AN ANALYSIS OF PERINATAL COVID-19 INFECTION AND  PRETERM BIRTHS IN A MICHIGAN-BASED COHORT       By   Katherine Patterson                A THESIS   Submitted to Michigan State University in partial fulfillment of the requirements  for the degree of  Epidemiology – Master of Science  2023  ABSTRACT Background: COVID-19 infection during pregnancy has been previously associated with an increased risk of preterm birth. This retrospective, population-level observational study uses data from the Michigan Department of Health and Human Service’s COVID-19 Pregnancy and Neonate Surveillance project, along with birth certificate and abstracted medical record data, to assess the risk of preterm birth amongst a 2020 cohort of Michigan residents who tested positive for SARS-CoV-2 during pregnancy. Methods: Logistic regression analysis produced odds ratios, 95% confidence intervals, chi-square values, and p-values. Several covariates were evaluated as potential confounders, though none met the threshold to require adjustment. To examine possible effect modification, the model was stratified by pre-pregnancy BMI, race, and trimester of infection. Results: The odds of preterm birth was 27% higher amongst the COVID-positive pregnancies in this cohort compared to COVID-negative pregnancies (95% CI: 0.99-1.63; p=0.062), but these results were not statistically significant at p<0.05. Exploratory findings did identify higher odds of preterm birth amongst pregnancies that tested positive for COVID-19 between 14-26 weeks (OR: 2.10; 95% CI: 1.30-3.39; p=0.002), as well as those who were a race other than non-Hispanic Asian or white (OR: 1.49; 95% CI: 1.08-2.07; p= 0.016). Conclusions: While this study could not conclude that there was an association between prenatal COVID-19 infection and preterm birth in this cohort, stratification did find increased risk amongst non-white/Asian pregnancies and second trimester infections. As COVID-19 continues into endemicity, vaccination and other preventative measures should be prioritized prior to conception and during pregnancy.   iii ACKNOWLEDGEMENTS I’d like to express my deepest thanks to my thesis committee chair, Dr. Misra, for all her support, patience, and guidance during this process. I would also like to express my gratitude to my committee members Dr. Margerison and Dr. Zhang for their continuous support. Furthermore, I would like to thank Chris Fussman and his colleagues at the MDHHS working on the COVID-19 Pregnancy and Neonate Surveillance project, as without their time and assistance this research would not exist. Thank you to my classmates, professors, and all in the Department of Epidemiology and Biostatistics for their inspiration and aid during my degree program. Finally, to my family, friends, and those who provided me with endless love and support during this time– thank you. I couldn’t have done it without you.      iv  TABLE OF CONTENTS INTRODUCTION .......................................................................................................................... 1 BACKGROUND ............................................................................................................................ 2 COVID-19 AND PREGNANCY ................................................................................................... 5 METHODS ..................................................................................................................................... 8 RESULTS ..................................................................................................................................... 14 DISCUSSION ............................................................................................................................... 17 STUDY LIMITATIONS .............................................................................................................. 20 CONCLUSION ............................................................................................................................. 22 REFERENCES ............................................................................................................................. 23 APPENDIX ................................................................................................................................... 31   1   INTRODUCTION COVID-19 has become one of the deadliest pandemics of the 21st century so far, causing more than three years of massive social and economic disruption.1 Though outbreaks of other coronaviruses (such as SARS and MERS) with higher case fatality rates have occurred over the past few decades, Coronavirus disease 2019 (COVID-19) has displayed more efficient interpersonal transmission while still being able to cause severe illness. Certain COVID-19 variants appear to have a longer incubation period than these other coronaviruses, and evidence also supports both presymptomatic and aysmptomatic transmission.2,3 COVID-19 infection during pregnancy has been repeatedly associated with an increased risk of severe illness, as well as a number of adverse maternal and birth outcomes.4  This retrospective, population-level observational study used Michigan Department of Health and Human Services (MDHHS) data from the COVID-19 Pregnancy and Neonate Surveillance Project, in conjunction with Michigan birth certificate and abstracted medical record data, to both describe and analyze the risk of preterm birth amongst a 2020 cohort of Michigan residents who received a positive test for SARS-CoV-2 during their pregnancies. This project used de-identified data and did not involve the direct use of human subjects or biological specimens.    2   BACKGROUND A Timeline of COVID-19 in 2020 At the end of 2019, the World Health Organization began investigating a cluster of unusual viral pneumonia cases in Wuhan City, one of the largest cities in central China. Within a few weeks, a novel betacoronavirus, now known as Severe Acute Respiratory Coronavirus 2 (SARS-CoV-2), was identified as the likely cause of the outbreak.2 By the beginning of 2023, the WHO had reported over 743 million cases and 6.7 million deaths globally, though the true numbers are likely much higher.5,6 The majority of epidemiologists and public health officials consider COVID-19 to be an endemic disease. In late January of 2020, the CDC announced the first confirmed case of COVID-19 in the United States (Figure 1).7 By mid-March, the WHO officially declared COVID-19 a pandemic. Shortly afterwards, the US further restricted international travel and passed the CARES act, while individual states began issuing stay-at-home mandates for residents intended to slow the spread of the virus.7 In Michigan, Governor Gretchen Whitmer issued an executive order on March 23rd that prohibited in-person, non-essential business or activities in the state, building on previous orders that banned large gatherings, closed K-12 schools and limited the operations of gyms, movie theatres and dine-in restaurant services.8 The stay-at-home order was lifted in early June, but certain restrictions on in-person dining and education were reinstated when COVID-19 cases began rapidly rising during October. In November, the state was averaging over 6,000 cases a day. By the end of 2020, Michigan had 640,105 confirmed cases of COVID-19, and 13,019 deaths.9  Epidemiology of COVID-19 The primary route of SARS-CoV-2 transmission is through direct, person-to-person contact. Respiratory secretions containing the virus are released while an infected individual is  3   breathing, talking, sneezing, or coughing. Close contact (within 6 feet) with a person who has COVID-19 may result in the inhalation of airborne respiratory particles containing the virus, or through other direct contact between the secretions and mucosal membranes. Infection through touching contaminated surfaces (then touching the eyes, mouth, or nose) is less common, but still possible. Long-range airborne transmission is not believed to be a primary mode of infection, though evidence suggests that it still does occur, particularly in enclosed, poorly ventilated spaces.10 In addition to symptomatic carriers, pre-symptomatic or asymptomatic transmission is also common.11 It has been estimated that over a third of COVID-19 infections are aysmptomatic.12 COVID-19 primarily affects the respiratory system, where it can induce respiratory distress syndrome. It is also known to impact the cardiovascular, gastrointestional, and nervous systems.13 The majority of symptomatic SARS-CoV-2 infections remain mild, and do not require medical intervention. However, individuals with COVID-19 can experience a spectrum of clinical manifestations and a wide range of illness severity.14,15 A number of medical conditions have been associated with an increased risk of severe COVID-19 illness, including cancer, asthma, diabetes, cardiovascular disease, obesity, advanced/untreated HIV infection, chronic lung, liver, or kidney disease, as well as those who have received immunosuppressive therapies or organ transplants. Smoking, pregnancy, and advanced age (≥ 65 years old) have also been associated with severe illness.15   Virology of SARS-CoV-2 Like other coronaviruses, SARS-CoV-2 is a single-stranded, positive sense RNA virus. It infects host cells through its spike protein (S protein), which binds to a cell’s angiotensin-converting enzyme 2 (ACE2) receptor. To enter a cell, S proteins must be cleaved at two sites. The first cleavage site (S1/S2) facilitates protein recognition by the host cell’s ACE2 receptor. The  4   second site (S2′) is necessary in triggering membrane fusion and viral entry. Host enzyme proteases such as type II transmembrane serine protease 2 (TMPRSS2), furin, and cathepsin L are thought to further facilitate viral entry.16,17 Cells with ACE2 and TMPRSS2 co-expression are believed to be at the greatest risk of infection by SARS-CoV-2.18  Mutations to the original, “wild-type” SARS-CoV-2 strain from 2019 have resulted in the emergence (and dominance) of variants over time. The major SARS-CoV-2 variants of concern include B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.671.2 (Delta), and B.1.1.529 (Omicron). Evidence suggests that these variants and their subvariants differ in transmissability, severity, and risk of reinfection.19      5   COVID-19 AND PREGNANCY In 2020, the CDC reported 51,903 maternal COVID-19 cases in the United States, though the true number of infections is likely much higher.20 The majority of pregnant people experience only mild or asymptomatic COVID-19, and most infants do not test positive for COVID-19 following delivery.21 Despite this, a growing body of research suggests that pregnancy is a risk factor for severe illness, ICU admittance, and mortality from COVID-19.22 Furthermore, maternal SARS-CoV-2 infection has also been associated with a higher risk of multiple adverse birth outcomes, including preterm birth and NICU admission.23,24,25,26 SARS-CoV-2 antibodies have been detected in some neonates, suggesting that their immune activity may have been altered by maternal infection.21 Limited maternal-fetal transmission is also believed to occasionally occur, though the vast majority of neonates do not test positive for COVID-19 infection or fetal IgM antibodies following birth, implying that fetal infection is rare and that the placenta provides some degree of protection against in-utero transmission.13,28 COVID-19 infection-related inflammation can occur in the placenta without direct placental infection as a result of maternal immune activation.27 Increased levels of inflammatory cytokines have been found both at the maternal-fetal interface and in cord blood. It is currently unclear whether they are solely maternal cytokines or if they are also being produced by the fetus.28 Activated maternal T cells and natural killer cells have also been observed in the decidua of COVID-19 positive pregnancies.28 Though uncommon, evidence of placental infection has been observed during pregnancies complicated by COVID-19 and has been associated with an increased risk of stillbirth.29,30 A few studies have estimated the rate of placental infections to be 7% or less.13,27,29,30,31,32,33 It has been hypothesized that the relative rarity of placental infection may be due to a low rate of ACE2- 6   TMPRSS2 co-expression in placental tissue.28,34,35 Higher SARS-CoV-2 viremia (which has been associated with increased disease severity) may still allow a route for placental infection. SARS-CoV-2 infection has also been associated with the development of placentitis, a severe inflammatory syndrome that causes vascular and placental lesions. Placentitis is considered a risk factor for fetal distress and death, with severe placental damage linked to fetal hypoxic-ischemic injury, fetal distress, intrauterine growth restriction, and miscarriage or stillbirth.28,32,36  Researchers have found links between prenatal COVID-19 infection and other adverse birth outcomes. One large systematic review (including 29 studies with a total of 197,196 neonates) concluded that neonates born to COVID-19 pregnancies had a higher risk of NICU admission compared to those born without perinatal COVID-19 infections (OR: 2.18; 95% CI: 1.46-3.26).24 Maternal COVID-19 infection has also been associated with an increased risk of preeclampsia which can cause fetal growth restriction and preterm birth (though confounding is possible because of several shared risk factors for COVID-19).37 A higher rate of fetal growth restriction amongst severe and critical COVID-19 patients has been observed, compared to mild cases (adjusted OR: 2.73; 95% CI: 1.03–7.25).38 The stage of pregnancy at infection may also play a role in the risk of adverse outcomes. Increased vulnerability to maternal stress and infections (such as influenza) in the second and third trimesters have been associated with increased rates of preterm birth, poor fetal growth, and low birth weight.39  COVID-19 and Preterm Birth Preterm birth (delivery occurring before 37 weeks of gestation) has been associated with serious neonatal morbidity, such as intraventricular hemorrhage and necrotizing enterocolitis. It has also been linked to cerebral palsy, delayed development, and other long-term chronic conditions such as asthma and a higher risk of infections.40,41 Prior to the SARS-CoV-2 pandemic,  7   approximately 8% of singleton births in the US were delivered preterm. The majority were late preterm births, occurring between 34-36 weeks of gestation.42  A growing body of research has found that COVID-19 infection during pregnancy is associated with an increased risk of preterm birth, and that greater disease severity may further increase the risk.23,24,25,26,43,44,45,46 In studies that differentiate the types of preterm births, some have reported higher risks of both iatrogenic and spontaneous preterm births, while others have only found an increased risk of iatrogenic births.43,47,48 One large population-based study reported that a COVID-19 diagnosis was associated with a 50% increased risk for spontaneous preterm birth (adjusted rate ratio: 1.5, 95% CI: 1.2-1.7).43 The observed risk of iatrogenic preterm births with COVID-19 infection has largely been attributed to clinical decision-making and risk management. Doctors may elect to prematurely deliver to save a severely ill patient or to address other complications (such as preeclampsia/eclampsia or fetal distress) that may pose a threat to the health of the infant following maternal infection.24,47 The relationship between SARS-CoV-2 infection and iatrogenic preterm birth appears stronger in those diagnosed during the later preterm period, which supports this theory.24 The potential biological mechanisms linking spontaneous preterm birth to maternal SARS-CoV-2 infection are not yet well understood. It is plausible that infection-related inflammation plays a significant role in facilitating spontaneous preterm birth or preterm premature rupture of membranes (PPROM); this would be in line with what has been observed in other coronaviruses, as well as in other viral respiratory infections like influenza.21,49,50   8   METHODS Research Objectives This research sought to expand upon an existing body of work by using newly available state-level surveillance data for Michigan, and is the first to use this dataset to study COVID-19 infection and preterm birth in this population during 2020. The first objective of the study was to produce descriptive statistics for the 2020 cohort of the Michigan COVID-19 Pregnancy and Neonate Surveillance Project, including the prevalence of COVID-19 diagnosis in preganancy. The second objective was to compare the preterm birth rates of this cohort against a sample of pregnant people from Michigan during the same time period who did not test positive for SARS-CoV-2 during their pregnancy. This also included a further exploration of the role of pre-pregnancy BMI and race/ethnicity on the outcomes. It was hypothesized that maternal SARS-CoV-2 infection in this cohort would be associated with an increased risk of preterm birth.  Study Design This retrospective cohort study used population-level data from 2020 to examine COVID-19 infection and preterm birth amongst pregnant people in Michigan. This was done by linking two separate sets of state-level data that were provided by the Michigan Department of Health and Human Services (MDHHS) for use in this thesis project. The first contained health information from individuals who experienced a lab-confirmed COVID-19 infection during their pregnancy. These data were collected through birth certificates and medical record abstractions by the MDHHS COVID-19 Pregnancy and Neonate Surveillance Project, as part of the CDC’s Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET) program. Secondly, Michigan birth certificate data for 2020 was provided. Information on COVID-19 infection was not recorded in Michigan’s Certificate of Live Birth form. Instead, the MDHHS  9   provided a separate file linking birth certificate file numbers with CDC identification numbers assigned to COVID-19 cases recorded by SET-NET. This allowed for dataset linkage and the selection of an unexposed cohort. There was no active data collection involved in this thesis project. The Institutional Review Board from MSU ruled this study exempt from approval for human subjects research, as it utilizes deidentified data previously collected by the MDHHS. The MDHHS owns the data used in this study, and all relevant parties entered into a data use agreement.   Study Population and Sample Selection The study population were all individuals who delivered in Michigan between January 1st and December 31st, 2020. During this time period, 104,169 birth certificates were issued by the state for approximately 102,270 deliveries. Figure 2 shows the selection process of the study sample and analytic sample from the original study population. This study sample included 96,591 individuals with singleton pregnancies (multiple or missing plurality: n=3,759) who were 18-39 years old at delivery (<18 or >39 years at delivery or missing age: n=3,819). Pregnancies that concluded with miscarriage, termination or stillbirth prior to delivery were also excluded from the study sample due to the selection of preterm delivery as the primary outcome. The birth certificate records used by this study collect information from live birth worksheets, and therefore the dataset should not contain pregnancy outcomes besides live births. Data from SET-NET did include one linked pregnancy whose an outcome was reported as a stillbirth or intrauterine fetal demise. On closer examination however, this outcome date was over six months after the date of birth (which was months prior to the first reported COVID-19 case in Michigan), and therefore may be a data collection error and not a stillbirth. This pregnancy was later excluded from the analytic sample, as it was also a post-natal COVID-19 infection.  10   Prior to analysis, additional inclusion criteria were applied to the study sample. The gestational age of the infant must have been at least 22 completed weeks’ gestation (<22 weeks or missing gestational age: n=171). They must have a birthweight of 500 grams or higher (<500 grams or missing birthweight: n=78). Individuals who tested positive for COVID-19 needed laboratory confirmation prior to their delivery date (post-natal positive COVID-19 test: n=6). Finally, missing information on sex was excluded (n=3). Following these exclusions, the analytic sample included 96,333 pregnancies.  Measures The adverse birth outcome of interest in this study is preterm birth. Preterm birth was defined as delivery occurring before 37 completed weeks of gestation. It is a dichotomous variable that was extrapolated from dataset variables providing the estimated gestational age at the outcome of the pregnancy. The variables used to measure this outcome for both cohorts originated from the birth certificate dataset, and not the SET-NET dataset. COVID-19 exposure status was determined by a positive polymerase chain reaction (PCR) laboratory test result that occurred during the pregnancy and had been captured by the MDHHS COVID-19 Pregnancy and Neonate Surveillance Project. Suspected cases without recorded confirmatory PCR testing were not included in the exposure sample, nor were cases diagnosed through antigen testing alone.  Following inclusion and exclusion criteria, the sample used for analysis included 96,333 pregnancies (99.73% of the selected sample). Inclusion and exclusion criteria were applied following dataset linkage. Individuals with missing information on maternal age, delivery date, and plurality were excluded from the study sample sample (Figure 2). Those with missing information on gestational age, birthweight, and sex were further excluded from the analytic sample. Prior to analysis, this study excluded births with an estimated gestational age below 22  11   weeks, or a birthweight less than 500 grams. This was due to the very low likelihood of survival for these infants, as well as the possibility of inaccurate data entry.  Binary SET-NET variables (0/1) were recoded to match the formatting of corresponding variables in the birth certificate dataset (1/2). A new variable estimating gestational age at infection was produced. The number of days in between the delivery date and the date of the first positive laboratory test result were counted, then subtracted from the estimated gestational age at delivery (in days) provided by SETNET. This variable measuring estimated gestational age at infection was then coverted into weeks. A second variable was created to group estimated gestational age at COVID-19 infection into the trimester of infection. Pre-pregnancy body mass index (BMI) was calculated using pre-pregnancy weight (kilograms) and height (meters) (kilograms2/meters). Bridged race variables were recoded into 6 combined categories, as well as a binary race variable (white/non-white). INTERGROWTH 21st z-scores and centiles measuring fetal growth were produced using a separate desktop application for newborn size, before being re-added to the dataset as new variables.51 The data file used by the INTERGROWTH application was further deidentified by assigning a new identification number to each observation and limiting variables to birthweight, sex, and gestational age.  The dataset variable for race included 19 different categories. From this, I created two generalized race variables to use in the analysis- one binary (white, non-white), and one categorical (white, Black, Asian, Native American/Alaskan, Hawaiian/Pacific Islander/Other Asian, other). Hispanic ethnicity was not recorded within this original bridged race variable and was instead recorded as a separate binary variable. Though more Hispanic individuals were in the COVID-19 positive group (13.9% vs 6.8%; p=<0.0001)(Table 1), there was no difference in ethnicity between preterm and term births (Table 2). However, a new variable was created to include Hispanic  12   ethnicity as an additional category within race for subgroup analysis because it appeared to account for large amounts of missing data in the original race variable. 84.1% of “other race” (2208/2627) and 39.2% of individuals with missing data on race (211/539) were also reported as being Hispanic. All respondents who were recorded as Hispanic were then placed in this new category within the race variable (n=6576). Table 7 shows how these new variables were produced, as well as more detailed information on race in this sample (a breakdown of the original race categories prior to adding ethnicity have been included as Table 8). An assessment of the interaction between COVID-19 status and the new binary race variable that included Hispanic ethnicity in the non-white group appears to support this decision, as it produced evidence of a positive additive interaction (RERIOR= 0.83 > 0; 95% CI: 0.58-1.15).   Statistical Analysis All data analysis was performed using SAS Studio. Figures and tables were produced using Excel and SAS. Covariates and general descriptive characteristics (including sociodemographic and health information) were produced for both the study sample and the analytic sample (Table 1). To assess bias in the selection process of the analytic sample, chi-square tests of independence and t-tests for differences in means were performed to measure differences between the study sample and analytic sample (Table 3), as well as the data included versus excluded in the analytic sample following the application of exclusion criteria (Table 4). This analysis included COVID-19 infection, health and delivery information, and variables measuring age, race and socioeconomic status. The only variable reaching statistical significance (p<0.05) that was not directly included in the exclusion process was maternal ICU admission, with the analytic sample reporting a lower prevalence of maternal ICU admissions than the study sample. This could have been influenced by the decision to exclude related factors such as neonatal death, very low  13   gestational age and very low birth weight. The missingness of data within the analytic sample was also calculated by both exposure (Table 5) and outcome (Table 6), and found to be minimal. Missing data was 3% or lower for almost all variables, with the majority below 1%.  The only exception was observed within only the preterm birth outcome group, where all three included STI variables (Chlamydia, Gonorrhea, and Syphilis) reported 5.9% missingness. Otherwise, alcohol use during pregnancy and pre-pregnancy BMI were the two variables with the highest rates of missing data. This is in line with previous findings examining the validity of birth certificate data.52   Logistic regression was performed to assess the relationship between prenatal COVID-19 infection and preterm birth. This analysis produced odds ratios, 95% confidence intervals, chi-square values, and p-values. Several covariates were evaluated as potential confounders, including age, socioeconomic status (through education level and source of payment), parity, smoking, and pre-pregnancy BMI, as they have previously been associated with COVID-19 infection (including a higher risk of infection severity) and preterm birth in the literature.23,43,53,54,55,56 However, after calculating the magnitude of confounding, none of these covariate-adjusted models exceeded the 10% threshold indicating the presence of confounding.57 Therefore, the final model remains unadjusted. This model was stratified by race and pre-pregnancy BMI, as both may fall on the causal pathway and function as effect modifiers. While I had some information that could be as a proxy for disease severity (e.g., maternal ICU admission), pre-pregnancy obesity status may also be a risk factor for severity as obese individuals have been found to be more affected by COVID-19. When I performed further subgroup analysis on race, both the white and Asian subgroups showed no observed increase in risk (Figure 5).  Based on these findings, I decided to combine white and Asian categories for my reference group when I stratified the final model by race.  14   RESULTS There were 96,333 people who met the inclusion criteria and were included in the analytic sample. The majority (63.7%) were between 25-34 years old at delivery, with a mean age of 28.7 years (IQR: 25-34). 68.5% of pregnant people in the analytic sample were non-Hispanic white and 19.2% were non-Hispanic Black. Approximately 6.9% of individuals in the sample reported Hispanic ethnicity, while 3.7% were NH Asian, 1.1% were NH Native American or Native Alaskan, 0.2% were NH Native Hawaiian or Pacific Islander (or other asian), and 0.4% identified as some other race/ethnicity. The overall prevalence of preterm birth (<37 weeks) was 8.05% in the analytic sample (Table 1). In the analytic sample, 692 individuals (0.7%) received a positive COVID-19 test during their pregnancy. Nearly three quarters of these positive tests occurred during the third trimester of pregnancy, while 18.6% were during the second trimester. 70.6% of the COVID-positive cohort had reported at least one symptom of COVID-19. Amongst pregnancies who had a COVID-19 positive test result, the prevalence of preterm birth was 9.97% (69/692 pregnancies), while the COVID-19 negative cohort had a prevalence of 8.03% (7,684/95,641 pregnancies). The COVID-positive cohort was less frequently white (55.6% vs 68.6%; p=<0.0001), more often Hispanic (13.9% vs 6.8%; p=<0.0001), and had more patients insured by Medicaid (47% vs 39.6%; p=0.0003). They had more gestational hypertesion (10.6% vs 8.3%; p=0.0273) and chylamidia diagnoses (2.3% vs 1.4%; p=0.0375). They also had a higher percentage of recorded ICU admissions for both birthgivers (1.2% vs 0.1%; p=<0.0001) and infants (8.7% vs 6.6%; p=0.026) (Table 1). Figure 3 shows the distribution of positive test results by month, with the majority occuring April-May and July-October of 2020.  15   Table 2 describes the characteristics of the sample by outcome status. There were 7,753 preterm births in the sample. Of these births, 86.3% were mildly preterm (32-36 weeks) and 13.7% were very preterm (<32 weeks). NICU admission was 41.7% for the preterm infants and 3.5% for full term. Neonatal mortality was higher amongst the preterm birth group (1% vs 0%; p=<0.0001), and more infants were small for gestational age (7.1% vs 4.4%; p=<.0001). Though the majority of deliveries for both were spontaneous, there were more caesaran procedures reported for preterm births than full term (46.1% vs 29.3%; p=<0.0001). Preterm births were more prevalent amongst non-white pregnancies (36.9% vs 26.7%; p=<0.0001). Despite only making up 19.2% of the total sample, 29% of preterm infants were delivered by NH Black birthgivers. Those who delivered preterm were also more likely to have gestational hypertension, diabetes, clamydia, gonnhorea, and to report smoking during the pregnancy.  Within this sample, the odds of a preterm birth was 1.27 times higher amongst COVID-positive pregnancies compared to COVID-negative pregnancies (95% CI: 0.99-1.63; p=0.062). Inclusion of any covariates, as well as combinations thereof, changed the odds very little and did not support the inclusion of the covariates as confounders in the final model. During further exploration of these data, odds ratios for preterm birth were calculated by trimester of infection (Figure 4). While no statistically significant differences were observed for infections in the 1st or 3rd trimesters, the odds of preterm birth were 2.10 times higher amongst pregnancies who tested positive during their 2nd trimester, compared to those without COVID infection (95% CI: 1.30-3.39; p=0.002). I also produced models stratified by suspected effect modifiers, including race (white/Asian and non-white/Asian) and pre-pregnancy BMI (Figures 6 and 7). Amongst NH non-white, non-Asian pregnant people, the odds of preterm birth was found to be 49% higher amongst the COVID-positive cohort (OR: 1.49; 95% CI: 1.08-2.07; p= 0.016). I chose to add Asian- 16   Americans to my reference group when stratifying by race, as they had no observed increase in risk when I stratified by each individual race category. No statistically significant results were observed within the BMI categories.  17   DISCUSSION I found that the odds of preterm birth were higher among Michigan births in which the birth-giver tested positive for COVID-19 prenatally. This difference was not statistically significant, however it does border on significance (p=0.06 > 0.05) and therefore still may hold some clinical importance. A limited number of studies published early in the pandemic did not initially find an association between COVID-19 infection and preterm birth or reported a population-level reduction in preterm birth in the months following the initial wave COVID-19 “lockdown” policies.47,58,59 Despite this, maternal SARS-CoV-2 infection has since been repeatedly associated with a increased risk of preterm birth.23,24,25,26,43,44,45,46 This study may have simply lacked the power to detect a statistically significant difference in odds, because of the very small number of COVID-19 infections in the cohort (n=692) and the modest effect size of the observed odds ratio.  The odds of preterm birth were higher amongst pregnancies who tested positive during their 2nd trimester (OR: 2.10; 95% CI: 1.30-3.39), though not for those with 1st and 3rd trimester infections (Figure 4). Prior research has found respiratory infections during the second and third trimesters to relate to increased rates of preterm birth, and other adverse birth outcomes.39 This association between trimester of infection and preterm birth has been observed in COVID-specific studies as well, particularly amongst symptomatic cases occuring during the third trimester.60,61 The effect of COVID-19 infection on preterm birth was determined to be different by race (NH white/Asian vs non-white/non-Asian), with non-Asian minorities experiencing higher odds of preterm birth (Figure 6). Prior to the pandemic, minority communities in the US experienced higher rates of poor birth outcomes, including preterm birth, low birth weight, and infant mortality, as well as maternal mortality and morbidity.62 The causes of these inequities are multi-faceted.  18   Socioeconomic and health-related risk factors (such as chronic stress) have been associated with many poor birth outcomes. Systemic racism and race-based discriminatory treatment both cause and further contribute to disparities in perinatal health.63,64 These same communities have been disproportionally impacted by the COVID-19 pandemic.65,66 In many areas of the country, Black, Latinx, and Native/Indigenous populations have had a notably higher risk of infection and of COVID-associated mortality when compared to white populations.67,68 Racial and socioeconomic disparities in COVID-19 infection risk and severity have also specifically been observed during pregnancy, though this data is more limited.43,69,70 One Michigan-based cohort study found that, compared to white patients, Black patients had twice the risk of being diagnosed with COVID-19 during their pregnancy (35.9% vs. 18.3%; RR=1.96; 95% CI: 1.6-2.4).73 Furthermore, several other perinatal comorbidities that have been associated with both COVID-19 disease severity and adverse birth outcomes disproportionally burden non-white communities in the US, including diabetes, hypertension and obesity.43,47,55 This study found that within the non-white, non-Asian pregnant group, the odds of preterm birth was 49% higher amongst the COVID-positive cohort (95% CI: 1.08-2.07; p= 0.016). It is difficult to measure the impact of COVID-19 mitigation policies on birth outcomes.74 Several studies observed lower than expected preterm birth rates in certain groups during the early months of the pandemic, which has led some researchers to theorize that “lockdown” periods (which placed restrictions on non-essential, in-person work or gatherings, and led to the widespread adoption of work-from-home orders) may have contributed to a reduction in preterm births.47,57 These policies may have indirectly affected preterm birth rates for some through a reduction in workplace-related stress or physical strain, a general decrease in circulating infections, or even lower air pollution.54,75,76,77 For many however, the pandemic-related social and economic  19   turmoil of 2020 could have contributed to higher levels of stress and anxiety during pregnancy, while the strain placed on healthcare services from COVID-19 may have led to an overall reduction in the quantity and quality of care, including exacerbating preexisting racial and socioeconomic inequities.70,75,77,78,79 Importantly, low-income and minority (particularly Black, Latinx, and Native American) workers were more likely to be in-person essential workers and therefore would’ve been excluded from many of the potential benefits of work-from-home orders. These workers also experienced a higher risk of COVID-19 infection than white, high-income workers in similarly high exposure workplaces.80 These same groups are often found to have the highest risk of preterm birth.62 Additionally, it has been proposed that an increased rate of miscarriage or stillbirth in high-risk populations (either associated with COVID-19 infection, or through other indirect effects of the pandemic) could slightly decrease preterm birth rates as well, though this relationship has not been as well-studied.81,82,83  20   STUDY LIMITATIONS My findings may have been further influenced by several factors. The primary limitation of this study was the state of testing during the first year of the pandemic generally within Michigan, but also in pregnancy. By late spring of 2020 many hospitals had begun requiring COVID-19 tests following admittance, including to labor and delivery units, but there was no systematic effort to test throughout pregnancy. While the true prevalence of SARS-CoV-2 infections in the pregnant population is unknown at any point in the pandemic, it is likely higher than reported. During this time, clinical and public health guidance was rapidly evolving. This study necessarily could only include SARS-CoV-2 cases that were identified by laboratory PCR testing, which may have contributed to undercounting.53  The individuals included in this study also do not necessarily have similar circumstances surrounding their testing. For example, while some tests may have been conducted due to hospital admittance or delivery requirements, others may have come only after a known exposure or symptom presentation. Symptomatic cases (especially moderate and severe cases) prior to delivery may therefore be overrepresented in this sample, while mild and asymptomatic cases earlier in pregnancy may have been missed. Furthermore, nearly one third of positive tests occurred within a day of their delivery date, many of which were asymptomatic. This could bias the results towards the null hypothesis. Accessibility issues surrounding healthcare, testing and treatment options also likely contributed to missed cases.72,73 This would’ve been especially prevalent in the earlier months of the pandemic. Though public health institutions were able to utilize existing perinatal health surveillance networks such as SET-NET, early collection of SARS-CoV-2 infection information could have suffered from a lack of standardization and delayed system implementation  21   that possibly impacted the quality of data, including a lag in testing availability. As an observational study, there is the possibility of additional unknown confounding.  Several studies suggest that birth certificate data may underreport several recorded variables.84 Additional information on other potential causes of preterm births were also limited, including infections such as influenza, urinary tract infections, yeast infections, and sexually transmitted infections. The data used in this study does not distinguish between iatrogenic and spontaneous births. This study only includes pregnancies that concluded with a live delivery and does not include cases of miscarriage or stillbirth. There is limited data on first trimester infections, especially for those who were not yet aware of their pregnancy when they contracted COVID-19. Only 692 perinatal cases were included in this cohort study, significantly less than what was expected. This contributed to the low power of the study, and the small sample of COVID-positive pregnancies made it difficult to identify potential differences between subgroups.  The generalizability of these findings may be limited, based on the unique circumstances surrounding both the time and geographic area. Vaccines were not yet widely available (and only recently recommended during pregnancy), so it is also assumed that individuals included in this sample were unvaccinated. During this period, it is reasonable to believe that it was the first infection amongst those who tested positive, as the reinfection rate was very low prior to the emergence of new strains of SARS-CoV-2. These results are not generalizable for subsequent reinfections, as some studies suggest a decreased risk of severe outcomes.85,86 The sample also predates the dominance of Delta and Omicron variants in the United States.     22   CONCLUSION This study used a Michigan-specific dataset to explore the relationship between maternal SARS-CoV-2 infection and a major adverse birth outcome during the first year of the pandemic. I did not observe a statistically significant association between prenatal COVID-19 infection and preterm birth for this cohort. However, exploratory findings did identify an increased risk of preterm birth amongst COVID-positive pregnancies in the second trimester (OR: 2.10; 95% CI: 1.30-3.39; p=0.002), as well as amongst NH non-white or Asian-American individuals (OR: 1.49; 95% CI: 1.08-2.07; p= 0.016). More work is needed to better assess the role of symptom severity, disease variant, or pregnancy-specific risk factors. Further studies incorporating a greater number of COVID-positive pregnancies will allow for improved subgroup analysis. Preterm birth has previously been associated with a higher risk of severe health complications for the newborn, including mortality. It also places significant emotional and financial burdens on families. Even a small increase in the rate of preterm birth associated with COVID-19 infection would create a significant public health burden, due to the high rate of exposure. Preventative measures, such as vaccination, should continue to be prioritized during pregnancy. As COVID-19 moves into endemicity, further epidemiological work is necessary to help guide maternal health policy and decision-making.         23   REFERENCES  1.  Pak A, Adegboye OA, Adekunle AI, Rahman KM, McBryde ES, Eisen DP. Economic Consequences of the COVID-19 Outbreak: the Need for Epidemic Preparedness. Front Public Health. 2020;8:241. Published 2020 May 29. doi:10.3389/fpubh.2020.00241   2.  Carvalho T, Krammer F, Iwasaki A. The first 12 months of COVID-19: a timeline of immunological insights. Nat Rev Immunol. 2021;21(4):245-256. doi:10.1038/s41577-021-00522-1   3.  Mugglestone MA, Ratnaraja NV, Bak A, et al. 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Lancet Infect Dis. 2022;22(6):781-790. doi:10.1016/S1473-3099(22)00143-8   31   APPENDIX Table 1: Sociodemographic and delivery-related health characteristics of amongst Michigan women aged 18-39 who delivered singleton pregnancies in 2020 by COVID-19 exposure.      TOTAL COVID-POS COVID-NEG   N=96,333 N=692 N=95,641   N (Wt%) N (Wt%) N (Wt%) Chi-Sq P-Value  Trimester of COVID-19 infection                   1st (<14 wks) 51 (7.4) 51 (7.4)           2nd (14-26 wks) 129 (18.6) 129 (18.6)           3rd (27+ wks) 512 (74.0) 512 (74.0)         Symptomatic infection                   Yes 481 (70.6) 481 (70.6)           No 200 (29.4) 200 (29.4)         Race (white/not white)                   White 69439 (72.5) 426 (62.9) 69013 (72.6) 31.27 <.0001**   Not white 26355 (27.5) 251 (37.1) 26104 (27.4)     Race categories                    White 69439 (72.5) 426 (62.9) 69013 (72.6) 56.39 <.0001**   Black 18733 (19.6) 173 (25.6) 18560 (19.5)       Asian  3931 (4.1) 26 (3.8) 3905 (4.1)       Native American/Alaska Native 808 (0.8) 6 (0.9) 802 (0.8)       Native Hawaiian/Pac. Islander/Other Asian 256 (0.3) 2 (0.3) 254 (0.3)       Other 2627 (2.7) 44 (6.5) 2583 (2.7)     Ethnicity                   Hispanic 6576 (6.8) 96 (13.9) 6480 (6.8) 54.67 <.0001**   Not Hispanic 89487 (93.2) 593 (86.1) 88894 (93.2)     Race/Ethnicity (combined variable)           NH White 65727 (68.5) 382 (55.6) 65345 (68.6) 81.53   <.0001**   NH Black 18420 (19.2) 171 (24.9) 18249 (19.1)     Hispanic 6576 (6.9) 96 (14.0) 6480 (6.8)     NH Asian 3578 (3.7) 26 (3.8) 3876 (4.1)     NH Native American/Alaska Native 1060 (1.1) 6 (0.9) 730 (0.8)     NH Hawaiian/Pac. Islander/Asian 225 (0.2) 1 (0.1) 224 (0.2)    NH Other 419 (0.4) 5 (0.7) 414 (0.4)   Age at pregnancy outcome (yrs)                   <20 3127 (3.2) 20 (2.9) 3107 (3.2) 6.64 0.1563   20-24 18649 (19.4) 151 (21.8) 18498 (19.3)       25-29 31011 (32.2) 234 (33.8) 30777 (32.2)       30-34 30318 (31.5) 190 (27.5) 30128 (31.5)       35-39 13228 (13.7) 97 (14.0) 13131 (13.7)     Highest level of education                   Did not complete high school 9257 (9.7) 73 (10.7) 9184 (9.7) 31.24 <.0001**   High school graduate or GED 26052 (27.2) 216 (31.6) 25836 (27.2)       Some college, no degree 21394 (22.4) 149 (21.8) 21245 (22.4)       Associate degree 8360 (8.7) 81 (11.9) 8279 (8.7)       Bachelor’s degree 19700 (20.6) 120 (17.6) 19580 (20.6)       Advanced or professional degree 10904 (11.4) 44 (6.4) 10860 (11.4)         32   Table 1 (cont’d)  Pre-pregnancy Body Mass Index                   <18.5 2689 (2.8) 18 (2.6) 2671 (2.8) 22.22 <.0001**   18.5-24 37774 (39.7) 215 (31.5) 37559 (39.8)       25-29 24833 (26.1) 191 (28.0) 24642 (26.1)       30+ 29817 (31.3) 259 (37.9) 29558 (31.3)     Payment source                   Private insurance 54734 (57.0) 352 (51.1) 54382 (57.1) 19.04 0.0003**   Medicaid 38059 (39.7) 324 (47.0) 37735 (39.6)       Self-pay 2026 (2.1) 6 (0.9) 2020 (2.1)       Other 1145 (1.2) 7 (1.0) 1138 (1.2)     Parity                   Nulliparous 37064 (38.6) 255 (37.0) 36809 (38.6) 0.72 0.3965   Multiparous  59014 (61.4) 434 (63.0) 58580 (61.4)     Smoking during pregnancy                   Yes 14805 (15.4) 73 (10.6) 14732 (15.5) 12.57 0.0004**   No 81229 (84.6) 618 (89.4) 80611 (84.5)     Alcohol during pregnancy                   Yes 657 (0.7) 2 (0.3) 655 (0.7) 1.60 0.2063   No 94238 (99.3) 681 (99.7) 93557 (99.3)     Gestational hypertension                   Yes 7954 (8.3) 73 (10.6) 7881 (8.3) 4.87 0.0273*   No 88184 (91.7) 617 (89.4) 87567 (91.7)     Gestational diabetes                   Yes 6513 (6.8) 56 (8.1) 6457 (6.8) 1.98 0.1594   No 89625 (93.2) 634 (91.9) 88991 (93.2)     Chlamydia           Yes 1344 (1.4) 16 (2.3) 1328 (1.4) 4.33 0.0375*   No 93813 (98.6) 665 (97.7) 93148 (98.6)   Gonorrhea           Yes 324 (0.3) 2 (0.3) 322 (0.3) 0.04 0.8333   No 94833 (99.7) 679 (99.7) 94154 (99.7)   Syphilis           Yes 109 (0.1) 0 (0.0) 109 (0.1) 0.79 0.3751   No 95048 (99.9) 681 (100.0) 94367 (99.9)   Maternal ICU admission                   Yes 121 (0.1) 8 (1.2) 113 (0.1) 59.06 <.0001**   No 96019 (99.9) 682 (98.8) 95337 (99.9)     Sex                   Male 49342 (51.2) 361 (52.2) 48981 (51.2) 0.25 0.6168   Female 46991 (48.8) 331 (47.8) 46660 (48.8)     Final route of delivery                   Spontaneous 64096 (66.5) 456 (65.9) 63640 (66.6) 1.28 0.5285   Cesarean 29562 (30.7) 221 (31.9) 29341 (30.7)       Other (forceps, vacuum) 2656 (2.8) 15 (2.2) 2641 (2.8)     Preterm birth (<37wks)                   Preterm 7753 (8.0) 69 (10.0) 7684 (8.0) 3.48 0.062   Full term 88580 (92.0) 623 (90.0) 87957 (92.0)     Prematurity (wks)                   <32  1060 (1.1) 9 (1.3) 1051 (1.1) 3.51 0.1728   32-36 6693 (6.9) 60 (8.7) 6633 (6.9)       37+ 88580 (92.0) 623 (90.0) 87957 (92.0)      33   Table 1 (cont’d)  Birthweight (grams)                   <1500 933 (1.0) 8 (1.2) 925 (1.0) 2.06 0.5601   1500-2999 23152 (24.0) 181 (26.2) 22971 (24.0)       3000-4499 71124 (73.8) 495 (71.5) 70629 (73.8)       4500+ 1124 (1.2) 8 (1.2) 1116 (1.2)     Neonatal mortality                   Yes 99 (0.1) 0 (0.0) 99 (0.1) N/A N/A   No 96221 (99.9) 692 (100.0) 95529 (99.9)     NICU admission                   Yes 6333 (6.6) 60 (8.7) 6273 (6.6) 4.95 0.026*   No 89910 (93.4) 632 (91.3) 89278 (93.4)     Small for Gestational Age (<10th percentile)                   Yes 4445 (4.6) 49 (7.1) 4396 (4.6) 9.66 0.0019**   No 91807 (95.4) 642 (92.9) 91165 (95.4)     Fetal growth (centile) MEAN 95% CI MEAN 95% CI MEAN 95% CI T Statistic P-Value      62.77 62.59, 62.94 58.65 56.51, 60.79 62.80 62.62, 62.97 -3.81 0.0002** Note: COVID-19 positivity was determined by record of a laboratory-confirmed positive PCR test occurring during the pregnancy. Those with COVID-19 tests performed after the delivery date were considered COVID-negative (n=5). Compared to the COVID-19 negative group, the COVID-19 positive group had more NICU admissions, maternal ICU admissions, gestational hypertension, smoking, and overweight and obese BMI. Those with COVID were more hispanic and less white (both original and combined race/ethnicity), and more were Black. There were more small for gestational age (<10%) infants and had a lower average percentile fetal growth. They had more medicaid users than the COVID-19 negative group. P<0.10; P<0.05*; P<0.01** Missing data (COV-POS): symptomatic=11, race (original)=15, race/ethnicity=5, educ. level=9, pre-preg. BMI=9, payment=3, parity=3, smoking=1, alcohol=9, gest. hypertension=2, gest. diabetes=2, chlamydia=11, gonorrhea=11, syphilis=11, mat. ICU=2, SGA (<10%)=1, FG (%)=1. Missing data (COV-NEG): race (origional)=524, race/ethnicity=323, educ. level=657, pre-preg. BMI=1211, payment=366, parity=252, smoking=298, alcohol=1429, gest. hypertension=193, gest. diabetes=193, chlamydia=1165, gonorrhea=1165, syphilis=1165, mat. ICU=191, delivery route=19, NICU=90, neo. mort.=13, SGA (<10%)=80, FG (%)=80      34   Table 2: A comparison of sociodemographic and delivery-related health characteristics between preterm and term births amongst singleton pregnancies born to Michigan women aged 18-39 years in 2020.      PRETERM BIRTH FULL TERM BIRTH         N=7,753 N=88,580         N (Wt%) N (Wt%) Chi-Sq P-Value  COVID-19 infection during pregnancy               Yes 69 (0.9) 623 (0.7) 3.48 0.062   No 7684 (99.1) 87957 (99.3)     Trimester of COVID-19 infection               1st (<14 wks) 5 (7.2) 46 (7.4) 5.48 0.065   2nd (14-26 wks) 20 (29.0) 109 (17.5)       3rd (27+ wks) 44 (63.8) 468 (75.1)     Symptomatic infection               Yes 43 (63.2) 438 (71.5) 1.99 0.158   No 25 (36.8) 175 (28.5)     Race               White 4873 (63.1) 64566 (73.3) 370.74 <.0001**   Not white 2849 (36.9) 23506 (26.7)     Race categories                White 4873 (63.1) 64566 (73.3) 537.01 <.0001**   Black 2268 (29.4) 16465 (18.7)       Asian  252 (3.3) 3679 (4.2)       Native American/Native Alaskan 81 (1.0) 727 (0.8)       Native Hawaiian/Pacific Islander/Asian 17 (0.2) 239 (0.3)       Other 231 (3.0) 2396 (2.7)     Ethnicity               Hispanic 518 (6.7) 6058 (6.9) 0.30 0.587   Not Hispanic 7218 (93.3) 82269 (93.1)     Race/Ethnicity (combined variable)               NH White 4603 (59.5) 61124 (69.2) 540.76 <.0001**   NH Black 2243 (29.0) 16177 (18.3)     Hispanic 518 (6.7) 6058 (6.9)     NH Asian 251 (3.2) 3651 (4.1)     NH Native American/Alaska Native 76 (1.0) 660 (0.7)     NH Hawaiian/Pac. Islander/Asian 14 (0.2) 211 (0.2)    NH Other 33 (0.4) 386 (0.4)   Age at pregnancy outcome (yrs)               <20 285 (3.7) 2842 (3.2) 80.68 <.0001**   20-24 1540 (19.9) 17109 (19.3)       25-29 2323 (30.0) 28688 (32.4)       30-34 2310 (29.8) 28008 (31.6)       35-39 1295 (16.7) 11933 (13.5)         MEAN 95% CI MEAN 95% CI T Statistic P-Value      28.83 28.7, 29.0 28.67 28.63, 28.7 2.69 0.0071**     35   Table 2 (cont’d)  Highest level of education               Did not complete high school 913 (11.9) 8344 (9.5) 288.89 <.0001**   High school graduate or GED 2469 (32.2) 23583 (26.8)       Some college, no degree 1804 (23.5) 19590 (22.3)       Associate degree 629 (8.2) 7731 (8.8)       Bachelor’s degree 1203 (15.7) 18497 (21.0)       Advanced or professional degree 645 (8.4) 10259 (11.7)     Pre-pregnancy Body Mass Index               <18.5 277 (3.7) 2412 (2.8) 113.86 <.0001**   18.5-24 2678 (35.5) 35096 (40.1)       25-29 1893 (25.1) 22940 (26.2)       30+ 2706 (35.8) 27111 (31.0)         MEAN 95% CI MEAN 95% CI T Statistic P-Value      28.66 28.48, 28.84 27.78 27.74, 27.83 9.70 <.0001** Payment source               Private insurance 3845 (49.8) 50889 (57.7) 229.10 <.0001**   Medicaid 3657 (47.4) 34402 (39.0)       Self-pay 105 (1.4) 1921 (2.2)       Other 110 (1.4) 1035 (1.2)     Parity               Nulliparous 3029 (39.2) 34035 (38.5) 1.42 0.233   Multiparous  4696 (60.8) 54318 (61.5)     Smoking during pregnancy               Yes 1518 (19.7) 13287 (15.0) 116.60 <.0001**   No 6198 (80.3) 75031 (85.0)     Alcohol during pregnancy               Yes 62 (0.8) 595 (0.7) 2.00 0.1571   No 7481 (99.2) 86757 (99.3)     Gestational hypertension               Yes 1257 (16.3) 6697 (7.6) 709.73 <.0001**   No 6462 (83.7) 81722 (92.4)     Gestational diabetes               Yes 706 (9.1) 5807 (6.6) 74.75 <.0001**   No 7013 (90.9) 82612 (93.4)     Chlamydia         Yes 152 (2.1) 1192 (1.4) 25.58 <.0001**   No 7142 (97.9) 86671 (98.6)   Gonorrhea         Yes 37 (0.5) 287 (0.3) 6.48 0.0109*   No 7257 (99.5) 87576 (99.7)   Syphilis         Yes 7 (0.1) 102 (0.1) 0.24 0.6254   No 7287 (99.9) 87761 (99.9)   Maternal ICU admission               Yes 60 (0.8) 61 (0.1) 282.55 <.0001**   No 7676 (99.2) 88343 (99.9)     Sex               Male 4142 (53.4) 45200 (51.0) 16.40 <.0001**   Female 3611 (46.6) 43380 (49.0)        36   Table 2 (cont’d)  Final route of delivery               Spontaneous 4062 (52.4) 60034 (67.8) 956.68 <.0001**   Cesarean 3575 (46.1) 25987 (29.3)       Other (forceps, vacuum) 115 (1.5) 2541 (2.9)     Prematurity (wks)               <32  1060 (13.7) 0 (0.0) N/A N/A   32-36 6693 (86.3) 0 (0.0)       37+ 0 (0.0) 88580 (100.0)         MEAN 95% CI MEAN 95% CI T Statistic P-Value      34.09 34.03, 34.15 38.98 38.97, 38.99 -156.89 <.0001** Birthweight (grams)               <1500 906 (11.7) 27 (0.0) 21505.22 <.0001**   1500-2999 5509 (71.1) 17643 (19.9)       3000-4499 1322 (17.1) 69802 (78.8)       4500+ 16 (0.2) 1108 (1.3)         MEAN 95% CI MEAN 95% CI T Statistic P-Value      2387.07 2371.25, 2402.88 3383.85 3380.74, 3386.96 -123.56 <.0001** NICU admission               Yes 3229 (41.7) 3104 (3.5) 16894.34 <.0001**   No 4515 (58.3) 85395 (96.5)     Neonatal mortality               Yes 74 (1.0) 25 (0.0) 595.84 <.0001**   No 7677 (99.0) 88544 (100.0)     Small for Gestational Age (<10%)               Yes 545 (7.1) 3900 (4.4) 115.53 <.0001**   No 7147 (92.9) 84660 (95.6)     Fetal growth (z score) MEAN 95% CI MEAN 95% CI T Statistic P-Value      0.32 0.3, 0.35 0.47 0.46, 0.48 -11.59 <.0001** Note: Preterm birth was defined as delivery <37 completed weeks’ gestation. Compared to the full term group, preterm group had more caesarian deliveries, NICU admissions, maternal ICU admissions, neonatal mortality, gestational hypertension, gestational diabetes, and smoking. Those who delivered preterm were also older, less educated, had a higher mean pre-pregnancy BMI. The preterm group was less white (both original and combined race/ethnicity variables). There were more small for gestational age (<10%) infants and had a lower average percentile fetal growth. They had more males. They also had more medicaid users than those who delivered to term. P<0.10; P<0.05*; P<0.01** MISSING (Preterm): race (original)=31, race/ethnicity=15, educ. level=90, pre-preg. BMI=199, payment=36, parity=28, smoking=37, alcohol=210, gest. hypertension=34, gest. diabetes=34, chlamydia=459, gonorrhea=459, syphilis=459, mat. ICU=17, delivery route=1, NICU=9, neo. mort.=2, SGA (<10%)=61, FG (%)=61 MISSING (Full Term): race (original)=508, race/ethnicity=313, educ. level=576, pre-preg. BMI=1021,payment=333, parity=227, smoking=262, alcohol=1228, gest. hypertension=161, gest. diabetes=161, chlamydia=717, gonorrhea=717, syphilis=717, mat. ICU=176, delivery route=18, NICU=81, neo. mort.=11, SGA (<10%)=20, FG (%)=20   37   Table 3: A comparison of sociodemographic and delivery-related health characteristics amongst the study sample of Michigan women aged 18-39 who delivered singleton pregnancies in 2020 and the analytic sample.      STUDY SAMPLE ANALYTIC SAMPLE     N=96,591 N=96,333     (missing) N (Wt%) (missing) N (Wt%) Chi-Sq P-Value  COVID-19 infection during pregnancy                   Yes   699 (0.7)   692 (0.7) 0.04 0.8444   No   95892 (99.3)   95641 (99.3)     Symptomatic infection 95903     95652           Yes   487 (70.8)   481 (70.6) 0.01 0.9298   No   201 (29.2)   200 (29.4)     Race 543     539           White   69581 (72.4)   69439 (72.5) 0.09 0.7614   Not white   26467 (27.6)   26355 (27.5)     Age at pregnancy outcome (years)                   <20 years   3141 (3.3)   3127 (3.2) 0.02 0.9999   20-24 years   18710 (19.4)   18649 (19.4)       25-29 years   31088 (32.2)   31011 (32.2)       30-34 years   30386 (31.5)   30318 (31.5)       35-39 years   13266 (13.7)   13228 (13.7)     Highest level of education 677     666           Did not complete high school   9288 (9.7)   9257 (9.7) 0.11 0.9998   High school graduate or GED   26143 (27.3)   26052 (27.2)       Some college, no degree   21466 (22.4)   21394 (22.4)       Associate degree   8374 (8.7)   8360 (8.7)       Bachelor’s degree   19732 (20.6)   19700 (20.6)       Advanced or professional degree   10911 (11.4)   10904 (11.4)     Pre-pregnancy Body Mass Index 1255     1220           <18.5   2704 (2.8)   2689 (2.8) 0.05 0.9966   18.5-24   37844 (39.7)   37774 (39.7)       25-29   24881 (26.1)   24833 (26.1)       30+   29907 (31.4)   29817 (31.3)     Payment source 382     369           Private insurance   54829 (57.0)   54734 (57.0) 0.15 0.9845   Medicaid   38187 (39.7)   38059 (39.7)       Self-pay   2045 (2.1)   2026 (2.1)       Other   1148 (1.2)   1145 (1.2)     Parity 263     255           Nulliparous   37179 (38.6)   37064 (38.6) 0.02 0.9021   Multiparous    59149 (61.4)   59014 (61.4)     Smoking during pregnancy 304     299           Yes   14852 (15.4)   14805 (15.4) 0.01 0.9433   No   81435 (84.6)   81229 (84.6)     Alcohol during pregnancy 1457     1438           Yes   661 (0.7)   657 (0.7) 0.01 0.9274   No   94473 (99.3)   94238 (99.3)        38   Table 3 (cont’d)  Gestational hypertension 206     195           Yes   7964 (8.3)   7954 (8.3) 0.01 0.903   No   88421 (91.7)   88184 (91.7)     Gestational diabetes 206     195           Yes   6523 (6.8)   6513 (6.8) 0.01 0.9318   No   89862 (93.2)   89625 (93.2)     Chlamydia 1201   1176       Yes   1346  (1.4)   1344 (1.4) 0.00 0.9707   No   94044  (98.6)   93813 (98.6)   Gonorrhea 1201   1176       Yes   327 (0.3)   324 (0.3) 0.01 0.903   No   95063 (99.7)   94833 (99.7)   Syphilis 1201   1176       Yes   110 (0.1)   109 (0.1) 0.00 0.9455   No   95280 (99.9)   95048 (99.9)   Maternal ICU admission 198     193           Yes   123 (0.1)   121 (0.1) 0.02 0.8797   No   96270 (99.9)   96019 (99.9)     Sex 3                 Male   49460 (51.2)   49342 (51.2) 0.01 0.9354   Female   47128 (48.8)   46991 (48.8)     Final route of delivery 20     19           Spontaneous   64306 (66.6)   64096 (66.5) 0.07 0.9648   Cesarean   29607 (30.7)   29562 (30.7)       Other (forceps, vacuum)   2658 (2.8)   2656 (2.8)     Estimated gest. age at delivery (wks)   MEAN 95% CI   MEAN 95% CI T Stat. P-Value      47 38.55 38.5, 38.6   38.59 38.58, 38.6 6.20 <.0001* Preterm birth (<37wks) 47                 Preterm   7940 (8.2)   7753 (8.0) 3.96 0.0467   Full term   88604 (91.8)   88580 (92.0)     Classification of prematurity (wks) 47                 <32    1243 (1.3)   1060 (1.1) 26.55 <.0001*   32-36   6697 (6.9)   6693 (6.9)       37+   88604 (91.8)   88580 (92.0)     NICU admission 100     90           Yes   6377 (6.6)   6333 (6.6) 0.13 0.7202   No   90114 (93.4)   89910 (93.4)     Neonatal mortality 13     13           Yes   234 (0.2)   99 (0.1) 77.57 <.0001*   No   96344 (99.8)   96221 (99.9)     Birthweight (grams) 15                 <1500 grams   1118 (1.2)   933 (1.0) 30.10 <.0001*   1500-2999 grams   23172 (24.0)   23152 (24.0)       3000-4499 grams   71161 (73.7)   71124 (73.8)       4500+ grams   1125 (1.2)   1124 (1.2)     Small for Gestational Age (<10th percentile) 289     81           Yes   4481 (4.7)   4445 (4.6) 0.27 0.606   No   91821 (95.3)   91807 (95.4)     Fetal growth (z score)   MEAN 95% CI   MEAN 95% CI T Stat. P-Value      289 0.456 0.45, 0.46 81 0.458 0.45, 0.46 -0.55 0.5852  39   Table 3 (cont’d)  Notes: The study sample (n=96,591) contained singleton pregnancies delivered from 1/1/2020-12/31/2020 to individuals aged 18-39 at time of delivery, who received a birth certificate from the state of Michigan and did not conclude in miscarraige or stillbirth. Individuals with missing maternal age, delivery date, and/or plurality were not included in the study sample. The analytic sample (n=96,333) derived from the study sample further excluded infants with a gestational age <22 weeks (n=124) and/or a birthweight <500 grams (n=175). Observations with missing information on gestational age (n=47), birthweight (n=15), and/or sex (n=3) were also excluded. Compared to the study sample, the analytic sample had fewer neonatal mortalities and very preterm births (<32 weeks), and higher birthweight and mean gestational age. P<0.05; P<0.01*     40   Table 4: Assessing differences in sociodemographic and delivery-related health characteristics in the pregnancy cohort by inclusion and exclusion criteria.      Included (1) Excluded (2)       N=96,333 N=258         N (Wt%) N (Wt%) Chi-Sq P-Value  COVID-19 infection during pregnancy               Yes 692 (0.7) 7 (2.7) 14.25 0.0002*   No 95641 (99.3) 251 (97.3)     Symptomatic infection               Yes 481 (70.6) 6 (85.7) 0.76 0.3829   No 200 (29.4) 1 (14.3)     Race               White 69439 (72.5) 142 (55.9) 34.89 <.0001*   Not white 26355 (27.5) 112 (44.1)     Age at pregnancy outcome (yrs)               <20 3127 (3.2) 14 (5.4) 8.97 0.0619   20-24 18649 (19.4) 61 (23.6)       25-29 31011 (32.2) 77 (29.8)       30-34 30318 (31.5) 68 (26.4)       35-39 13228 (13.7) 38 (14.7)     Highest level of education               Did not complete high school 9257 (9.7) 31 (12.6) 41.05 <.0001*   High school graduate or GED 26052 (27.2) 91 (36.8)       Some college, no degree 21394 (22.4) 72 (29.1)       Associate degree 8360 (8.7) 14 (5.7)       Bachelor’s degree 19700 (20.6) 32 (13.0)       Advanced or professional degree 10904 (11.4) 7 (2.8)     Pre-pregnancy Body Mass Index               <18.5 2689 (2.8) 15 (6.7) 23.36 <.0001*   18.5-24 37774 (39.7) 70 (31.4)       25-29 24833 (26.1) 48 (21.5)       30+ 29817 (31.3) 90 (40.4)     Payment source               Private insurance 54734 (57.0) 95 (38.8) 60.67 <.0001*   Medicaid 38059 (39.7) 128 (52.2)       Self-pay 2026 (2.1) 19 (7.8)       Other 1145 (1.2) 3 (1.2)     Parity               Nulliparous 37064 (38.6) 115 (46.0) 5.80 0.0161   Multiparous  59014 (61.4) 135 (54.0)     Smoking during pregnancy               Yes 14805 (15.4) 47 (18.6) 1.93 0.1645   No 81229 (84.6) 206 (81.4)     Alcohol during pregnancy               Yes 657 (0.7) 4 (1.7) 3.33 0.0681   No 94238 (99.3) 235 (98.3)     Gestational hypertension               Yes 7954 (8.3) 10 (4.0) 5.80 0.016   No 88184 (91.7) 237 (96.0)      41   Table 4 (cont’d)  Gestational diabetes               Yes 6513 (6.8) 10 (4.0) 2.90 0.0885   No 89625 (93.2) 237 (96.0)     Chlamydia         Yes 1344 (1.4) 2 (0.9) 0.51 0.4739   No 93813 (98.6) 231 (99.1)    Gonorrhea         Yes 324 (0.3) 3 (1.3) 6.10 0.0135*   No 94833 (99.7) 230 (98.7)    Syphilis         Yes 109 (0.1) 1 (0.4) 2.00 0.1575   No 95048 (99.9) 232 (99.6)   Maternal ICU admission               Yes 121 (0.1) 2 (0.8) 8.75 0.0031*   No 96019 (99.9) 251 (99.2)     Sex               Male 49342 (51.2) 118 (46.3) 2.49 0.1146   Female 46991 (48.8) 137 (53.7)     Final route of delivery               Spontaneous 64096 (66.5) 210 (81.7) 27.03 <.0001*   Cesarean 29562 (30.7) 45 (17.5)       Other (forceps, vacuum) 2656 (2.8) 2 (0.8)     Estimated gestational age at delivery (wks) MEAN 95% CI MEAN 95% CI T Statistic P-Value      38.59 38.58, 38.6 22.74 21.79, 23.68 -33.19 <.0001* Preterm birth (<37wks)               Preterm 7753 (8.0) 187 (88.6) 1811.07 <.0001*   Full term 88580 (92.0) 24 (11.4)     Classification of prematurity (wks)               <32  1060 (1.1) 183 (86.7) 12147.08 <.0001*   32-36 6693 (6.9) 4 (1.9)       37+ 88580 (92.0) 24 (11.4)     NICU admission               Yes 6333 (6.6) 44 (17.7) 49.93 <.0001*   No 89910 (93.4) 204 (82.3)     Neonatal mortality               Yes 99 (0.1) 135 (52.3) 29033.15 <.0001*   No 96221 (99.9) 123 (47.7)     Birthweight (grams)               <1500 933 (1.0) 185 (76.1) 11968.44 <.0001*   1500-2999 23152 (24.0) 20 (8.2)       3000-4499 71124 (73.8) 37 (15.2)       4500+ 1124 (1.2) 1 (0.4)     Small for Gestational Age (<10%)               Yes 4445 (4.6) 36 (72.0) 511.43 <.0001*   No 91807 (95.4) 14 (28.0)     Fetal growth (z score) MEAN 95% CI MEAN 95% CI T Statistic P-Value      0.46 0.45, 0.46 -2.87 -4.64, -1.01 -3.64 0.0007*     42   Table 4 (cont’d)  Notes: The inclusion group (n=96,333) contained individuals in the study sample and who had an infant at 22+ weeks gestation and over 500 grams. The exclusion group (n=258) contained those who were included in the study sample but had infants with a gestational age <22 weeks or a birthweight <500 grams (n=175), and/or had missing data for gestational age (n=47), birthweight (n=15), and/or sex (n=3). The excluded individuals had more COVID-19 infections, NICU and maternal ICU admissions, spontaneous births, gestational hypertension, gonorrhea diagnoses, neonatal mortality, small for gestational age infants, very preterm births (<32 weeks), and was less white. They had a lower mean fetal growth z score, birthweight, and gestational age. There were more underweight and obese BMI in the excluded group, had more that reported their education level as some college or less, and fewer used private insurance. P<0.05; P<0.01*   43   Table 5: Variables with missing data in analytic sample by COVID infection status.    COVID-POS COVID-NEG TOTAL   N=692 N=95,641 N=96,333   N Missing % Missing N Missing % Missing Missing % Missing Symptomatic infection 681 11 1.6%           Race (white/nonwhite) 687 5 1.5% 95318 323 0.3% 328 0.3% Highest level of education 683 9 1.3% 94984 657 0.7% 666 0.7% Pre-pregnancy Body Mass Index 683 9 1.3% 94430 1211 1.3% 1220 1.3% Payment source 689 3 0.4% 95275 366 0.4% 369 0.4% Parity 689 3 0.4% 95389 252 0.3% 255 0.3% Smoking during pregnancy 691 1 0.1% 95343 298 0.3% 299 0.3% Alcohol during pregnancy 683 9 1.3% 94212 1429 1.5% 1438 1.5% Gestational hypertension 690 2 0.3% 95448 193 0.2% 195 0.2% Gestational diabetes 690 2 0.3% 95448 193 0.2% 195 0.2% Chlamydia 681 11 1.6% 94476 1165 1.2% 1176 1.2% Gonorrhea 681 11 1.6% 94476 1165 1.2% 1176 1.2% Syphilis 681 11 1.6% 94476 1165 1.2% 1176 1.2% Maternal ICU admission 690 2 0.3% 95450 191 0.2% 193 0.2% Final route of delivery 692 0 0.0% 95622 19 0.0% 19 0.0% NICU admission 692 0 0.0% 95551 90 0.1% 90 0.1% Neonatal mortality 692 0 0.0% 95628 13 0.0% 13 0.0% Small for Gest. Age (<10th percentile) 691 1 0.1% 95561 80 0.1% 81 0.1% Fetal growth (percentile) 691 1 0.1% 95561 80 0.1% 81 0.1% Notes: No variable for either exposure category met the 5% threshold of concern for missingness.     44   Table 6: Variables with missing data in analytic sample for preterm and term births.    PRETERM BIRTH FULL TERM BIRTH TOTAL   N=7,753 N=88,580 N=96,333   N Missing % Missing N Missing % Missing Missing % Missing Race (white/nonwhite) 7738 15 0.2% 88267 313 0.4% 328 0.3% Highest level of education 7663 90 1.2% 88004 576 0.7% 666 0.7% Pre-pregnancy Body Mass Index 7554 199 2.6% 87559 1021 1.2% 1220 1.3% Payment source 7717 36 0.5% 88247 333 0.4% 369 0.4% Parity 7725 28 0.4% 88353 227 0.3% 255 0.3% Smoking during pregnancy 7716 37 0.5% 88318 262 0.3% 299 0.3% Alcohol during pregnancy 7543 210 2.7% 87352 1228 1.4% 1438 1.5% Gestational hypertension 7719 34 0.4% 88419 161 0.2% 195 0.2% Gestational diabetes 7719 34 0.4% 88419 161 0.2% 195 0.2% Chlamydia 7294 459 5.9% 87863 717 0.8% 1176 1.2% Gonorrhea 7294 459 5.9% 87863 717 0.8% 1176 1.2% Syphilis 7294 459 5.9% 87863 717 0.8% 1176 1.2% Maternal ICU admission 7736 17 0.2% 88404 176 0.2% 193 0.2% Final route of delivery 7752 1 0.0% 88562 18 0.0% 19 0.0% NICU admission 7744 9 0.1% 88499 81 0.1% 90 0.1% Neonatal mortality 7751 2 0.0% 88569 11 0.0% 13 0.0% Small for Gestational Age (<10th percentile) 7692 61 0.8% 88560 20 0.0% 81 0.1% Fetal growth (percentile) 7692 61 0.8% 88560 20 0.0% 81 0.1%  Notes: Chlamydia, Gonorrhea, and Syphilis had 5.9% missingness within the preterm birth group. No other variables for either outcome category met the 5% threshold of concern for missingness.     45   Table 7: New race-related variables (including Hispanic ethnicity) used in sample analysis.  Binary  N (%) Categorical N (%) Original  N (%) Variable Variable Variable White 65727 68.46% NH White 65727 68.46% White 65262 67.98% White (Bridged) 465 0.48% Non-white 30278 31.54% NH Black 18420 19.19% Black 17377 18.10% Black (Bridged) 1043 1.09% NH Asian 3578 3.73% Asian Indian 382 0.40% Chinese 354 0.37% Filipino 1835 1.91% Japanese 455 0.47% Korean 226 0.24% Vietnamese 99 0.10% Other Asian 227 0.24% Hispanic* 6576 6.85% Hispanic* 6576 6.85% NH Native American/ 1060 1.10% American Indian 204 0.21% Native Alaskan Native American/Alaskan (Bridged) 856 0.89% NH Native Hawaiian/ 225 0.23% Native Hawaiian 13 0.01% Pac. Islander/ Guamanian/Chamorro 4 0.00% Other Asian Samoan 1 0.00%   Other Pacific Islander 41 0.04%   Asian or Pacifica Islander (Bridged) 166 0.17% Other 419 0.44% Other Race 419 0.44%  Notes: The new race variable measured standard bridged race data from the birth certificates, with Hispanic ethnicity integrated from a separate measure into an additional category. Missing=328    46   Table 8: Original race-related variables used in sample analysis.  Binary  Variable N (%) Categorical Variable N (%) Original  Variable N (%) White 69439 (72.49) White 69439 (72.49) White 68823 (71.84) White (Bridged) 616 (0.64) Non-white 26355 (27.51) Black 18733 (19.56) Black 17552 (18.32) Black (Bridged) 1181 (1.23) Asian 3931 (4.10) Asian Indian 1840 (1.92) Chinese 455 (0.47) Filipino 244 (0.25) Japanese 99 (0.10) Korean 228 (0.24) Vietnamese 204 (0.21) Other Asian 861 (0.90) Native American/ Native Alaskan 808 (0.84) American Indian 408 (0.43) Native American/Alaskan (Bridged) 400 (0.42) Native Hawaiian/ Pac. Islander/ Other Asian 256 (0.27) Native Hawaiian 14 (0.01) Guamanian/Chamorro 15 (0.02) Samoan 1 (0.00) Other Pacific Islander 46 (0.05) Asian or Pacifica Islander (Bridged) 180 (0.19) Other 2627 (2.74) Other Race 2627 (2.74) Notes: The original variable measured standard bridged race data from the birth certificates. Ethnicity (Hispanic/non-Hispanic) was recorded a separate variable. Missing=539     47   Figure 1. Timeline of COVID-19 in Michigan during 2020.   Note: Major COVID-19 news and policy decisions for the state of Michigan during the first year of the pandemic.6,8     48   Figure 2. Selection process (inclusion and exclusion criteria) of the analytic sample from the study population.       49   Figure 3. Distribution of COVID-19 cases in 2020 cohort, by month.     Note: Laboratory confirmed COVID-19 cases during pregnancy in the state of Michigan in 2020 that were included in analytic sample. All included also gave birth in 2020.       50   Figure 4. Forest plot of odds ratios and 95% confidence interval for COVID-19 in pregnancy and preterm birth. Stratified by trimester of COVID-19 infection.   Note: Unadjusted odds ratios and 95% confidence intervals, stratified by the trimester of COVID-19 infection (ref=no covid infection). Logistic regression analysis of COVID exposure (ref=no) and preterm birth <37 weeks (ref=no) in the sample (n=96,333) produced odds ratios and 95% CI. Likelihood ratio chi-squared tests produced p-values. The observed odds of preterm birth were 1.27 times higher for COVID -19 positive pregnancies compared to those without COVID-19, but the findings are not statistically significant (p=0.06). In those with a COVID-19 infection during their second trimester of pregnancy, the odds of preterm birth were 2.10 times that of COVID-negative pregnancies and is statistically significant (p=0.002).  P<0.05       51   Figure 5. Forest plot of odds ratios and 95% confidence interval for COVID-19 infection and preterm birth. Stratified by categories of race and ethnicity.   Note: Unadjusted odds ratio and 95% confidence interval, stratified by race (ref=NH white). Logistic regression analysis of COVID exposure (ref=no) and preterm birth <37 weeks (ref=no) in the sample (n=96,333) produced odds ratios and 95% CI. Likelihood ratio chi-squared tests produced p-values. No statistically significant difference was found for any of the race categories. Missing=328. P<0.05     52   Figure 6. Forest plot of odds ratios and 95% confidence interval for COVID-19 infection and preterm birth. Stratified by race (NH white/NH Asian vs. non-white/non-Asian).   Note: Unadjusted odds ratio and 95% confidence interval, stratified by race (ref=NH white/Asian). Logistic regression analysis of COVID exposure (ref=no) and preterm birth <37 weeks (ref=no) in the sample (n=96,333) produced odds ratios and 95% CI. Likelihood ratio chi-squared tests produced p-values. In non-white/Asians, the odds of preterm birth were higher for those with a COVID-positive pregnancy (OR: 1.49, 95% CI: 1.08-2.07, p= 0.0162). Missing=328. P<0.05     53   Figure 7. Forest plot of odds ratios and 95% confidence interval for COVID-19 infection and preterm birth. Stratified by pre-pregnancy BMI.   Note: Unadjusted odds ratio and 95% confidence interval, stratified pre-pregnancy BMI categories (ref=normal weight). Logistic regression analysis of COVID exposure (ref=no) and preterm birth <37 weeks (ref=no) in the sample (n=96,333) produced odds ratios and 95% CI. Likelihood ratio chi-squared tests produced p-values. No statistically significant difference was found for any of the pre-pregnancy BMI categories. Missing=1220. P<0.05