{"paper_id":"4cffccb7-c5f3-498a-9058-3c7e141f7d81","body_text":"Preconception, prenatal and postnatal air pollution exposure and risk of neurodevelopmental disorders among Medicaid recipients | 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 Article Preconception, prenatal and postnatal air pollution exposure and risk of neurodevelopmental disorders among Medicaid recipients Matthew Shupler, Xinye Qiu, Krista Huybrechts, Sonia Hernandez Diaz, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7773518/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 Studies examining the association between ambient PM 2.5 exposure and risk of neurodevelopmental disorders (NDDs) in children have yielded mixed results. We conducted a cohort study spanning 2001–2013 among 1,547,244 Medicaid enrollees to quantify the association between zip-code level preconception (12-week), prenatal (37-week), and postnatal (3-year) PM 2.5 exposure and NDD risk among US children. Cox proportional hazards models were used to estimate risks of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), learning difficulty, developmental speech or language disorder, developmental coordination disorder (DCD), intellectual disability (ID) and behavioral disorder, adjusting for demographics, behavioral factors, meteorological characteristics, and area-level socioeconomic indicators. Distributed lag models examined critical exposure windows and cumulative risks of PM 2.5 exposures. A 10 µg/m 3 increase in cumulative prenatal PM 2.5 exposure was associated with 1.17 (95%CI:[1.00,1.37]) times greater ASD risk. A critical exposure window for ASD existed during gestational weeks 23–31 (HR:1.09 95%CI:[1.02,1.17]). Another critical prenatal exposure window occurred for ADHD at 16–22 weeks gestation (HR:95%CI:[1.01, 1.06]). Cumulative postnatal PM 2.5 exposure was linked with increased DCD risk (HR:1.77 95%CI:[0.93,3.34]), with a critical exposure window at age 2 (HR:1.67 95%CI:[1.13, 2.47]). Prenatal and postnatal PM 2.5 exposure may contribute to increased risk of multiple NDD among US children from low-income families. Earth and environmental sciences/Environmental social sciences/Environmental impact Health sciences/Health care/Public health/Epidemiology Health sciences/Neurology/Neurological disorders/Neurodevelopmental disorders/Autism spectrum disorders Neurodevelopmental delays autism spectrum disorder attention-deficit/hyperactivity disorder PM2.5 prenatal postnatal Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Neurodevelopmental disorders (NDDs) are characterized by delays in social, cognitive, and emotional functioning during early childhood 1 and diagnosed in approximately 10–15% of children in the US, 2 posing a lifelong health burden. 3 , 4 NDDs can arise from a complex interplay of genetic, biological, and environmental influences, 1,5 including ambient air pollution exposure. Exposure to fine particulate matter (PM 2.5 ), a major component of ambient air pollution, during preconception, prenatal, and early postnatal periods may cause permanent changes in the developing brain and increase the risk of cognitive, behavioral, and motor delays in children. 6 , 7 Existing epidemiological evidence has provided conflicting findings, with some studies showing that PM 2.5 exposure is associated with increased NDD risk, 8,9 and others reporting mixed results. 10 , 11 Potential explanations for contrary findings include various methods for ascertaining NDDs, inadequate temporal resolution for PM 2.5 exposure assessment (e.g. trimester or pregnancy averages), differing degrees of spatial resolution for quantifying PM 2.5 levels, differences in population characteristics and confounding control, heterogeneity in PM 2.5 composition and smaller sample sizes for less common NDDs. 12 – 14 Studies have also reported differential associations between PM 2.5 exposure and NDD risk by sex 15 , 16 , race/ethnicity, 17,18 and socioeconomic status (SES). 19 , 20 Longitudinal studies assessing early-life exposure windows can help delineate the effects of ambient PM 2.5 exposures on neurodevelopment. 21 Leveraging a national large population-based cohort of 1.5 million births and high-resolution geospatial air pollution data, this longitudinal study investigates whether PM 2.5 exposure during preconception, prenatal, and postnatal periods is associated with NDD risk among Medicaid enrollees. We also investigate effect measure modification by sex, race/ethnicity and urbanicity to determine if certain subpopulations may have higher susceptibility to NDD due to PM 2.5 exposure. RESULTS The final analytic sample included 1,547,244 mother-child pairs, nearly all of which were singleton births (n=1,526,817; 99%). The most common NDD were developmental speech or language disorder (n=73,911), ADHD (n=67,338), behavioral disorder (n=47,671) and ASD (n=12,942). The median age of diagnosis for ASD, ADHD and any NDD was 3.7 years (inner quartile range (IQR): 3.0), 6.0 years (IQR: 2.4) and 5.0 years (IQR: 3.7), respectively. The median total follow-up time was 7.4 years (IQR: 5.2). PM 2.5 concentrations gradually declined during the study period (eResults 1), with a median weekly prenatal exposure of 11.26 μg/m 3 and median annual postnatal exposure of 10.49 μg/m 3 . The proportion of White children was higher in the lower exposure (below median) group, while Black/African American and Hispanic/Latino children were more prevalent in the higher exposure (above/at median) group (Table 1). Substance use indicators (smoking, alcohol, drug use) were more common in the lower PM 2.5 exposure group. Median household income and educational attainment were lower in higher PM 2.5 exposure areas. PM 2.5 concentrations during the preconception, prenatal and postnatal exposure periods were highly correlated (Spearman correlation coefficients: 0.81-0.88). Table 1. Cohort characteristics stratified by median preconception/prenatal and postnatal PM 2.5 exposure (n=1,547,244) Preconception/Prenatal PM 2.5 exposure Postnatal PM 2.5 exposure Total Below median (11.26 μg/m 3 ) Above/at median (11.26 μg/m 3 ) Below median (10.49 μg/m 3 ) Above/at median (10.49 μg/m 3 ) Individual variables Sex of child (Male) [n (%)] 781,529 (50.5) 391,458 (50.7) 390,071 (50.3) 391,374 (50.6) 390,155 (50.4) Maternal race or ethnic group [n (%)] White 642,592 (41.5) 361,151 (46.6) 281,441 (36.3) 365,548 (47.3) 277,044 (35.8) Black/African-American 502,273 (32.4) 210,007 (27.1) 292,266 (37.8) 209,267 (27.0) 293,006 (37.8) Asian/Other Pacific 50,193 (3.2) 28,203 (3.6) 21,990 (2.8) 27,812 (3.6) 22,381 (2.9) Hispanic/Latino 300,551 (19.4) 137,214 (17.7) 163,337 (21.1) 133,557 (17.3) 166,994 (21.6) Unknown 51,635 (3.3) 37,047 (4.8) 14,588 (1.9) 37,427 (4.8) 14,208 (1.8) Maternal age at delivery [mean (sd)] 24.5 (5.9) 24.1 (5.8) 25.0 (5.9) 25.0 (5.9) 24.1 (5.8) Substance abuse [n (%)] 20,809 (1.3) 12,674 (1.6) 8,135 (1.1) 13,012 (1.7) 7,797 (1.0) Smoking [n (%)] 30,942 (2.0) 19,486 (2.5) 11,456 (1.5) 19,801 (2.6) 11,141 (1.4) Alcohol abuse [n (%)] 7,913 (0.5) 4,986 (0.6) 2,927 (0.4) 5,035 (0.6) 2,878 (0.4) Poor nutrition [n (%)] 6,473 (0.4) 4,609 (0.6) 1,864 (0.2) 4,697 (0.6) 1,776 (0.2) Folate 55,791 (3.6) 29,964 (3.9) 25,827 (3.4) 30,611 (4.0) 25,180 (3.3) BMI [mean (sd)] 27.7 (1.4) 27.8 (1.6) 27.7 (1.1) 27.8 (1.6) 27.7 (1.1) Area-level variables Median household income (thousands ($)) [mean (sd)] 41.9 (15.0) 44.6 (15.9) 39.3 (13.7) 45.0 (15.9) 38.9 (13.4) Owner-occupied housing value [mean (sd)] 180.8 (142.4) 194.3 (151.7) 167.9 (131.6) 202.3 (156.4) 159.6 (123.5) Owner-occupied housing units [% (sd)] 57.7 (19.3) 60.2 (18.7) 55.3 (19.6) 59.5 (19.2) 55.9 (19.3) Above 65 years and did not complete high school [% (sd)] 35.8 (15.6) 31.2 (14.6) 40.2 (15.3) 30.8 (14.4) 40.6 (15.3) Above 65 years and living below the poverty level [% (sd)] 13.7 (8.5) 12.9 (8.5) 14.5 (8.4) 13.0 (8.6) 14.4 (8.3) Population Density (people/km²) [mean (sd)] 2,670 (5,530) 2,382 (5,457) 2,959 (5,587) 2,616 (5,794) 2,724 (5,252) Nearest hospital (km) [mean (sd)] 5.5 (6.9) 6.4 (7.9) 4.7 (5.5) 6.3 (7.9) 4.8 (5.6) sd= standard deviation Preconception and prenatal exposures In fully adjusted models, cumulative (37-week) prenatal PM 2.5 exposure was associated with increased ASD risk (HR: 1.17, 95%CI: [1.00, 1.37]) (Figure 1). The exposure-response curve showed increased ASD risk at higher levels of preconception/ prenatal PM 2.5 exposure (eResults 2). No associations were found between cumulative preconception or prenatal PM 2.5 exposure and any other NDD among the overall study population. Preconception and prenatal critical exposure windows A critical exposure window for increased ASD risk occurred during weeks 23-31 of gestation, with a 10 μg/m 3 increase in cumulative PM 2.5 exposure associated with 1.09 times (95%CI:[1.02, 1.17]) the risk of ASD (Figure 1). Additionally, a 10 μg/m 3 increase in PM 2.5 levels at 16-22 weeks gestation was associated with 1.03 times (95%CI:[1.01, 1.06]) the risk of ADHD. Prenatal PM 2.5 exposure at gestational weeks 1-15 was associated with reduced ADHD risk (0.90 95%CI:[0.85,0.94]). Postnatal exposures After adjustment for prenatal/preconception PM 2.5 exposure, postnatal PM 2.5 exposure was associated with higher DCD risk (1.77 95%CI:[0.93, 3.40]) (Figure 3). The exposure-response curve showed increased DCD risk at higher levels of postnatal PM 2.5 exposure (eResults 3). No associations were found between cumulative postnatal PM 2.5 concentrations and risk of other NDD. In models without inclusion of preconception/prenatal PM 2.5 exposure as a covariate, there were no substantial changes in the HRs for postnatal PM 2.5 exposure (eResults 4). Susceptible windows in the postnatal period Postnatal PM 2.5 levels at age 0 years were associated with ADHD risk (1.18 95%CI:[0.99, 1.41]) (eResults 5; Figure 2). Postnatal PM 2.5 exposure at age 1 and 2 years was highly associated with risk of ID (1.96 95%CI:[1.08, 3.55]) and DCD (1.67 95%CI:[1.13, 2.47]), respectively. Effect modification during the prenatal exposure period We identified that race/ethnicity was an effect modifier in the association between prenatal PM 2.5 exposure and ADHD risk (p interaction <0.001). The association between cumulative prenatal PM 2.5 exposure and ADHD risk was also higher among Hispanic participants (1.12 95%CI: [0.94, 1.33]), compared with Black (0.89 95%CI:[0.75, 1.04]) and White (0.79 95%CI:[0.70, 0.89]) individuals. Additionally, there was an association between prenatal PM 2.5 exposure and ID risk among Hispanic enrollees (1.57 95%CI:[1.02, 2.43]), but not White individuals (0.94 95%CI:[0.61, 1.48]). The sex of the child (eResults 6) and urbanicity (eResults 7) did not modify the association between preconception/prenatal exposure and NDD risk. When stratifying models by US region, there were not substantial differences in the association between cumulative preconception and prenatal PM 2.5 exposure with NDD risk (eResults 8). Effect modification during the postnatal exposure period Participants’ race/ethnicity modified the association between postnatal PM 2.5 exposure and ADHD (p interaction =0.001) risk (Figure 4). Specifically, Black (1.10 95%CI:[0.79, 1.53]) and Hispanic (1.28 95%CI:[0.91, 1.81]) enrollees had an elevated risk of ADHD due to PM 2.5 exposure, while White participants did not (0.90 95%CI:[0.69, 1.16]). The child’s sex (eResults 9) and urbanicity (eResults 10) did not modify the association between postnatal PM 2.5 exposure and risk of any NDD. Sensitivity analyses In a sensitivity analysis that included models containing shorter maximum lags (26- and 30-week) to account for different gestation durations, HRs (eResults 11) and the shape of the lag-response curves (eResults 12) were similar. When including only children with an NDD diagnosis at age 3 or later (less severe cases), the HRs were similar to those generated from the main models (eResults 13). When adjusting for average ambient NO 2 concentrations, minimal changes in the HRs occurred (eResults 14). Finally, accounting for censoring weights did not alter the findings (eResults 15). DISCUSSION In this national study of more than 1.5 million children, we found that prenatal PM 2.5 exposure was associated with increased risk of ASD, with a sensitive window of exposure identified during weeks 23–31. We also found that prenatal PM 2.5 exposure in gestational weeks 16–22 and the first year of life was associated with an increased risk of ADHD. Previous studies have similarly reported that late pregnancy PM 2.5 22 and PM 10 23 exposures had the strongest association with increased ASD risk. A large number of past studies 10 , 12 – 14 , 24 – 26 have reported associations between both prenatal and postnatal PM 2.5 exposure and ASD risk. Regarding ADHD, another study identified the same prenatal window of susceptibility (gestational weeks 16–22), but found that ages 1–3 years were a more vulnerable postnatal period. 27 However, two Asian studies (in Taiwan 28 and China 29 ) found significant associations between first-year PM 2.5 exposures and ADHD risk. In our results, we identified a protective effect of prenatal PM 2.5 exposure on ADHD risk during gestational weeks 1–15. This protective effect may be due to live birth bias, which arises when the selective survival between conception and birth skews the distribution of prenatal PM 2.5 exposures among the subset of remaining live births. 30 Life birth bias could also lead to underestimation of the associations between PM 2.5 exposure and ASD risk. Beyond ASD and ADHD, we identified a potential association between postnatal PM 2.5 exposure and the development of DCD, with a possible susceptible exposure window occurring during age 2 years. Recently, a Chinese study found that average PM 2.5 exposure during age 0–3 years was associated with increased odds of reduced motor performance. 31 Biological mechanisms that may explain the link between PM 2.5 exposure and NDD include penetration of PM across the blood-brain barrier, which can impede neuronal function, 32 and oxidative stress and neuroinflammation, 33 which can dysregulate neurodevelopmental processes. 34 For ASD specifically, prenatal PM 2.5 exposure may impact risk via placental toxicity resulting in impaired oxygenation and nutrient transport to the fetus and therefore disruption of central nervous system development. 6 , 35 ASD and ADHD risk due to postnatal PM 2.5 exposure was higher among Black and Hispanic Medicaid enrollees compared with White individuals. Additionally, the risk of ASD, ADHD and ID due to prenatal PM 2.5 exposure was higher among Hispanic Medicaid recipients compared with Black and White individuals. Similarly, other air pollution epidemiological studies have reported higher risk among racial/ethnic minorities, 24,36 with potential explanations being that Black and Hispanic communities in the US are generally closer to freeways and industrial facilities 37 and face negative psychosocial stressors related to systemic racism. 38 – 43 These social determinants may exacerbate the effects of PM 2.5 exposure on NDD risk through neuroendocrine, vascular, or immune mechanisms involved in the body’s stress response. 44 To our knowledge, this is one of the first national studies to evaluate the association between PM 2.5 concentrations and NDD risk among Medicaid recipients, an underrepresented population in environmental pregnancy studies; our analysis also included understudied NDDs beyond ASD and ADHD. Our large sample size of > 1.5 million Medicaid enrollees enabled us to examine effect modification by several sociodemographic factors, which have been less investigated due to a more homogenous study population or an insufficient sample size. 10 We additionally adjusted for a large number individual and area-level confounding variables, enhancing the robustness of our findings. Our PM 2.5 exposures were derived from a validated ensemble model with high spatiotemporal resolution, thereby reducing potential exposure misclassifications from studies relying on more sparse monitoring networks. 45 , 46 Outcome misclassification was reduced as the NDD definitions were validated with high predictive values; outcome misclassification would be non-differential and bias toward a null finding. We undertook multiple sensitivity analyses to ensure that biases (e.g. informative censoring, outcome misclassification) did not affect our results. Finally, we accounted for residential mobility during pregnancy, since we obtained zip codes at the LMP and at birth. Although the ability to detect susceptible exposure windows is a strength of using DLMs, there were many critical windows examined, which carries a higher risk of identifying a false positive association due to larger number of comparisons. However, the susceptible windows we identified for more commonly studied outcomes, such as ASD 22 and ADHD, 27 have been acknowledged in other studies. Exposure misclassification may exist when assigning zip code-level PM 2.5 exposures, as they are not representative of individual PM 2.5 levels. 47 However, a recent study found that associations between individual and zip code-level PM 2.5 estimates and health outcomes are similar. 48 Zip code–level PM 2.5 exposures may help mitigate residual confounding from unmeasured individual-level factors, such as time-activity patterns, because these proxy measures are more distal from personal behaviors. 49 As a result, they are less influenced by individual-level variable confounding and less prone to reverse causation. 50 Additionally, zip code-level data can be more informative for air pollution policy recommendations, which are typically implemented at larger geographical levels. Conclusion Our study contributes to growing evidence that prenatal PM 2.5 exposure is a plausible risk factor for ASD and ID, and that postnatal PM 2.5 exposure may increase ADHD and DCD risk. As several NDD, such as ASD and ADHD, are being increasingly diagnosed in the US, 2,6 it is critical to continue enacting policies that lower PM 2.5 exposures to protect early child development. METHODS We constructed a population-based cohort study of pregnant women enrolled in Medicaid from 2001–2013. Medicaid is a state and federal health insurance program available to low-income US individuals and covers medical expenses of > 40% of births nationally. 51 Medicaid beneficiary enrollment and healthcare utilization claims were obtained from the Medicaid Analytic Extract (MAX) dataset and Transformed Medicaid Statistical Information System Analytic Files (TAF); 52 cohort creation methods have been described elsewhere. 53 Pregnant women enrolled in Medicaid are predominantly younger, more racially and ethnically diverse, and economically disadvantaged compared to the general population. The study population consisted of liveborn children of females aged 12–55 years old enrolled in Medicaid at least three months before the date of their estimated last menstrual period (LMP) to at least one month after delivery. We defined the cohort based on the estimated date of conception (DOC), which was approximated by assuming the DOC fell two weeks after the LMP date, assuming a 28-day cycle. To minimize fixed cohort bias, 42 we restricted inclusion to individuals with a DOC between January 1, 2001, and December 31, 2013. We excluded pregnancies with a DOC after December 31, 2013 to ensure a minimum of three years of follow-up, given that exposure data (described in the following section) were available through December 31, 2016. 42 Postnatal follow-up time was calculated as the number of days from a child’s birth until either their enrollment in Medicaid ended, the development of a NDD, death, or study period end, whichever came first, with up to 12 years of maximum follow-up. Children with any chromosomal or genetic abnormality were excluded. Exposure assessment Average zip code-level PM 2.5 concentrations were assigned to each mother based on their residential zip code. The PM 2.5 measurements were obtained from an ensemble model that estimates spatiotemporally resolved concentrations at a 1 km 2 \\(\\:\\:\\) grid resolution across the contiguous US from 2000-2016. 48 The model has been applied in several epidemiological studies. 36 , 54 , 55 PM 2.5 concentration predictions were aggregated at a zip code level by inverse distance averaging the four nearest grid cells to the residential zip code centroid. 36 , 56 If zip codes at the LMP and delivery differed, we attributed 50% of the pregnancy exposure duration to each zip code, which has been done previously. 57 Outcome variables Outcome data from the MAX database included a total of seven NDDs: autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), learning disability, developmental speech or language disorders, developmental coordination disorder (DCD), intellectual disability (ID), and behavioral disorder. Validated algorithms have been applied to confirm the presence of NDDs, with event rates in this cohort aligned with US statistics (eTable 1). 58–60 Covariates We adjusted for individual and area-level factors, as in prior studies. 57 , 61 Individual-level variables included maternal age, maternal race/ethnicity, substance abuse, smoking, alcohol abuse, poor nutrition, folate use during pregnancy, birth year, and season of conception (eTable 2). We included race/ethnicity as a covariate as a proxy for life experiences of structural racism. 62 Area-level SES variables included median household income, median value of owner-occupied housing, percentage of population living below the poverty level, percentage of population with less than high school education, percentage of owner-occupied housing units, and population density 63 as a proxy for urbanicity (eTable 2). Area-level SES data were obtained from the 2000 and 2010 US Census and linearly interpolated. After 2010, annual estimates were obtained from the American Community Survey, which has been used in previous epidemiological studies. 54 , 64 We acquired county-level body mass index (BMI) annually from 2000–2012 from the Behavioral Risk Factor Surveillance System. 65 We also adjusted for zip code-level mean Normalized Difference Vegetation Index (NDVI) 66 , 67 as a proxy for greenness exposure, and distance to the nearest hospital as a surrogate measurement for local healthcare access (eTable 2). 68 Meteorological variables Daily ambient air surface temperatures and relative humidity across the contiguous US were obtained at a 4 km 2 spatial resolution from a hybrid model 69 using NASA’s Land Data Assimilation System Phase 2 (NLDAS-2) 70 and the Parameter-elevation Regressions on Independent Slopes Model (PRISM). 71 Meteorological data was also aggregated by zip code. Statistical Analysis Hazard ratios (HRs) with 95% CIs were estimated using stratified Cox proportional hazard models. We ran separate Cox models for preconception, prenatal and postnatal periods with mutual adjustments for prior windows of exposure. Variables that violated the proportional hazards assumption were included as stratification factors in the models. Subsequently, we allowed the baseline hazard to vary by child’s sex, birth year, race/ethnicity and county-level Federal Information Processing Systems (FIPS) code (used to uniquely identify geographic areas). We used the child’s zip code at birth as a cluster index to account for similar characteristics among individuals living in closer proximity. A distributed lag model (DLM) was used to examine single-week (weeks 0–48: 12-week preconception and 37-week prenatal period) and cumulative (weeks 0–11 and 12–48) lag effects of preconception and prenatal PM 2.5 exposure and NDD risk. We used another DLM to assess single-year (from age 0–3 years) and cumulative (year 0–3) lag effects of postnatal PM 2.5 exposure with NDD risk. 72 When parameterizing exposure-response and lag-response relationships for the DLMs, we selected the degrees of freedom (df) with the lowest Akaike Information Criterion (AIC). For PM 2.5 concentrations, the best model fit was a linear exposure-response relationship (eResults 1); the lag-response relationship was modeled using 6 df for the lag effects. We also used DLMs to characterize meteorological exposures. After evaluation of the AIC, both temperature and relative humidity were modeled with a linear exposure-response and 4 df for the lag-response relationship. To obtain a smoothed lag-response shape for the postnatal period, we used monthly distributed lags by assigning participants’ average annual postnatal PM 2.5 value to all 12 months of that year to construct a monthly exposure history. Given that the monthly exposure values within a year were identical, the shape of the monthly lag curve was driven by the modeling constraints rather than the month-to-month variation in exposure. Thus, we focused our inference on the cumulative effect over annual windows rather than month-specific effects. Postnatal exposure models were also adjusted for average preconception/prenatal PM 2.5 exposure. HRs presented in the paper were expressed per 10 µg/m 3 increase in PM 2.5 concentration, with a selected baseline PM 2.5 level of 9 µg/m 3 - the current National Ambient Air Quality Standard. As sex, race/ethnicity, and urbanicity 73 (population density of > or < 500 people/zip code) have been shown in previous studies to modify the association between PM 2.5 exposure and NDD risk, we examined interaction by including multiplicative interaction terms in models. For all interactions, we used a product term between effect modifier and the overall mean PM 2.5 concentration during the respective period (e.g. prenatal, postnatal). If the interaction term was significant, we performed stratified analyses. We further stratified our models into US regions (Northeast, Southeast, Midwest, West and Southwest) as a crude analysis to examine whether differences in temperature patterns and air pollution particle composition across the contiguous US may alter the associations between PM 2.5 exposure and NDD risk. Sensitivity Analyses To check for selection bias due to differential loss to follow-up, we built models using inverse probability of censoring weights (IPCW), estimated as a function of all covariates included in the main analysis. 74 , 75 For each participant, the conditional probability of remaining uncensored through their follow-up was estimated from the model’s predicted survival function. The IPCW was defined as the inverse of this survival probability, which corresponds to the product of conditional inverse probabilities of remaining under follow-up up to that time. To check for the impact of the duration of gestation in our associations, 76,77 we re-ran models with shorter lag windows (26 weeks, 30 weeks). We additionally adjusted models for ambient NO 2 concentrations, 78 which are correlated with PM 2.5 concentrations and may have independent impacts on NDD risk. 79 We conducted an additional analysis only among children diagnosed with an NDD after age 3 years to focus on cases whose etiology may be more strongly driven by environmental factors. 80 , 81 Our interpretation of the results focused on the strength of the adjusted HR and its precision (width of 95% CI), rather than only on statistical significance at an alpha < 0.05 level. 51 However, statistical significance was relied on when identifying potential susceptible windows of exposure using the DLMs. All analyses were conducted in R version 4.1.1. 82 The study protocol was approved by Harvard TH Chan School of Public Health (IRB23-1090), Mass General Brigham (2022P002615), and Rutgers School of Public Health (2024000897). References Thapar A, Cooper M, Rutter M. Neurodevelopmental disorders. 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Clinical Genetic Testing in Autism Spectrum Disorder in a Large Community-Based Population Sample. JAMA Psychiatry . 2020;77(9):979-981. doi:10.1001/jamapsychiatry.2020.0950 R Core Team. R: A language and environment for statistical computing. Published online 2017. http://www.R-project.org/ Additional Declarations There is NO Competing Interest. Supplementary Files SupplementalInformationSept242025Clean.docx Preconception, prenatal and postnatal air pollution exposure and risk of neurodevelopmental disorders among Medicaid recipients Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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08:32:59\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":116992,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLag-response relationship between PM\\u003csub\\u003e2.5 \\u003c/sub\\u003econcentration (10 μg/m\\u003csup\\u003e3\\u003c/sup\\u003e increase) and NDD during preconception and prenatal period (adjusted for temperature, relative humidity, individual and area-level covariates). Hazard ratios [95% CI] for association between preconception and prenatal PM\\u003csub\\u003e2.5 \\u003c/sub\\u003eexposure and neurodevelopmental delays at the cumulative lag (12 weeks for preconception and 37 weeks for prenatal).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7773518/v1/acd346e1553bb7b84b625499.png\"},{\"id\":95276984,\"identity\":\"b592b964-a3a2-4060-87c5-31d3bdbc6d3c\",\"added_by\":\"auto\",\"created_at\":\"2025-11-06 08:32:58\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":44850,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLag-response relationship between PM\\u003csub\\u003e2.5 \\u003c/sub\\u003econcentration (10 μg/m\\u003csup\\u003e3\\u003c/sup\\u003e increase) and NDD during postnatal period (adjusted for temperature, relative humidity, individual and area-level covariates, and preconception/prenatal PM\\u003csub\\u003e2.5 \\u003c/sub\\u003elevels). Hazard ratios [95% CI] for association between preconception and prenatal PM\\u003csub\\u003e2.5 \\u003c/sub\\u003eexposure and neurodevelopmental delays at the cumulative lag (0-3 years).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7773518/v1/48c12d4453d0aa7e66ebb021.png\"},{\"id\":95313255,\"identity\":\"84d27f6c-36a9-45c1-9bed-61a7f543b11a\",\"added_by\":\"auto\",\"created_at\":\"2025-11-06 15:51:11\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":493023,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLag-response relationship between PM\\u003csub\\u003e2.5 \\u003c/sub\\u003econcentration (10 μg/m\\u003csup\\u003e3\\u003c/sup\\u003e increase) and NDD during preconception and prenatal period (adjusted for temperature, relative humidity, individual and area-level covariates, and preconception/prenatal PM\\u003csub\\u003e2.5 \\u003c/sub\\u003elevels) stratified by race/ethnicity. Hazard ratios [95% CI] for association between preconception and prenatal PM\\u003csub\\u003e2.5 \\u003c/sub\\u003eexposure and neurodevelopmental delays at cumulative lag (12 weeks for preconception and 37 weeks for prenatal).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7773518/v1/11c791025ccc60678bbadd6d.png\"},{\"id\":95276999,\"identity\":\"5806b42d-9053-4e15-83ea-fab598d49025\",\"added_by\":\"auto\",\"created_at\":\"2025-11-06 08:33:00\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":154917,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLag-response relationship between PM\\u003csub\\u003e2.5 \\u003c/sub\\u003econcentration (10 μg/m\\u003csup\\u003e3\\u003c/sup\\u003e increase) and NDD during postnatal period (adjusted for temperature, relative humidity, individual and area-level covariates, and preconception/prenatal PM\\u003csub\\u003e2.5 \\u003c/sub\\u003elevels) stratified by race/ethnicity. Hazard ratios [95% CI] for association between postnatal PM\\u003csub\\u003e2.5 \\u003c/sub\\u003eexposure and neurodevelopmental delays at cumulative lag (0-3 years).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7773518/v1/0ada9eddebe9dcc79a37fd20.png\"},{\"id\":104405242,\"identity\":\"bb93f4b9-0a5c-4d76-89ea-1d058e0b5eae\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:22:16\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1705397,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7773518/v1/8ed5ce17-6a51-402b-a5cb-a87a9b50ca84.pdf\"},{\"id\":95313923,\"identity\":\"5da66f38-c74f-40ca-b184-44eab9788a41\",\"added_by\":\"auto\",\"created_at\":\"2025-11-06 15:52:14\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":542719,\"visible\":true,\"origin\":\"\",\"legend\":\"Preconception, prenatal and postnatal air pollution exposure and risk of neurodevelopmental disorders among Medicaid recipients\",\"description\":\"\",\"filename\":\"SupplementalInformationSept242025Clean.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7773518/v1/3c620ee18d7f1efb3ef3fb5d.docx\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"Preconception, prenatal and postnatal air pollution exposure and risk of neurodevelopmental disorders among Medicaid recipients\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eNeurodevelopmental disorders (NDDs) are characterized by delays in social, cognitive, and emotional functioning during early childhood \\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e and diagnosed in approximately 10\\u0026ndash;15% of children in the US,\\u003csup\\u003e2\\u003c/sup\\u003e posing a lifelong health burden.\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e NDDs can arise from a complex interplay of genetic, biological, and environmental influences,\\u003csup\\u003e1,5\\u003c/sup\\u003e including ambient air pollution exposure. Exposure to fine particulate matter (PM\\u003csub\\u003e2.5\\u003c/sub\\u003e), a major component of ambient air pollution, during preconception, prenatal, and early postnatal periods may cause permanent changes in the developing brain and increase the risk of cognitive, behavioral, and motor delays in children.\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eExisting epidemiological evidence has provided conflicting findings, with some studies showing that PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure is associated with increased NDD risk,\\u003csup\\u003e8,9\\u003c/sup\\u003e and others reporting mixed results.\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e Potential explanations for contrary findings include various methods for ascertaining NDDs, inadequate temporal resolution for PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure assessment (e.g. trimester or pregnancy averages), differing degrees of spatial resolution for quantifying PM\\u003csub\\u003e2.5\\u003c/sub\\u003e levels, differences in population characteristics and confounding control, heterogeneity in PM\\u003csub\\u003e2.5\\u003c/sub\\u003e composition and smaller sample sizes for less common NDDs.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR13\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e Studies have also reported differential associations between PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and NDD risk by sex\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e, race/ethnicity,\\u003csup\\u003e17,18\\u003c/sup\\u003e and socioeconomic status (SES).\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eLongitudinal studies assessing early-life exposure windows can help delineate the effects of ambient PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposures on neurodevelopment.\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e Leveraging a national large population-based cohort of 1.5\\u0026nbsp;million births and high-resolution geospatial air pollution data, this longitudinal study investigates whether PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure during preconception, prenatal, and postnatal periods is associated with NDD risk among Medicaid enrollees. We also investigate effect measure modification by sex, race/ethnicity and urbanicity to determine if certain subpopulations may have higher susceptibility to NDD due to PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure.\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003eThe final analytic sample included 1,547,244 mother-child pairs, nearly all of which were singleton births (n=1,526,817; 99%). The most common NDD were developmental speech or language disorder (n=73,911), ADHD (n=67,338), behavioral disorder (n=47,671) and ASD (n=12,942). The median age of diagnosis for ASD, ADHD and any NDD was 3.7 years (inner quartile range (IQR): 3.0), 6.0 years (IQR: 2.4) and 5.0 years (IQR: 3.7), respectively. The median total follow-up time was 7.4 years (IQR: 5.2). PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003econcentrations gradually declined during the study period (eResults 1), with a median weekly prenatal exposure of 11.26 \\u0026mu;g/m\\u003csup\\u003e3\\u0026nbsp;\\u003c/sup\\u003eand median annual postnatal exposure of 10.49 \\u0026mu;g/m\\u003csup\\u003e3\\u003c/sup\\u003e. The proportion of White children was higher in the lower exposure (below median) group, while Black/African American and Hispanic/Latino children were more prevalent in the higher exposure (above/at median) group (Table 1). Substance use indicators (smoking, alcohol, drug use) were more common in the lower PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure group. Median household income and educational attainment were lower in higher PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure areas. PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003econcentrations during the preconception, prenatal and postnatal exposure periods were highly correlated (Spearman correlation coefficients: 0.81-0.88).\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1. Cohort characteristics stratified by median preconception/prenatal and postnatal\\u0026nbsp;PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure (n=1,547,244)\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"636\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 24.5283%;\\\" colspan=\\\"2\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 46.8553%;\\\" colspan=\\\"4\\\"\\u003ePreconception/Prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 26.8868%;\\\" colspan=\\\"2\\\"\\u003ePostnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003eTotal\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003eBelow median (11.26 \\u0026mu;g/m\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003eAbove/at median (11.26 \\u0026mu;g/m\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003eBelow median (10.49 \\u0026mu;g/m\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003eAbove/at median (10.49 \\u0026mu;g/m\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIndividual variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eSex of child (Male) [n (%)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e781,529 (50.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e391,458 (50.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e390,071 (50.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e391,374 (50.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e390,155\\u0026nbsp;(50.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eMaternal race or ethnic group [n (%)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;White\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e642,592 (41.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e361,151 (46.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e281,441 (36.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e365,548 (47.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e277,044 (35.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Black/African-American\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e502,273 (32.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e210,007 (27.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e292,266 (37.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e209,267 (27.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e293,006 (37.8)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Asian/Other Pacific\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e50,193 (3.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e28,203 (3.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e21,990 (2.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e27,812 (3.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e22,381 (2.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Hispanic/Latino\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e300,551 (19.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e137,214 (17.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e163,337 (21.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e133,557 (17.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e166,994 (21.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;Unknown\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e51,635 (3.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e37,047 (4.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e14,588 (1.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e37,427 (4.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e14,208 (1.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eMaternal age at delivery [mean (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e24.5 (5.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e24.1 (5.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e25.0 (5.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e25.0 (5.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e24.1 (5.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eSubstance abuse [n (%)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e20,809 (1.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e12,674 (1.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e8,135 (1.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e13,012 (1.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e7,797 (1.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eSmoking [n (%)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e30,942 (2.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e19,486 (2.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e11,456 (1.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e19,801 (2.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e11,141 (1.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eAlcohol abuse\\u0026nbsp;[n (%)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e7,913 (0.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e4,986 (0.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e2,927 (0.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e5,035 (0.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e2,878 (0.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003ePoor nutrition\\u0026nbsp;[n (%)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e6,473 (0.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e4,609 (0.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e1,864 (0.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e4,697 (0.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e1,776 (0.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eFolate\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e55,791 (3.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e29,964 (3.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e25,827 (3.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e30,611 (4.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e25,180 (3.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eBMI [mean (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e27.7 (1.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e27.8 (1.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e27.7 (1.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e27.8 (1.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e27.7 (1.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eArea-level variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eMedian household income (thousands ($)) [mean (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e41.9 (15.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e44.6 (15.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e39.3 (13.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e45.0 (15.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e38.9 (13.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eOwner-occupied housing value [mean (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e180.8 (142.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e194.3 (151.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e167.9 (131.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e202.3 (156.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e159.6 (123.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eOwner-occupied housing units\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e[% (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e57.7 (19.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e60.2 (18.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e55.3 (19.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e59.5 (19.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e55.9 (19.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eAbove 65 years and did not complete high school [% (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e35.8 (15.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e31.2 (14.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e40.2 (15.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e30.8 (14.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e40.6 (15.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eAbove 65 years and living below the poverty level [% (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e13.7 (8.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e12.9 (8.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e14.5 (8.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e13.0 (8.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e14.4 (8.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003ePopulation Density (people/km\\u0026sup2;) [mean (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e2,670 (5,530)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e2,382 (5,457)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e2,959 (5,587)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e2,616 (5,794)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e2,724 (5,252)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eNearest hospital (km) [mean (sd)]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e5.5 (6.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e6.4 (7.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e4.7 (5.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003e6.3 (7.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 90px;\\\"\\u003e\\n \\u003cp\\u003e4.8 (5.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003esd= standard deviation\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ePreconception and prenatal exposures\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn fully adjusted models, cumulative (37-week) prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure was associated with increased ASD risk (HR: 1.17, 95%CI: [1.00, 1.37]) (Figure 1). The exposure-response curve showed increased ASD risk at higher levels of preconception/ prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure (eResults 2). No associations were found between cumulative preconception or prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure and any other NDD among the overall study population.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ePreconception and prenatal critical exposure windows\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA critical exposure window for increased ASD risk occurred during weeks 23-31 of gestation, with a 10 \\u0026mu;g/m\\u003csup\\u003e3\\u003c/sup\\u003e increase in cumulative PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure associated with 1.09 times (95%CI:[1.02, 1.17]) the risk of ASD (Figure 1). Additionally, a 10 \\u0026mu;g/m\\u003csup\\u003e3\\u003c/sup\\u003e increase in PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003elevels at 16-22 weeks gestation was associated with 1.03 times (95%CI:[1.01, 1.06]) the risk of ADHD. Prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure at gestational weeks 1-15 was associated with reduced ADHD risk (0.90 95%CI:[0.85,0.94]).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003ePostnatal exposures\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAfter adjustment for prenatal/preconception PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure, postnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure was associated with higher DCD risk (1.77 95%CI:[0.93, 3.40]) (Figure 3). The exposure-response curve showed increased DCD risk at higher levels of postnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure (eResults 3). No associations were found between cumulative postnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003econcentrations and risk of other NDD. In models without inclusion of preconception/prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure as a covariate, there were no substantial changes in the HRs for postnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure (eResults 4).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSusceptible windows in the postnatal period\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePostnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003elevels at age 0 years were associated with ADHD risk (1.18 95%CI:[0.99, 1.41]) (eResults 5; Figure 2). Postnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure at age 1 and 2 years was highly associated with risk of ID (1.96 95%CI:[1.08, 3.55]) and DCD (1.67 95%CI:[1.13, 2.47]), respectively.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eEffect modification during the prenatal exposure period\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe identified that race/ethnicity was an effect modifier in the association between prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure and ADHD risk (p\\u003csub\\u003einteraction\\u003c/sub\\u003e\\u0026lt;0.001). The association between cumulative prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure and ADHD risk was also higher among Hispanic participants (1.12 95%CI: [0.94, 1.33]), compared with Black (0.89 95%CI:[0.75, 1.04]) and White (0.79 95%CI:[0.70, 0.89]) individuals. Additionally, there was an association between prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure and ID risk among Hispanic enrollees (1.57 95%CI:[1.02, 2.43]), but not White individuals (0.94 95%CI:[0.61, 1.48]). The sex of the child (eResults 6) and urbanicity (eResults 7) did not modify the association between preconception/prenatal exposure and NDD risk. When stratifying models by US region, there were not substantial differences in the association between cumulative preconception and prenatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure with NDD risk (eResults 8).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eEffect modification during the postnatal exposure period\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants\\u0026rsquo; race/ethnicity modified the association between postnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure and ADHD (p\\u003csub\\u003einteraction\\u003c/sub\\u003e=0.001) risk (Figure 4). Specifically, Black (1.10 95%CI:[0.79, 1.53]) and Hispanic (1.28 95%CI:[0.91, 1.81]) enrollees had an elevated risk of ADHD due to PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure, while White participants did not (0.90 95%CI:[0.69, 1.16]). The child\\u0026rsquo;s sex (eResults 9) and urbanicity (eResults 10) did not modify the association between postnatal PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure and risk of any NDD. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSensitivity analyses\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn a sensitivity analysis that included models containing shorter maximum lags (26- and 30-week) to account for different gestation durations, HRs (eResults 11) and the shape of the lag-response curves (eResults 12) were similar. When including only children with an NDD diagnosis at age 3 or later (less severe cases), the HRs were similar to those generated from the main models (eResults 13). When adjusting for average ambient NO\\u003csub\\u003e2\\u003c/sub\\u003e concentrations, minimal changes in the HRs occurred (eResults 14). Finally, accounting for censoring weights did not alter the findings (eResults 15).\\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eIn this national study of more than 1.5\\u0026nbsp;million children, we found that prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure was associated with increased risk of ASD, with a sensitive window of exposure identified during weeks 23\\u0026ndash;31. We also found that prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure in gestational weeks 16\\u0026ndash;22 and the first year of life was associated with an increased risk of ADHD.\\u003c/p\\u003e\\u003cp\\u003ePrevious studies have similarly reported that late pregnancy PM\\u003csub\\u003e2.5\\u003c/sub\\u003e\\u003csup\\u003e22\\u003c/sup\\u003e and PM\\u003csub\\u003e10\\u003c/sub\\u003e\\u003csup\\u003e23\\u003c/sup\\u003e exposures had the strongest association with increased ASD risk. A large number of past studies\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR13\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR25\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e have reported associations between both prenatal and postnatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and ASD risk. Regarding ADHD, another study identified the same prenatal window of susceptibility (gestational weeks 16\\u0026ndash;22), but found that ages 1\\u0026ndash;3 years were a more vulnerable postnatal period.\\u003csup\\u003e\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e However, two Asian studies (in Taiwan\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e and China\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e) found significant associations between first-year PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposures and ADHD risk. In our results, we identified a protective effect of prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure on ADHD risk during gestational weeks 1\\u0026ndash;15. This protective effect may be due to live birth bias, which arises when the selective survival between conception and birth skews the distribution of prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposures among the subset of remaining live births.\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e Life birth bias could also lead to underestimation of the associations between PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and ASD risk. Beyond ASD and ADHD, we identified a potential association between postnatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and the development of DCD, with a possible susceptible exposure window occurring during age 2 years. Recently, a Chinese study found that average PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure during age 0\\u0026ndash;3 years was associated with increased odds of reduced motor performance.\\u003csup\\u003e\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eBiological mechanisms that may explain the link between PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and NDD include penetration of PM across the blood-brain barrier, which can impede neuronal function,\\u003csup\\u003e32\\u003c/sup\\u003e and oxidative stress and neuroinflammation,\\u003csup\\u003e33\\u003c/sup\\u003e which can dysregulate neurodevelopmental processes.\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e For ASD specifically, prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure may impact risk via placental toxicity resulting in impaired oxygenation and nutrient transport to the fetus and therefore disruption of central nervous system development.\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eASD and ADHD risk due to postnatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure was higher among Black and Hispanic Medicaid enrollees compared with White individuals. Additionally, the risk of ASD, ADHD and ID due to prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure was higher among Hispanic Medicaid recipients compared with Black and White individuals. Similarly, other air pollution epidemiological studies have reported higher risk among racial/ethnic minorities,\\u003csup\\u003e24,36\\u003c/sup\\u003e with potential explanations being that Black and Hispanic communities in the US are generally closer to freeways and industrial facilities\\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e and face negative psychosocial stressors related to systemic racism.\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR39 CR40 CR41 CR42\\\" citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e These social determinants may exacerbate the effects of PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure on NDD risk through neuroendocrine, vascular, or immune mechanisms involved in the body\\u0026rsquo;s stress response.\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo our knowledge, this is one of the first national studies to evaluate the association between PM\\u003csub\\u003e2.5\\u003c/sub\\u003e concentrations and NDD risk among Medicaid recipients, an underrepresented population in environmental pregnancy studies; our analysis also included understudied NDDs beyond ASD and ADHD. Our large sample size of \\u0026gt;\\u0026thinsp;1.5\\u0026nbsp;million Medicaid enrollees enabled us to examine effect modification by several sociodemographic factors, which have been less investigated due to a more homogenous study population or an insufficient sample size.\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e We additionally adjusted for a large number individual and area-level confounding variables, enhancing the robustness of our findings. Our PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposures were derived from a validated ensemble model with high spatiotemporal resolution, thereby reducing potential exposure misclassifications from studies relying on more sparse monitoring networks.\\u003csup\\u003e\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e Outcome misclassification was reduced as the NDD definitions were validated with high predictive values; outcome misclassification would be non-differential and bias toward a null finding. We undertook multiple sensitivity analyses to ensure that biases (e.g. informative censoring, outcome misclassification) did not affect our results. Finally, we accounted for residential mobility during pregnancy, since we obtained zip codes at the LMP and at birth.\\u003c/p\\u003e\\u003cp\\u003eAlthough the ability to detect susceptible exposure windows is a strength of using DLMs, there were many critical windows examined, which carries a higher risk of identifying a false positive association due to larger number of comparisons. However, the susceptible windows we identified for more commonly studied outcomes, such as ASD\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e and ADHD,\\u003csup\\u003e27\\u003c/sup\\u003e have been acknowledged in other studies.\\u003c/p\\u003e\\u003cp\\u003eExposure misclassification may exist when assigning zip code-level PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposures, as they are not representative of individual PM\\u003csub\\u003e2.5\\u003c/sub\\u003e levels.\\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e However, a recent study found that associations between individual and zip code-level PM\\u003csub\\u003e2.5\\u003c/sub\\u003e estimates and health outcomes are similar.\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e Zip code\\u0026ndash;level PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposures may help mitigate residual confounding from unmeasured individual-level factors, such as time-activity patterns, because these proxy measures are more distal from personal behaviors.\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u003c/sup\\u003e As a result, they are less influenced by individual-level variable confounding and less prone to reverse causation.\\u003csup\\u003e\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e Additionally, zip code-level data can be more informative for air pollution policy recommendations, which are typically implemented at larger geographical levels.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eOur study contributes to growing evidence that prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure is a plausible risk factor for ASD and ID, and that postnatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure may increase ADHD and DCD risk. As several NDD, such as ASD and ADHD, are being increasingly diagnosed in the US,\\u003csup\\u003e2,6\\u003c/sup\\u003e it is critical to continue enacting policies that lower PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposures to protect early child development.\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n \\u003cp\\u003eWe constructed a population-based cohort study of pregnant women enrolled in Medicaid from 2001\\u0026ndash;2013. Medicaid is a state and federal health insurance program available to low-income US individuals and covers medical expenses of \\u0026gt;\\u0026thinsp;40% of births nationally.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e Medicaid beneficiary enrollment and healthcare utilization claims were obtained from the Medicaid Analytic Extract (MAX) dataset and Transformed Medicaid Statistical Information System Analytic Files (TAF);\\u003csup\\u003e52\\u003c/sup\\u003e cohort creation methods have been described elsewhere.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u003c/sup\\u003e Pregnant women enrolled in Medicaid are predominantly younger, more racially and ethnically diverse, and economically disadvantaged compared to the general population.\\u003c/p\\u003e\\n \\u003cp\\u003eThe study population consisted of liveborn children of females aged 12\\u0026ndash;55 years old enrolled in Medicaid at least three months before the date of their estimated last menstrual period (LMP) to at least one month after delivery. We defined the cohort based on the estimated date of conception (DOC), which was approximated by assuming the DOC fell two weeks after the LMP date, assuming a 28-day cycle. To minimize fixed cohort bias,\\u003csup\\u003e42\\u003c/sup\\u003e we restricted inclusion to individuals with a DOC between January 1, 2001, and December 31, 2013. We excluded pregnancies with a DOC after December 31, 2013 to ensure a minimum of three years of follow-up, given that exposure data (described in the following section) were available through December 31, 2016.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003ePostnatal follow-up time was calculated as the number of days from a child\\u0026rsquo;s birth until either their enrollment in Medicaid ended, the development of a NDD, death, or study period end, whichever came first, with up to 12 years of maximum follow-up. Children with any chromosomal or genetic abnormality were excluded.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eExposure assessment\\u003c/h2\\u003e\\n \\u003cp\\u003eAverage zip code-level PM\\u003csub\\u003e2.5\\u003c/sub\\u003e concentrations were assigned to each mother based on their residential zip code. The PM\\u003csub\\u003e2.5\\u003c/sub\\u003e measurements were obtained from an ensemble model that estimates spatiotemporally resolved concentrations at a 1 km\\u003csup\\u003e2\\u003c/sup\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003egrid resolution across the contiguous US from 2000-2016.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e The model has been applied in several epidemiological studies.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e\\u003c/sup\\u003e PM\\u003csub\\u003e2.5\\u003c/sub\\u003e concentration predictions were aggregated at a zip code level by inverse distance averaging the four nearest grid cells to the residential zip code centroid.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e\\u003c/sup\\u003e If zip codes at the LMP and delivery differed, we attributed 50% of the pregnancy exposure duration to each zip code, which has been done previously.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eOutcome variables\\u003c/h2\\u003e\\n \\u003cp\\u003eOutcome data from the MAX database included a total of seven NDDs: autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), learning disability, developmental speech or language disorders, developmental coordination disorder (DCD), intellectual disability (ID), and behavioral disorder. Validated algorithms have been applied to confirm the presence of NDDs, with event rates in this cohort aligned with US statistics (eTable 1).\\u003csup\\u003e58\\u0026ndash;60\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eCovariates\\u003c/h2\\u003e\\n \\u003cp\\u003eWe adjusted for individual and area-level factors, as in prior studies.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u003c/sup\\u003e Individual-level variables included maternal age, maternal race/ethnicity, substance abuse, smoking, alcohol abuse, poor nutrition, folate use during pregnancy, birth year, and season of conception (eTable 2). We included race/ethnicity as a covariate as a proxy for life experiences of structural racism.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e Area-level SES variables included median household income, median value of owner-occupied housing, percentage of population living below the poverty level, percentage of population with less than high school education, percentage of owner-occupied housing units, and population density\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e\\u003c/sup\\u003e as a proxy for urbanicity (eTable 2). Area-level SES data were obtained from the 2000 and 2010 US Census and linearly interpolated. After 2010, annual estimates were obtained from the American Community Survey, which has been used in previous epidemiological studies.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e We acquired county-level body mass index (BMI) annually from 2000\\u0026ndash;2012 from the Behavioral Risk Factor Surveillance System.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u003c/sup\\u003e We also adjusted for zip code-level mean Normalized Difference Vegetation Index (NDVI)\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e\\u003c/sup\\u003e as a proxy for greenness exposure, and distance to the nearest hospital as a surrogate measurement for local healthcare access (eTable 2).\\u003csup\\u003e68\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eMeteorological variables\\u003c/h2\\u003e\\n \\u003cp\\u003eDaily ambient air surface temperatures and relative humidity across the contiguous US were obtained at a 4 km\\u003csup\\u003e2\\u003c/sup\\u003e spatial resolution from a hybrid model\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e\\u003c/sup\\u003e using NASA\\u0026rsquo;s Land Data Assimilation System Phase 2 (NLDAS-2)\\u003csup\\u003e70\\u003c/sup\\u003e and the Parameter-elevation Regressions on Independent Slopes Model (PRISM).\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e\\u003c/sup\\u003e Meteorological data was also aggregated by zip code.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eHazard ratios (HRs) with 95% CIs were estimated using stratified Cox proportional hazard models. We ran separate Cox models for preconception, prenatal and postnatal periods with mutual adjustments for prior windows of exposure. Variables that violated the proportional hazards assumption were included as stratification factors in the models. Subsequently, we allowed the baseline hazard to vary by child\\u0026rsquo;s sex, birth year, race/ethnicity and county-level Federal Information Processing Systems (FIPS) code (used to uniquely identify geographic areas). We used the child\\u0026rsquo;s zip code at birth as a cluster index to account for similar characteristics among individuals living in closer proximity.\\u003c/p\\u003e\\n \\u003cp\\u003eA distributed lag model (DLM) was used to examine single-week (weeks 0\\u0026ndash;48: 12-week preconception and 37-week prenatal period) and cumulative (weeks 0\\u0026ndash;11 and 12\\u0026ndash;48) lag effects of preconception and prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and NDD risk. We used another DLM to assess single-year (from age 0\\u0026ndash;3 years) and cumulative (year 0\\u0026ndash;3) lag effects of postnatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure with NDD risk.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eWhen parameterizing exposure-response and lag-response relationships for the DLMs, we selected the degrees of freedom (df) with the lowest Akaike Information Criterion (AIC). For PM\\u003csub\\u003e2.5\\u003c/sub\\u003e concentrations, the best model fit was a linear exposure-response relationship (eResults 1); the lag-response relationship was modeled using 6 df for the lag effects. We also used DLMs to characterize meteorological exposures. After evaluation of the AIC, both temperature and relative humidity were modeled with a linear exposure-response and 4 df for the lag-response relationship.\\u003c/p\\u003e\\n \\u003cp\\u003eTo obtain a smoothed lag-response shape for the postnatal period, we used monthly distributed lags by assigning participants\\u0026rsquo; average annual postnatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e value to all 12 months of that year to construct a monthly exposure history. Given that the monthly exposure values within a year were identical, the shape of the monthly lag curve was driven by the modeling constraints rather than the month-to-month variation in exposure. Thus, we focused our inference on the cumulative effect over annual windows rather than month-specific effects. Postnatal exposure models were also adjusted for average preconception/prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure.\\u003c/p\\u003e\\n \\u003cp\\u003eHRs presented in the paper were expressed per 10 \\u0026micro;g/m\\u003csup\\u003e3\\u003c/sup\\u003e increase in PM\\u003csub\\u003e2.5\\u003c/sub\\u003e concentration, with a selected baseline PM\\u003csub\\u003e2.5\\u003c/sub\\u003e level of 9 \\u0026micro;g/m\\u003csup\\u003e3\\u003c/sup\\u003e - the current National Ambient Air Quality Standard.\\u003c/p\\u003e\\n \\u003cp\\u003eAs sex, race/ethnicity, and urbanicity\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e\\u003c/sup\\u003e (population density of \\u0026gt;\\u0026thinsp;or \\u0026lt;\\u0026thinsp;500 people/zip code) have been shown in previous studies to modify the association between PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and NDD risk, we examined interaction by including multiplicative interaction terms in models. For all interactions, we used a product term between effect modifier and the overall mean PM\\u003csub\\u003e2.5\\u003c/sub\\u003e concentration during the respective period (e.g. prenatal, postnatal). If the interaction term was significant, we performed stratified analyses. We further stratified our models into US regions (Northeast, Southeast, Midwest, West and Southwest) as a crude analysis to examine whether differences in temperature patterns and air pollution particle composition across the contiguous US may alter the associations between PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and NDD risk.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eSensitivity Analyses\\u003c/h2\\u003e\\n \\u003cp\\u003eTo check for selection bias due to differential loss to follow-up, we built models using inverse probability of censoring weights (IPCW), estimated as a function of all covariates included in the main analysis.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e\\u003c/sup\\u003e For each participant, the conditional probability of remaining uncensored through their follow-up was estimated from the model\\u0026rsquo;s predicted survival function. The IPCW was defined as the inverse of this survival probability, which corresponds to the product of conditional inverse probabilities of remaining under follow-up up to that time.\\u003c/p\\u003e\\n \\u003cp\\u003eTo check for the impact of the duration of gestation in our associations,\\u003csup\\u003e76,77\\u003c/sup\\u003e we re-ran models with shorter lag windows (26 weeks, 30 weeks). We additionally adjusted models for ambient NO\\u003csub\\u003e2\\u003c/sub\\u003e concentrations,\\u003csup\\u003e78\\u003c/sup\\u003e which are correlated with PM\\u003csub\\u003e2.5\\u003c/sub\\u003e concentrations and may have independent impacts on NDD risk.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e\\u003c/sup\\u003e We conducted an additional analysis only among children diagnosed with an NDD after age 3 years to focus on cases whose etiology may be more strongly driven by environmental factors.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e,\\u003cspan class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eOur interpretation of the results focused on the strength of the adjusted HR and its precision (width of 95% CI), rather than only on statistical significance at an alpha\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 level.\\u003csup\\u003e\\u003cspan class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e However, statistical significance was relied on when identifying potential susceptible windows of exposure using the DLMs. All analyses were conducted in R version 4.1.1.\\u003csup\\u003e82\\u003c/sup\\u003e The study protocol was approved by Harvard TH Chan School of Public Health (IRB23-1090), Mass General Brigham (2022P002615), and Rutgers School of Public Health (2024000897).\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eThapar A, Cooper M, Rutter M. Neurodevelopmental disorders. \\u003cem\\u003eThe Lancet Psychiatry\\u003c/em\\u003e. 2017;4(4):339-346. doi:10.1016/S2215-0366(16)30376-5\\u003c/li\\u003e\\n\\u003cli\\u003eXu G, Strathearn L, Liu B, Yang B, Bao W. Twenty-Year Trends in Diagnosed Attention-Deficit/Hyperactivity Disorder Among US Children and Adolescents, 1997-2016. \\u003cem\\u003eJAMA Network Open\\u003c/em\\u003e. 2018;1(4):e181471. doi:10.1001/jamanetworkopen.2018.1471\\u003c/li\\u003e\\n\\u003cli\\u003eFaraone SV, Biederman J, Mick E. The age-dependent decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies. \\u003cem\\u003ePsychological Medicine\\u003c/em\\u003e. 2006;36(2):159-165. doi:10.1017/S003329170500471X\\u003c/li\\u003e\\n\\u003cli\\u003eMonk C, Lugo-Candelas C, Trumpff C. Prenatal Developmental Origins of Future Psychopathology: Mechanisms and Pathways. \\u003cem\\u003eAnnual Review of Clinical Psychology\\u003c/em\\u003e. 2019;15(Volume 15, 2019):317-344. doi:10.1146/annurev-clinpsy-050718-095539\\u003c/li\\u003e\\n\\u003cli\\u003eLyall K, Croen L, Daniels J, et al. The Changing Epidemiology of Autism Spectrum Disorders. \\u003cem\\u003eAnnual Review of Public Health\\u003c/em\\u003e. 2017;38(Volume 38, 2017):81-102. doi:10.1146/annurev-publhealth-031816-044318\\u003c/li\\u003e\\n\\u003cli\\u003eHa S. Air pollution and neurological development in children. \\u003cem\\u003eDevelopmental Medicine \\u0026amp; Child Neurology\\u003c/em\\u003e. 2021;63(4):374-381. doi:10.1111/dmcn.14758\\u003c/li\\u003e\\n\\u003cli\\u003eCotter DL, Campbell CE, Sukumaran K, et al. Effects of ambient fine particulates, nitrogen dioxide, and ozone on maturation of functional brain networks across early adolescence. \\u003cem\\u003eEnvironment International\\u003c/em\\u003e. 2023;177:108001. doi:10.1016/j.envint.2023.108001\\u003c/li\\u003e\\n\\u003cli\\u003eSuades-Gonz\\u0026aacute;lez E, Gascon M, Guxens M, Sunyer J. Air Pollution and Neuropsychological Development: A Review of the Latest Evidence. \\u003cem\\u003eEndocrinology\\u003c/em\\u003e. 2015;156(10):3473-3482. doi:10.1210/en.2015-1403\\u003c/li\\u003e\\n\\u003cli\\u003eClifford A, Lang L, Chen R, Anstey KJ, Seaton A. Exposure to air pollution and cognitive functioning across the life course \\u0026ndash; A systematic literature review. \\u003cem\\u003eEnvironmental Research\\u003c/em\\u003e. 2016;147:383-398. doi:10.1016/j.envres.2016.01.018\\u003c/li\\u003e\\n\\u003cli\\u003eYu X, Rahman MM, Wang Z, et al. Evidence of susceptibility to autism risks associated with early life ambient air pollution: A systematic review. \\u003cem\\u003eEnvironmental Research\\u003c/em\\u003e. 2022;208:112590. doi:10.1016/j.envres.2021.112590\\u003c/li\\u003e\\n\\u003cli\\u003eVolk HE, Perera F, Braun JM, et al. Prenatal air pollution exposure and neurodevelopment: A review and blueprint for a harmonized approach within ECHO. \\u003cem\\u003eEnvironmental Research\\u003c/em\\u003e. 2021;196:110320. doi:10.1016/j.envres.2020.110320\\u003c/li\\u003e\\n\\u003cli\\u003eChun H, Leung C, Wen SW, McDonald J, Shin HH. Maternal exposure to air pollution and risk of autism in children: A systematic review and meta-analysis. \\u003cem\\u003eEnvironmental Pollution\\u003c/em\\u003e. 2020;256:113307. doi:10.1016/j.envpol.2019.113307\\u003c/li\\u003e\\n\\u003cli\\u003eFlores-Pajot MC, Ofner M, Do MT, Lavigne E, Villeneuve PJ. Childhood autism spectrum disorders and exposure to nitrogen dioxide, and particulate matter air pollution: A review and meta-analysis. \\u003cem\\u003eEnviron Res\\u003c/em\\u003e. 2016;151:763-776. doi:10.1016/j.envres.2016.07.030\\u003c/li\\u003e\\n\\u003cli\\u003eLam J, Sutton P, Kalkbrenner A, et al. A Systematic Review and Meta-Analysis of Multiple Airborne Pollutants and Autism Spectrum Disorder. \\u003cem\\u003ePLOS ONE\\u003c/em\\u003e. 2016;11(9):e0161851. doi:10.1371/journal.pone.0161851\\u003c/li\\u003e\\n\\u003cli\\u003eChiu YHM, Hsu HHL, Coull BA, et al. Prenatal particulate air pollution and neurodevelopment in urban children: Examining sensitive windows and sex-specific associations. \\u003cem\\u003eEnvironment International\\u003c/em\\u003e. 2016;87:56-65. doi:10.1016/j.envint.2015.11.010\\u003c/li\\u003e\\n\\u003cli\\u003eLertxundi A, Andiarena A, Mart\\u0026iacute;nez MD, et al. 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Published online 2017. http://www.R-project.org/\\u003cstrong\\u003e\\u003c/strong\\u003e\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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\":\"Neurodevelopmental delays, autism spectrum disorder, attention-deficit/hyperactivity disorder, PM2.5, prenatal, postnatal\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7773518/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7773518/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eStudies examining the association between ambient PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and risk of neurodevelopmental disorders (NDDs) in children have yielded mixed results. We conducted a cohort study spanning 2001\\u0026ndash;2013 among 1,547,244 Medicaid enrollees to quantify the association between zip-code level preconception (12-week), prenatal (37-week), and postnatal (3-year) PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure and NDD risk among US children. Cox proportional hazards models were used to estimate risks of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), learning difficulty, developmental speech or language disorder, developmental coordination disorder (DCD), intellectual disability (ID) and behavioral disorder, adjusting for demographics, behavioral factors, meteorological characteristics, and area-level socioeconomic indicators. Distributed lag models examined critical exposure windows and cumulative risks of PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposures. A 10 \\u0026micro;g/m\\u003csup\\u003e3\\u003c/sup\\u003e increase in cumulative prenatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure was associated with 1.17 (95%CI:[1.00,1.37]) times greater ASD risk. A critical exposure window for ASD existed during gestational weeks 23\\u0026ndash;31 (HR:1.09 95%CI:[1.02,1.17]). Another critical prenatal exposure window occurred for ADHD at 16\\u0026ndash;22 weeks gestation (HR:95%CI:[1.01, 1.06]). Cumulative postnatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure was linked with increased DCD risk (HR:1.77 95%CI:[0.93,3.34]), with a critical exposure window at age 2 (HR:1.67 95%CI:[1.13, 2.47]). Prenatal and postnatal PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure may contribute to increased risk of multiple NDD among US children from low-income families.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Preconception, prenatal and postnatal air pollution exposure and risk of neurodevelopmental disorders among Medicaid recipients\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-06 08:32:51\",\"doi\":\"10.21203/rs.3.rs-7773518/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"4f2c0f4c-fbac-46b6-833d-a62808dab084\",\"owner\":[],\"postedDate\":\"November 6th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":57446713,\"name\":\"Earth and environmental sciences/Environmental social sciences/Environmental impact\"},{\"id\":57446714,\"name\":\"Health sciences/Health care/Public health/Epidemiology\"},{\"id\":57446715,\"name\":\"Health sciences/Neurology/Neurological disorders/Neurodevelopmental disorders/Autism spectrum disorders\"}],\"tags\":[],\"updatedAt\":\"2026-03-09T16:41:33+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-06 08:32:51\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7773518\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7773518\",\"identity\":\"rs-7773518\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}