a??? 2 Elwin}. New” «baidf x .QL. . ue.qu a: ‘5: 5 . . .‘ :1 az.uh.‘x : .. 1 Ex. 343.19 .t ‘. ,.p .\uza:; v t..l..1 .13 a: (1:... 4 qua ‘ t. 5...!) ‘ | 3W V 2: ~ 7 a! 13.9»; ‘uflud. Jan. . . 5’" 9 mm _m m . ’(gift" .1. 1.3I. .1112; .\ |..:o ...A_ in. QarmaaawaRw-‘ l“‘¥s!l#‘fi“‘fi**’9 ,g! .1 ‘ 21:1: .3‘ . .4 LIJIITW II n- = . - lVllCn gan State UI’Iw-ery This is to certify that the thesis entitled HEALTH'CARE DEMAND IN MICHIGAN: AN EXAMINATION OF THE MICHIGAN CERTIFICATE OF NEED ACUTE CARE BED NEED METHODOLOGY presented by Mark Jeffrey Finn has been accepted towards fulfillment of the requirements for the Master of Arts degree in Geography 1 4j/W/ / flajor Professor’s Signature 30 0d a 7 Date MSU is an afiinnative-action, equal-opportunity employer — ‘a-._. *fififl_“ - PLACE IN RETURN BOX to remove this checkout from your record. To AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE 6/07 p:ICIRC/DateDue.indd-p.1 HEALTH CARE DEMAND IN MICHIGAN: AN EXAMINATION OF THE MICHIGAN CERTIFICATE OF NEED ACUTE CARE BED NEED METHODOLOGY By Mark Jeflrey Finn A THESIS Submitted to Michigan State University In partial fulfillment of the requirements for the degree of MASTER OF ARTS Department of Geography 2007 ABSTRACT HEALTH CARE DEMAND IN MICHIGAN: AN EXAMINATION OF THE MICHIGAN CERTIFICATE OF NEED ACUTE CARE BED NEED METHODOLOGY By Mark Jeffrey Finn Health care demand, or the demand of a health care system for facilities and services based on a measurement of a population’s health care need, in Michigan is defined by the Michigan Certificate of Need Commission under its acute care bed need methodology and administrated by the Michigan Department of Community Health (MDCH). Michigan’s current acute care bed need methodology was last redefined in 1978 and has since been recommended by one of its co-creator to be abandoned. This thesis presents a detailed background investigation of the history of Michigan’s acute care bed need methodology and employs geospatial analysis techniques to examine the methodology using the Michigan Inpatient Data Base (MIDB): a database of every acute care hospital discharge in Michigan between 2001 and 2005. Results indicate the current bed need methodology is not defining an acute health care system that reflects patient utilization trends. Uncertainty in the acute care bed need methodology is introduced by the input of committees to interpret results, the sole use of the MIDB for utilization data, and the reliance on the 5-digit ZIP code as the primary geographic unit of analysis. A new acute care bed need methodology should be created in Michigan which should: 1) focus on the reduction of beds; 2) redefine the existing criteria for the movement of beds to other licensed sites; 3) incorporate measures of geographic access; 4) integrate outpatient discharges; 5) and include forecasts of related admissions and procedures. Copyright by MARK JEFFREY FINN 2007 l\/ This tl: los This thesis is dedicated to my wife Heather whose sacrifices, which were realized by our loss of precious time together, were for me the most humbling and painfiJl of all. iv ACKNOWLEDGEMENTS I wish to express my sincere gratitude to my chairman and advisor, Dr. Joseph P. Messina I am indebted to him for his guidance, patience, and concern in helping me prepare this thesis. I offer my warmest gratitude to the members of my committee, Dr. Bruce Wm. Pigozzi and Dr. Ashton M. Shortridge for their helpfulness and valuable input in helping me prepare this thesis. I wish to recognize and thank Larry Horvath, Irma Lopez, and the rest of the team at the Michigan Department of Community Health Certificate-of—Need Division for their cooperation and support. A special word of thanks is extended to Stan Nash at the Michigan Department of Community Health who greatly assisted me in this study by providing data, answering questions, and discussing the historical development of Michigan’s acute care bed need methodology. Additionally, I gratefully acknowledge funds from the Michigan Department of Community Health Grant (61 -9242). Thank you to Dr. Sue C. Grady, Dr. Judy Olson, Lindsay Campbell, Ed Hartwick, Ivan J . Ramirez, Eric Sandberg, and Pariwate (Perry) Varnakovida for their help with this study. Also, thank you to Dr. Randall J. Schaetzl and Dr. Richard E. Groop for providing me with additional research opportunities and support while studying at Michigan State University. “in f) W TABLE OF CONTENTS LIST OF TABLES ....................................................................................................... viii LIST OF FIGURES ........................................................................................................ ix 1 Introduction ............................................................................................................. l l . 1 Introduction .............................................................................................. 1 1.2 Literature Review ..................................................................................... 7 1.2.1 Medical Geography and Health Care Utilization ....................................... 7 1.2.2 History of Health Care Planning and CON ............................................. 13 1.2.3 Michigan CON ....................................................................................... 25 1.2.4 Measurement of Demand ....................................................................... 33 1.2.5 Michigan’s Acute Care Bed Need Methodology ..................................... 38 2 Methods ................................................................................................................ 59 2.1 Computer Architecture and Data ............................................................ 59 2.1.1 Computer Architecture (System and Programs Used) ............................. 59 2.1.2 Michigan Inpatient Data Base (MIDB) ................................................... 60 2.1.3 Michigan Acute Care Hospitals List ....................................................... 65 2.2 Calculating Michigan Acute Care Bed Need ........................................... 66 2.3 Evaluation of Michigan CON Acute Care Bed Need Methodology ......... 69 2.3.1 30 Minutes Travel Time Calculation ...................................................... 70 2.3.2 Average Travel Distance for Patients Traveling Longer than 30 Minutes Calculation ............................................................................................. 84 2.3.3 Proximity to the Nearest Acute Care Hospital Outside FSA Calculation. 87 2.3.4 Hospital Hierarchical Movement of Patient Visits Outside 30 Minutes Travel Time Analysis ............................................................................. 88 2.3.5 HSA Commitment Index Calculation ..................................................... 93 3 Results and Discussion .......................................................................................... 95 3.1 Results of 30 Minutes Travel Time Analysis .......................................... 95 3.2 Results of Average Travel Distance Analysis on Patients Traveling Longer than 30 Minutes Travel Time ............................................................... 110 3.3 Results of Calculation of Proximity to the Nearest Acute Care Hospital Outside FSA ......................................................................................... 1 19 3.4 Results of Hospital Hierarchical Movement Analysis of Patient Visits Outside 30 Minutes Travel Time .......................................................... 121 3.5 Results of HSA Commitment Index Analysis and Discussion of F SAs. 127 4 Conclusions ......................................................................................................... 144 4.1 Overview ............................................................................................. 144 4.2 Major Findings ..................................................................................... 149 4.3 Future Research ................................................................................... 155 vi “.1“ Zhuv AD I Apr P. “1'. APPC OCI Appcn Pytl. ArC( Append Pyt hc Other Append i.“ Pythor visited 30 min Appendix Patient Appendix Results mlnmes Ofkfich: eSUhS C lIlUleg Literature C Appendix 1 .................................................................................................................. 158 Factors Identified in Early Literature as Influencing Bed Needs for General Hospitals (Palmer 1956) .......................................................................................................... 158 Appendix 2 .................................................................................................................. 160 Michigan HSAs and FSAs ....................................................................................... 160 Appendix 3 .................................................................................................................. 164 Python code to calculate age groups from age field for the MIDB 2004 and 2005 fixed width text files ......................................................................................................... 164 Appendix 4 .................................................................................................................. 165 Occupancy Rate Table ............................................................................................. 165 Appendix 5 .................................................................................................................. 167 Python code to select ZIP codes intersecting FSA 30 minutes travel time areas in ArcGIS .................................................................................................................... 167 Appendix 6 .................................................................................................................. 168 Python code to calculate nearest point distance between hospitals within an FSA to all other hospitals. ........................................................................................................ 168 Appendix 7 .................................................................................................................. 169 Python code creates a new field within table SIZE2 which indicates whether the patient visited a larger, smaller or similar sized hospital compared to the largest hospital in its 30 minutes travel area .............................................................................................. 169 Appendix 8 .................................................................................................................. 170 Patient Visits Traveling Longer than 30 Minutes for Acute Care ............................. 170 Appendix 9 .................................................................................................................. 235 Results of T-test to compare the percentage of patients traveling longer than 30 minutes for acute care for FSA 30 minutes travel time service areas and the entire State Of Michigan ............................................................................................................. 235 Appendix 10 ................................................................................................................ 237 Results of Hospital Hierarchical Movement Analysis of Patient Visits Outside 30 Minutes Travel Time ............................................................................................... 237 Literature Cited ........................................................................................................... 267 vii g r 1' Al . Ta Tat LIST OF TABLES Table 1. Mistyped, Nonexistent, or Null Values in the MIDB ........................................ 63 Table 2. Shortest Radial Distance fi'om a Hospital in a FSA to a Hospital Outside a FSA ........................................................................................................... 120 viii Fig. Fig. Fig. ‘ Fig. 8 Fig. 9 Fig. I( Fig. ll Fig. 12. Fig. 13. 30 Mim Fig. l Fig, I Fig. N Fig. 17 Fig. 18. Fig. I9, Fig. 20. . LIST OF FIGURES Fig. 1. Integration of the basic elements of a health care delivery system ........................ 2 Fig. 2. Classification scheme for the studies of general interest in medical geography ..... 8 Fig. 3. Duration of Certificate of Need Regulation by State .......................................... 18 Fig. 4. Process for developing Certificate of Need review standards ............................. 27 Fig. 5. Michigan Certificate of Need application review process ................................... 29 Fig. 6. Services covered by Certificate of Need in states surrounding Michigan ............ 31 Fig. 7. Epidemiological model of the delivery of health care services ........................... 34 Fig. 8. First Facility Service Areas in Michigan, 1946 .................................................. 41 Fig. 9. Facility Service Areas in Michigan, 1955 ........................................................... 43 Fig. 10. Facility Service Areas in Michigan, 1975 ......................................................... 47 Fig. 11. Hospital System Areas in Michigan, 2007 ....................................................... 49 Fig. 12. SQL Query to find unique values in MIDB ...................................................... 61 Fig. 13. SQL Query to find discernable data errors in MIDB ........................................ 62 30 Minutes Travel Time Calculation Fig. 14. 30 Minutes Travel Time in Acute Care Hospitals in FSA 1A ..................... 72 Fig. 15. Diagram of automated ESRI ArcGIS analysis in Python ............................ 73 Fig. 16. SQL Query to create table OUTSIDEI ...................................................... 74 Fig. 17. SQL Query to create table OUTSIDE2 ...................................................... 75 Fig. 18. SQL Query to create table OUTSIDE3 ...................................................... 75 Fig. 19. SQL Query to create table ALLDISCHARGES ......................................... 76 Fig. 20. SQL Query to create table OUTSIDE4 ...................................................... 76 ix M73 Fi Fi Fi Fi Fi, Fig Fig Avera; Fig Fig. Fig. Fig. Fig. . Fig. .7 Proximit Fig. 3. HOSpital I A"illysis Fig. 35. Fig. 36. F18- 37, 5 H8- 38. s Fig- 39, S F lg. 40. S Fig. 21. Areas Outside 30 Minutes Travel Time to Acute Care Hospitals ................ 77 Fig. 22. Diagram of ESRI ArcGIS analysis to calculate overlapping areas .............. 79 Fig. 23. SQL Queries to create tables OVERLAP2 and ZIP2 .................................. 80 Fig. 24. SQL Query to create table PERC_OVERLAP ........................................... 80 Fig. 25. SQL Query to create table P100 ................................................................. 81 Fig. 26. SQL Query to create table P100_OVERLAP ............................................. 81 Fig. 27. Number of Overlaping F SAs by ZIP code .................................................. 83 Average Travel Distance for Patients Traveling Longer than 30 Minutes Calculation Fig. 28. Diagram of ESRI ArcGIS analysis to calculate point distance .................... 84 Fig. 29. SQL Query to create table DISTl .............................................................. 85 Fig. 30. SQL Query to create table DIST2 .............................................................. 85 Fig. 31. SQL Query to create table DIST3 .............................................................. 86 Fig. 32. SQL Query to create table DIST4 .............................................................. 86 Fig. 33. SQL Query to create table DISTS .............................................................. 86 Proximity to the Nearest Acute Care Hospital Outside F SA Calculation Fig. 34. Diagram of ESRI ArcGIS analysis to calculate Near(Analysis) function 88 Hospital Hierarchical Movement of Patient Visits Outside 30 Minutes Travel Time Analysis Fig. 35. SQL Query to create table SIZE] ............................................................... 90 Fig. 36. SQL Query to create table SIZE2 ............................................................... 90 Fig. 37. SQL Query 1 to sum hospital visits by size ................................................ 91 Fig. 38. SQL Query 2 to sum hospital visits by size ................................................. 92 Fig. 39. SQL Query 3 to sum hospital visits by size ................................................. 92 Fig. 40. SQL Query to combine previous three queries ............................................ 92 )\N:§ Resr F11 Fig Fig. Fig. Fig 4 Fig. 5 Fig. 5 Fig 52 Fig. 53. Rem-”ts 0f .1 mm“ Tr; Fig. 54, HSA Commitment Index Calculation Fig. 41. SQL Query to total discharges by ZIP code ................................................ 93 Fig. 42. SQL Query to combine indices .................................................................. 94 Fig. 43. SQL Query to calculate five year totals ...................................................... 94 Results of 30 Minutes Travel Time Analysis Fig. 44. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. 45. 46. 47. 48. 49. 50. 51. 53. Percentage of Patients Traveling Outside 30 Minutes FSA Travel Areas 2001 ........................................................................................................... 97 Percentage of Patients Traveling Outside 30 Minutes F SA Travel Areas 2002 ........................................................................................................... 98 Percentage of Patients Traveling Outside 30 Minutes FSA Travel Areas 2003 ........................................................................................................... 99 Percentage of Patients Traveling Outside 30 Minutes FSA Travel Areas 2004 ......................................................................................................... 100 Percentage of Patients Traveling Outside 30 Minutes FSA Travel Areas 2005 ......................................................................................................... 101 Percentage of Michigan Out-of-State Hospitals Visits 2001 - 2003 .......... 103 Scatterplot of the average percentage of patients traveling outside 30 minutes FSA travel time areas versus total population .............................. 104 Top 90% of Hospital Visits Traveling Outside 30 Minutes Travel Time FSA Area from ZIP Codes 48834, 48865, and 48846 ....................................... 106 . Graph of average percentage of patients traveling outside 30 Minutes FSA travel time areas ............................................................................... 108 Statistical Comparison of FSA Percentage of Patients Traveling Longer than 30 Minutes Travel Time for Acute Care to the State of , Michigan Percentage ................................................................................ 109 Results of Average Travel Distance Analysis for Patients Traveling Longer than 30 Minutes Travel Time Fig. 54. Scatterplot of the average travel distance by patients outside 30 minutes FSA travel time versus the average percentage of patients traveling xi N 7' outside 30 minutes F SA travel time .......................................................... 112 Fig. 55. Average Travel Distance (mi) of Patients Traveling Outside 30 Minutes F SA Travel Areas 2001 ............................................................................ 114 Fig. 56. Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes FSA Travel Areas 2002 ............................................................................ 115 Fig. 57. Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes F SA Travel Areas 2003 ............................................................................ 116 Fig. 58. Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes FSA Travel Areas 2004 ............................................................................ 117 Fig. 59. Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes F SA Travel Areas 2005 ............................................................................ 118 Results of Hospital Hierarchical Movement Analysis of Patient Visits Outside 30 Minutes Travel Time Fig. 60. 2005 Patient Travel Outside 30 Minutes FSA Travel Areas Bivariate Map: Percentage of All Patients and Percentage Traveling Outside to Smaller Hospitals ...................................................................................... 126 Results of HSA Commitment Index Analysis and Discussion of FSAs Fig. 61. HSA 1 Southeast Commitment Index 2001 - 2005 ................................... 128 Fig. 62. HSA 2 Mid—South Commitment Index 2001 - 2005 ................................. 130 Fig. 63. HSA 3 Southwest Commitment Index 2001 - 2005 .................................. 131 Fig. 64. HSA 4 West Commitment Index 2001 - 2005 .......................................... 132 Fig. 65. HSA 5 Genesee Commitment Index 2001 - 2005 ..................................... 133 Fig. 66. HSA 6 East Central Commitment Index 2001 - 2005 ............................... 135 Fig. 67. HSA 7 North Central Commitment Index 2001 - 2005 ............................. 136 Fig. 68. HSA 8 Upper Peninsula Commitment Index 2001 - 2005 ........................ 137 Results of HSA Commitment Index Analysis and Discussion of FSAs Fig. 69. Facility Service Areas Comparison 1946 Areas & 2007 Clusters ............. 140 xii .‘h s z, Fig. 70. Facility Service Areas Comparison 1975 Areas & 2007 Clusters ............. 141 Fig. 71. Metropolitan Detroit 2007 Hospitals — FSAs ............................................ 142 Images in this thesis are presented in color. xiii ZEN 1 Introduction 1.1 Introduction Health care is the provision of services and supplies by agents of the health services or professions to individuals or communities with the express goal of achieving an optimal state of well-being. A study of total health care would include consideration for housing, nutrition, education, employment, politics, and other topics all of which underlie ‘quality of life’ and together promote or damage ‘health’ (Joseph and Philips 1984). The definition of health care used here focuses on primary health services or ‘ill-health’ services. Specifically, health care is used to describe the utilization of acute care provided by general hospitals. Acute care refers to medical services for patients with or at risk for acute or active medical conditions in a variety of ambulatory and inpatient settings (Gourevitch, Caronna, and Kalkut 2005). A health care system is the organizational structure by which health care is provided. From a health care planning perspective, an ideal health care system provides an entire population with access to the broadest possible range of quality health services to prevent disease, promote wellness, and improve quality of life at reasonable costs and geographic efficiency (Michigan Department of Community Health 2007). From a medical geography perspective, an ideal health care system would precisely locate health care services and resources over space based on a clear understanding of future health care demand to provide optimal, potential accessibility to a population while overcoming all physical and sociO-economic barriers. Health care provision is complex and difficult to effectively manage. A health care system includes practitioners, administrators, researchers, facilities, specialized services and equipment, suppliers, private and public funding, international, federal and state regulation, political and business interests, Health Maintenance Organizations (HMOs), purchasers and payers of health care services, and health care consumers. A health care delivery system must account for many interacting elements such as disease agents, hosts and vectors; cultural, economic, and environmental conditions; caregivers, insurance, government regulation, human behavior, and intervention (Fig. 1). External St'mu" Cultural Individual Response: Buffering System: . Parental Example; 4 AttIude Peer Group Example; Toma! Illngss; Prior Experience; es 0 Educational Back round; Illness _’ Care Available; ——+ Religious Beligfs; Attitude Toward Effects of Advertising Medical System and Media; and Practitioners; Medical Technology; Political and Financial Status. Economic Status; Barriers to Care. l I Health and Well Being ‘ Interaction of the basic elements of a health care delivery system; redrafied by Mark Finn, fi'om Meade and Earickson 2000 Fig. l According to Hunter (1974), the locational planning of health services must take into account: (1) the scope of the services provided (based on disease surveillance and its spatial patterns in the locality or in the region); (2) a realistic view of resources, needs, and finance; (3) the geographical distribution of the population at risk; (4) the projected population changes through natural increase or decrease and migration; and (5) disease forecasting. N _ 9 f1) W Health care need and demand are not the same. In an ideal world, health needs should generate an appropriate demand, which then could be supplied in a systematic way by a health care delivery system. Black (1983) explained why this was not the case in reality: since actual health care needs are distorted by faulty perceptions, there is often little relationship between perceived needs and demand; and supply does not match need or demand. Demand is usually based on a measurement of utilization. Utilization is evidence that access to health care services has been achieved (Fiedler 1981). Demand vis-a-vis access is impacted by age, sex, ethnic origin, socioeconomic status, and insurance (Rothberg 1982; Anderson 1973; Ringel 2002); ecological factors such as distance and travel time (Shannon, Bashshur, and Metzner 1969; Acton 1975; McGuirk and Porell 1984); and individual factors such as patient behaviors and physicians’ practices (Anderson and Newman 1973; Eisenberg 2002). The focus of this research is on health care demand, the demand by a health care system for services based on a measurement of a population’s health care need, limited to the State of Michigan. Health care demand in the majority of states is defined by the state government through hospital certificate-of-need regulation (CON). Michigan first enacted its CON law in 1972 as Public Act 256. CON law in Michigan is regulated by the Certificate of Need Commission and administered by the Michigan Department of Community Health (MDCH) Certificate-of—Need Division. Federal support of CON ended in 1986 with the repeal of the National Health Planning and Resources Development Act mainly due to concerns it had failed to reduce the nation’s aggregate health care costs. Three years prior, 1983, Congress had passed legislation to move away fi‘om health care planning regulation to health care fee regulation to control the cost of 31' 1'65 of H progr about by the Program balancin develops Conover Universit} (Conover; of MDCH. Whether the meaSUres to The prim by the MD(‘ mCaSUre the CON PTOgra, duplicative St‘ health care. The Federal Government gave itself the authority under the diagnosis related group (DRG) system to establish the purchasing price for identifiable services for Medicare. Presently only fourteen states have eliminated their CON programs while the rest of the country’s CON programs operate under state firnding. In a nationwide comparison of CON programs conducted by a consultant for the State of Washington, Michigan tied with North Carolina as having the most effective CON program in the country (Piper 2005). However at the state level, there has been concern about Michigan’s CON Program. A 2002 performance audit of MDCH-CON conducted by the Auditor General of Michigan stated MDCH had failed to evaluate the state’s CON Program in order to determine whether the CON Program was achieving its goal of balancing cost, quality, and access issues and ensuring that only needed services are developed in Michigan (McTavish 2002). MDCH responded to this audit by contracting Conover and Sloan of the Center for Health Policy, Law and Management at Duke University (nationally recognized experts in the field) to evaluate CON in Michigan (Conover and Sloan 2003). A follow-up audit report by the Auditor General of Michigan of MDCH-CON in 2005 indicated the Conover and Sloan report did not conclude whether the CON Program was achieving its stated pro gram goal and MDCH still lacked measures to evaluate the performance of the CON pro gram (McTavish 2005). The primary objective of this research is to explore the efficacy of the methods used by the MDCH-CON to measure health care demand in the State of Michigan and to measure the efficiency of their existing methods of assessing demand to demonstrate the CON Program is achieving its goal of balancing cost, quality, and access and eliminating duplicative services. The questions this research is designed to address are: pron et a1. or inc ofCO. OVeruti means c(impetii duPlicati It is complexit 1. How do the current methods used by MDCH-CON represent real demand? 2. What is the level of uncertainty of these methods and what are the sources of the uncertainty? 3. How might demand measures be methodologically improved? There are established statistical methods (Diehr et a1. 1999; Keeler and Ying 1996) and econometric methods (Drurrunond et a1. 2005) for the evaluation of a hospital’s and health care system’s costs. The Dartmouth Atlas of Health Care has developed a method to assess health care quality using a population-based approach (Fisher and Wennberg 2003). There is also extensive literature on health care access (Fiedler 1981) including methods to measure access in medical geography (Guagliardo 2004; Joseph and Philips 1984; Thomas 1992; Ricketts et a1. 1994; Albert, Gesler, and Levergood 2000) and epidemiology (Clement and Wan 2001); and methods to locate health care facilities to promote access in operations research (Rushton 1987; Rahman and Smith 2000; Toregas et a1. 1971; Harper et a1. 2005). Roemer’s Law (Roemer 1961), a bed built is a bed filled or increasing supply will increase admissions or length of stay, has been the foundation of CON legislation to eliminate duplicative services in the interest of reducing costs and overutilization. The methods used by MDCH-CON to measure demand are largely a means to eliminate duplicative services. Potentially, methods used to measure competition and antitrust issues in health care systems could be used to identify duplicative services (see Sohn 2002; Schramm and Renn 1984). It is unreasonable for a study of this size to address all the aforementioned complexities of health care, health care systems, individual demand, and locational planning of health care services to create an ideal health care system for the State of Michigan, but the fundamental issue of demand for health care services by a health care system can be addressed. Hence, assumptions must be made concerning variables outside the scope of this research This thesis will evaluate MDCH-CON’s methods for defining Michigan’s health care demand for general hospitals based on acute care hospital bed need (Certificate of Need Commission 2007b). MDCH-CON methods utilize hospital discharge records recorded in the Michigan Inpatient Data Base (MIDB) and total population estimates and projections by ZIP code from the US. Census for a given planning year. These methods place an emphasis on planning with reference to a demonstrated demand by assuming past hospital utilization shed light on future usage. For the purposes of evaluating Michigan’s methods to define health care demand, this thesis will adopt those methods’ assumptions and limit its scope of variables to those found in its datasets. This thesis is organized to provide the reader with an understanding of the problem and the methods used to solve it. The remainder of Chapter 1 is a literature review providing a background on medical geography and health care utilization, the history of health care planning and CON, Michigan CON, a discussion on the measurement of demand, and Michigan’s acute care bed need methodology over time. Chapter 2 introduces the methods used in this study to examine Michigan’s acute care bed need methodology. Chapter 3 presents the results of this examination and discussion. Chapter 4 closes with an overview of the thesis, major findings, and suggestions for future research. 1.2 Literature Review The first section discusses medical geography and health care utilization. The second section presents an overview of the history of health care planning and Certificate of Need (CON). The third section discusses Michigan CON. The fourth section provides an overview of the measurement of demand. The final section discusses Michigan’s acute care bed need methodology over time. 1.2.1 Medical Geography and Health Care Utilization Medical geography is the “geographical analysis of health, disease, mortality, and health care” (Johnston et a1. 2000). It is an “integrative, multistranded subdiscipline” drawing freely from other social, physical, and biological sciences (Meade and Earickson 2000). The concept of geography in health has been present since the Hippocratic School’s book “On Airs, Waters and Places” in the fiflh century BC. (Barrett 1980). Hippocrates’ ecological perspective on health and disease continued to be philosophically important, even dominant, until the emergence of germ theory in the later half of the nineteenth century (Meade and Earickson 2000). The term, medical geography, did not appear in literature until Leonhard Ludwig Finke used it in 1792 in a three volume study, “Attempt at a General Medico-Practical Geography [Versuch einer allgemienen medicinisch-praktischen Geographie]” (Finke 1792-95). Other eighteenth and nineteenth century physicians carried on the Hippocratic tradition; their explanation of disease distribution and etiology and the beginnings of disease mapping have been researched extensively by Barrett (1980, 1991, 1993, 1996, 1998). Despite its early origins, medical geography never experienced an explosive grth until the late 19705 and early 19803 (Meade and Earickson 2000). Figure 2 presents a classification scheme of the studies of general interest in the field of medical geography. Medical Geography I I Disease Ecology Diet & Nutrition Health Care Delivery 1 I I - - Malnutrition & Deficiency Food 8- NutrItIon Diseases I l I . . . . . Spatio Disease MappIng ASSOCIatlve Analysns Epidemiological I I L I I Spatial Patterns of Patient Travel - - Location of - Health Facilities and Accessibility PatIent BehaVIor l Health Facilities Health Education Classification scheme for the studies of general interest in medical geography; redrafted by Mark Finn, from Akhtar 1982. Fig. 2 In the early 19905, an epistemological and methodological debate in the discipline over the role of PLACE in medical geography resulted in a push for a “post-medical” geography of health (Kearns 1993, 1994a, 1994b; Kearns and Moon 2002; Paul 1994; for an overview of earlier debates in medical geography see Barrett 1986). This resulted in a division within the discipline between medical geography and health geography. Health geography, or the geography of health and health care, is “a sub—discipline focused on the dynamic, and recursive, relationship between health, health services, and PLACE, and on the impact of both health services and the health of population groups on the vitality of places” (Johnston et a1. 2000). The adoption of the biomedical model of disease by conventional medical geographers has been the main critique by health geographers of medical geography. Critics argue that a socio-ecological model is needed to replace biomedicine. I will adopt a medical geography theoretical approach for this thesis. The study of health care under medical geography, or the geography of medical care, emerged in the 19603 by medical geographers who felt the spatial organization of health institutions in an area were more important to study than disease ecology due to these institutions’ important role in the persistence and elimination of diseases (Akhtar 1982). The geography of medical care had as Shannon (1980) put it “somewhat ignoble beginnings” in the covers of the “American Journal of Insanity” compared to the aristocratic-Hippocratic roots of medical geography. Edward Jarvis observed in 1851 that “the people in the vicinity of lunatic hospitals send more patients to them than those at a greater distance” (Jarvis 1851). This observation of distance decay in rates of utilization has since been referred to as ‘Jarvis’ Law’ (see chapter 7 “Jarvis’ Law and the Utilization of Mental Health Care” in Joseph and Phillips 1984). Literature on health care delivery in the geography of medical care has identified many factors outside basic geographic proximity to hazards and epidemiology that influence health care utilization. Factors include: Demographics. The ratio of women to men in a population will affect utilization of health care services. Studies have consistently shown women use more health care services than men (Cleary, Mechanic, and Greenley 1982; Hrbbard and Pope 1983; Waldron 1983; Verbrugge and Wingard 1987; Bertakis et al. 2000). It has long been believed that an aging population results in higher utilization of health care services. This has not been the case in some recent literature on acute care utilization (Reinhardt 2003; Busse, Krauth, and Schwartz 2002). Age is associated with an increase in the prevalence of chronic conditions and functional limitations. The elderly do have a higher rate of utilization for many procedures and are prescribed more drugs which may reduce the prevalence of other conditions (Bernstein et a1. 2003), but it remains arguable whether overall acute care utilization does or does not increase. Race and ethnicity influence utilization rates. Racial and ethnic disparities in health care have been extensively documented. Minority race or ethnicity has been linked to a reduced regular source of care (CDC 1998), fewer physician visits, and lower total expenditures on health care services (Fiscella, Franks, and Clancy 1998). Appreciable disparities also exist in health-care by race, Hispanic ethnicity, and English fluency (Fiscella et al. 2002). It is difficult to isolate racial and ethnic disparities in health care from socioeconomic disparities due to their close intertwining in American society (Navarro 1990). However, research has shown socioeconomic position appears to be more of a determinant in health care utilization (Mutchler and Burr 1991; Fiscella et a1. 2000). Lower socioeconomic position is associated with lower overall health care use, even among those with insurance (Fiscella, Franks, and Clancy 1998; Newacheck, Hughes, and Stoddard 1996; Wood et a1. 1990; F iscella et a1. 2000). Insurance. An individual’s access to third party payment methods such as private insurance, Medicare, and Medicaid enable utilization by providing individuals with a better ability to purchase health care. However, insurance through managed care programs such as Health Maintenance Organizations (HMOs) and Preferred Provider Organizations (PPOs) can prevent utilization through cost controls which offer volume price discounts for physicians, gatekeeper restraints on specialty consultations, drug 10 7; a formularies, prior authorization of tests and admissions, and retrospective denial of payment for unnecessary services (Zelman and Berenson 1998). Interrelationships between the utilization of health care and the demand for health insurance exist such as: (1) if the cost of out-of-pocket costs for care in a health plan fall, more individuals will enroll, and those already enrolled will make use of additional services; (2) individuals with poor health tend to choose insurance with high benefits, individuals with good health avoid such insurance due to its high cost; (3) the presence of insurance can undermine an individual’s incentive to pursue health services, or since insurance shields the individual from paying for the full cost of services, the individual consumes more services than if he or she had no insurance (Ringel et al. 2002; Zweifel and Manning 2000). Distance and Travel Time. There is a distance decay in rates of utilization known as ‘Jarvis’ Law’ (see chapter 7 “Jarvis’ Law and the Utilization of Mental Health Care” in Joseph and Phillips 1984). Individuals who are closer to health care services are more likely to utilize them. Distance and travel time from a patient’s home to a facility has been found to be an important variable in differences in utilization (Shannon, Bashshur, and Metzner 1969; Acton 1975; McGuirk and Porell 1984). Individuals seeking specialized treatment are more willing to travel further than for primary and preventive services (Simon and Smith 1973). Behavior. Social and perceptual variables influence an individual’s utilization of health care services. Health perceptions define how individuals perceive their health whether they perceive that they are healthy or ill (Davies and Ware 1981). Psychological distress influences health perceptions and may result in healthy individuals developing ll symptoms that result in health-related concerns and a diminished sense of well-being (Manning and Wells 1992). A review of 30 studies on health care utilization indicated that religion created significant differences in utilization rates (Schiller and Levin 1988). Advertising also influences individual behavior to pursue health care. Concerns over Direct-to-Consumer advertising and their effects on health care have emerged in 2002 in the New England Journal of Medicine (Rosenthal et a1. 2002; Holrner 2002). Medical Advancements and Changes in Medicine. Medical and technological advancements in health care have both reduced and encouraged utilization. For example, antibiotics and public health initiatives have dramatically reduced the need for services to treat infectious diseases. However, other factors, such as increases in the prevalence of chronic disease, may contribute tO increases in overall utilization. New procedures and technologies, therapeutic technologies such as corrective eye surgeries, elective cosmetic surgery, and the direct marketing of drugs may increase utilization. Decreasing supply (hospital closures, large number of physicians retiring), ambulatory surgery, alternative sites of care (assisted living), and changes in practice patterns (encouraging self-care and healthy lifestyles; reducing length of hospital stay) reduce utilization (Bernstein et al. 2003) Physicians. Individual physician decision making has a substantial influence on the utilization of medical services (Eisenberg 2002). Physicians control referrals, return visits, entry to hospitals, and access to prescribed medicines. The patient does not have sufficient knowledge to evaluate the quality and extent of services supplied and must rely on the physician to make decisions. There has been debate over whether physicians 12 r “ii «ow actually influence demand in their own self interest, and models have been created to identify this supplier-induced demand (Feldman and Sloan 1988; Rice and Labelle 1989; Grytten and Sorensen 2001). Although spatial analysis techniques applied to medical geography have existed for decades, in recent years the application of Geographic Information Science has become a new focus in the geography of medical care and medical geography in general (Albert, Gesler, and Levergood 2000; Cromley and McLafferty 2002; Lawson and Kleinman 2005; McLafferty 2003). Medical geography, as with U. S. geography as a whole, is perceived fragmented and yet to identify its core and focus on its future (Meade and Earickson 2000). In their conclusion on the section entitled “The Course of Medical Geography”, Meade and Earickson (2000) conclude quoting John Hunter (1974); The application of geographical concepts and techniques to health-related problems places medical geography, so defined, in the very heart or mainstream of the discipline of geography. I would suggest that there is no professional geographer, whatever his or her systematic bent or regional interest, who cannot effectively apply a measure of his or her particular skills or regional insights towards the understanding, or at least partial understanding, of a health problem. This is the essential challenge of medical geography. (pp. 3-4) 1.2.2 History of Health Care Planning and CON In the 19203 and 19303 the intellectual foundations of health care planning began in the United States through philanthrOpically supported programs promoting rural health care and later urban programs (Melhado 2006a). Influenced by the British humanitarian movement of the eighteenth century, United States health care was characterized by 13 26 voluntary charitable organizations and philanthropy for over two hundred years (Duffy 1990). Private philanthropy dominated medical education and research until 1940 and was the principle source of capital for hospital construction until the mid-19603 (Rosen 1965; Terenzio 1978). Planning was a private, voluntary effort on the part of hospital administrators and financers. The Great Depression and Second World War greatly affected health care capital availability as private and public giving declined (Terenzio 1978). With the creation of the Blue Cross, the first major provider of hospitalization insurance, in the 19303, the modern hospital system was founded on voluntary prepayment of hospital costs through private health insurance. Health insurance provided hospitals with an alternative to private philanthropy for financial support. It also moved the American health care system substantially toward realizing two major aspirations: (1) that everyone should have access to high quality medical care without barriers based on an individual’s ability to pay; and (2) that all health care should be based on the most advanced scientific methods of treatment (Payton and Powsner 1980). The cost of health care skyrocketed in the United States with more people being able to afford services and hospitals increasing their rates to pay for equipment for new advanced treatment methods: between 1929 and 1960 per capita medical expenditures went up 5 percent per year, between 1940 and 1960 the percentage of the civilian population with some form of voluntary health insurance septupled, and from the late twenties to the late fifties the average annual number of physician visits per person nearly doubled (Lerner and Anderson 1963). 14 N 'r; <3 W “Hierarchical regionalism” is a phrase introduced by Fox (1986a) to summarize three assumptions that were the basis of policy for the organization of health services in the United States. These assumptions are: (1) the causes of and cures for most diseases are usually discovered in the laboratories of teaching hospitals and medical schools; (2) these discoveries are then disseminated down hierarchies of investigators, institutions, and practitioners, which serve particular geographical areas; (3) a central goal of health policy is stimulating the creation of hierarchies in regions which lack them and making existing ones more efficient. Hierarchical regionalism was the fundamental basis of policies to plan, build, and equip hospitals in the United States since the 19403. It manifests itself in America’s hierarchical health care system, focused on urban centers and their medical schools and research hospitals, with community hospitals in smaller towns, and rural clinics out in the periphery (see also Fox 1986b). In 1946, the Hospital Survey and Construction Act (Public Law 79-725), popularly known as the Hill-Burton Act, was the first federal legislation attempting to organize the US. health care delivery system The Hill—Burton Act was a state-federal partnership that subsidized the construction of mainly rural hospitals in “needy” areas. Need was defined as a bed-to-population ratio. The Hill-Burton Act led to the establishment of many state health planning agencies and forced states to identify general hospital service areas as a condition for funding of hospital construction (Meade and Earickson 2000): furthering hierarchical regionalism in American health care planning. The Hill-Burton Act required two-thirds financing fi'om nonfederal sources which provided an initiative for fund- raisers to seek contributors for capital projects and acted as a catalyst in attracting funds from philanthropists (Terenzio 1978). However, the demand for hospitals in the postwar 15 period resulted in a rapid growth of voluntary hospitals built without federal assistance in disregard of state plans set forth under the Hill-Burton Act. This resulted in the over allocation of beds in certain areas, shortages in others, and an enormous amount of duplication (Stevens 1971 ). It was not until health insurance became widely available in the mid-19503 that rising hospital costs began to be perceived nationally as an inherent problem of the hospital industry. Hospital leaders feared the public criticism and loss of legitimacy as a voluntary system serving the public interest would result in governmental controls. As a result Of the contradictory demands of providing better and more accessible health care services while keeping costs down, hospitals undertook planning as “a form of altruistic self- limitation in the public interest” and pushed for a national planning movement (Melhado 2006a) Passage of federal Medicare and Medicaid legislation in 1965 brought on a near-crisis in public finance and led the federal government to take an active role in health care planning by encouraging the creation of state and regional planning agencies through the Comprehensive Health Planning Act of 1966 which offered various grants for studies and health demonstrations (Payton and Powsner 1980). Policy makers attempted to restrain the rate at which health care costs increased as they did in the past using strategies of hierarchical regionalism. These attempts included mandated peer review of the inpatient services physicians ordered for Medicare patients, incentives to create health maintenance organizations, the establishment of state and regional planning organizations, and Certificate-of-Need programs to inhibit new hospital construction and regulate the diffusion of new expensive medical technology (Fox 1986a). 16 Certificate-of—Need (CON) began as a community health planning council composed ' to evaluate the need for hospital beds in of local businesses and the Blue Cross Rochester, New York. This council led to the passage of the first CON legislation by the State of New York in 1966 (Citizens Research Council of Michigan 2005). Certificate-of- Need (CON) programs attempted tO control costs by regulating supply (McGinley 1995). The intent of CON regulation was to control health care costs by limiting expenditures for new health care facilities and equipment, preventing duplication and unnecessary use of expensive and sophisticated services, and increasing the quality of health care procedures (Michigan Legislative Service Bureau 2002). CON regulations were also intended to ensure adequate access to health care services. A CON program is established by state law. It requires health care service providers to demonstrate a need (defined by law or regulation in that state) for the creation, upgrading or modernization, expansion, relocation and acquisition of services and beds (Citizens Research Council of Michigan 2005). By 1968, the American Hospital Association publicly supported CON laws and began lobbying efforts to encourage CON regulation across the United States (Havighurst 1973). In seven years, thirty states had enacted CON regulations including Michigan (Fig. 3). Many of these state regulations were brought about by the lobbying efforts of hospitals which profited from state regulations by restricting the entry of competitors into their markets (Wolfson 2001). ' The Blue Cross dominated the health care market as the majority ofnon-profit hospitals relied on them for financial support (Payton and Powsner 1980). Around the time CON legislation was passed in Michigan, the Blue Cross financed 55 percent of all health care in Michigan, had agreements with over 90 percent of Michigan Hospitals, had legislative authority to withhold its participation from any licensed hospital on the basis of need (deprived of authority by Public Act 233, 1972), and consequently had the power to force hospitals out of business who did not participate in Blue Cross (House Fiscal Agency 1974). 17 Duration Of CON regulation by state; Redrafted by Mark Finn, from American Health Planning Association 2004. From 1966 to 1975, 30 states voluntarily started CON prior to the beginning of federally mandated CON under the National Health Planning Act. Of those 30 states, eight voluntarily terminated their programs after the end of these mandates in 1983. Of the remaining 20 states, six states terminated their CON after mandates ended. Fig. 3 l8 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 111 1111 11111 11111 E31111 11111 1 111 1111 11111 11111 '1111 11111 11 111 1111 11111 11111 [Q1111 11111 11 111 1111 11111 11111 [31111 11111 11 111 1111 11111 1111 1111 11111 11 111 1111 11111 1111 {31111 11111 11 111 1111 11111 1111 ,1111 11111 11 111 1111 11111 1111 1 F11111 11111 11 1111 1111 11111 1111 1 [51111111111 11 1111 1111111111 111111 E31111111111 11 1111 1111111111 111111 [$111111111111111111111111111111111111 E3111111111111111111111111111111111111 0 J J 0 J J J J 0 (DJ J~)0 J~JJ UhJJ J f3111111111111111111111111111111111111 [5111111111111111111111111111111111111 [91111111111111111111111111111111 [$111111111111111111111111111111 f911111111111111111111111111 {3111111111111111111111111 ($1111111111111111111 511111111111111 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 333 333333 033 333333 iJOIJQIJOiJQNJO\JQ . 333 33333333 [a33333333333333333333333333333333333333333333333333 {833333333333333333333333333333333333333333333333333 5333333333333333333333333333333333333333333333333333 E333333333333333333333333333333333333333333333333333 333333333333333333333333333333333333333333333333 3333 a) :26 g Oa§§2 0>§ §€ C .89 $933 c -z~ Co Bite .9: area Hm CC'D III—225 35%29532 0 1 m 0 3 o Mandatory-terminated The historical motives for CON regulation are complex and open to interpretation. According to Melhado (2006b): “What was in essence rationalization in the eyes of planners and their supporters was cartelization and self-serving in the eyes of their critics.” Health planning agencies like CON determined the size of a community’s hospital bed supply—a form of output restriction—and allocated areas of responsrbility both geographically and by activity—creating market divisions (Havighurst 1973). Output restriction and market division are classic characteristics of a cartel (McGinley 1995). Payton and Powsner (1980) provide a scathing analysis of CON in Michigan consistent with the cartel characterization. Payton and Powsner argue the three objectives of CON were: (1) to restore public confidence in the voluntary hospitals and their financing arm, the Blue Cross, in order to deflect growing pressure for government regulation of hospital costs and government-sponsored compulsory health insurance; (2) to protect the dominance of the existing large voluntary teaching hospitals; and (3) to channel hospital grth in the developing suburbs into large, full-service, general hospitals. Further they argue areawide planning was used through CON by the Blue Cross to ensure dominance in the health care market by eliminating “cream skimmersz” and to ward off government action for control and/or socialized health care by demonstrating voluntary collective self-discipline. The presence of small hospitals in large urban areas was theoretically inconsistent with the tiered regional hospital model or, in other words, hierarchical regionalism. 2 “Cream Skimmers” was a term given to small private hospitals ofl'ering lower than average costs for health care compared to larger hospitals. larger hospitals need primary care patients to subsidize more expensive treatments for other patients, provide subjects for their teaching programs, and protect their market for referral services (Payton and Powsner 1980). 20 E .u. 1333 2 {DI According to Havighurst (1973), the impact of third party payment and the prevalence of nonprofit firms may have provided dispensation for cartel-like behavior in the hospital industry. Third party coverage such as private insurance, Medicare, and Medicaid insulate consumers from concern regarding direct payment for services (Wolfson 2001). As a result, consumers could not be relied on to exert market discipline since suppliers could build new facilities and equipment knowing third party payers would cover their costs, and facilities were prone to the “medical arms race” in which hospitals competed for the best physicians, who in turn could attract patients (Conover and Sloan 2003). Research has shown increased competition in the health care services has the opposite effect of what is expected in conventional markets: hospital costs tend to be higher in areas with greater competition (Robinson and Luft 1987) and lengths of stay tend to be longer (Robinson and Lqu 1988). In 1974, the federal government passed the National Health Planning and Resources Development Act, Public Law 93-64] (National Health Planning Act) in response to the continuing escalation in cost of health care, growing concern over quality of care, and the emergence of national health insurance as a major policy issue (Werlin, Walcott, and Joroff 1976). The National Health Planning Act was enacted to establish a system for the regional planning of health services. The National Health Planning Act superseded the Hill-Burton Act and denied states funding from certain federal programs such as Medicare and Medicaid if the states did not have a state agency issuing certificates of need for new health care facilities and expenditures by 1980 (Cordato 2005). By 1980, every state had enacted CON regulation except Louisiana (later enacted in 1991) (Fig. 3). 21 j; a» I r" Federal support for CON ended in 1986 with the repeal of the National Health Planning Act because of concerns it had failed to reduce the nation’s aggregate health care costs and was beginning to produce detrimental effects in small communities3 (McGinley 1995). It was left to the discretion of each state as to whether to continue with CON without federal support; many states did (Fig. 3). On the national level, health care planning had come to an end primarily due to its failure to control costs. Currently fourteen states have eliminated CON programs: Arizona, California, Colorado, Idaho, Indiana, Kansas, Minnesota, New Mexico, North Dakota, Pennsylvania, South Dakota, Texas, Utah, and Wyoming. Only Indiana and Wisconsin have restored CON following repeal. Since Ohio dropped CON for hospitals and all other services except long term care, the State has seen the construction of 150 additional surgery centers and 300 additional diagnostic imaging centers; the State of Ohio has since proposed bringing back their CON law for hospitals (Jackson 2002). Virginia was scheduled to eliminate its program in 2002, but retained it (Jackson 2002). For an in depth discussion of the decline of national health care planning, see Melhado (2006). During the period of federal deregulation of health care planning in the 19803, Congress discovered a new way to control the cost of health care: health care fee 3 Small communities considered CON insensitive to local community needs. Congressional Representative Rowland of the Eight District of Georgia expressed this to Congress at the time: At first glance, the idea [ of certificate-ofneed] may have looked pretty good. In practice, the eflect of certificate of need on health care costs has been dubious, at best. And the program has certainly been insensitive in many instances to the true needs of our communities. The citizens of Putnam County are proud of their 20-year-old community hospital. They build it with local funding, without using any Federal Hill-Burton funds, and they still support it locally. They are proud enough to have recently approved a 1-cent sales tax to renovate the facility. They are not seeking an expansion. The hospital has always had 50 beds, and that '3 what they propose to maintain. However, when Putnam County authorities went to the State health planning agency for the required approval under the certificate of need program this year, they ran into unexpected trouble. The agency looked over the request for the locally funded hospital improvements and decided to deny it—unless the hospital eliminated ten beds. (134 Cong. Rec. H9455—01 as cited in McGinley 1995) 22 l. N ‘9. V" " regulation. In 1983, Congress passed legislation incorporating prospective payment into Medicare. Instead of reimbursing hospitals for the service costs set by the hospital, Medicare was now to pay a set flat fee for service, varying by type of diagnosis, arranged into 467 diagnosis related groups (DRGs). The DRG system gave the federal government the authority to establish the purchase price for identifiable services for Medicare beneficiaries (Stevens 1989). DRGs are still used today and are defined by the Health Care Financing Administration. Health care in the 19903 was substantially altered by the rise of managed care as a method to finance and deliver health care over traditional insurance (Gaynor and Haas- Wilson 1999). Unlike traditional third party insurance which does not restrict either the provider or treatment choices of patients or doctors, managed care programs, such as Health Maintenance Organizations (HMOs) and Preferred Provider Organizations (PPOs), limit both provider and treatment to reduce costs. Managed care proposed to control costs through volume price discounts for physicians, gatekeeper restraints on specialty consultations, drug formularies, prior authorization of tests and admissions, and retrospective denial of payment for unnecessary services (Zelman and Berenson 1998). This led to increased price competition in certain areas; “creaming”, the over-provision of services to low cost patients, “skimping”, the under-provision of services to high cost patients, and “dumping”, the explicit avoidance of high cost patients (Ellis 1998). Managed care caused other interrelated alterations in health care including the horizontal consolidation within markets for insurance, hospital services, and physician services and the blurring of the vertical distinctions between these markets (Gaynor and Haas-Wilson l 999). 23 Managed care was developed by employers, insurers, and some physician groups as a private sector alternative to governmental regulation (Robinson 2001). Managed care programs marketed themselves primarily to employers based on their ability to reduce the cost of health care benefits. In the past, third party insurance insulated consumers from considerations of cost and quality when choosing providers. Managed care programs restricted the consumer’s access to health care and increased the role of employers in health care. While managed care was an economic success, it was a cultural and political failure (Robinson 2001). The private health insurance industry in the United States changed its strategic focus, product design, and pricing policy as a result of the backlash against managed care (Robinson 2004). In the 20003, the health care system became increasingly consumer driven. Robinson (2001) keyed the term consumerism as the new era of health care where consumer preference dictates the priority setting of health care. Robinson saw the rise of consumerism in the United States as evidence of the rejection of professional, governmental, and corporate mechanisms for allocating health care resources. This assertion is dubious for the entire United States because the majority of states still employ governmental mechanisms such as CON (Fig. 3). Several states abandoned CON due to its failure to control health care costs, but quality and access to health care played a greater role in Michigan CON than in other states in the initial decision to adopt CON and decisions made thereafter under CON regulation (Conover and Sloan 2003). 24 a... 77 my R 2 1.2.3 Michigan CON Michigan first enacted its Certificate of Need law in 1972 as Public Act 256. Over the years the law was amended several times (Michigan Legislative Service Bureau 2002). While the National Health Planning Act was repealed in 1986, Michigan’s CON law was not. The goal of Michigan’s CON Program is to balance cost, quality, and access issues and ensure only needed health services are developed in Michigan. The Program’s ability to meet these goals was significantly affected by courts early on by overturning denied applications. To address this, Michigan’s CON Reform Act of 1988 was passed to create a clear, systematic standards development process and reduce the number of services requiring CON approval. The Act also created the CON Commission, with membership appointed by the Governor and responsibility for approving CON review standards. The Commission also has the authority to make recommendations to revise the list of covered clinical services subject to CON review. The CON Section within the Michigan Department of Community Health is responsible for day-to-day operations of the program, including making decisions on CON applications consistent with the review standards set by the Commission. In 1993, additional amendments to the Act required ad hoc committees to be appointed by the Commission to provide expert assistance in the formation of review standards. The Act was amended again in 2002 expanding the Commission to 11 members, eliminating ad hoc committees, and establishing the use of standard advisory committees (SACs) or other private consultants/organizations for , professional and technical assistance (Michigan Department of Community Health 2006). 25 Algl The Commission consists of 11 members (five members from one major political party and six from another) appointed by the governor with the advice and consent of the Senate. The Corrmrission must include: 0 Two individuals representing hospitals 0 One individual representing physicians engaged in the practice of medicine 0 One individual representing physicians engaged in the practice Of osteopathic medicine and surgery 0 One individual who is a physician of a school of medicine or osteopathic medicine 0 One individual representing nursing homes 0 One individual representing nurses 0 One individual representing a company that is self-insured for health coverage 0 One individual representing a company that is not self-insured for health coverage (Certificate of Need Commission 2005) Thus, two-thirds of the Commission are providers and about one-third are consumers/payers/purchasers (Citizens Research Council of Michigan 2005). There are four SACs: Open Heart Standard Advisory Committee, Cardiac Catheterization Standard Advisory Committee, Nursing Home Standard Advisory Committee, and CT Standard Advisory Committee; and a new Medical Technology Advisory Committee (Certificate of Need Commission 2007a). The Commission may use SACs to assist in the development of proposed CON review standards (Fig. 4). 26 The CON Commission may convene a standard advisory committee or request the services of private consultants. V The CON Commission, standard advisory committee (within 6 months of the ——> appointment), or private consultant. develops draft standards. Michigan DepaItment of Community Health (MDCH) provides staff assistance. V If a standard advisory committee or private consultant is appointed to draft standards, the recommendations for the proposed standards would be presented to the CON Commission. V MDCH and the Attorney General's office considers the Commission's, advisory committee's, or private consultant's, as applicable, proposed standards and may submit to the CON Commission proposed CON review standards that differ from the recommendations. V The CON Commission approves l ‘ PROPOSED standards. I CON Commission holds public hearing no less Sent to joint legislative committee for 30-day than 30 days prior to the CON Commission mtg. comment period along with a copy of the Public at which FINAL action will be taken. Hearing Notice and a concise summary of the expected impact of the proposed action (no less than 30 days prior to the CON Commission mtg. at which FINAL action will be taken). The CON Commission meets to approve. I disapprove. or revise proposed FINAL CON review standards. I V Standards are disapproved Standards are approved by the Commission. by the Commission. L The standards are returned to MDCH, Approved standards to joint legislative committee and standard advisory committee, or private the Governor along with a concise summary of the consultant, as applicable. for further work. expected impact of theproposed FINAL action for a 45-day review period that commences on a Legislative session day and includes at least 9 legislative days. I V Standards are approved by the joint Standards are disapproved by the joint legislative legislative committee and the Governor. committee and the Governor by concurrent resolution. V The CON review standards become effective and binding The standards do not become effective OR are on all parties and are sent to the Office of Regulatory returned to the CON Commisswn whom may Reform to be published in the Michigan Register. return the disapproved standards to MDCH. standard advisory committee, or private consultant, as applicable, for further work. Process for developing Certificate of Need review standards; redratted by Mark Finn, fiom Certificate of Need Commission 2007c Fig. 4 27 A person or entity is required to obtain a certificate of need, unless elsewhere specified in Part 222, for any of the following activities: 0 Acquire an existing health facility or begin operation of a health facility at a site that is not currently licensed for that type of health facility. 0 Make a change in the bed capacity of a health facility. 0 Initiate, replace, or expand a covered clinical service. 0 Make a covered capital expenditure (Legislative Council 2003). The CON application process includes five steps (Fig. 5): 1. Letter of Intent filed and processed prior to submission of an application, 2. CON application filed on appropriate date as defined in the CON Administrative Rules, 3. Application reviewed by the Program Review Section, 4. Issuance of Proposed Decision by the Bureau in which the Program Review Section resides, (appeal if applicant disagrees with the Proposed Decision issued), 5. Issuance of the Final Decision by the MDCH Director (Michigan Department of Community Health 2006). There are three types of CON review, each with an established time line by which MDCH CON Section must issue a proposed decision: nonsubstantive—45 days, substantive individual—120 days, and comparative—150 days (involving competitive applications for limited resources by two or more applicants) (Michigan Department of Community Health 2006). 28 21.? CON Section notifies ,---.-_-------_-----. _ CON Section receives Letter LOI to local of Intent (LOI) from applicant |f app|icab|e review agency 1 year applicant of required forms within 15 days I _ . . Application to local Apggfit‘ggcstg: to reVIew agency Additional information ’eV'eW Local review agen 15 days application review Request for additional 90 da 3 30 days information 30 days . (substanytive/ (non-substantIve) ti 15 days Yes ‘ comp ara V6) Additional information Recommendations received from a- licant sent ‘0 CON 39°90" Application deemed com o lete Substantive IndIVIdual Revrsw Non-substantive Review Proposed DeCIsIon Proposed DeClSIOn Approval Final deClSlon Final deCISIon hearing Reconsider, If applIcabIe Withdraw request Final decision Designated Application Dates Nonsubstantive—any workday Substantive-1 st workday of month Comparative—tat workday of Feb., June. or Oct. Final deClSlon Request for Potential Comparative 30 days Comparative Grouping Comparative Review Single proposed decision 90 days unless waived Michigan Certificate of Need application review process; redrafted by Mark Finn, Certificate of Need Commission 2003 Fig. 5 29 The American Health Planning Association conducts an annual survey of all 50 states and the District of Columbia CON Programs and compiles the information in its National Directory * Certificate of Need Programs * Health Planning Agencies. Michigan is classified by the American Health Planning Association in the mid-range of states for scope of CON coverage and monetary review thresholds. However, cross state comparisons are difficult due to state differences in service taxonomy, evaluation criteria, and state distinctions as to which entity provides the service such as limiting MRI CON coverage to inpatient hospitals (Citizens Research Council of Michigan 2005). Figure 6 presents a comparison of the services covered by CON in Michigan and surrounding states. Michigan is the most similar to Illinois in the number of services covered. Ohio and Wisconsin fall well below Michigan; New York provides greater coverage; and Indiana is excluded because it does not have a CON program. 30 2" Service Michigan Illinois New York Ohio Wisconsin USA Diagnostic Equipment 4 2 5 0 0 Cardiac Catheterization 0 J J J J 32 T Scanners J J J J J 21 MRI Scanners J J J J O 30 PET Scanners J J J J J 24 Ultra-Sound J J J 'J J 5 Surgical-Related 5 4 5 0 0 AmbulatorySurgical Centers J J J J J 30 Gamma Knives J J J 'J J 25 Lithotripiers J O J J U 26 Open Heart Units . J J J J J 34 Organ Transplant Units ”J J J O 'J 24 Other Acute Care 7 10 11 0 1 Acute Care Services J J J J J 32 Air Ambulance J 'J 'J J ‘J 12 Burn Care J J 'J J J 14 Business Computers J J J J J 4 Medical Office BUIldings J J J J J 3 Mobile HI h Tech J J J J J 20 Neonatal CU J J J J J 27 Obstetrical . J J J J 'J 1 0 Psychiatric Servrces J J J J J 29 Radiation Therapy J J J J -J 30 Rehabilitation J J 'J J J 30 Renal Dial sis J J J J J 23 Subacute are J J J “J J 12 Substance Abuse J J J J 'J 28 Swmg Beds J J J J J 17 Long Term Care-Related 1 2 4 1 2 38 Home Health J J 'J J J 24 ICF/MR J J J J J 26 Long Term Care .. . J J J J J 38 ReSIdentIal Care FaCIlItIes J J 'J J J 6 Grand Total 17 18 25 1 3 Services covered by Certificate of Need in states surrounding Michigan; blue dots indicated covered services, and red dots indicate services not covered; redrafted by Mark Finn, from American Health Planning Association 2004 Fig. 6 The Michigan Auditor General issued a performance audit of Michigan’s CON program in 2002 that contained five findings (McTavish 2002). Four of the findings related to costs and revenues of the program including fee structure, monitoring approved CON projects, application fee refunds, and monitoring compliance with CON review standards. The fifth finding was that: [MDCH], in conjunction with the CON Commission, had not evaluated the CON Program in order to determine whether the CON Program was achieving its goal of balancing cost, quality, and access issues and ensuring that only needed services are developed in Michigan (McTavish 2002). This was considered a “material condition,” meaning that: 31 ...a condition existed which that could impair the ability of management to operate a program in an eflective and efi‘icient manner and/or could adversely affect the judgment of an interested person concerning the eflectiveness and efficiency of the program (McTavish 2002). MDCH responded to this audit through a subcontract administered by the Michigan Public Health Institute (MPHI) to Conover and Sloan of the Center for Health Policy, Law and Management at Duke University (nationally recognized experts in the field) to evaluate CON in Michigan (Conover and Sloan 2003). In the past, Conover and Sloan wrote several articles critical of health care regulation (Sloan 1983, 1981; Sloan and Steinwald 1980), including a summary of the findings of an extensive examination of CON for the Delaware Health Care Commission (Conover and Sloan 1998), indicating regulation in general and CON regulation do not reduce health care spending; removal of CON regulations does not lead to a surge in acquisition of facilities and costs; and that CON regulation does not have much affect on quality of care but may improve access (Sloan and Steinwald 1980; Conover and Sloan 1998) for the uninsured, underinsured, and inner city population (at the expense of access in suburban areas). Their conclusions about Michigan CON were consistent with their 1998 paper. A follow-up audit report of MDCH-CON in 2005 indicated the Conover and Sloan report did not conclude whether the CON Program was achieving its stated program goals and MDCH still lacked measures to evaluate the performance of the CON program (McTavish 2005). “...[MDCH] had not developed quantifiable goals and objectives to help in evaluating the overall performance of the CON Program” (McTavish 2005). The Federal Trade Commission and the Department of Justice issued a report in July of 2004 titled “Improving Health Care: A Dose of Competition”. It recommended states 32 l\ r' reconsider CON programs because they are not successful in containing health care costs, and they pose serious anticompetitive risks that usually outweigh their purported economic benefits (Federal Trade Commission 2004). Supporters of the Michigan CON Program state that Michigan’s approach is different from other programs because standards are established by an independent commission that are tied to quantifiable requirements and cover all types of providers wishing to offer service (Citizens Research Council of Michigan 2005). In recent years, three prominent subjects of debate over Michigan’s CON Program have been (1) its overall value, (2) its standards used to evaluate applications and for monitoring ongoing operations of certificate recipients, and (3) the inability of certain hospitals in Detroit to open new hOSpitals in suburban locations (Citizens Research Council of Michigan 2005). 1.2.4 Measurement of Demand The epidemiological model of the delivery of health care services does not include demand (Fig. 7). The model does however include need which is not the same as demand. Health care need in epidemiology is defined as any self-perceived deviation from societal norms of health or problem detected by a health profession (Oleske 2001). 33 Health Status organizational ~personnel Characteristics -technology — Utilization of Health Care Services -accessiblity -knowledge —availability -attitudes -beliefs -dem raphics physical environment -lifesl e -economic environment -social environment I from Oleske 2001 Fig. 7 A wide variety of definitions of need have been developed, and “it may be an illusion to suppose that there might ever be a consensus about the meaning of needs” (Culyer 1998). From a legal perspective, statutes have been constructed requiring the establishment of need as a precondition for the construction and operation of hospitals and other facilities (Case 1975). However, the concepts of need and demand are difficult to define for health care (Boulding 1966; see also Asadi—Lari, Packham, and Gray 2003). From an environmental sociology perspective, Bradshaw’s taxonomy of need can be defined as: normative need, which is determined for individuals by professionals; felt need, which is expressed by individuals themselves; expressed need, which leads to a demand for service; and comparative need, which is professionally determined for certain Population population subgroups (Bradshaw 1972). 34 I Epidemiological model of the delivery of health care services; redrafted by Mark Finn, I need of phj derive health prod UCt H ea geograp DCpendi fell need demand CON usi Ontside 0 cost, pm JOnes 20. health Ca need. Cons: of need 1 (Palmer . correlatir (New Yr "umberS Demand is an economist’s term related to supply and need. A population’s health care need should generate an appropriate demand, which then should be supplied in the form of physicians, facilities, and services by a health care delivery system. Health care has a derived, rather than a direct, demand (Grossman 1972). Consumers have a demand for health but cannot purchase it directly; they must purchase services that are used to produce health (Ringel et al. 2002). Health care demand is an approximation of consumer (patient) need. In medical geography, health care need and demand can be very different (Ricketts et a1. 1994). Depending on how one measures them, demand can be mistakenly used as a proxy for felt need. Differences in measurements, data sources, and experimental design affect the demand for health care services. Demand for the most part is defined by each state’s CON using a bed to population need ratio. The measurement of health care demand outside of a state’s CON program is found in econometric literature which is focused on cost, production, insurance, income, and capacity rather than access and quality (see Jones 2000; Grossman 2000). Again, the focus of this research is on the demand by a health care system for services based on a measurement of a population’s health care need. Consideration for the number and distribution of hospital beds related to the demand of need for services began to appear after 1920 as a general subject for consideration (Palmer 1956). In 1920, the New York Academy of Medicine conducted the first study correlating the need for general hospital beds with the population served in a given area (New York Academy of Medicine 1921). The study’s conclusion established actual numbers and types of beds needed and recognized that a central group or agency was 35 m... 7: vi. .. 2 needed to coordinate the use of hospital beds throughout the city. No prior efforts were made to relate the construction of hospital facilities to the requirements of their service area (Rosenthal 1964). The two most ambitious early efforts to estimate bed requirements were the 1945 Public Health Service study and the 1947 study of the Commission of Hospital Care (Rosenthal 1964). The Public Health Service study (Mountin, Pennell, and Hoge 1945) established the 4.5 beds per 1,000 people rule used by the Hill-Burton Act and introduced the concept of health service areas. The Commission of Hospital Care study (1947) introduced a new approach to estimating bed needs fiorn utilization data: bed-death ratio. The bed-death ratio used an estimate of the proportion of deaths that would occur in a hospital and the predicted death rate of a population to predict general hospital bed requirements (Rosenthal 1964). Several states used this method including Michigan and New York (Rosenthal 1964). Michigan’s bed-death ratio is discussed further in Section 1.2.5. The Hill-Burton Act’s original methodology that required states to not exceed 4.5 beds per thousand people, except in rural or sparsely populated areas, was revised in the 19603 to include three major criteria for assessing bed need: 1) population (projected for five years); 2) use rates (i.e., the number of bed-days used by the population); and 3) an occupancy factor (i.e., the average percentage of beds maintained for patient care that are filled; for general hospital beds this average was 80 percent). An adjustment factor was also incorporated to help smaller hospitals adapt to fluctuating demands and emergencies (Melum 1975). The Hill-Burton formula consisted of. Use Rate = Number of patient days / current population 36 Average Daily Census (ADC) = Use rate * projected population / 365 days Projected hospital bed need = ADC / .80 occupancy + 10 adjustment factor In 1972, the Hill-Burton occupancy level standard was changed fi’om 80 percent to 85 percent, and the + 10 adjustment factor was dropped in 1973 (Melum 1975). Also in 1973, the Federal Government altered its regulations regarding Hill-Burton’s projection of existing use patterns in Federal Policy Memorandum No. A-1-73 allowing states to 1) discontinue the use of projected use rates, 2) use a maximum use rate in areas where the bed need formula reflects an unrealistic and excessive demand, and 3) use a minimum use rate in areas where there is no previous record of hospital utilization (Technical Advisory Committee 1977). The 1974 National Health Planning Act superseded the Hill- Burton Act, and bed need methodologies were defined by each state’s CON program. See Melum (1975) for an overview of different states’ bed need methodologies at the time. Many factors influence the demand for hospital beds. A literature review of early studies on measuring bed needs for general hospitals compiled a list of 30 factors influencing bed needs (Appendix 1). However, these factors do not directly influence bed need or demand; they directly influence access and utilization. In the past few decades, these factors have been present as major topics in the study of health care access and utilization (see Section 1.2.1 for a discussion of health care utilization). “Roemer’s Law” had a profound effect on the measurement of a health care system’s demand for services using hospital beds. In 1961, Roemer published a study which indicated hospital bed expansion in one region increased utilization, despite the absence of major changes in the morbidity of a population (Roemer 1961). This casual generality of a bed built is a bed filled or increasing supply will increase admissions or length of 37 . 2} 3H,. stay has become known as “Roemer’s Law” in health services research (Kroneman and Nagy 2001). Roemer’s observation has been the foundation of CON legislation to eliminate duplicative services in the interest of reducing costs and overutilization. Several studies on hospital utilization have shown Roemer’s observation is not valid in all cases including: a study on rural Iowa which showed utilization was more related to the number of unique hospital services than bed supply (Rohrer 1990); a study in the Netherlands which found a positive correlation between bed supply and length of stay but not admission rates using both micro and macro level data (van Doorslaer and van Vliet 1989); and a study in Germany using a regression analysis of 13 different economic and social variables which could not contradict the effect of Roemer’s Law in Germany but concluded the validity of Roemer’s Law in Germany was (at least partially) due to the existing hospital planning (Kopetsch 2006). 1.2.5 Michigan’s Acute Care Bed Need Methodology Michigan’s original standards for defining health care demand was outlined by the Michigan Hospital Study Committee in the Michigan Hospital Survey Report (Michigan Hospital Study Committee 1946). Their formula for estimating need for general hospital beds initially did not follow the guideline of 4.5 beds per 1,000 people set by the Hill- Burton program. Data on sickness (utilization) were rarely available and expensive, so general hospital bed need was related to the incidence of births and deaths. Their formula for incidence of birth assumed for each birth one bed is needed for an average length-of- stay of 11 days. This would require about 3 occupied beds per year for each hundred births. The Bed-Death Ratio was used for the calculation of the incidence of deaths. At 38 BI the t hosp ratio equal for an be hos Mi and re; in aCCt Bunon FSAs h Cement Centers, hierarch HOSpitaI medically UHIVer-Sl'. the time in the United States, statistics showed the public used about 250 days of general hospital care for each death and correlated sickness in a general hospital. The bed-death ratio was 250 divided by 365 days—which equals .685 or about .7; each hospital death equals seven-tenths of a bed issued for one year. The calculation of occupied beds needed for an area was the product of the bed-death ratio and the number of deaths expected to be hospitalized. Michigan’s original facility service areas (FSAs), or a geographic area of available and readily accessible health care services used in regional health planning, were defined in accordance with the United States Public Health Service regulations set by the Hill Burton Act‘. The Hill-Burton Act required state planning agencies to divide a state into FSAs based on a hierarchical system with base areas at the top centered on a medical center-teaching hospital, followed by regional hospital centers, community hospital centers, and public health and medical service centers. Federal law thus imposed a hierarchical health care system: (1) Teaching Center Hospital, (2) Regional Center Hospital, (3) Area Center Hospital, and (4) Community Hospital. In Michigan, the medical/teaching centers were the University of Michigan in Ann Arbor and Wayne University in Detroit. The regional centers were to have 200 beds or greater; the community hospital centers required 50 beds or greater serving 15,000-20,000 people or less if the hospital was more than 30 miles from a “good hospital”; and the public health and medical service centers were areas too small to justify 50 beds but were large enough or isolated. It was assumed people travel to the nearest hospital for minor illnesses- 4 Michigan has changed its naming convention over time: “hospital service areas” changed to “facility service areas” or FSAs and are presently called “hospital subareas”. To eliminate confusion, this thesis will use FSA to refer to hospital service areas, despite being later renamed hospital subareas. The acronym HSA will only refer to Health Systems Agencies as designated under the 1974 National Health Planning Act. 39 usually a corrrmunity hospital-and for major illness physicians recommend they go to regional or teaching hospitals. It was also assumed the regions would conform to trade areas recognized by commercial marketing agencies (Michigan Hospital Study Committee 1946). The delineation of F SAs was tentatively delineated in an “experimental, or trial and error basis” taking into consideration natural barriers, such as lakes and rivers (Michigan Hospital Study Committee 1946). The Michigan Hospital Study Committee commented on how unfortunate it was that county lines and these FSAs did not coincide (Figure 8). 40 ‘V El HOW ....... Facility Service Areas + Teaching Hospital Center , '3’ Regional Hospital Center E + Area Center Hospital I Community Hospital ——-———- FSA Boundary 75 E::1Miles .‘ W ‘ , 1:4,3oo,ooo . "r"1"+ . j _ - _ - "’ ... .. .- .. First Facility Service Areas in Michigan, 1946; redrafted by Mark Finn, from Michigan Hospital Study Committee 1946 Fig. 3 In the 1955 Michigan State Hospital Plan, the FSAs were redefined: the FSA boundaries were mapped alongside US. Census Minor Civil Division (MCD) borders; all out-of-state hospitals were removed other than South Bend and Sturgis, IN and Toledo, 41 r I ’5 C1\r\‘ OH: hiera Survc was a nevert arounc pseudo nearly 1 bed-den thouSan. Per thou for base redistribi hOSpitaIs OH; several community hospitals were added throughout the state changing the hierarchy; and a teaching hospital center was added to Grand Rapids (Office of Hospital Survey and Construction 1954) (Fig. 9). This teaching hospital center in Grand Rapids was a proposed site for a third medical school in Michigan that was never built but nevertheless factored into the delineation of FSAs. Except for the consolidation of areas around Muskegon and Stambaugh, when comparing the FSA boundaries between the pseudo-Thiessen Polygons in 1946 to the MCD aligned borders in 1955, they appear nearly identical. This is surprising considering the 1946 use of the incidence of births and bed-death ratio and the 1955 use of the Hill-Burton Act requirement of 4.5 beds per thousand people for the entire population of a state—individual areas could be 2.5 beds per thousand for rural, 4.0 beds per thousand for intermediate, and 4.5 beds per thousand for base areas (Office of Hospital Survey and Construction 1954). Possibly, the redistribution of hierarchical categories among hospitals and the addition of community hospitals helped maintain identical FSAs over the decade. 42 , .3 2/ h I... I... 2 Facility Service Areas If Teaching Hospital Center '1‘} Regional Hospital Center + Area Center Hospital - Community Hospital —--— FSA Boundary 75 1:: Miles 1:4.300,000 Facility Service Areas in Michigan, 1955; redrafted by Mark Finn, from Office of Hospital Survey and Construction 1954 Fig. 9 Despite slight increases in the population and bed requirements for each category to reflect population growth, the definition of FSAs for Michigan defined under the Hill- Burton Act (later Hill-Harris Act) remained the same until 1963 (Office of Hospital Survey and Construction 1954, 1957, 1959; Michigan Department of Health 1961, 1963). 43 The bed need methodology did change in the 1955 Michigan State Hospital Plan. Instead of the incidence of births and death, the new methodology used guidelines set by the US. Commission on Hospital Care. It was assumed individuals in each FSA should be able to receive at least 1,300 hospital days care per thousand: 1,000 days per thousand in local (community) hospitals, 200 days in regional hospitals; and 100 or more days in base, or teaching, hospitals. Each FSA was assigned a need of 1,300 hospital days per thousand peOple. The numbers were summed and divided by 365 to obtain an estimated average daily census. TO allow for the fact that hospitals cannot Operate at 100 percent of occupancy, the Commission on Hospital Care formula was modified to include an occupancy factor: the square root of the estimated average daily census was multiplied by 2.5 (Office of Hospital Survey and Construction 1957). The total number of beds needed in each F SA was the estimated average daily census plus the occupancy factor. When the use of this formula resulted in percents of occupancy in excess of 85%, it was not used. Instead, the estimated average daily census was divided by .85 to get the number of beds needed. Est. Avg. Daily Census = 1300 beds * FSA Population / 1000 / 365 Total Bed Need = 2.5 * SQRT(Est. Avg. Daily Census) Despite slight alterations in hospital days per thousand and calculations of total population for specific regions of Michigan (particularly the Detroit Metro), the bed need methodology for Michigan remained the same until 1965 (Office of Hospital Survey and Construction 1954, 1957, 1959; Michigan Department of Health 1961, 1963, 1965). However in 1961 long-term care was separated from acute care bed need, and their bed 44 o/\ m need methodology was renamed acute care bed need (Michigan Department of Health 1961) The first significant change in methodologies since 1955 was in the definition of FSAs in 1963. Prior to 1963, in the Michigan Department of Health’s Michigan State Plan for Hospital and Medical Facilities Construction the creation Of FSAs was described as follows: Under the regulations of the United States Public Health Service, the State Agency is required to divide the State into hospital service areas [FSAs]. These areas serve as the basis for developing the general hospital construction program. For purposes of this Plan, hospital service areas [FSAS] in Michigan shall be designated respectively (a) base, (b) regional center, and (c) community. These areas as defined below conform with the United States Public Health Service definitions for base, intermediate, and rural areas (Michigan Department Of Health 1961). In 1963, the Michigan Department of Health’s Michigan State Plan for Hospital and Medical Facilities Construction described the creation of FSAs as follows: Under the regulations of the United States Public Health Service, the State Agency administering the Hill-Burton program must divide the state into health facility service areas [F SAs]. These areas serve as a basis for developing the construction program. They have been set up in terms of normal trading areas, taking into consideration population distribution, transportation and trade patterns, travel distance and data indicating the residence of patients served by existing hospitals. In general, boundaries of health facility service areas [F SAs] are so drawn that, with a few exceptions in the northern part of the state, no person in Michigan is more than 30 minutes travel time from an acute care facility (Michigan Department of Health 1963). The description goes on to describe the designation of base, regional centers, and community F SAs as was written in the previous Plan. 1963 was the first time important geographic factors were taken into consideration. However, nothing else was written in the document to indicate how these factors were derived or used in the definition of 45 m 7.? Ow. Z F SAs. In 1966, the Michigan State Plan for Hospital and Medical Facilities Construction included two maps showing average 24 hour traffic flow in 1962 and Michigan’s 1960 Census population distribution. Nothing was written in the Plan to indicate who made the maps or how they were incorporated in the definition of FSAs. The 1963 definition of FSAs for Michigan remained the same until 1978 (Michigan Department of Public Health 1966, 1967, 1968, 1969, 1970, 1973, 1974, 1975; Nash 2007A) (Fig. 10). The hierarchical system was retained with the additional criteria that patient referral patterns be present up through the hierarchy. The FSAs are known as the 77 Hill-Burton subareas. 46 N t; 2.3 W “i Facility Service Areas —— FSA Boundary , 75 ' 1::IMlles .. .- 1:4,3oo,ooo " " . _ 7 Facility Service Areas in Michigan, 1975; redrafted by Mark Finn, fiom Michigan Department of Public Health 1975 (hospital locations not shown in original map) Fig. 10 The second significant change in methodologies since 1955 was in the calculation of acute care bed need in 1965. Federal regulations now required an occupancy factor of 80% + 10 acute care facilities. The formula assumed existing patterns of utilization would 47 continue into the future, and that the only need for additional beds was to accommodate population growth and current overcrowding. The formula penalized the low-utilization areas where lack of facilities prevented the natural level of utilization fiom developing and rewarded high utilization areas. The State of Michigan received Federal approval to modify the formula for estimating bed need to (1) provide a mechanism for upgrading low utilization areas, (2) provide a ceiling for high utilization areas, and (3) provide a realistic method for estimating bed need in the Detroit metrOpolitan area (Michigan Department of Public Health 1965). Essentially differing totals of hospital days per thousand were applied to specific regions in the state and the occupancy factor for acute care facilities was adjusted. Acute care bed need dramatically changed in the early ‘703 as Federal Policy Memorandum No. A-l-73 allowed changing of the formula used to determine acute care bed need. Michigan’s acute care bed need formula of the early ‘70s added age adjustments, referral adjustments, and obstetrical use rates (Michigan Department of Public Health 1973, 1974, 1975). The 1974 National Health Planning Act required all states to define areawide Health Systems Agencies (HSAs) for health care planning. Michigan politically defined 8 HSAs without any geographic or scientific consideration. These regions mapped to county boundaries and the City of Detroit are displayed in Figure 11 as they appear today. 48 Hospital System Areas 2007 wI 1- Southeast - 1 1- City of Detroit - 2 - Mid-South - 3- Southwest I jg] 4- West - 5 - Genesee I:j 6 - East Central - 7 - North Central - ‘ -. A‘s I i A A - 8 Upper Peninsula 1; 3,.» fl?” , . in :- glee} $32.3 I use 75 fig; ] 1:1Miles '3 " if}: r 12.4 300, 000 ‘ ' Hospital System Areas in Michigan, 2007; redrafted by Mark Finn, from Citizens Research Council of Michigan 2005 Fig. 11 In the mid 19703, the Acute Care Bed Need Methodology Project was initiated to revise Michigan’s Hill-Burton rooted bed need methodology (Technical Advisory Committee 1977). The Project was developed by the Michigan Association of Areawide 49 Comprehensive Planning Agencies, in cooperation with the office of Health and Medical Affairs and the Michigan Department of Public Health. The purpose of this Project was to develop the most reliable and equitable methodology available for projecting hospital bed need for the State of Michigan. A Technical Advisory Committee, representing the major health interests in the state was appointed to assist the project. Appointments to this committee were made by: Each health planning agency in Michigan - Blue Cross and Blue Shield of Michigan - Bureau of Hospital Administration of the University of Michigan - Commission on Professional and Hospital Activities (Advisory Participant) - Greater Detroit Area Hospital Council - Michigan Department of Commerce-Insurance Commission - Michigan Department of Mental Health - Michigan Department of Public Health — Michigan Hospital Association - Michigan Osteopathic Hospital Association - Michigan Society of Osteopathic Physicians and Surgeons - Office of Health and Medical Affairs. The Bureau of Hospital Administration of the University of Michigan was retained as technical consultant to the project. An initial Project recommendation was for the State of Michigan to adopt a normative approach for planning medical/surgical services. The normative approach incorporated a measure of expressed met demand and “expert judgment” to make decisions about 50 appropriate hospital use. In this way, “. . .the standards chosen reflect what a community ggm to be like as seen through the eyes of a group of well-meaning community professionals as well as what it is as measured by expressed met deman ” (Technical Advisory Committee 1977). A weakness mentioned by the Project about their recommendations for a methodology was that a satisfactory decision rule for grouping hospitals based on patient origin data had not been researched. Michigan’s acute care bed need methodology developed out of the recommendations, research, and compilation of patient data of the Project. This methodology is still used today. The CON Review Standards for Hospital Beds (Certificate of Need Commission 2007b) details the Michigan CON Commission’s current standards of measuring health care demand including (1) the definition of facility service areas (FSAs); (2) the determination of needed hospital bed supply; (3) bed need; (4) the requirements for approval of new beds in a hospital; (5) replacement of beds in a hospital in a replacement zone; and (6) relocation of existing licensed hospital beds. The standards for defining FSAs were written in 1978 by J. William Thomas, John R. Griffith, and Paul Durance of the Program and Bureau of Hospital Administration, School of Public Health, University of Michigan (Thomas, Griffith, and Durance 1979). Seventy-one FSAs were defined using aggregate hospital patient discharge data fi'om 1976. These FSAs remained the same for the entire state until the CON Commission developed a new set for just the southern Lower Peninsula and Traverse City area of Michigan in 2002. The work group at the time decided to keep the same areas for the Upper Peninsula and northern Lower Peninsula after running the methodology several times with different parameters and levels of aggregation (Nash 2007). Each hospital in the State of Michigan is assigned to a 51 FSA until the CON Commission revises these FSAs. The FSAs are no longer mapped by the Commission and are listed in Appendix 2. These FSAs can be amended to reflect new sites assigned to a specific hospital service area, hospital closures, and licensure actions. These FSAs are to be updated at the direction of the Commission no later than two years after the official date of the federal decennial census, provided that population data at the federal ZIP code level, derived from the federal decennial census, are available; and final MIDB data are available to the Department for that same census year. The 1978 method to define FSAs, developed by Thomas, Griffith, and Durance (1979), was a two step approach. The first step involved defining three sets of F SAs with three different objective models. The second step relied on a subjective panel of “experts” whom selected FSAs they felt were reasonable. The first step built off previous “relevance indices” or clustering methods for defining FSAs: Lembcke’s Equal Likelihood Method (Poland and Lembcke 1962), Griffith’s Relevance Index Method (1978), and a variant on Lembcke’s method developed by Gittelsohn and Wennberg (1977). Poland and Lembcke defined FSAs by aggregating ZIP codes to a hospital where 50% or more of the p0pulation utilize the hospital. Gittelsohn and Wennberg used the same approach but specified 60% or more. Griffith used a relevance index where instead of assigning an entire population to a hospital, the size of each hospital service population is calculated by multiplying each ZIP code’s total population by the percentage of patients fi'om the ZIP code who use that hospital, and finally summing these values over all ZIP codes. Thomas, Griffith, and Durance (1979) argued none of these methods worked well when applied to hospitals in large urban areas. Their argument was Lembcke’s Equal 52 Likelihood Method and Gittelsohn and Wennberg’s variant did not work well because urban hospitals typically have few ZIP codes with relevance indices greater than 50%. Additionally, Griffith’s method could not create well—defined geographic areas for service communities with small relevance index values. Thomas, Griffith, and Durance proposed a method, based on Griffith’s aforementioned relevance index method, to assign hospitals to clusters to maximize the average relevance index while constraining the maximum number of hospitals per cluster and/or minimum number of clusters formed which they called the “max relevance algorithm”. Letting relevance index Rij be the proportion of residents of areal unit i utilizing hospital(s) of cluster j, R; approaches an upper limit of 1.0 as more hospitals are added to cluster j. They used two other heuristic techniques, a “greedy heuristic” and a max-flow/min-cut algorithm, to determine near- optirnal solutions for comparison to their own technique. As Thomas, Griffith, and Durance (1979) describe them, the “greedy heuristic” and max-flow/min-cut algorithm form clusters by partitioning ZIP codes into non- overlapping cluster service areas and utilize a “patient flow” matrix developed from patient origin data. For a region with N areal units, each ij element of the N by N matrix gives the number of patients residing in areal unit i who utilize hospitals located in areal unit j plus the number residing in j who use hospitals in i. Both algorithms also require that M < N of the areal units be selected as cluster service area centers, where M is the number of clusters to be formed. Cluster area centers were selected using methods defined in Thomas (1979). Greedy heuristic. The “greedy heuristic” is not well described in Thomas, Griffith, and Durance (1979) or Thomas (1979). The greedy heuristic builds up cluster areas 53 through the sequential assignment of areal units based on areas sharing the greatest amount of patient flow until all areal units have been assigned to a cluster. Max-flow/min-cut algorithm. Thomas, Griffith, and Durance (1979) misrepresent this algorithm by describing the max-flow/min-cut algorithm as a technique that: ...defines clusters by ”cutting” the region into ever-smaller pieces. As a first step, the region is divided into two cluster service areas. Another cut is then made to yield three clusters. The next cut yields four clusters, etc. Each areal unit in the region is considered to represent one node of a network; and the capacity of the are connecting areal units i and j in the network is defined to be the patient flow shared between i and j. Ford and Fulkerson ’s [1956] max-flow/min-cut theorem then provides a basis for locating optional partitions (1979). The max-flow/min-cut algorithm is actually a special kind of linear programming problem for working with networks which states, “The maximum possible flow from left to right through a network is equal to the minimum value among all simple cut-sets” (Elias, Feinstein, and Shannon 1956). In other words, the maximum flow in a network is bound by its bottleneck. The network might represent communication channels, a railroad system, a power feeding system, or a network of pipes, provided it is possible to assign a definite max allowed rate of flow over a given segment or branch (Elias, Feinstein, and Shannon 1956). The max-flow/min-cut algorithm cannot define “clusters by “cutting” a region into ever-smaller pieces” (Thomas, Griffith, and Durance 1979). Ford and Fulkerson (1956), cited by Thomas, Griffith, and Durance, never indicate the max-flow/min- cut algorithm could either delineate a geographic area or cluster points. Initial attempts to define hospital clusters in the Detroit Metro with the greedy heuristic and max- flow/min-cut algorithm failed due to the number of ij paths in the patient flow data which made them intractable for the computer to process. Both 54 algorithms were reapplied independently to each ZIP code. Neither algorithm was able to locate an acceptable solution for the Detroit hospitals. Solutions contained single hospital clusters and one large cluster containing around 30 hospitals and 60 ZIP code areas. Additional calculations were made with certain large hospitals removed from the data, but problems with single hospital areas embedded in larger cluster areas and unacceptably large cluster areas in the central city continued to occur (Thomas, Griffith, and Durance 1979). Max relevance algorithm. The first step in the max relevance algorithm is to calculate a population-weighted average relevance index Rj for each hospital. Letting: Pi population of areal unit 1'; dij = number of patients from areal unit i treated at hospital j; D,- = E dij = total patients from areal unit i; j Ij = {i|(dij / Di) } 20:}, set of areal units for which the individual relevance values (dij / D,) of hospital j exceeds or equals 01, where a is specified 0 $01 51. Then R, = E P,(dij / Di) if Ij EP,‘ 16 If After Rj is calculated for each individual hospital, the hospital with the smallest Rj is identified and is grouped with the hospital having the greatest individual relevance in hospital j ’5 home areal unit to form a cluster. A new value of Rja- is determined as above, 55 L. N where j* refers to the two-hospital cluster. Values of Rj and Rjau are again scanned to identify the minimum Rj. The identified hospital or cluster is grouped with the hospital or cluster having the greatest individual relevance in the identified hospital’s home areal unit. When a cluster j* is identified for clustering, its home areal unit is assumed to be the areal unit of the hospital having the highest R5]- among the cluster hospitals’ home areas. This iterative process terminates when: (1) all hospitals have been aggregated into a single large cluster; (2) a user-specified number of iterations have been completed; or (3) all identified clusters are stable. Condition (3) occurs when no cluster serves more than 0: of the patients in the home areal unit of any other cluster (where a is a percentage of the home area discharges). Thomas, Griffith, and Durance completed the first step of the 1978 method to define FSAs on the Detroit Metro and for the most part discarded the greedy heuristic and max- flow/min-cut algorithm cluster results for the “max relevance algorithm” results. Thomas, Griffith, and Durance’s predilection for Griffith’s own “max relevance algorithm” may explain why they choose techniques for comparison which could not be computed and ultimately failed; one of which was questionable at best as to its relevance for delineating geographic areas or clustering. The second step of the process relied on a subjective “group of experts familiar with the local area”. The experts included four representatives from Detroit area hospitals, one fiom the health systems agency staff, one from the Greater Detroit Area Hospital Council, and two non-provider board members of the health systems agency. The experts selected clusters to define FSAs which in their opinion were the most reasonable and 56 made the final determination as to which hospitals were to be clustered together. Although this methodology was originally specified to define FSAs for communities in large metropolitan areas (meaning Detroit), the max relevance algorithm was applied to the rest of Michigan and additional cluster review panels were formed to modify the results. This methodology, which has become known as the Gn‘fiith Methodology, is the current methodology used by MDCH-CON to assign new hospitals to existing FSAs and define new FSAs. The methodology has been slightly modified to exclude ZIP codes (i) with a market forecast factor less than .05. The market forecast factor is the number of total inpatient discharges indicated by a market survey (created by an applicant applying for the approval to build a new licensed site for a hospital not MDCH) divided by the base year discharges. Also, F SAs or clusters with R]- scores less than .10 for all ZIP codes (areal units) are deleted from the computation (Certificate of Need Commission 2007b). The Griffith Methodology was last applied to the State of Michigan in 2002 where, as previously mentioned, results were discarded in favor of the 1978 FSAs for approximately 75% of the state (FSAs for the southern Lower Peninsula (Detroit) and Traverse City were modified). An accurate recreation of the Griffith Methodology to define FSAs is impossible due to the entanglement of political, business, and perceived public interests ascribed by expert committees composed of individuals representing hospitals, health systems, councils, and insurance providers. Michigan’s acute care bed need methodology (discussed in detail in Section 2.2) relies on the FSAs defined by the Griffith Methodology. The bed need methodology has come under fire in recent years. In Conover and Sloan’s evaluation of Michigan acute 57 1.. t 7p m bed CON methodology (2003), they concluded no evidence indicates CON impacts costs/availability of hospital beds; nor would lifting restrictions on beds result in a surge in building of new facilities; and no evidence suggests CON for beds affects quality. The strongest case for continuing CON for hospital beds was for access. Key informants suggested in their study that if CON regulation for beds continued, the following improvements could be made: a) fix bed need methodology so that it is based on more current data; b) increase flexibility by permitting transfers of beds within hospital systems; and c) develop a mechanism to take excess capacity offline. The debate over the acute care bed need methodology continued, and MDCH contacted Prof. Griffith at the University of Michigan to lend support to his 1978 methods. Griffith responded with a letter on January 14, 2004 writing: I can no longer support the bed-need methodology as being in the best interest of the people of Michigan. The material [Larry Horvath, Manager of Michigan 's CON Program] submitted frequently references the fact the methodology is old, and that conditions have changed, but it understates the magnitude and implications of those changes. Medical care itself: health insurance, information availability, and population needs have changed to an extent that makes the approach of approving hospital investment based on counts of total beds inappropriate... my recommendation is that the bed need methodology be abandoned (Griffith 2004). 58 \— J. 2 Methods 2.1 Computer Architecture and Data The Michigan Department of Community Health CON Section and the Michigan Certificate of Need Commission (MDCH-CON) utilize three data sets to determine health care demand within the State of Michigan. The primary data set is the Michigan Inpatient Data Base (MIDB). The two secondary datasets are population projections created by the Michigan Department of Transportation (MDOT) and decennial census data compiled by the United States Census Bureau. The presented research in this thesis will only utilize these three data sets, along with a list of acute care hospitals in Michigan provided by MDCH. 2.1.1 Computer Architecture (System and Programs Used) All analyses were computed on a Sun Microsystems Ultra 20 Workstation with a 2.61 GHz AMD Opteron Processor 152 and 3.37 GB of RAM. The workstation was running Microsoft Windows XP Professional. Additionally a Sun Microsystems Fire V402 Server containing MySQL 5.0.22 was remotely logged into from the Workstation using a JDBC connector to Star Office Base 8. The server ran Sun Solaris 10 (x86) with two 2.39 GHz AMD Opteron 850 Processors and 4.03 GB of RAM. Additional software used for this research includes Microsoft Access and Excel 2007, DBF Viewer 2000, SPSS 15.0, R 2.5.1, Python 2.5, ESRI ArcGIS 9.2, ESRI Arc/Info 9.2, and Adobe Illustrator and Fireworks CS3. In the proceeding methods subsections, each section will indicate which 59 N programs were used for analysis. Illustrator and Fireworks were used throughout for the creation of maps and figures. 2.1.2 Michigan Inpatient Data Base (MIDB) The MIDB is a database containing inpatient discharge records for all Michigan hospitals and Michigan residents discharged from hospitals in bordering states not including Ontario, Canada for a calendar year (Certificate of Need Commission 2007b). Inpatient refers to a patient in residence in a hospital for at least one fiill night. A hospital discharge represents the release or dismissal of a patient fi'om a hospital after a procedure or course of treatment. Hospital discharge records usually contain demographic information about the patient, primary and secondary diagnoses, diagnostic procedures, treatment procedures, length of stay, and insurance status (Cromley and McLafferty 2002). The data are compiled for the State of Michigan by the Michigan Health and Hospital Association The MIDBs for 2001, 2002, 2003, 2004, and 2005 were given to researchers at the Michigan State University, Department of Geography by MDCH for research in health care access within the State of Michigan. The MIDB for each year contains over 1.1 million individual discharge records, totaling over 5.8 million records. Each discharge record contains the hospital ID number and patient’s home 5-digit ZIP code, sex, age group, date of discharge, length of stay, International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) primary diagnosis code, and Health Care Financing Administration (HCFA) defined Diagnoses Related Group (DRG) code. ICD- 9-CM is based on the World Health Organization's Ninth Revision, International 60 (It. 1/ an», Classification of Diseases (ICD-9) and is the official system of assigning codes to diagnoses and procedures associated with hospital utilization in the United States (National Center for Health Statistics 2007). DRG is a system used to classify inpatients into groups based on common characteristics expected to require similar service for payment of hospitalization in Medicare. The MIDBs for 2001, 2002, 2003, 2004, and 2005 contain 100% of all inpatient hospital visits with few discernable data errors. Discemable data errors were detected by running a SELECT DISTINCT query for each field and comparing the output to known values. For example: SELECT DISTINCT DISCHARGE.SeX, Count(DISCHARGE.SeX) AS total FROM DISCHARGE GROUP BY DISCHARGE.seX; DISCHARGE SEX!ERR_CHQ Distinct sex E> sex Cou nt(sex) total Fig. 12 This query selects distinct values from the sex field in the database DISCHARGE and returns the total for each value. The result of this query run on the MIDB for 2005 was: + ----- + -------- + I sex I total I + ————— + -------- + I O I 10 I I 1 | 489267 I I 2 | 700285 I + ————— + -------- + Out of the 1,189,562 records in 2005, 10 individuals’ sex could not be identified or were not properly recorded and were given the unknown sex default value of “0”. Total discernable errors that are not record keeping codes, such as “0” in the sex field, and are actually mistyped, nonexistent, or null are recorded in Table 1. Mistyped or nonexistent 61 ZIP codes are identified by comparing ZIP code values to US. Census 2000 5-Digit ZIP Code Tabulation Areas (ZCT As) Cartographic Boundary Files (shapefiles) for Michigan, Indiana, Ohio, and Wisconsin. Mistyped or nonexistent primary diagnosis codes are identified by comparison to the ICD-9-CM rich text files available on the Center for Disease Control’s National Center for Health Statistics (NCHS) Web sites. Mistyped or nonexistent DRG codes are identified by comparison to the official HCFA DRG codes. The query used for this comparison matches the result of the previous SELECT DISTINCT query to a listing of known values and find records that do not match. For example, the ZIP code query is below SELECT TOTALZIP.midb_zip, Sum(TOTALZIP.total) AS total, KNOWNZIPS.zip FROM TOTALZIP LEFT JOIN KNOWNZIPS ON TOTALZIP.midb_zip = KNOWNZIPS.zip GROUP BY TOTALZIP.midb_zip HAVING KNOWNZIPS.Zip IS Null; TOTALZIP KNOWNZIPS ZIP_ERR_CHQ midb_zip at zip :1) midb_zip Sum(total) total Fig. 13 This query uses a left join to combine all the distinct values found in the MIDB for the ZIP code field (T 0TALZIP.midb_zip) to a known list of ZIP codes (KNOWNZIPSzip). The ZIP codes that are not found in KNOWZIPS (Is Null) are selected. The previous SELECT DISTINCT query was alternatively changed to count hosp_id to identify the 5 Establishing a mistyped or nonexistent ICD-9-CM code was difficult. Many codes were used several thousand times which suggests they were not mistyped. Also the majority of codes that did not appear in the NCHS files did appear on the Wisconsin Department of Health and Family Services listing of diagnosis codes Web site on Feb 21, 2007 Available from within.dhfs.state.wi.us/helpfiles/dlookupbrowse.html. Only codes with obvious errors, such as blank spaces and unnecessary letters, were classified as mistyped or nonexistent. 62 2., a Null values in the midb_zip field. Values identified as record keeping codes and not mistyped or nonexistent values were verified with MDCH. Field 2001 2002 . 2003 . 2004 I 2005 ‘ Total Age Group 0 0 O 0 ~ 0 ‘ 0 Date of Visit 1 84 ’ 0 O 0 85 DRG 0 3 . O 0 O - 3 Hospltal ID 0 82 . 0 . 0 0 ‘ 82 Length of Stay 0 0 0 0 0 g 0 Patient ZIP Code 7 0 94 0 ‘ . 0 ; ,0 _ 94 Primary Diagnosis 461 , 332 ‘ 354 ' 152 217 1516 Sex 0 ; 43 .= 0 0 0 43 Mistyped, Nonexistent, or Null Values in the MIDB Table 1 Out of the over 5.8 million records in the MIDB only 1,823 individual record fields were unidentifiable. For the sake of simplicity, assuming none of the 1,823 errors fall within the same record, only 0.031% of all five years data combined contain discernable errors. These records were not removed because MDCH-CON does not remove them in their analysis of hospital bed demand (Certificate of Need Commission 2007b). This study will assume no errors of omission or comission are present in this database due to record keeping standards required by hospitals accepting Medicare and Medicaid (Soc. Sec. Act, Titles XVIII and XIX) and Maternal and Child Health Services (Soc. Sec. Act, Title V); the Health Insurance Portability and Accountability Act, Title II and the Department of Health and Human Services; Michigan Public Act 481 of 2006; and individual hospital administration policies. The MIDB in the format provided by MDCH-CON is defined as a limited dataset and partially de-identified health information because it contains patient 5-digit ZIP codes and the month and day of discharge (National Institutes of Health 2007). Health data containing elements which can be used to re-identify individual records such as S-digit ZIP codes and discharge dates are considered Protected Health Information (PHI) under 63 25: ,\ the Standards for Privacy of Individually Identifiable Health Information (U. S. Dept. of Health and Human Services 2002). This thesis was authorized for the research use and limited disclosure of the MIDB by MDCH-CON and was granted a waiver of the Authorization requirement by the Michigan State University Institutional Review Board (IRB # 07-362 / APP # i02484; P.I. Dr. Joseph Messina). The MIDB was received from MDCH-CON in fixed width text files for four of the five years and one year in an old DBF4 file format most programs could not open. The DBF4 data file was converted to a comma-delimited text file using DBF Viewer 2000. DBF Viewer 2000 is a program developed specifically for viewing and converting old DBF file formats. The 2004 and 2005 MIDB text files did not have the age group calculated as per the CON standards for hospital bed demand (Certificate of Need Commission 2007b). These two files unlike the other years did have a patient age field. A corrected age group field was created by running a short Python script (Appendix 3) on the text files, and the patient age field was discarded as an unnecessary field for this study and potential unique identifier. The five years of data were imported into a Microsoft Access database and a MySQL database. Microsoft Access was used because of its ease of exporting data in multiple formats, ability to handle the large database and interface with ESRI Geospatial Databases. A second copy of the MIDB was created by first exporting the data from Microsoft Access as comma-delimited text files and second importing the data into hAySCflx LOAD DATA LOCAL INFILE 'C:/midb2005.txt' INTO TABLE DISCHARGE FIELDS TERMINATED BY ',' LINES TERMINATED BY '\r\n'; 64 MySQL was selected because of its accessibility with scripting languages such as Python, speed, and ability to handle the large database. No data were lost or corrupted from the transfer from Microsoft Access to MySQL. 2.1.3 Michigan Acute Care Hospitals List After inquiries to MDCH, no accurate, up-to-date table of Michigan acute care hospitals with hospital ID codes, Health Systems Agency (HSA), facility service area (F SA), and addresses was provided to researchers at the Michigan State University, Department of Geography. An accurate table of hospital IDs and hospital names only was provided for years 2003, 2004, 2005, and 2007. A separate listing of every health facility in Michigan with address and phone number was also provided. This listing was missing several acute care hospitals and contained incorrect addresses. In the process of finding accurate hospital addresses online and geocoding the addresses, it was discovered the complete list of acute care hospitals in the 2007 CON Review Standards for Hospital Beds (Certificate of Need Commission 2007b) (Appendixl) contained closed hospitals such as the Greater Detroit Hospital and Medical Center (closed 1999), Renaissance Hospital & Medical Centers (closed 1999), Riverside Osteopathic Hospital (closed 2002), and St. John Northeast Community Hospital (closed 2003); merged hospitals such as Samaritan Health Center (acquired by Bay Regional Medical Center in 1982); and hospitals which have changed names such as Select Specialty Hospital - Wyandotte (now Henry Ford Wyandotte Hospital). The list was also missing hospitals found in the July 2, 2007 Bed Inventory (Certificate of Need Program 2007b). 65 7“ \ N % The July 2, 2007 Bed Inventory did contain bed counts for closed hospitals such as Riverside Osteopathic Hospital, Select Specialty Hospital-Flint, and Select Specialty Hospital-Western Michigan. Closed hospitals are allowed to keep their beds as an asset, and MDCH-CON will temporarily continue to cOImt the beds as part of the calculation of bed need for the FSA (Public Act 238). The list of hospitals with associated FSA and HSA found in the 2007 CON Review Standards for Hospital Beds (Appendix 2) was used unmodified for this study because these hospitals were used in the calculation of acute care bed need for the State of Michigan regardless of hospital closures. Each Michigan acute care hospital found in the 2007 CON Review Standards for Hospital Beds document was geocoded using Yahoo! Maps Web Services - Geocoding API6. The coordinates were independently verified with Digital Orthophoto Quadrangles from the Center for Geographic Information — State of Michigan and imagery available through Yahoo! Maps. A shapefile of the geocoded hospitals was created in ESRI ArcGIS and projected to Michigan GeoRef. 2.2 Calculating Michigan Acute Care Bed Need The Michigan acute care bed need calculation for a FSA is made using the MIDB and population estimates and projections by ZIP code in the following 13 step methodology. Step 1: All hospital discharges for normal newborns (DRG 391) and psychiatric patients (ICD-9-CM codes 290 through 319 as a principal diagnosis) are excluded. 6 This process was automated using an online batch geocoder created by the author of this thesis for MDCH. The online geocoder is a CGI script that sends an address to the Yahoo! Maps Web Services — Geocoding API and returns latitude and longitude. Available at http://health.geo.msu.edu/geocoder.htm. 66 Step 2: Step 3: Step 4: Step 5: For each F SA discharge, calculate the total number of patient days for the following age groups: ages 0 (excluding normal newborns) through 14 (pediatric), ages 15 through 44, female ages 15 through 44 (DRGs 370 through 375 — obstetrical discharges), ages 45 through 64, ages 65 through 74, and ages 75 and older. Data from non-Michigan residents are included for each specific age group. For each FSA, calculate the relevance index (%Z) for each ZIP code and for each of the following age groups: ages 0 (excluding normal newborns) through 14 (pediatric), ages 15 through 44, female ages 15 through 44 (DRGs 370 through 375 — obstetrical discharges), ages 45 through 64, ages 65 through 74, and ages 75 and older. The relevance index is the number of inpatient hospital patient days provided by a specified FSA from a specific ZIP code divided by the total number of inpatient hospital patient days provided by all hospitals to that specific ZIP code. For each FSA, multiply each ZIP code %Z calculated in Step 3 by its base year ZIP code and age group specific year population. The result will be the ZIP code allocations by age group for each FSA. For each FSA, calculate the FSA base year population by age group by adding together all ZIP code population allocations calculated in Step 4 for each specific age group in that FSA. The result will be six population age groups for each FSA. 67 .1 7 \N Step 6: Step 7: Step 8: Step 9: Step 10: Step1]: For each FSA, calculate the patient day use rates for age groups: ages 0 (excluding normal newborns) through 14 (pediatric), ages 15 through 44, female ages 15 through 44 (DRGs 370 through 375 — obstetrical discharges), ages 45 through 64, ages 65 through 74, and ages 75 and older by dividing the results of Step 2 by the results of Step 5. For each FSA, multiply each ZIP code %Z calculated in Step 3 by its respective planning year ZIP code and age group specific year population. The results will be the projected ZIP code allocations by age group for each FSA. For each FSA, calculate the FSA projected year population by age group by adding together all projected ZIP code population allocations calculated in Step 7 for each specific age group. The result will be six population age groups. For each FSA, calculate the FSA’s projected patient days for each age group by multiplying the six projected populations by age group calculated in Step 8 by the age specific use rates identified in Step 6. For each FSA, calculate the adult medical/surgical FSA projected patient days by adding together the following age group specific projected patient days calculated in Step 9: ages 15 through 44, ages 45 through 64, ages 65 through 74, and ages 75 and older. For each FSA, calculate the FSA projected average daily census (ADC) for three age groups: 0 (excluding normal newborns) through 14 (pediatric), female ages 15 through 44 (DRGs 370 through 375 — 68 Step 12: Step 13: 2.3 The methods presented in this section were used to evaluate how well Michigan’s obstetrical discharges), and adult medical/surgical by dividing the results calculated in Step 10 by 365 (or 366 if the planning year is a leap year). Round each ADC to a whole number. This will give three ADC computations per FSA. For each FSA and age group, select the appropriate occupancy rate from the occupancy rate table in Appendix 4. For each FSA and age group, calculate the FSA projected bed need number of hospital beds for the FSA by age group by dividing the ADC calculated in Step 11 by the appropriate occupancy rate determined in Step 9. To obtain the hospital bed need, add the three age group bed projections together. Round any part of a bed up to a whole bed. Evaluation of Michigan CON Acute Care Bed Need Methodology current acute care hospital system, as defined under the acute care bed need methodology, represents actual patient utilization trends using the 30 minutes travel time rule. The first section details the calculation of acute care patient discharges (visits) traveling outside 30 minutes facility service area (FSA) travel time areas by ZIP code. The second section calculates the average travel distance for patients traveling outside the 30 minutes FSA travel time areas by ZIP code. The third section identifies the nearest hospital to each FSA. The fourth section describes the methods used to analyze the hierarchical movement of patients to different sized hospitals outside 30 minutes FSA 69 travel time areas as compared to the size of the largest nearby hospital. The fifth section computes a commitment index for each Health Systems Agency (HSA). The focus of this thesis is on health care demand, the demand by a health care system for services based on a measurement of a population’s health care need, limited to the State of Michigan. Need for acute care health services is defined in Michigan by realized access, or utilization, found in the MIDB. Factors influencing access such as patient behavior and socio-economic status or doctor referral networks will not be investigated as this research is an evaluation of a health care delivery system not population behavior. The data sets used are limited to those used by MDCH in the calculation of acute care bed need. 2.3.1 30 Minutes Travel Time Calculation The 30 minutes travel time criterion is used by the State of Michigan in the definition of limited access areas to acute care. 30 minutes travel time to acute care hospitals in Michigan was calculated by researchers in the Department of Geography at Michigan State University working on the same grant project from the Michigan Department of Community Health as the author of this thesis. The methods used are thoroughly discussed in Messina et al. (2006). In brief, a raster model of travel time was created, as opposed to a road network model. A road network model assumes all travel begins on a road or the network leaving wide gaps in statewide coverage. A raster model was used to eliminate the significant gaps in statewide coverage a road network leaves because in many cases these gaps comprise areas with a) road networks too new to be included in the public system; b) areas of undocumented private or national road 70 2. C designations (particularly private hospital roads); or c) urban districts with significant industrial facilities. The grid model required more computational power and storage than a network model, but it provided a complete spatial representation of the acute care hospitals and health coverage in Michigan. The final raster model was comprised of 1- kilometer cells whose values indicate the approximate travel time to the nearest acute care hospital for each FSA. This required the development of intermediate raster models representing the cost, in minutes, to traverse each cell. The raster model was created using a road network which was publicly available from the Michigan Center for Geographic Information. Speed limits for road types were based on the speed limits of representative roads in the Mid-Michigan area. The PATHDISTANCE fimction in ESRI Arc/Info GRID was selected for the travel time methodology as opposed to Euclidean distance functions as Euclidean distance functions fail to effectively model transportation networks and variations in landscape. The PATHDISTANCE function determines the shortest weighted distance fi'om each cell to the nearest cell in the set of source cells. The cost used to weight distances was based on the slowest speed limit of any road within a particular 1 km cell. This conservative estimate was used given the risks of underestimating actual travel time to the nearest hospital. While the final individual FSA grids were not published in Messina et a1. (2006) and were combined to create a statewide map for that publication, the FSA grids were appreciatively provided in GRID and shapefile format for this thesis. Figure 14 shows the grid in orange created for FSA 1A. 71 I: . E1143 2:? II 30 Minute Travel 11me to Acute Care Hospitals rm in FSA 1A - Within 30 Minutes II Outside 30 Minutes j: [3 ZIP codes I“; I .. ,, .. e Other Hospitals -; ' to, « , . cl}: FSA1A Hospitals 5" 4350.000 ~ ‘ .‘r’it'j VMamFinn‘ The first step in demonstrating a significant percentage of patients travel longer than 30 minutes instead of accessing nearby hospitals was to create a table of ZIP codes within 30 minutes travel time of acute care hospitals for each F SA for comparison to the patient discharge records in the MIDB (Figure 15). A S-digit ZIP code shapefile of Michigan 72 was downloaded fi'om the United States Census Bureau Web site]. ESRI ArcGIS 9.2 was used to run a Select by Location — Intersect for each FSA 30 minutes travel time shapefile on the Census ZIP code shapefile. This selected all ZIP codes that geographically touched the 30 minutes travel time shapefile for a specific FSA. The result was exported as a new shapefile. A Python script was written to simplify the selection and creation of a new shapcfile process in ArcGIS for each F SA (Appendix 5). All of the shapefile data tables were combined in Microsoft Excel to make a three field table with ZIP code, FSA, and a combined ZIP code/F SA field such as “489172A” for ZIP code 48917 and FSA 2A. The combined field was added to facilitate future queries using the CONCATENATE function in Excel. This table was called WIN-IIN30 and represented all possible combinations of patient travel from a home ZIP code to a FSA within 30 minutes travel time. The WI T HIN30 table was added to the MIDB Microsoft Access database. Python Script repeat for every FSA ESRI ArcGIS Analysis J I /7 Select By . _ Create shapefile Location - Intersect — from Selection 6' a FSA 4F 30 min. FSA 4F 30 min. shapefile of Mich. travel time grid travel time ZlPs Fig. 15 7 Census 2000 5-Digit ZIP Code Tabulation Areas (ZCTAs) Cartographic Boundary Files were available fi'om http:l/www.census.gov/geo/www/cob/252000.html. The Michigan ZIP code shapefile was reprojected to Michigan GeoRef. This shapefile included many hydrological ZIP code areas, where ZIP codes are drawn around rivers and lakes, and large land areas (generally larger than 25 square miles), where insufficient information was available for the Census Bureau to determine the 5-digit codes. The hydrological ZIP codes will appear as water features on all subsequent ZIP code maps. The unknown ZIP code areas will be labeled as “excluded” and colored grey on all subsequent ZIP code maps. These excluded areas make up a significant portion of the Upper Peninsula of Michigan as they represent State and National Forests and are taken into consideration for this analysis. 73 The second step was to generate a table of every combination of patient home ZIP code to hospital FSA discharge appearing in the MIDB with the total number of patient discharges by combination ZIP code/F SA field. Since F SAs do not appear in the MIDB, but hospital IDs do, an additional table provided by MDCH was used containing FSA definitions by hospital called HOSP_KE Y (Nash 2007B). The following SQL code created the table in Microsoft Access: SELECT DISCHARGE.hOSp_id, DISCHARGE.midb_Zip, HOSP_KEY.fsa, [DISCHARGE.midb_zip] & [HOSP_KEY.fsa] AS zipfsa, Count(DISCHARGE.hosp_id) AS total FROM DISCHARGE LEFT JOIN HOSP_KEY ON DISCHARGE.hOSp_id = HOSP_KEY.hosp_id GROUP BY DISCHARGE.hOSp_id, DISCHARGE.midb_zip, HOSP_KEY.fsa; DISCHARGE HOSP_KEY OUT SIDE1 hosp_id = hosp_id hosp_id midb_zip midb_zip fsa E’) fsa midb_zip 8. fsa zipfsa Cou nt(hosp_id) total Fig. 16 The SQL code created a table called OUT SIDE] which listed the hospital visited (hosp_id), patient ZIP code (midb_zip), FSA (fsa), combined ZIP code and FSA (zipfsa), and total visits by ZIP Code/F SA combination (total). The third step was to extract combinations of actual patient discharges from OUT SIDE] that did not appear in the 30 nrinutes travel time analysis table WITHIN30. This was accomplished using a SQL query which left joined the two tables on zipfsa, and selected the null join values. This SQL query compares two tables and finds records without matches: SELECT OUTSIDE1.zipfsa, OUTSIDEl.midb_zip, Sum(OUTSIDE1.total) AS 74 a I total, FROM OUTSIDE1 LEFT JOIN WITHIN30 ON WITHIN30.zipfsa OUTSIDE1.zipfsa=WITHIN30.zipfsa GROUP BY OUTSIDEl.zipfsa, OUTSIDE1.midb_zip, WITHIN30.zipfsa HAVING WITHIN30.Zipfsa IS Null; OUTSIDE1 WITHIN30 OUTSIDE2 zipfsa zipfsa Q zipfsa midb_zip midb_zip Sum(tota|) total Fig. 17 The use of the WITHIN30 table, based on the Census ZIP code shapefile, eliminates all out-of-state ZIP codes and post office boxes from this analysis. The resulting table was called OUT SIDEZ. The fourth step was to sum up the outside 30 minutes travel time visits found in OUT SIDEZ for each ZIP code using a SELECT and SUM SQL query. SELECT OUTSIDE2.midb_zip, Sum(OUTSIDE2.total) AS total FROM OUTSIDE2 GROUP BY OUTSIDE2.midb_Zip; OUTSIDE2 OUTSIDES midb_zip 12> midb_zip Sum(total) total Fig. 18 The resulting table was called OUT SIDE3 and contained all patient discharges in Michigan where the patient traveled longer than 30 minutes to an acute care hospital. The fifth step was to run a separate query to total patient discharges in the MIDB by ZIP code. SELECT discharge.midb_zip, Count(discharge.midb_zip) AS total FROM discharge GROUP BY discharge.midb_zip; 75 :5. I DISCHARGE ALLDISCHARGES midb_zip Q midb_zip Cou nt(midb_zip) total Fig. 19 This table, ALLDISCHARGES, was finally joined to the OUTSIDE3 table by ZIP code for the purpose of calculating the percentage of patients traveling longer than 30 minutes to an acute care hospital by ZIP code. SELECT ALLDISCHARGES.midb_zip, ALLDISCHARGES.total AS all, OUTSIDE3.total AS out FROM ALLDISCHARGES LEFT JOIN outside3 ON ALLDISCHARGES.midb_zip = OUTSIDE3.midb_zip; ALLDISCHARGES OUTSIDE3 OUTSIDE4 midb_zip = midb_zip midb_zip total :> all total out Fig. 20 Additional considerations were taken into account for this 30 minutes travel time analysis. First, as indicated in Messina et a1. (2006) analysis, there are areas in Michigan without a single acute care hospital located within 30 minutes travel time (Figure 21). The inclusion of these areas in the total statewide calculation of patients traveling longer than 30 minutes for acute care could distort conclusions. Second, a modifiable areal unit problem (MAUP) (Openshaw 1984) exists when selecting ZIP codes that touch the 30 minutes travel time shapefiles. As shown in Figure 10, many ZIP codes are only partially overlapped by the 30 minutes travel time shapefile. The inclusion of all discharges fiom a population living throughout the ZIP code when only small portions of the ZIP code are within the 30 minutes travel time could skew calculations. 76 Areas Outside 30 Minutes Travel Time to Acute Care Hospitals - Outside 30 Minutes Within 30 Minutes m Excluded I]; Excluded & Outside 30 Min. 0 Acute Care Hospital 75 :ZZIMiIes 1:4.300,000 Fig. 21 Additional steps were taken to produce alternative outside 30 minutes travel time results by excluding areas without any acute care access within 30 minutes and excluding ZIP codes only partially within 30 minutes travel time to deal with MAUP. 77 First, polygonal area was calculated and added to each ZIP code in the Census ZIP code shapefile using Hawth’s Analysis Tools for ArcGISS, a third party extension for ArcGIS, Add/Update Area & Perimeter Field tool. Second, a Select by Location — Intersect was run on the ZIP code shapefile to select all ZIP codes that geographically touched the 30 minutes travel time shapefile for the entire State of Michigan (Figure 21). The result was exported as a new shapefile. Second, the new shapefile of ZIP codes with the added polygonal area field were combined with the statewide 30 minutes travel time shapefile in ArcGIS for the Overlay — Intersect Tool to compute the geometric intersection of the two shapefiles as a new shapefile. This new shapefile contained the original ZIP code polygonal area field and the ZIP code each feature overlapped. The Hawth’s Add/Update Area & Perimeter Field tool was again used to add the polygonal area of the intersection to the new shapefile. 8 Beyer, H. L. 2004.1-Iawth’s Analysis Tools for ArcGIS. Available at http://www.spatialecology.com/htools. 78 a l ESRI ArcGIS Analysis shapefile calculate shapefile data table area field data table S-digit ZIP code shapefile of Mich. I . f 4 3. ”%mm -. 3:93. 5,. “.4; Select By I“ ’ " Location - Intersect Create shapefile from Selection * ,9". ‘2' 5—digit ZIP code Outside 30 min. Outside 30 min. shapefile of Mich. travel time travel time ZlPs I I ."' .”r i... ‘ . I". f ”r A tomatically if" ‘4 — ’1‘? U Overlay- intersect ‘ .- - (PX creates shapefile . » y. 1,4 "eat- from intersection Outside 30 min. Geometric travel time ZlPs travel time intersection I ‘V' in, ~ -. . f’ f” I . ' g ‘ ' shapefile calculate shapefile - - N data table area field data table Geometric intersection Fig. 22 Third, the DBF of the shapefile was imported into Access, and two independent queries were run to sum the unique ZIP code overlapping polygonal areas and ZIP code polygonal areas to deal with non-contiguous ZIP codes and islands of overlapping areas. The two queries used the same SQL select statement as the one below: 79 SELECT DISTINCT OVERLAF.Zip, FROM OVERLAP GROUP BY OVERLAF.Zip; Distinct Distinct Fig. 23 The two queries were joined by ZIP code and divided to compute percent overlap: SELECT OVERLAP2.Zip, OVERLAP zip Sum(area2) OVERLAP zip Sum(area) Sum(OVERLAP.area) e) ::> OVERLAP2.area OVERLAP2 zip area ZIP2 zip area AS ziparea, ZIP2.area / OVERLAP2.area AS perc_over FROM OVERLAP2 INNER JOIN ZIP2 ON OVERLAP2.zip = GROUP BY OVERLAP2.zip, OVERLAP2.area; OVERLAP2 zip area area Fig. 24 The above query was opened in Excel and ten representing 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% overlap of areas where ZIP codes fall outside 30 minutes travel time to acute care hospitals. These columns were filled with ‘1 ’s if the percent overlap exceeded the given percentage. The spreadsheet was then imported back into Access along with the table created earlier containing total patient discharges and discharges outside 30 minutes travel time by ZIP code. ZIP2 = zip area / area 80 OVERLAP2.area, ZIP2.area, ZIP2.area / PERC_OVERLAP zip overarea zlparea perc_over additional columns were added AS area overarea, ZIP2.area ZIP2.zip Fourth, 11 tables were created to eliminate ZIP code records containing the 10 varying percentages of overlap or any overlap at all by first creating two field tables of ZIP codes and the ‘1’s indicating ZIP code overlap for a given percentage, and then comparing these tables to the table containing total patient discharges and discharges outside 30 minutes travel time by ZIP code: SELECT PERC_OVERLAP.Zip, PERC_OVERLAP.p100 FROM PERC_OVERLAP WHERE PERC_OVERLAP.plOO='1'; PERC_IEOVERLAP P100 zip C9 zip p100 = '1' p100 Fig.25 SELECT OUTSIDE4.*, PlOO.plOO FROM totals LEFT JOIN 9100 ON OUTSIDE.zip = P100.zip WHERE P100.p100 IS Null; OUTSIDE4 P100 P100_OVERLAP midb_zip = zip E> midb_zip * all fields * all fields p100 ls Null Fig. 26 The resulting tables contained total visits and visits outside 30 minutes with records removed according to overlap percentage. These tables were imported into ArcMap and individually joined to the Census ZIP code shapefile and exported as new shapefiles. The previously used Python script written to facilitate the selection and creation of a new shapefile process in ArcGIS was again run for each FSA for each percentage of overlap (Appendix 5). Each resulting shapefiles’ data table included 30 minutes travel time ZIP code totals based on a specific percentage of overlap and FSA. 81 e \l\l Final maps were created in ESRI ArcGIS and graphs were made in SPSS. Census 2000 data were included on the graphs. The Census data were exported to an Access database from the Census 2000 Summary file 1--National file CD and joined by ZIP code to the ZIP code shapefile. The FSA 30 minutes travel time grids used in this analysis significantly overlap where FSAs are close together. Figure 27 shows the degree of FSA 30 minutes travel time area overlap mapped to ZIP codes. This observation is noted not as a limitation of the methods used in this thesis but as a limitation of the current definition of FSAs in Michigan. 82 ;~.~~ ,,, r'\,. .7 r \ e“i'i-',.. "fit 2" 513‘;- él'i’; harem»: : .-, i_ ‘7] e7 ‘ .1335 LEVI-13$ unfilgtiq - .i. A. ..t. -.,. . ,7" J- Wu” -; E: A: {is}. 19km; I..7‘?- e. v <‘ h _ ‘ufi ,i' .. .- «—.. 3.11%; L3 in we Number of Overlaping FSAs - by ZIP code v' -6-7 83 2.3.2 Average Travel Distance for Patients Traveling Longer than 30 Minutes Calculation The first step in the calculation of average travel distance for patients traveling longer than 30 minutes for acute care health was to calculate the radial distance between each hospital in Michigan to the centroid of every ZIP code. The shapefile of geocoded hospitals was imported into ArcGIS, the previously projected to Michigan GeoRef Census ZIP code shapefile was also imported into ArcGIS, and the centroid of each ZIP code polygon was identified using the Feature to Point tool. The calculation of radial distance between each hospital and every ZIP code centroid was done using Hawth’s Analysis Tools for ArcGIS Distance Between Points (Between Layers) tool. Hawth’s tool was used over ArcGIS’s Point Distance tool because it enabled the selection of unique fields to identify calculated distances in the output file such as ZIP code and hospital ID. ESRI ArcGIS Analysis I was i“ 3 geocoded 5-digit ZIP code hospitals shapefile of Mich. centroids Distance Between Points (Between Layers) calculated distances table hospitals centroids Fig. 28 84 The output comma delimited shapefile was imported into Microsoft Access where a SELECT DISTINCT query was run to average distances between hospitals and non- contiguous ZIP codes. SELECT DISTINCT OUTPUT.hosp_id, OUTPUT.zip, Avg(OUTPUT.distance) AS meters FROM OUTPUT GROUP BY OUTPUT.hosp_id, OUTPUT.zip; OUTPUT DIST1 Distinct zip zip hosp_id E> hosp_id Average(distance) meters Fig. 29 A second query was run to calculate distance in miles. SELECT DIST1.hosp_id, DIST1.zip, DIST1.meters, DIST1.meters*0.000621371192 AS miles FROM DIST1; DIST1 DIST2 zip zip hosp_id I:> hosp_id meters meters meters * 0000621371 192 miles Fig. 30 The resulting table contained the radial distance between each hospital in Michigan to the centroid of every ZIP code. The second step in the calculation of average travel distance for patients traveling longer than 30 minutes for acute care was to combine the table of radial distance measurements with the MIDB to calculate average travel distance. The MIDB, table DISCHARGE, is combined with HOSP_KEY table and the previously created DIST 2 table in two independent left joins. 85 SELECT HOSP_KEY.fsa, DISCHARGE.hosp_id, DISCHARGE.midb_zip, [DISCHARGE.midb_zip] & [HOSP_KEY.fsa] AS zipfsa, DIST2.miles FROM (DISCHARGE LEFT JOIN DIST2 ON (DISCHARGE.midb_Zip = DIST2.zip) AND (DISCHARGE.hOSp_id = DIST2.hosp_id)) LEFT JOIN HOSP_KEY ON DISCHARGE.hosp_id = HOSP_KEY.hosp_id; DISCHARGE HOSP_KEY DIST2 DIST3 Fsa fsa hosp_id = hosp_id = hosp_id I2> hosp_id midb_zip = zip midb_zip midb_zip & fsa zipfsa miles miles Fig. 31 Next, all visits within 30 minutes were removed from DIST 3 using the table WITHIN30 created earlier, and null distance values (resulting fiom out-of-state visits, post office boxes, and unique identifier database codes) were removed. SELECT DIST3.midb_zip, DIST3.miles FROM DIST3 LEFT JOIN WITHIN30 ON DIST3.zipfsa = WITHIN30.zipfsa WHERE DIST3.miles Is Not Null AND WITHIN30.zipfsa Is Null; DIST3 WITHIN30 DIST4 midb_zip Q midb_zip zipfsa ¢ zipfsa miles it " " miles Fig. 32 Finally, the distances were averaged by ZIP code. SELECT DISTINCT DIST4.midb_zip, Avg(DIST4.miles) AS avg_dist FROM DIST4 GROUP BY DIST4.midb_zip; DIST4 DIST5 midb_zip r:{> midb_zip Avg(miles) avg_dist Fig. 33 86 Final maps of table DIST 5 were created in ESRI ArcGIS and graphs were made in SPSS. 2.3.3 Proximity to the Nearest Acute Care Hospital Outside FSA Calculation The proximity to nearby hospital alternatives for acute care needs to be taken into consideration when looking at utilization trends. The previously geocoded and projected to Michigan GeoRef Michigan acute care hospitals shapefile was used to run a Near(Analysis) calculation in ESRI Arc/Info 9.2 to determine the distance from each point in a FSA cluster of hospital points to the nearest hospital. A Python script was written to facilitate the selection and creation of shapefiles for hospitals within each FSA and outside each FSA using ESRI ArcGIS 9.2 and then run the Near(Analysis) on the resulting shapefiles (Appendix 6). The resulting shapefile data tables were sorted ascending in Microsoft Access and the shortest distance to an acute care hospital for each F SA was recorded in a Microsoft Excel worksheet. 87 Python Script repeat for every FSA ESRI ArcGIS Analysis I I 14,7 7 <- r- - {sh Select By Create shapefile Attribute - from Selection FSA == 2A ) > ;W./ Hospitals in shapefile of SA FS == 2A I I I? Z (Tm/:1}: C" 34:13: “ err . Select By I“ .17: I? m Create shapefile Attribute - .' ; .'.', .‘ , ,'. .. from Selection FSA <> 2A ‘13,? '._',-.'- "P, - fie“;- .1 rifts-.1 geocoded Hospitals not shapefile of hospitals in FSA 2A FSA <> 2A I V . . :_':: v.‘ : .I “ 7 . Near(AnalySIS) 1‘... '.-" / shapefile . i_°.:l~'. '. f", . data table " ‘ 41‘s,, I" " ' Adds distance and shapefile opt shapefile of shapefile of '0 Of nearest pomt FSA == FSA <> 2A FSA == 2A Fig. 34 Time Analysis 2 3 4 Hospital Hierarchical Movement of Patient Visits Outs1de 30 Minutes Travel Hospitals in Michigan were initially defined within a hierarchy under the Hill-Burton Act Today their relative sizes based on the number of acute care beds fit to a certain extent these old hierarchical definitions with smaller community hospitals or rural 88 .4 hospitals, medium sized regional hospitals, and large research and teaching hospitals. An analysis of hierarchical movement on the basis of moving from smaller hospitals to larger, larger to smaller, or same sized hospitals would further illustrate utilization patterns where patients travel outside 30 minutes FSA travel time areas for acute care. The first step in identifying the hierarchical movement of patients traveling outside 30 minutes F SA travel time in relation to their F SA service area hospitals was to assign a hierarchical classification to Michigan hospitals and create hierarchical movement criteria. Since no hierarchical system exists today in Michigan, and the number of acute care hospital beds was used to distinguish between hospitals in the hierarchical system defined under the Hill-Burton Act, hospital acute care bed count was used to distinguish between hospital sizes. Hospitals considered larger had to have 1.25 times as many beds, and hospitals considered smaller had to have .75 times as few beds. This fractional scale was used instead of a finite bed count due to the variability in hospital sizes in Michigan which vary fiom the single digits up to a little over 1000 beds. The number of hospital beds in each Michigan acute care hospital was added to the Microsoft Access database containing the MIDB. The bed counts were taken fi'om the July 2, 2007 Bed Inventory available from the Michigan Department of Community Health Certificate of Need Program Web site). Two tables were created: one with hospital names, IDs, and number of beds; and one with each FSA and the maximum number of beds at the FSA’s largest hospital. The first query in Access assigned the maximum number of beds at a hospital within 30 minutes to each ZIP code. The previously created WITHIN30 table, which excludes ZIP codes falling outside 30 minutes travel time service areas, was used for this query to reduce unnecessary computations. 9 http://www.michigan.gov/documents/mdch/HOSPBEDINVJAN07_l 82193_7.pdf 89 SELECT WITHIN30.zip, Max(BEDS.maxofbeds) AS maxofbeds FROM WITHIN30 LEFT JOIN BEDS ON WITHIN30.fsa = BEDS.fsa GROUP BY WITHIN30.Zip; iii—lirrHlliao leans SIZE1 zip zip fsa = sa :9 MaxQnaxofbeds) maxofbeds Fig. 35 The resulting table SIZE] contains the maximum number of beds at the largest facility among overlapping FSA 30 minutes service areas. SIZE 1 was combined with the OUT SIDE] table, created previously to total visits in the MIDB by ZIP code and hospital ID, and the HOSP_BED table which contained hospital name, ID, and number of beds. The difference between the maximum number of beds available at a facility within 30 minutes and the visited facility’s bed count was calculated. SELECT OUTSIDEl.midb_zip, OUTSIDE1.total, SIZE1.maXOfbeds, HOSP_BED.beds, SIZE1.maXOfbeds — HOSP_BED.beds AS difference FROM (OUTSIDE1 INNER JOIN SIZEI ON OUTSIDEl.midb_zip = SIZE1.zip) LEFT JOIN HOSP_BED ON OUTSIDEl.fsa = HOSP_BED.fsa GROUP BY OUTSIDEl.midb_zip, OUTSIDEl.total, SIZEl.maXOfbeds, HOSP_BED.BEDS, SIZE1.maxofbeds - HOSP_BED.beds; OUTSIDE1 SIZE1 HOSP_BED SIZE2 midb_zip = zip midb_zip fsa = fsa fsa total 12> total maxofbeds maxofbeds beds beds maxofbeds - beds difference Fig. 36 90 The resulting table SIZE2 contained records with null values for maxofbeds because the record’s ZIP code did not fall within any FSA 30 minutes service area. These were removed in the next step. Due to the difficulty in writing if-then-else statements in Access, SIZE2 was exported as a comma delimited text file and processed in Python (Appendix 7). The Python script checked to determine whether the difference value of each record was positive or negative and then created a new field in the table and assigned it a -l for visits traveling to smaller hospitals or 1 for visits traveling to larger hospitals if the ratio of maxofbeds to beds was less than or greater than 25%. A value of 0 was given to visits traveling to similar sized hospitals when the ratio of maxofbeds to beds was not greater than 25%. The resulting table was imported back into Access and named SIZE3. Three queries were written to separately sum the total number of visits fi'om a ZIP code to larger, smaller, and similar sized hospitals. These resulting tables were then joined to a table of Michigan ZIP codes. SELECT SIZE3.zip, Sum(SIZE3.total) AS down, SIZE3.move FROM SIZE3 GROUP BY SIZE3.zip, SIZE3.move HAVING SIZE3.move=-l; SIZE3 SIZE4A zip zip Sum(total) E> down move = -1 Fig. 37 and SELECT SIZE3.zip, Sum(SIZE3.total) AS up, SIZE3.move FROM SIZE3 GROUP BY SIZE3.zip, SIZE3.move 91 HAVING SIZE3.move=l; Fig. 38 and Fig. 39 and combined with SELECT SIZE3.zip, FROM SIZE3 GROUP BY SIZE3.zip, SIZE3 zip Sum(total) move = 1 SIZE4B zip UP Sum(SIZE3.total) AS same, HAVING SIZE3.move=0; SELECT MIZIPS.Zip, FROM LEFT JOIN SIZE4B ON MIZIPS.Zip=SIZE4B.ZIP) SIZE3 Zip Sum(total) move = 0 SIZE4A.down, ON MIZIPS.zip=SIZE4C.ZIP; MIZIPS zip SIZE3.move SIZE4C zip same SIZE4B.up, Fig. 40 SIZE4A SIZE4B zip = zip down UP SIZE3.move SIZE4C.same ((MIZIPS LEFT JOIN SIZE4A ON MIZIPS.Zip=SIZE4A.ZIP) LEFT JOIN SIZE4C Final maps of table SIZE5 were created in ESRI ArcGIS. 92 SIZE4C SIZES zip zip Q down UP same same 2.3.5 HSA Commitment Index Calculation A commitment index (CI) is the calculation of the number of patients traveling to a hospital from a ZIP code divided by the number of patient visits to the hospital fi'om all ZIP codes. The calculation of each HSA’s CI was entirely performed in Microsoft Access. The first query in Access calculated the total number of visits from a ZIP code to hospitals within a HSA using the MIDB database DISCHARGE and a table which identifies the HSA for each hospital HOSP_KE Y. SELECT DISCHARGE.midb_zip, Count(DISCHARGE.midb_zip) AS total FROM DISCHARGE RIGHT JOIN HOSP_KEY ON DISCHARGE.hOSp_id = HOSP_KEY.hosp_id GROUP BY DISCHARGE.midb_zip, HOSP_KEY.hsa HAVING HOSP_KEY.hsa = '1'; DISCHARGE HOSP_KEY Cl01_1 midb_zip = midb_zip I:> midb_zip Cou nt(midb_zip) total hsa='f Fig. 41 This query was run for each year for each HSA 1 through 8. The resulting 40 output tables were combined by HSA with a list of known ZIP codes to create 8 tables of total visits by ZIP code. SELECT KNOWNZIPS.zip, CIOl_1.total AS totalOl, CI02_1.total AS totalOZ, C103_l.total AS tota103, CIO4_1.total AS totalO4, CI05_l.total AS totalOS FROM ((((KNOWNZIPS LEFT JOIN CIOl_l ON KNOWNZIPS.zip=CI01_1.midb_zip) LEFT JOIN CIOZ_1 ON KNOWNZIPS.zip=CIOZ_1.midb_zip) LEFT JOIN C103_1 ON KNOWNZIPS.zip=CI03_1.midb_zip) LEFT JOIN CIO4_l ON 93 KNOWNZIPS.zip=CIO4_l.midb_zip) LEFT JOIN CIOS_1 ON KNOWNZIPS.zip=CIOS_l.midb_zip; K’ZIPS CI01 Zip zip total Cl02 Zip total Cl03 le total C|04 zip total CI05 Zip total t:> Cl2 zip totaIO1 total02 total03 total04 total05 “K'ZIPS” meaning “KNOWNZIPS”; “zip” means “midb_zip”; names shortened for graphic display Fig. 42 An additional query was run to sum the visits for all five years into a single column for each HSA table. SELECT C12.zip, CI2.tota101 + C12.tota102 + CIZ.totalO3 + C12.tota104 + C12.tota105 AS totall_5 FROM CI2 GROUP BY CI2.zip, C12.tota101 + C12.tota102 + C12.tota103 + C12.tota104 + CI2.totalOS; Cl2 CI3 Zip ED zip total01 + total02 + totaI03 + total04 + total05 total1j Fig. 43 The final tables were exported in DBF4 format and mapped in ESRI ArcGIS using the Percent of Total - Normalization option. 94 3 Results and Discussion 3.1 Results of 30 Minutes Travel Time Analysis The use of 30 minutes travel time areas for assessing geographic access was first incorporated, although vaguely described, in 1963 to Michigan’s acute care bed need methodology (Michigan Department of Health 1963)(see Section 1.2.5). The movement fiom distance measurements to travel time measurements as a meaningfiil indicator of geographic accessibility was discussed in Bosanac et aL (1976). Bosanac et a1. indicated 30 minutes travel time became the standard for assessing geographic access in health care planning in the 19705 after several states including Virginia, Wisconsin, Pennsylvania, and Kentucky adopted it. Today, the State of Michigan defines a limited access area as a geographic area containing a population of 50,000 or more based on the planning year, not within 30 minutes drive time of an existing licensed acute care hospital with 24 hour/7 days a week emergency services, and utilizing the slowest route available as defined by the Michigan Department of Transportation (Certificate of Need Commission 2007b). It has been recommended to the Michigan CON Hospital Beds Standard Advisory Committee (CON-HBSAC) that the 30 minutes travel time rule be expanded to all areas regardless of population size (Hospital Beds Standard Advisory Committee 2004). Research has been conducted identifying areas in Michigan exceeding 30 minutes travel time to an acute care hospital (Messina et a1. 2006). In a report published in the Michigan CON-HBSAC meeting minutes August 2, 2006, the author of this thesis expanded on this study to measure actual percentages of patients traveling outside 30 minutes FSA travel time areas using MIDB patient discharge data (Hospital Beds 95 l\i Standard Advisory Committee 2006). These results for evaluating 30 minutes travel time in Michigan are an extension of Hospital Beds Standard Advisory Committee (2006) and Messina et a1. (2006). The purpose of Michigan’s acute care bed need methodology was to develop an acute care hospital system which represented health care demand or actual patient utilization trends. It was assumed that past utilization records and state total population projections would inform planners of future usage, and patients would seek the nearest hospital. By demonstrating that a significant percentage of patients travel longer than 30 minutes instead of accessing nearby hospitals and some local hospitals are almost entirely avoided by their 30 minutes service area population, it can be shown the current acute care hospital system in Michigan as defined by the acute care bed need methodology is failing to represent health care demand and actual patient utilization trends. Figures 44-48 show a choropleth map of the percentage of inpatient discharges in Michigan where the patient traveled outside the hospital’s F SA 30 minutes travel time areas for 2001-2005. These maps exclude out-of-state patient visits and post office boxes. 96 30 Minutes FSA Travel Area Percentage of Patients Traveling Outside - 75% - 100% - 45% - 74% - 25% - 44% W1 10% - 24% State Average = 9.9% 0 Acute Care Hospital 97 Percentage of Patients Traveling Outside 30 Minutes FSA Travel Area 2002 - 75% - 100% - 45% - 74% - 25% - 44% I”: 10% - 24% II..- 0% - 9% r: Excluded State Average = 93% 0 Acute Care Hospital 75 ZZZ] Miles 1 24.300.000 Fig. 45 98 Percentage of Patients Traveling Outside 30 Minutes FSA Travel Area 2003 - 75% - 100% - 45% - 74% - 25% - 44% 2 10% - 24% i4; 0% - 9% [_~ Excluded State Average = 9.9% 0 Acute Care Hospital 75 1: Miles 1:4,3oo,000 Fig. 46 99 Percentage of Patients Traveling Outside 30 Minutes FSA Travel Area - 75% -100% - 45% - 74% - 25% - 44% 10% - 24% [44 0% - 9% i— Excluded ..I State Average = 10.0% 0 Acute Care Hospital 75 Miles 1 :4,300,000 Fig. 47 Percentage of Patients Traveling Outside 30 Minutes FSA Travel Area 2005 - 75% - 100% - 45% - 74% - 25% - 44% W 10% - 24% {4; 0% - 9% l— Excluded State Average =10.3% 0 Acute Care Hospital 75 1 4.300.000 Fig. 48 The spatial distribution of patients traveling outside 30 minutes F SA travel time areas for acute care creates a distinct pattern which is present over all five years. Few patients travel longer than 30 minutes in metropolitan areas, particularly urban areas with multiple hospitals. Michigan’s largest cities, which form a belt across the south central Lower 101 Peninsula, create a large area of patients not traveling outside 30 minutes centered around Grand Rapids, Kalamazoo, Battle Creek, Lansing, Saginaw, Bay City, Ann Arbor, and Detroit. Also the areas around Traverse City and Marquette, the largest cities in the northern Lower Peninsula and Upper Peninsula respectively, have few patients traveling outside 30 minutes. Predominantly rural areas experience the highest percentage of patients traveling outside 30 minutes FSA travel time areas for acute care including the majority of the Upper Peninsula and northern Lower Peninsula. Areas bordering the Great Lakes such as the many smaller peninsulas in Michigan and the Thumb10 also have high percentages of patients traveling outside 30 minutes; although the Leelanau Peninsula, just north of Traverse City, has a low percentage despite the travel limitations imposed by the surrounding water. Michigan’s southern border with Indiana and Ohio has a high percentage of patients traveling outside 30 minutes as well despite the removal of Michigan out-of-state hospital visits from the analysis. A map of the percentage of Michigan patients traveling to out-Of-state hospitals versus all hospital visits was previously created by the author of this thesis for the combined years of 2001, 2002, and 2003 (Hospital Beds Standard Advisory Committee 2006). Figure 49, a redrafted version of this map, shows a large percentage of Michigan patients are traveling out-of-state along the State’s borders with Wisconsin, Indiana, and Ohio. Despite having the most densely clustered and largest hospitals in the state located a short distance north, the area of Michigan south of Detroit and Ann Arbor and '0 The Thumb of Michigan is a region so named because the Lower Peninsula is shaped like a mitten. The Thumb refers to the extended peninsula that stretches northward into Lake Huron and Saginaw Bay formed by Huron, Sanilac, and Tuscola counties. 102 bordering Ohio still has a large percentage of Michigan patients traveling to out-of-state hospitals. Percentage of Michigan Out-of-State Hospital Visits 2001 - 2003 - 40% ~ 97% - 10% - 39% ';_ j 0% - 9% {—7 Excluded State Average = 4.4% 0 Acute Care Hospital 75 Miles 124,300,000 Fig. 49 A scatterplot of the average percentage of patients traveling outside 30 minutes FSA travel time areas for acute care for 2001 through 2005 versus total population by ZIP code was created in SPSS (Figure 50). 103 24000-1 .. E 18000- i" . 1'6 r-v — of a ‘5.- o ai- - E 12000- a. _. s S .'. _ " ' _ o I l- E.” . . ' 6000- :n‘ig‘ '- - ' u‘ I-..' . ' -' a. I I - “Ii-3&3. .3 " ,"° -' . ' ' , .- ii ‘-';,,:,:"."-_- <,°-.‘_'. ,i' , - ' . - i . ffi'} . g 0"- -l I ~ - cl 0‘ 5.....- ‘.l: n‘fiz'éflzfi I. u .I.‘ r..‘ '4" c I i l i i l 0% 25% 50% 75% 1 00% Percent Outside Visits Fig. 50 The scatterplot is nonlinear and negatively sloped. Clustering occurs about the x and y axes indicating high variability in the percentage of outside 30 minutes F SA travel time areas for ZIP codes with very small populations or high variability in total population for ZIP codes with very low percentages of patients traveling outside 30 minutes. Overall, the percentage of patients traveling outside 30 minutes FSA travel time areas is inversely related to population size. As previously presented in Figures 44-48, ZIP codes with large populations (urban) have lower percentages of patients traveling outside 30 minutes, and small populated areas (rural) have higher percentages of patients traveling outside 30 minutes. 104 Looking back at Figures 44-48 a spatial pattern emerges when drawing circular thresholds on the maps around large cities approximating patient commitment to the large hospitals or groups of hospitals within the cities. For example, small circles can be pictured around Grand Rapids, Alpena, and Marquette; and large circles can be pictured around the Detroit-Ann Arbor Metro and Bay City-Saginaw area. The ridge of high percentages traveling outside 30 minutes FSA travel time areas for acute care occurring south to southwest of the Detroit Metro and Ann Arbor areas extending up between Grand Rapids and Lansing appears to be the result of an edge effect. This ridge could be described as an element of distance decay where the fiiction of distance to a major hospital, such as in between Lansing and Grand Rapids, is equal, and patients must decide to travel one way or the other. The size of these circular areas varies; ZIP codes with high percentages traveling outside 30 minutes occur on the edges of or in between these circular areas. A spider diagram map of the ridge between Grand Rapids and Lansing was previously created by the author of this thesis for the combined years of 2001, 2002, and 2003 to show patient destinations for the top 90% of hospital discharges (Hospital Beds Standard Advisory Committee 2006). Figure 51, a redraited version of the Grand Rapids and Lansing spider diagram, shows the high percentage of patients traveling outside 30 minutes FSA travel time areas for acute care might be the result of an edge effect. The ridge between Lansing and Grand Rapids is an element of distance decay where the friction of distance to Lansing or Grand Rapids hospitals results in movement to both cities, but predominantly Grand Rapids. 105 'llll‘ N; 2 Percentage of Patients Traveling Outside 30 Minute FSA Travel Areas 2001- 2003 TOp 90% Of Hospital Visits Traveling OutSide 30 Minutes Travel Time FSA Areas fi'om ZIP Codes 48834, 48865, and 48846; Legend for blue spider diagram removed for confidentiality. Fig. 51 Alternative outside 30 minutes FSA travel time area results which excluded areas without any acute care access within 30 minutes and excluded ZIP codes only partially within 30 minutes travel time to deal with the modifiable areal unit problem are presented in Appendix 8. Appendix 8 includes detailed tables of the total visits and percent visits outside 30 minutes FSA travel time areas for acute care for the State of Michigan and each FSA for years 2001 through 2005. Total indicates no elimination of ZIP codes which overlap areas where there is no acute care hospital within 30 minutes travel time; Any indicates the elimination of all ZIP codes which overlap; and the percentages 106 indicate the percentage of overlap required for the elimination of ZIP codes. Each table contains a map showing the 30 minutes service area for each FSA by ZIP code. Between 2001 and 2005, Michigan experienced a consistent statewide increase in the number of in-state, acute care patient discharges and the number of patients traveling longer than 30 minutes travel time for acute care. The percentage of patients traveling outside 30 minutes F SA travel time areas for acute care has fluctuated but increased overall. The average percentage of patients traveling outside 30 minutes FSA travel time areas for 2001 and 2005 for the entire State of Michigan is 10.00%. The exclusion of areas of Michigan without acute care hospitals within 30 minutes at varying percentages decreased the state average by 3.15% at most. Time-trend analysis was not conducted because the acute bed methodology evaluates bed need using a years worth of discharges, so five years of data would be insufficient for time-trend analysis. The percentage of patients traveling outside 30 minutes FSA travel time areas for acute care by F SA varies significantly. A histogram was created of the average percentage for 2001 through 2005 for each FSA (Figure 52). 107 Count 10% 20% 30% 40% 50% 60% Percent Outside 30 min. Fig. 52 The majority of FSAs throughout Michigan are around the State average of 10.00%. The distribution is not normal; there are peaks around 25% and 35%, and a drop off occurs after 35%. All 60 calculated percentages of patients traveling outside 30 minutes FSA travel time areas for acute care for each FSA in Appendix 8 were compared in R with a Welch's Two Sample t-test to determine whether the values were significantly higher or lower than the State of Michigan’s percentages. A Welch’s t-test is an adaptation of the Student's t-test intended for use with two samples having unequal variances. Figure 53 is a map of Michigan FSAs indicating whether their percentages of patients traveling outside 30 minutes FSA travel time areas for acute care are statistically higher (red), lower (blue), or not significantly higher or lower (black) than the state average. FSAs considered larger have a t value greater than 2, and FSAs considered smaller have at value less than -2. The results of the t-test are listed in Appendix 9. 108 {757’ ,8E/ " .‘ BE ,2? ~88 . fir/I” ,7 ‘31; l 80 5' / "VI/TIT 8A . 7 86 86 , WEI/{WHT—‘l 5 2J4 / 7\.__8t,'i . I .‘ l “VI—4“, .,..I. ‘ l i _i 77“, ‘ 7 ¥\‘J‘,;_ 1 \\ C “#ka ‘ éF ‘ I" "5 spy/fl [8‘] R): \‘7 “A. V , 9H J \ I) 2 , >CI~E\\\‘. .1 i/ i \ I7 i\\- :fl .’ v 84‘7;___1i l 7C\\ A If 7 ; r—l t‘- l“ 7“".\ N t at: 71;, 770T ; 7g. ' , ("4 I l I ‘\ :71 ‘ 2,12 I»_.-. '_i;'_ v.11" H44; 5‘ 7%" VP VF t7H I l i f’ i" ‘ 1 “ “'T’""""‘; 271-. _l MIG l ,-...1§A,_L3A' ‘ 1 SD iSEJX 4"/\ 35-371c76ci i -‘66663§ significantly Higher (4.3 i 33 i 69% 607527 ”SI-E ‘6H\ . . 1:725 1'48 4H 6F“, F9 6” ‘- Not Significantly “‘46 ‘7‘; «714' 4'4F 7 [441 7 --_. - \, , . 2 r ..-I .IH‘”- -,,I..l ‘ \ Higher or Lower 49 14H 7 4K7 ZAiSA 535:1” 1G I432.” -L. l -.i...ee I 1 1i 8' 1' ti L N l ‘ I l ‘ ignilcan y ower 7 L iSAlzzl 11H1L1A1i‘1ff 39 " 3A 66331 ED /ec 3A5 l l 23 2 inititlfg 70 , ~t is “1"“1 “"1 M. / .30 9A 3Eizc l 202 1d 1:] lies 30 ,7 3A27 l ’ 1:4.300.ooo Mark Finn Statistical Comparison of FSA Percentage of Patients Traveling Longer than 30 Minutes Travel Time for Acute Care to the State Of Michigan Percentage; Note labels are non- overlapping, so the map shows only approximate distribution of hospitals by FSA. Fig. 53 The entire State of Michigan has a significantly higher percentage of patients traveling outside 30 minutes FSA travel time areas for acute care except for the belt of major cities across the south central Lower Peninsula and the largest city in the Upper 109 Peninsula, Marquette. Other than Muskegon (4G) and Owosso (5A), these exceptions are for the most part significantly lower than the state average. The calculation of percent overlap to deal with MAUP was meant to be a rough estimate. The results in Appendix 8 show little change in overall percentages and totals. The areas with significant differences were the result of a units problem where a relatively small FSA 30 minutes service area population loses a few visits and dramatically changes internal variation. More complex methods, such as dasymetric mapping with census block group data, would have been employed in this research if the results had indicated a significant distortion occurred due to MAUP. 3.2 Results of Average Travel Distance Analysis on Patients Traveling Longer than 30 Minutes Travel Time As indicated by the results of the 30 minutes travel time analysis, the areas in Michigan with a significant percentage of patients traveling outside 30 minutes FSA travel time areas are rural areas with limited access to acute care in terms of distance, number of alternative hospital choices, and access to larger hospitals. Analysis of average travel distance for these outside 30 minutes travel time distances should indicate that areas with higher percentages of patients traveling longer than 30 minutes also travel further distances. The distances calculated reflect radial distances between ZIP code centroids and hospitals. Alternative representations of distance using transportation networks and estimates of population cores such as dasymetric mapping with Census tract data or geocoded patient addresses could be used to possibly better approximate average travel 110 distance in future studies. However, these approaches are unnecessary for the research presented in this thesis. MDCH-CON relies on ZIP codes as the primary unit of analysis for its acute care bed need methodology. Conducting analysis on millions of patient addresses using ZIP codes instead of geocoding addresses reduces the amount of error propagated by current geocoding technologies in health studies (Krieger et al. 2001; Oliver et a1. 2005; Ward et a1. 2005). The aim of these present methods is to present a generalized representation of patient utilization focused on acute care facilities not population behavior while preserving the confidentiality of the MIDB discharge records. A scatterplot of average travel distance by patients traveling outside 30 minutes F SA travel time within a ZIP code versus the percentage of patients traveling outside 30 minutes FSA travel time areas was created in SPSS (Figure 54). 111 300'- E '- i a 200'-1 E D O 5’ - -' g . ...g I...- 1001 “.1" .'.' ' - . I ." :33" ' '..'.'- .. 7. ' ii. ' i .‘2 El‘fii‘h;.{i:i :. :- ' .' Z: .- '- i 'WILHH-s“ .:'.. '. ..- -'='-. - I; 51' 3 s "f" '19,. " " '.- | I T l l i . 0% 25% 50% 75% 100% Percent Outside Visits Fig. 54 The scatterplot is nonlinear and negatively sloped. The right side of the scatterplot shows high percentages of visits outside 30 minutes FSA travel time areas with a weak correlation to average travel distance. The points on the right side are highly variable, but relatively evenly distributed suggesting an even number of ZIP codes exist with patients traveling both long and short distances. The left side of the scatterplot showing low percentages of visits outside 30 minutes FSA travel time areas is tightly clustered with a negative slope. It appears ZIP codes with a higher percentage of patients traveling outside 30 minutes F SA travel time areas travel shorter distances on average. However, there are quite a few ZIP codes on the left side where patients travel great distances on average. 112 This scatterplot is difficult to read due to the unit of analysis, the ZIP code. ZIP codes are not evenly distributed throughout Michigan; urban areas have smaller, more concentrated numbers of ZIP codes, and rural areas have large sparse ZIP codes. The results of the 30 minutes travel time analysis indicated rural areas have high percentages of patients traveling outside 30 minutes FSA travel time areas for acute care, and urban areas have low percentages. Therefore the right side of the scatterplot showing high percentages predominantly represents rural Michigan, and the left side of the scatterplot showing low percentages predominantly represents urban Michigan. Patients in rural ZIP codes equally travel long and short distances, but not as far as some patients in urban ZIP codes. Patients in urban ZIP codes travel greater distances on average when traveling outside 30 minutes F SA travel time areas. In the few urban ZIP codes where there is a large percentage of visits outside 30 minutes FSA travel time areas the average travel distance is shorter. Identifying areas where patients are traveling great distances for acute care, particularly in and around urban areas, should indicate the current acute care hospital system in Michigan as defined by the acute care bed need methodology is failing to represent health care demand and actual patient utilization trends if patients are traveling such distances and avoiding nearby hospitals. Figures 55-59 show a choropleth map of the average travel distance in miles of patients traveling outside 30 minutes FSA travel areas for acute care for 2001-2005. These maps exclude out-of-state patient visits and post office boxes. 113 Fig. 55 Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes FSA Travel Areas 2001 - 175 - 344 - 100 - 174 - 75 - 99 .9 3;; 50 - 74 _ 21 - 49 ”T Excluded State Average = 72.5 miles 0 Acute Care Hospital Miles ' - ‘ Kt- “ tfi‘h'; is?" - 1'4.300.°00 1 '9 .‘ ' 1.9-.“ .v , W 2 114 Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes FSA Travel Areas 2002 - 175 - 369 - 100 - 174 - 75 - 99 {EVA 50 - 74 . ‘ 22 - 49 _ , j Excluded State Average = 74.6 miles 0 Acute Care Hospital 75 : Miles 1:4.3oo.ooo Fig. 56 115 Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes FSA Travel Areas 2003 - 175 - 384 - 100 - 174 - 75 - 99 3313,? 50 - 74 ___l 22 - 49 “T Excluded State Average = 73.4 miles 0 Acute Care Hospital 75 I: Miles 1:4,aoo.ooo Fig. 57 116 Fig. 58 Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes FSA Travel Areas 2004 - 175 - 333 - 100 - 174 - 75 - 99 sec 50 - 74 4; 21 - 49 __ Excluded State Average = 72.6 miles 0 Acute Care Hospital 75 Miles 1:4.300.000 ll7 ..» . a t i . . r yeweose- . .x‘ ”Emir Average Travel Distance (mi.) of Patients Traveling Outside 30 Minutes FSA Travel Areas 2005 - 175 - 37o - 100 - 174 - 75 - 99 — 50 - 74 _____ Z 24 - 49 *5” Excluded State Average = 72.5 miles 0 Acute Care Hospital a 75 Miles 124,300,000 Fig. 59 The spatial distribution of the average travel distance of patients traveling outside 30 minutes FSA travel time areas for acute care creates a distinct pattern which is present all five years. As indicated in the scatterplot, there is an equal distribution of rural areas where patients on average travel short and long distances. Rural areas of concern where patients travel long distances on average include the western tip of the Upper Peninsula 118 (Gogebic County), the eastern half of the Upper Peninsula, the northern tip of the Lower Peninsula, the area surrounding Alpena, and the southwestern corner of the Lower Peninsula. Areas of concern in and around urban areas include the area surrounding the largest city in the Upper Peninsula, Marquette, the expansive area surrounding the largest city in the northern Lower Peninsula, Traverse City, the area north of Bay City, around Grand Rapids—particularly between Grand Rapids, Muskegon, and Holland, between Ann Arbor and Detroit, and west of Pontiac. When comparing Figures 55-59 to the percentage of patients traveling outside 30 minutes FSA travel areas maps (Figures 44-48), the average distance maps look like the exact opposite of the percentage of patients traveling outside 30 minutes FSA travel areas maps. Detroit and the central Lower Peninsula just north of the urban population belt both have low percentages of patients traveling outside 30 minutes FSA travel areas and average travel distances. There are no large or moderately sized areas in Michigan with both high percentages of patients traveling outside 30 minutes FSA travel areas and average travel distances. Analysis of average travel distance of patients traveling outside 30 minutes FSA travel time areas indicates populations in areas with higher percentages of patients traveling longer than 30 minutes do not travel fiirther distances compared to populations in areas with lower percentages of patients traveling longer than 30 minutes. 3.3 Results of Calculation of Proximity to the Nearest Acute Care Hospital Outside FSA Consideration for proximity to nearby hospital alternatives for acute care can affect utilization trends. The measurement of proximity to the nearest acute care hospital 119 outside an FSA was calculated to identify areas where the presence of hospital alternatives may affect utilization trends. Dist. Dist. FSA (mi.) FSA (mi.) 1A 2.8 58 19.7 18 2.5 5C 19.7 10 6.5 BA 23.5 10 2.5 68 15.2 1E 3.6 6C 15.3 1F 6.6 60 16.0 16 11.9 6E 11.8 1H 11.2 6F 11.8 1| 11.9 66 15.5 1J 16.0 6H 13.9 2A 19.0 6| 13.9 28 19.1 7A 19.3 2C 18.6 78 19.3 2D 13.6 70 30.5 3A 11.6 70 25.6 38 19.1 7E 30.5 SC 9.2 7F 21.3 30 9.2 76 26.3 3E 18.6 7H 23.8 4A 23.0 7| 23.0 48 13.0 8A 46.6 40 13.0 88 38.8 40 23.0 80 34.0 4E 20.1 80 26.8 4F 12.0 8E 26.8 46 20.0 8F 34.0 4H 19.0 86 37.9 4| 12.0 8H 43.8 4J 19.0 8| 37.1 4K 15.9 8J 37.1 4L 11.6 8K 44.1 5A 19.0 8L 47.4 Shortest Radial Distance fiom a HOSpital in a F SA to a Hospital Outside a FSA Table 2 The average shortest radial distance from a hospital in a FSA to a hospital outside a FSA is 20 miles. FSA 8L (Sault Ste. Marie) is the furthest from any hospital with 47.4 miles, and FSA 18 (Warren) is the closest to any hospital with 2.5 miles. A comparison of Figures 55-59 of the average travel distance of patients traveling outside 30 minutes 120 FSA travel time areas for acute care to Table 3 above reveals several areas in Michigan where patients travel great distances when living in close proximity to alternative hospitals. These areas include the northern Lower Peninsula, the area just north of Bay City, the southwest Lower Peninsula, and between Ann Arbor and Detroit. 3.4 Results of Hospital Hierarchical Movement Analysis of Patient Visits Outside 30 Minutes Travel Time Michigan’s health care system was developed to be a hierarchical delivery system under the Hill-Burton era with base areas centered on a medical center-teaching hospital, followed by regional hospital centers, community hospital centers, and public health and medical service centers. Although the definition of Michigan acute care hospitals within this hierarchy ended with the Griffith Methodology, the post WWII Hill-Burton era of rapid hospital construction left its hierarchical imprint on Michigan with the selective building of hospitals to balance the hierarchy, restrictions on hospital expansions, and limitations on hospital relocation. Outside of state regulation, most hospitals across the United States are integrated into communities through ties with area physicians and other health care providers, clinics, outpatient facilities, and other practitioners (Gourevith, Caronna, and Kalkut 2005). It is assumed local area providers will refer patients to nearby hospitals within this network, and it is assumed patients will seek out nearby hospitals for service versus traveling a great distance. It is assumed patients will be referred up the hierarchy to larger facilities or patients may choose larger facilities over smaller facilities. 121 If the Michigan acute care health system as defined under the acute care bed need methodology is accurately representing utilization trends, it could be assumed patients in urban areas that travel outside 30 minutes for health care would travel to larger hospitals such as the large teaching hospitals found in the urban center and not to smaller rural hospitals. It could also be assumed these urban patients would travel to hospitals the same size as their local hospitals as a result of referral or in response to competition or advertising. Urban patients or rural patients passing up nearby hospitals for significantly smaller hospitals could indicate some form of avoidance. The combination of the results of this hierarchical movement analysis with the results of the patient travel outside of 30 minutes travel time analysis and proximity to nearby hospital alternatives outside a FSA analysis should demonstrate patient avoidance of certain acute care hospitals regardless of nearby alternatives and hospital size. Appendix 10 shows the results of the hierarchical movement analysis of patient visits outside 30 minutes travel time with six maps for each year between 2001 and 2005: three maps of the total number of visits to smaller, similar sized, and larger hospitals normalized by Census 2000 total population; and three maps of the percentage of visits to smaller, similar sized, and larger hospitals versus all patient visits outside 30 minutes travel time. The spatial distribution of patients traveling outside 30 minutes F SA travel time areas for acute care to smaller, similar sized, and larger hospitals (see Section 2.3.2 for specific definition) create distinct patterns which are present over all five years. Patient Travel to Smaller Hospitals Outside 30 Minutes FSA Travel Areas. The state average for the normalized total number of visits to smaller hospitals outside 30 minutes FSA travel areas does not vary ftom 0.9 between 2001 and 2005 except in 2001 at 1.0. 122 The maximum ZIP code normalized totals between 2001 and 2005 is roughly 5.7 except for in 2001 when an extreme 29.3 value for a single ZIP code in Lakeland, Michigan (near Ann Arbor) appears as the result of a units problem where the population of this ZIP code is only 50 people. The greatest number of visits to smaller hospitals outside 30 minutes FSA travel areas occurs throughout the southern LP. The areas with the highest normalized totals are around Saginaw, Hastings, southeast of Lansing, and throughout Metro Detroit. These areas have a large number of patients traveling to smaller hospitals compared to the rest of Michigan. Percentage of Patient Travel to Smaller Hospitals Outside 30 Minutes FSA Travel areas. The state average percentage decreases from 52.0% in 2001 to 49.5% in 2002 and then decreases to 49.4% in 2004, and remains the same in 2005. The maximum percentage for a ZIP code is 99% throughout the five year period. The majority of the Lower Peninsula has a high percentage of patients traveling outside 30 minutes FSA travel areas to smaller hospitals than the hospitals within 30 minutes travel time. The areas with the highest percentages, where over 80% of patients travel to smaller hospitals, are in the urban population belt across the Lower Peninsula. The high values in the southeast Lower Peninsula in and around the Detroit Metro are the result of having the largest hospitals in Michigan within 30 minutes travel time. Patient Travel to Similar Sized Hospitals Outside 30 Minutes FSA Travel Time. The state average for the normalized total of visits to similar sized hospitals outside 30 minutes FSA travel areas is .19 with little variation between 2001 and 2005. This average is significantly lower than the average going to smaller hospitals. The normalized totals throughout the state are low. The maximum value decreases from 3.68 in 2001 to 1.20 in 123 2005. The areas with the highest normalized totals are around Saginaw Bay, Flint, Muskegon, and south of Kalamazoo down to the Indiana border. Percentage of Patient Travel to Similar Sized Hospitals Outside 30 Minutes FSA Travel Areas. The state average percentage decreases fi'om 19.4% in 2001 to 17.4% in 2004 and 2005. The maximum percentage for a ZIP code decreases from 100% to 84% between 2001 and 2005. Few areas in Michigan have a high percentage of patient travel to similar sized hospitals. The areas with the highest percentages, where over 80% of patients travel to similar sized hospitals, in the Upper Peninsula include the area in between Bessemer and Ontonagon; the areas around L’Anse and Iron Mountain; and within Menominee County. In the Lower Peninsula, only Flint has high percentages, over 60%, of patient travel to similar sized hospitals. Flint is an interesting case as it falls in between Lansing, Saginaw, Bay City, and northern Metro Detroit. All of these areas contain similar sized hospitals to those in Flint. Patient Travel to Larger Hospitals Outside 30 Minutes FSA Travel Areas. The state average for the normalized total of visits to larger hospitals is .01 in 2001 and .05 for 2002 through 2005. This average is significantly lower than the average going to smaller hospitals and the average going to similar sized hospitals. The maximum value for a ZIP code fluctuates between 1.00 and .76. The majority of patients in Michigan do not travel to larger hospitals when traveling outside their 30 minutes FSA travel areas. The only areas where patients are traveling to larger hospitals are those just outside 30 minutes of a major city in rural areas. For example, a distinct 30 minutes travel border encircles northern Grand Rapids. The areas in Michigan with the highest normalized totals are 124 those on the 30 minutes periphery boundary of northern Grand Rapids, northern Metro Detroit, and eastern Bay City-Saginaw. Percentage of Patient Travel to Larger Hospitals Outside 30 Minutes FSA Travel Areas. The state average percentage fluctuates between 7.2% and 7.8% between 2001 and 2005. This percentage is much lower than the average percentage of patients going to smaller hospitals and the average percentage of patients going to similar sized hospitals. The maximum percentage for a ZIP code fluctuates between 80% and 87% for the five year period. Two ZIP codes in Sanilac County are the only areas in Michigan containing a high percentage, greater than 80%. Figure 60 shows a bivariate map of 2005 patient travel outside 30 minutes F SA travel areas with the percentage of all patients traveling outside 30 minutes and the percentage traveling outside 30 minutes to smaller hospitals. 125 - 2005 Patient Travel Outside _ 4‘“ 73‘ 30 Minute FSA Travel Area 0 Acute Care Hospital ”j; i ‘ Excluded Z; Percentage of All Patients 66% - 100% 33% - 66% 0% - 33% ”be 6‘6; 199} O 0 Percentage Traveling Outside , a?! to Smaller Hospitals ' °\ 70 “‘93 4. .¢' Miles 39”... 1:4.300,000 Mark Finn 2005 Patient Travel Outside 30 Minutes FSA Travel Areas Bivariate Map: Percentage of All Patients and Percentage Traveling Outside to Smaller Hospitals Fig. 60 Figure 60 depicts the combination of the results of this hierarchical movement analysis for patients traveling to smaller hospitals and the results of the patient travel outside of 30 minutes travel time analysis (Section 3.1). Areas where there is a local 126 hospital avoidance as revealed by areas with a high percentage of patients traveling outside 30 minutes travel time and high percentage traveling to smaller hospitals, indicated in medium to dark blues and greens on the map, the central Upper Peninsula and portions of Mackinac County; the northeastern Lower Peninsula, Thumb of Michigan, and west central Michigan north of Grand Rapids; and particularly high areas along Michigan’s border with Indiana and Ohio. Incorporating the proximity to alternative hospitals outside of a FSA analysis (Section 3.3) indicates that the central Upper Peninsula, portions of Mackinac County, and northeastern Lower Peninsula are areas in Michigan where patients are avoiding nearby acute care hospitals regardless of nearby alternatives and hospital size. 3.5 Results of HSA Commitment Index Analysis and Discussion of FSAs Griffith’s index of commitment, or commitment index (CI), measures the number of admissions to a hospital x from area y divided by total admissions to hospital x (1972). The commitment index is predominantly used for the definition of medical service areas (Ricketts et a1. 1994). This thesis will use the commitment index to evaluate the definition of HSAs in Michigan. This analysis focuses on the hospitals within a HSA (Appendix 2) in relation to the population within the MDCH-CON defined HSA area (Figure 11). ZIP codes with a high CI located in another HSA’s area would demonstrate a patient utilization trend incongruent to Michigan’s current HSA definitions. Maps of ZIP code commitment indices for each HSA for the combined years of 2001 through 2005 127 were created to show the relative market share of patients utilizing acute care for each HSA (Figures 61-68). HSA1 Southeast Commitment Index 2001-2005 " - 1.0%- 1.5% (i I] Excluded 20 It): HSA1 Hospitals Miles 0 Other Acute Care Hospitals 1:1,260,000 Fig. 61 Figure 61 shows the CI for HSA l - Southeast. The distribution of ZIP codes with high CIs is congruent overall to the geographic definition of the HSA (shown on the 128 small inset map). A few outliers are present around Ann Arbor, Adrian, and southeastern Sanilac County, but their CI values are very low. Compared to the other HSAs (Figures 61 -67), the values of C1 by ZIP code are significantly lower in HSA 1 — Southeast. This is an artifact of the unit of analysis, the ZIP code. ZIP codes are small and highly concentrated in southeastern Michigan because of the large Metropolitan Detroit population. The areas with the highest CIs within the HSA are Roseville, Detroit, Westland, Taylor, Wyandotte, and Riverview. 129 / HSA 2 Mid-South Commitment Index 7 2001-2005 ’ - 2.0% -4.9% ,- - 1.0%- 1.9% ' - o.5%-o.9% :9}: E3 0.2%-o.4% 5::1 0.0% - 0.1% - Lf:1 Excluded 4? HSA2 Hospitals 0 Other Acute Care Hospitals 25 ' l _1 Miles 1:1,400.000 Fig. 62 Figure 62 shows the CI for HSA 2 — Mid-South. The distribution of ZIP codes with high CIs is congruent overall to the geographic definition of the HSA. Most of the immediate outliers are the result of ZIP code boundaries not matching county boundaries which are the political boundaries HSA are based upon. One outlier with a moderate CI is present around Owosso. Two outliers with low CIs appear north of the HSA around Mt. 130 Pleasant and south of Alma. The areas with the highest CIs within the HSA are Lansing, Charlotte, Jackson, and Adrian. ..- ::~:.i gs, I .1 . HSA 3 Southwest ‘55" Commitment Index ' 2001-2005 l - 2.0%-4.9% t: - 1.0%- 1.9% - 0.5% -o.9% :' {1:;10.2%-0.4% {" , 0.0% -o.1% 3'," :1 Excluded 11‘“ “J: HSA3 Hospitals > Other Acute Care Hospitals (LE 1 Miles 1 :1 330.000 Fig. 63 Mark Finn Figure 63 shows the CI for HSA 3 — Southwest. The distribution of ZIP codes with high CIs is congruent overall to the geographic definition of the HSA. Most of the immediate outliers are the result of ZIP code boundaries not matching HSA/county 131 boundaries. Allegan County is an area with moderately high CIs which is located outside HSA 3. However, many of the ZIP codes in Allegan County have boundaries that extend into HSA 3. One outlying area with a low CI is present around Adrian. The areas with the highest CIs within the HSA are the ZIP codes around St. Joseph, Kalamazoo, Battle Creek, Three Rivers, Sturgis, and Coldwater. ‘, ‘ HSA 4 West Commitment Index 2001-2005 - 2.0% - 3.0% - 1.0% - 1.9% - 0.5% - 0.9% g, g 0.2% - 0.4% ,, ' 0.0% - 0.1% :2 Excluded .41. HSA4 Hospitals 132 Figure 64 shows the CI for HSA 4 — West. The distribution of ZIP codes with high CIs is completely congruent to the geographic definition of the HSA. The only outliers are the result of ZIP code boundaries not matching HSA/county boundaries. The areas with the highest CIs within the HSA are around Muskegon, Grand Rapids, and Holland. rrr" Maiiifinh- ,» ”1 'i . .J ill 1 : l '. l... . .._ ?, ".. ..,..:' ‘71: 1‘ HSA 5 Genesee «,1. , Commitment Index fir"? if?!“ I, 2001-2005 ' 1 - 2.0% -6.6% -1.0%-1.9% ~ .~ .. ,1 .._ _ . . - i-o.5%-o.9% *1“ ._ ‘ ' * ; ' . of u "g . " ~__ . .. 1.7: "‘4' 'g T at ..--t ,4 ‘ 0.2%-0.4% " r g ‘1 ‘41 , —. r ‘ {.4- f, 11 ‘ V' L 7 ’— H .: :13 _ ‘ "f" f 1‘34““; 00%-0.1% _;: ..l l 1.; 31:11.97“ 3*" ,. Excluded , ‘ L3,: V " .384“; 1". ‘1" HSAS Hospitals N L" ‘g- 9 ‘ . . ‘ 0 , 122““ EMHOS - OtherAcute Care Hospitals ., ' 1 ‘- 1K 1- .;_‘§. 1950.000 W- . _ I .4 ' 1' 1' 7 a" Fig. 65 133 Figure 65 shows the CI for HSA 5 — Genesee. The distribution of ZIP codes with high C13 is not congruent to the geographic definition of the HSA. Two large ZIP codes overlapping the Genesee, Livingston, and Oakland county borders have high CI, 48430 and 48442. According to Census 2000 data, the aforementioned ZIP code with the higher CI along these borders and with the greatest geographic area in Livingston County outside HSA 5, 48430, has a total population of 12,816. The largest city in this ZIP code, Fenton, has a population of 10,582 and is located within Genesee County or within HSA 5. Knowing that 82.6% of ZIP code 48430’s population lives within HSA 5, this ZIP code will not be considered an outlying area of concern. ZIP code 48442 however, has a total population of 7,336 with 6,135 people living in Holly, a small city in Oakland County. Knowing that 83.6% of ZIP code 48442’s population lives outside HSA 5, this ZIP code is considered an outlying area of concern. A few ZIP codes not intersecting HSA 5’s borders with low CIs are located in Oakland County and Tuscola County. HSA 5 has the highest overall CI out all the HSAs in Michigan; it is also geographically smallest with the fewest ZIP codes. The areas with the highest CIs within the HSA are Owosso, Flint, Grand Blanc, Lapeer, and Fenton. 134 f HSAGEast Central Commitment Index 2001-2005 {j - 2.0%- 5.9% 5 -1.0%-1.9% - 0.5%-0.9% 1::1 0.2% - 0.4% t .6. . . g. :j0.0%-0.1% ‘l‘ s -. 1 Jr: a . ( 4 : : "L 1' L r/VlrrJLLl .. 1 :1 Excluded e HSA 6 Hospitals 0 Other Acute Care Hospitals .. ,:L/\::_4 l/\ / zy/ I ; ”14¢ L/ t Figure 66 shows the CI for HSA 6 - East Central. The distribution of ZIP codes with high CIs is congruent overall to the geographic definition of the HSA. All but one ZIP code outlier are the result of ZIP code boundaries not matching HSA/county boundaries. This outlier is located in central Oscoda County. The areas with the highest CIs within the HSA are Mt. Pleasant, Midland, Bay City, and Saginaw. 135 q l r / ~~vr I HSA 7 North Central Commitment Index _ y ‘ 2001-2005 ~71 r9. :5- 74: 2_ - 2.0%-5.3% i 2,, ‘ ‘“ ~ - 1.0%-1.9% 1‘. l 4.5, ‘» - 0.5%-0.9% . N 0.2%-0.4% " 0.0% -0.1% L :1 Excluded 11> HSA7 Hospitals , 0 Other Acute Care Hospitals 4 l_"'\ / ’ >t.. '. .-\' 5 .. I... " x t . flee .1 E t: $1». " . . .' 3.1 31:1 fur: A); .3313; 4}?“ E/Jw 5 1:3; a } fiwj . l 11 .. Firs-Plies “13 1J4: {If awn-(fly— Fig 67 l jiwr [1 {£8 ..‘J Figure 67 shows the CI for HSA 7 — Northern Central The distribution of ZIP codes with high CIs is not congruent to the geographic definition of the HSA. Several ZIP codes with moderate sized C1 are present in the Upper Peninsula counties of Chippewa and Mackinac and the Lower Peninsula county of Roscomrnon. There are several ZIP code outliers which are the result of ZIP code boundaries not matching HSA/county 136 boundaries. A few ZIP codes not intersecting HSA 7’s borders with low CIs are located in Osceola, Mecosta, and Iosco Counties. There are many areas with high CIs within the HSA around the larger cities of the northern Lower Peninsula: Cheboygan, Petoskey, Gaylord, Alpena, Traverse City, Grayling, Manistee, and Cadillac. 3 U) 23, a to .s§ a? C ox fig lg .30 I. \°<\°\"\° 0- ¢E§32323t31 8% gquooogzg ==""'g‘°b ogééfifiéfiég .....x (tNr-oooluza wE m‘g'} =5 n 8 L_r:!__l Ir ‘ Fig. 68 137 Figure 68 shows the CI for HSA 8 — Upper Peninsula The distribution of ZIP codes with high C13 is congruent to the geographic definition of the HSA. HSA 8 is surrounded by water, except for its western border with Wisconsin, and has only Mackinaw Bridge to connect it to the Lower Peninsula. This FSA has no outlying ZIP codes with C13. The areas with the highest CIs within the HSA are Laurium, Marquette, Ishpeming, Iron Mountain, Escanaba, and Sault Ste. Marie. Based on the commitment index analysis, with a few exceptions, Michigan’s HSAs closely align with patient commitment indices. The commitment index analysis differs from the FSA 30 minutes travel time service areas analysis as the commitment index analysis focuses more on Michigan’s hospitals than on Michigan’s population. Comparison of the HSA commitment index analysis results (Figures 61-68) to the FSA 30 minutes travel time service area analysis results (Figures 44-48) shows that ZIP codes in Michigan with high commitment indexes to nearby hospitals have low percentages of patients traveling outside 30 minutes travel time for acute care. This generality does not hold true for areas in the northern Lower Peninsula and Upper Peninsula where there are many ZIP codes with high commitment indices and high percentages of patients traveling outside 30 minutes travel time. This is indicative of the methods differing focuses: while the portion of visits to hospitals in that HSA is high relative to all visits to hospitals within that HSA, the portion is low relative to all hospital visits from the population. Michigan’s FSAs are difficult to evaluate as they have no geographical basis to their assignment. They are clusters of acute care hospitals that should be defined by a max relevance algorithm based on aggregate acute care utilization data from 1976 for the majority of Michigan—the southern Lower Peninsula (Metro Detroit) and Traverse City 138 area of Michigan were redefined in 2002 (Nash 2007). However, both times an expert committee ultimately decided on whether to accept the results. Figures 69 and 70 draw geographic comparisons between the 2007 F SA clusters and the 1946 and 1975 FSA areas. FSAs that have been split up into smaller FSAs are indicated by dotted red lines, and FSAs that have been combined are indicated by solid red lines. 139 L‘A1__L Facility Service Areas Comparison 1946 Areas 8. 2007 Clusters ' 2007 Hospitals —_ 2007 FSA Clusters ”"1 1946 FSAs Differences ----- Partioning — Consolidation 1:4,3oo,ooo Fig. 69 Upon visual examination of Figure 69, it is apparent the greatest changes between 1946 and 2007 occurred in the southem Lower Peninsula. Outside of Metro Detroit where partitioning occurred, the majority of changes resulted in the consolidation of FSAs. The partitioning of FSAs occurred throughout the central Lower Peninsula. The entire Upper Peninsula and portions of the northern Lower Peninsula have remained 140 unchanged. This could be the result of the expert committees resistant to change in the redefinition of FSAs in 1978 and 2002. Facility Service Areas Comparison 1975 Areas & 2007 Clusters ° 2007 Hospitals -—— 2007 FSA Clusters If“, 1975 FSAs Differences ----- Partioning — Consolidation 50 Miles 1:4.300,000 Fig. 70 Comparing Figure 69 to Figure 70, the FSA locations and distribution appear quite similar between 1946 and 1975. Many of the FSA partioning and consolidations in the northern Lower Peninsula and southwestern Lower Peninsula remain the same indicating 141 these changes likely occurred in the 1978 definition of FSAs. One significant change has been the consolidation after 1975 of the FSAs which were partitioned between 1946 and 1975 in Metro Detroit. A close examination of the 2007 Metro Detroit FSAs (Figure 71) shows that the expert committee defined FSAs are oddly split based on political boundaries. Metropolitan Detroit 2007 Hospitals - FSAs c3 0 3 cc c 2 .c m (c E 142 The northern Wayne County line which is formed by 8 Mile Road, a defacto cultural and economic dividing line and boundary between the City of Detroit and Detroit’s northern suburbs in Oakland and Macomb Counties, distinctly separates FSAs 1D and 1E from the Oakland County 1A FSA and Macomb County 1B. The Oakland County— Macomb County line divides FSA 1A from 1B. Detroit and Grosse Pointe form together and are separated along Detroit’s western border fi'om the western Wayne County suburbs 1E and the southwestern 1C. 143 4 Conclusions 4.1 Overview The structure and spatial organization of health care systems are the result of decades of politics, business, payment mechanisms, social programs, scientific planning, utilization, medical advancements, and population changes. Certificate-of—Need Programs (CON) were created to manage the growth of these systems by controlling health care costs, preventing the duplication of services, and increasing the quality of and access to health care. Federal support for Certificate—of—Need Programs ended in 1986 with the repeal of the National Health Planning Act, and the health care planning movement in the United States declined. In Michigan and the majority of states, their CON Programs continued with state finding. The American Health Planning Association (2004) places Michigan’s CON Program in the mid-range of states for scope of CON coverage and monetary review thresholds. Compared to surrounding states, Michigan covers more services under CON than Ohio, Wisconsin, and Indiana (no CON program), is most similar to Illinois, and covers fewer services than New York. In a nationwide comparison of CON programs conducted by a consultant for the State of Washington, Michigan ranked along side North Carolina as having the most effective CON program in the country (Piper 2005). However at the state level, a 2002 Auditor General of Michigan audit report and 2005 follow up audit report stated MDCH had failed to evaluate the state’s CON Program in order to determine whether the CON Program was achieving its goal of balancing cost, quality, and access 144 issues and ensuring that only needed services are developed in Michigan (McTavish 2002,2005) The purpose of this thesis was to explore the efficacy of the methods used by the Michigan Department of Community Health — Certificate-of-Need Division and the Certificate-of-Need Commission (MDCH-CON) to measure health care demand in the State of Michigan. Health care demand in Michigan is defined under MDCH-CON’s acute care hospital bed need methodology (Certificate of Need Commission 2007b). This definition of health care demand is indicative of the demand of a health care system for services based on a measurement of a population’s health care need, not individual demand. Michigan’s original acute care bed need methodology was outlined in 1946 by the Michigan Hospital Study Committee. Their formula for estimating need for general hospital beds was based on the Hill-Burton Act guideline of 4.5 beds per 1,000 people. Bed need was calculated using a bed-death ratio and the incidence of birth (assuming an average length-of-stay of 11 days). Michigan’s original facility service areas (FSAs), were defined in accordance with the Hill-Burton Act and looked similar to Thiessen Polygons (Figure 8). In 1955 the FSAs were redefined and mapped to U.S. Census Minor Civil Division borders (Figure 9). Also in 1955, the bed need methodology was changed to incorporate the guidelines set by the U.S. Commission on Hospital Care using a measure of hospital days per thousand people. The FSAs in Michigan again changed in 1963 taking into consideration population distribution, transportation and trade patterns, travel distance and data indicating the residence of patients served by existing hospitals. These FSAs remained the same until 1978 (Figure 10). In 1961, long-term care beds were 145 separated from the general bed need methodology, and the bed need methodology was renamed acute care bed need. In 1965, the acute care bed need methodology incorporated an occupancy factor required by Federal regulation which was modified under Federal approval for the State of Michigan. The acute care bed need methodology dramatically changed in the early 19705 as Federal Policy Memorandum No. A-l-73 allowed changing of the formula used to determine acute care bed need. Michigan’s new formula incorporated age adjustments, referral adjustments, and obstetrical use rates. The National Health Planning and Resources Development Act of 1974 required all states to define area wide Health Systems Agencies (HSAs) for health planning. Michigan politically defined 8 HSAs without any geographic or scientific consideration. These regions mapped to county boundaries and the City of Detroit, are displayed in Figure 11, as they appear today. The CON Review Standards for Hospital Beds (Certificate of Need Commission 2007b) details the Michigan CON Commissions current standards for measuring health care demand. Their standards or methodology was defined in 197 8 by Thomas, Griffith, and Durance (1979) as a two step approach. The first step of the methodology was to generate proposed F SAs using a relevance clustering algorithm based on patient utilization data. The second step of the methodology relied on a subjective panel of “experts” who selected FSAs they felt reasonable based on their knowledge of the area This methodology came to be known as the Griffith Methodology. The methodology was last applied to the State of Michigan in 2002 where results were discarded in favor of the 1978 FSAs for approximately 75% of the state (FSAs for the southern Lower Peninsula (Detroit) and Traverse City were modified). An accurate recreation of the Griffith 146 Methodology to define FSAs is impossible due to the entanglement of political, business, and perceived public interests ascribed by expert committees composed of individuals representing hospitals, health systems, councils, and insurance providers. Griffith wrote a letter to Larry Horvath, Manager of Michigan’s CON Program, on January 4, 2004, indicating he no longer could support his bed need methodology as being in the best interest of the people of Michigan and recommended the bed need methodology be abandoned. The current acute care bed need methodology used by MDCH-CON to represent real demand measures demand uses patient discharge data fiom the Michigan Inpatient Data Base (MIDB) in combination with U.S. Census Bureau total population statistics and population projections by ZIP code. The methodology measures a relevance index for each FSA based on patient days for specific age groups and obstetrical discharges within ZIP codes; these ZIP code relevance indices are combined with base year total population and projected population and divided by 365 to create the F SA projected average daily census; the FSA projected average daily census is divided by occupancy rates listed in a table (Appendix 4) to calculate the F SA projected bed need. The analysis presented in this thesis to explore the methods used by MDCH-CON to measure health care demand utilized the same data sets used in the calculation of the acute care bed need methodology: the Michigan Inpatient Data Base (MIDB), a database of acute care hospital discharges, and U.S. Census total population estimates and projections by ZIP code. Further, the acute care bed need methodology placed an emphasis on planning with reference to a demonstrated demand by assuming past 147 hospital utilization shed light on future usage, and this thesis also adopted this assumption and limited its scope of variables to those found in the methodology’s datasets. The State of Michigan defines a limited access area as a geographic area containing a population of 50,000 or more based on the planning year, not within 30 minutes drive time of an existing licensed acute care hospital with 24 hour/7 days a week emergency services, and utilizing the slowest route available as defined by the Michigan Department of Transportation (Certificate of Need Commission 2007b). It has been recommended to the Michigan CON Hospital Beds Standard Advisory Committee (CON-HBSAC) the 30 minutes travel time rule be expanded to all areas regardless of population size (Hospital Beds Standard Advisory Committee 2004). The purpose of Michigan’s acute care bed need methodology was to develop an acute care hospital system which represented health care demand or actual patient utilization trends. The 30 minutes travel time rule was used to determine whether the current acute care hospital system in Michigan as defined by the acute care bed need methodology was failing to represent health care demand and actual patient utilization trends. The methods used in this exploration calculated the percentage of patient visits traveling further than 30 minutes travel time and those patients’ average radial travel distance; identified the nearest hospital to each FSA; analyzed the hierarchical movement of patients to differing sized hospitals traveling further than 30 minutes travel time; and computed a commitment index for the evaluation of HSAs. Results of this analysis indicated a significant percentage of patients travel longer than 30 minutes instead of accessing nearby hospitals and some local hospitals are almost entirely avoided by their 30 minutes service area population. Additionally, HSAs despite 148 being mapped to county boundaries reflect utilization trends well, while FSAs greatly overlapped in their 30 minutes travel time service areas and changed very little since 1946. 4.2 Major Findings How do the current methods used by MDCH—CON represent real demand? If the current methods used by MDCH-CON, as defined in the acute care bed need methodology, represent real demand, then Michigan’s acute care system would reflect patient utilization trends. This thesis evaluated whether Michigan’s acute care system reflected patient utilization based on the 30 minutes travel time limit. Results indicated an increasing percentage of Michigan’s population was traveling further than 30 minutes for acute care between 2001 and 2005 (Section 3.1). Outside of major metropolitan areas, there was a statistically significant problem with Michigan’s population traveling further than 30 minutes for acute care (Figure 53). The most extreme cases were in the Upper Peninsula where 60% of the 30 minutes travel time service area patients around FSAs 81, 8J, and 8K traveled further than 30 minutes for acute care instead of visiting nearby hospitals (Appendix 8). Travel distance analysis (Section 3.2) indicated that despite access to arguably the highest quality and quantity of care, Michigan’s urban population was traveling the fiirthest when traveling outside 30 minutes travel time areas for acute care. Even when taking into consideration the relative proximity to nearby hospital alternatives (Section 3.3) and the size of these hospital alternatives (Section 3.4), there are several hospitals in Michigan avoided by their 30 minutes service area population. These areas include the central Upper Peninsula, portions of Mackinac County, and 149 northeastern Lower Peninsula. Michigan’s acute care bed need methodology is not adequately representing real demand. What is the level of uncertainty of these methods and what are the sources of the uncertainty? Uncertainty in the acute care bed need methodology is introduced by the input of committees to interpret results, the sole use of the Michigan Inpatient Data Base for utilization data, and the reliance on the 5-digit ZIP code as the primary geographic unit of analysis. The acute care bed need methodology was not recreated for this study due to its being highly subjective: results for the definition of FSAs in 1978 were discarded by the expert committees at the time, and many Michigan FSAs continued to be defined by Hill-Burton era methods; results for 2002 were discarded for the entire state except for around Detroit and Traverse City, and now the Detroit Metro’s FSA divisions are distinctly related to political boundaries and not utilization (Section 3.5). For exanrple, the northern Wayne County line which is formed by 8 Mile Road, a de facto cultural and economic dividing line and boundary between the City of Detroit and Detroit’s northern suburbs in Oakland and Macomb Counties, distinctly separates FSAs 1D and 1E fi‘om the Oakland County 1A FSA and Macomb County 1B. The 30 minutes service areas for these four FSAs notably overlap each other (Figure 27). No matter the findings produced by the methodology, political, business, and perceived public interests ultimately bias the final results by expert committees composed of individuals representing hospitals, health systems, councils, and insurance providers. 150 Additional uncertainty is the result of the acute care bed need methodology use of the Michigan Inpatient Data Base (MIDB) for utilization data. This data set was originally created out of the mid 19703 Michigan Acute Care Bed Need Methodology Project as a collaborative effort (Michigan Department of Public Health 1976). The dataset is complete and has few discernable errors. However, the use of the MIDB solely for understanding acute care utilization reflects the outdated nature of the 1978 methodology. Due in part to advances in medical treatment; the rapid growth of home care and new, for-profit ambulatory surgery centers; and Congress creating the diagnosis related group (DRG) system in 1983 which restricted lengths of stay under strict discharge planning to maximize profitability for hospitals from Medicare patients, there has been a dramatic increase in outpatient utilization (Feinglass and Holloway 1991). Using the 2003 Annual Hospital Statistical Questionnaire / Table 5 of all operating room discharges in Michigan, 180,404 cases from freestanding facilities would be discounted fiom the acute care bed need methodology (Michigan Department of Conununity Health 2003). The U.S. Postal Service defined 5-digit ZIP code is the primary geographic unit of analysis used by the acute care bed need methodology. ZIP (Zone Improvement Plan) codes are created to facilitate mail delivery for the U.S. Postal Service; they are assigned to a section of a street, a collection of streets, an establishment, structure, or group of post office boxes (Albert, Gesler, and Levergood. 2000). Compounding problems arise from using a non-related administrative boundary to aggregate data into areas (Kirby 1996). ZIP codes encompass neighborhoods with highly divergent economic, social, and environmental characteristics and cross political and census boundaries, making it difficult to overlay data for GIS analysis (Kirby 1996; Cromley and McLafferty 2002). 151 ZIP codes are not evenly distributed throughout Michigan; urban areas have smaller, more concentrated numbers of ZIP codes, and rural areas have large sparse ZIP codes. Additionally, several ZIP codes in Michigan cross three county boundaries, and many ZIP codes are non-contiguous making distance measurements difficult. How might demand measures be methodologically improved? The focus of the Michigan’s acute care bed need methodology is still on identifying the need for the expansion of a health care system by licensing new beds and constructing new hospitals. This emphasis on expansion is a holdover from the beginnings of the health care planning movement during the Hill-Burton / post WWII era of construction of hospitals to provide for America’s growing population. Currently, every FSA in Michigan is in excess capacity of acute care beds totaling 6,879 beds or 25% of all acute care bed in the state according to the July 2, 2007 Bed Inventory (Certificate of Need Program 2007b). Even closed hospitals are allowed to keep their beds. These excess beds are not being utilized; the 2005 average acute care hospital occupancy rate was only 56.7% (Certificate of Need Program 20073). Additionally, official state population projections created by the Michigan Department of Transportation (2003), indicate a substantial decline in Wayne County and other areas between 2005 and 2030. Wayne County also has the highest concentration of hospitals and highest number of acute care beds in the state. The acute care bed need methodology should be improved by focusing on the reduction of beds. The current requirements for the relocation of existing licensed hospital beds to another licensed site are not subject to a distance limitation, but are limited to either acute 152 care hospitals within the same F SA or if the hospital at the existing licensed hospital site has operated at an adjusted occupancy rate of 80% or above for the previous, consecutive 24 months based on its licensed and approved hospital bed capacity, hospitals within the same HSA (Certificate of Need Commission 2007b). Michigan’s current FSAs are unfit to be used as bed relocation requirements because they are by and large politically constructed, particularly around the Detroit Metro (see Section 3.5), and do not reflect current patient utilization trends. The distinct politically defined boundaries of FSAs between inner-city Detroit and its suburbs could explain recent concerns over the inability of certain hospitals in Detroit to open new hospitals in suburban locations (Citizens Research Council of Michigan 2005). The acute care bed need methodology should be improved by redefining the existing criteria for the movement of beds to other licensed sites either through the development of new criteria or redefinition of FSAs. The acute care bed need methodology is a patient origin method. It takes into account the patient home ZIP code and the hospital visited to calculate patient days for specific age groups and obstetrical discharges by patient home ZIP code. In Conover and Sloan’s evaluation of Michigan acute care bed methodology (2003), they concluded the strongest case for continuing CON for acute care hospital beds was for access. Incorporating measures of geographic access would improve the acute care bed need methodology by helping to reduce the problem of patients being referred or forced due to limited access to travel further than 30 minutes for acute care. The incorporation of geographic access would require the development of criteria for measuring distance fiom hospitals; hospitals generally take up large areas and a statewide standard would need to be imposed on whether to measure from the comer or centroid of the property or building, 153 front door, or geocoded street address. Due to the aforementioned problems with ZIP codes, distances would need to be measured from patient home addresses for the greatest degree of accuracy. Criteria would also need to be determined for how distance should be measured. The acute care bed need methodology does not represent current health care utilization trends. As mentioned above, thousands of outpatient discharges are not reflected in the methodology. In addition, Griffith (2004) points out several ways medical care, health insurance, information availability, and population needs have changed since 1978 in his letter ending his support for the acute care bed need methodology. A new acute care bed need methodology ought to be created. The assumption that all acute care procedures are uniform and interchangeable in the estimation of acute care need should be amended. Data are available in the Michigan Inpatient Data Base (MIDB) to forecast acute care need based on similar admissions and procedures such as Diagnosis Related Groups. Griffith (2004) takes this a step further saying that acute care need should be based on population need for a disease or service instead of based on beds: An approach based on population need for a disease or service is substantially superior to one based on beds. It measures patients rather than facilities, indicates the resources more specifically and more accurately, and reveals important considerations about community health (Griffith 2004). The use of beds as the primary unit of acute care need determination and resource allocation has long been the standard in health care planning and should not change. The purpose of Certificate-of—Need regulation is to manage the growth of a state’s health care system, so the focus of its need methodology should be based on facilities—their location, capacities, and available services-—and the continual improvement of the health 154 care system to meet the needs of the population. In the past, separate CON review standards have been created when necessary in Michigan for allocating resources based on surgical services and procedures such as: bone marrow, heart, lung, liver, and pancreas transplants; Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scanners, and Positron Emission Tomography (PET) Scanners; and open heart surgery, cardiac catheterization, and urinary lithotripters. A new acute care bed need methodology should be created which incorporates admissions and procedures into its assessment of need, but population admissions and procedures should not substitute facility beds and services. 4.3 Future Research A new acute care bed need methodology ought to be created in Michigan which should: 1) focus on the reduction of beds; 2) redefine the existing criteria for the movement of beds to other licensed sites; 3) incorporate measures of geographic access; 4) integrate outpatient discharges; 5) and include forecasts of related admissions and procedures. Additional research should be conducted to identify the impacts of Michigan’s excess capacity on costs, quality, access issues, unnecessary utilization, and duplicative services. This research could then be used to persuade state policy makers and planners of the need for the reduction of health care services in Michigan. Impact studies should also be conducted on areas of Michigan which could be affected by possible consolidation, closure, conversion, restructuring, and reallocation of the State’s health care system. 155 This thesis identifies several potential areas for future research in addition to the recommendations to improve the acute care bed need methodology discussed above. First, the spider diagram (Figure 51) and the 30 minutes travel time maps (Figures 44-48) may have identified a utilization based hierarchy to Michigan acute care hospitals based on a distance decay of how far patients are willing to travel based on perceptions of hospitals, referrals, or the services offered. An analysis of acute care hospital markets could be firrther researched to aid health planners in the redefinition of FSAs and HSAs. Second, the fact that Michigan’s urban population was traveling the furthest distance when traveling outside 30 minutes travel time areas for acute care despite access to arguably the highest quality and quantity of care needs to be investigated further (Section 3.2). These visits were usually to smaller facilities relative to nearby hospitals which would suggest these patients were seeking specialized services (Section 3.4). Investigating travel distances based on acute care procedure may help identify the cause of this utilization trend. Third, the out-of-state travel map (Figure 49) showed a significant percentage of Michigan residents travel out-of-state for acute care when living just south of Ann Arbor and Detroit. A health geography study of this area incorporating surveys and discharge data would help determine why this is occurring. Need is endlessly redefined as the definition of good health continually changes, or by determining that what was once a medically or morally acceptable failure is no longer tolerable such as the death of a low birthweight baby, often as a function of technological possibility (Callahan 1991-1992). As our definition of need continually changes with medical advancements, changes in health care utilization, and changes within the population, so does our definition of access. The measurement of health care demand in Michigan must be continually updated 156 and reevaluated to ensure adequate access to health care and that the needs of the population are met by the health care system. 157 Appendix 1 Factors Identified in Early Literature as Influencing Bed Needs for General Hospitals (Palmer 1956) 1. 10. ll. 12. l3. 14. Availability and competency of physicians and other professional personnel in an area, including specialists. The degree to which hospital staffs are open or closed to physicians of the locality. Attitudes and customs of local physicians toward hospital care. Inability of physicians to make home calls in remote areas. Distance from the nearest hospital or from hospitals in adjacent areas. The prevalence of substandard hospitals in an area Availability and effectiveness of other types of medical facilities in an area, including diagnostic or treatment clinics, chronic disease and other long-term hospitals, nursing and convalescent homes, rehabilitation facilities, medical schools, government hospitals, home care programs, social welfare services. Advances in medical science, including the changes in medical techniques and the development of new drugs and technical equipment, which may affect the length of hospital stay or the number of hospitalizations. Disease prevention activities in an area. The awareness or attitudes of the general public toward the need and value of hospital care. The quality and extent of health education in an area. The purchasing power of the local population, as reflected by the levels of income and the prevailing prices paid for goods and services. The extent of coverage by hospital insurance in the area. The extent to which fee hospital and medical services are provided in the area by private and governmental agencies. 158 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. Occupancy rates. Length of stay and number of admissions per bed per year. Size of the hospital or hospitals in an area. Selective admissions. Changes in hospital administrative techniques. Coordination and integration of hospital and other medical services. The extent to which hospital beds in a city are used for non-residents. The housing situation in an area, including the existence of any shortages and the number of smaller dwelling units. The adequacy of transportation facilities. Trends in size of population, including seasonal fluctuations. Population density and distribution (urban or rural). Composition of population (age, race, sex, marital status). Cultural characteristics of the population, including educational level, local customs and mores, and religious affiliations. Morbidity rates of the population (prevalence and incidence). Industrial, occupational, and recreational hazards in an area. Climate and topography of a region, as they affect the prevalence and endemicity of specific diseases. 159 Appendix 2 Michigan HSAs and FSAs (Certificate of Need Commission 2007b) HSA FSA Hospital Name City 1 - Southeast 1A North Oakland Med Centers Pontiac 1A Pontiac Osteopathic Hospital Pontiac 1A St. Joseph Mercy — Oakland Pontiac 1A Select Specialty Hospital - Pontiac Pontiac 1A Crittenton Hospital Rochester 1A Huron Valley - Sinai Hospital Commerce Twp 1A Wm Beaumont Hospital Royal Oak 1A Wm Beaumont Hospital - Troy Troy 1A Providence Hospital Southfield 1A Great Lakes Rehabilitation Hospital Southfield 1A Straith Hospital for Special Surg Southfield 1A The Orthopaedic Specialty Hospital Madison Heights 1A St. John Oakland Hospital Madison Heights 1A Southeast Michigan Surgical Hospital Warren 18 Bi-County Community Hospital Warren 13 St. John Macomb HOSpital Warren 10 Oakwood Hosp And Medical Center Dearborn 10 Garden City Hospital Garden City 1C Henry Ford —Wyandotte Hospital Wyandotte 10 Select Specialty Hosp Wyandotte Wyandotte 1C Oakwood Annapolis Hospital Wayne 10 Oakwood Heritage Hospital Taylor 10 Riverside Osteopathic Hospital Trenton 1C Oakwood Southshore Medical Center Trenton 10 Kindred Hospital - Detroit Lincoln Park 10 Sinai-Grace Hospital Detroit 10 Rehabilitation Institute of Michigan Detroit 10 Harper University Hospital Detroit 10 St. John Detroit Riverview Hospital Detroit 10 Henry Ford Hospital Detroit 10 St. John Hospital & Medical Center Detroit 10 Children's Hospital of Michigan Detroit 10 Detroit Receiving Hospital 8. Univ Hlth Detroit 10 St. John Northeast Community Hosp Detroit 10 Kindred Hospital—Metro Detroit Detroit 10 SCCI Hospital-Detroit Detroit 10 Greater Detroit Hosp—Medical Center Detroit 10 Renaissance Hosp & Medical Centers Detroit 10 United Community Hospital Detroit 10 Harper-Hutzel Hospital Detroit 10 Select Specialty Hosp—NW Detroit Detroit 10 Bon Secours Hospital Grosse Pointe 160 HSA FSA Hospital Name City 1 — Southeast (cont) 1D Cottage Hospital Grosse Pointe Farm 1E Botsford General Hospital Farmington Hills 1E St. Mary Mercy Hospital Livonia 1F Mount Clemens General Hospital Mt. Clemens 1F Select Specialty Hosp - Macomb Co. Mt. Clemens 1F St. John North Shores Hospital Harrison Twp. 1F St. Joseph's Mercy Hosp 8: Hlth Serv Clinton Twp 1F St. Joseph's Mercy Hospital 8: Health Mt. Clemens 1G Mercy Hospital Port Huron 1G Port Huron Hospital Port Huron 1H St. Joseph Mercy Hospital Ann Arbor 1H University Of Michigan Health System Ann Arbor 1H Select Specialty Hosp—Ann Arbor Ann Arbor 1H Chelsea Community Hospital Chelsea 1H Saint Joseph Mercy Livingston Hosp Howell 1H Saint Joseph Mercy Saline Hospital Saline 1H Forest Health Medical Center Ypsilanti 1H Brighton Hospital Brighton 1| St. John River District Hospital East China 1J Mercy Memorial Hospital Monroe 2 — Mid-Southern 2A Clinton Memorial Hospital St. Johns 2A Eaton Rapids Medical Center Eaton Rapids 2A Hayes Green Beach Memorial Hosp Chariotte 2A lngham Reg Med Cntr (Greenlawn) Lansing 2A lngham Reg Med Cntr (Pennsylvania) Lansing 2A Edward W. Sparrow Hospital Lansing 2A Sparrow - St. Lawrence Campus Lansing 28 Carelink of Jackson Jackson 28 W. A. Foote Memorial Hospital Jackson 2C Hillsdale Community Health Center Hillsdale 20 Emma L. Bixby Medical Center Adrian 20 Herrick Memorial Hospital Tecumseh 3 - Southwest 3A Borgess Medical Center Kalamazoo 3A Bronson Methodist Hospital Kalamazoo 3A Borgess-Pipp Health Center Plainwell 3A Lakeview Community Hospital Paw Paw 3A Bronson — Vicksburg Hospital Vicksburg 3A Pennock Hospital Hastings 3A Three Rivers Area Hospital Three Rivers 3A Sturgis Hospital Sturgis 3A Sempercare Hospital at Bronson Kalamazoo 38 Fieldstone Ctr of Battle Crk. Health Battle Creek 38 Battle Creek Health System Baffle Creek 38 Select Spec Hosp—Battle Creek Battle Creek 38 SW Michigan Rehab. Hosp. Battle Creek 38 Oaklawn Hospital Marshall 30 Community Hospital Watervliet 30 Lakeland Hospital - St. Joseph St. Joseph 30 Lakeland Specialty Hospital Berrien Center 161 HSA FSA Hospital Name City 3 — Southwest (cont) 30 South Haven Community Hospital South Haven 30 Lakeland Hospital - Niles Niles 30 Lee Memorial Hospital Dowagiac 3E Community Hlth Ctr Of Branch Co Goldwater 4 - West 4A Memorial Medical Center Of West MI Ludington 4B Kelsey Memorial Hospital Lakeview 4B Mecosta County General Hospital Big Rapids 40 Spectrum Hlth-Reed City Campus Reed City 40 Lakeshore Community Hospital Shelby 4E Gerber Memorial Hospital Fremont 4F Carson City Hospital Carson City 4F Gratiot Community Hospital Alma 4G Hackley Hospital Muskegon 4G Mercy Gen Hlth Partners-(Sherman) Muskegon 4G Mercy Gen Hlth Partners-(Oak) Muskegon 4G Lifecare Hospitals of Western Ml Muskegon 46 Select Spec Hosp—Western MI Muskegon 46 North Ottawa Community Hospital Grand Haven 4H Spectrum Hlth—Blodgett Campus E. Grand Rapids 4H Spectrum HIth—Buttemorth Campus Grand Rapids 4H Spectrum Hlth—Kent Comm Campus Grand Rapids 4H Mary Free Bed Hospital & Rehab Ctr Grand Rapids 4H Metropolitan Hospital Grand Rapids 4H Saint Mary's Mercy Medical Center Grand Rapids 4| Sheridan Community Hospital Sheridan 4| United Memorial Hospital 8. LTCU Greenville 4J Holland Community Hospital Holland 4J Zeeland Community Hospital Zeeland 4K lonia County Memorial Hospital lonia 4L Allegan General Hospital Allegan 5 - GLS 5A Memorial Healthmre Owosso 58 Genesys Reg Med Ctr—Hlth Park Grand Blanc 5B Hurley Medical Center Flint 58 Mclaren Regional Medical Center Flint 58 Select Specialty Hospital-Flint Flint SC Lapeer Regional Hospital Lapeer 6 - East 6A West Branch Regional Medical Cntr West Branch 6A Tawas St Joseph Hospital Tawas City 68 Central Michigan Community Hosp Mt. Pleasant 60 Mid-Michigan Medical Center-Clare Clare GD Mid-Michigan Medical Cntr - Gladwin Gladwin 60 Mid-Michigan Medical Cntr - Midland Midland 6E Bay Regional Medical Center Bay City 6E Bay Regional Medical Ctr-West Bay City 6E Samaritan Health Center Bay City 6E Bay Special Care Bay City 6E Standish Community Hospital Standish 6F Select Specialty Hosp—Saginaw Saginaw 6F Covenant Medical Centers, Inc Saginaw 162 HSA FSA Hospital Name Clty 6 - East (cont) 6F Covenant Medical Cntr-N Michigan Saginaw 6F Covenant Medical Cntr-N Harrison Saginaw 6F Healthsource Saginaw Saginaw 6F St. Mary's Medical Center Saginaw 6F Caro Community Hospital Caro 6F Hills And Dales General Hospital Cass City 6G Harbor Beach Community Hosp Harbor Beach 66 Huron Medical Center Bad Axe 66 Scheurer Hospital Pigeon 6H Deckerville Community Hospital Deckerville 6H Mckenzie Memorial Hospital Sandusky 6| Mariette Community Hospital Mariette 7 - Northern Lower 7A Cheboygan Memorial Hospital Cheboygan 7B Charlevoix Area Hospital Charlevoix 7B Mackinac Straits Hospital St. Ignace 78 Northern Michigan Hospital Petoskey 7C Rogers City Rehabilitation Hospital Rogers City 70 Otsego Memorial Hospital Gaylord 7E Alpena General Hospital Alpena 7F Kalkaska Memorial Health Center Kalkaska 7F Leelanau Memorial Health Center Northport 7F Munson Medical Center Traverse City 7F Paul Oliver Memorial Hospital Frankfort 7G Mercy Hospital - Cadillac Cadillac 7H Mercy Hospital - Grayling Grayling 7| West Shore Medical Center Manistee 8 - Upper Peninsula 8A Grand View Hospital Ironwood 88 Ontonagon Memorial Hospital Ontonagon 8C Iron County General Hospital Iron River 80 Baraga County Memorial Hospital L'anse 8E Keweenaw Memorial Medical Center Laurium 8E Portage Health System Hancock 8F Dickinson County Memorial Hospital Iron Mountain 8G Bell Memorial Hospital lshpeming 8G Marquette General Hospital Marquette 8H St. Francis Hospital Escanaba 8| Munising Memorial Hospital Munising 8J Schoolcraft Memorial Hospital Manistique 8K Helen Newberry Joy Hospital Newberry 8L Chippewa Co. War Memorial Hosp Sault Ste Marie 163 Appendix 3 Python code to calculate age groups from age field for the MIDB 2004 and 2005 fixed width text files. # Author : Mark J. Finn # Program : calc_age_g.py # Created : June 2007 # Description : this file calculates age group for MID82004 and # MIDBZOOS f = open("MID82005.txt", 'r') f2 = open("MIDB2005age_g.txt", "w") for line in f.readlines(): #Format 0123 hosp id 4 sex 567 AGE if int(line[5:8]) > 120: agegroup "99" elif int( line 8]) >= 75: agegroup "75 TO 120 YRS" [5 elif int(line[5: 8]) >= 65: agegroup = "65 TO 74 YRS" elif int(line[5 8]) >= 45: agegroup = "45 TO 64 YRS" elif int(line[5: 8]) >= 15: [5 agegroup "15 TO 44 YRS" elif int(line 8]) >= 0: agegroup "0 TO 14 YRS" else: agegroup = "99" f2.write(line[:-1]+ agegroup + '\n') f.close() f2.close() print "done" 164 Appendix 4 Occupancy Rate Table (Certificate of Need Commission 2007b) Adult Medical/Surgical Pediatric Beds Beds Bods ADC>= ADC< Occup Start Stop ADC>= ADC< Occup Start Stop 30 0.60 <=50 30 0.50 <=50 31 32 0.60 52 52 30 33 0. 50 61 66 32 34 0.61 53 56 34 40 0.51 67 79 35 37 0.62 57 60 41 46 0.52 80 88 38 41 0.63 61 65 47 53 0.53 89 100 42 46 0.64 66 72 54 60 0.54 101 111 47 50 0.65 73 77 61 67 0.55 112 121 51 56 0.66 78 85 68 74 0.56 . 122 131 57 63 0.67 86 94 75 80 0.57 132 139 64 70 0.68 95 103 81 87 0.58 140 149 71 79 0.69 104 114 88 94 0.59 150 158 80 89 0.70 115 126 95 101 0.60 159 167 90 100 0.71 127 140 102 108 0.61 168 175 101 114 0.72 141 157 109 114 0.62 176 182 115 130 0.73 158 177 115 121 0.63 183 190 131 149 0.74 178 200 122 128 0.64 191 198 150 172 0.75 201 227 129 135 0.65 199 206 173 200 0.76 228 261 136 142 0.66 207 213 201 234 0.77 262 301 143 149 0.67 214 220 235 276 0.78 302 350 1 50 1 55 0.68 221 226 277 327 0.79 351 410 156 162 0.69 227 232 328 391 0.80 411 484 163 169 0.70 233 239 392 473 0.81 485 578 170 176 0.71 240 245 474 577 0.82 579 696 177 183 0.72 246 252 578 713 0.83 697 850 184 189 0.73 253 256 714 894 0.84 851 894 190 196 0.74 257 2627 895 0.85 >=1054 197 0.75 >=263 cont. 165 Obstetric Beds Beds ADC>= ADC< Occup Start Stop 30 0.50 <=50 30 33 0.50 61 66 34 40 0.51 67 79 41 46 0.52 80 88 47 53 0.53 89 100 54 60 0.54 101 111 61 67 0.55 112 121 68 74 0.56 122 131 75 80 0.57 132 139 81 87 0.58 140 149 88 94 0.59 150 158 95 101 0.60 159 167 102 108 0.61 168 175 109 114 0.62 176 182 115 121 0.63 183 190 122 128 0.64 191 198 129 135 0.65 199 206 136 142 0.66 207 213 143 149 0.67 214 220 1 50 1 55 0.68 221 226 156 162 0.69 227 232 163 169 0.70 233 239 170 176 0.71 240 245 1 77 1 83 0.72 246 252 184 189 0.73 253 256 190 196 0.74 257 262 197 0.75 >=263 166 Appendix 5 Python code to select ZIP codes intersecting FSA 30 minutes travel time areas in ArcGIS # Author : Mark J. Finn # Program : select_intersect.py # Created : June 2007 # Description : this file creates shapefiles of select by location # - intersection in ArcGIS import arcgisscripting gp = arcgisscripting.create() # Create the Geoprocessor fsas == ('lA', 'lB', 'lrr, '1D', '13', '1r', '1G', '11P, '11', 'lJ', '2A', '28', '2c', '20', '3A', '38', '3c', '30', '3E', '4A', '48', '4c', '4D', I4El' '4F', 14Gl’ '4H', '4I', I4JI' '4K', '4L', 'SA', '58., rSCr’ '6A', '6B', '6C', '6D', '68', '6F', '6G', '6H', '6I', '7A', '78', '7c', '7D', '73', '7r', '7G', '7H', '71', '8A', '88', '8C', 'BD', '88', 'ar', 'ac', '8H', '81', '8J', '8K', '8L') gp.MakeFeatureLayer("e:/michigan.mdb/ZIPS", "ZIPs_lyr") for x in fsas: try: # Make a layer from the feature class gp.MakeFeatureLayer("e:/michigan.mdb/fsa" + X, "fsa" + X + fl—lyr") # Select all ZIP codes that overlap with FSA 30 minutes service areas gp.SelectLayerByLocation("ZIPs_lyr", "intersect", "fsa" + X + lI—lyr") # Write the selected features to a new featureclass gp.GopyFeatures("ZIPs_lyr", "e:/michigan.mdb/" + x) print x except: # If an error occurred print the message to the screen print gp.GetMessages() print 'Done' 167 Appendix 6 Python code to calculate nearest point distance between hospitals within an F SA to all other hospitals. # Author : Mark J. Finn # Program : proximity.py # Created : July 2007 # Description : this file creates shapefiles of select by # attribute and calculates Arc/Info Near(Analysis) # function gp = arcgisscripting.create() # Create the Geoprocessor gp.workspace = "f:/michigan.mdb" gp.toolbox = "analysis" fsas == ('lA', '18', '1C', '1D', '1E', '1F', '1G', 'lH', '1I', 'lJ', '2A', '28', '2C', '2D', IBAl’ '3B', I3Cl’ '3D', I3Er’ '4A', I481, O4Cl, '4D', '43', '4r', '4G', '4H', '41', '4J', '4K', '4L', '5A', '58', '5c', '6A', '6B', '6C', '6D', '6E', '6F', '6G', '6H', '6I', '7A', '73', '7c', '70', '7E', '7r', '7G', '7H', '71', '8A', '88', '8C', 'BD', '8E', 'sr', IBGI’ '8H', '8I', '8J', 'BK', IBLI) gp.MakeFeatureLayer("Fz/michigan.mdb/hosps", "hosps_lyr") for x in fsas: try: # Make a layer from the feature class of hospitals of a FSA gp.SelectLayerByAttribute("hosps_lyr", "NEW_SELECTION", " [fsa] = l" + X + rrrrr) # Write the selected features to a new featureclass gp.CopyFeatures("hosps_lyr", "f:/michigan.mdb/is_" + x) # Make a layer from the feature class of hospitals not of the #FSA gp.SelectLayerByAttribute("hosps_lyr", "NEW_SELECTION", " [fsa] <> 7" + X + HI") # Write the selected features to a new featureclass gp.CopyFeatures("hosps_lyr", "f:/michigan.mdb/not_" + x) # Calculate Point Distance gp.near("is_" + x, "not_" + x, "1000000000", "LOCATION", "NO_ANGLE") print x except: # If an error occurred print the message to the screen print gp.GetMessages() print 'Done' 168 Appendix 7 Python code creates a new field within table SIZE2 which indicates whether the patient visited a larger, smaller or similar sized hospital compared to the largest hospital in its 30 minutes travel area # Author : Mark J. Finn # Program : size3.py # Created : July 2007 # Description : this file reads in the comma delimitated text file # size2.txt and creates a new field indicating # comparative hospital size import re f = open("f:/size2.txt", 'r') f2 = open("f:/size3.txt", "w") for line in f.readlines(): x = re.split(',',line) if len(x[4]) > 1: # eliminate nulls if float(x[4]) > O and float(x[3])/float(x[2]) < .75: f2.write(line[:-1] + ',—l\n') elif float(x[4]) < O and float(x[2])/float(x[3]) < .75: f2.write(line[:-l] + ',1\n') else: f2.write(line[:-1] + ',O\n') f.close() f2.close() print 'Done' 169 Appendix 8 Patient Visits Traveling Longer than 30 Minutes for Acute Care 2001 2002 EM“ 8"“ ALL our %OUT ALL our %OUT TOTAL 1117283 110957 9.93% 1125475 111478 9.90% Any 953717 66127 6.93% 957190 64933 6.78% a 10% 1040707 82052 7.88% 1046709 81324 7.77% ,2 20% 1059482 87226 8.23% 1066125 86853 8.15% o 30% 1068399 89489 8.38% 1075677 89392 8.31% 5 40% 1083200 93752 8.66% 1090659 93789 8.60% “a 50% 1088237 95569 8.78% 1095749 95645 8.73% s 60% 1093920 97095 8.88% 1101445 97184 8.82% § 70% 1097030 98078 8.94% 1104366 98093 8.88% g 80% 1100088 99407 9.04% 1107784 99589 8.99% 90% 1103309 101198 9.17% 1111178 101441 9.13% 100% 1112144 107960 9.71% 1120531 108634 9.69% 2003 2004 EM“ 5'1“" ALL our %OUT ALL our %our TOTAL 1140423 112785 9.89% 1150134 115484 10.04% Any 968094 64643 6.68% 978310 67396 6.89% a 10% 1059828 81858 7.72% 1070033 84756 7.92% g 20% 1079738 87561 8.11% 1089916 90382 8.29% o 30% 1089696 90147 8.27% 1099904 93000 8.46% 5 40% 1104913 94670 8.57% 1115078 97468 8.74% “a 50% 1110311 96550 8.70% 1120099 99216 8.86% *5 60% 1115933 98112 8.79% 1125525 100729 8.95% § 70% 1119018 99028 8.85% 1128753 101780 9.02% g 80% 1122296 100507 8.96% 1132056 103272 9.12% 90% 1125783 102379 9.09% 1135518 105149 9.26% 100% 1135326 109779 9.67% 1145138 112544 9.83% 2005 Entire State ALL OUT %OUT . TOTAL 1162229 119496 10.28% WW“!"O'APIW‘d'x ”398 Any 986529 69300 7.02% m... mm... 10% 1080718 87358 8.08% SQRsideSOMhutes 12% 20% 1100643 93610 8.51% SW 0 30% 1110665 96244 8.67% . e. .1 m 5 40% 1126708 101006 8.96% ...n...‘ “a 50% 1131877 102893 9.09% :_”V°'°z"’°°"” *5 60% 1137364 104550 9.19% 514‘ admtmmw ”i"- § 70% 1140709 105668 9.26% - one. How: 3 80% 1144156 107276 9.38% 6 puma... 90% 1147622 109081 9.50% 100% 1157215 116498 10.07% All maps created by Mark Finn. 170 2001 2002 FSA 1‘ ALL our %OUT ALL our %our TOTAL 563620 19005 3.37% 564101 17478 3.10% _A_nL 552693 17828 3.23% 553022 16378 2.96% n 10% 560323 18443 3.29% 560671 16943 3.02% ,g 20% 560323 18443 3.29% 560671 16943 3.02% o 3011 560323 18443 3.29% 560671 16943 3.02% 5 401.1 561818 18738 3.34% 562197 17246 3.07% '5 5011.1 561818 18738 3.34% 562197 17246 3.07% a 60% 562857 18875 3.35% 563285 17353 3.08% § 711% 562857 18875 3.35% 563285 17353 3.08% g 811% 563362 18960 3.37% 563830 17438 3.09% 9111131 563362 18960 3.37% 563830 17438 3.09% 100% 563620 19005 3.37% 564101 17478 3.10% 2003 2004 FSA 1‘ ALL our %OUT ALL our %OUT TOTAL 570960 15408 2.70% 578838 16059 2.77% Any 559094 14187 2.54% 567164 14862 2.62% a 10%_ 567510 14860 2.62% 575423 15577 2.71% 1:" 20%_ 567510 14860 2.62% 575423 15577 2.71% o 30%_ 567510 14860 2.62% 575423 15577 2.71% 5 40%_ 569088 15179 2.67% 576973 15833 2.74% “6 50%_ 569088 15179 2.67% 576973 15833 2.74% 'a 60% 570146 15272 2.68% 578021 15947 2.76% § 70%_ 570146 15272 2.68% 578021 15947 2.76% g 80%_ 570661 15350 2.69% 578520 16022 2.77% 90v_._ 570661 15350 2.69% 578520 16022 2.77% 100% 570960 15408 2.70% 578838 16059 2.77% 2005 FSA 1“ ALL our %Our {1 TOTAL 582441 16503 2.83% . Any 570639 15201 2.66% a 10151 578902 15896 2.75% 3 201731 578902 15896 2.75% o 30% 578902 15896 2.75% 5 mi 580547 16238 2.80% "a 501 580547 16238 2.80% a 60% 581612 16377 2.82% g 70%_ 581612 16377 2.82% g BO'L 582115 16453 2.83% 901g 582115 16453 2.83% 100% 582441 16503 2.83% 171 2001 2002 FSA "3 ALL OUT %OUT ALL_ OUT %OUT TOTAL 405412 1 1 190 2.76% 403501 10502 2.60% An 1 402963 10870 2.70% 401015 10237 2.55% a 10%_ 403868 10968 2.72% 401868 10310 2.57% g 20%_ 403868 10968 2.72% 401868 10310 2.57% o 30%_ 403868 10968 2.72% 401868 10310 2.57% 5 40¢1.__ 403868 10968 2.72% 401868 10310 2.57% '5 50% 403868 10968 2.72% 401868 10310 2.57% E 61 11. 404907 1 1 105 2.74% 402956 10417 2.59% § 711% 404907 11105 2.74% 402956 10417 2.59% g 811% 405412 11190 2.76% 403501 10502 2.60% 911%_ 405412 11190 2.76% 403501 10502 2.60% 100% 405412 11190 2.76% 403501 10502 2.60% 2003 2004 FSA "3 ALL our %OUT ALL OUT %OUT TOTAL 407262 9235 2.27% 410785 961 1 2.34% Any 404757 9003 2.22% 408339 9351 2.29% a 10% 405689 9064 2.23% 409238 9422 2.30% ‘t' 20% 405689 9064 2.23% 409238 9422 2.30% g 30% 405689 9064 2.23% 409238 9422 2.30% O 40% 405689 9064 2.23% 409238 9422 2.30% ‘5 50% 405689 9064 2.23% 409238 9422 2.30% E 60% 406747 9157 2.25% 410286 9536 2.32% § 70% 406747 9157 2.25% 410286 9536 2.32% g 80% 407262 9235 2.27% 410785 961 1 2.34% 90% 407262 9235 2.27% 410785 9611 2.34% 100% 407262 9235 2.27% 410785 9611 2.34% 2005 FSA "3 ALL_ our %OUT .; TOTAL 409135 9392 2.30% ' Any 406566 9109 2.24% Q 10%_ 407567 9177 2.25% g 2011 407567 9177 2.25% g 30%_ 407567 9177 2.25% 40% 407567 9177 2.25% '5 50%_ 407567 9177 2.25% *5 60% 408632 9316 2.28% § 70% 408632 9316 2.28% o 80% 409135 9392 2.30% °' 90% 409135 9392 2.30% 100% 409135 9392 2.30% 172 2001 2002 FSA 1° ALL our %OUT ALL OUT %OUT TOTAL 455505 17650 3.87% 455816 16123 3.54% An 1 453120 16581 3.66% 453277 15136 3.34% G 10%_ 455505 17650 3.87% 455816 16123 3.54% g 20% 455505 17650 3.87% 455816 16123 3.54% o 30%_ 455505 17650 3.87% 455816 16123 3.54% 5 40%_ 455505 17650 3.87% 455816 16123 3.54% "6 50%_ 455505 17650 3.87% 455816 16123 3.54% '5 60% 455505 17650 3.87% 455816 16123 3.54% § 711% 455505 17650 3.87% 455816 16123 3.54% g 111% 455505 17650 3.87% 455816 16123 3.54% 111% 455505 17650 3.87% 455816 16123 3.54% 1 111% 455505 17650 3.87% 455816 16123 3.54% 2003 2004 FSA 1° ALL our %OUT ALL OUT %OUT TOTAL 457675 13303 2.91% 462850 13771 2.98% Any 454988 12364 2.72% 460094 12898 2.80% 10% 457675 13303 2.91% 462850 13771 2.98% g 20% 457675 13303 2.91% 462850 13771 2.98% o 30% 457675 13303 2.91% 462850 13771 2.98% 5 40% 457675 13303 2.91% 462850 13771 2.98% ‘5 50% 457675 13303 2.91% 462850 13771 2.98% a 60% 457675 13303 2.91% 462850 13771 2.98% § 70% 457675 13303 2.91% 462850 13771 2.98% g 80% 457675 13303 2.91% 462850 13771 2.98% 90% 457675 13303 2.91% 462850 13771 2.98% 100% 457675 13303 2.91% 462850 13771 2.98% 2005 FSA 1° ALL our %OUT . TOTAL 463551 13721 2.96% Any 460780 12797 2.78% 10% 463551 13721 2.96% E 20% 463551 13721 2.96% o 30% 463551 13721 2.96% 5 40% 463551 13721 2.96% ‘6 50% 463551 13721 2.96% a 60% 463551 13721 2.96% § 70% 463551 13721 2.96% g 80% 463551 13721 2.96% 90% 463551 13721 2.96% 100% 463551 13721 2.96% 173 2001 2002 FSA 1° ALL OUT %OUT ALL OUT %O_ur_ TOTAL 502470 15976 3.18% 503070 14313 2.85% Any 500381 15616 3.12% 500918 14062 2.81% a may 502470 15976 3.18% 503070 14313 2.85% g 201 ._ 502470 15976 3.18% 503070 14313 2.85% o 30' ._ 502470 15976 3.18% 503070 14313 2.85% 5 40'_._ 502470 15976 3.18% 503070 14313 2.85% ‘6 50%_ 502470 15976 3.18% 503070 14313 2.85% is 60% 502470 15976 3.18% 503070 14313 2.85% § 70%_ 502470 15976 3.18% 503070 14313 2.85% g 80% 502470 15976 3.18% 503070 14313 2.85% 90%_ 502470 15976 3.18% 503070 14313 2.85% 100% 502470 15976 3.18% 503070 14313 2.85% 2003 2004 FSA 1° ALL OUT %OUT ALL our %OUT TOTAL 508488 11360 2.23% 514016 11713 2.28% Any 506123 11239 2.22% 511566 11582 2.26% a 10% 508488 11360 2.23% 514016 11713 2.28% g 20% 508488 11360 2.23% 514016 11713 2.28% o 30% 508488 11360 2.23% 514016 11713 2.28% 5 40% 508488 11360 2.23% 514016 11713 2.28% '5 50% 508488 11360 2.23% 514016 11713 2.28% *5 60% 508488 11360 2.23% 514016 11713 2.28% § 70% 508488 11360 2.23% 514016 11713 2.28% g 80% 508488 11360 2.23% 514016 11713 2.28% 90% 508488 11360 2.23% 514016 11713 2.28% 100% 508488 11360 2.23% 514016 11713 2.28% 2005 FSA 1° ALL our %OUT TOTAL 515896 11811 2.29% Any 513368 11644 2.27% a 10% 515896 11811 2.29% g 20% 515896 11811 2.29% «)1 30% 515896 11811 2.29% O 40% 515896 11811 2.29% “5 50% 515896 11811 2.29% a 60% 515896 11811 2.29% § 70% 515896 11811 2.29% g 80% 515896 11811 2.29% 90% 515896 11811 2.29% 100% 515896 11811 2.29% 174 2001 2002 FSA "5 ALL our %OUT ALL our %OUT TOTAL 442971 13985 3.16% 442032 12394 2.80% Any_ 442661 13922 3.15% 441705 12347 2.80% a 10%_ 442971 13985 3.16% 442032 12394 2.80% g 201 ._ 442971 13985 3.16% 442032 12394 2.80% o 301 ._ 442971 13985 3.16% 442032 12394 2.80% 5 40' ._ 442971 13985 3.16% 442032 12394 2.80% '6 50'_._ 442971 13985 3.16% 442032 12394 2.80% '5 60% 442971 13985 3.16% 442032 12394 2.80% § 711% 442971 13985 3.16% 442032 12394 2.80% g 811' . 442971 13985 3.16% 442032 12394 2.80% 911¢ ._ 442971 13985 3.16% 442032 12394 2.80% 100' . 442971 13985 3.16% 442032 12394 2.80% 2003 2004 FSA ‘5 ALL our %OUT ALL our %OUT TOTAL 445536 10276 2.31% 452359 10737 2.37% An 445170 10256 2.30% 452018 10709 2.37% 10% 445536 10276 2.31% 452359 10737 2.37% 5' 20% 445536 10276 2.31% 452359 10737 2.37% o 30% 445536 10276 2.31% 452359 10737 2.37% 5 40% 445536 10276 2.31% 452359 10737 2.37% '5 50% 445536 10276 2.31% 452359 10737 2.37% a 60% 445536 10276 2.31% 452359 10737 2.37% § 70% 445536 10276 2.31% 452359 10737 2.37% g 80% 445536 10276 2.31% 452359 10737 2.37% 90% 445536 10276 2.31% 452359 10737 2.37% 100% 445536 10276 2.31% 452359 10737 2.37% 2005 FSA "5 ALL our %OUT TOTAL 454971 10905 2.40% Any 454636 10876 2.39% 10% 454971 10905 2.40% 5’ 20% 454971 10905 2.40% o 30% 454971 10905 2.40% 5 40% 454971 10905 2.40% '5 50% 454971 10905 2.40% '5 60% 454971 10905 2.40% § 70% 454971 10905 2.40% g 80% 454971 10905 2.40% 90% 454971 10905 2.40% 100% 454971 10905 2.40% 175 2001 2002 FSA 1F ALL OUT %OUT ALL OUT %OUT TOTAL 316594 9789 3.09% 314994 10010 3.18% An 310145 8878 2.86% 308327 9161 2.97% 10%_ 312881 9137 2.92% 311085 9371 3.01% g 20%_ 312881 9137 2.92% 311085 9371 3.01% o 30%_ 312881 9137 2.92% 311085 9371 3.01 % 5 40%_ 314376 9432 3.00% 312611 9674 3.09% ‘6 50%_ 314376 9432 3.00% 312611 9674 3.09% a 60% 315831 9659 3.06% 314178 9885 3.15% § 711% 315831 9659 3.06% 314178 9885 3.15% g 811% 316336 9744 3.08% 314723 9970 3.17% 911% 316336 9744 3.08% 314723 9970 3.17% 100% 316594 9789 3.09% 314994 10010 3.18% 2003 2004 FSA 1': ALL OUT %OUT ALL OUT %OUT TOTAL 317995 9773 3.07% 320247 10025 3.13% Any 311182 8890 2.86% 313693 9231 2.94% a 10% 314067 9105 2.90% 316381 9443 2.98% t“ 20% 314067 9105 2.90% 316381 9443 2.98% o 30% 314067 9105 2.90% 316381 9443 2.98% 5 40% 315645 9424 2.99% 317931 9699 3.05% “5 50% 315645 9424 2.99% 317931 9699 3.05% a 60% 317181 9637 3.04% 319430 9913 3.10% § 70% 317181 9637 3.04% 319430 9913 3.10% g 80% 317696 9715 3.06% 319929 9988 3.12% 90% 317696 9715 3.06% 319929 9988 3.12% 100% 317995 9773 3.07% 320247 10025 3.13% 2005 FSA 1F ALL OUT %OUT TOTAL 319508 9924 3.1 1% Any 312585 8963 2.87% a 10% 315478 9202 2.92% g 20% 315478 9202 2.92% g 30% 315478 9202 2.92% O 40% 317123 9544 3.01% “a 50% 317123 9544 3.01% E 60% 318679 9798 3.07% § 70% 318679 9798 3.07% g 80% 319182 9874 3.09% 90% 319182 9874 3.09% 100% 319508 9924 3.1 1% 176 2001 2002 FSMG ALL OUT %OUT ALL OUT %OUT TOTAL 25034 3542 14.15% 25434 3590 14.11% An 17836 2101 11.78% 17990 2073 11.52% 10%_ 18541 2238 12.07% 18654 2204 11.82% g 20%_ 20821 2583 12.41% 21135 2569 12.16% a 30%_ 20821 2583 12.41% 21135 2569 12.16% 5 40%_ 22368 2897 12.95% 22656 2903 12.81% “6 50%_ 22796 2999 13.16% 23024 2993 13.00% a 60% 24084 3258 13.53% 24380 3278 13.45% § 70%_ 24084 3258 13.53% 24380 3278 13.45% g 80% 24084 3258 13.53% 24380 3278 13.45% 90%_ 24505 3409 13.91% 24859 3461 13.92% 100% 25034 3542 14.15% 25434 3590 14.11% 2003 2004 FSMG ALL OUT %OUT ALL OUT %OUT TOTAL 26586 3735 14.05% 26761 3881 14.50% Any 18834 2084 11.07% 19177 2405 12.54% 10%_ 19485 2214 11.36% 19907 2561 12.86% g 20%_ 22026 2564 11.64% 22486 2938 13.07% 0 30%_ 22026 2564 11.64% 22486 2938 13.07% 5 40%_ 23730 2918 12.30% 24062 3228 13.42% '6 50%_ 24151 3023 12.52% 24445 3312 13.55% a 60% 25510 3353 13.14% 25718 3573 13.89% § 70% 25510 3353 13.14% 25718 3573 13.89% g: 80% 25510 3353 13.14% 25718 3573 13.89% 90% 25997 3549 13.65% 26164 3732 14.26% 100% 26586 3735 14.05% 26761 3881 14.50% 2005 FSMG ALL OUT %OUT TOTAL 27340 3955 14.47% Any 19441 2232 11.48% a 10% 20181 2363 11.71% g 20% 22769 2807 12.33% g 30% 22769 2807 12.33% O 40% 24430 3171 12.98% '5 50% 24814 3259 13.13% a 60% 26184 3574 13.65% g 70% 26184 3574 13.65% g 80% 26184 3574 13.65% 90% 26728 3795 14.20% 100% 27340 3955 14.47% 177 2001 2002 FSA 1 H ALL OUT %OUT ALL OUT %OUT TOTAL 280737 15446 5.50% 286586 14317 5.00% An 1 271250 13981 5.15% 276603 12806 4.63% a 10%_ 279821 15119 5.40% 285574 13934 4.88% g 20%_ 280321 15438 5.51% 286100 14299 5.00% o 30%_ 280737 15446 5.50% 286586 14317 5.00% 5 40%_ 280737 15446 5.50% 286586 14317 5.00% "6 50%_ 280737 15446 5.50% 286586 14317 5.00% 1: 60% 280737 15446 5.50% 286586 14317 5.00% § 711% 280737 15446 5.50% 286586 14317 5.00% g 111% 280737 15446 5.50% 286586 14317 5.00% 111% 280737 15446 5.50% 286586 14317 5.00% 1 11% 280737 15446 5.50% 286586 14317 5.00% 2003 2004 FSA 1" ALL OUT %OUT ALL OUT %OUT TOTAL 289573 13047 4.51% 296850 14518 4.89% An 279108 11521 4.13% 286287 13034 4.55% a 10%_ 288484 12645 4.38% 295735 14131 4.78% g 20%_ 289075 13025 4.51% 296314 14494 4.89% o 30%_ 289573 13047 4.51% 296850 14518 4.89% 5 40%_ 289573 13047 4.51% 296850 14518 4.89% “6 50%_ 289573 13047 4.51% 296850 14518 4.89% a 60% 289573 13047 4.51% 296850 14518 4.89% g 70%_ 289573 13047 4.51% 296850 14518 4.89% g 80%_ 289573 13047 4.51% 296850 14518 4.89% 90%_ 289573 13047 4.51% 296850 14518 4.89% 100% 289573 13047 4.51% 296850 14518 4.89% 2005 FSA 1” ALL OUT %OUT TOTAL 303019 14938 4.93% Any 292312 13384 4.58% a 10% 301981 14538 4.81% g 20% 302543 14926 4.93% o 30% 303019 14938 4.93% 5 40% 303019 14938 4.93% “6 50% 303019 14938 4.93% *a 60% 303019 14938 4.93% § 70% 303019 14938 4.93% w 80% 303019 14938 4.93% °' 90% 303019 14938 4.93% 100% 303019 14938 4.93% 178 179 FSA 1' 2001 2002 ALL OUT %OUT ALL OUT %OUT TOTAL 24380 3624 14.86% 24652 3638 14.76% An 19310 2661 13.78% 19432 2624 13.50% Q 10%_ 20569 2933 14.26% 20612 2897 14.05% g 20%“ 22106 3132 14.17% 22346 3130 14.01% 0 30%_ 22106 3132 14.17% 22346 3130 14.01% 5 40%_ 23082 3303 14.31% 23321 3317 14.22% "6 50%_ 23510 3405 14.48% 23689 3407 14.38% a 60% 24109 3536 14.67% 24348 3549 14.58% § 70%_ 24109 3536 14.67% 24348 3549 14.58% g 80%_ 24109 3536 14.67% 24348 3549 14.58% 90%_ 24109 3536 14.67% 24348 3549 14.58% 100% 24380 3624 14.86% 24652 3638 14.76% 1=s A 1' zone. 2004 ALL OUT %OUT ALL OUT %OUT TOTAL 25815 3792 14.69% 26132 4042 15.47% Any 20348 2690 13.22% 20718 3014 14.55% a 10%_ 21564 2949 13.68% 21988 3304 15.03% g 20%_ 23350 3174 13.59% 23868 3552 14.88% a 30%_ 23350 3174 13.59% 23868 3552 14.88% 5 4o_ 68482 3113 4.55% 69147 3212 4.65% E 60% 68482 3113 4.55% 69147 3212 4.65% § 70% 68482 3113 4.55% 69147 3212 4.65% a H N» 68482 3113 4.55% 69147 3212 4.65% N N» 68482 3113 4.55% 69147 3212 4.65% 1 M “K; 68482 3113 4.55% 69147 3212 4.65% FS A 4J 2003 2004 ALL OUT %OUT ALL OUT %OUT TOTAL 69897 3385 4.84% 69242 3380 4.88% An! 65336 3276 5.01% 64626 3245 5.02% 10% 69897 3385 4.84% 69242 3380 4.88% 20% 69897 3385 4.84% 69242 3380 4.88% 30% 69897 3385 4.84% 69242 3380 4.88% 40% 69897 3385 4.84% 69242 3380 4.88% 69897 3385 4.84% 69242 3380 4.88% 60% 69897 3385 4.84% 69242 3380 4.88% 70% 69897 3385 4.84% 69242 3380 4.88% 80% 69897 3385 4.84% 69242 3380 4.88% 90% 69897 3385 4.84% 69242 3380 4.88% 1 00% 69897 3385 4.84% 69242 3380 4.88% Percent Of Overlap 0| 0 33 2005 FSA“ ALL OUT %OUT TOTAL 69683 3413 4.90% Any 64775 3296 5.09% a 10%; 69683 3413 4.90% g 20%_ 69683 3413 4.90% o 30%_ 69683 3413 4.90% 5 4o%_ 69683 3413 4.90% “a 50%_ 69683 3413 4.90% a 60% 69683 3413 4.90% g 70%_ 69683 3413 4.90% g 80%; 69683 3413 4.90% 90% 69683 3413 4.90% 100% 69683 3413 4.90% 199 2001 2002 FSA“ ALL OUT %OUT ALL OUT %OU_T_ TOTAL 29568 4419 14.95% 30665 4484 14.62% Any 21852 3996 18.29% 22424 4004 17.86% 10%_ 29568 4419 14.95% 30665 4484 14.62% g" 20%_ 29568 4419 14.95% 30665 4484 14.62% g 30%. 29568 4419 14.95% 30665 4484 14.62% 0 may 29568 4419 14.95% 30665 4484 14.62% '6 50%_ 29568 4419 14.95% 30665 4484 14.62% ‘a 60% 29568 4419 14.95% 30665 4484 14.62% § 70% 29568 4419 14.95% 30665 4484 14.62% g 801k 29568 4419 14.95% 30665 4484 14.62% 90% 29568 4419 14.95% 30665 4484 14.62% 100% 29568 4419 14.95% 30665 4484 14.62% 2003 2004 ”A“ ALL OUT %OUT ALL OUT %OUT TOTAL 30280 4682 15.46% 30672 4705 15.34% Any 22121 4175 18.87% 22446 4201 18.72% 10% 30280 4682 15.46% 30672 4705 15.34% g 20%_ 30280 4682 15.46% 30672 4705 15.34% 0 30%_ 30280 4682 15.46% 30672 4705 15.34% 5 40%_ 30280 4682 15.46% 30672 4705 15.34% '6 50%_ 30280 4682 15.46% 30672 4705 15.34% a 60% 30280 4682 15.46% 30672 4705 15.34% g 70% 30280 4682 15.46% 30672 4705 15.34% 3 801771 30280 4682 15.46% 30672 4705 15.34% 90151 30280 4682 15.46% 30672 4705 15.34% 100% 30280 4682 15.46% 30672 4705 15.34% 2005 FSA“ ALL OUT %OUT TOTAL 31904 5065 15.88% Any 23198 4587 19.77% a 10% 31904 5065 15.88% g 20% 31904 5065 15.88% 0 30% 31904 5065 15.88% 5 40% 31904 5065 15.88% '5 50% 31904 5065 15.88% a 60% 31904 5065 15.88% § 70% 31904 5065 15.88% g 80% 31904 5065 15.88% 90% 31904 5065 15.88% 100% 31904 5065 15.88% 200 2001 2002 FSA 4" ALL OUT %OUT ALL OUT %OUT TOTAL 56609 3102 5.48% 57231 3308 5.78% An 1 52912 3020 5.71% 53314 3190 5.98% a 1 11% 56609 3102 5.48% 57231 3308 5.78% g :11 1% 56609 3102 5.48% 57231 3308 5.78% 5; .111% 56609 3102 5.48% 57231 3308 5.78% O 411%_ 56609 3102 5.48% 57231 3308 5.78% ‘5 50%_ 56609 3102 5.48% 57231 3308 5.78% E 60% 56609 3102 5.48% 57231 3308 5.78% § 711% 56609 3102 5.48% 57231 3308 5.78% g 111 1% 56609 3102 5.48% 57231 3308 5.78% 111 1% 56609 3102 5.48% 57231 3308 5.78% 100% 56609 3102 5.48% 57231 3308 5.78% 2003 2004 FSA 4" ALL OUT %OUT ALL OUT %OUT TOTAL 58053 3410 5.87% 58939 3515 5.96% Any 54080 3300 6.10% 54915 3408 6.21% a 10% 58053 3410 5.87% 58939 3515 5.96% g 20%_ 58053 3410 5.87% 58939 3515 5.96% o 30%_ 58053 3410 5.87% 58939 3515 5.96% 5 40%_ 58053 3410 5.87% 58939 3515 5.96% '5 50%_ 58053 3410 5.87% 58939 3515 5.96% z 60% 58053 3410 5.87% 58939 3515 5.96% § 70%_ 58053 3410 5.87% 58939 3515 5.96% g 80%_ 58053 3410 5.87% 58939 3515 5.96% 90%_ 58053 3410 5.87% 58939 3515 5.96% 100% 58053 3410 5.87% 58939 3515 5.96% 2005 FSA 4" ALL OUT %OUT TOTAL 6162 3814 6.19% An 57331 3691 6.44% 101 61622 3814 6.19% g 20%_ 61622 3814 6.19% a 30%_ 61622 3814 6.19% 5 4o'v_._ 61622 3814 6.19% “a 50%_ 61622 3814 6.19% a 60% 61622 3814 6.19% g 70%_ 61622 3814 6.19% g 80%_ 61622 3814 6.19% 90%_ 61622 3814 6.19% 100% 61622 3814 6.19% 201 2001 2002 FSA 5" ALL OUT %OUT ALL OUT %OUT TOTAL 37717 3155 8.36% 38421 3336 8.68% Any 29439 2398 8.15% 30316 2654 8.75% 10%_ 35910 3019 8.41% 36815 3247 8.82% g 20%_ 35910 3019 8.41% 36815 3247 8.82% o 30%_ 35910 3019 8.41% 36815 3247 8.82% 5 40%_ 37717 3155 8.36% 38421 3336 8.68% "6 50%_ 37717 3155 8.36% 38421 3336 8.68% E 60% 37717 3155 8.36% 38421 3336 8.68% § 71 1% 37717 3155 8.36% 38421 3336 8.68% g 01 1% 37717 3155 8.36% 38421 3336 8.68% 01 1% 37717 3155 8.36% 38421 3336 8.68% 101 1% 37717 3155 8.36% 38421 3336 8.68% 2003 2004 FSA 5‘ ALL OUT %OUT ALL OUT %OUT TOTAL 38769 3421 8.82% 40311 3569 8.85% Any 30305 2631 8.68% 31402 2750 8.76% 10% 37094 3309 8.92% 38656 3449 8.92% g 20% 37094 3309 8.92% 38656 3449 8.92% o 30% 37094 3309 8.92% 38656 3449 8.92% 5 40% 38769 3421 8.82% 40311 3569 8.85% '5 50% 38769 3421 8.82% 40311 3569 8.85% a 60% 38769 3421 8.82% 40311 3569 8.85% § 70% 38769 3421 8.82% 40311 3569 8.85% g 80% 38769 3421 8.82% 40311 3569 8.85% 90% 38769 3421 8.82% 40311 3569 8.85% 100% 38769 3421 8.82% 40311 3569 8.85% 2005 FSA 5‘ ALL OUT %OUT TOTAL 41177 3706 9.00% Any_ 32269 2935 9.10% a 10%_ 39374 3586 9.1 1% '6 2°": 39374 3586 9.1 1% o 30%_ 39374 3586 9.1 1% 5 40%_ 41177 3706 9.00% '5 50%y 41 177 3706 9.00% a 60% 41 177 3706 9.00% § 70%_ 41 177 3706 9.00% g 80%_ 41177 3706 9.00% 90%; 41177 3706 9.00% 100% 41177 3706 9.00% 202 2001 2002 FSA 5'3 ALL OUT %OUT ALL OUT %OUT TOTAL 139921 8816 6.30% 140750 9020 6.41% _A_ny_ 123832 7474 6.04% 124369 7712 6.20% 1 0% 137175 8576 6.25% 138031 8810 6.38% g 20% 138232 8641 6.25% 139154 8880 6.38% 0 :1o%_ 139102 8756 6.29% 140016 8983 6.42% 5 40%_ 139921 8816 6.30% 140750 9020 6.41% “a 50%_ 139921 8816 6.30% 140750 9020 6.41% E 60‘ 139921 8816 6.30% 140750 9020 6.41% g 70% 139921 8816 6.30% 140750 9020 6.41% g 00% 139921 8816 6.30% 140750 9020 6.41% 00% 139921 8816 6.30% 140750 9020 6.41% 100% 139921 8816 6.30% 140750 9020 6.41% 2003 2004 FSA 53 ALL OUT %OUT ALL OUT %OUT TOTAL 141577 9360 6.61% 143851 9653 6.71% An 124350 7879 6.34% 126556 8185 6.47% 10% 138802 9142 6.59% 141158 9452 6.70% g" 20% 139899 9218 6.59% 142242 9526 6.70% o 30% 140816 9311 6.61% 143112 9603 6.71% 5 40% 141577 9360 6.61% 143851 9653 6.71% '5 50% 141577 9360 6.61% 143851 9653 6.71% E 60% 141577 9360 6.61% 143851 9653 6.71% § 70% 141577 9360 6.61% 143851 9653 6.71% g 80% 141577 9360 6.61% 143851 9653 6.71% 90% 141577 9360 6.61% 143851 9653 6.71% 100% 141577 9360 6.61% 143851 9653 6.71% 2005 FS" 53 ALL OUT %OUT TOTAL 147140 10312 7.01% Any 129583 8861 6.84% 10% 144307 10108 7.00% a“ 20% 145432 10182 7.00% o 30% 146322 10259 7.01% 5 40% 147140 10312 7.01% "6 50% 147140 10312 7.01% E 60% 147140 10312 7.01% § 70% 147140 10312 7.01% g 80% 147140 10312 7.01% 90% 147140 10312 7.01% 100% 147140 10312 7.01% 203 2001 2002 FSA 5c ALL OUT %OUT ALL OUT %OUT TOTAL 76161 7261 9.53% 76089 7416 9.75% An 61859 5316 8.59% 61407 5464 8.90% 10%_ 72483 6546 9.03% 72292 6720 9.30% 3 20% 72483 6546 9.03% 72292 6720 9.30% '5 30%— 73353 6661 9.08% 73154 6823 9.33% 5 40%— 74443 6928 9.31% 74251 7086 9.54% '6 50%— 74443 6928 9.31% 74251 7086 9.54% *5 60%? 75482 7065 9.36% 75339 7193 9.55% 8 70% 75482 7065 9.36% 75339 7193 9.55% 3 80%— 75482 7065 9.36% 75339 7193 9.55% “' 90117 75903 7216 9.51% 75818 7376 9.73% 100% 76161 7261 9.53% 76089 7416 9.75% 2003 2004 FSA“: ALL OUT %OUT ALL OUT %OUT TOTAL 77288 7835 10.14% 77863 8001 10.28% Any 61645 5730 9.30% 62319 5873 9.42% 10% 73373 7132 9.72% 73983 7372 9.96% 5 20% 73373 7132 9.72% 73983 7372 9.96% o 30% 74290 7225 9.73% 74853 7449 9.95% 5 40% 75444 7488 9.93% 76051 7691 10.11% '6 50% 75444 7488 9.93% 76051 7691 10.11% '5 60% 76502 7581 9.91% 77099 7805 10.12% g 70% 76502 7581 9.91% 77099 7805 10.12% g 80% 76502 7581 9.91% 77099 7805 10.12% 90% 76989 7777 10.10% 77545 7964 10.27% 100% 77288 7835 10.14% 77863 8001 10.28% 2005 FSA 5° ALL OUT %OUT TOTAL 79080 8588 10.86% Any 63288 6285 9.93% 10%_ 74961 7789 10.39% g 20%_ 74961 7789 10.39% c» 30%_ 75851 7866 10.37% 5 40%__ 77145 8178 10.60% '5 50% 77145 8178 10.60% s 60% 78210 8317 10.63% g 70%_ 78210 8317 10.63% g 80%_ 78210 8317 10.63% 90% 78754 8538 10.84% 100% 79080 8588 10.86% 204 2001 2002 FSA“ ALL OUT %OUT ALL OUT %OUT TOTAL 18373 6904 37.58% 18746 7181 38.31% _An 7166 2340 32.65% 7307 2419 33.11% 10% 11419 3604 31.56% 11617 3717 32.00% 5‘ 20% 13489 4507 33.41% 13634 4645 34.07% 5 EL 14970 5147 34.38% 15286 5367 35.11% 5 40%_ 14970 5147 34.38% 15286 5367 35.11% '5 50%_ 14970 5147 34.38% 15286 5367 35.11% .. 60% 15662 5477 34.97% 15920 5675 35.65% g 711% 15662 5477 34.97% 15920 5675 35.65% g 80% 16382 5797 35.39% 16595 6012 36.23% 90% 17870 6590 36.88% 18246 6852 37.55% 101% 18373 6904 37.58% 18746 7181 38.31% 2003 2004 FSA“ ALL OUT %OUT ALL OUT %OUT TOTAL 19118 7317 38.27% 20387 7841 38.46% Any 7601 2526 33.23% 8359 2748 32.87% 10% 11802 3897 33.02% 12757 4203 32.95% g 20% 13973 4930 35.28% 15056 5261 34.94% 0 30% 15751 5678 36.05% 16805 5999 35.70% 5 40% 15751 5678 36.05% 16805 5999 35.70% “.5 50% 15751 5678 36.05% 16805 5999 35.70% E 60% 16411 5985 36.47% 17442 6337 36.33% g 70% 16411 5985 36.47% 17442 6337 36.33% g 80% 17058 6264 36.72% 18154 6653 36.65% 90% 18682 7044 37.70% 19837 7475 37.68% 100% 19118 7317 38.27% 20387 7841 38.46% 2005 FSA 6‘ ALL OUT %OUT TOTAL 19819 7647 38.58% Any 7575 2420 31.95% a 10% 12145 3949 32.52% ‘t‘ 20% 14396 5053 35.10% a 30% 16251 5832 35.89% 5 40% 16251 5832 35.89% '5 50%_ 16251 5832 35.89% a 60% 16874 6152 36.46% {1 70%_ 16874 6152 36.46% g 80%; 17617 6490 36.84% 90%; 19328 7303 37.78% 100% 19819 7647 38.58% 205 2001 2002 FSASB ALL OUT %OUT ALL OUT %OUT TOTAL 24804 5168 20.84% 25621 5920 23.11% An 16575 3017 18.20% 17045 3392 19.90% a 10%_ 21515 4365 20.29% 22234 4954 22.28% 't' 20%_ 22468 4480 19.94% 23240 5094 21.92% o 30% 24804 5168 20.84% 25621 5920 23.11% 5 4°21. 24804 5168 20.84% 25621 5920 23.11% “a 50%_ 24804 5168 20.84% 25621 5920 23.11% E 60% 24804 5168 20.84% 25621 5920 23.11% g 70%_ 24804 5168 20.84% 25621 5920 23.11% g 80% 24804 5168 20.84% 25621 5920 23.11% 90% 24804 5168 20.84% 25621 5920 23.11% 100% 24804 5168 20.84% 25621 5920 23.11% 2003 2004 FSA ‘3 ALL OUT %OUT ALL OUT %OUT TOTAL 25956 5886 22.68% 26514 5698 21.49% Any 17074 3355 19.65% 17510 3197 18.26% 10% 22319 4879 21.86% 22835 20.42% g 20% 23342 5023 21.52% 23834 4787 20.08% 0 ' 30% 25956 5886 22.68% 26514 5698 21.49% 5 40% 25956 5886 22.68% 26514 5698 21.49% '5 50% 25956 5886 22.68% 26514 5698 21.49% a 60% 25956 5886 22.68% 26514 5698 21.49% § 70% 25956 5886 22.68% 26514 5698 21.49% g 80% 25956 5886 22.68% 26514 5698 21.49% 90% 25956 5886 22.68% 26514 5698 21.49% 100% 25956 5886 22.68% 26514 5698 21.49% 2005 FSA 63 ALL OUT %OUT TOTAL 26030 5855 22.49% Any 17365 3423 19.71% 10% 22545 4883 21.66% g 20% 23484 5014 21.35% a 30% 26030 5855 22.49% 5 40% 26030 5855 22.49% ‘6 50% 26030 5855 22.49% a 60% 26030 5855 22.49% g 70% 26030 5855 22.49% 8 80% 26030 5855 22.49% 90% 26030 5855 22.49% 100% 26030 5855 22.49% 206 ' 2001 2002 FSA 6‘: ALL OUT %OUT ALL OUT %OUT TOTAL 20416 5679 27.82% 21213 6298 29.69% Any 6749 1670 24.74% 6994 1791 25.61% a 10%_ 13021 3481 26.58% 13579 3826 28.18% g 20%_ 15620 3918 25.08% 16280 4360 26.78% 0 30%_ 17956 4606 25.65% 18661 5186 27.79% 5 40%_ 18934 4954 26.16% 19583 5564 28.41% '5 50%_ 18934 4954 26.16% 19583 5564 28.41% ‘5 60% 18934 4954 26.16% 19583 5564 28.41% g 70% 18934 4954 26.16% 19583 5564 28.41% g 110% 19292 5136 26.63% 19917 5708 28.66% 00% 20416 5679 27.82% 21213 6298 29.69% 101% 20416 5679 27.82% 21213 6298 29.69% 2003 2004 FSA 6c ALL OUT %OUT ALL OUT %OUT TOTAL 21071 6189 29.37% 21620 6239 28.86% Any 6858 1776 25.90% 7291 1737 23.82% 10% 13205 3746 28.37% 13727 3706 27.00% g 20% 15990 4301 26.90% 16424 4185 25.48% a 30% 18604 5164 27.76% 19104 5096 26.68% 5 40% 19536 5498 28.14% 19964 5484 27.47% ‘6 50% 19536 5498 28.14% 19964 5484 27.47% a 60% 19536 5498 28.14% 19964 5484 27.47% g 70% 19536 5498 28.14% 19964 5484 27.47% g 80% 19806 5628 28.42% 20310 5662 27.88% 90% 21071 6189 29.37% 21620 6239 28.86% 100% 21071 6189 29.37% 21620 6239 28.86% 2005 FSA“ ALL OUT %OUT TOTAL 21792 6518 29.91% Any 7374 1897 25.73% a 10% 13792 3875 28.10% g 20% 16471 4477 27.18% 10 30% 19017 5318 27.96% 5 40% 20073 5734 28.57% '5 50% 20073 5734 28.57% a 60% 20073 5734 28.57% g 70% 20073 5734 28.57% g 80% 20441 5948 29.10% 90% 21792 6518 29.91% 100% 21792 6518 29.91% 207 2001 2002 FSA 6° ALL OUT %OUT ALL OUT %OUT TOTAL 75928 9153 12.05% 76821 10043 13.07% Any 54608 5203 9.53% 55203 5647 10.23% a 10%_ 68369 7556 11.05% 69225 8239 11.90% g 20%_ 71903 8147 11.33% 72838 8911 12.23% a 30%_ 74389 8773 11.79% 75412 9669 12.82% 5 40%_ 75208 8833 11.74% 76146 9706 12.75% ‘6 50%_ 75208 8833 11.74% 76146 9706 12.75% *5 60% 75208 8833 11.74% 76146 9706 12.75% § 70%_ 75208 8833 11.74% 76146 9706 12.75% g 80%_ 75928 9153 12.05% 76821 10043 13.07% 90% 75928 9153 12.05% 76821 10043 13.07% 100% 75928 9153 12.05% 76821 10043 13.07% 2003 2004 FSA 6° ALL OUT %OUT ALL OUT %OUT TOTAL 77026 9828 12.76% 77701 10315 13.28% Any 55638 5539 9.96% 56035 5780 10.31% 10% 69157 8018 11.59% 69737 8485 12.17% g 20% 72904 8729 11.97% 73470 9140 12.44% 0 30%_ 75618 9500 12.56% 76250 9949 13.05% 5 40%_ 76379 9549 12.50% 76989 9999 12.99% “6 50%_ 76379 9549 12.50% 76989 9999 12.99% a 60% 76379 9549 12.50% 76989 9999 12.99% § 70%_ 76379 9549 12.50% 76989 9999 12.99% g 80% 77026 9828 12.76% 77701 10315 13.28% 90% 77026 9828 12.76% 77701 10315 13.28% 100% 77026 9828 12.76% 77701 10315 13.28% 2005 FSAs” ALL OUT %OUT TOTAL 77995 10439 13.38747 Any 55838 5866 10.51% 10% 70137 8572 12.22% 5 20% 73787 9324 12.64% 0 30% 76434 10048 13.15% 5 40% 77252 10101 13.08% “a 50% 77252 10101 13.08% a 60% 77252 10101 13.08% g 70% 77252 10101 13.08% g 80% 77995 10439 13.38% 90% 77995 10439 13.38% 100% 77995 10439 13.38% 208 2001 2002 FSA 6'5 ALL OUT %OUT ALL OUT %OUT TOTAL 97104 8827 9.09% 97714 9104 9.32% Am 78944 5961 7.55% 79364 6162 7.76% 10%_ 94094 8508 9.04% 94705 8802 9.29% g" 20%_ 96285 8767 9.11% 96980 9067 9.35% o 30% 96285 8767 9.11% 96980 9067 9.35% 5 40% 97104 8827 9.09% 97714 9104 9.32% '6 50%_ 97104 8827 9.09% 97714 9104 9.32% E 60%_ 97104 8827 9.09% 97714 9104 9.32% § 70% 97104 8827 9.09% 97714 9104 9.32% g 80%_ 97104 8827 9.09% 97714 9104 9.32% 90% 97104 8827 9.09% 97714 9104 9.32% 100% 97104 8827 9.09% 97714 9104 9:me 2003 2004 FSA GE ALL OUT %OUT ALL OUT %OUT TOTAL 96338 9186 9.54% 96682 9755 10.09% An 78264 6188 7.91% 78423 6467 8.25% a 10% 93308 8862 9.50% 93673 9453 10.09% t“ 20% 95577 9137 9.56% 95943 9705 10.12% 0 30%_ 95577 9137 9.56% 95943 9705 10.12% 5 40%_ 96338 9186 9.54% 96682 9755 10.09% '3 50%_ 96338 9186 9.54% 96682 9755 10.09% E 60% 96338 9186 9.54% 96682 9755 10.09% § 70%_ 96338 9186 9.54% 96682 9755 10.09% g 80%_ 96338 9186 9.54% 96682 9755 10.09% 90%_ 96338 9186 9.54% 96682 9755 10.09% 100% 96338 9186 9.54% 96682 9755 10.09% 2005 FSA 6'5 ALL OUT %OUT TOTAL 97921 9618 9.82% Any 78832 6344 8.05% a 10% 94791 9273 9.78% g 20%_ 97103 9565 9.85% o 30%_ 97103 9565 9.85% 5 40%; 97921 9618 9.82% '5 50%_ 97921 9618 9.82% s 60% 97921 9618 9.82% § 70%_ 97921 9618 9.82% g 80% 97921 9618 9.82% 90‘ 0 97921 9618 9.82% 100% 97921 9618 9.82% 209 2001 2002 FSA “F ALL OUT %OUT ALL OUT %OUT TOTAL 108097 9426 8.72% 108676 9394 8.64% An 87363 7609 8.71% 87764 7553 8.61% 10%_ 101361 8808 8.69% 101929 8769 8.60% 5 20¢ . 105017 9144 8.71% 105752 9165 8.67% o 30' ._ 106290 9290 8.74% 107070 9305 8.69% 5 40¢ ._ 108097 9426 8.72% 108676 9394 8.64% '6 50'_._ 108097 9426 8.72% 108676 9394 8.64% 2 60% 108097 9426 8.72% 108676 9394 8.64% § 70% 108097 9426 8.72% 108676 9394 8.64% g 80%_ 108097 9426 8.72% 108676 9394 8.64% 90%_ 108097 9426 8.72% 108676 9394 8.64% 100% 108097 9426 8.72% 108676 9394 8.64% 2003 2004 FSA 6': ALI;__ OUT %OUT ALL OUT %OUT TOTAL 108497 9842 9.07% 107806 9958 9.24% An 87158 7908 9.07% 86599 7969 9.20% 10% 101582 9183 9.04% 101067 9381 9.28% 5 20% 105447 9602 9.1 1% 104847 9733 9.28% g 30% 106822 9730 9.11% 106151 9838 9.27% o 40% 108497 9842 9.07% 107806 9958 9.24% “5 50% 108497 9842 9.07% 107806 9958 9.24% z 60% 108497 9842 9.07% 107806 9958 9.24% § 70% 108497 9842 9.07% 107806 9958 9.24% g: 80% 108497 9842 9.07% 107806 9958 9.24% 90% 108497 9842 9.07% 107806 9958 9.24% 100% 108497 9842 9.07% 107806 9958 9.24% 2005 FSA 6F ALL OUT %OUT TOTAL 109660 9961 9.08% Any 87825 7984 9.09% a 10% 102833 9344 9.09% g 20% 106536 9732 9.13% o 30% 107857 9841 9.12% 5 40% 109660 9961 9.08% “5 50% 109660 9961 9.08% *a 60% 109660 9961 9.08% § 70% 109660 9961 9.08% g 80% 109660 9961 9.08% 90% 109660 9961 9.08% 100% 109660 9961 9.08% 210 2001 2002 FSA“; ALL OUT %OUT ALL OUT %OUT TOTAL 10529 2930 27.83% 10737 2874 26.77% An 9422 2641 28.03% 9684 2607 26.92% a may 10529 2930 27.83% 10737 2874 26.77% g 20%_ 10529 2930 27.83% 10737 2874 26.77% a 30% 10529 2930 27.83% 10737 2874 26.77% 5 40%_ 10529 2930 27.83% 10737 2874 26.77% ”5 50%_ 10529 2930 27.83% 10737 2874 26.77% :5 60% 10529 2930 27.83% 10737 2874 26.77% § 70% 10529 2930 27.83% 10737 2874 26.77% g 111% 10529 2930 27.83% 10737 2874 26.77% 111% 10529 2930 27.83% 10737 2874 26.77% 110% 105% 2930 27.83% 10737 2874 26.77% 2003 2004 FSASG ALL OUT %OUT ALL OUT %OUT TOTAL 10804 3060 28.32% 10324 2880 27.90% Any 9707 2731 28.13% 9324 2606 27.95% a 10% 10804 3060 28.32% 10324 2880 27.90% g 20% 10804 3060 28.32% 10324 2880 27.90% a 30% 10804 3060 28.32% 10324 2880 27.90% 5 40% 10804 3060 28.32% 10324 2880 27.90% ”a 50% 10804 3060 28.32% 10324 2880 27.90% '5 60% 10804 3060 28.32% 10324 2880 27.90% g 70% 10804 3060 28.32% 10324 2880 27.90% g 80% 10804 3060 28.32% 10324 2880 27.90% 90% 10804 3060 28.32% 10324 2880 27.90% 100% 10804 3060 28.32% 103& 2880 27.90% 2005 FSAGG ALL OUT %OUT TOTAL 10885 2910 26.73% Any 9816 2602 26.51% a 10% 10885 2910 26.73% g 20% 10885 2910 26.73% E: 30% 10885 2910 26.73% O 40% 10885 2910 26.73% ‘5 50% 10885 2910 26.73% *5 60% 10885 2910 26.73% § 70% 10885 2910 26.73% g 80% 10885 2910 26.73% 90% 10885 2910 26.73% 100% 10885 2910 26.73% 211 2001 2002 FSAs" ALL OUT %OUT ALL OUT %OUT TOTAL 10458 3479 33.27% 10306 3391 32.90% An 7131 2569 36.03% 7164 2554 35.65% a 10%_ 7844 2919 37.21% 7768 2841 36.57% g 20%_ 8587 3065 35.69% 8515 2973 34.91% a 30%_ 8587 3065 35.69% 8515 2973 34.91% 5 40% 9158 3208 35.03% 9061 3120 34.43% ‘6 50% 9586 3310 34.53% 9429 3210 34.04% a 6011? 10458 3479 33.27% 10306 3391 32.90% g 711% 10458 3479 33.27% 10306 3391 32.90% g 110% 10458 3479 33.27% 10306 3391 32.90% 90% 10458 3479 33.27% 10306 3391 32.90% 100% 10458 3479 33.27% 10306 3391 32.90% 2003 2004 FSA 6” ALL OUT %OUT ALL OUT %OUT TOTAL 10525 3591 34.12% 10195 3440 33.74% Any 7205 2682 37.22% 7066 2614 36.99% 10% 7828 3002 38.35% 7679 2928 38.13% :8 20% 8583 3127 36.43% 8378 3057 36.49% g 30% 8583 3127 36.43% 8378 3057 36.49% O 40% 9223 3276 35.52% 8990 3195 35.54% “a 50% 9644 3381 35.06% 9373 3279 34.98% E 60% 10525 3591 34.12% 10195 3440 33.74% § 70% 10525 3591 34.12% 10195 3440 33.74% g 80% 10525 3591 34.12% 10195 3440 33.74% 90% 10525 3591 34.12% 10195 3440 33.74% 100% 10525 3591 34.12% 10195 3440 33.74% 2005 FSA 6” ALL OUT %OUT . TOTAL 10623 3650 34.36% Any 7262 2672 36.79% a 10% 7937 3026 38.13% g 20% 8705 3195 36.70% 51 30% 8705 3195 36.70% O 40% 9360 3362 35.92% “5 50% 9744 3450 35.41% E 60% 10623 3650 34.36% g 70% 10623 3650 34.36% g 80% 10623 3650 34.36% 90% 10623 3650 34.36% 100% 10623 3650 34.36% 212 FSA 6' 2001 2002 ALL OUT %OUT ALL OUT %OUT TOTAL 20096 5405 26.90% 20360 5280 25.93% An 14585 3878 26.59% 14923 3789 25.39% 10%_ 17491 4850 27.73% 17726 4715 26.60% g 20%_ 18234 4996 27.40% 18473 4847 26.24% a 30%_ 19104 5111 26.75% 19335 4950 25.60% 5 40%_ 19675 5254 26.70% 19881 5097 25.64% ‘6 50%_ 19675 5254 26.70% 19881 5097 25.64% *5 60% 19675 5254 26.70% 19881 5097 25.64% § 70% 19675 5254 26.70% 19881 5097 25.64% g 110% 19675 5254 26.70% 19881 5097 25.64% 110% 20096 5405 26.90% 20360 5280 25.93% 100% 20096 5405 26.90% 20360 5280 25.93% 2003 2004 FSA 6' ALL OUT %OUT ALL OUT %OUT TOTAL 21009 5722 27.24% 20589 5662 27.50% Any 15290 4183 27.36% 15051 4117 27.35% 10% 18210 5159 28.33% 17962 5159 28.72% g 20% 18965 5284 27.86% 18661 5288 28.34% 0 30% 19882 5377 27.04% 19531 5365 27.47% 5 40% 20522 5526 26.93% 20143 5503 27.32% “a 50% 20522 5526 26.93% 20143 5503 27.32% a 60% 20522 5526 26.93% 20143 5503 27.32% § 70% 20522 5526 26.93% 20143 5503 27.32% g 80% 20522 5526 26.93% 20143 5503 27.32% 90% 21009 5722 27.24% 20589 5662 27.50% 100% 21009 5722 27.24% 20589 5662 27.50% 2005 FSA“ ALL OUT %OUT TOTAL 21521 5920 27.51% An 15598 4234 27.14% 10% 18664 5286 28.32% g 20% 19432 5455 28.07% 0 30% 20322 5532 27.22% 5 40% 20977 5699 27.17% '3 50% 20977 5699 27.17% a 60% 20977 5699 27.17% g 70% 20977 5699 27.17% g 80% 20977 5699 27.17% 90% 21521 5920 27.51% 100% 21521 5920 27.51% 213 2001 2002 FSA 7“ ALL OUT %OUT ALL OUT %OUT TOTAL 6769 1125 16.62% 7051 1088 15.43% Am 392 82 20.92% 276 29 10.51% a 10%_ 5141 592 11.52% 5388 491 9.11% g 20%_ 5141 592 11.52% 5388 491 9.11% g 30%.. 5141 592 11.52% 5388 491 9.11% O 40% 6079 738 12.14% 6356 663 10.43% "6 50% 6079 738 12.14% 6356 663 10.43% E 60% 6079 738 12.14% 6356 663 10.43% g 70% 6079 738 12.14% 6356 663 10.43% g 10% 6079 738 12.14% 6356 663 10.43% 00% 6557 1009 15.39% 6855 959 13.99% 101% 6769 1125 16.62% 7051 1088 15.43% 2003 2004 FSA” ALL OUT %OUT ALL OUT %OUT TOTAL 7280 1475 20.26% 7288 1365 18.73% Any 290 43 14.83% 290 29 10.00% a 10% 5486 759 13.84% 5535 697 12.59% g 20% 5486 759 13.84% 5535 697 12.59% 0 30% 5486 759 13.84% 5535 697 12.59% 5 40% 6400 928 14.50% 6425 834 12.98% '5 50% 6400 928 14.50% 6425 834 12.98% a 60% 6400 928 14.50% 6425 834 12.98% g 70% 6400 928 14.50% 6425 834 12.98% g 80% 6400 928 14.50% 6425 834 12.98% 90% 7034 1321 18.78% 7053 1239 17.57% 100% 7280 1475 20.26% 7288 1365 18.73% 2005 FSA 7“ ALL OUT %OUT TOTAL 7040 1336 18.98% Any 272 35 12.87% a 10% 5312 682 12.84% a 20% 5312 682 12.84% E 30% 5312 682 12.84% O 40% 6292 875 13.91% *5 "50' % 6292 875 13.91% '5 60% 6292 875 13.91% g 70% 6292 875 13.91% g 80% 6292 875 13.91% 90% 6828 1199 17.56% 100% 7040 1336 18.98% 214 2001 2002 FSMB ALL OUT %OUT ALL OUT %OUT TOTAL 14430 1624 11.25% 14896 1715 11.51% An 6088 573 9.41% 6040 649 10.75% a 10% 10653 1034 9.71% 10977 1100 10.02% g 20¢(L 11956 1307 10.93% 12328 1366 11.08% a 30%_ 11956 1307 10.93% 12328 1366 11.08% 5 40%_ 13898 1517 10.92% 14258 1561 10.95% “6 50%_ 13988 1546 11.05% 14345 1586 11.06% '5 60% 13988 1546 11.05% 14345 1586 11.06% § 711% 13988 1546 11.05% 14345 1586 11.06% g 811% 14430 1624 11.25% 14896 1715 11.51% 90%_ 14430 1624 11.25% 14896 1715 11.51% 100% 14430 1624 11.25% 14896 1715 11.51% 2003 2004 FSMB ALL OUT %OUT ALL OUT %OUT TOTAL 14834 1993 13.44% 14668 2102 14.33% Any 5892 601 10.20% 5690 710 12.48% 10% 10868 1230 11.32% 10708 1348 12.59% g 20% 12292 1595 12.98% 12025 1696 14.10% 0 30% 12292 1595 12.98% 12025 1696 14.10% 5 40% 14269 1855 13.00% 13997 1932 13.80% “a 50% 14336 1882 13.13% 14085 1974 14.01% *a 60% 14336 1882 13.13% 14085 1974 14.01% g 70% 14336 1882 13.13% 14085 1974 14.01% g 80% 14834 1993 13.44% 14668 2102 14.33% 90% 14834 1993 13.44% 14668 2102 14.33% 100% 14834 1993 13.44% 14668 2102 14.33% 2005 FSA” ALL OUT %OUT TOTAL 14822 2191 14.78% Any 5921 746 12.60% a 10% 10831 1362 12.58% g 20% 12118 1745 14.40% a 30% 12118 1745 14.40% 5 40% 14161 2021 14.27% “.5 50% 14257 2066 14.49% a 60% 14257 2066 14.49% § 70% 14257 2066 14.49% a 80% 14822 2191 14.78% 90% 14822 2191 14.78% 100% 14822 2191 14.78% 215 2001 2002 FSA 7c ALL OUT %OUT ALL OUT %OUT TOTAL 5444 1944 35.71% 5596 1994 35.63% An 0 0 0 0 o 0 10%_ 2862 541 18.90% 2954 533 18.04% g 20%_ 2862 541 18.90% 2954 533 18.04% a 30%_ 2862 541 18.90% 2954 533 18.04% 5 401 3044 600 19.71% 3117 591 18.96% '6 50%_ 3044 600 19.71% 3117 591 18.96% a 60% 3044 600 19.71% 3117 591 18.96% § 7o¢._ 3044 600 19.71% 3117 591 18.96% g 80'._ 3480 724 20.80% 3600 731 20.31% 901._ 3958 995 25.14% 4099 1027 25.05% 100'. 4770 1711 35.87% 4892 1753 35.83% 2003 2004 FSA TO ALL OUT %OUT ALL OUT %OUT TOTAL 5651 2390 42.29% 5348 2241 41.90% An 0 0 0 0 0 0 a 10%_ 2827 662 23.42% 2690 599 22.27% g 20%_ 2827 662 23.42% 2690 599 22.27% 0 301 2827 662 23.42% 2690 599 22.27% 5 40%_ 3013 738 24.49% 2845 656 23.06% "5 50%_ 3013 738 24.49% 2845 656 23.06% E 60% 3013 738 24.49% 2845 656 23.06% g 70% 3013 738 24.49% 2845 656 23.06% g 80% 3430 873 25.45% 3270 822 25.14% 90% 4064 1266 31.15% 3898 1227 31.48% 100% 4990 2100 42.08% 4759 1979 41.58% 2005 FSA 7° ALL OUT %OUT TOTAL 5531 2271 41.06% Any 0 o o a 10% 2877 674 23.43% t“ 20% 2877 674 23.43% 0 30% 2877 674 23.43% 5 40% 3068 750 24.45% '5 50% 3068 750 24.45% E 60% 3068 750 24.45% g 70% 3068 750 24.45% g 80% 3579 947 26.46% 90% 4115 1271 30.89% 100% 4935 2016 40.85% 216 2001 2002 FSMD ALL OUT %OUT ALL OUT %OUT TOTAL 11550 2803 24.27% 12145 2955 24.33% Any 3361 863 25.68% 3590 946 26.35% a 10%_ 7909 1623 20.52% 8433 1757 20.83% g 20% 10291 1978 19.22% 10866 2085 19.19% 6 30%_ 10485 2073 19.77% 11030 2166 19.64% 5 40%_ 10595 2131 20.11% 11151 2241 20.10% "6 50%_ 10595 2131 20.11% 11151 2241 20.10% s 60% 10595 2131 20.11% 11151 2241 20.10% § 70%_ 10595 2131 20.11% 11151 2241 20.10% g 80% 10595 2131 20.11% 11151 2241 20.10% 90%_ 10595 2131 20.11% 11151 2241 20.10% 100% 11550 2803 24.27% 12145 2955 24.33% 2003 2004 FSA") ALL OUT %OUT ALL OUT %OUT TOTAL 12550 3149 25.09% 12470 3144 25.21% An 3824 1070 27.98% 3636 1041 28.63% a 10% 8648 1927 22.28% 8650 1930 22.31% g 20% 11184 2275 20.34% 11174 2324 20.80% g 30% 11374 2365 20.79% 11381 2425 21.31% O 40% 11483 2426 21.13% 11464 2468 21.53% '5 50% 11483 2426 21.13% 11464 2468 21.53% E 60% 11483 2426 21.13% 11464 2468 21.53% g 70% 11483 2426 21.13% 11464 2468 21.53% g 80% 11483 2426 21.13% 11464 2468 21.53% 90% 11483 2426 21.13% 11464 2468 21.53% 100% 12550 3149 25.09% 12470 3144 25.21% 2005 FSA") ALL OUT %OUT TOTAL 12684 3325 26.21% Any 3845 1106 28.76% a 10% 8820 2024 22.95% g 20% 11317 2439 21.55% 0 30% 11528 2544 22.07% 5 40% 11618 2595 22.34% ‘5 50% 11618 2595 22.34% a 60% 11618 2595 22.34% {3 70% 11618 2595 22.34% g 80% 11618 2595 22.34% 90% 11618 2595 22.34% 100% 12684 3325 26.21% 217 2001 2002 FSA“ ALL OUT %OUT ALL OUT %OUT TOTAL 5148 1176 22.84% 5282 1195 22.62% An 0 o 0 0 0 0 101 2862 541 18.90% 2954 533 18.04% g 20%_ 3278 639 19.49% 3319 623 18.77% 0 30%_ 3278 639 19.49% 3319 623 18.77% 5 40%_ 3460 698 20.17% 3482 681 19.56% ‘6 50% 3460 698 20.17% 3482 681 19.56% E 60% 3460 698 20.17% 3482 681 19.56% § 701._ 3460 698 20.17% 3482 681 19.56% 3 80'1_ 4121 886 21.50% 4197 903 21.52% 901._ 4272 939 21.98% 4352 964 22.15% 100'. 4325 964 22.29% 4410 985 22.34% 2003 2004 FSA "5 ALL OUT %OUT ALL OUT %OUT TOTAL 5038 1396 27.71% 4751 1281 26.96% Any 0 0 0 0 0 0 10% 2827 662 23.42% 2690 599 22.27% g 20% 3229 790 24.47% 3067 702 22.89% c 30% 3229 790 24.47% 3067 702 22.89% 5 40% 3415 866 25.36% 3222 759 23.56% “a 50% 3415 866 25.36% 3222 759 23.56% a 60% 3415 866 25.36% 3222 759 23.56% g 70% 3415 866 25.36% 3222 759 23.56% g 80% 4046 1061 26.22% 3843 990 25.76% 90% 4193 1120 26.71% 3971 1036 26.09% 100% 4244 1142 26.91% 4028 1057 26.24% 2005 FSA 75 ALL OUT %OUT TOTAL 5160 1419 27.50% Any 0 0 o a 10% 2877 674 23.43% g 20% 3277 777 23.71% 4; 30% 3277 777 23.71% O 40% 3468 853 24.60% '5 50% 3468 853 24.60% E 60% 3468 853 24.60% § 70% 3468 853 24.60% g 80% 4182 1121 26.81% 90% 4327 1167 26.97% 100% 4383 1188 27.10% 218 2001 2002 FSA 7F ALL OUT %OUT ALL OUT %OUT TOTAL 19377 1788 9.23% 19848 1899 9.57% An 6955 482 6.93% 7324 539 7.36% a 10%_ 14252 1156 8.11% 14766 1274 8.63% g 20% 15632 1387 8.87% 16176 1508 9.32% o 30%_ 16391 1441 8.79% 16938 1574 9.29% 5 40%_ 17535 1525 8.70% 18023 1678 9.31% "6 50%_ 18499 1696 9.17% 18932 1817 9.60% z 60% 18629 1703 9.14% 19080 1829 9.59% § 70%_ 18629 1703 9.14% 19080 1829 9.59% g 80% 19191 1757 9.16% 19655 1871 9.52% 90%_ 19247 1764 9.17% 19710 1876 9.52% 100% 19377 1788 9.23% 19846 1899 9.57% 2003 2004 FSA 7F ALL OUT %OUT ALL OUT %OUT TOTAL 20059 1886 9.40% 20517 1932 9.42% Any 7262 558 7.68% 7253 524 7.22% a 10% 14782 1284 8.69% 15082 1283 8.51 % 1:11 20% 16276 1492 9.17% 16644 1526 9.17% o 30% 17005 1551 9.12% 17386 1594 9.17% 5 40% 18101 1653 9.13% 18587 1707 9.18% “.5 50% 19059 1791 9.40% 19571 1835 9.38% a 60% 19225 1804 9.38% 19710 1842 9.35% § 70% 19225 1804 9.38% 19710 1842 9.35% g 80% 19848 1859 9.37% 20301 1903 9.37% 90% 19921 1871 9.39% 20363 1909 9.37% 100% 20059 1886 9.40% 20517 1932 9.42% 2005 FSA 7F ALL OUT %OUT TOTAL 20944 2108 10.06% Any 7157 587 8.20% a 10% 15222 1386 9.11% g 20% 16779 1626 9.69% o 30% 17538 1709 9.74% 5 40% 18804 1814 9.65% '5 50% 19946 1988 9.97% a 60% 20059 1997 9.96% § 70% 20059 1997 9.96% g 80% 20695 2063 9.97% 90% 20758 2070 9.97% 100% 20944 2108 10.06% 219 2001 2002 FSA-’6 ALL OUT %OUT ALL OUT %OUT TOTAL 9287 2287 24.63% 9378 2460 26.23% Am 1327 452 34.06% 1350 495 36.67% 10%_ 4439 1164 26.22% 4688 1339 28.56% g 20%_ 5605 1522 27.15% 5835 1743 29.87% 1» 30%_ 6163 1554 25.21% 6381 1778 27.86% 5 40%_ 6850 1732 25.28% 7059 1983 28.09% "5 50%_ 8488 2060 24.27% 8571 2243 26.17% *5 60% 8488 2060 24.27% 8571 2243 26.17% § 70% 8488 2060 24.27% 8571 2243 26.17% g 80%_ 9287 2287 24.63% 9378 2460 26.23% 90% 9287 2287 24.63% 9378 2460 26.23% 100% 9287 2287 24.63% 9378 2460 26.23% 2003 2004 FSMG ALL OUT %OUT ALL OUT %OUT TOTAL 9566 2421 25.31% 9520 2422 25.44% Any 1394 474 34.00% 1428 497 34.80% 10% 4643 1263 27.20% 4760 1324 27.82% :3 20% 5883 1706 29.00% 5919 1710 28.89% 0 30% 6419 1740 27.11% 6445 1739 26.98% 5 40% 7076 1919 27.12% 7081 1947 27.50% “a 50% 8700 2183 25.09% 8713 2177 24.99% E 60% 8700 2183 25.09% 8713 2177 24.99% g 70% 8700 2183 25.09% 8713 2177 24.99% g 80% 9566 2421 25.31% 9520 2422 25.44% 90% 9566 2421 25.31% 9520 2422 25.44% 100% 9566 2421 25.31% 9520 2422 25.44% 2005 FSA 76 ALL_ OUT %OUT TOTAL 10450 2900 27.75% An 1412 566 40.08% 10% 4947 1516 30.64% g 20% 6253 2031 32.48% 0 30% 6807 2083 30.60% 5 40% 7606 2300 30.24% '5 50% 9552 2614 27.37% *5 60% 9552 2614 27.37% g 70% 9552 2614 27.37% g 80% 10450 2900 27.75% 90% 10450 2900 27.75% 100% 10450 2900 27.75% 220 2001 2002 FSA” ALL OUT %OUT ALL OUT %OUT TOTAL 10116 3319 32.81% 10495 3460 32.97% Any 1879 813 43.27% 1870 780 41.71% a 10%_ 3941 1189 30.17% 4081 1211 29.67% g 20% 5991 1648 27.51% 6192 1733 27.99% 0 30%_ 7453 2294 30.78% 7768 2403 30.93% 5 40% 7453 2294 30.78% 7768 2403 30.93% '5 50% 8272 2458 29.71% 8524 2533 29.72% a 60% 8272 2458 29.71% 8524 2533 29.72% g 70% 8272 2458 29.71% 8524 2533 29.72% g 80% 8992 2778 30.89% 9199 2870 31.20% 90%_ 10116 3319 32.81% 10495 3460 32.97% 100% 10116 3319 32.81% 10495 3460 32.97% 2003 2004 FSA” ALL OUT %OUT ALL OUT %OUT TOTAL 11118 3588 32.27% 11239 3610 32.12% An 2138 941 44.01% 2024 872 43.08% 10% 4434 1372 30.94% 4330 1311 30.28% g 20% 6690 1893 28.30% 6698 1877 28.02% 0 _ 30% 8394 2616 31.17% 8401 2602 30.97% 5 40%_ 8394 2616 31.17% 8401 2602 30.97% ‘5 5°‘L 9206 2748 29.85% 9217 2717 29.48% a 60% 9206 2748 29.85% 9217 2717 29.48% 8 7011.4 9206 2748 29.85% 9217 2717 29.48% g 80‘L 9853 3027 30.72% 9929 3033 30.55% 90% 11118 3588 32.27% 11239 3610 32.12% 100% 11118 3588 32.27% 11239 3610 32.12% 2005 FSA 7” ALL OUT %OUT TOTAL 11730 3859 32.90% Am 2070 925 44.69% = Q 10%_ 4433 1397 31.51% g 20%_ 6836 2018 29.52% 0 30%_ 8663 2794 32.25% 5 40%_ 8663 2794 32.25% “a 50%_ 9636 2951 30.62% 2 60% 9636 2951 30.62% § 70%_ 9636 2951 30.62% g 80%_ 10379 3289 31.69% 90%_ 11730 3859 32.90% 100% 11730 3859 32.90% 221 222 2001 2002 FSA" ALL OUT %OUT ALL OUT %OUT TOTAL 10402 2743 26.37% 10240 2913 28.45% Any 3255 674 20.71% 3257 728 22.35% 10%_ 9002 2226 24.73% 8839 2395 27.10% g 20% 9002 2226 24.73% 8839 2395 27.10% 0 30%_ 9203 2248 24.43% 9055 2426 26.79% 5 40%_ 9543 2313 24.24% 9378 2511 26.78% "5 50%_ 9543 2313 24.24% 9378 2511 26.78% ‘5 60% 9543 2313 24.24% 9378 2511 26.78% g 70%_ 9543 2313 24.24% 9378 2511 26.78% g 80% 9991 2517 25.19% 9879 2721 27.54% 90%_ 9991 2517 25.19% 9879 2721 27.54% 100% 10402 2743 26.37% 10240 2913 28.45% 2003 2004 FSA 7' ALL OUT %OUT ALL OUT %OUT TOTAL 10671 2900 27.18% 10790 3125 28.96% Any 3328 715 21.48% 3406 838 24.60% 10% 9271 2385 25.73% 9359 2567 27.43% g 20% 9271 2385 25.73% 9359 2567 27.43% 5 30% 9464 2410 25.46% 9575 2606 27.22% 5 40% 9801 2494 25.45% 9928 2701 27.21% '5 50% 9801 2494 25.45% 9928 2701 27.21% a 60% 9801 2494 25.45% 9928 2701 27.21% § 70% 9801 2494 25.45% 9928 2701 27.21% g 80% 10311 2703 26.21% 10407 2911 27.97% 90% 10311 2703 26.21% 10407 2911 27.97% 100% 10671 2900 27.18% 10790 3125 28.96% 2005 FSA 7' ALL OUT %OUT TOTAL 11294 3504 31.03% Any 3446 847 24.58% 5 10% 9736 2890 29.68% g 20% 9736 2890 29.68% 5 30% 9941 2921 29.38% 5 40% 10324 3020 29.25% '5 50% 10324 3020 29.25% a 60% 10324 3020 29.25% § 70% 10324 3020 29.25% g 80% 10843 3254 30.01% 90% 10843 3254 30.01% 100% 11294 3504 31.03% 2001 2002 FSA“ ALL OUT %OUT ALL OUT %OUT TOTAL 1550 469 30.26% 1498 499 33.31% An 53 24 45.28% 0 0 0 10%_ 356 132 37.08% 323 119 36.84% 1% -20'_._ 1235 361 29.23% 1193 400 33.53% 0 30%_ 1235 361 29.23% 1193 400 33.53% 5 40%_ 1235 361 29.23% 1193 400 33.53% ‘5 50%_ 1235 361 29.23% 1193 400 33.53% s 60% 1235 361 29.23% 1193 400 33.53% § 70% 1235 361 29.23% 1193 400 33.53% g 80%_ 1471 424 28.82% 1409 454 32.22% 90%__ 1471 424 28.82% 1409 454 32.22% 100% 1550 469 30.26% 1498 499 33.31% 2003 2004 FSA 8‘ ALL OUT %OUT ALL_ OUT %OUT TOTAL 1551 544 35.07% 1539 584 37.95% Any 0 0 o o 0 0 a 10% 333 140 42.04% 318 125 39.31% g 20% 1251 432 34.53% 1177 430 36.53% 5 30%_ 1251 432 34.53% 1177 430 36.53% 5 40%_ 1251 432 34.53% 1177 430 36.53% ‘5 50%_ 1251 432 34.53% 1177 430 36.53% *5 60% 1251 432 34.53% 1177 430 36.53% § 70%_ 1251 432 34.53% 1177 430 36.53% g (10%_ 1479 505 34.14% 1442 533 36.96% 9011 1479 505 34.14% 1442 533 36.96% 100% 1551 544 35.07% 1539 584 37.95% 2005 FSA 3‘ ALL OUT %OUT TOTAL 1827 844 46.20% Any 0 0 0 a 10% 405 185 45.68% g zo'gfl 1424 654 45.93% 0 30%: 1424 654 45.93% 5 40%_ 1424 654 45.93% '5 50%_ 1424 654 45.93% a 60% 1424 654 45.93% § 70%_ 1424 654 45.93% g 80%_ 1718 790 45.98% 90%_ 1718 790 45.98% 100% 1827 844 46.20% 223 FSA“ ALL OUT %OUT ALL OUT %OUT TOTAL 1969 919 46.67% 2013 914 45.40% Any 45 27 60.00% 0 0 0 10%_ 81 39 48.15% 0 0 0 E 20%_ 81 39 48.15% 0 o 0 5 30%_ 81 39 48.15% 0 0 0 5 40%_ 1437 568 39.53% 1626 616 37.88% '5 50%_ 1437 568 39.53% 1626 616 37.88% 15 60% 1437 568 39.53% 1626 616 37.88% § 70%_ 1437 568 39.53% 1626 616 37.88% g 80%_ 1643 670 40.78% 1626 616 37.88% 90% 1643 670 40.78% 1626 616 37.88% 100% 1925 879 45.66% 1948 855 43.89% FSA BB 2003 2004 ALL OUT %OUT ALL OUT %OUT TOTAL 1927 1017 52.78% 1871 949 50.72% Any 0 0 0 0 o 0 5 10% 0 0 0 0 0 0 g 20% 0 0 0 0 o 0 5 30% 0 0 o 0 0 0 5 40% 1571 725 46.15% 1499 645 43.03% *5 50% 1571 725 46.15% 1499 645 43.03% s 60% 1571 725 46.15% 1499 645 43.03% § 70% 1571 725 46.15% 1499 645 43.03% g 80% 1571 725 46.15% 1499 645 43.03% 90% 1571 725 46.15% 1499 645 43.03% ""1'00% 1873 965 51.52% 1810 895 49.45% 2005 FSA 83 ALL OUT %OUT TOTAL 2098 990 47.19% Any 0 0 0 5 10% 0 0 0 11:11 20% 0 0 0 5 30% 0 0 0 5 40% 1754 713 40.65% '5 50% 1754 713 40.65% '5 60% 1754 713 40.65% § 70% 1754 713 40.65% a 80% 1754 713 40.65% 90% 1754 713 40.65% 100% 2046 944 46.14% 224 2001 2002 FSA 8° ALL OUT %OUT ALL OUT %OUT TOTAL 3672 1406 38.29% 4197 1695 40.39% Any 308 161 52.27% 87 35 40.23% 10%_ 2254 973 43.17% 2567 1103 42.97% g 20%_ 2296 998 43.47% 2567 1103 42.97% 5 30%_ 2296 998 43.47% 2567 1103 42.97% 5 40%_ 2296 998 43.47% 2567 1103 42.97% '5 50%_ 3672 1406 38.29% 4197 1695 40.39% a 60% 3672 1406 38.29% 4197 1695 40.39% § 70% 3672 1406 38.29% 4197 1695 40.39% g 80%_ 3672 1406 38.29% 4197 1695 40.39% 90%_ 3672 1406 38.29% 4197 1695 40.39% 100% 3672 1406 38.29% 4197 1695 40.39% 2003 2004 FSA 8° ALL OUT %OUT ALL OUT %OUT TOTAL 4117 1605 38.98% 3964 1621 40.89% Any 55 21 38.18% 102 69 67.65% 10% 2437 1077 44.19% 2426 1153 47.53% 8: 20% 2437 1077 44.19% 2426 1153 47.53% t 5 30% 2437 1077 44.19% 2426 1153 47.53% 5 40% 2437 1077 44.19% 2426 1153 47.53% '5 50%y 4117 1605 38.98% 3964 1621 40.89% a 60% 4117 1605 38.98% 3964 1621 40.89% g 70%_ 4117 1605 38.98% 3964 1621 40.89% g 80%_ 4117 1605 38.98% 3964 1621 40.89% 90%__ 4117 1605 38.98% 3964 1621 40.89% 100% 4117 1605 38.98% 3964 1621 40.89% 2005 FSA 8° ALL OUT %OUT TOTAL 3760 1626 43.24% Any 138 106 76.81% 5 10% 2294 1118 48.74% g 20% 2294 1118 48.74% 5 30% 2294 1118 48.74% 5 40% 2294 1118 48.74% “a 50% 3760 1626 43.24% a 60% 3760 1626 43.24% § 70% 3760 1626 43.24% g 80% 3760 1626 43.24% 90% 3760 1626 43.24% 100% 3760 1626 43.24% 225 2001 2002 FSA 8” ALL OUT %OUT ALL OUT %OUT TOTAL 1771 668 37.72% 2013 671 33.33% Any 291 107 36.77% 254 76 29.92% 10%_ 1061 429 40.43% 1047 383 36.58% g 20%_ 1108 449 40.52% 1103 417 37.81% 5 30%_ 1623 630 38.82% 1801 605 33.59% 5 40%_ 1623 630 38.82% 1801 605 33.59% ‘5 50%_ 1623 630 38.82% 1801 605 33.59% a 60% 1645 637 38.72% 1848 626 33.87% § 70%_ 1645 637 38.72% 1848 626 33.87% g 80%_ 1645 637 38.72% 1848 626 33.87% 90% 1645 637 38.72% 1848 626 33.87% 100% 1771 668 37.72% 2013 671 33.33% 2003 2004 FSA” ALL OUT %OUT ALL OUT %OUT TOTAL 2078 745 35.85% 2085 788 37.79% Any 290 100 34.48% 315 124 39.37% 10% 1140 456 40.00% 1154 497 43.07% g 20% 1169 468 40.03% 1201 522 43.46% 5 30% 1836 655 35.68% 1878 733 39.03% 5 40% 1836 655 35.68% 1878 733 39.03% ‘5 50% 1836 655 35.68% 1878 733 39.03% s 60% 1890 676 35.77% 1910 744 38.95% § 70% 1890 676 35.77% 1910 744 38.95% g 80% 1890 676 35.77% 1910 744 38.95% 90% 1890 676 35.77% 1910 744 38.95% 100% 2078 745 35.85% 2085 788 37.79% 2005 FSA 8” ALL OUT %OUT TOTAL 2068 743 35.93% Any 352 125 35.51% 5 10% 1170 471 40.26% g 20% 1210 488 40.33% g 30% 1880 698 37.13% O 40% 1880 698 37.13% ‘3 50% 1880 698 37.13% a 60% 1903 706 37.10% § 70% 1903 706 37.10% g 80% 1903 706 37.10% 90% 1903 706 37.10% 100% 2068 743 35.93% 226 2001 2002 Percent of Overlap 5487 1587 28.92% 5763 1646 28.56% 4193 1127 26.88% 4078 1112 27.27% 4435 1224 27.60% 4329 1 185 27.37% 4582 1273 27.78% 4618 1278 27.67% 5097 1454 28.53% 5316 1466 27.58% 5158 1475 28.60% 5316 1466 27.58% 5158 1475 28.60% 5316 1466 27.58% 5201 1489 28.63% 5357 1484 27.70% 5201 1489 28.63% 5357 1484 27.70% 5487 1587 28.92% 5763 1646 28.56% 5487 1587 28.92% 5763 1646 28.56% 5487 1587 28.92% 5763 1646 28.56% 2003 2004 ALL OUT %OUT ALL OUT %OUT Percent of Overlap 5978 1 702 28.47% 5806 1648 28.38% 4346 1193 27.45% 4112 1124 27.33% 4615 1274 27.61% 4373 1215 27.78% 4908 1368 27.87% 4725 1 321 27.96% 5575 1555 27.89% 5402 1 532 28.36% 5575 1 555 27.89% 5402 1 532 28.36% 5575 1555 27.89% 5402 1 532 28.36% 5624 1572 27.95% 5452 1 550 28.43% 5624 1572 27.95% 5452 1550 28.43% 5978 1 702 28.47% 5806 1 648 28.38% 5978 1 702 28.47% 5806 1648 28.38% 5978 1702 L28.47% 5806 1648 28.38% 2005 ALL OUT %OUT xlxlxl x xl xl xl xl xl Percent of Overlap 5909 1 742 29.48% 4257 1203 28.26% 4521 1294 28.62% 4826 1406 29.1 3% 5496 1 61 6 29.40% 5496 1 61 6 29.40% 5496 1 61 6 29.40% 5537 1636 29.55% 5537 1636 29.55% 5909 1 742 29.48% 5909 1 742 29.48% 5909 1 742 29.48% 227 2001 2002 FSA “F ALL OUT %OUT ALL OU_1_ %OUT TOTAL 5448 1434 26.32% 5428 1658 30.55% An 2255 595 26.39% 2118 612 28.90% 10%_ 2255 595 26.39% 2118 612 28.90% g 20%_ 2255 595 26.39% 2118 612 28.90% 5 30¢._ 2255 595 26.39% 2118 612 28.90% 5 40'._ 2255 595 26.39% 2118 612 28.90% '5 50%_ 3343 907 27.13% 3373 1076 31.90% a 60% 5224 1337 25.59% 5201 1551 29.82% g 70% 5224 1337 25.59% 5201 1551 29.82% g 110‘. 5249 1344 25.60% 5240 1568 29.92% 90'._ 5249 1344 25.60% 5240 1568 29.92% 100'. 5448 1434 26.32% 5428 1658 30.55% 2003 2004 FSA" ALL OUT %OUT ALL OUT %OUT TOTAL 5202 1567 30.12% 5029 1605 31.91% An 2011 642 31.92% 2001 682 34.08% 5 10%_ 2011 642 31.92% 2001 682 34.08% g 20% 2011 642 31.92% 2001 682 34.08% 5 30%_ 2011 642 31.92% 2001 682 34.08% 5 40%_ 2011 642 31.92% 2001 682 34.08% ‘5 50%; 3265 1016 31.12% 3164 1058 33.44% *5 60% 4971 1479 29.75% 4820 1507 31.27% g 70%_ 4971 1479 29.75% 4820 1507 31.27% g 80%_ 5014 1489 29.70% 4874 1534 31.47% 90%_ 5014 1489 29.70% 4874 1534 31.47% 100% 5202 1567 30.12% 5029 1605 31.91% 2005 FSA 8F ALL OUT 960$ TOTAL 5132 1763 34.35% Any 2102 743 35.35% 10% 2102 743 35.35% :9 20% 2102 743 35.35% 5 30% 2102 743 35.35% 5 40% 2102 743 35.35% ‘3 50%_ 3241 1165 35.95% a 60% 4913 1664 33.87% g 70%_ 4913 1664 33.87% g 80‘V_5_ 4967 1680 33.82% 90%_ 4967 1680 33.82% 100% 5132 1763 34.35% 228 2001 2002 FSA 86 ALL OUT %OUT ALL OUT %OUT TOTAL 7272 440 6.05% 7581 481 6.34% An 81 6 7.41% 0 0 0 101 1665 74 4.44% 1802 76 4.22% :3 20%_ 1665 74 4.44% 1802 76 4.22% 5 30%_ 1665 74 4.44% 1802 76 4.22% 5 40%_ 5589 306 5.48% 5930 318 5.36% '5 50%_ 6045 330 5.46% 6374 360 5.65% E 60% 6045 330 5.46% 6374 360 5.65% § 70% 6045 330 5.46% 6374 360 5.65% g 80' ._ 6804 383 5.63% 7083 406 5.73% 90' . 6855 403 5.88% 7083 406 5.73% 100' . 7272 440 6.05% 7581 481 6.34% 2003 2004 FSA 8° ALL OUT__y %OUT ALL OUT %OUT TOTAL 7710 480 6.23% 7754 495 6.38% Any 0 0 0 0 0 0 5 10% 1790 84 4.69% 1806 92 5.09% g 20% 1790 84 4.69% 1806 92 5.09% 5 30% 1790 84 4.69% 1806 92 5.09% 5 40% 5956 323 5.42% 5962 358 6.00% “a 50% 6391 344 5.38% 6412 394 6.14% E 60% 6391 344 5.38% 6412 394 6.14% § 70% 6391 344 5.38% 6412 394 6.14% g 80% 7249 403 5.56% 7278 452 6.21% 90% 7249 403 5.56% 7278 452 6.21% 100% 7710 480 6.23% 7754 495 6.38% 2005 FSA 8‘5 ALL OUT %OUT TOTAL 7782 527 6.77% Any 0 0 0 Q 10% 1752 110 6.28% g 20% 1752 110 6.28 A, 5 30% 1752 110 6.28% 5 40% 6108 393 6.43% “5 50% 6468 41 1 6.35% *5 60% 6468 411 6.35% § 70% 6468 41 1 6.35% g 80% 7340 482 6.57% 90% 7340 482 6.57% 100% 7782 527 6.77% 229 2001 2002 FSAs" ALL OUT %OUT ALL OUT %OUT TOTAL 9266 3531 38.11% 9099 3554 39.06% An 108 43 39.81% 113 46 40.71% 10%_ 108 43 39.81% 113 46 40.71% g" 20%_ 1194 438 36.68% 1268 492 38.80% 5 30%_ 1194 438 36.68% 1268 492 38.80% 5 40%_ 1194 438 36.68% 1268 492 38.80% ‘5 50%_ 1588 629 39.61% 1721 717 41.66% E 60% 1588 629 39.61% 1721 717 41.66% § 70%_ 1588 629 39.61% 1721 717 41.66% g 80% 1867 780 41.78% 2028 858 42.31% 90%_ 1867 780 41.78% 2028 858 42.31% 100% 2324 996 42.86% 2532 1109 43.80% 2003 2004 FSA 8” ALL OUT %OUT ALL OUT %OUT TOTAL 9863 3931 39.86% 9625 3776 39.23% An 108 53 49.07% 111 48 43.24% 5 10% 108 53 49.07% 111 48 43.24% 1:“ 20% 1318 506 38.39% 1246 461 37.00% 5 30% 1318 506 38.39% 1246 461 37.00% 5 40% 1318 506 38.39% 1246 461 37.00% “a 50% 1771 734 41.45% 1639 657 40.09% a 60% 1771 734 41.45% 1639 657 40.09% g 70% 1771 734 41.45% 1639 657 40.09% g: 80% 2063 882 42.75% 1953 829 42.45% 90% 2063 882 42.75% 1953 829 42.45% 100% 2575 1136 44.12% 2437 1096 44.97% 2005 FSA 8” ALL OUT %OUT TOTAL 9260 3811 41.16% Any_ 88 29 32.95% 5 10%_ 88 29 32.95% g 20%_ 1148 470 40.94% 5 30%_ 1148 470 40.94% 5 40%_ 1148 470 40.94% '5 50%_ 1480 643 43.45% *5 60% 1480 643 43.45% § 70%_ 1480 643 43.45% g: 80%_ 1790 818 45.70% % 1790 818 45.70% 100%— 2288 1089 47.60% 230 2001 2002 ALL OUT %OUT ALL OUT %OUT Percent Of Overlap 1831 1136 62.04% 1916 1193 62.27% 0 0 0 0 0 0 0 0 0 0 0 0 532 315 59.21% 644 400 62.1 1% 618 377 61.00% 734 461 62.81% 618 377 61.00% 734 461 62.81% 1436 924 64.35% 1457 920 63.14% 1526 1002 65.66% 1564 1014 64.83% 1526 1002 65.66% 1564 1014 64.83% 1831 1136 62.04% 1916 1193 62.27% 1831 1136 62.04% 1916 1193 62.27% 1831 1136 62.04% 1916 1193 62.27% ALL OUT %OUT ALL OUT %OUT Percent of Overlap 2087 1182 56.64% 1954 1132 57.93% 0 0 0 0 0 0 0 0 0 0 0 0 626 346 55.27% 61 1 358 58.59% 726 410 56.47% 690 408 59.13% 726 410 56.47% 690 408 59.13% 1577 924 58.59% 1486 887 59.69% 1680 1010 60.12% 1585 969 61.14% 1680 1010 60.12% 1585 969 61.14% 2087 1182 56.64% 1954 1132 57.93% 2087 1182 56.64% 1954 1132 57.93% 2087 1 182 56.64% 1954 1 1 32 57.93% Percent of Overlap xlxlxlx xlxlxlxlxl 2005 . -. ., .. - . .. ALL OUT %OUT .‘ ‘ “Rm 1864 1185 63.57% . 0 o 0 o 0 0 561 378 67.38% 624 415 66.51% 624 415 66.51% 1431 931 65.06% 1528 1012 66.23% 1528 1012 66.23% 1864 1185 63.57% 1864 1185 63.57% 1864 1185 63.57% 231 2001 2002 FSA” ALL OUT %OUT ALL OUT %OUT TOTAL 2316 1442 62.26% 2200 1332 60.55% An 0 0 0 0 0 0 5 10%_ 59 40 67.80% 56 43 76.79% g 20%_ 59 40 67.80% 56 43 76.79% 5 30'._ 139 89 64.03% 138 93 67.39% 5 40' ._ 139 89 64.03% 138 93 67.39% '5 50'._ 1639 1061 64.73% 1460 901 61.71% ‘5' 60'. 1639 1061 64.73% 1460 901 61.71% g 70'._ 1639 1061 64.73% 1460 901 61.71% 3 80‘1_ 1734 1136 65.51% 1549 971 62.69% 90'_._ 1875 1243 66.29% 1700 1079 63.47% 100° 2316 1442 62.26% 2200 1332 60.55% 2003 2004 FSA“ ALL OUT %OUT ALL OUT %OUT TOTAL 2486 1461 58.77% 2420 1470 60.74% Any 0 0 o 0 0 0 5 10% 71 55 77.46% 54 43 79.63% 11:11 20% 71 55 77.46% 54 43 79.63% 5 30% 136 101 74.26% 135 93 68.89% 5 40% 136 101 74.26% 135 93 68.89% '5 so%_ 1712 1019 59.52% 1647 983 59.68% E 60% 1712 1019 59.52% 1647 983 59.68% § 70%_ 1712 1019 59.52% 1647 983 59.68% g 80%_ 1835 1110 60.49% 1771 1085 61.26% 90% 1981 1205 60.83% 1897 1181 62.26% 100% 2486 1461 58.77% 2420 1470 60.74% 2005 FSA 8" ALL OUT %OUT TOTAL 2392 1468 61.37% Any 0 0 0 5 10% 50 32 64.00% g 20% 50 32 64.00% '5 30% 148 86 58.11% 5 40% 148 86 58.11% '5 50% 1618 988 61.06% E 60% 1618 988 61.06% {s 70% 1618 988 61.06% g 80% 1732 1079 62.30% 90% 1859 1176 63.26% 100% 2392 1468 61.37% 232 2001 2002 FSA“ ALL OUT %OUT ALL OUT %OUT TOTAL 1407 808 57.43% 1571 917 58.37% An 0 0 0 o 0 0 5 10%_ 0 0 0 0 0 0 =5 20%_ 0 0 0 0 0 0 5 30%_ 241 160 66.39% 326 211 64.72% 5 40%_ 324 223 68.83% 402 266 66.17% “5 50%_ 1023 604 59.04% 1132 641 56.63% ‘5 60% 1023 604 59.04% 1132 641 56.63% § 70%_ 1023 604 59.04% 1132 641 56.63% g 80% 1257 706 56.17% 1435 809 56.38% 90%_ 1282 722 56.32% 1435 809 56.38% 100% 1407 808 57.43% 1571 917 58.37% 2003 2004 FSA 8" ALL OUT %OUT ALL OUT_ %OUT TOTAL 1573 918 58.36% 1442 889 61.65% Any 0 0 0 0 0 o 5 10% 0 0 0 0 0 0 g 20% 0 o 0 0 0 0 5 ” 30% 312 195 62.50% 239 164 68.62% 5 40% 403 253 62.78% 337 234 69.44% ‘5 50% 1145 657 57.38% 972 609 62.65% s 60% 1145 657 57.38% 972 609 62.65% g 70% 1145 657 57.38% 972 609 62.65% g 80% 1409 795 56.42% 1266 762 60.19% 90% 1409 795 56.42% 1266 762 60.19% 100% 1573 918 58.36% 1442 889 61.65% 2005 FSA 8K ALL OUT %OL TOTAL 1480 947 63.99% Any 0 0 0 10% 0 0 0 18; 20% 0 o 0 5 30% 303 222 73.27% 5 40% 400 286 71.50% “a 50% 1038 671 64.64% 2 60% 1038 671 64.64% § 70% 1038 671 64.64% g 80% 1323 827 62.51% 90% 1323 827 62.51% 100% 1480 947 63.99% 233 2001 2002 FSA“ ALL OUT %OUT ALL OUT %OUT TOTAL 19667 6421 32.65% 20848 6944 33.31% Am 0 0 0 0 0 0 5 10% 91 28 30.77% 138 57 41.30% g 20%? 18395 6115 33.24% 19359 6523 33.69% 5 3017L 18395 6115 33.24% 19359 6523 33.69% 5 40%; 18911 6173 32.64% 19870 6576 33.10% “a 50% 18911 6173 32.64% 19870 6576 33.10% a 611% 18955 6189 32.65% 19944 6611 33.15% § 70% 18955 6189 32.65% 19944 6611 33.15% g 110% 19509 6358 32.59% 20651 6865 33.24% 00% 19509 6358 32.59% 20651 6865 33.24% 100% 19667 6421 32.65% 20846 6944 33.31% 2003 2004 FSA“ ALL_ OUT %OUT ALL OUT %OUT TOTAL 18958 5990 31.60% 19631 6279 31.99% Any 0 0 0 0 0 0 5 10% 96 29 30.21% 105 25 23.81% g 20% 17523 5512 31.46% 18136 5849 32.25% 5 30% 17523 5512 31.46% 18136 5849 32.25% 5 40% 18014 5576 30.95% 18619 5913 31.76% '5 50% 18014 5576 30.95% 18619 5913 31.76% a 60% 18069 5614 31.07% 18675 5937 31.79% g 70% 18069 5614 31.07% 18675 5937 31.79% g 80% 18724 5879 31.40% 19396 6183 31.88% 90% 18724 5879 31.40% 19396 6183 31.88% 100% 18958 5990 31.60% 19631 6279 31.99% 2005 FSA 8" ALL OUT %OUT TOTAL 20199 6858 33.95% Any 0 0 0 5 10% 100 46 46.00% g 20% 18696 6397 34.22% g 30% 18696 6397 34.22% O 40% 19242 6481 33.68% '5 50% 19242 6481 33.68% a 60% 19305 6509 33.72% § 70% 19305 6509 33.72% g 80% 19985 6765 33.85% 90% 19985 6765 33.85% 100% 20199 6858 33.95% 234 Appendix 9 Results of T-test to compare the percentage of patients traveling longer than 30 minutes for acute care for FSA 30 minutes travel time service areas and the entire State of Michigan R results for Welch Two Sample t-test FSA t (If p 95% Conf. Interval mean x mean y 1A 52.316 70.097 < 2.2e—16 0.056138 0.060588 0.087497 0.029134 1 B 57.683 65.599 < 2.2e—1 6 0.061012 0.065388 0.087497 0.024297 1C 46.703 84.444 < 2.2e-16 0.052800 0.057497 0.087497 0.032348 1 D 52.660 83.177 < 2.2e—1 6 0.059531 0.064204 0.087497 0.025629 1E 53.543 77.179 < 2.2e-16 0.059142 0.063711 0.087497 0.026070 1 F 53.320 60.300 < 2.2e-16 0.055015 0.059303 0.087497 0.030338 1G -27.281 116.568 < 2.28-16 -0.046787 -0.040454 0.087497 0.131117 1H 33.191 78.247 < 2.2e—1 6 0.035945 0.040532 0.087497 0.049258 1 l -44. 542 1 00. 708 < 229-1 6 -0.058985 -0.053955 0.087497 0.143967 1J -3.532 74.067 0.0007145 -0.017374 -0.004841 0.087497 0.098604 2A 6.357 1 13.986 4.39E-09 0.006055 0.01 1536 0.087497 0.078701 28 -5.301 92.030 7.87E-07 -0.016046 -0.007299 0.087497 0.099169 2C -57.141 85.874 < 2.2e—1 6 -0.143059 -0.133439 0.087497 0.225746 20 -23.446 75.609 < 2.29-16 -0.076518 -0.064535 0.087497 0.158023 3A -6.266 88.367 1 .32E-08 -0.009903 -0.005134 0.087497 0.095015 38 -7.172 102.500 1 .19E-10 -0.017658 -0.010007 0.087497 0.101329 SC -44.838 99.957 < 226-1 6 -0.093087 -0.085198 0.087497 0.176640 30 -61.014 86.422 < 2.2e—16 -0.151043 -0.141511 0.087497 0.233774 3E -42.372 81.636 < 2.2e-16 -0.115980 -0.105577 0.087497 0.198275 4A -1 17.835 91 .609 < 2.28-16 -0.265431 -0.256631 0.087497 0.348527 48 -102.312 93.928 < 229-1 6 -0.223793 -0.215272 0.087497 0.307029 4C -84.544 69.983 < 229-1 6 -0.314913 -0.300398 0.087497 0.395152 40 -78.037 104.630 < 2.2e-16 -0.150515 -0.143056 0.087497 0.234282 4E -51 .682 78.885 < 2.2e—1 6 -0.148785 -0.137749 0.087497 0.230763 4F -83.503 1 16.402 < 226-1 6 -0.121942 -0.116292 0.087497 0.206613 4G 1 .176 99.774 0.2425 -0.001020 0.003988 0.087497 0.086013 4H 52.966 59.665 < 2.26-16 0.054489 0.058767 0.087497 0.030869 41 -58.141 114.701 < 2.2e-16 -0.099475 -0.092920 0.087497 0.183694 4J 36.677 62.899 < 228-1 6 0.037578 0.041910 0.087497 0.047753 4K -39.009 111.109 < 226-1 6 -0.071319 -0.064423 0.087497 0.155368 4L 25.845 69.214 < 2.2e—1 6 0.026514 0.030949 0.087497 0.058765 5A -0.1 19 68.496 0.9057 -0.002344 0.002080 0.087497 0.087629 58 19.359 70.728 < 228-1 6 0.019420 0.023881 0.087497 0.065846 5C -8.683 99.544 7.78E-14 -0.01 3447 -0.008445 0.087497 0.098443 6A -102.093 81 .169 < 2.28-16 -0.274691 -0.264189 0.087497 0.356937 68 -69.021 104.792 < 2.26-16 -0.133304 -0.125859 0.087497 0.217078 6C -90.459 95.623 < 2.2e-16 -0.193989 -0.185658 0.087497 0.277320 235 R results for Welch Two Sample t-teet cont. FSA t df p 95% Conf. Interval mean x mean y 6D -23.130 117.131 < 228-1 6 0039605 0.033358 0.087497 0.123978 GE 0241 106.105 8.18E-07 0009427 0.004252 0.087497 0.094336 6F 0351 59.075 3.34E-08 0353880 0.184316 0.087497 0.356594 6G -138.280 111.817 < 2.28-16 0190287 0.184911 0.087497 0.275095 6H -119.063 92.294 < 2.2e—1 6 0265585 0.256870 0.087497 0.348724 6| -127.310 116.660 < 2.2e-16 0.185264 0.179588 0.087497 0.269923 7A -13.031 68.337 < 2.2e-16 0058989 0.043323 0.087497 0.138653 78 -16.685 89.383 < 2.2e-16 0042725 0.033633 0.087497 0.125676 70 -17.330 55.293 < 228-16 0189516 0.150233 0.087497 0.257371 7D -41.136 72.826 < 2.2e-16 0141094 0.128054 0.087497 0.222070 7E -37.874 63.122 < 2.28—16 0152111 0.136865 0.087497 0.231985 7F -3.221 1 12.030 0.001674 0007070 0.001684 0.087497 0.091874 76 -43.692 66.808 < 228-1 6 01951 19 0.178069 0.087497 0.274091 7H -46.843 64.735 < 228-1 6 0241598 0.221838 0.087497 0.319215 71 -60.142 75.766 < 228-16 0186107 0.174175 0.087497 0.267638 8A 06.339 57.180 < 2.26-16 0293283 0.262650 0.087497 0.365463 88 48.555 44.916 < 2.2e—1 6 0365660 0.336531 0.087497 0.438592 8C —41.290 60.952 < 229-1 6 0361711 0.328295 0.087497 0.432500 80 02.984 71.518 < 2.2e-1 6 0291031 0.277375 0.087497 0.371700 8E -143.202 112.934 < 2.2e-16 0198853 0.193425 0.087497 0.283636 8F -54.079 67.573 < 2.2e-16 0229138 0.212828 0.087497 0.308479 8G 20.908 113.723 < 229-1 6 0.027404 0.033141 0.087497 0.057224 8H -75.951 66.934 < 228-1 6 0330381 0.313460 0.087497 0.409417 8| -112.409 54.362 < 2.2e-16 0536095 0.517310 0.087497 0.614199 8J -69.871 55.958 < 2.2e-16 0574443 0.542421 0.087497 0.645929 8K -77.556 46.240 < 226-1 6 0539099 051 1827 0.087497 0.612959 8L 04.038 63.439 < 2.2e-16 0247957 0.232952 0.087497 0.327951 236 Appendix 10 Results of Hospital Hierarchical Movement Analysis of Patient Visits Outside 30 Minutes Travel Time \. :3 .1 . - . .1 -‘ 1‘. .. {1&9 “ ‘5 ‘1 I _'j 4331894. Patient Travel tO Smaller Hospitals Outside 30 Minutes FSA Travel Areas“ ' 2001 - 3.5 - 29.3 - 2.0 - 3.4 - 1.0 - 1.9 3 0.5 - 0.9 _y; 0.0 . 0.4 i“ Excluded State Average = 1.0 0 Acute Care Hospital * Normalized by Total Population 75 '48 5 Miles 1 :4,300.000 237 Percentage Of Patient Trave to Smaller Hospitals Outside 30 Minutes FSA Travel Areas‘ 2001 - 80% - 99% - 60% - 79% - 40% - 59% f '41:} 20% - 39% __ 0% - 19% ’7‘] Excluded State Average = 52.0% 0 Acute Care Hospital " Percent of all Outside 30 Min. Visits 75 Miles 1:4.300.000 238 Patient Travel tO Similar Sized . Hospitals Outside 30 Minutes " FSA Travel Areas‘ ' 2001 - 0.70 - 3.68 - 0.35 - 0.69 - 0.20 - 0.34 A; 0.10 - 0.19 0.00 - 0.09 1___ F— Excluded State Average = 0.2 0 Acute Care Hospital " Normalized by Total Population 75 Miles 1 24,300,000 239 Percentage of Patient Travel to Similar Sized Hospitals Outside 30 Minutes FSA Travel Areas‘ 2001 - 80% -100% - 60% - 79% - 40% - 59% ;.":- :.- 20% - 39% _ 0% -19% a“ Excluded State Average = 19.4% 0 Acute Care Hospital * Percent of all Outside 30 Min. Visits 75 " -' * :2: Miles 04,300,000 240 I Patient Travel to Larger _ “a? j 13" Hospltals Outside 30 Minutes 8495*“ FSA Travel Areas‘ ,‘fi; ' 2001 . 1’41" ..5 p - 030-097 - 0.20- 0.29 - 0.10 - 0.19 :3 0.05 - 0.09 '___: 0.00 - 0.04 —‘ Excluded State Average = 0.1 0 Acute Care Hospital ' Normalized by Total Population 75 ;;"""~ = [. l_—____—_:lMlles ‘ ‘ > 124,300,000 241 Percentage of Patient Travel ' ‘g‘é “1...: -. 1" to Larger Hospitals Outside 5 14 ‘9' ll 1 30 Minutes FSA Travel Areas" . . Fifi-LEE!!! L 5") w was... - 80% - 82% g: fl 1 ‘ #1333 - 60% - 79% .’§ - 40% - 59% .t ‘_ @2083ng 9’1 ‘ 1' W; 0% -19% ‘. {3335? Excluded . 51%;}? State Average = 7.4% " $49!; 0 Acute Care Hospital ‘ $33 ' Percent of all Outside 30 Min. msits 5"!" V. 4‘81 75 1::- . V ' ' V‘I“I‘ 1 :4,300,000 242 Patient Travel to Smaller Hospitals Outside 30 Minutes FSA Travel Areas‘ 2002 - 3.5 - 5.7 - 2.0 - 3.4 - 1.0 - 1.9 E 0.5 - 0.9 ”y; 0.0 - 0.4 — Excluded State Average = 0.9 0 Acute Care Hospital Normalized by Total Population 1:5]Miles * 1:..4300000 243 t . Percentage Of Patient Travel to Smaller Hospitals Outside 30 Minutes FSA Travel Areas‘ 2002 - 80% - 99% - 60% - 79% - 40% - 59% ” f; 20% - 39% W’ 0% -19% 7'77 Excluded State Average = 49.5% 0 Acute Care Hospital * Percent of all Outside 30 Min. Visits v 75 ’ "" ‘ Miles 1 :4,300,000 244 Patient Travel to Similar Sized . Hospitals Outside 30 Minutes { FSA Travel Areas“ 2002 - 0.70 - 2.04 - 0.35 - 0.89 - 0.20 - 0.34 238; 0.10 - 0.19 L; 0.00 - 0.09 1‘— Excluded State Average = 0.18 0 Acute Care Hospital * Normalized by Total Population 75 . 1:2:Mlles . _ .‘.1_“~.‘1.‘ 1:4.300,000 - 245 Percentage of Patient Travel to Similar Sized Hospltals Outside ‘ 30 Minutes FSA Travel Areas* 2002 - 80% -100% - 60% - 79% - 40% - 59% 33.-:53 - - ' ‘ Mag} 20% 39% 7.,0' 1. ___ 0%-19% 13...“..51 *6 45.51. . xcluded *3 4‘1. 00' - a“ €2.14 t w ' Q 7' State Average = 17.6% 0 Acute Care Hospital " Percent of all Outside 30 Min. Visits 75 \ Miles 1:4,300,000 246 Patient Travel tO Larger Hospitals Outside 30 Minutes FSA Travel Areas* 2002 - 0.30 - 0.76 - 0.20 — 0.29 - 0.10 - 0.19 ”'7’ 0.05 - 0.09 *N 0.00 - 0.04 —T Excluded State Average = 0.05 0 Acute Care Hospital ' Normalized by Total Population 75 ‘ ‘“ " iles 124,300,000 247 Percentage of Patient Travel to Larger Hospitals Outside 30 Minutes FSA Travel Areas* . 2002 - 80% - 84% - 60% - 79% - 40% - 59% 1% 20% - 39% yyyy 0% - 19% #_' Excluded State Average = 7.5% 0 Acute Care Hospital ‘ Percent of all Outside 30 Min. Visits .1 if" Miles 5%. a, ngfi , ~ ‘ 124,300,000 1" 248 Patient Travel to Smaller Hospitals Outside 30 Minutes _3, , FSA Travel Areas* ’ 2003 - 3.5 - 5.7 - 2.0 - 3.4 - 1.0 - 1.9 "7: 0.5 - 0.9 9 0.0 - 0.4 _T Excluded State Average = 0.9 0 Acute Care Hospital * Normalized by Total Population 75 9: Miles . 1:4.300,000 249 .7“ Percentage Of Patient Travel to Smaller Hospitals Outside 30 Minutes FSA Travel Areas* 2003 - 80% - 99% - 60% - 79% - 40% - 59% 7f 20% - 39% _ 0% -19% "Hi Excluded State Average = 49.5% 0 Acute Care Hospital . . . Percent of all Outside 30 Min. Visits ' 75 " ’ " Miles 1 :4.300,000 250 Patient Travel to Similar Sized '2 Hospitals Outside 30 Minutes 1' FSA Travel Areas* ‘ 2003 - 0.70 - 1.73 - 0.35 - 0.69 - 0.20 - 0.34 0.10 - 0.19 L; 0.00 - 0.09 {fl Excluded State Average = 0.19 0 Acute Care Hospital " Normalized by Total Population 75 '~ Miles ' 1 4,300,000 251 Percentage of Patient Travel to Similar Sized Hospitals Outside 30 Minutes FSA Travel Areas* 2003 - 80% - 95% - 60% - 79% - 40% - 59% 11' 20% - 39% ":3 0% - 19% T", Excluded State Average = 17.5% 0 Acute Care Hospital ' Percent of all Outside 30 Min. Visits 75 Miles 124,300,000 252 Patient Travel to Larger Hospitals Outside 30 Minutes FSA Travel Areas* 2003 t - 0.30 - 0.86 .3, 5’" , - 0.20 - 0.29 p f“ 4 1.“.3‘, I - 0.10-0.19 $55 5. 9‘ If 0.05-0.09 ‘94,}: , . , W- 0.00- 0.04 ‘5‘ ' , ”“7“. 3, z '- . , Excluded 91¢ .I'. State Average = 0.05 ‘1’" 0 Acute Care Hospital ' Normalized by Total Population 75 " ' Miles 1:4,300,000 253 PercentageOfPatlent Travel .. ,, 1:111?“ to Larger Hospitals Outside i: 4*}gfigifl 30 Minutes FSA Travel Areas* Egfii‘“ i 2003 “7'5 *5!" b -80%-82% 1 '1 . ‘ fig’ ' W7" - 60%-79% ' 4“ Wed - 40%-59% tag“ K a: i1 ‘ . ‘ . 1.1 1: 7‘. ti:- m 20% - 39% 1 "3 0°/ .19% V”:- 511% :7: o ‘ 29:91:54 j Excluded 11...; , 774‘ s 3 ‘e State Average = 7.7% ' ‘ ' any,“ ,3 5 Acute Care Hospital ‘ $153,qu : ”v ’ ' Percent of all Outside 30 Min. Visits . , 2.3.1-{15:11. -~ "£4?'t2'&fi“$1.i 75 . i' , . .. 3:9 .8"? 68?“ Miles A '. Z, 45' 1‘: L5 13.211‘.‘ 1:4,300.000 254 gas-45,539. 4,, 1.1 ,, J 1411"“ 41 I “1. Patient Travel to Smaller Hospitals Outside 30 Minutes FSA Travel Areas* 2004 - 3.5 - 5.5 - 2.0 - 3.4 - 1.0 - 1.9 ”3”“ 0.5 - 0.9 ‘__y 0.0 - 0.4 . ;T Excluded State Average = 0.9 0 Acute Care Hospital " Normalized by Total Population 75 " pl 1 345. 54.99} ' 3 * 1- 1.. 4. 1:4.300,000 255 Percentage Of Patient Travel to Smaller Hospitals Outside 30 Minutes FSA Travel Areas* - 80% - 99% - 60% - 79% - 40% - 59% _ 20% - 39% ’ 0% -19% v—"" Excluded State Average = 49.4% 0 Acute Care Hospital ' Percent of all Outside 30 Min. Visits .. , . 75 :Miies 1:4,300,000 256 Patient Travel to Similar Sized it, Hospitals Outside 30 Minutes j" FSA Travel Areas* 2004 - 0.70 - 1.21 - 0.35 - 0.69 - 0.20 - 0.34 0.10-0.19 §_,_, 0.00 - 0.09 {T Excluded State Average = 0.19 0 Acute Care Hospital " Normalized by Total Population 75 Miles 1:4,300,000 .rt 257 Percentage of Patient Travel to Similar Sized Hospitals Outside 30 Minutes FSA Travel Areas* 2004 - 80% - 87% - 60% - 79% - 40% - 59% 8*}‘45 ‘ 20%-39% A V ‘9’ a; '1' )fi _ 0% -19% ‘fiwbqu‘ ...__._ I p ‘ ‘1' 1 I, .Excluded , 5.193155" ‘50:“! ”£3.55.- State Average= 17.4% , 05‘,th a“; fig-gt 4".»011. -.- 7" r F 0 Acute Care Hospital I *9 * Percent Of all Outside 30 Min. Vlsits . "P :53. ‘1. gift? ‘ 1.1-'1' ‘* .. 75 ‘ . :zj. uptnfl't. 1:101” 335} Q1 a; 1:4.300,000 ~- » 258 ' I Patient Travel to Larger Hospitals Outside 30 Minutes FSA Travel Areas* 2004 - 0.30 - 0.91 - 0.20 - 0.29 - 0.10 - 0.19 :2 0.05 - 0.09 W; 0.00 - 0.04 '—T Excluded State Average = 0.05 0 Acute Care Hospital ' Normalized by Total Populatlon 75 _ Miles 1:4.3oo,ooo 259 Percentage of Patient Travel to Larger Hospitals Outside _ .4; 30 Minutes FSA Travel Areas* _ ' 2004 ‘ - 80% - 37% - 60% - 79% - 40% - 59% fig 20% - 39% 1:9,: ,, , f “It 0% -19% - '3" Wk” .7 _5 Excluded '- fiéfifihgfifl , , [1‘ State Average= 7.2% ' . 'i- 53.9.!659) _, f AtC H ill #51“ ”Q”. . C e are 03 a ' 7 , 5‘ ” " ~ mitt.» “wee-i. tea ‘PercentofallOutslde 30 Min. Visits 7 .{fiaflgrzgtg3yigxiggyfw i ~. ' ..-‘~~ r:- o »g 75 :4; ; .3 5i“ *fi-‘ffifiy'fifii'iflf‘ggfi3 1-4 300 000 ”"93 cimaékzz’mfl 3%! r .— 260 Patient Travel to Smaller Hospitals Outside 30 Minutes ' FSA Travel Areas* 2005 - 3.5 - 5.7 - 2.0 - 3.4 - 1.0 - 1.9 s 0.5 - 0.9 w 0.0 - 0.4 _—7 Excluded State Average = 0.9 0 Acute Care Hospital ‘ Normalized by Total Population 75 Miles I ‘ ' 1:4,3oo,ooo ~ 261 ,‘i..- 7’ Percentage of Patient Travel to Smaller Hospitals Outside 30 Minutes FSA Travel Areas* ‘ 2005 - 80% - 99% - 60% - 79% - 40% - 59% . 20% - 39% fl __ 0% - 19% if Excluded State Average = 49.4% 0 Acute Care Hospital , , ‘ Percent of all Outside 30 Min. Visits 75 ‘- “ Miles 1 :4.300.000 262 --—-—-———-— .l-I Patient Travel to Similar Sized : Hospitals Outside 30 Minutes ’ FSA Travel Areas* ' 2005 - 0.70 -1.20 - 0.35 - 0.69 - 0.20 - 0.34 32%-? 0.10 - 0.19 L; 0.00 - 0.09 [‘5 Excluded State Average = 0.19 0 Acute Care Hospital " Nomialized by Total Population 75 Miles " 1:4,3oo,ooo 263 Percentage of Patient Travel to Similar Sized Hospitals Outside 30 Minutes FSA Travel Areas* 2005 - 80% - 84% - 60% - 79% - 40% - 59% €353§§ 20% - 39% ,_ 0% -19% 4 A .1. . i: “ ‘- , . a Excluded “9.517181: ‘ ‘ fi‘.. 77 7 fi‘xl't‘%ifi'$s%g&@ia lull“ . l ' , 0 Acute Care Hospital ’ Percent of all Outside 30 Min. Visits 75 :3 ':' _ . :Miles 'v‘ 1' 1:4,300,000 = ° ‘ x “v. ‘ State Average 17.4/0 flag, “fly: $1,. diet! * 264 Patient Travel to Larger ,. 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