7 an arm; M—nulwamym . f 1-,, ,‘ .‘. . ..' MMAM‘HFL as}. -kf " L .4qu wq‘ 4‘ g THESIS VERSITY LIBRARIES 111111111111111111111111111 1111121111111 3 1293 010332 111 This is to certify that the dissertation entitled SPECIAL EDUCATION OUTCOMES FOR PREMATURE AND/OR LON BIRTH WEIGHT INFANTS, AND THE EFFECTS OF PREMATURITY, INTRA- UTERINE GROWTH RETARDATION, AND SOCIOECONOMIC STATUS ON LEARNING DISABILITIES presented by Michael Thomas Monroe has been accepted towards fulfillment of the requirements for Ph.D. degree m CounselinqL Educational Psychology, and Special Education g1 104223? jor professor Date February 25, 1994 MS U is an Affirmative Action/Equal Opportunity Institution 0- 12771 LIBRARY Mlchlgan State University PLACE IN RETURN BOX to remove We checkout from your record. TO AVOID FINES retum on or More dd. duo. DATE DUE DATE DUE DATE DUE ”A; éfi éOOO 1| '1 1 MSU leAn Affirmative Action/Equal Opportunity Institution ' WWI SPECIAL EDUCATION OUTCOMES FOR PREMATURE AND/OR LOW BIRTH WEIGHT INFANTS, AND THE EFFECTS OF PREMATURITY, INTRA- UTERINE GROWTH RETARDATION, AND SOCIOECONOMIC STATUS ON LEARNING DISABILITIES BY Michael Thomas Monroe A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Counseling, Educational Psychology, and Special Education 1 994 ABSTRACT SPECIAL EDUCATION OUTCOMES FOR PREMATURE AND/OR Low BIRTH WEIGHT INFANTS, AND THE EFFECTS OF PREMATURITY, INTRA- UTERINE GROWTH RETARDATION, AND SOCIOECONOMIC STATUS ON LEARNING DISABILITIES By Michael Thomas Monroe Long-term special education outcomes were investigated for three distinct groups Of premature and/or low birth weight infants, premature appropriate for gestational age (PTAGA), preterm small for gestational age (PTSGA), and full-term small for gestational age (FTSGA), then compared with local population rates. A total Of 42 PTAGA, 26 PTSGA, and 14 FTSGA infants were followed until they were approximately 8 to 14 years of age. Intelligence, academic achievement, grade retentions, educational services, and the Chronology of program decisions were described and compared for each group, and the impact of gender and socioeconomic status (SES). Group comparisons involved matching PTSGA and PTAGA Children on the basis Of gestational age, and FTSGA and PTAGA children on birthweight. Matches also involved gender, SES, and birth date. The second part of the study more closely assessed learning disabilities for these groups using standard score discrepancy and regression analysis discrepancy Michael Thomas Monroe models. IEPC identified learning disabled (LD) subjects were compared with matched local LD students with normal neonatal backgrounds to locate distinct patterns of learning disabilities. The strength of relationship for ability/achievement discrepancies with socioeconomic and neonatal variables was addressed using multiple regression analysis. The at-riSk group's total average of handicaps exceeded local rates by 8% to 17.6%. Learning disabilities were 1.87 more likely, and severe multiple impairments were 29 to 54 times more evident for the three groups. Upper-middle SES subjects had a larger proportion Ofthe handicaps, although these findings were based on an unrepresentative population. Grade retention rates ranged from 28.6% to 50% for the three groups. Lower SES males were retained more than higher SES subjects. No significant differences in IQ or academic achievement were detected between PTSGA/PTAGA or FTSGA/PTAGA matched groups. Lower SES subjects had significantly lower achievement than higher SES matches in four of eight areas. Comparing at-risk lEPC identified LD and local LD children, one Significant factor was identified, perceptual organization. For lEPC identified LD subjects, a small, yet significant relationship was evident regarding lower maternal education and smaller family size only. A significant small neonatal and gender/LD relationship was found with regression analysis discrepancy and standard score discrepancy methods. Dissertation Advisor: Dr. Harvey Clarizio ACKNOWLEDGMENTS lwould like to thank Dr. David Sciamanna and John Wallen for their ideas and advice, which contributed to the accomplishment Of this dissertation. Much appreciation is also extended to Dr. Harvey Clarizio, for without his encouragement and advice this project would not have been accomplished. A very Special note Of thanks goes out to my friend and companion, Candace Henig-Monroe, for her understanding, care, and support. An additional note of appreciation goes out to my parents, Edith and George Monroe, who always encouraged me to try to help others and never to stop learning. TABLE OF CONTENTS Page LIST OF TABLES ............................................... vii LIST OF ABBREVIATIONS ......................................... xi Chapter I. INTRODUCTION ...................................... 1 ll. REVIEW OF LITERATURE .............................. 6 Prematurity and Low Birth Weight ......................... 6 Intellectual Outcomes .................................. 9 Academic Outcomes .................................. 20 Special Education Outcomes ........................... 25 Grade Retention ..................................... 28 Gender and Race-Related Influences ..................... 30 Learning Disabilities .................................. 32 LD Characteristics-Epidemiological Studies ............... 35 Socioeconomic Status and Learning Disabilities ............. 36 Review of Literature Summary .......................... 40 Ill. METHODOLOGY .................................... 44 Rationale ........................................... 44 Definitions and Formulas .............................. 46 Method 1: Standard Score Discrepancy ............. 46 Method 2: Regression Analysis .................... 47 Hypotheses ......................................... 48 Subjects ........................................... 50 Procedures ......................................... 55 Data Analysis ....................................... 57 Limitations .......................................... 57 IV. RESULTS AND DISCUSSION .......................... 6O Hypothesis 1 ........................................ 6O Hypothesis 2 ........................................ 71 Hypothesis 3 ........................................ 79 Hypothesis 4 ........................................ 91 Hypothesis 5 ........................................ 95 Hypothesis 6 ....................................... 100 V. SUMMARY AND RECOMMENDATIONS ................. 110 Implications for Future Research ....................... 115 Implications for Practice and Policy ...................... 116 APPENDICES A. Consent to Participate in Research (RNICU Graduates) ..... 119 B. Consent to Participate in Research (Eaton Intermediate School District LI) .................................. 123 C. E. W. Sparrow Hospital Research Approval ............... 127 D. Eaton Intermediate School District Research Approval ...... 128 E. PTSGA/FTSGA Subject POOI .......................... 129 F. Subject Data ....................................... 130 G. Michigan Special Education Guidelines .................. 134 H. Tests Used ........................................ 141 I. Test Reliabilities .................................... 142 J. Extended Tables .................................... 159 LIST OF REFERENCES ......................................... 173 vi Table 10. 11. 12. 13. LIST OF TABLES Page Intellectual Outcomes for Composite Groups of Pre-Term and/or Low Birth Weight Infants Versus Full-Term ........... 10 Intellectual Outcomes for Full-Term SGA Versus Full-Term AGA ...................................... 13 Intellectual Outcomes for Pre-Term SGA Versus Full-Term AGA ...................................... 14 Intellectual Outcomes for Pre-Term SGA Versus Full-Term AGA ...................................... 15 Intellectual Outcomes for Pre-Term SGA Versus Full-Term SGA ...................................... 16 Intellectual Outcomes for Pre-Term SGA Versus Pre-Term AGA ...................................... 18 Long-Term Intellectual and Achievement Outcomes ......... 22 Special Education Outcomes ........................... 26 Subject Characteristics ................................ 52 Matched Groups’ Comparisons .......................... 53 Birthweights/Gestational Ages .......................... 53 Intermediate School District Special Education Incidence-- Grade 2 Through 8 Averages ........................... 61 Mid-Michigan ISD Special Education Incidence, by Gender ............................................ 61 vii 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. Mid-Michigan ISD Special Education Incidence Totals- Grades 2 Through 8 .................................. 62 PTAGA, PTSGA, FTSGA. and Pooled Special Education Rates Versus Michigan Averages ........................ 62 PTAGA, PTSGA, FTSGA, and Pooled Special Education Incidence, by Gender ................................. 67 PTAGA and PTSGA Special Education Incidence, by SES Level .......................................... 68 FTSGA and Pooled Special Education Incidence, by SES Level .......................................... 69 Special Services ..................................... 72 Average Age of Special Education Referral/Placement ....... 73 PTAGA, PTSGA, FTSGA, and Pooled Retentions-Special Education Incidence .................................. 74 PTAGA and PTSGA Regular and Special Education Retentions, by SES and Gender ......................... 77 FTSGA and Pooled Regular and Special Education Retentions, by SES and Gender .......................... 7 PTSGA Test Data (Entire Sample) ....................... 8O FTSGA Test Data (Entire Sample) ....................... 81 PTAGA Test Data (Entire Sample) ....................... 81 Total Sample LD Discrepancies ......................... 83 PTSGA--Total Sample LD Discrepancies .................. 83 FTSGA--Total Sample LD Discrepancies .................. 84 PTAGA--Total Sample LD Discrepancies .................. 84 Matched PTSGA/PTAGA Test Data ...................... 86 viii 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. Matched FTSGA/PTAGA Test Data ...................... 87 Matched PTSGA/PTAGA LD Discrepancies—Standard Score Method ............................................ 89 Matched PTSGA/PTAGA LD DiscrepanciesuRegreSSion Analysis Method ..................................... 89 Matched FTSGA/PTAGA LD Discrepancies—Standard Score Method ............................................ 91 Matched FTSGA/PTAGA LD Discrepancies-Regression Analysis Method ..................................... 91 Higher Versus Lower SES Test Data--t-Tests ............... 93 Higher Versus Lower SES LD Discrepancies ............... 94 At-Risk LD Versus Local LD WISC-R t-Test Comparisons ..... 96 Psychometric Ability/Achievement Discrepancies in IEPC Identified LD Subjects ............................ 98 At-Risk LD Versus Local LD Achievement t-Test Comparisons ........................................ 99 LD Correlations .................................... 100 IPEC LD Subjects/Regression Analysis .................. 102 Standard Score Method--18-Point Discrepancy/ Regression Analysis ................................. 104 Standard Score Method--20—Point Discrepancy/ Regression Analysis ................................. 104 Standard Score Method--24-Point Discrepancy/ Regression Analysis ................................. 105 Regressed IO Method--18—Point Discrepancy/ Regression Analysis ................................. 106 Regressed IQ Method-~20-Point Discrepancy/ Regression Analysis ................................. 107 ix 49. J1. J2. J3. J4. J5. J6. J7. J8. J9. J10. J11. J12. J13. J14. J15. Regressed IQ Method-—24-Point Discrepancy/ Regression Analysis ................................. 108 Intermediate School District Special Education Incidence, Grades 2 Through 8 ......................... 159 Mid-Michigan ISD Special Education Incidence. by Gender ........................................... 160 PTAGA Special Services .............................. 161 PTSGA Special Services .............................. 162 FTSGA Special Services .............................. 162 Number of Special Education Students by Diagnostic Category and by Age: Michigan Audited Report for 1989 . . . . 163 PTSGA Total Sample LD Discrepancies .................. 164 FTSGA Total Sample LD Discrepancies .................. 165 PTAGA Total Sample LD Discrepancies .................. 166 Matched PTSGA/PTAGA LD Discrepancies-Standard Score Method ........................................... 167 Matched PTSGA/PTAGA LI DiscrepanCies--Regression Analysis Method .................................... 168 Matched FTSGA/PTAGA LD Discrepancies-Standard Score Method ........................................... 169 Matched FTSGA/PTAGA LD DiscrepanciesnRegreSSion Analysis Method .................................... 170 Higher Versus Lower SES LD Discrepancies .............. 171 Psychometric Ability/Achievement Discrepancies in IEPC Identified LD Subjects ................................ 172 AGA awn DAC 1330 El EMI FTSGA FT GA HI ICU IEPC IUGR IO POHI PTAGA PTSGA PT RDS RNKHJ SAT SES SGA SU SXI VWSC WISC-R \NRAT \NRATJ? LIST OF ABBREVIATIONS Appropriate for Gestational Age Birth Weight Developmental Assessment Clinic Eaton Intermediate School District Emotionally Impaired Educable Mentally Impaired Full-Term Small for Gestational Age Full-Term Gestational Age Hearing Impaired Intensive Care Unit Individual Educational Planning Committee Intrauterine Growth Retardation Intelligence Quotient Physically or Otherwise Health Impaired Pre-Term Appropriate for Gestational Age Pre-Term Small for Gestational Age Pre-Term Respiratory Distress Syndrome Regional Neonatal Intensive Care Unit Stanford Achievement Test Socioeconomic Status Small for Gestational Age Speech and Language Impaired Severely Multiply Impaired Wechsler Intelligence Scale for Children Wechsler Intelligence Scale for Children-Revised Wide Range Achievement Test Wide Range Achievement Test-Revised xi CHAPTER I INTRODUCTION Historically, most premature (gestational age of 37 weeks orless), low birth weight (less than 2500 grams) infants died in the early 19003. Those that survived were relatively free from handicapping conditions (McCormick, 1985; Stewart, Reynolds, 8 Lipscomb, 1981). This pattern changed during the 19503 when mortality rates decreased and the incidence of handicaps increased (Hack, Fanaroff, 8 Merkatz, 1979; Stewart et al., 1981). However, from the 19603 until the present, both mortality and major handicap rates have decreased (Hack et al., 1979; McCormick, 1985; Stewart et al., 1981). For infants weighing 1500 grams and less, there is a 25% to 45% mortality rate, and an 8% to 19% incidence of moderate to severe handicap (cerebral palsy, mental retardation, profound hearing loss, visual impairment, and hydrocephalus) (McCormick, 1989; Shapiro, McCormick, Starfield. 8 Crawley, 1983; Stewart et al., 1981). This compares to a 1% mortality rate for all live births in the United States (U.N. Children’s Fund, 1985) and a 2% to 3% disability rate (Healy, 1983). However, questions remain regarding the prevalence and etiology of less severe handicaps, particularly Specific learning disabilities (LD), for this at-risk population. This is an important 2 issue as some authors have predicted that the numbers of children with less severe types of handicaps will increase with the decrease in mortality and major handicap rates (Fitzhardinge, 1976; McCormick, 1989). Consequently, it is important for researchers and practitioners from the fields of medicine, psychology, and education to determine whether premature, low birth weight infants indeed have a greater prevalence of handicaps, particularly for less severe conditions (specific learning disabilities). Likewise, it is important to identify specific risk factors in the premature, low birth weight population, and to note their relative impact on special education outcomes. If it can be demonstrated that particular factors ortypes of premature, low birth weight infants remain at greater risk for specific handicaps, then physicians, school psychologists, and special educators will need to more Closely monitor these children’s progress through their school years. And should it be found that different groups have higher prevalence rates of the so-called less severe handicaps, it could be useful to ascertain whether premature, low birth weight infants represent or display a distinct pattern or type Of disability. With these considerations in mind, it will be important to clarify how prematurity, low birth weight, intrauterine growth retardation, socioeconomic status, and gender relate to learning disabilities. As some researchers have proposed that Children who are born too early or too little have suffered central nervous system insult and therefore are at-risk for developing later learning disabilities, it is important to accurately assess and 3 follow homogeneous groups of premature and/or low birth weight infants into their school years. Some investigators have found some early developmental delays to be transient and that between 65% and 75% of these children attend normal school programs (Drillien, Thomson, 8 Burgoyne, 1980; Kitchen et al., 1980; Kitchen et al., 1987; Lefebvre, Bard, Veilleux, 8 Martel, 1988). Other researchers have found, however, that between 24% and 64% of this at-risk population receive some form of special education service (Lefebvre et al., 1988; Nickel, Bennett, 8 Lamson, 1982; Sell, Gaines, Gluckman, 8 Williams, 1985). Furthermore, it has been Shown that environmental factors progressively impact cognitive and academic outcomes for this at-risk population from birth onward. Consequently, it will be important to obtain a better understanding of these relationships as they relate to long-term outcomes. The goals of this research were as follows: 1. To describe the incidence of identified Special education handicaps for three specific groups of premature and/or low birth weight infants at school age (the preterm appropriate for gestational age [PTAGA], the full-term small for gestational age [FTSGA], and the premature small for gestational age [PTSGA]). And to compare these results with the local population rates on the basis of gender, socioeconomic status (SES), and age at the time of the referral. 2. To describe grade retentions, remedial education programs, Special education programs and services, and the chronology of placement decisions for 4 the three at-risk groups of premature and/or low birth. weight children. And to compare these results with local population results. 3. To determine the prevalence of psychometrically identified learning disabilities using an 18, 20, and 24 standard score point ability/achievement discrepancy and regression analysis (2, 5, and 6.5 percentile cutting scores). Controlling for gender and socioeconomic status, to compare preterm AGA and preterm SGA infants with matched gestational ages, and preterm AGA infants with full-term SGA Children with matched birth weights. And to describe the severity of the ability/achievement discrepancies, intelligence test results, and academic achievement for these groups. 4. To determine the prevalence of psychometrically identified learning disabilities (using the previous goal’s statistical procedures) for high and low SES groups of preterm AGA, PTSGA, and FTSGA infants matched on the basis Of gender, SES, and age. And to describe the severity Of the ability/achievement discrepancies, intelligence test results, and academic achievement for these groups. 5. To investigate whether at-risk learning disabled (LD) infants display a distinct pattern of learning disabilities in comparison with the general LD population. And to describe and compare the severity of the ability/achievement discrepancies, intelligence test results, and academic achievement for these groups. 5 6. . To assess the strength of relationship for psychometrically and Individual Educational Planning Committee (IEPC) identified learning disabilities with race, gender, family marital status, maternal and paternal education, maternal and paternal occupation, birth order, family size, low birth weight, prematurity, intrauterine growth retardation (IUGR), episodes of otitis media, birth asphyxia, length of Intensive Care Unit (ICU) hospitalization, need for and time on ventilator, grade of intracranial hemorrhage, and seizures. CHAPTER II REVIEW OF LITERATURE This review of literature will analyze and integrate two broad areas of research. The first area of investigation involves long-term educational outcome studies for premature and/or low birth weight infants. This review starts by examining the critical identifying characteristics of this at—risk population, progresses to intellectual and academic outcomes, and then describes socioeconomic and gender-related influences. Finally, it will describe the research on this at-risk group's involvement in special education programs, grade retentions, and other educational interventions. The second area of investigation focuses on research related to the relationship of learning disabilities with prematurity and/or low birth weight. Beginning with classification and definition issues of learning disabilities, this review looks at etiological questions by first exploring large-scale epidemiological studies, then relating learning disabilities to SES, gender, and perinatal factors. r t r' n Studying the educational handicaps of premature, low birth weight infants has proven troublesome because of the group's heterogeneous nature. For 6 7 example, classifying infants by gestational age and birth. weight involves three specific patterns and, often, multiple etiologies (Blackman. 1984; Korones, 1986; Behrman, Vaughan, 8 Nelson, 1983). These are: (a) premature infants who are appropriately grown for their gestational age (AGA), (b) small for gestational age (SGA) infants who have slow intrauterine growth rates and delivered at or later than 37 weeks, and (C) the small for date or SGA, premature infants with a retarded rate Ofintrauterine growth and early delivery. SGA orintrauterine growth retardation (IUGR) has been defined by neonatalogists as birth weight/ gestational age ratio falling below the 10th percentile, or scoring 2 or more standard deviations below the mean on population-based intrauterine growth curves. The more severe cut-Off has sometimes been used as a means of adjusting for ethnic or nationality differences, or denoting extremes of the SGA population (Hoffman 8 Bakketeig, 1984). Gestational age typically is determined by counting the time since the first day of the last menstrual period, or using a postnatal system developed by Dubowitz and his co-workers (Dubowitz, Dubowitz, 8 Goldberg, 1970). Intrauterine growth retardation occurs in approximately one-third of all low birth weight infants (Behrman et al., 1983; Korones, 1986). According to Behrman et al. (1983), premature AGA births are associated with conditions where there is an inability of the uterus to retain the fetus, interference with the course of the pregnancy, premature separation of the placenta, or a stimulus to cause uterine contractions prior to term. Intrauterine growth retardation is associated with conditions that interfere with the circulation and efficiency 8 . of the placenta, with the development or growth of the fetus, or with the general health and health and nutrition of the mother. (p. 442) Furthermore, lower levels of SES positively correlate with both prematurity and low birth weight (Behrman et al., 1983; Korones, 1986). Other attempts at classifying premature, low birth weight infants included differentiating intrauterine growth patterns (symmetrical versus asymmetrical physical proportions), etiology (genetics, congenital infections, maternal conditions, maternal ingestions, and placental abnormalities), congenital malformations, and perinatal complications (perinatal asphyxia, hypoglycemia, and polycythemia) according to Allen (1984), Behrman et al. (1983), and Korones (1986). Consequently, it must be recognized that premature, low birth weight infants represent a common outcome of a heterogeneous population that researchers are attempting to classify in order to more clearly define risk parameters for specific groups of children. Children who are born too early are considered to be at special risk because of their susceptibility to cerebral hemorrhage (Hunt, 1986). And children who are born too little are thought to be at-risk because of the evidence of fetal malnutrition (Teberg, Walther, 8 Pena, 1988). Regarding the association between prematurity and/or low birth weight and less severe handicaps, Fitzhardinge (1976) proposed that these infants may have suffered some degree of central nervous system insult. If a major handicap has not occurred, it is assumed that minor dysfunctions are inevitable. For these Children, it is postulated that neurological sequelae will be subtle and possibly not evidenced until faced with school-aged tasks requiring higher levels Of central nervous 9 system functioning. However, as prospective studies have shown (and will be discussed shortly), there is a wide variance of outcomes. Environmental factors as well as brain plasticity have powerful modulating effects on severe neonatal problems (Hunt, 1986). Sameroff and Chandler (1975), in their review of prematurity, found little evidence to support the proposition that being born too early alone produced poor developmental outcomes. n II t In spite of these definitional and Classification issues, much has been learned about the cognitive abilities of premature, low birth weight infants. According to reviews studying premature, low birth weight infants (Caputo 8 Mandell, 1970; Cohen, 1986; Kopp, 1983; Kopp 8 Parmelee, 1979) and studies controlling for the effects of SES (Drillien et al., 1980; Dunn, 1986; Klein, Hack, M J 8 Breslau, 1989; Lindahl, Michelsson, Helenius, 8 Parre, 1988; Lloyd, Wheldall, 8 Perks, 1988; Noble-Jamieson, Lukeman, Silverman, 8 Davies, 1982; Rubin, Rosenblatt, 8 Balow, 1973; Wiener, 1968), intelligence for this undifferentiated group of Children is significantly lower than that for full-term, normal birth weight infants by 7.4 standard score points on average (Table 1), generally using the Wechsler Intelligence Scale for Children (WISC). However, the average IQ for the low birth weight group is still within the average range for the vast majority Of 1 studies. It was also found that premature and/or low birth weight infants from lower socioeconomic groups, on average, score 14 points lower than groups of infants from middle- to upper-class groups at 4 to 10 years of age (Drillien et al., 10 02 8 .38 momflntsw .8“. 9.3 «mu/So 5: <8 m2 .80va E A 88: ._n s 26: 85”. 8E 83 wow“. SE 3 m2 S m 28 w mo; m; “8.0. a SE SE wm> 38? E to ._n a coonSoBoZ 4o 3 3 a no a 4 1 2: R9 «9 mo 2: mm m <8 8. m: 42 we we «S N 5E 8qu SE 2; 03mm SE S E F ”8.0, mooomv S 88: ._m a 55:0 SE SE 8m“. NE mm> mooomv 22>) 2-..: 88: Locos) O. mESE £995 EB 32 5ch 6.9.03 Co 8:03 BquEoo .8 928030 $302.95 H_. 29m... 11 g 0.89:2 .1. mu. GEES u n. @_NE II. E 820 526. u 0.. 330 Loan: n O: ES. :2 u E 8.9.ch u ._.n_ 850QO 6: u m2 cde 25:26 mocmm___9c_ :92: O. mocmEcotoa n 58.: O. .82? n. > O. 368 28088208 u mmm Ego; 5.3 so. u 39 come £995 5.5 u Em :mmE mom .mcozflmmm u <0 mam Ecozfimmm .2 835063 n 3. ace; Now“. 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Whereas there is an average 6.4 pre-term AGA advantage in studies (Table 6) controlling for SES effects (Drillien, 1970; Fitzhardinge, Kalman, Ashby, & Pape, 1978; Francis-Williams & Davies, 1974; Pena et al., 1987; Robertson et al., 1990; Vohr & Hack, 1982), most researchers have not matched these groups on the basis of gestational age. To ignore matching on this factor results in comparing two heterogeneous groups with different levels of physical and neurological maturation that have different sets of perinatal and neonatal complications. Thus, to match groups- on gestational age would allow investigators to accurately assess the outcomes of intrauterine growth retardation aside from maturational effects. Only Pena et al. (1987) and Robertson et al. (1990) have used this matching strategy. Interestingly, their results are contradictory. Pena et al. (1987) noted significant IQ differences at 40 weeks in favor of the PTAGA child, whereas Robertson et al. (1990) found no differences in intelligence scores at 8 years of age. Differences between these investigators’ results may relate to the subjects’ ages at the time of evaluation, group SES differences. or yet-undetermined variables. In general, research on premature and/or low birth weight infants” cognitive abilities has typically noted only group means and differences, rather than 18 mmafitsm 9.; {8&0 <0<\E mogutsm x: NE 9? m.mm&0 <00\E w 88: ._m «0 82.08”. mamfitsm 0? 5&0 <0<\E 8:205. So. v m 008 F ”Em <0<\E 5.5 80. as. <00\E 9.3 5&0 <00\E 9.2 S :00: ._m .0 «can. 0.5.0 F ”Em 9? awn/Mm (03E 009 ESE cos: 8 cos: t 9.; 8&0 <00\E F 800: x8: 0 Eo> l. moothSm 0.; 9&0 3 8 <0<\E 0 <00\E .0. $5: .6 6 005222“. mtmfitsm 9.; 8&0 <0<\E moafitsm 8 N0 9.; 2&0 005 NE. 2.3: $3 a mem____>>.w_ocm¢ movmwngm 0.; 8&0 <0 <00 €9.05 .9 008830 3:80:95 “m 0500. 19 identifying individual differences and the prevalence of specific ranges. of ability. The use of idiosyncratic terms and nonstandard levels of functioning, rather than identifying abilities by using standard descriptions of mental handicap, such as provided by the intelligence classification system developed by the American Association on Mental Deficiency (Grossman, 1983), has made comparisons between studies difficult. Likewise, the lack of studies controlling for SES or noting distinct low birth weight subtypes has further complicated the pursuit of accurate conclusions. Overall, studies have used intelligence tests that denote a single composite score, rather than a multidimensional score. The Wechsler Intelligence Scales for Children (WISC) and its revised edition (WISC-R) have been the tests most frequently used with school-aged children. As the WISC and WISC-R yield verbal and performance scores, some investigators have looked for a distinct pattern with premature and/or low birth weight infants. In studies controlling for SES at school age, specific patterns have not been evidenced (Hunt, Cooper, 8 Tooley, 1988; Klein et al., 1989; Lindahl et al., 1988; Neligan et al., 1976; Nickle et al., 1982; Noble-Jamieson et al., 1982; Wallace et al., 1982). Likewise, Hunt et al. (1988) noted WISC-R profiles of premature, low birth weight infants at 8 to 11 years of age to be identical to Kaufman’s (1979) proportions of verbal/performance discrepancies. Unfortunately, most studies did not classify their subjects by homogeneous groupings (pre-term AGA, full-term SGA, or pre- 20 term SGA). Only full-term SGA children were assessed (Neligan et al., 1976), and significant profile differences were not evidenced. MW Academic achievement has been assessed by few investigators of premature and/or low birth weight infants at school age. Word recognition skills were assessed most frequently, with less emphasis given to written spelling, and mathematical calculation skills. A variety of achievement tests were used, with the Wide Range Achievement Test (WRAT) being the most popular and the Neale Analysis of Reading Ability (a word recognition test) being in distant second place. The results were presented inanumberofways. Some studies compared their subjects to control groups or to test norms by noting test score means, whereas others noted statistical significance without presenting scores. And some researchers identified abnormality by establishing subjectively based cutting scores. Studies that matched groups to control for the impact of SES and noted test scores had results suggesting that perinatal events affect later academic performance, but mainly to the extent that they were related to intelligence (Drillien, 1980; Klein et al., 1989; Lindahl et al., 1988; Neligan et al., 1976; Nickel et al., 1982; Rubin et al., 1973; Westwood, Kramer, Munz, Lovett, & Watters, 1983; Wiener, 1968; Wiener, Rider, Oppel, & Harper, 1968). Controlling for the effects of SES when comparing groups is important, due to its high correlation to both achievement and ability tests (Sattler, 1988). This relationship, therefore, 21 needs to be addressed, given its impact on the cognitive development of premature and/or low birth weight infants at school age. With reading recognition skills (Table 7), pre-term, low birth weight children a X: (N ~—__ scored 4.7 standard score points belowthe scores of full-term, normal birth weight controls (Klein et al., 1989; W., 1976; Rubin et al., 1973; Westwood et al., 1983; Wiener et al., 1968). Mathematical skills (Table 7) were 7 points below \ (Klein et al., 1989; Rubin et al., 1973; WW3; Wiener et al., 1968). Spelling skills (Table 7) were also noted to be significantly less than those of normal controls (Lindahl et al., 1988; Rubin et al., 1973). Regarding differences between full-term SGA and full-term normal birth weight children, Westwood et al. (1983) noted standard score differences of 6 points for reading recognition, a 5 point written spelling difference, and a 6 point math calculation difference. it also should be noted that Westwood’s SGA group came from a higher SES level than his normal comparison group. Comparing ability test means with achievement test means, there is a 3.9 point discrepancy for reading recognition tests, a 9.1 point written spelling discrepancy, and a 15.8 point math calculation discrepancy. Academic outcomes for low birth weight and : g *t: ; SGA infants at school age have been followed by only one researcher (Dunn, ._ l 1986), and he did notfind significant differences between low birth weight and full- fl term SGA groups of children. Unfortunately, these study groups did not have similar socioeconomic_backgrounds, as the low birth weight groups cases came from families whose parents had low educational and occupational levels. Rubin 22 5.05 02 0.00 0.00 a 9 v 0.00 02 0.53 0.2: ; NI .8 :8 020uts0 9.; 00&0 Si; 0.00 0.00 0.0.0 0.00 0000V E 0.2 A000: ._0 .0 .322 02 02 00 v0 e 02 02 00 08 0 02 02 09 09 N 02 02 0: 09 F 000 <0m 02 02 09 0: F ”000 082v .E a <00 50 800: ._m .0 55.5 02 02 0.00000 ~00 <00Eom 0:0 8:60:95 509.08: K 030... 23 0o_E000o0 n 0.0. E0030 00:09:95 n O. 0800 0:00:90 H mm 0390 2508002000 n wmm 500:. 20.0; 5:: n :50 £0.03 5...: 30. u .50: 0.0.0.9” H. 90.: u 5. 95803.50: E009: :o_.0o:00o:o>0n. :00::00-v.ooo0oo>> n mm00>> 000: :o_::0oo0 05000”. :o:.0>-t:m n .Em>m .00: :o_._: 000w. 050090 :0:o:om n Hmmm .00: m:_000m 00:9:00 500.01 u .EmI .000. E0E0>0_ o< 0:00:90 u H0Eo< 0 :0m 00.>> n h> :09: 000 .0:o=0.00m u <0 8.9 :9 u E E:9-0.0 u :0 000 6529000 .9 900090000 u >m 0x2. omn<0 00:25 :0 02 00 00 089v E 0 .000: .0 .0 :02 .020 02 9.0 0.0 0.09 <0<: 00< >03w 002.28 K 0.00: 24 et al. (1973) attempted to address pre-term and SGA ability/achievement outcomes and to control for SES effects. This did not occur because they established their low birth weight cut-off at <2500 grams, a group with more favorable outcomes. Furthermore, their criteria for SGA (GA <37 weeks and BWT >2500 grams) were above the Dubowitz et al. (1970) 10% ratio. Thus, accurate academic outcomes for full-term and pre-term SGA children are unknown. Other investigators studied highly selected groups of at-risk children so that their results are not representative of the broader PTAGA population. For example, Klein et al. (1989) studied only PTAGA infants without neurological impairments and Nickel et al. (1982) investigated only infants having birth weights less than 1000 grams. Thus, accurate ability/achievement outcomes for pre-term AGA infants, as well as how they compare with other at-risk groups, are not known. Research on premature, low birth weight infants have not used the federal definition and criteria to identify specific learning disabilities. To make this even more confusing, different researchers have used subjective criteria to identify learning disabilities with this at-risk population. And many have not addressed academic concerns at all. Likewise, information regarding oral expression, listening comprehension, written expression, basic reading skill, reading comprehension, and mathematical reasoning is unknown for any category of this population while controlling for the influence of socioeconomic factors (Aman & Singh, 1983; Balow, Rubin, 8- Rosen, 1975/76; Caputo & Mandell, 1970; Cohen, 25 1986). Furthermore, comparisons between achievement and ability for_individual students have been investigated by only one researcher (Hunt at al., 1988). She noted a significant discrepancy (1.5 standard deviation ability/achievement discrepancy for 16.7% of her low birth weight population). However, specific achievement areas were not identified from their WRAT scores. Consequently, there is a need for investigators to study areas addressed by the federal definition of learning disabilities for homogeneous groupings of premature, low birth weight infants (pre—term AGA, full-term SGA, and pre-term SGA). It is also important that future studies control for socioeconomic factors affecting these at-risk children. i ti n t m Researchers (Table 8) have found that the prevalence of mental retardation (IQ less than 70) occurs at a 3% to 8% rate for infants weighing less than 1500 grams (Lefebvre et al., 1988; Nickel et al., 1982; Sell et al., 1985; Stewart, 1983). Unfortunately, these studies did not differentiate between premature AGA, full-term SGA, and pre-term SGA groupings. Also, the populations they followed tended to overrepresent groups from lower socioeconomic levels. Thus, current estimates of mental retardation may present a skewed set of results. Higher prevalence rates (5% to 8%) were generally found for birth weights less than 1000 grams (Kitchen et al., 1980; Nickels et al., 1982; Stewart, 1983) than for birth weights greater than 2000 grams (3% to 6%). Prevalence rates of mental retardation for this population are significantly higher E0E:0.::o:_0E 0:.:0S0:.:. .0258 u 25:0 E0690 .00 .0.0000 0050000 .0: u 9n. 02 00300.0 m:.::00_ u 0.. 00:.00E. 5.00: 00.03050 :0 300.050 n .100. 00:.00E. 000005. .0 :00000 n :m 00:.00E. 0:000: n .I 00:.00E. >__030.> u _> 8:00:90: .95.: n «:2 $3. 000 0800.000 :9 :95 n <00 000 .0:o..0..00m n <0 :00... 0.0.0:. 5:... n :20 00.0.0000 .0: u 02 6:0. :0: u E E..0..0:0 n :n. 20.0.... 0:... .so. u 2.0. 26 02 000.0 02 .00.0 000.0 02 00:0 0009 v <00 5 E 0-0 .009. ..0 .0 0300.0. <00 02 00000u:>>0 02 000.00 000.0 000.. 02 000.. 00:0. 9.; 00n<0 0-0 .009. .0 .0 :00 02 02 o0: A00.0 02 000.0 000.: :20 009v 22.0 02 02 000 c00.0 02 000.0 e00.0 22.0 3-9 .009. ..0 .0 =0. 02 02 02 °00.0 o00.0 000,. c00.0 0000? 2.0. 0 .009. 0030.0 009 $9 n00.9 02 n00.0 _:._> 000.0 0009v E 0.9 .009. ..0 .0 .0022 02 02 02 .000 .000 000.0 .000. 0000V 2.0. 0 .009. ..0 .0 5:05. 0:0 02 0. :0 .100 .I _> 05. 00>: 00< .020 .00.:0050 8.000000 0.0000 .0 0.00: 27 than the overall 1% to 2% incidence noted by the US. Department of Education (1984). Although mental handicaps have long been the focus of study, it has been difficult to compare and integrate these findings into a clear picture because of the lack of standardization of sampling and test procedures, as well as with problems in accurately reporting the degree of retardation (T eberg et al., 1988). Higher prevalence rates than the United States population have been noted for all other areas (Table 8) of special education handicaps. For example, visual impairment ranged from 1% to 5%; profound hearing impairment ranged from 3% to 4%; and physical or other health impairments occurred in 1% to 8% of their at-risk subjects according to Kitchen et al. (1980), Nickel et al. (1982), Hill et al. (1984), Lefebvre et al. (1988), Sell et al. (1985), and Stewart (1983). This compares to an .08% to .07% rate of visual impairment, a .14% incidence of orthopedic impairment, and a .13% rate for health impairment (US. Department of Education, 1984). Specific perinatal events were generally cited as responsible for many of these severe handicapping conditions according to Behrman et al. (1983) and Korones (1986). For instance, high bilirubin counts related to deafness, seizures, and/or grade 3 to 4 intracranial hemorrhages related to cerebral palsy, and retrolental fibroplasia due to too much oxygen at birth caused visual impairments. When looking at less severe handicaps (Table 8), speech and language impairment was found to range between 5% and 17% for PTAGA and PTSGA Children (Hill et al., 1984; Sell et al., 1985). Hill et al. (1984) found 17% of their 28 SGA infants received special education services in this area, with Sell et al. (1985) noting 5% of her neonatal intensive care graduates getting this assistance. These results compare with a 2% to 3% national incidence rate (US. Department of Education, 1984). With specific learning disabilities (Table 8), rates varied from 8% to 23% (Lefebvre et al., 1988; Nickel et al., 1982; Sell et al., 1985), compared to the United States population incidence rate of 4% to 4.4% (US. Department of Education, 1984). However, the few studies that noted LD prevalence rates were limited by the fact that the populations being assessed were either highly selected (Nickel and Lefebvre studied only infants weighing less than 1000 grams, with an overrepresentation of lower socioeconomic level subjects), or too general (Sell followed an undifferentiated population of neonatal intensive care graduates, with no data available regarding SGA infants, race, or SES). Despite the lowering of mortality and morbidity rates with premature, low birth weight infants and the increase in research on intellectual functioning and educational sequelae, there is a need to learn more about the prevalence and etiology of many of their resulting handicaps, particularly the less severe outcomes. GLaQeBBjQMLOQ Grade retention rates for premature and/or small for gestational age infants have largely been ignored in most long-term studies. Perhaps because of differences in educational systems or cultural backgrounds, researchers in Japan, the United Kingdom, and Australia have not addressed this issue. Likewise, 29 Shepard and Smith (1989) reported that essentially no children are retained annually in these countries. In the United States, researchers note retention rates from 17% (Rubin et al., 1973) for pre-term infants at 7 years of age to 55% (Wiener, 1968) at 12 to 13 years of age. Unfortunately, these studies did not specify SES or at-risk groupings for these children. Klein et al. (1989) noted that for an average SES group of infants weighing less than 1500 grams at 9 years of age, 40% had repeated a grade. This rate compares to an 11% rate for a matched group of full-term, normal controls. Thus, it appears that low birth weight children do have difficulty progressing through the graded school systems of the United States. l-lowever, information is not available to differentiate between small for gestational age and premature infants’ grade retention outcomes. To further complicate the grade retention issue, there are no comprehensive data documenting the numbers of children retained by grade each year. Rose, Medway, Cantrell, and Marus (1983), however, collected retention rates for the 1979-80 and preceding school years from the 15 states that had gathered this information. The states, primarily from the South, reported average yearly retention rates ranging from 3.5% (Arizona) to 8.9% (Mississippi). Because a number of the states had retention rates larger than 100%, it was assumed that at a minimum, 15% of the retentions were individuals repeating for a second time. Thus, over the 13-year school period, 30.5% to over 100% of these states’ students were retained. N/ i 3 t? ‘ié / 30 Regarding retention rates for LD children in Indiana during the 1987-88 school year, McLeskey and Grizzle (1992) found that 54% of the third-grade LD students and 61% of the sixth-grade LD students had been retained. Based on a stratified random population sample of Indiana students from similar-sized school districts and geographical locations, these investigators found that 25% of the third graders and 26% of the sixth graders had been retained. According to Klein et al. (1989), it appears that premature and low birth weight children are more at-risk of being retained during their school years in the United States than normal children. However, it is unknown how much higher their retention rate is than that of the general population, or whether it is higher than that of children with disabilities who do not have a history of prematurity and/or low birth weight. r n R - l t l Although there appears to be a tendency toward lower mean IQ scores (2 to 4 points) for undifferentiated groups of male pre-term and SGA infants (Dunn, 1986; Eaves et al., 1970; Portnoy, Callias, Wolke, 8 Gamsu, 1988; Rubin et al., 1973; Wallace et al., 1982), these findings have been disputed. Francis-Williams and Davies (1974) noted that males have a 6 to 7 point advantage. However, these studies’ results rarely approached statistical significance. Regarding gender differences for pre-term AGA infants, Neligan et al. (1976) found none. And for SGA children, the results were contradictory. Fitzhardinge and Steven (1972) found females to score 6 IQ points higher than males, whereas Neligan et 31 al. (1976) noted males to perform 2 IQ points better. Unfortunately, these investigators did not control for SES. Only Rubin et al. (1973) identified SES groupings by gender. Their results noted a nonsignificant 3.7 lQ point advantage for pre-term, low birth weight (<2500 grams) females. However, these young girls were from lower socioeconomic levels than pre-term male comparisons. Therefore, one might conclude that there may be a tendency toward lower male IQ performance, but controlled data regarding specific results for all groups of premature and/or low birth weight infants are lacking. Regarding gender-related academic differences for premature and/or low birth weight infants, little is known about the specific performance of these at-risk children. Current studies generally have focused on relatively heavier pre-term children (birth weights less than 2500 grams). Rubin et al. (1973) found no gender differences in reading recognition, spelling and math calculation skills. Wallace et al. (1982) found no differences when assessing reading recognition skills. Dunn (1986) identified significant reading recognition and spelling differences favoring females at only the fourth-grade level, but noted consistent writing differences for the third through fifth grades. Because Rubin et al. (1973) were the only researchers to address socioeconomic influences, little is known about male-female differences for this at-risk population. Concerning racial influences for low birth weight infants, only one study (Wiener et al., 1968) assessed intellectual outcomes. These investigators found a 14 point IQ difference between White and African American infants. However, 32 these results must be viewed with great caution as the study did not control for socioeconomic factors. With academic outcomes, only one study (Wiener, 1968) assessed African American and White low birth weight infants. White children weighing between 2000 and 2500 grams at 10 to 12 years of age had a 26 point standard score advantage for reading recognition skills. Infants weighing less than 2000 grams had an 18 standard score point reading recognition advantage. A similar trend was noted for math calculation skills (2000 to 2500 grams: 21 point advantage, <2000 grams: 17 point advantage). However, regression analysis noted that socioeconomic class was the most important variable regarding academic achievement for these children. Studies identifying special education handicap rates by race or gender for premature and/or low birth weight infants have not been pursued. l r' [1' I'll Like premature, low birth weight infants, specific learning disabilities have been difficult to assess because they also represent a heterogeneous population with numerous, complex, and sometimes conflicting etiologies (Aman & Singh, 1983; Cohen, 1986; Hammill, 1990; Keogh, 1982). Aman and Singh (1983), for example, have identified brain damage, maturational lag, genetic inheritance, cerebral dominance, and ocular system factors (dominant eye, abnormal eye movements, and visual perception) as physiological bases of etiology, with 33 personality and environmental factors (social class, family size, geographical location, and instruction) as nonphysiological bases. In addition to classification systems based on etiology, McKinney (1987) noted that specific learning disability subtyping research represents a promising approach in understanding this heterogeneous population from the perspective of dividing it into more homogeneous groups that reflect specific patterns of disability. McKinney described classification systems based on ability profiles, lQ/achievement discrepancies, academic performance, neuropsychological development, and functioning patterns, using multivariate statistical techniques. Unfortunately, the vast majority of these studies used clinic-referred samples that were not representative of the general population, or did not control for such important variables as age, race, gender, or SES. Another problem with subgroup research involved the issue of external validation. For example, most of the neuropsychological studies did not link their subtypes with actual academic performance or learning disabilities. And last, there is a need for researchers to assess stability of subtype membership orthe relationship between subtypes and long-term academic outcomes. With these limitations in mind, McKinney noted general consensus among these studies regarding the following subgroups: (a) visual perceptual/perceptual-motor process deficits and average to above- average language abilities, (b) linguistic and auditory process deficits with intact perceptual and/or perceptual-motor skills, (c) mixed perceptual/linguistic deficits, and (d) normal-appearing individuals. The normal subgroup also includes 34 concerns with motivation, pedagogical factors, attention disorders, and memory deficits. Likewise, Sameroff and Chandler (1975) noted three possible models of development that may explain specific learning disabilities: (a) a main effect model that involves specific causal factors (environmental or constitutional influences), (b) an interaction model that views development as a function of the contribution of both constitutional and environmental factors that affect each other, and (c) a transactional or reciprocal model that proposes, for example, that biological risk is not the most significant factor in the outcome of neurological impairment, but it is the nature of the transactions within the social milieu where a child grows up. In other words, a child with an impairment and his environment change each other; thus, it is not possible to use a main effect model or an interactive model to understand development. In the pursuit of addressing each etiology and attempting to be comprehensive, numerous definitions of specific learning disabilities exist. Unfortunately, researchers following premature, low birth weight infants have not consistently used any specific developmental model or definition, and have not followed the predominant educational definition and its criteria followed in the United States. Public Law 94-142, the Education for All Handicapped Children Act of 1975, defines specific learning disability as follows: a disorder in one or more of the basic psychological processes involved in understanding or in using language, spoken or written, which may manifest itself in an imperfect ability to listen, speak, read, write, spell, or to do mathematical calculations. The term includes conditions as perceptual 35 handicap, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia. The term does not include children who have learning disabilities which are primarily the result of visual, hearing, or motor handicaps, or mental retardation, or emotional disturbance, or environmental, cultural, or economic disadvantage. (USOE, 1977, p. 65083) The 1977 EedeLaLBegjster further set the criterion that there must be a severe discrepancy between achievement and intellectual ability, although the amount of discrepancy and specific procedures were left to the states to determine. Currently, all states quantify a severe discrepancy by using at least one of four methods (Frankenberger & Harper, 1987). They are: (a) deviation from grade score, (b) expectancy formulas, (0) standard score comparisons, and (d) regression analysis. Standard score comparisons were the most widely used. Of the large-scale prospective epidemiological studies that identified learning disabilities for basic reading skills by using a discrepancy model, prevalence rates varied according to cutting scores used and geographical locations. Rutter and Yule (1975) used a 2 or more standard deviation difference between ability and achievement with a regression formula, and found a 2.28% rate on the Isle of Wight and a 6.3% rate in inner London for 9 to 10 year olds. Gjessing and Karlsen (1989) used a 1 to 1.5 standard deviation discrepancy with a regression formula and found a 5% incidence for Nonlvegian second graders and a 2% incidence for ninth graders. Both studies noted a 3:1 disability ratio in favor of males, significantly more reading difficulties among first-degree relatives, 36 and significant speech and, language delays. Rutter and Yule noted lower verbal IQ than performance IQ scores, larger family sizes, and a slight tendency toward premature births and low birth weights relating to reading retardation. Whereas Gjessing and Karlsen noted dyslexia to be correlated with poor fine and gross motor coordination, fine tactile sensation difficulties, problems with left-right discrimination, concentration concerns, and a higher incidence of behavioral and emotional problems. Unfortunately, these researchers did not assess prematurity and low birth weight factors. Of retrospective studies noting below grade level academic results, Kawi and Pasamanick (1959) noted reading disorders occur much more frequently with children having perinatal abnormalities. However, this clinic-based population study did not take SES into account. Malmquist (1958) also found significant (p < .05) relationships between low birth weight and prematurity, and poor reading. Likewise, SES was not addressed in this Swedish study. i n mi t t rni ' i'ti The relationship of SES to learning disabilities for school-aged children remains a largely unstudied area with little empirical data. In contrast, there is much evidence that higher SES/LD students have better educational, employ- ment, and occupational outcomes as adults than lower SES/LD individuals (Schouhaut & Satz, 1983; Spreen, 1988). Regarding the school-age LD population, only the large-scale epidemiological studies from England (Rutter & Yule, 1975) and Norway (Gjessing 8 Karlsen, 1989) directly address the LD/SES 37 issue. Both sets of researchers defined SES on the basis of paternal occupation alone. Rutter and Yule (1975) determined reading retardation or learning disabilities in an approach similar to the current federal LD definition and criteria. They found a slight tendency for reading retardation to be less common in children of nonmanual workers, rather than an excess of identified children from lower social classes in their Isle of Wight and inner London studies. They also noted a strong association between larger families and parents with poor reading skills to reading retardation. Gjessing and Karlsen (1989) found that SES accounted for only 20% of the variance relating to dyslexia. They thought this low relationship was based on Non/vegian demographics; Norway is culturally and socioeconomically more homogeneous than the United States. The fourteenth annual Report to Congress on the Implementation of the Individuals With Disabilities Act (US. Department of Education, 1992) suggested that SES and racial factors may be related to higher incidences of learning disabilities and other special education handicaps. Specifically, 21.6% of secondary LD school children are African American, whereas only 12% of the general population of secondary school children are African American. This same report found that 57% of African American youth, 49% of Hispanic youth, and 25% of White youth were from families earning less than $12,000 per year. Research has shown that children living in poverty or from low SES families are less likely to receive adequate health care and nutrition. And poor health care and nutrition are related to increased incidences of developmental delays and 38 disabilities (Children’s Defense Fund, 1991). However, this implication is contradicted by data identifying 8.4% of secondary school LD youth as Hispanic versus 13% of the general population of secondary school children as Hispanic. In comparison to national handicap rates, Hispanic youths are underrepresented in other impairment areas (mental retardation--5%, emotional impairment-—6%, multiple disabilities-42.1%, hearing impairment--11.5%, visual impairments-- 8.1%, and deaf-blindness-5.8°/o). Hispanics are overrepresented for only orthopedic impairments (15.1%) and other health impairments (22.5%). Thus, special education data documenting the relationship of SES to disability are unclear. Regarding regression analysis studies predicting intellectual and academic achievement performance from socioeconomic and perinatal factors, Rubin and Balow (1977) noted maternal age, education, and SES to be correlated with IQ at 7 years ([ = .47), WRAT reading at 7 years (:1 = .40), SAT spelling at 9 years (:1 = .36), and SAT arithmetic and 9 years ([ = .34). Neonatal variables had weaker correlations with IQ ([ = .23), WRAT reading (r = .23), SAT spelling ([ = .25), and SAT arithmetic ([ = .25). These findings support SES-related results regarding IQ and achievement outcomes for White subjects from the Collaborative Project (Broman, Nichols, & Kennedy, 1975) and the British National Child Development Study (Davie, Butler, & Goldstein, 1972). Correlations at age 7 consistently fell between 0.24 and 0.42 and accounted for 6% to 17% of the variance. 39 Looking at the special education data in the United States, Satz and Friel (1974) noted a tendency of low SES to correlate with learning disabilities, when trying to establish an early risk index factor at the kindergarten level. Concerning LD prevalence rates from the 1978 and 1980 Civil Rights Survey of Elementary and Secondary Schools, Gelb and Mizokawa (1986) found comparable results for Whites (3.2%), African Americans (3.2%), and Hispanics (3.2%) versus the national rates (3.2%). Only Asian (1.4%) and American Indian (4.1%) groups had variable prevalence rates. However, the relationship between race and learning disabilities was not assessed. Gelb and Mizokawa (1986) attempted to investigate the SES/LD relationship using these surveys and 1970 census data. Their SES indices included children below the poverty level, infant mortality, public aid, average family income, and educational costs. Learning disability identification was positively related to family income and educational costs, and negatively related to living below the poverty level and dependence on public welfare. Research-based conclusions regarding SES and learning disabilities were also conspicuously absent in textbooks on the subject (Adamson 8 Adamson, 1978; Adelman 8 Taylor, 1983; Clarizio 8 McCoy, 1983; Gelfand, Jenson, 8 Drew, 1982; Lerner, 1981; Mercer, 1983). Of those texts that addressed this relationship, Gelfand et al. (1982) discussed the issue from a broad perspective, noting "it is evident that environmental influence has played an important role in psychological theories and research. This has affected the field of learning 4O disabilities as well. Precise identification of environmental impact remains elusive” (p. 225). Clarizio and McCoy (1983) addressed the incidence of specific learning disabilities in relation to SES, noting the "research is difficult to interpret" (p. 213). They further noted that some authors have found a higher frequency of learning disabilities for upper-class children, suggesting that these families were possibly better able to afford professional evaluations and treatment. Unfortunately, there were no other systematic attempts to assess learning disabilities and socioeconomic factors. Consequently, empirically based conclusions cannot be drawn at this time. Review ef Literature Summary 1. From the 19603, both mortality and major handicap rates have decreased for premature and/or low birth weight infants. Currently, 7% of those surviving birth in the United States weigh less than 2500 grams, and approximately 1% weigh less than 1500 grams. There is a 25% to 45% mortality rate, and 8% to 19% prevalence of moderate to severe handicaps for infants weighing less than 1500 grams. Decreases in these rates have been attributed to improvements in pediatric care, obstetric advances, and innovations in medical technology. 2. Questions remain regarding the prevalence and etiology of less severe handicaps, particularly specific learning disabilities. This is a particular concern as some authors have predicted that the numbers of children with less 41 severe handicaps will increase with the decrease in mortality and major handicap rates. 3. Studying premature and/or low birth weight infants regarding educational handicaps has proven difficult due to this group’s heterogeneity. Current classification schemes involve gestational age and birth weight criteria. Three patterns have been identified: (a) premature infants who are appropriately grown for their gestational age, (b) small for gestational age and born at or later than 37 weeks, and (c) premature, small for gestational age. 4. Learning disabilities have been difficult to assess because it represents a heterogeneous population with numerous, complex, and sometimes conflicting etiologies. Unfortunately, researchers following premature and/or low birth weight infants have not consistently used any specific developmental model or definition of learning disabilities. And they have :not used the predominant educational definition and criteria mandated in the United States, as noted by Federal Law 94-142. 5. Large-scale prospective epidemiological studies have identified learning disabilities using a discrepancy model and regression analysis. Prevalence rates varied from 2% to 6% depending on cutting scores, geographical setting, and age. LD boys outnumbered girls by a 3:1 ratio. Likewise, LD children had more first-degree relatives with reading problems, and had significantly more speech and language problems than normal comparisons. 42 Regression analysis found maternal age, education, and SES to be more highly correlated with intellectual and academic outcomes than perinatal factors. 6. The relationship between SES and learning disabilities remains a largely unstudied area, with few empirically based conclusions. 7. Intelligence test scores of premature and/or low birth weight infants were lower than those of full-term, normal birth weight infants by 7.4 IQ points, on average. It was also found that pre-term, low birth weight infants from lower socioeconomic groups score, on average, 14 IQ points lower than infants from middle- and upper-class groups at 4 to 10 years of age. Likewise, the effects of an adverse socioeconomic environment progressively impact lQ from 2 years onwards. 8. Full-term SGA infants have le 5.6 points lower, on average, than full-term, normal birth weight comparisons. Pre-term SGA infants are 12.5 points lower, and pre-term AGA infants are 11.8 points lower. Comparisons between premature SGA and premature AGA infants were contradictory, possibly due to methodological concerns. 9. Academic achievement of premature and/or low birth weight infants at school age has been investigated by few researchers. Word recognition skills have generally been evaluated most frequently, with much less emphasis given to written spelling and mathematical calculation skills. 10. Reading recognition skills for preterm and/or low birth weight children were 4.7 standard score points below full-term normal birth weight 43 controls, and 7 points lower on mathematical calculation tests. Accurate academic outcomes for full-term SGA and pre-term SGA children are unknown. No investigator has used the federal definition and criteria to study learning disabilities with this at-risk population. 11. Higher prevalence rates have been found for all categories of educationally defined special education handicaps for premature and/or low birth weight infants at school age. However, it is difficult to draw valid conclusions due to the lack of standardization of at-risk types, variable test procedures, and studies not controlling for the effects of SES. 12. Grade retention rates for premature and/or low birth weight children at school age have generally not been studied. Furthermore, rates forthe general population have not been systematically assessed. 13. Regarding gender-related influences, there is a tendency for lower male IQ performance for premature and/or low birth weight infants. However, there is little data to support this relationship with academic outcomes. CHAPTER III METHODOLOGY Rationale In attempting to answer the questions that were generated in the previous sections, two general areas of investigation were pursued. The first area of research addressed descriptive long-term outcomes for three distinct groups of premature, low birth weight infants (premature AGA, full-term SGA, and premature SGA infants). This study identified prevalence rates for special education handicaps forthese groups bytheir gender, socioeconomic status, and age at thetime of the referral and then made comparisons to local handicap rates. A systematic analysis of these groupings’ outcomes was attempted to better define their prognosis at school age. While previous studies have focused on neonatal outcomes for specific birth weights and gestational ages of at-risk infants, this study further described characteristics such as handicaps using current federal special education criteria and definitions, grade retentions, special services and programs, and the chronology of program decisions for the three groupings. This information can assist practitioners in the fields of education, psychology, and medicine to make more informed decisions for these children and their care-giving institutions. 44 45 The second part of the study was designed to look more closely at the long-term outcomes of less severe handicaps, or learning disabilities, for these three distinct groups of at-risk infants. To control for school district IEPC eligibility decisions and examiner differences, ability/achievement discrepancies (using both standard score differences and regression analysis models to determine the severity of ability/achievement discrepancy) were computed using independent testing results. This studythen compared the at-risk groups’ ability and academic achievement test results, and learning disabilities. To control for maturational factors, gestational ages were matched for premature AGA and SGA groups. To compare premature AGA and full-term SGA infants, subjects were matched by their birth weights. Subjects in these groups were also matched on the basis of age, gender, and SES. Unlike previous studies that have not controlled for these factors, it was anticipated that questions pertaining to how these at-risk groups compare with each other could be accurately addressed. The comparisons of discrepancies and learning disability incidence obtained by using different procedural models should help practitioners from different theoretical perspectives better understand outcome results. Another major difference between this study and previous research in this area involved comparing high and low SES groups of pre-term AGA and SGA infants matched on the basis of gender, age, and SES. Gender differences were also investigated. There was no reliable information regarding these concerns for the three at-risk groups, as well as for LD students in general. 46 This study investigated whether these at-risk infants displayed a distinct pattern of learning disabilities in comparison to the general population. LD pre- term and LD SGA children were compared with a local LD population matched on the basis of gender, age, and SES. Unlike previous studies that assessed reading recognition, spelling, and math calculation skills versus intellectual ability, this study used the more comprehensive current LD educational definition, which included reading recognition, reading comprehension, written expression, math reasoning, and math calculation. Using.this broader definition allowed for exploration of specific patterns of learning disabilities. Finally, perinatal and socioeconomic variables were assessed in relation to LD outcomes for psychometrically and school certified LD students. These variables included race, gender, family marital status, maternal and paternal education, maternal and paternal occupation, birth order, family size, low birth weight, prematurity, intrauterine growth retardation, episodes of otitis media, birth asphyxia, length of Intensive Care Unit (ICU) hospitalization, need for and time on ventilator, grade of intracranial hemorrhage, and seizures. Because learning disabilities have various etiologies, it was important to discern the strength of this condition’s relationships to possible influential factors. Deflfltionsandflmsflas Wm Currently, most states use a standard score discrepancy model (Frankenberger 8 F ronzaglio, 1991) in comparing IQ and academic achievement 47 when determining learning disabilities. Typically, a specific value was selected as the eligibility criterion by individual school districts. Academic achievement! ability test discrepancies of 18 to 22 standard score points are typically used to qualify children as LD in the mid-Michigan area. II II | 2, B . g l . The next most widely used method in identifying LD students (Franken- berger 8 Fronzaglio, 1991) involves a regression analysis procedure outlined by Wilson and Cone (1984). For purposes of comparison with other regression analysis methods, it was assumed that the mean standard score for the population being sampled is 100 and the standard deviation is 15 for each test used. A 0.6 test intercorrelation level was used, based on Reynolds’s (1990) recommended formula (r.y = 70—5 (My) when r),y is unknown. Wilson and Cone (1984) suggested the following regression equation: A Y = I,y Sy/Sx (IQ -3?) + v where Y = the expected achievement for a given IQ rxy = the IQ - achievement correlation Sy = the standard deviation of the achievement scores )_( = the mean IQ Sx = the standard deviation of the IQ scores V = the overall mean achievement 48 Hypotheses Based on the previous observations and considerations, the following hypotheses were tested. 1. The incidence of special education handicaps in all categories was thought to be higherfor pre-term AGA, full-term SGA, and pre-term SGA than the general local population. No differences were anticipated between pre-term AGA and SGA rates, and full-term SGA students were expected to have a lower proportion of special education eligibility than the other at-risk groups. Furthermore, it was anticipated that gender-related incidences would reflect local population patterns, whereas lower SES groups would be overrepresented by higher rates of special education outcomes. 2. Premature and/or low birth weight children were thought to receive more special services, be retained at a higher rate, and be involved with special programs at earlier ages than the general population. Full-term SGA students would be involved with fewer services and programs than pre-term SGA and AGA children. 3. It was hypothesized that there would be a greater proportion of psychometrically identified LD students using both regression analysis methods and standard score discrepancy methods in determining a significant discrepancy between IQ and academic achievement for pre-term AGA and SGA students than IEPC identified students. It was anticipated that matched PTSGA IQ and achievement test means would be significantly lower than matched PTAGA 49 results. This pattern was postulated to continue with PTAGA test results being higher than matched FTSGA scores. Furthermore, it was thought that there would be a greater number of significant ability/achievement discrepancies, and they would be more severe for pre-term AGA children than full-term SGA children. Likewise, PTSGA infants would have a greater number of and more severe ability/ achievement discrepancies than PTAGA students. 4. It was thought that there would be more psychometrically identified learning disabilities with lower SES premature and/or low birth weight children using standard score and regression analysis methods than matched groups of higher SES pre-term and/or low birth weight children. Likewise, it was anticipated that there would be a greater number of and more severe ability/achievement discrepancies using regression analysis and standard score methods for lower SES at-risk children than matched at-risk children from higher SES groups. It was also thought that lower SES subjects would have significantly lower ability and achievement test scores than a matched group of at-risk higher SES children. 5. It was hypothesized that there would be significant differences between at-risk LD subjects identified by lEPCs and a matched group of local LD students with normal neonatal backgrounds. It was thought that at-risk children would have significantly lower ability and achievement tests, more severe ability/achievement discrepancies, and greater numbers of academic discrepancies. 50 6. It was postulated that prenatal and perinatal factors would be significantly related to learning disabilities for both psychometrically and IEPC identified LD subjects. Prenatal and perinatal factors included low birth weight, intrauterine growth retardation,.episodes of otitis media, birth asphyxia, length of ICU hospitalization, need for and time on ventilator, grade of intracranial hemorrhage, and seizures. Sums The preliminary subject pool for this study was established by identifying all premature and/or low birth weight infants born at Sparrow Hospital in Lansing, Michigan, and placed in its Regional Neonatal Intensive Care Unit (RNICU) from January 1, 1977, to January 1, 1984. Prematurity was defined as birth at 37 weeks gestation or earlier. Small for gestational age (SGA) was characterized by a 10% or less birth weight on standard population growth curves at birth. In addition to these defining features, subjects were also formally diagnosed by hospital neonatalogists. Infants transferred into the hospital’s RNICU were not included in this study because of the many uncontrolled variables associated with the decision to transport at-risk infants to regional neonatal intensive care facilities from smaller hospitals, as well as their highly variable outcomes (McCormick, 1989). In addition to these inclusion criteria, study subjects needed to have been regularlyfollowed by Sparrow Hospital’s Developmental Assessment Clinic(DAC) until at least 5 years of age. The AGA and SGA subjects were obtained from this pool of monitored children. 51 Twenty-five FTSGA and 48 PTSGA children were located and question- naires and consent forms sent to their parents. After obtaining permission from the children’s parents and Sparrow Hospital, medical records and school files were reviewed. Fifty-six percent of the FTSGA and 54% of the PTSGA families consented to be included in the study. PTAGA subjects were chosen on the basis of matching characteristics with SGA subjects. Specific matching criteria included no more than 2 week gestational age differences, no more than 250 grams weight differences, and not more than a 4 month difference in the date of birth was allowed. For SES matching criteria, only a one-level discrepancy was allowed. Tables 9, 10, and 11 show the descriptive statistics for the study’s subjects and groupings regarding gender, race, family SES, grade placement, and averages and ranges of birth weights and gestational ages. A total of 42 premature AGA infants, 26 pre-term SGA infants, and 14 full- term SGA infants were followed until they were approximately 8 to 14 years of age. White children represented the vast majority (92%) of the children in the population, which contrasts with US. Census data that showed African American and other minorities made up over 25% of the total population (1980 Census of Population). Further examination of Table 9 shows that subjects in the upper- middle and middle-class groups were overrepresented. The underrepresentation of minority, lower-middle class, and lower-class children may possibly have been related to difficulties in participating in long-term hospital follow-up visits, reluctance to become engaged in research studies, or the inability of this 52 Table 9: Subject characteristics. Characteristic PTAGA PTSGA FTSGA Total Gender Male 23 13 4 40 Female 19 13 10 42 Total 42 26 14 82 Race White 40 23 13 76 Black/Other 2 3 1 6 EamthSES Level 1 7 4 1 12 Level 2 22 10 9 41 Level 3 10 6 2 18 Level 4 1 6 2 9 Level 5 2 0 0 2 r nt 2nd grade 0 0 2 2 3rd grade 7 5 2 14 4th grade 8 4 2 14 5th grade 4 3 1 8 6th grade 10 5 3 18 7th grade 5 3 2 10 8th grade 8 5 2 15 Ungraded 0 1 0 1 53 Table 10: Matched groups’ characteristics. Characteristic PTSGA 8 FTSGA 8 Higher SES 8 At-Risk LD PTAGA PTAGA Lower SES 8 Local LD Gender Male 13 4 8 10 Female 8 7 8 6 Total 21 11 16 16 Level 1 3 0 3 0 Level 2 9 7 5 5 Level 3 I 4 4 o 6 Level 4 5 0 6 5 Level 5 l 0 0 2 0 PTSGA 21 0 6 1 FTSGA 0 11 4 3 PTAGA 21 11 6 4 FTAGA 0 0 0 8 Table 11: Birthweights/gestational ages. E] AGA Birthweight ave: Gestational age ave: Age: 2093.039 34.028 wks. 8.1 yrs-13.7 yrs. Range: 9649-33459 Range: 27.0-37.0 wks. ELSQA Birthweight ave.: Gestational age ave.: Age: 1322.59 33.577 wks. 8.4 yrs-13.9 yrs. Range: 5389-20709 Range: 27.5-37.0 wks. ELSE/A Birthweight ave.: Gestational age ave.: Age: 2225.99 39.31 wks. 7.10 yrs-13.0 yrs. Range: 14509-27309 Range: 38.0-42.5 wks. EeelelelAGA, PTSGA, ELSQA Birthweight ave.: Gestational age ave.: A e: 1871.49 34.787 wks. 7.10 yrs-13.9 yrs. 54 researcher to locate families over time as their involvement with the hospital was over. Gender representation was generally equal for PTAGA and PTSGA subjects. However, females were overrepresented for FTSGA subjects by a greater than 2:1 ratio. Average birth weights and gestational ages were comparable to previous studies’ averages that compared these at-risk groups of infants. A comparison group of LD students (matched on the basis of age, SES, and having no history of premature birth or being small for gestational age) was obtained from the Eaton Intermediate School District. All of these students were evaluated by Michigan certified school psychologists. Individual intelligence and achievement tests, as well as other special education evaluations and records, were reviewed after obtaining consent from the Eaton Intermediate School District and the parents of individual LD children. Local special education populations were determined by compiling handicap rates from Ingham Intermediate School District, Eaton Intermediate School District, Shiawassee Intermediate School District, and Clinton Intermediate School District. These districts were chosen because 86% (71 of 83) of the subjects resided within them. Subjects were followed from approximately 8 to 14 years of age because of the difficulty in establishing accurate ability/achievement discrepancies at earlier ages, and due to the fact that the majority of LD students are identified by the fourth grade (D'Amato, Dean, Rattan, 8 Nickell, 1988). 55 Emcedures The data for this study were obtained by reviewing each subject’s DAC file and school cumulative records. Following discharge from the RNICU, infants were followed by the Developmental Assessment Clinic personnel. Current functioning levels were assessed beginning around 6 months of age and continuing until 7 years of age at regular intervals. Most subjects were examined on a yearly basis. On the recommendation of the neonatalogists, children from distant areas, having less serious medical backgrounds, or followed by other medical clinics were followed every 18 months. On each visit to the DAC, the child and his or her parent(s) were seen by certified or licensed personnel (audiologists, school psychologists, physical therapists, neonatalogists, and pediatric nurses). In addition to evaluations by each of these professionals, current physical weights and measurements were obtained for each child. Psychological functioning was assessed, depending on the age of the child, by the Bayley Scales of Infant Development, the Stanford-Binet Intelligence Scale: Form L-M, orthe Stanford-Binet Intelligence Scale: Fourth Edition, and the Wide Range Achievement Test or Wide Range Achievement Test-Revised (when the child reached school age). After each visit, the scores for each child along with descriptive behavioral observations were recorded in the child’s DAC records. RNICU discharge summaries and neonatalogist overview reports of individual DAC visits were also included in each child’s file. The investigator, a Michigan certified school psychologist, reviewed each child’s cumulative educational file (CA 60). This allowed for the retrieval of group 56 achievement test data, records of grade retentions, involvement in supplemental or special education programs, special education reports and plans, as well as the chronology of service decisions. The most recent test scores for each subject were used to compare intelligence and academic achievement test results. Typically, this procedure involved comparing an individually administered intelligence test completed when a child was 6 to 7 years old at the DAC, with a group achievement test administered during the 1990-91 school year. Occasion- ally, a subject’s last achievement test was an individually administered instrument. When this occurred, grade norms were used. This practice was used because group achievement tests all utilized grade-based norms. Retention was defined as a child’s nonentry into school. Therefore, to be considered retained, it was not necessary that children be involved in develop- mental kindergartens, young 5’s programs, or that their school or parent had retained them. Rather, retention was based solely on the subject’s age in comparison with the common standard of being 5 years old on the first day of kindergarten. Because information documenting SES was not always gathered by the DAC or noted in school records, a questionnaire and consent to review files was sent to the family of each prospective subject. In this questionnaire (Appendix A), specific information was requested regarding parental occupation, family composition, and highest level of education for each family member. SES was then determined by using Hollingshead’s (1975) index. This index was validated 57 by analyzing data from the 1970 US. Census. SES levels were obtained by scaling the education and occupation of parents, and noting family composition. t A i Data collected dealing with categories of special education eligibility, test results, remedial programs, grade retentions, and models of learning disabilities were analyzed using frequency counts, percentages, and other descriptive statistics. Data not specifically discussed in the results and discussion chapter are located in extended tables (Appendix J). Psychological and educational tests were compared, when necessary, by converting their standard scores to a mean of 100 and a standard deviation of 15. To determine the significance of differences between variables for premature and/or low birth weight groups, as well as matched groupings, t-tests were calculated- Critical values of statistical significance were based on p values being equal to or less than .05. Multiple regression analysis and Pearson product moment correlations were used to determine the relationships between variables related to learning disabilities. Limitatiene Perhapsthe most important limitation of this study is the nonrandom nature of the sample of children being investigated. The students are not representative of the United States population according to their ethnic backgrounds, gender. SES, or geographical residence. In comparison with previous research on premature and/or low birth weight children at school age (Tables 5 and 6), this 58 .study’s population was comparable on the basis of numbers of subjects, birthweight, gestational age, and gender. However, this at-risk group had more upper-middle-level SES subjects than Drillien (1970), Pena et al. (1987), and Robertson et al. (1990). A second limitation ofthis research was related to its long-term nature and attrition problems. Fifty-six percent of the FTSGA and 54% of the PTSGA families consented to be included in the study. A number of subjects were lost due to their parents’ deciding not to participate in the hospital’s periodic follow-ups and the DAC. Others were lost because they left the mid-Michigan area without leaving forwarding addresses. And still more were not included in the study because their parents decided against consenting to allow their children’s records to be reviewed. Consequently, the relatively low number of subjects available for this long-term study limited the scope and depth of possible statistical procedures and analysis, as well as intergroup comparisons for these at-risk children. Also, the relatively low numbers limited the power of statistical procedures. Third, checking for examiner accuracy in scoring the various tests was not possible. Data collected for this study consisted principally of reports and some test protocol face sheets. Therefore, it was not possible to perform a random check to determine the accuracy of the various test scores. A fourth limitation involved the current educational classification system noting learning disabilities. The standard score ability/achievement discrepancy procedure predominantly used in Michigan best predicts achievement levels with le between 90 and 129 (Dore-Boyce, Misher, 8 McGuire, 1975). It underidenti- 59 fies low-ability students and overidentifies high-IQ students due to the regression toward the mean effect (Beck, 1984; Cone 8 Wilson, 1981; Shepard, 1980). Likewise, Michigan and other states have eligibility criteria that exclude LD children from special education who supposedly can make progress in regular education classes without special education support. This policy has the effect of eliminating brighter students who are achieving at or close to their grade placements from being considered as LD. Besides these measurement-related issues, there is evidence that LD classification decisions reached by multidisciplinary teams are sometimes based on subjective information rather than empirical data (Coles, 1987; Epps, Ysseldyke, 8 McGue, 1984; Ysseldyke, Algozzine, Richey, 8 Graden, 1982). The unreliability ofdiagnosing children with learning disabilities could result in the erroneous inclusion of ineligible LD children (false positives) and the overlooking of children who are truly LD (false negatives) in the study. A final limitation involves relying on the use of different psychological and educational instruments, administered by different individuals at different times, and then making psychometric comparisons. This situation was further complicated for retained subjects, where age-normed intelligence test scores were compared with grade-normed achievement test scores in determining LD discrepancies. For these children, it is likely that ability/achievement discrepan- cies are underrepresented. CHAPTER IV RESULTS AND DISCUSSION HmotbesisJ The first hypothesis stated that: ”The incidence of special education handicaps in all categories was thought to be higher for preterm AGA, full-term SGA,and preterm SGA than the general local population." Table 12 illustrates special education incidence rates by handicap and grades 2 through 8 averages for each of the four intermediate school districts (lSDs) in the mid-Michigan area where 86% (n = 71) of the subjects resided, as well as total area incidence rates. Table 13 notes average grades 2 through 8 incidence rates for the four districts by gender. Tables 14 and 15 compare the individual at-rlsk groups and pooled special education incidence rates with local population rates. It can be seen that, for five of seven categories, incidence rates were higher by 1.62 to 36.92 times. Only speech and language impairment (SLI) and emotional impairment (El) rates were roughly similar to the general population (.86 and .80 times the normal incidence). Furthermore, the pooled at-risk group total special education incidence was 2.3 times higher than the local special education population. Because of the limited number of subjects, an average of grade 2 through grade 8 rates was compared, rather than individual grade-by-grade comparisons. In contrast to the hypothesis 60 61 Table 12: Intermediate school district specialeducation incidence--Grade 2 through 8 averages. Eaton ISD Clinton ISD Shiawassee ISD Ingham ISD POHI .0025 .0016 .0021 .0038 HI .0005 .0007 .0010 .0030 SXI .0015 .0013 .0007 .0015 EMI .0057 .0083 .0089 .0075 LD .0510 .0670 .0380 .0530 SLI .0230 .0380 .0340 .0260 El .0130 .0210 .0160 .0150 Total .0980 .1390 .1030 .1060 Key: POHI = Physically or otherwise health impaired HI = Hearing impaired SXI = Severely multiply impaired EMI = Educable mentally impaired LD = Learning disabled SLI = Speech and language impaired El = Emotionally impaired Table 13: Mid-Michigan ISD special education incidence, by gender. Males Females POHI .0011 .0011 HI .0010 .0011 SXI .0007 .0008 EMI .0043 .0034 LD .0360 .0160 SLI .0180 .010 El .0120 .0036 I Total .0720 .0350 62 Table 14: Mid-Michigan ISD special education incidence totals--Grades 2 through 8. Grade 2 to 8 2 3 4 5 6 7 8 Ave- POHI .004 .004 .004 .003 .003 .002 .002 .0031 HI .002 .001 .002 .002 .002 .002 .002 .0019 SXI .002 .001 .002 .001 .001 .001 .001 .0013 EMI .007 .006 .008 .008 .008 .006 .009 .0074 LD .015 .037 .054 .061 .068 .062 .065 .0520 SLI .044 .049 .043 .029 .017 .008 .006 .0280 El .005 .011 .012 .018 .018 .021 .022 .015 Total .079 .109 .125 .132 .117 .102 .107 .1100 Table 15: PTAGA, PTSGA, FTSGA, and pooled special education rates versus Michigan averages. PTAGA Handicap PTSGA Handicap FTSGA Handicap Sp.Ed. Ratio Sp. Ed. Ratio Sp. Ed. Ratio Incidence (PTAGA to Incidence (PTSGA to Incidence (FTSGA to Mich. Ave.) Mich. Ave.) Mich. Ave.) POHI .095 30.65 .000 0 1 .000 0 HI .000 0 .000 0 I .071 37.37 SCI .048 36.92 .038 29.23 .071 54.62 EMI .000 0 .038 5.14 .000 0 LD .119 2.29 .077 1.48 .071 1.37 SLI .000 0 .038 1.36 .071 2.54 El .024 1.60 .000 0 .000 0 Total .286 2.60 .191 1.74 .284 2.58 63 Table 15: Continued. Pooled Rates Versus Michigan Averages Sp. Ed. Incidence MiéiéMlifigigggcip ’ (:oingCAE-gzmo ' MlCh. Ave.) POHI .048 .0031 15.48 Hl .012 .0019 6.32 SCI . .048 .0013 36.92 EMI .012 .0074 1.62 LD .097 .0520 1.87 SLI .024 .0280 .86 El .012 .0150 .80 Total .253 .1100 2.30 that PTAGA and PTSGA groups would have the greatest numbers of special education handicaps, FTSGA subjects had almost the same overall handicap rate (28.4%) as PTAGA infants (28.6%) and more than PTSGA subjects (19.1%). Furthermore, the FTSGA group had special education handicaps in four of seven areas, the same ratio as PTAGA and PTSGA subjects. Looking at previous reports, premature children were found to have physical or otherwise health impairments (POHI) in the 1% to 8% range. The current study’s pooled results were comparable (4.8%). However, no POHI individuals were identified in either the FTSGA or PTSGA groups. As this pattern has not been described or studied by other investigators, it will be interesting to see whether this trend holds true in future long-term studies. 64 Hearing impaimtent’s (HI) pooled incidence. was considerably higher (1.2% versus 0.19%) than local special education rates. Whether the one female FTSGA with a hearing impairment out of the entire sample population represented a chance occurrence or a trend is unknown. It is apparent that this study’s limited numbers will not settle this question. In any event, this study’s results were less than previous researchers’ rates (3% to 4%). Severe multiple impairments (SXI) were notably higher than local special education rates for all at-risk groupings. As multiple impairment has not been specifically documented in previous studies, the pooled 4.8% rate was 36.92 times higher than local special education rates, and denotes an area of great concern for parents of preterm children and our society in general. Furthermore, SXI rates for all at-risk groups were high (PTSGA = 3.8%, PTAGA = 4.8%, and FTSGA = 7.1%). Previous investigators found 3% to 8% of the premature and low birth weight infants to be only mentally impaired (IQ less than 70). The current study’s combined severely multiply impaired and educable mentally impaired (EMI) results (4.8% + 1.2% = 6.0%) were also within that range. However, the four mentally impaired subjects in this study had the additional disadvantage of having one or more serious handicaps (health impairment, physical impairment, visual impairment, and/or hearing impairment). It also should be noted that none of these SXI subjects were included in other special education categories. They were represented only in this one handicap category. 65 Learning disabilities were also uniformly higher for all groupings of at—risk infants at school age (9.7% at-risk subjects versus 5.2% local special education rates). In comparison to previous studies, the current incidence is nearer to the Lefebvre et al. (1988) lower estimate (8%), but still 1.87 times greater than local population rates. Only pooled speech and language impairment and emotional impairment rates were lower than the general population. However, PTSGA and FTSGA subjects did have higher rates (1.36 and 2.54 times greater than local population rates) for speech and language impairment. This condition was absent in PTAGA children. Emotional impairment was documented for only one PTAGA child. As emotional impairment was not an outcome studied in prior studies of preterm children, the current results suggest that it is probably not a major outcome for premature or low birth weight infants at school age. The 5% to 17% incidence of speech and language impairments noted by previous studies was not evident in the current results. This higher SLI incidence might be the result of following subjects to only 5 to 7 years of age, a time where higher rates are more likely to be found in the general population. In summary, it cannot be said that each at-risk group had a higher incidence for every handicap category. When the at-risk groups had representation in an individual handicap category, their rates were much higher than local rates. With the exception of speech and language impairment and emotional impairment, pooled results were larger, on average, for each special education classification area. It 66 was also apparent that each group's total average number of handicaps was considerably greater than the local special education rates. The second part of the first hypothesis predicted that special education incidence results, by gender, would reflect local special education patterns and that lower SES groups would have higher handicap rates. Table 16 illustrates that gender-related patterns generally follow the local population trends for four of seven categories (EMI, LD, SLI, and El) with the pooled at-risk groups. Given the small numbers of subjects in this study and the relatively low probability of many of the handicapping conditions, it is inappropriate to imply that the results are representative of the premature and low birth weight population. However, it is interesting that the approximate 3:1 ratio of males having more learning disabilities than females was apparent in the current study, as well as males having slightly higher incidences of emotional impairments, educable mental impairments, and speech and language impairments. At odds with general population patterns, physically or otherwise health impairment and severe multiply impairment rates favored males. Table 16 shows that many of the individual and special education categories were minimally or not represented in the various at-risk groups. Therefore, comparisons were not made. As previous studies have not addressed gender differences for these at-risk groups, it will be interesting to follow future investigations that have larger numbers of subjects. Regarding SES and special education outcomes, Tables 17 and 18 note an ' unanticipated set offindings. By SES category, the highest (1) and lowest (5) SES- 67 Table 16: PTAGA, PTSGA, FTSGA, and pooled special education incidence, by genden PTAGA (n = 42) PTSGA (n = 26) FTSGA (n = 14) ll Male Female Male Female Male Female POHI 3 1 0 0 O 0 HI 0 0 0 0 0 1 SCI 1 1 1 0 1 0 EMI 0 0 1 a 0 0 0 LD 4 1 2 i 0 0 1 SLI 0 0 1 0 1 0 El 1 0 0 0 0 0 Total 9 3 5 0 2 2 Pooled At-Risk Groups Michigan Incidence by (n = 84) Gender (Grades 2 to 8) Male Female Total Male Female POHI 3 1 4 .0011 .0011 HI 0 1 1 .0010 .0011 SCI 3 1 4 .0007 .0008 EMI 1 0 1 .0043 .0034 LD 6 2 8 .0360 .0160 SLI 2 0 2 .0180 .0100 El 1 0 1 .012 .0036 Total 16 5 21 .0720 .0350 68 _o>m_ so $0.8 $0.9. $0.9. .8 OS 08.... $5: 02.2 0% mmm .8 88.29. B .x. OS o\oFFNN o\..F..NN Osman $4.8 OS §N 48.8 $4.8 .89 .22 Fe 4. .96. mum o m w 9 F. 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The upper-middle SES level (2) had the greatest number of special education handicaps (70% of the total number of handicaps versus 50% of the total number of subjects). Middle and lower-middle SES levels had slightly lower than expected handicap rates (Level 3: 20% versus 21 .9%, and Level 4: 10% versus 10.9%) with pooled results. Also, more of the high- incidence handicaps, categories that are generally considered less severe, seemed more likely to occur at middle to lower SES levels. Level 2, conversely, had a broad representation of all of the special education handicapping conditions. Previous research of premature and low birth weight children generally found more handicaps in lower SES—level groupings. In that light, it would be expected that the higher SES- level groups would have less severe handicaps. In the current study, this was found for only the highest SES level (no handicaps in 14.6% of the subject population). The greater than expected upper-middle SES handicap rates would appear to be contrary to previous findings. A possible explanation for these results might be based on the fact that the subjects of this study were primarily from middle to upper SES-level groups, with relatively few low SES subjects involved, and that the study lacked enough subjects to represent all low incidence categories. Individual at-risk groups showed similar patterns of underrepresenting the highest and lowest SES levels. However, more variability was apparent. PTSGA handicaps were overrepresented at the middle SES level (40% versus 23.1%), with upper-middle and lower-middle-Ievel rates being similar (Level 2: 40% versus 38.5%, and Level 4: 20% versus 23.1%). In contrast, FTSGA rates were variably 71 higher at those levels (Level 2: 75% versus 64.3%, and Level 4: 25% versus 14.3%) and had no handicaps at the middle level. PTAGA results, having the largest number of subjects, reflected the pooled group rates. In summary, it was difficult to accurately compare the at-risk groups’ gender- based patterns of handicaps with local rates due to the limited number of subjects in this study. However, it was evident that pooled at-risk rates were comparable for four of seven special education categories. Regarding the hypothesis that lower SES groups have higher rates of handicaps, pooled group results showed that this was not accurate. For all at-risk groups, the highest and lowest SES levels had no handicaps. The upper-middle SES level had the largest concentration of handicapped subjects using PTAGA, FTSGA, and pooled results. However, it seems likely that the very limited number of lower SES subjects involved in this study contributed to the variability of these findings. H th i The second hypothesis stated that "Premature and/or low birth weight children were thought to receive more special services, be retained at a higher rate, and be involved with special programs at earlier ages than the general population. Full-term SGA students would be involved with fewer services and programs than preterm SGA and AGA children." In addition to the previously noted higher incidence of special education handicaps, Table 19 notes the three at-risk groups’ involvement with special education and regular education remedial programs. According to the Michigan 72 Table 19: Special services. Number Percent PIAGA Receiving special education 12 28.6 Receiving remedial programs 6 14.3 Receiving special programs 16 38.1 Not receiving special programs 26 61.9 ELSEA Receiving special education 5 19.2 Receiving remedial programs 3 11.5 Receiving special programs 11 42.3 Not receiving special programs 15 57.7 EISQA Receiving special education 4 28.6 Receiving remedial programs 1 7.1 Receiving special programs 6 42.9 Not receiving special programs 8 57.1 Department of Education (personal communication), federally funded remedial programs in reading and math are provided for one in eight to nine students in the state. Each school district defines its own criteria for student eligibility, with more monies and programs being available for lower SES children. Mid-Michigan remedial programs address highly variable needs, and therefore are quite different from one another. With this perspective in mind, the incidence of at-risk subjects involved with remedial programs varied from 7.1% to 14.3%. As predicted, FTSGA subjects (7.1%) received the least amount of additional services. Only PTAGA (14.3%) were more apt to be involved with regular-education-based remedial 73 services than the general population. PTSGA subjects had similar results to the general population incidence rate of 11% to 12.5%. Thus, full-term SGA subjects received fewer regular education special services and programs than the general population. However, all three groups received many more special services, when combining regular education and special education, than did local populations. Regarding the initial age of special education programming, Table 20 revealed few differences. All the subjects referred for special education evaluations and determined to be eligible for services by IEPCs were identified within the state of Michigan’s peak placement ages for each handicap classification. Consequently, it appeared that current Child Find operations implemented by lSDs in the mid- Michigan area identified handicapped children as readily as Sparrow Hospital’s RNICU. Table 20: Average age of special education referral/placement. Category Age (Yrs.) Number Michigan--Peak Referral Ages POHI 1.5 yrs. I 4 1-2 yrs. HI 3.0 yrs. 1 3-8 yrs. SXI 2.3 yrs. . 3 1-2 yrs. EMI 4.0 yrs. 1 3-9 yrs. LD 6.75 yrs. 8 7-10 yrs. SLI 4.0 yrs. 5 3-7 yrs. El 8.0 yrs. 1 6-15 yrs. 74 Retentions, illustrated by Table 21, indicated seemingly high rates for at-risk groups identified with special education handicaps PTAGA--58.3%, PTSGA= 100%, FTSGA-50%, and pooled groups-66.7%). Whether these group averages are greater than local or national comparisons is unknown due to the lack of data regarding retention rates for all special education categories. However, these rates did not appear unusual when compared with the Rose et al. (1983) findings for 15, primarily southern, states that retained from 30.5% to over 100% of their students in a 13-year period. Table 21: PTAGA, PTSGA, FTSGA, and pooled retentions-Special education incidence. PTAGA I PTSGA # in Retained (Gender) % # in Retained (Gender) % 1 Sp. Ed. M F Retained Sp. Ed. M F Retained POHI 4 2 i 0 50% 0 0 0 0% HI 0 0 0 0% 0 0 0 0% sxr 2 1 1 100% 1 ‘ 1 o 100% EMI 0 0 0 0% 1 1 0 100% L0 5 2 1 60% 2 2 0 100% SLI 0 0 0 0% 1 1 0 100% El 1 0 0 0% O 0 0 0% Total 12 5 2 58. 3% 5 5 0 100% 75 Table 21: Continued. FTSGA Pooled Retentions # in Retained (Gender) % # in Retained (Gender) % Sp. Ed. M F Retained Sp. Ed. M F Retained POHI 0 0 0 0% 4 2 0 50% HI 1 1 0 100% 1 0 0 0% SXI 1 1 0 100% 4 3 1 100% EMI 0 0 0 0% 1 1 0 100% L0 1 0 0 0% 8 4 1 62.5% SLI 1 1 0 100% 2 2 0 100% El 0 0 0 0% 1 0 O 0% Total 4 2 0 50% 21 12 2 66.7% Looking at individual categories, this study found that all of the subjects who were retained had severe multiple impairments, educable mental impairments, and speech and language impairments. Retentions were somewhat less likely forthe LD (62.5%) and POHI (50%). No retentions occurred with HI or El subjects. It should be noted that the HI, EMI, El, and SLI categories had only one to subjects identified. It is possible that these findings may be chance occurrences rather than population- based trends. Learning disability retention rates for PTAGA and PTSGA subjects were somewhat higher than rates found by McLeskey and Grizzle (1992) for third-grade LD (54%) and sixth-grade LD (61%) students in Indiana. No FTSGA LD subjects were retained. However, only one individual of the 14 FTSGA subjects had this diagnosis. f0 thl no: 76 Thus, it appeared that premature and/or low birth weight children had very high retention rates, particularly for children with mental impairments (SXI, EMI). It is not possible to say whether these rates are higher than local and national retention rates for special education students due to the lack of comparative data. Furthermore, LD subjects from PTAGA and PTSGA groups had greater rates of retention than current comparable findings. For students not involved with special education programs, Tables 22 and 23 show that PTSGA subjects had the highest incidence (38.1%) of retentions, followed by FTSGA (18.2%) and PTAGA (14.3%). This pattern was continued when looking atthetotal group rates (combining regular education and special education findings). PTSGA had the largest percentage of retained subjects (50%), followed by FTSGA (35.7%) and PTAGA (28.6%). 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