Preterm and Early-Term Delivery After Heat Waves in 50 US Metropolitan Areas.

Importance: Heat waves are increasing in frequency, intensity, and duration and may be acutely associated with pregnancy outcomes. Objective: To examine changes in daily rates of preterm and early-term birth after heat waves in a 25-year nationwide study. Design, Setting, and Participants: This cohort study of singleton births used birth records from 1993 to 2017 from the 50 most populous US metropolitan statistical areas (MSAs). The study included 53 million births, covering 52.8% of US births over the period. Data were analyzed between October 2022 and March 2023 at the National Center for Health Statistics. Exposures: Daily temperature data from Daymet at 1-km2 resolution were averaged over each MSA using population weighting. Heat waves were defined in the 4 days (lag, 0-3 days) or 7 days (lag, 0-6 days) preceding birth. Main Outcomes and Measures: Daily counts of preterm birth (28 to <37 weeks), early-term birth (37 to <39 weeks), and ongoing pregnancies in each gestational week on each day were enumerated in each MSA. Rate ratios for heat wave metrics were obtained from time-series models restricted to the warm season (May to September) adjusting for MSA, year, day of season, and day of week, and offset by pregnancies at risk. Results: There were 53 154 816 eligible births in the 50 MSAs from 1993 to 2017; 2 153 609 preterm births and 5 795 313 early-term births occurring in the warm season were analyzed. A total of 30.0% of mothers were younger than 25 years, 53.8% were 25 to 34 years, and 16.3% were 35 years or older. Heat waves were positively associated with daily rates of preterm and early-term births, showing a dose-response association with heat wave duration and temperatures and stronger associations in the more acute 4-day window. After 4 consecutive days of mean temperatures exceeding the local 97.5th percentile, the rate ratio for preterm birth was 1.02 (95% CI, 1.00-1.03), and the rate ratio for early-term birth was 1.01 (95% CI, 1.01-1.02). For the same exposure, among those who were 29 years of age or younger, had a high school education or less, and belonged to a racial or ethnic minority group, the rate ratios were 1.04 (95% CI, 1.02-1.06) for preterm birth and 1.03 (95% CI, 1.02-1.05) for early-term birth. Results were robust to alternative heat wave definitions, excluding medically induced deliveries, and alternative statistical model specifications. Conclusions and Relevance: In this cohort study, preterm and early-term birth rates increased after heat waves, particularly among socioeconomically disadvantaged subgroups. Extreme heat events have implications for perinatal health.

Associations between satellite-derived estimates of PM2.5 species concentrations for organic carbon, elemental carbon, nitrate, and sulfate with birth weight and preterm birth in California during 2005-2014.

BACKGROUND: Characterizing the spatial distribution of PM2.5 species concentrations is challenging due to the geographic sparsity of the stationary monitoring network. Recent advances have enabled valid estimation of PM2.5 species concentrations using satellite remote sensing data for use in epidemiologic studies. OBJECTIVE: In this study, we used satellite-based estimates of ambient PM2.5 species concentrations to estimate associations with birth weight and preterm birth in California. METHODS: Daily 24 h averaged ground-level PM2.5 species concentrations of organic carbon, elemental carbon, nitrate, and sulfate were estimated during 2005-2014 in California at 1 km resolution. Birth records were linked to ambient pollutant exposures based on maternal residential zip code. Linear regression and Cox regression were conducted to estimate the effect of 1 µg/m3 increases in PM2.5 species concentrations on birth weight and preterm birth. RESULTS: Analyses included 4.7 million live singleton births having a median 28 days with exposure measurements per pregnancy. In single pollutant models, the observed changes in mean birth weight (per 1 µg/m3 increase in speciated PM2.5 concentrations) were: organic carbon -3.12 g (CI: -4.71, -1.52), elemental carbon -14.20 g (CI: -18.76, -9.63), nitrate -5.51 g (CI: -6.79, -4.23), and sulfate 9.26 g (CI: 7.03, 11.49). Results from multipollutant models were less precise due to high correlation between pollutants. Associations with preterm birth were null, save for a negative association between sulfate and preterm birth (Hazard Ratio per 1 µg/m3 increase: 0.973 CI: 0.958, 0.987).

Effects of air pollution on adverse birth outcomes and pregnancy complications in the U.S. state of Kansas (2000-2015).

Neonatal mortality and morbidity are often caused by preterm birth and lower birth weight. Gestational diabetes mellitus (GDM) and gestational hypertension (GH) are the most prevalent maternal medical complications during pregnancy. However, evidence on effects of air pollution on adverse birth outcomes and pregnancy complications is mixed. Singleton live births conceived between January 1st, 2000, and December 31st, 2015, and reached at least 27 weeks of pregnancy in Kansas were included in the study. Trimester-specific and total pregnancy exposures to nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), and ozone (O3) were estimated using spatiotemporal ensemble models and assigned to maternal residential census tracts. Logistic regression, discrete-time survival, and linear models were applied to assess the associations. After adjustment for demographics and socio-economic status (SES) factors, we found increases in the second and third trimesters and total pregnancy O3 exposures were significantly linked to preterm birth. Exposure to the second and third trimesters O3 was significantly associated with lower birth weight, and exposure to NO2 during the first trimester was linked to an increased risk of GDM. O3 exposures in the first trimester were connected to an elevated risk of GH. We didn't observe consistent associations between adverse pregnancy and birth outcomes with PM2.5 exposure. Our findings indicate there is a positive link between increased O3 exposure during pregnancy and a higher risk of preterm birth, GH, and decreased birth weight. Our work supports limiting population exposure to air pollution, which may lower the likelihood of adverse birth and pregnancy outcomes.

Maternal obesity and childhood asthma risk: Exploring mediating pathways.

BACKGROUND: Growing evidence for the effect of maternal obesity on childhood asthma motivates investigation of mediating pathways. OBJECTIVE: To investigate if childhood body mass index (BMI), gestational weight gain (GWG) and preterm birth mediate the association of maternal obesity on childhood asthma risk. METHODS: We used electronic medical records from mother-child pairs enrolled in Kaiser Permanente Northern California integrated healthcare system. Children were followed from their birth (2005-2014) until at least age 4 (n = 95,723), age 6 (n = 59,230) or age 8 (n = 25,261). Childhood asthma diagnosis at each age was determined using ICD-9/10 codes and medication dispensings. Prepregnancy BMI (underweight [<18.5], normal [18.5-24.9], overweight [25-29.9], obese [≥30] kg/m2 ) were defined using height and weight measurements close to the last menstrual period date. Child's BMI (Centers for Disease Control and Prevention BMI-for-age percentiles: underweight [<5th], normal [5th-85th], overweight [85th-95th], obese [>95th]) were obtained using anthropometric measurements taken the year preceding each follow-up age. GWG (delivery weight-prepregnancy weight) was categorised based on Institutes of Medicine recommendations (inadequate, adequate, excessive). Implementing first causal inference test (CIT) then causal mediator models (to decompose the natural direct and indirect effects), we examined the potential mediating effect of childhood BMI, GWG, and preterm birth on the association between prepregnancy BMI (continuous and categorical) and childhood asthma. RESULTS: Overall, risk of childhood asthma increased as prepregnancy BMI increased (age 4 risk ratio: 1.07, 95% confidence interval: 1.04, 1.09, per 5 kg/m2 increase in BMI; similar for age 6 and 8). CIT identified childhood BMI and preterm birth, but not GWG as potential mediators. Causal mediation models confirmed childhood BMI, but not preterm birth, as having a partial mediating effect. Results were similar for age 6 and 8, and when continuous mediators (instead of binary) were assessed. CONCLUSIONS: Childhood overweight/obesity has a modest mediating effect on the association between prepregnancy BMI and childhood asthma.

Confounding by Conception Seasonality in Studies of Temperature and Preterm Birth: A Simulation Study.

BACKGROUND: Seasonal patterns of conception may confound acute associations between birth outcomes and seasonally varying exposures. We aim to evaluate four epidemiologic designs (time-stratified case-crossover, time-series, pair-matched case-control, and time-to-event) commonly used to study acute associations between ambient temperature and preterm births. METHODS: We conducted simulations assuming no effect of temperature on preterm birth. We generated pseudo-birth data from the observed seasonal patterns of birth in the United States and analyzed them in relation to observed temperatures using design-specific seasonality adjustments. RESULTS: Using the case-crossover approach (time-stratified by calendar month), we observed a bias (among 1,000 replicates) = 0.016 (Monte-Carlo standard error 95% CI: 0.015-0.018) in the regression coefficient for every 10°C increase in mean temperature in the warm season (May-September). Unbiased estimates obtained using the time-series approach required accounting for both the pregnancies-at-risk and their weighted probability of birth. Notably, adding the daily weighted probability of birth from the time-series models to the case-crossover models corrected the bias in the case-crossover approach. In the pair-matched case-control design, where the exposure period was matched on gestational window, we observed no bias. The time-to-event approach was also unbiased but was more computationally intensive than others. CONCLUSIONS: Most designs can be implemented in a way that yields estimates unbiased by conception seasonality. The time-stratified case-crossover design exhibited a small positive bias, which could contribute to, but not fully explain, previously reported associations.

Prepregnancy body mass index and risk of childhood asthma.

BACKGROUND: Growing evidence suggests that maternal obesity may affect the intrauterine environment and increase a child's risk of developing asthma. We aim to investigate the relationship between prepregnancy obesity and childhood asthma risk. METHODS: Cohorts of children enrolled in Kaiser Permanente Northern California integrated healthcare system were followed from birth (2005-2014) to age 4 (n = 104,467), 6 (n = 63,084), or 8 (n = 31,006) using electronic medical records. Child's asthma was defined using ICD codes and asthma-related prescription medication dispensing. Risk ratios (RR) and 95% confidence intervals (95% CIs) for child's asthma were estimated using Poisson regression with robust error variance for (1) prepregnancy BMI categories (underweight [<18.5], normal [18.5-24.9], overweight [25-29.9], obese 1 [30-34.9], and obese 2/3 [≥35]) and (2) continuous prepregnancy BMI modeled using cubic splines with knots at BMI category boundaries. Models were adjusted for maternal age, education, race, asthma, allergies, smoking, gestational weight gain, child's birth year, parity, infant sex, gestational age, and child's BMI. RESULTS: Relative to normal BMI, RRs (95%CIs) for asthma at ages 4, 6, and 8 were 0.91 (0.75, 1.11), 0.95 (0.78, 1.16), and 0.97 (0.75, 1.27) for underweight, 1.06 (0.99, 1.14), 1.08 (1.01, 1.16), and 1.03 (0.94, 1.14) for overweight, 1.09 (1.00, 1.19), 1.12 (1.03, 1.23), 1.03 (0.91, 1.17) for obese 1, and 1.10 (0.99, 1.21), 1.13 (1.02, 1.25), 1.14 (0.99, 1.31) for obese 2/3. When continuous prepregnancy BMI was modeled with splines, child's asthma risk generally increased linearly with increasing prepregnancy BMI. CONCLUSIONS: Higher prepregnancy BMI is associated with modestly increased childhood asthma risk.

Gestational Weight Gain and Birth Outcome: A Comparison of Methods of Accounting for Gestational Age.

Cross-sectional studies of total gestational weight gain (GWG) and perinatal outcomes have used different approaches to operationalize GWG and adjust for duration of gestation. Using birth records from California (2007-2017), Nevada (2010-2017), and Oregon (2008-2017), we compared 3 commonly used approaches to estimate associations between GWG and cesarean delivery, small-for-gestational-age birth, and low birth weight (LBW): 1) the Institute of Medicine-recommended GWG ranges at a given gestational week, 2) total weight gain categories directly adjusting for gestational age as a covariate, and 3) weight-gain-for-gestational-age z scores derived from an external longitudinal reference population. Among 5,461,130 births, the 3 methods yielded similar conclusions for cesarean delivery and small-for-gestational-age birth. However, for LBW, some associations based on z scores were in the opposite direction of methods 1 and 2, paradoxically suggesting that higher GWG increases risk of LBW. This was due to a greater proportion of preterm births among those with high z scores, and controlling for gestational age in the z score model brought the results in line with the other methods. We conclude that the use of externally derived GWG z scores based on ongoing pregnancies can yield associations confounded by duration of pregnancy when the outcome is strongly associated with gestational age at delivery.

Acute association between heatwaves and stillbirth in six US states.

BACKGROUND: Heatwaves are becoming more frequent and may acutely increase the risk of stillbirth, a rare and severe pregnancy outcome. OBJECTIVES: Examine the association between multiple heatwave metrics and stillbirth in six U.S. states. METHODS: Data were collected from fetal death and birth records in California (1996-2017), Florida (1991-2017), Georgia (1994-2017), Kansas (1991-2017), New Jersey (1991-2015), and Oregon (1991-2017). Cases were matched to controls 1:4 based on maternal race/ethnicity, maternal education, and county, and exposure windows were aligned (gestational week prior to stillbirth). County-level temperature data were obtained from Daymet and linked to cases and controls by residential county and the exposure window. Five heatwave metrics (1 categorical, 3 dichotomous, 1 continuous) were created using different combinations of the duration and intensity of hot days (mean daily temperature exceeding the county-specific 97.5th percentile) during the exposure window, as well as a continuous measure of mean temperature during the exposure window modeled using natural splines to allow for nonlinear associations. State-specific odds ratios (ORs) and 95% confidence intervals (CI) were estimated using conditional logistic regression models. State-specific results were pooled using a fixed-effects meta-analysis. RESULTS: In our data set of 140,428 stillbirths (553,928 live birth controls), three of the five heatwave metrics examined were not associated with stillbirth. However, four consecutive hot days during the previous week was associated with a 3% increase in stillbirth risk (CI: 1.01, 1.06), and a 1 °C average increase over the threshold was associated with a 10% increase in stillbirth risk (CI: 1.04, 1.17). In continuous temperature analyses, there was a slight increased risk of stillbirth associated with extremely hot temperatures (≥ 35 °C). DISCUSSION: Most heat wave definitions examined were not associated with acute changes in stillbirth risk; however, the most extreme heatwave durations and temperatures were associated with a modest increase in stillbirth risk.

Statistical Comparison and Assessment of Four Fire Emissions Inventories for 2013 and a Large Wildfire in the Western United States.

Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations.

Estimating Heat-Related Exposures and Urban Heat Island Impacts: A Case Study for the 2012 Chicago Heatwave.

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi-layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2-3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat-related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat-related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat-related health impacts.

A cross-sectional analysis of associations between environmental indices and asthma in U.S. counties from 2003 to 2012.

BACKGROUND: To capture the impacts of environmental stressors, environmental indices like the Air Quality Index, Toxic Release Inventory, and Environmental Quality Index have been used to investigate the environmental quality and its association with public health issues. However, past studies often rely on relatively small sample sizes, and they have typically not adjusted for important individual-level disease risk factors. OBJECTIVE: We aim to estimate associations between existing environmental indices and asthma prevalence over a large population and multiple years. METHODS: Based on data availability, we assessed the predictive capability of these indices for prevalent asthma across U.S. counties from 2003 to 2012. We gathered asthma data from the U.S. CDC Behavioral Risk Factor Surveillance System by county and used multivariable weighted logistic regression models to estimate the associations between the environmental indices and asthma, adjusting for individual factors such as smoking, income level, and obesity. RESULTS: Environmental indices showed little to no correlation with one another and with prevalent asthma over time. Associations of environmental indices with prevalent asthma were very weak; whereas individual factors were more substantially associated with prevalent asthma. SIGNIFICANCE: Our study suggests that an improved environmental index is needed to predict population-level asthma prevalence.

CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH.

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.

Time-series analysis of daily ambient temperature and emergency department visits in five US cities with a comparison of exposure metrics derived from 1-km meteorology products.

BACKGROUND: Ambient temperature observations from single monitoring stations (usually located at the major international airport serving a city) are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error. METHODS: We conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution. RESULTS: Across cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center. CONCLUSION: Epidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.

Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta.

BACKGROUND: Short-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations. METHODS: Logic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency department visits for 12 outcomes in the Atlanta metropolitan area. RESULTS: For the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient. CONCLUSION: Logic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.

Acute associations between heatwaves and preterm and early-term birth in 50 US metropolitan areas: a matched case-control study.

BACKGROUND: The effect of heatwaves on adverse birth outcomes is not well understood and may vary by how heatwaves are defined. The study aims to examine acute associations between various heatwave definitions and preterm and early-term birth. METHODS: Using national vital records from 50 metropolitan statistical areas (MSAs) between 1982 and 1988, singleton preterm (< 37 weeks) and early-term births (37-38 weeks) were matched (1:1) to controls who completed at least 37 weeks or 39 weeks of gestation, respectively. Matching variables were MSA, maternal race, and maternal education. Sixty heatwave definitions including binary indicators for exposure to sustained heat, number of high heat days, and measures of heat intensity (the average degrees over the threshold in the past 7 days) based on the 97.5th percentile of MSA-specific temperature metrics, or the 85th percentile of positive excessive heat factor (EHF) were created. Odds ratios (OR) for heatwave exposures in the week preceding birth (or corresponding gestational week for controls) were estimated using conditional logistic regression adjusting for maternal age, marital status, and seasonality. Effect modification by maternal education, age, race/ethnicity, child sex, and region was assessed. RESULTS: There were 615,329 preterm and 1,005,576 early-term case-control pairs in the analyses. For most definitions, exposure to heatwaves in the week before delivery was consistently associated with increased odds of early-term birth. Exposure to more high heat days and more degrees above the threshold yielded higher magnitude ORs. For exposure to 3 or more days over the 97.5th percentile of mean temperature in the past week compared to zero days, the OR was 1.027 for early-term birth (95%CI: 1.014, 1.039). Although we generally found null associations when assessing various heatwave definitions and preterm birth, ORs for both preterm and early-term birth were greater in magnitude among Hispanic and non-Hispanic black mothers. CONCLUSION: Although associations varied across metrics and heatwave definitions, heatwaves were more consistently associated with early-term birth than with preterm birth. This study's findings may have implications for prevention programs targeting vulnerable subgroups as climate change progresses.

Prepregnancy body mass index and spina bifida: Potential contributions of bias.

BACKGROUND: Epidemiologists have consistently observed associations between prepregnancy obesity and spina bifida in offspring. Most studies, however, used self-reported body mass index (potential for exposure misclassification) and incompletely ascertained cases of spina bifida among terminations of pregnancy (potential for selection bias). We conducted a quantitative bias analysis to explore the potential effects of these biases on study results. METHODS: We included 808 mothers of fetuses or infants with spina bifida (case mothers) and 7,685 mothers of infants without birth defects (control mothers) from a population-based case-control study, the National Birth Defects Prevention Study (1997-2011). First, we performed a conventional epidemiologic analysis, adjusting for potential confounders using logistic regression. Then, we used 5,000 iterations of probabilistic bias analysis to adjust for the combination of confounding, exposure misclassification, and selection bias. RESULTS: In the conventional confounding-adjusted analysis, prepregnancy obesity was associated with spina bifida (odds ratio 1.4, 95% confidence interval: 1.2, 1.7). In the probabilistic bias analysis, we tested nine different models for the combined effects of confounding, exposure misclassification, and selection bias. Results were consistent with a weak to moderate association between prepregnancy obesity and spina bifida, with the median odds ratios across the nine models ranging from 1.1 to 1.4. CONCLUSIONS: Given our assumptions about the occurrence of bias in the study, our results suggest that exposure misclassification, selection bias, and confounding do not completely explain the association between prepregnancy obesity and spina bifida.

Use of antihistamine medications during early pregnancy and selected birth defects: The National Birth Defects Prevention Study, 1997-2011.

BACKGROUND: It is estimated that approximately 10-15% of pregnant women report antihistamine use during pregnancy. Although antihistamines are generally considered safe during pregnancy, results from published studies are inconsistent. METHODS: Using a case-control study design we analyzed 41,148 pregnancies (30,091 cases and 11,057 controls) from the National Birth Defects Prevention Study (1997-2011). Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals for 64 birth defect groupings in relation to early pregnancy exposure to 14 distinct antihistamines. Models were adjusted for maternal age, race, parity, education level, prenatal care, folic acid use, smoking and alcohol use, and study site. RESULTS: Approximately 13% of cases and controls were exposed to an antihistamine during early pregnancy. Analyses were restricted to those defects where more than five cases were exposed to the antihistamine of interest, generating 340 analyses which yielded 20 (5.9%) significant positive associations (adjusted ORs ranging from 1.21 to 4.34). CONCLUSIONS: Only a few of our findings were consistent with previous studies. There is a lack of strong evidence to conclude that birth defects are associated with exposure to antihistamines during early pregnancy.

Acute associations between PM2.5 and ozone concentrations and asthma exacerbations among patients with and without allergic comorbidities.

Acute effects of outdoor air pollution on asthma exacerbations may vary by asthma phenotype (allergic vs nonallergic). Associations of ambient PM2.5 and ozone concentrations with acute asthma visits (office, urgent, emergency, and hospitalization) were investigated using electronic medical records. International Classification of Disease codes were used to identify asthmatics, and classify them based on the presence or absence of an allergic comorbidity in their medical records. Daily 24-h average PM2.5, 8-h maximum ozone, and mean temperature were obtained from a centralized monitor. Using a time-stratified case-crossover approach, pollutant concentrations were modeled using moving averages and distributed lag nonlinear models (lag 0-6) to examine lag associations and nonlinear concentration-response. The adjusted odds ratios for a 10 µg/m3 increase in 3-day moving average (lag 0-2) PM2.5 in the two-pollutant models among patients with and without allergic comorbidities were 1.10 (95% confidence interval [CI]: 1.07, 1.13) and 1.05 (95% CI: 1.02, 1.09), respectively; and for a 20 ppb increase in 3-day moving average (lag 0-2) ozone were 1.08 (95% CI: 1.02, 1.14) and 1.00 (95% CI: 0.95, 1.05), respectively. Estimated odds ratios among patients with allergic comorbidities were consistently higher across age, sex, and temperature categories. Asthmatics with an allergic comorbidity may be more susceptible to ambient PM2.5 and ozone.

Source apportionment of primary and secondary PM2.5: Associations with pediatric respiratory disease emergency department visits in the U.S. State of Georgia.

We developed a hybrid chemical transport model and receptor model (CTM-RM) to conduct source apportionment of both primary and secondary PM2.5 (particulate matter ≤2.5 µm in diameter) at 36 km resolution throughout the U.S. State of Georgia for the years 2005 and 2007. This novel source apportionment model enabled us to estimate and compare associations of short-term changes in 12 PM2.5 source concentrations (agriculture, biogenic, coal, dust, fuel oil, metals, natural gas, non-road mobile diesel, non-road mobile gasoline, on-road mobile diesel, on-road mobile gasoline, and all other sources) with emergency department (ED) visits for pediatric respiratory diseases. ED visits for asthma (N = 49,651), pneumonia (N = 25,558), and acute upper respiratory infections (acute URI, N = 235,343) among patients aged ≤18 years were obtained from patient claims records. Using a case-crossover study, we estimated odds ratios per interquartile range (IQR) increase for 3-day moving average PM2.5 source concentrations using conditional logistic regression, matching on day-of-week, month, and year, and adjusting for average temperature, humidity, and holidays. We fit both single-source and multi-source models. We observed positive associations between several PM2.5 sources and ED visits for asthma, pneumonia, and acute URI. For example, for asthma, per IQR increase in the source contribution in the single-source model, odds ratios were 1.022 (95% CI: 1.013, 1.031) for dust; 1.050 (95% CI: 1.036, 1.063) for metals, and 1.091 (95% CI: 1.064, 1.119) for natural gas. These sources comprised 5.7%, 2.2%, and 6.3% of total PM2.5 mass, respectively. PM2.5 from metals and natural gas were positively associated with all three respiratory outcomes. In addition, non-road mobile diesel was positively associated with pneumonia and acute URI.

Associations of wildfire smoke PM2.5 exposure with cardiorespiratory events in Colorado 2011-2014.

BACKGROUND: Substantial increases in wildfire activity have been recorded in recent decades. Wildfires influence the chemical composition and concentration of particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5). However, relatively few epidemiologic studies focus on the health impacts of wildfire smoke PM2.5 compared with the number of studies focusing on total PM2.5 exposure. OBJECTIVES: We estimated the associations between cardiorespiratory acute events and exposure to smoke PM2.5 in Colorado using a novel exposure model to separate smoke PM2.5 from background ambient PM2.5 levels. METHODS: We obtained emergency department visits and hospitalizations for acute cardiorespiratory outcomes from Colorado for May-August 2011-2014, geocoded to a 4 km geographic grid. Combining ground measurements, chemical transport models, and remote sensing data, we estimated smoke PM2.5 and non-smoke PM2.5 on a 1 km spatial grid and aggregated to match the resolution of the health data. Time-stratified, case-crossover models were fit using conditional logistic regression to estimate associations between fire smoke PM2.5 and non-smoke PM2.5 for overall and age-stratified outcomes using 2-day averaging windows for cardiovascular disease and 3-day windows for respiratory disease. RESULTS: Per 1 µg/m3 increase in fire smoke PM2.5, statistically significant associations were observed for asthma (OR = 1.081 (1.058, 1.105)) and combined respiratory disease (OR = 1.021 (1.012, 1.031)). No significant relationships were evident for cardiovascular diseases and smoke PM2.5. Associations with non-smoke PM2.5 were null for all outcomes. Positive age-specific associations related to smoke PM2.5 were observed for asthma and combined respiratory disease in children, and for asthma, bronchitis, COPD, and combined respiratory disease in adults. No significant associations were found in older adults. DISCUSSION: This is the first multi-year, high-resolution epidemiologic study to incorporate statistical and chemical transport modeling methods to estimate PM2.5 exposure due to wildfires. Our results allow for a more precise assessment of the population health impact of wildfire-related PM2.5 exposure in a changing climate.