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BackgroundResidential proximity to major roadways, and prenatal exposures to particulate matter 1000 m away from a major roadway, those resided 50-100 m [RR: 2.12 (1.00-4.52)] and 100-500 m [RR: 2.07 (1.02-4.22)] away had twice the risk of failing the communication domain. Prenatal exposures to both PM2.5 and ozone during various pregnancy windows had weak but significant associations with failing any developmental domain with effects ranging from 1.6% to 2.7% for a 10 μg/m3 increase in PM2.5 and 0.7%-1.7% for a 10 ppb increase in ozone. Average daily postnatal ozone exposure was positively associated with failing the overall screening by 8 months [3.3% (1.1%-5.5%)], 12 months [17.7% (10.4%-25.5%)], and 30 months [7.6%, (1.3%-14.3%)]. Findings were mixed for postnatal PM2.5 exposures.ConclusionsIn this prospective cohort study, proximity to major roadway and prenatal/early-life exposures to PM2.5 and O3 were associated with developmental delays. While awaiting larger studies with personal air pollution assessment, efforts to minimize air pollution exposures during critical developmental windows may be warranted.

Background Mechanisms underlying racial and ethnic disparities in robot-assisted radical prostatectomy (RARP) vs open radical prostatectomy (ORP) are unclear. We sought to test 2 physician-level hypotheses: 1) Segregated Treatment and 2) Differential Treatment. Methods This observational study used the New York State Cancer Registry linked to discharge records and included patients undergoing radical prostatectomy for localized prostate cancer from October 1, 2008 to December 31, 2018. For hypothesis 1, we examined the association between patient race and ethnicity and treating surgeon RARP use (high-use surgeons, low-use surgeons, and surgeons at non-RARP facilities). For hypothesis 2, we determined the association between patient race and ethnicity and receipt of RARP when matching on treating surgeon, age, year of procedure, and Gleason group. We explored the role of insurance in both analyses. Results This study included 18 926 patients (8.0% Hispanic, 16.9% non-Hispanic Black, 75.1% non-Hispanic White), with a mean age of 60.4 ± 7.1 years. Compared with non-Hispanic White patients, Hispanic and non-Hispanic Black patients had higher odds of being treated by low-RARP-use surgeons (odds ratio [OR] = 2.16, 95% confidence interval [CI] = 1.20 to 3.88; OR = 1.76, 95% CI = 1.06 to 2.94, respectively) and by surgeons at non-RARP facilities (OR = 4.19, 95% CI = 2.18 to 8.07; OR = 4.60, 95% CI = 2.58 to 8.23, respectively). In the matched cohorts, Hispanic and non-Hispanic Black patients were less likely to receive RARP than non-Hispanic White patients (OR = 0.78, 95% CI = 0.62 to 0.98; OR = 0.75, 95% CI = 0.57 to 1.00, respectively). These associations were partially attenuated after accounting for insurance. Conclusions Racial and ethnic disparities in RARP use are related to patients being treated by different surgeons and treated differently by the same surgeons. Identifying and addressing multilevel barriers to equitable surgical treatment is needed to reduce disparities among prostate cancer patients. [ABSTRACT FROM AUTHOR]

Background: Individual measures of socioeconomic status (SES) have been associated with an increased risk of neural tube defects (NTDs); however, the association between neighborhood SES and NTD risk is unknown. Using data from the National Birth Defects Prevention Study (NBDPS) from 1997 to 2011, we investigated the association between measures of census tract SES and NTD risk. Methods: The study population included 10,028 controls and 1829 NTD cases. We linked maternal addresses to census tract SES measures and used these measures to calculate the neighborhood deprivation index. We used generalized estimating equations to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) estimating the impact of quartiles of census tract deprivation on NTDs adjusting for maternal race-ethnicity, maternal education, and maternal age at delivery. Results: Quartiles of higher neighborhood deprivation were associated with NTDs when compared with the least deprived quartile (Q2: aOR = 1.2; 95% CI = 1.0, 1.4; Q3: aOR = 1.3, 95% CI = 1.1, 1.5; Q4 (highest): aOR = 1.2; 95% CI = 1.0, 1.4). Results for spina bifida were similar; however, estimates for anencephaly and encephalocele were attenuated. Associations differed by maternal race-ethnicity. Conclusions: Our findings suggest that residing in a census tract with more socioeconomic deprivation is associated with an increased risk for NTDs, specifically spina bifida. [ABSTRACT FROM AUTHOR]

Background: Gastroschisis prevalence more than doubled between 1995 and 2012. While there are individual‐level risk factors (e.g., young maternal age, low body mass index), the impact of environmental exposures is not well understood. Methods: We used the U.S. Environmental Protection Agency's Environmental Quality Index (EQI) as a county‐level estimate of cumulative environmental exposures for five domains (air, water, land, sociodemographic, and built) and overall from 2006 to 2010. Adjusted odds ratios (aOR) and 95% confidence interval (CI) were estimated from logistic regression models between EQI tertiles (better environmental quality (reference); mid; poorer) and gastroschisis in the National Birth Defects Prevention Study from births delivered between 2006 and 2011. Our analysis included 594 cases with gastroschisis and 4105 infants without a birth defect (controls). Results: Overall EQI was modestly associated with gastroschisis (aOR [95% CI]: 1.29 [0.98, 1.71]) for maternal residence in counties with poorer environmental quality, compared to the reference (better environmental quality). Within domain‐specific indices, only the sociodemographic domain (aOR: 1.51 [0.99, 2.29]) was modestly associated with gastroschisis, when comparing poorer to better environmental quality. Conclusions: Future work could elucidate pathway(s) by which components of the sociodemographic domain or possibly related psychosocial factors like chronic stress potentially contribute to risk of gastroschisis. [ABSTRACT FROM AUTHOR]

Background: Two strong risk factors for gastroschisis are young maternal age (<20 years) and low/normal pre‐pregnancy body mass index (BMI), yet the reasons remain unknown. We explored whether neighborhood‐level socioeconomic position (nSEP) during pregnancy modified these associations. Methods: We analyzed data from 1269 gastroschisis cases and 10,217 controls in the National Birth Defects Prevention Study (1997–2011). To characterize nSEP, we applied the neighborhood deprivation index and used generalized estimating equations to calculate odds ratios and relative excess risk due to interaction. Results: Elevated odds of gastroschisis were consistently associated with young maternal age and low/normal BMI, regardless of nSEP. High‐deprivation neighborhoods modified the association with young maternal age. Infants of young mothers in high‐deprivation areas had lower odds of gastroschisis (adjusted odds ratio [aOR]: 3.1, 95% confidence interval [CI]: 2.6, 3.8) than young mothers in low‐deprivation areas (aOR: 6.6; 95% CI: 4.6, 9.4). Mothers of low/normal BMI had approximately twice the odds of having an infant with gastroschisis compared to mothers with overweight/obese BMI, regardless of nSEP (aOR range: 1.5–2.3). Conclusion: Our findings suggest nSEP modified the association between gastroschisis and maternal age, but not BMI. Further research could clarify whether the modification is due to unidentified biologic and/or non‐biologic factors. [ABSTRACT FROM AUTHOR]

Background: Neighborhood-level socioeconomic position has been shown to influence birth outcomes, including selected birth defects. This study examines the un derstudied association between neighborhood- level socioeconomic position during early pregnancy and the risk of gastroschisis, an abdominal birth defect of increasing prevalence. Methods: We conducted a case-control study of 1,269 gastroschisis cases and 10,217 controls using data from the National Birth Defects Prevention Study (1997-2011). To characterize neighborhood-level socioeconomic position, we conducted a principal component analysis to construct two indices--Neighborhood Deprivation Index (NDI) and Neighborhood Socioeconomic Position Index (nSEPI). We created neighborhood-level indices using census socioeconomic indicators corresponding to census tracts associated with addresses where mothers lived the longest during the periconceptional period. We used generalized estimating equations to estimate odds ratios (ORs) and 95% confidence intervals (CIs), with multiple imputations for missing data and adjustment for maternal race-ethnicity, household income, education, birth year, and duration of residence. Results: Mothers residing in moderate (NDI Tertile 2 aOR = 1.23; 95% CI = 1.03, 1.48 and nSEPI Tertile 2 aOR = 1.24; 95% CI = 1.04, 1.49) or low socioeconomic neighborhoods (NDI Tertile 3 aOR = 1.28; 95% CI = 1.05, 1.55 and nSEPI Tertile 3 aOR = 1.32, 95% CI = 1.09, 1.61) were more likely to deliver an infant with gastroschisis compared with mothers residing in high socioeconomic neighborhoods. Conclusions: Our findings suggest that lower neighborhood-level socioeconomic position during early pregnancy is associated with elevated odds of gastroschisis. Additional epidemiologic studies may aid in confirming this finding and evaluating potential mechanisms linking neighborhood-level socioeconomic factors and gastroschisis. [ABSTRACT FROM AUTHOR]

A broad spectrum of model-derived weather datasets are available in the US. Because each product integrates atmospheric conditions with different model processes, each produces different statistical biases. This study validated air temperature from NLDAS-2 and a novel statistically downscaled NLDAS-2 against observational weather station data for the state of Florida. We statistically downscaled NLDAS-2 to a 1-km grid product using MODIS land surface temperature. We investigated mean biases and Pearson correlation coefficients between daily observational data and the two model-derived datasets. We then calculated multiple Climate Extremes Indices to further scrutinize differences in capturing extreme temperatures. Finally, we quantified potential causes of systematic NLDAS-2 biases related to distance from the coast, urban heat island, land cover, and type of observational stations. Two model-derived datasets showed similar mean biases and correspondence with observational data, underestimating maximum temperature by 1°C and overestimating minimum temperature by 2°C. Extreme temperatures were well simulated in both datasets. However, we still found overestimated extreme minimum temperatures and underestimated extreme maximum temperatures. Systematic biases tended to be higher for coastal stations and grids having a high fraction of water. Our study suggests that including physical processes covering land surface and ocean interactions may improve the model accuracy. [ABSTRACT FROM AUTHOR]

Background: Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model.Methods: We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset.Results: For all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available.Conclusions: Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice. [ABSTRACT FROM AUTHOR]