Missing Shade, Missing Health: Tree Canopy Disparities and Neonatal Sepsis in a Midwestern U.S. City

Missing Shade, Missing Health: Tree Canopy Disparities and Neonatal Sepsis in a Midwestern U.S. City | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Missing Shade, Missing Health: Tree Canopy Disparities and Neonatal Sepsis in a Midwestern U.S. City Germysha Emily Little, Enbal Shacham, Kenan Li, Kimberly Enard, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7375744/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Neonatal sepsis is a critical health concern, particularly affecting low birthweight and preterm infants, with higher incidence rates among Black infants. In urban settings, environmental inequities, such as reduced tree canopy coverage, may influence neonatal health outcomes by affecting maternal stress levels and exposure to air pollution. This retrospective study analyzed electronic medical records from six SSM Health hospitals in St. Louis, Missouri, encompassing over 74,000 births between 2013 and 2022. Tree canopy coverage around maternal residences was quantified using geospatial data and categorized based on recommended residential thresholds. Logistic regression models assessed the association between tree canopy coverage and neonatal sepsis, adjusting for sociodemographic and clinical covariates. Among the infants studied, 2.3% were diagnosed with neonatal sepsis. Infants born to mothers residing in areas with below-recommended tree canopy coverage (<20%) exhibited a higher risk of neonatal sepsis (RR=1.15; 95%; CI: 1.07–1.23; p<0.05). Additional risk factors included younger maternal age and maternal smoking. Notably, average tree canopy coverage in the study area declined from 20.3% in 2013 to 17.9% in 2021. Lower residential tree canopy density urban neighborhoods is associated with an increased risk of neonatal sepsis. These findings underscore the importance of environmental considerations in maternal and child health strategies. Enhancing urban tree canopy coverage may serve as a structural intervention to mitigate neonatal health disparities and improve overall urban population health. Introduction Infant mortality is a significant public health issue throughout the world and is regarded as a highly sensitive measure of population health. 1 The top three leading causes of infant mortality are birth defects including congenital malformations, preterm birth and low birthweight, and sudden infant death syndrome. 2 In the U.S., Black infants are at an increased risk for infant mortality compared to white infants. 3 In 2019, the rate of infant mortality was 4.5 per 1,000 live births for white infants and the rate of infant mortality was 10.6 per 1,000 live births for Black infants. 4 Many societal factors including discrimination, income inequality, educational achievement gaps, residential segregation, psychosocial stress, and toxic environmental exposures contribute to the racial disparities of birth outcomes. 5 Addressing racial disparities should not be addressed only on an individual level but on a societal level by exploring social determinants of health that include discrimination, education and income gaps, and toxin exposure. Neonatal sepsis is a serious, life-threatening medical condition that affects nearly 3 million of all newborns and kills 750,000 infants every year across the world. 6 In the United States, neonatal sepsis affects over 3,000 infants and kills approximately 400 infants every year. 7 Neonatal sepsis is a bacterial infection that occurs within the first month of life that may cause inflammation and blood clotting throughout the body reducing blood flow which can lead to organ failure and death. 8 , 9 The incidence rate of neonatal sepsis is higher among premature and low birthweight infants and these infants are more likely to suffer from neonatal mortality. In addition, the incidence rate of neonatal sepsis is higher among African-American or Black infants compared to any other race. 9 Disproportionate rates of prematurity and low birthweight among Black infants contributes to the racial gap in sepsis-associated mortality. 10 Neonatal sepsis infant risk factors include low birth weight, prematurity, low Apgar score, decreasing gestational age, Black ethnicity, while maternal risk factors include advanced maternal age, premature rupture of membranes, cesarean delivery, and primiparity. 11 – 14 While there many studied risk factors, understanding the pathways of risk factors will allow a better approach to eliminate the risk of neonatal sepsis. Environmental exposures have a significant effect on maternal and birth outcomes and exacerbate health inequities. The American College of Obstetrics and Gynecology considers climate change, as related to environmental exposures, as an “an urgent women’s health concern and a major public health challenge.” 15 Exposure to climate change factors, including extreme heat and air pollution, is associated with an increased risk of low birthweight. 16 Greenspace and vegetation can reduce the risk of neonatal death-related illnesses by reducing exposure to air pollution, cooling environments, restoring attention, promoting physiological stress recovery, and encouraging physical activity. 17 Air pollution is associated with an increased risk for maternal morbidity and mortality and disproportionately affects people living in low-income environments. 18 Increased tree canopy coverage and density as well as greenspaces can serve as a method to reduce the exposure to air pollution. 19 There have been inconsistent results in the literature demonstrating the relationship between low birthweight and exposure to green space. Most previous studies have concluded that an increase in green space is associated with a decreased risk of low birthweight, 20 – 23 yet some studies did not find a significant association. 24 , 25 additionally, the relationship between green space and birthweight have been found to be modified by sociodemographic characteristics such as race/ethnicity. 22,26 Research shows that tree canopy coverage acts as an effect modifier in the relationship between the effect of limited urban residential tree canopy on perceived stress among pregnant women. 27 These findings suggest that future studies explored more details about how green spaces and trees may influence birthweight. Neonatal sepsis is a common neonatal complication associated with low birthweight infants. 28 Social determinants of health composed of structural and intermediary determinants impact care and access to quality healthcare leading to health inequities. By examining sociodemographic characteristics and environmental characteristics, there will be a better understanding of the complex relationship factors that impact infant morbidity and mortality. The purpose of this study is to measure and analyze the association of tree canopy density of prenatal home environments with neonatal sepsis. Methods Design The aim of the study was to test the association of tree canopy density and neonatal sepsis. This research study was accomplished by applying a retrospective framework to examine crude and adjusted regression models utilizing data sources described below. Data Sources This is a retrospective study that utilized medical record data of patients from six facilities within a regional integrated healthcare system (SSM Health St. Louis). These included four community hospitals, a high-risk delivery center with an associated Level III NICU, and a freestanding children’s hospital with a Level IV NICU. Data were extracted from the electronic medical record (EMR) data (Epic®) from patients born from January 01, 2013, to December 31, 2022. For babies admitted to the two NICUs, an expanded dataset was utilized from Vermont Oxford Network (VON), which contained additional variables hand abstracted for key outcomes. The dataset included variables related to birth, maternal characteristics, and neonatal morbidities and mortality. Shapefile data was utilized for geographical analysis from the US Census Bureau. TIGER/Line shapefile is a datafile containing mapping details and boundaries of the Missouri and Illinois region. The TIGER/Line shapefiles are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing Database. Study Population Eligible participants included infants born at five SSM St. Louis hospital locations between 2013 and 2021 with non-missing health outcome data during the study period. Measures Location The dataset included full addresses with street, city, state, and zip code. These addresses were geocoded to latitude and longitude to be used for geospatial analysis. Tree canopy coverage Tree canopy coverage was calculated based on location using data from the U.S. Forest Service Geospatial Technology and Applications Center. This was a continuous variable ranging from 0–100%. This variable was also categorized into a dichotomous variable (below recommended tree canopy coverage vs at or above recommended tree canopy coverage). “Below recommended tree canopy coverage” ranged from 0–19% and “at or above recommended tree canopy coverage” ranged from 20–100%. Statistical Analysis The primary outcome of this study was neonatal sepsis. The main predictor variable was recommended tree canopy coverage. A multivariable logistic regression was conducted to predict the likelihood of neonatal sepsis diagnosis based on tree canopy coverage. A chi-square test was conducted to test significance (p < 0.05). Covariates were added to the adjusted model if they were statistically significant. In order to conduct geospatial analysis, a shapefile to outline United States counties was obtained from the U.S. Census Bureau to organize Missouri counties. The shapefile contained data related to the mapping, scaling, and cartographic boundaries of the 114 counties in Missouri. Tree canopy coverage raster values were extracted based on mother’s address. Statistical analysis was conducted using SPSS 27 and ArcGIS Pro. 29 The Saint Louis University Institutional Review Board approved the exempt status of this study protocol. Results Infant descriptives The majority of infants were male (n = 38162, 51.5%). Slightly over half of infants were identified as white (n = 34715, 54.0%). Most infants were normal birthweight (n = 63833, 86.1%) and born at term ( ≥ 36 weeks) (n = 63292, 85.3%). Out of the study sample, 10854 (14.6%) infants were admitted to the NICU, 246 (2.6%) infants were diagnosed with neonatal sepsis, and 488 (0.7%) infants experienced neonatal death. Maternal descriptives The mean age among mothers was 26.7 (SD = 8.2) years with the majority of participants 25 to 34 years of age (n = 39772, 56.4%). The majority of mothers were not married (n = 38726, 55.9%) and the majority had public insurance (n = 44109, 59.8%). Most mothers reported receiving any prenatal care during their pregnancy (n = 10552, 97.3%). Over half of mothers reported not smoking (n = 44348, 65.0%), no drug use (n = 60434, 88.9%), and no alcohol use (n = 63146, 92.7%) at the time of labor. Overall, there was a decrease in tree canopy coverage since 2013. The mean tree canopy coverage of the study area in 2013 was 20.31%, compared to 2021 where the tree canopy coverage was 17.88%. Table 1 provides a summary of the sample characteristics. As shown in Table 2 , 246 (2.3%) infants were diagnosed with neonatal sepsis. Infant characteristics, including birthweight and gestational age were significantly associated with neonatal sepsis diagnosis (p < 0.05). Infants who were diagnosed with neonatal sepsis were more likely to be very low birthweight (n = 112, 6.4%; p < 0.05) and preterm (n = 168, 2.9%; p < 0.05). Maternal characteristics, including age and race, were significantly associated with neonatal sepsis diagnosis (p < 0.05). Mothers who were under the age of 18 were more likely to have an infant diagnosed with neonatal sepsis (n = 9; 7.0%), and mothers who identified as a race other than white or Black were more likely to have an infant diagnosed with neonatal sepsis (n = 14; 4.2%). Predictors of neonatal sepsis according to infant and maternal characteristics are reported in Table 3 . Mothers who had below the recommended tree canopy coverage (RR = 1.15, 95% CI: 1.07, 1.23; p < 0.05), between the ages of 18 and 24 (RR = 1.24, 95% CI: 1.14, 1.34; p < 0.05), and reported currently smoking (RR = 1.30, 95% CI: 1.18, 1.44; p < 0.05) had an increased risk of having an infant diagnosed with neonatal sepsis. Mothers who were not married (RR = 0.86, 95% CI: 0.79, 0.94; p < 0.05) and reported drug use (RR = 0.87, 95% CI: 0.79, 0.96; p < 0.05) had a decreased risk of neonatal sepsis. Discussion The purpose of this paper was to analyze the association of tree canopy density of prenatal home environments with a diagnosis of neonatal sepsis. Results from this study show that decreased tree canopy coverage was significantly associated with an increased risk of neonatal sepsis. Environmental exposures vary by neighborhoods. This study was conducted to assess how those prenatal exposures may play a role in ending up with a diagnosis of neonatal sepsis in this 10-year retrospective study. Overall, increased tree canopy and greenspace reduce the risk of adverse neonatal health outcomes. 17 In this study sample, the average tree canopy coverage of the study area was less than the recommended tree canopy coverage in a residential area. The American Forests’ Science Advisory Board recommends tree canopy coverage for residential areas to be at least 20%, yet the average tree canopy coverage among this sample was approximately 19%. 30 While the recommended tree canopy is based on development density, land use patterns, ordinances, and climate, it does not consider health needs and neighborhood pollution levels. It also interesting to note that the average tree canopy coverage was 20% in 2013 and overall decreased over the years to about 18% in 2021. The decrease in tree canopy coverage in the St. Louis area is likely due to the emerald ash borer that was discovered in 2013, which is a destructive pest that disrupts the tree’s ability to transport water and nutrients leading to the tree dying. 31 This further emphasizes the need to implement interventions that aim to increase tree canopy coverage improve infant health outcomes, and overall community health. Previous research has not been conclusive on the relationship between tree canopy and adverse neonatal health outcomes. While studies have concluded that an increase in green space is associated with a decreased risk of low birthweight, 20 – 23 some studies did not find a significant association. 24 , 25 It is important to note that green space and tree canopy have an important measurable difference. The normalized difference vegetation Index (NDVI) is the most widely-accepted measure in prior research to measure greenness, which includes percent tree canopy, quantity of street trees, and proximity to major greenspaces. 17 , 21 , 22 , 25 , 32 While, green space is broad, tree canopy coverage is a more specific measure. The data suggests that decreased tree canopy coverage is associated with an increased risk for neonatal sepsis. This is consistent with the literature indicating that decreased tree canopy coverage increases the exposure to air pollution leading to an increased risk of adverse birth outcomes, including infant mortality. 19 , 33 Lowering neonatal sepsis and infant mortality rates across the nation should focus on improving healthcare services, promoting land use policies to increase tree canopy coverage, as a structural determinant of health. This study has several limitations that should be considered when interpreting and applying the findings. Although most of the data were obtained from electronic medical records, including medical tests and vital signs, some variables were self-reported. Information on smoking, drug and alcohol use, marital status, and prenatal care was self-reported at a single point during the pregnancy, typically at the time of labor. Because of the stigma associated with substance use during pregnancy, some individuals may have underreported these behaviors, leading to social desirability bias. It is also unclear whether smoking, alcohol, or drug use occurred during pregnancy or in the perinatal period. Additionally, the self-reported data do not include information on the frequency or intensity of substance use, which limits the ability to interpret the results accurately. Finally, other important risk factors that may influence neonatal health outcomes were not included in the study due to a lack of available data. These factors include maternal health conditions, residential racial segregation, and nutritional habits, all of which have been shown in previous research to affect neonatal outcomes. 1 , 9 , 11 , 13 Conclusions Understanding the risk of neonatal sepsis using environmental risk factors, such as tree canopy is essential in reducing the risk of adverse infant health outcomes. The results of this study can be leveraged to implement effective practices and planning to increase tree canopy coverage to improve broad community health that benefit infants prenatally. In summary, the data suggests how environmental factors influence health outcomes. Public health interventions can include improving the quality and quantity of tree canopy coverage in order to effectively decrease the risk of low birthweight infants, and improve overall neighborhood health conditions. References Whitehouse C. The health of children. A review of research on the place of health in cycles of disadvantage. 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Tables Table 1 Characteristics of infants born at St. Louis SSM hospitals from 2013 to 2022 (n = 74,160) Characteristics n (%) or Mean ± SD Sex Female 35991 (48.5) Male 38162 (51.5) Race Black 26564 (41.3) White 34715 (54.0) Other 3033 (4.7) Birthweight 3125.3 ± 695.5 Very low birthweight ( 2500 g) 63833 (86.1) Preterm birth 37.8 ± 17.9 Yes 10868 (14.7) No 63292 (85.3) NICU admission 10854 (14.6) Neonatal sepsis diagnosis 246 (2.4) Neonatal death 488 (0.7) Maternal age 26.8 ± 8.2 Under 18 1192 (1.7) 18 to 24 years 20887 (29.6) 25 to 34 years 39772 (56.4) 35 to 44 years 8590 (12.2) Over 45 years 82 (0.1) Marital status Married 30527 (44.1) Not married 38723 (55.9) Insurance status Private 29604 (40.2) Public 44109 (59.8) Prenatal Care Yes 10552 (97.3) No 294 (2.7) Smoking status a Never smoked 44348 (65.0) Former smoker 15562 (22.8) Current smoker 8319 (12.2) Maternal drug use a Yes 7519 (11.1) No 60434 (88.9) Maternal alcohol use a Yes 4945 (7.3) No 63146 (92.7) Tree Canopy by Birth year 2021 17.88% 2020 18.06% 2019 17.92% 2018 17.99% 2017 18.64% 2016 18.64% 2015 19.34% 2014 20.04% 2013 20.31% a Smoking, drug, and alcohol use status of pregnant person during pregnancy is self-reported at labor. Table 2 Associations of characteristics and neonatal sepsis diagnosis among infants admitted to St. Louis Cardinal Glennon NICU between 2013 and 2022 (n = 10,854) Characteristic Diagnosed with neonatal sepsis Not diagnosed with neonatal sepsis p-value a n 246 10608 Sex 0.321 Female 116 (2.5) 4476 (97.5) Male 130 (2.2) 5703 (97.8) Race 0.048* Black 107 (2.6) 4023 (97.4) White 111 (2.2) 4944 (97.8) Other 14 (4.2) 318 (95.8) Birthweight < 0.001* Very low birthweight ( 2500 g) 79 (1.5) 5189 (98.5) Preterm birth < 0.001* Yes 168 (2.9) 5672 (97.1) No 78 (1.7) 4507 (98.3) Maternal age 0.003* Under 18 9 (7.0) 119 (93.0) 18 to 24 years 59 (2.9) 1990 (97.1) 25 to 34 years 76 (2.1) 3561 (97.9) 35 to 44 years 23 (2.2) 1043 (97.8) Over 45 years 0 (0.0) 19 (100.0) Marital status 55 (34.2) 2117 (32.0) 0.569 Married 55 (2.5) 2117 (97.5) Not married 106 (2.3) 4990 (97.7) Insurance status 0.404 Private 67 (2.2) 3034 (97.8) Public 176 (2.4) 7061 (97.6) Prenatal care b 0.204 Yes 236 (2.3) 9895 (97.7) No 10 (3.5) 277 (96.5) Smoking status c 0.311 Never smoked 94 (2.7) 3431 (97.3) Former smoker 32 (2.3) 1331 (97.7) Table 8. Continued Current smoker 21 (1.9) 1102 (98.1) Maternal drug use c 0.198 Yes 18 (1.8) 961 (98.2) No 126 (2.5) 4856 (97.5) Maternal alcohol use c 0.485 Yes 8 (1.9) 406 (98.1) No 138 (2.5) 5422 (97.5) a Chi-square test b Prenatal care is self-reported from mothers who had an infant admitted to the NICU and is characterized by receiving any prenatal care during pregnancy. c Smoking, drug, and alcohol use status of pregnant person during pregnancy is self-reported at labor. *Significance determined at p < 0.05 Table 3 Predictors of neonatal sepsis among infants born at St. Louis SSM hospitals between 2013 and 2021 (n = 63,195) Characteristic RR (95% CI) p-value aRR a (95% CI) p-value Tree canopy coverage At or above recommended tree canopy coverage REF REF REF REF Below recommended tree canopy coverage 1.22 (0.94, 1.59) 0.133 1.15 (1.07, 1.23) < 0.001* Sex Male REF REF REF REF Female 1.17 (0.90, 1.51) 0.235 1.03 (0.97, 1.10) 0.355 Race White REF REF REF REF Black 1.20 (0.91, 1.57) 0.190 1.05 (0.98, 1.14) 0.186 Other 1.91 (1.08, 3.40) 0.027* 0.87 (0.71, 1.08) 0.208 Maternal age 25 to 34 years REF REF REF REF Under 18 3.51 (1.76, 7.01) < 0.001* 1.23 (0.95, 1.59) 0.114 18 to 24 years 1.52 (1.08, 2.15) 0.016* 1.24 (1.14, 1.34) < 0.001* 35 to 44 years 0.95 (0.59, 1.53) 0.827 0.91 (0.83, 1.01) 0.070 Over 45 years 0 0.952 0.92 (0.44, 1.94) 0.834 Marital status Married REF REF REF REF Not married 0.90 (0.64, 1.25) 0.517 0.86 (0.79, 0.94) < 0.001* Insurance status Private REF REF REF REF Public 1.16 (0.87, 1.55) 0.309 1.00 (0.91, 1.10) 0.988 Prenatal care b Yes REF REF REF REF No 1.28 (0.66, 2.49) 0.468 1.12 (0.90, 1.41) 0.312 Smoking status c Never smoker REF REF REF REF Former smoker 0.87 (0.58, 1.31) 0.509 1.16 (1.07, 1.26) < 0.001* Current smoker 0.69 (0.43, 1.12) 0.136 1.30 (1.18, 1.44) < 0.001* Drug status c No REF REF REF REF Yes 0.68 (0.42, 1.12) 0.127 0.87 (0.79, 0.96) < 0.001* Alcohol use c No REF REF REF REF Yes 0.70 (0.34, 1.42) 0.320 0.94 (0.84, 1.04) 0.222 * Significance determined at p < 0.05 a Multivariate analysis controlled for sex, race, maternal age, marital status, insurance status, prenatal care, smoking status, drug use, and alcohol use. b Prenatal care is self-reported from mothers who had an infant admitted to the NICU and is characterized by receiving any prenatal care during pregnancy. c Smoking, drug, and alcohol use status is self-reported at birth. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7375744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502070173,"identity":"6791506b-837a-4a65-8a48-e518a64c9755","order_by":0,"name":"Germysha Emily Little","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3RP0vDQBjH8d95cFkuu5P3Fi4ILv4bfRuWQjIVOpWOVwrJorhmkb6FiG/g5AFdSmclDgWxk0PGDhFNMJOQs6PDfcfj+fA8cIDP93+zQMAMMMYBgvZB7EI4mxmrcQi+OwFrycD8RdTF9fu9rF8VOM/eKn2SLOZSo5pQL4mWT0dlmG4i83NYPCpIapavHCSPRRkaYh2hUcGl5mHqIIuNKGVN5x35SlRzGP90ELUvGiJo0BF7ieYwzhxEy1i83KY0TBuSL/UwKkiMH65WSf+W7FE8f9R0ehNk62o6PVMqm9+tt5Pj/i0W2JP49RG2d77dYgC2dU34fD6f7xuhoViUTcvd4AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9869-6808","institution":"Saint Louis University","correspondingAuthor":true,"prefix":"","firstName":"Germysha","middleName":"Emily","lastName":"Little","suffix":""},{"id":502070174,"identity":"aff4f244-726f-4adb-8d2b-cebc9a13f0d6","order_by":1,"name":"Enbal Shacham","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Enbal","middleName":"","lastName":"Shacham","suffix":""},{"id":502070175,"identity":"e228f94f-73af-402d-88f3-61a24fda92f7","order_by":2,"name":"Kenan Li","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Kenan","middleName":"","lastName":"Li","suffix":""},{"id":502070176,"identity":"a54abfb2-7806-4294-bf8f-8dbc5fcd4b48","order_by":3,"name":"Kimberly Enard","email":"","orcid":"","institution":"Saint Louis University","correspondingAuthor":false,"prefix":"","firstName":"Kimberly","middleName":"","lastName":"Enard","suffix":""},{"id":502070177,"identity":"20002bcd-454f-41a2-844e-f48881731f69","order_by":4,"name":"Justin Josephsen","email":"","orcid":"","institution":"SSM Health Saint Louis University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Justin","middleName":"","lastName":"Josephsen","suffix":""}],"badges":[],"createdAt":"2025-08-14 16:33:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7375744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7375744/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108803804,"identity":"098c6a44-f6eb-4992-94e6-311f69c5393b","added_by":"auto","created_at":"2026-05-08 15:07:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":461439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7375744/v1/293a57bd-402d-4399-9fed-a53efa829efd.pdf"}],"financialInterests":"","formattedTitle":"Missing Shade, Missing Health: Tree Canopy Disparities and Neonatal Sepsis in a Midwestern U.S. City","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfant mortality is a significant public health issue throughout the world and is regarded as a highly sensitive measure of population health.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The top three leading causes of infant mortality are birth defects including congenital malformations, preterm birth and low birthweight, and sudden infant death syndrome.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e In the U.S., Black infants are at an increased risk for infant mortality compared to white infants.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e In 2019, the rate of infant mortality was 4.5 per 1,000 live births for white infants and the rate of infant mortality was 10.6 per 1,000 live births for Black infants.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Many societal factors including discrimination, income inequality, educational achievement gaps, residential segregation, psychosocial stress, and toxic environmental exposures contribute to the racial disparities of birth outcomes.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Addressing racial disparities should not be addressed only on an individual level but on a societal level by exploring social determinants of health that include discrimination, education and income gaps, and toxin exposure.\u003c/p\u003e\u003cp\u003eNeonatal sepsis is a serious, life-threatening medical condition that affects nearly 3\u0026nbsp;million of all newborns and kills 750,000 infants every year across the world.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In the United States, neonatal sepsis affects over 3,000 infants and kills approximately 400 infants every year.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Neonatal sepsis is a bacterial infection that occurs within the first month of life that may cause inflammation and blood clotting throughout the body reducing blood flow which can lead to organ failure and death.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The incidence rate of neonatal sepsis is higher among premature and low birthweight infants and these infants are more likely to suffer from neonatal mortality. In addition, the incidence rate of neonatal sepsis is higher among African-American or Black infants compared to any other race.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Disproportionate rates of prematurity and low birthweight among Black infants contributes to the racial gap in sepsis-associated mortality.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Neonatal sepsis infant risk factors include low birth weight, prematurity, low Apgar score, decreasing gestational age, Black ethnicity, while maternal risk factors include advanced maternal age, premature rupture of membranes, cesarean delivery, and primiparity.\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e While there many studied risk factors, understanding the pathways of risk factors will allow a better approach to eliminate the risk of neonatal sepsis.\u003c/p\u003e\u003cp\u003eEnvironmental exposures have a significant effect on maternal and birth outcomes and exacerbate health inequities. The American College of Obstetrics and Gynecology considers climate change, as related to environmental exposures, as an \u0026ldquo;an urgent women\u0026rsquo;s health concern and a major public health challenge.\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Exposure to climate change factors, including extreme heat and air pollution, is associated with an increased risk of low birthweight.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Greenspace and vegetation can reduce the risk of neonatal death-related illnesses by reducing exposure to air pollution, cooling environments, restoring attention, promoting physiological stress recovery, and encouraging physical activity.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Air pollution is associated with an increased risk for maternal morbidity and mortality and disproportionately affects people living in low-income environments.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIncreased tree canopy coverage and density as well as greenspaces can serve as a method to reduce the exposure to air pollution.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e There have been inconsistent results in the literature demonstrating the relationship between low birthweight and exposure to green space. Most previous studies have concluded that an increase in green space is associated with a decreased risk of low birthweight,\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e yet some studies did not find a significant association.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e additionally, the relationship between green space and birthweight have been found to be modified by sociodemographic characteristics such as race/ethnicity.\u003csup\u003e22,26\u003c/sup\u003e Research shows that tree canopy coverage acts as an effect modifier in the relationship between the effect of limited urban residential tree canopy on perceived stress among pregnant women.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e These findings suggest that future studies explored more details about how green spaces and trees may influence birthweight.\u003c/p\u003e\u003cp\u003eNeonatal sepsis is a common neonatal complication associated with low birthweight infants.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Social determinants of health composed of structural and intermediary determinants impact care and access to quality healthcare leading to health inequities. By examining sociodemographic characteristics and environmental characteristics, there will be a better understanding of the complex relationship factors that impact infant morbidity and mortality. The purpose of this study is to measure and analyze the association of tree canopy density of prenatal home environments with neonatal sepsis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDesign\u003c/h2\u003e\u003cp\u003eThe aim of the study was to test the association of tree canopy density and neonatal sepsis. This research study was accomplished by applying a retrospective framework to examine crude and adjusted regression models utilizing data sources described below.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cp\u003e This is a retrospective study that utilized medical record data of patients from six facilities within a regional integrated healthcare system (SSM Health St. Louis). These included four community hospitals, a high-risk delivery center with an associated Level III NICU, and a freestanding children\u0026rsquo;s hospital with a Level IV NICU. Data were extracted from the electronic medical record (EMR) data (Epic\u0026reg;) from patients born from January 01, 2013, to December 31, 2022.\u003c/p\u003e\u003cp\u003eFor babies admitted to the two NICUs, an expanded dataset was utilized from Vermont Oxford Network (VON), which contained additional variables hand abstracted for key outcomes. The dataset included variables related to birth, maternal characteristics, and neonatal morbidities and mortality.\u003c/p\u003e\u003cp\u003eShapefile data was utilized for geographical analysis from the US Census Bureau. TIGER/Line shapefile is a datafile containing mapping details and boundaries of the Missouri and Illinois region. The TIGER/Line shapefiles are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing Database.\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eEligible participants included infants born at five SSM St. Louis hospital locations between 2013 and 2021 with non-missing health outcome data during the study period.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003cp\u003eThe dataset included full addresses with street, city, state, and zip code. These addresses were geocoded to latitude and longitude to be used for geospatial analysis.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTree canopy coverage\u003c/strong\u003e\u003cp\u003eTree canopy coverage was calculated based on location using data from the U.S. Forest Service Geospatial Technology and Applications Center. This was a continuous variable ranging from 0\u0026ndash;100%. This variable was also categorized into a dichotomous variable (below recommended tree canopy coverage vs at or above recommended tree canopy coverage). \u0026ldquo;Below recommended tree canopy coverage\u0026rdquo; ranged from 0\u0026ndash;19% and \u0026ldquo;at or above recommended tree canopy coverage\u0026rdquo; ranged from 20\u0026ndash;100%.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe primary outcome of this study was neonatal sepsis. The main predictor variable was recommended tree canopy coverage. A multivariable logistic regression was conducted to predict the likelihood of neonatal sepsis diagnosis based on tree canopy coverage. A chi-square test was conducted to test significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Covariates were added to the adjusted model if they were statistically significant.\u003c/p\u003e\u003cp\u003eIn order to conduct geospatial analysis, a shapefile to outline United States counties was obtained from the U.S. Census Bureau to organize Missouri counties. The shapefile contained data related to the mapping, scaling, and cartographic boundaries of the 114 counties in Missouri. Tree canopy coverage raster values were extracted based on mother\u0026rsquo;s address. Statistical analysis was conducted using SPSS 27 and ArcGIS Pro.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e The Saint Louis University Institutional Review Board approved the exempt status of this study protocol.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eInfant descriptives\u003c/strong\u003e\u003cp\u003eThe majority of infants were male (n\u0026thinsp;=\u0026thinsp;38162, 51.5%). Slightly over half of infants were identified as white (n\u0026thinsp;=\u0026thinsp;34715, 54.0%). Most infants were normal birthweight (n\u0026thinsp;=\u0026thinsp;63833, 86.1%) and born at term (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;36 weeks) (n\u0026thinsp;=\u0026thinsp;63292, 85.3%). Out of the study sample, 10854 (14.6%) infants were admitted to the NICU, 246 (2.6%) infants were diagnosed with neonatal sepsis, and 488 (0.7%) infants experienced neonatal death.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMaternal descriptives\u003c/strong\u003e\u003cp\u003eThe mean age among mothers was 26.7 (SD\u0026thinsp;=\u0026thinsp;8.2) years with the majority of participants 25 to 34 years of age (n\u0026thinsp;=\u0026thinsp;39772, 56.4%). The majority of mothers were not married (n\u0026thinsp;=\u0026thinsp;38726, 55.9%) and the majority had public insurance (n\u0026thinsp;=\u0026thinsp;44109, 59.8%). Most mothers reported receiving any prenatal care during their pregnancy (n\u0026thinsp;=\u0026thinsp;10552, 97.3%). Over half of mothers reported not smoking (n\u0026thinsp;=\u0026thinsp;44348, 65.0%), no drug use (n\u0026thinsp;=\u0026thinsp;60434, 88.9%), and no alcohol use (n\u0026thinsp;=\u0026thinsp;63146, 92.7%) at the time of labor. Overall, there was a decrease in tree canopy coverage since 2013. The mean tree canopy coverage of the study area in 2013 was 20.31%, compared to 2021 where the tree canopy coverage was 17.88%. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a summary of the sample characteristics.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 246 (2.3%) infants were diagnosed with neonatal sepsis. Infant characteristics, including birthweight and gestational age were significantly associated with neonatal sepsis diagnosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Infants who were diagnosed with neonatal sepsis were more likely to be very low birthweight (n\u0026thinsp;=\u0026thinsp;112, 6.4%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and preterm (n\u0026thinsp;=\u0026thinsp;168, 2.9%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Maternal characteristics, including age and race, were significantly associated with neonatal sepsis diagnosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Mothers who were under the age of 18 were more likely to have an infant diagnosed with neonatal sepsis (n\u0026thinsp;=\u0026thinsp;9; 7.0%), and mothers who identified as a race other than white or Black were more likely to have an infant diagnosed with neonatal sepsis (n\u0026thinsp;=\u0026thinsp;14; 4.2%).\u003c/p\u003e\u003cp\u003ePredictors of neonatal sepsis according to infant and maternal characteristics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Mothers who had below the recommended tree canopy coverage (RR\u0026thinsp;=\u0026thinsp;1.15, 95% CI: 1.07, 1.23; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), between the ages of 18 and 24 (RR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 1.14, 1.34; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and reported currently smoking (RR\u0026thinsp;=\u0026thinsp;1.30, 95% CI: 1.18, 1.44; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) had an increased risk of having an infant diagnosed with neonatal sepsis. Mothers who were not married (RR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.79, 0.94; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and reported drug use (RR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.79, 0.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) had a decreased risk of neonatal sepsis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe purpose of this paper was to analyze the association of tree canopy density of prenatal home environments with a diagnosis of neonatal sepsis. Results from this study show that decreased tree canopy coverage was significantly associated with an increased risk of neonatal sepsis. Environmental exposures vary by neighborhoods. This study was conducted to assess how those prenatal exposures may play a role in ending up with a diagnosis of neonatal sepsis in this 10-year retrospective study.\u003c/p\u003e\u003cp\u003eOverall, increased tree canopy and greenspace reduce the risk of adverse neonatal health outcomes.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e In this study sample, the average tree canopy coverage of the study area was less than the recommended tree canopy coverage in a residential area. The American Forests\u0026rsquo; Science Advisory Board recommends tree canopy coverage for residential areas to be at least 20%, yet the average tree canopy coverage among this sample was approximately 19%.\u003csup\u003e30\u003c/sup\u003e While the recommended tree canopy is based on development density, land use patterns, ordinances, and climate, it does not consider health needs and neighborhood pollution levels. It also interesting to note that the average tree canopy coverage was 20% in 2013 and overall decreased over the years to about 18% in 2021. The decrease in tree canopy coverage in the St. Louis area is likely due to the emerald ash borer that was discovered in 2013, which is a destructive pest that disrupts the tree\u0026rsquo;s ability to transport water and nutrients leading to the tree dying.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e This further emphasizes the need to implement interventions that aim to increase tree canopy coverage improve infant health outcomes, and overall community health.\u003c/p\u003e\u003cp\u003ePrevious research has not been conclusive on the relationship between tree canopy and adverse neonatal health outcomes. While studies have concluded that an increase in green space is associated with a decreased risk of low birthweight,\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e some studies did not find a significant association.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e It is important to note that green space and tree canopy have an important measurable difference. The normalized difference vegetation Index (NDVI) is the most widely-accepted measure in prior research to measure greenness, which includes percent tree canopy, quantity of street trees, and proximity to major greenspaces.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e While, green space is broad, tree canopy coverage is a more specific measure.\u003c/p\u003e\u003cp\u003eThe data suggests that decreased tree canopy coverage is associated with an increased risk for neonatal sepsis. This is consistent with the literature indicating that decreased tree canopy coverage increases the exposure to air pollution leading to an increased risk of adverse birth outcomes, including infant mortality.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Lowering neonatal sepsis and infant mortality rates across the nation should focus on improving healthcare services, promoting land use policies to increase tree canopy coverage, as a structural determinant of health.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be considered when interpreting and applying the findings. Although most of the data were obtained from electronic medical records, including medical tests and vital signs, some variables were self-reported. Information on smoking, drug and alcohol use, marital status, and prenatal care was self-reported at a single point during the pregnancy, typically at the time of labor. Because of the stigma associated with substance use during pregnancy, some individuals may have underreported these behaviors, leading to social desirability bias. It is also unclear whether smoking, alcohol, or drug use occurred during pregnancy or in the perinatal period. Additionally, the self-reported data do not include information on the frequency or intensity of substance use, which limits the ability to interpret the results accurately. Finally, other important risk factors that may influence neonatal health outcomes were not included in the study due to a lack of available data. These factors include maternal health conditions, residential racial segregation, and nutritional habits, all of which have been shown in previous research to affect neonatal outcomes.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUnderstanding the risk of neonatal sepsis using environmental risk factors, such as tree canopy is essential in reducing the risk of adverse infant health outcomes. The results of this study can be leveraged to implement effective practices and planning to increase tree canopy coverage to improve broad community health that benefit infants prenatally. In summary, the data suggests how environmental factors influence health outcomes. Public health interventions can include improving the quality and quantity of tree canopy coverage in order to effectively decrease the risk of low birthweight infants, and improve overall neighborhood health conditions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWhitehouse C. The health of children. A review of research on the place of health in cycles of disadvantage. J Royal Coll Gen Practitioners. 1982;32(237):249.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrganization WH. Infant mortality. 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatthews TJ, MacDorman MF, Thoma ME. Infant Mortality Statistics From the 2013 Period Linked Birth/Infant Death Data Set. Natl Vital Stat Rep Aug. 2015;6(9):1\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEly DM, Driscoll AK. Infant Mortality in the United States, 2019:Data From the Period Linked Birth/Infant Death File. Natl Vital Stat Rep Dec. 2021;70(14):1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurris HH, Hacker MR. Birth outcome racial disparities: A result of intersecting social and environmental factors. Semin Perinatol Oct. 2017;41(6):360\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.semperi.2017.07.002\u003c/span\u003e\u003cspan address=\"10.1053/j.semperi.2017.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSinnar S, Schiff S. The Problem of Microbial Dark Matter in Neonatal Sepsis. 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Health Place. 2019;57:200\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.healthplace.2019.04.012\u003c/span\u003e\u003cspan address=\"10.1016/j.healthplace.2019.04.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2019/05/01.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrazuleviciene R, Danileviciute A, Dedele A, et al. Surrounding greenness, proximity to city parks and pregnancy outcomes in Kaunas cohort study. Int J Hyg Environ Health. 2015;218(3):358\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijheh.2015.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ijheh.2015.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2015/05/01.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCusack L, Larkin A, Carozza S, Hystad P. Associations between residential greenness and birth outcomes across Texas. \u003cem\u003eEnvironmental Research\u003c/em\u003e. 2017/01/01/ 2017;152:88\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2016.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2016.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguemeni Tiako MJ, South E, Shannon MM, et al. Urban residential tree canopy and perceived stress among pregnant women. Environ Res. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2021.111620\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2021.111620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 10/01/ 2021;201:111620.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurphy S, Kochanek K, Xu J, Arias E, Mortality in the United States., 2020. NCHS Data Brief, no 427. Hyattsville, MD: National Center for Health Statistics. 2021. \u003cem\u003eSuggested citation Hoyert DL Maternal mortality rates in the United States\u003c/em\u003e. 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIBM. \u003cem\u003eCorp. Released 2023. IBM SPSS Statistics for Windows, Version 29.0.2.0 Armonk, NY: IBM Corp\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForests A. Why We No Longer Recommend a 40 Percent Urban Tree Canopy Goal.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEmerald Ash Border. Missouri Dept. of Agriculture. Accessed March 03. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://agriculture.mo.gov/plants/pests/emeraldash.php\u003c/span\u003e\u003cspan address=\"https://agriculture.mo.gov/plants/pests/emeraldash.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbelt K, McLafferty S. Green Streets: Urban Green and Birth Outcomes. 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Environ Health Perspect. 2008;116(6):791\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of infants born at St. Louis SSM hospitals from 2013 to 2022 (n\u0026thinsp;=\u0026thinsp;74,160)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%) or Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35991 (48.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38162 (51.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26564 (41.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34715 (54.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3033 (4.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirthweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3125.3\u0026thinsp;\u0026plusmn;\u0026thinsp;695.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery low birthweight (\u0026lt;\u0026thinsp;1500 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2326 (3.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birthweight (1500 g\u0026ndash;2500 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7993 (10.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal birthweight (\u0026gt;\u0026thinsp;2500 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63833 (86.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreterm birth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10868 (14.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63292 (85.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNICU admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10854 (14.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeonatal sepsis diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e246 (2.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeonatal death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e488 (0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnder 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1192 (1.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18 to 24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20887 (29.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25 to 34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39772 (56.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35 to 44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8590 (12.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOver 45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30527 (44.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38723 (55.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29604 (40.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44109 (59.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrenatal Care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10552 (97.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e294 (2.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44348 (65.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15562 (22.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8319 (12.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal drug use\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7519 (11.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60434 (88.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal alcohol use\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4945 (7.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63146 (92.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTree Canopy by Birth year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.88%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.06%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.99%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.64%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.64%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.34%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.04%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.31%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eSmoking, drug, and alcohol use status of pregnant person during pregnancy is self-reported at labor.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of characteristics and neonatal sepsis diagnosis among infants admitted to St. Louis Cardinal Glennon NICU between 2013 and 2022 (n\u0026thinsp;=\u0026thinsp;10,854)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiagnosed with neonatal sepsis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot diagnosed with neonatal sepsis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4476 (97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5703 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107 (2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4023 (97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4944 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e318 (95.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirthweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery low birthweight (\u0026lt;\u0026thinsp;1500 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1641 (93.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birthweight (1500 g\u0026ndash;2500 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3349 (98.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal birthweight (\u0026gt;\u0026thinsp;2500 g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5189 (98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreterm birth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5672 (97.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4507 (98.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnder 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (93.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18 to 24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1990 (97.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25 to 34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3561 (97.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35 to 44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1043 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOver 45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (34.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2117 (32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2117 (97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4990 (97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3034 (97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7061 (97.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrenatal care\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e236 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9895 (97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e277 (96.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3431 (97.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1331 (97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTable\u0026nbsp;8. Continued\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1102 (98.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal drug use\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e961 (98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4856 (97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal alcohol use\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e406 (98.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5422 (97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eChi-square test\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003ePrenatal care is self-reported from mothers who had an infant admitted to the NICU and is characterized by receiving any prenatal care during pregnancy.\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eSmoking, drug, and alcohol use status of pregnant person during pregnancy is self-reported at labor.\u003c/p\u003e\u003cp\u003e*Significance determined at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredictors of neonatal sepsis among infants born at St. Louis SSM hospitals between 2013 and 2021 (n\u0026thinsp;=\u0026thinsp;63,195)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaRR\u003csup\u003ea\u003c/sup\u003e (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTree canopy coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt or above recommended tree canopy coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow recommended tree canopy coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22 (0.94, 1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15 (1.07, 1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17 (0.90, 1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03 (0.97, 1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20 (0.91, 1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.05 (0.98, 1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.91 (1.08, 3.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.027*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.71, 1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25 to 34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnder 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.51 (1.76, 7.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.23 (0.95, 1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18 to 24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.52 (1.08, 2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.016*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24 (1.14, 1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35 to 44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95 (0.59, 1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.91 (0.83, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOver 45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92 (0.44, 1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90 (0.64, 1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86 (0.79, 0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16 (0.87, 1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.91, 1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrenatal care\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28 (0.66, 2.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12 (0.90, 1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87 (0.58, 1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.16 (1.07, 1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.69 (0.43, 1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30 (1.18, 1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug status\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68 (0.42, 1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.79, 0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70 (0.34, 1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94 (0.84, 1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eSignificance determined at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMultivariate analysis controlled for sex, race, maternal age, marital status, insurance status, prenatal care, smoking status, drug use, and alcohol use.\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003ePrenatal care is self-reported from mothers who had an infant admitted to the NICU and is characterized by receiving any prenatal care during pregnancy.\u003c/p\u003e\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eSmoking, drug, and alcohol use status is self-reported at birth.\u003c/p\u003e\u003cp\u003eREF\u0026thinsp;=\u0026thinsp;reference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7375744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7375744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Neonatal sepsis is a critical health concern, particularly affecting low birthweight and preterm infants, with higher incidence rates among Black infants. In urban settings, environmental inequities, such as reduced tree canopy coverage, may influence neonatal health outcomes by affecting maternal stress levels and exposure to air pollution.\nThis retrospective study analyzed electronic medical records from six SSM Health hospitals in St. Louis, Missouri, encompassing over 74,000 births between 2013 and 2022. Tree canopy coverage around maternal residences was quantified using geospatial data and categorized based on recommended residential thresholds. Logistic regression models assessed the association between tree canopy coverage and neonatal sepsis, adjusting for sociodemographic and clinical covariates.\nAmong the infants studied, 2.3% were diagnosed with neonatal sepsis. Infants born to mothers residing in areas with below-recommended tree canopy coverage (\u0026lt;20%) exhibited a higher risk of neonatal sepsis (RR=1.15; 95%; CI: 1.07–1.23; p\u0026lt;0.05). Additional risk factors included younger maternal age and maternal smoking. Notably, average tree canopy coverage in the study area declined from 20.3% in 2013 to 17.9% in 2021.\nLower residential tree canopy density urban neighborhoods is associated with an increased risk of neonatal sepsis. These findings underscore the importance of environmental considerations in maternal and child health strategies. Enhancing urban tree canopy coverage may serve as a structural intervention to mitigate neonatal health disparities and improve overall urban population health.","manuscriptTitle":"Missing Shade, Missing Health: Tree Canopy Disparities and Neonatal Sepsis in a Midwestern U.S. City","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 13:29:21","doi":"10.21203/rs.3.rs-7375744/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb511a1b-3e91-4df0-a861-543887a7b69e","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T18:34:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-26 13:29:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7375744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7375744","identity":"rs-7375744","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}