Association between particulate air pollution and hypertensive disorders in pregnancy: A retrospective cohort study.

BACKGROUND: Epidemiological findings regarding the association of particulate matter ≤2.5 µm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy. METHODS AND FINDINGS: A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 µg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochran's Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP. CONCLUSIONS: Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently.

Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy.

Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.

Modeling spatially varying compliance effects of PM2.5 exposure reductions on gestational diabetes mellitus in southern California: Results from electronic health record data of a large pregnancy cohort.

Gestational diabetes mellitus (GDM) is a major pregnancy complication affecting approximately 14.0% of pregnancies around the world. Air pollution exposure, particularly exposure to PM2.5, has become a major environmental issue affecting health, especially for vulnerable pregnant women. Associations between PM2.5 exposure and adverse birth outcomes are generally assumed to be the same throughout a large geographical area. However, the effects of air pollution on health can very spatially in subpopulations. Such spatially varying effects are likely due to a wide range of contextual neighborhood and individual factors that are spatially correlated, including SES, demographics, exposure to housing characteristics and due to different composition of particulate matter from different emission sources. This combination of elevated environmental hazards in conjunction with socioeconomic-based disparities forms what has been described as a "double jeopardy" for marginalized sub-populations. In this manuscript our analysis combines both an examination of spatially varying effects of a) unit-changes in exposure and examines effects of b) changes from current exposure levels down to a fixed compliance level, where compliance levels correspond to the Air Quality Standards (AQS) set by the U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality guideline values. Results suggest that exposure reduction policies should target certain "hotspot" areas where size and effects of potential reductions will reap the greatest rewards in terms of health benefits, such as areas of southeast Los Angeles County which experiences high levels of PM2.5 exposures and consist of individuals who may be particularly vulnerable to the effects of air pollution on the risk of GDM.

Reductions in NO2 and emergency room visits associated with California's goods movement policies: A quasi-experimental study.

INTRODUCTION: This study examines whether the "Emission Reduction Plan for Ports and Goods Movement" in California reduced air pollution exposures and emergency room visits among California Medicaid enrollees with asthma and/or chronic obstructive pulmonary disease. METHOD: We created a retrospective cohort of 5608 Medicaid enrollees from ten counties in California with data from 2004 to 2010. We grouped the patients into two groups: those living within 500 m of goods movement corridors (ports and truck-permitted freeways), and control areas (away from the busy truck or car permitted highways). We created annual air pollution surfaces for nitrogen dioxide and assigned them to enrollees' home addresses. We used a quasi-experimental design with a difference-in-differences method to examine changes before and after the policy for cohort beneficiaries in the two groups. RESULTS: The reductions in nitrogen dioxide exposures and emergency room visits were greater for enrollees in goods movement corridors than those in control areas in post-policy years. We found that the goods movement actions were associated with 14.8% (95% CI, -24.0% to -4.4%; P = 0.006) and 11.8% (95% CI, -21.2% to -1.2%; P = 0.030) greater reduction in emergency room visits for the beneficiaries with asthma and chronic obstructive pulmonary disease, respectively, in the third year after California's emission reduction plan. CONCLUSION: These findings indicate remarkable health benefits via reduced emergency room visits from the significantly improved air quality due to public policy interventions for disadvantaged and susceptible populations.

Part 3. Modeling of Multipollutant Profiles and Spatially Varying Health Effects with Applications to Indicators of Adverse Birth Outcomes.

The highly intercorrelated nature of air pollutants makes it difficult to examine their combined effects on health. As such, epidemiological studies have traditionally focused on single-pollutant models that use regression-based techniques to examine the marginal association between a pollutant and a health outcome. These relatively simple, additive models are useful for discerning the effect of a single pollutant on a health outcome with all other pollutants held to fixed values. However, pollutants occur in complex mixtures consisting of highly correlated combinations of individual exposures. For example, evidence for synergy among pollutants in causing health effects has been recently reviewed by Mauderly and Samet (2009). Also, studies cited in the Ozone Criteria Document (U.S. Environmental Protection Agency [U.S. EPA*] 2006) confirmed that synergisms between ozone and other pollutants have been demonstrated in laboratory studies involving humans and animals. Thus, the highly correlated nature of air pollution exposures makes marginal, single-pollutant models inadequate. This issue was raised in a report by the National Research Council (NRC 2004), which called for a multipollutant approach to air quality management. Here we present and apply a series of statistical approaches that treat patterns of covariates as a whole unit, stochastically grouping pollutant patterns into clusters and then using these cluster assignments as random effects in a regression model. Using this approach, the effect of a multipollutant pattern, or profile, is determined in a manner that takes into account the uncertainty in the clustering process. The models are set in a Bayesian framework, and in general, Markov chain Monte Carlo (MCMC) techniques (Gilks et al. 1998). For interpretation purposes, a best clustering is derived, and the uncertainty related to this best clustering is determined by utilizing model averaging techniques, in a manner such that consistent clustering obtained by the estimation process generally yields smaller standard errors while inconsistent clustering is generally associated with larger errors. These multivariate methods are applied to a range of different problems related to air pollution exposures, namely an association of multipollutant profiles with indicators of poverty and to an assessment of the association between measures of various air pollutants, patterns of socioeconomic status (SES), and birth outcomes. All of these studies involve an examination of regional-level exposures, at the census tract (CT) and census block group (CBG) levels, and individual-level outcomes throughout Los Angeles (LA) County. Results indicate that effects of pollutants vary spatially and vary in a complex interconnected manner that cannot be discerned using standard additive line ar models. Results obtaine d from these studies can be used to efficiently use limited resources to inform policies in targeting are as where air pollution reductions result in maximum health benefits.

Reference-free cell mixture adjustments in analysis of DNA methylation data.

MOTIVATION: Recently there has been increasing interest in the effects of cell mixture on the measurement of DNA methylation, specifically the extent to which small perturbations in cell mixture proportions can register as changes in DNA methylation. A recently published set of statistical methods exploits this association to infer changes in cell mixture proportions, and these methods are presently being applied to adjust for cell mixture effect in the context of epigenome-wide association studies. However, these adjustments require the existence of reference datasets, which may be laborious or expensive to collect. For some tissues such as placenta, saliva, adipose or tumor tissue, the relevant underlying cell types may not be known. RESULTS: We propose a method for conducting epigenome-wide association studies analysis when a reference dataset is unavailable, including a bootstrap method for estimating standard errors. We demonstrate via simulation study and several real data analyses that our proposed method can perform as well as or better than methods that make explicit use of reference datasets. In particular, it may adjust for detailed cell type differences that may be unavailable even in existing reference datasets. AVAILABILITY AND IMPLEMENTATION: Software is available in the R package RefFreeEWAS. Data for three of four examples were obtained from Gene Expression Omnibus (GEO), accession numbers GSE37008, GSE42861 and GSE30601, while reference data were obtained from GEO accession number GSE39981. CONTACT: andres.houseman@oregonstate.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling spatial effects of PM(2.5) on term low birth weight in Los Angeles County.

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

A cross sectional analysis of behaviors related to operating gas stoves and pneumonia in U.S. children under the age of 5.

BACKGROUND: Poorly ventilated combustion stoves and pollutants emitted from combustion stoves increase the risk of acute lower respiratory illnesses (ALRI) in children living in developing countries but few studies have examined these issues in developed countries. Our objective is to investigate behaviors related to gas stove use, namely using them for heat and without ventilation, on the odds of pneumonia and cough in U.S. children. METHODS: The National Health and Nutrition Examination Survey (1988-1994) was used to identify children < 5 years who lived in homes with a gas stove and whose parents provided information on their behaviors when operating their gas stoves and data on pneumonia (N = 3,289) and cough (N = 3,127). Multivariate logistic regression models were used to examine the association between each respiratory outcome and using a gas stove for heat or without ventilation, as well as, the joint effect of both behaviors. RESULTS: The adjusted odds of parental-reported pneumonia (adjusted odds ratio [aOR] = 2.08, 95% confidence interval [CI]: 1.08, 4.03) and cough (aOR = 1.66, 95% CI: 1.14, 2.43) were higher among children who lived in homes where gas stoves were used for heat compared to those who lived in homes where gas stoves were only used for cooking. The odds of pneumonia (aOR = 1.76, 95% CI: 1.04, 2.98), but not cough (aOR = 1.23, 95% CI: 0.87, 1.75), was higher among those children whose parents did not report using ventilation when operating gas stoves compared to those who did use ventilation. When considering the joint association of both stove operating conditions, only children whose parents reported using gas stoves for heat without ventilation had significantly higher odds of pneumonia (aOR = 3.06, 95% CI: 1.32, 7.09) and coughing (aOR = 2.07, 95% CI: 1.29, 3.30) after adjusting for other risk factors. CONCLUSIONS: Using gas stoves for heat without ventilation was associated with higher odds of pneumonia and cough among U.S. children less than five years old who live in homes with a gas stove. More research is needed to determine if emissions from gas stoves ventilation infrastructure, or modifiable behaviors contribute to respiratory infections in children.

A cross-sectional study of the association between ventilation of gas stoves and chronic respiratory illness in U.S. children enrolled in NHANESIII.

BACKGROUND: Gas stoves emit pollutants that are respiratory irritants. U.S. children under age 6 who live in homes where gas stoves are used for cooking or heating have an increased risk of asthma, wheeze and reduced lung function. Yet few studies have examined whether using ventilation when operating gas stoves is associated with a decrease in the prevalence of respiratory illnesses in this population. METHODS: The Third National Health and Nutrition Examination Survey was used to identify U.S. children aged 2-16 years with information on respiratory outcomes (asthma, wheeze, and bronchitis) who lived in homes where gas stoves were used in the previous 12 months and whose parents provided information on ventilation. Logistic regression models evaluated the association between prevalent respiratory outcomes and ventilation in homes that used gas stoves for cooking and/or heating. Linear regression models assessed the association between spirometry measurements and ventilation use in children aged 8-16 years. RESULTS: The adjusted odds of asthma (Odds Ratio [OR] = 0.64; 95% confidence intervals [CI]: 0.43, 0.97), wheeze (OR = 0.60, 95% CI: 0.42, 0.86), and bronchitis (OR = 0.60, 95% CI: 0.37, 0.95) were lower among children whose parents reported using ventilation compared to children whose parents reported not using ventilation when operating gas stoves. One-second forced expiratory volume (FEV1) and FEV1/FVC ratio was also higher in girls who lived in households that used gas stoves with ventilation compared to households that used gas stoves without ventilation. CONCLUSIONS: In homes that used gas stoves, children whose parents reported using ventilation when operating their stove had higher lung function and lower odds of asthma, wheeze, and bronchitis compared to homes that never used ventilation or did not have ventilation available after adjusting for other risk factors. Additional research on the efficacy of ventilation as an intervention for ameliorating respiratory symptoms in children with asthma is warranted.

Association between common variation at the FTO locus and changes in body mass index from infancy to late childhood: the complex nature of genetic association through growth and development.

An age-dependent association between variation at the FTO locus and BMI in children has been suggested. We meta-analyzed associations between the FTO locus (rs9939609) and BMI in samples, aged from early infancy to 13 years, from 8 cohorts of European ancestry. We found a positive association between additional minor (A) alleles and BMI from 5.5 years onwards, but an inverse association below age 2.5 years. Modelling median BMI curves for each genotype using the LMS method, we found that carriers of minor alleles showed lower BMI in infancy, earlier adiposity rebound (AR), and higher BMI later in childhood. Differences by allele were consistent with two independent processes: earlier AR equivalent to accelerating developmental age by 2.37% (95% CI 1.87, 2.87, p = 10(-20)) per A allele and a positive age by genotype interaction such that BMI increased faster with age (p = 10(-23)). We also fitted a linear mixed effects model to relate genotype to the BMI curve inflection points adiposity peak (AP) in infancy and AR. Carriage of two minor alleles at rs9939609 was associated with lower BMI at AP (-0.40% (95% CI: -0.74, -0.06), p = 0.02), higher BMI at AR (0.93% (95% CI: 0.22, 1.64), p = 0.01), and earlier AR (-4.72% (-5.81, -3.63), p = 10(-17)), supporting cross-sectional results. Overall, we confirm the expected association between variation at rs9939609 and BMI in childhood, but only after an inverse association between the same variant and BMI in infancy. Patterns are consistent with a shift on the developmental scale, which is reflected in association with the timing of AR rather than just a global increase in BMI. Results provide important information about longitudinal gene effects and about the role of FTO in adiposity. The associated shifts in developmental timing have clinical importance with respect to known relationships between AR and both later-life BMI and metabolic disease risk.

Exploring data from genetic association studies using Bayesian variable selection and the Dirichlet process: application to searching for gene × gene patterns.

We construct data exploration tools for recognizing important covariate patterns associated with a phenotype, with particular focus on searching for association with gene-gene patterns. To this end, we propose a new variable selection procedure that employs latent selection weights and compare it to an alternative formulation. The selection procedures are implemented in tandem with a Dirichlet process mixture model for the flexible clustering of genetic and epidemiological profiles. We illustrate our approach with the aid of simulated data and the analysis of a real data set from a genome-wide association study.

Informal confidential voting interviewing in a sexual risk assessment of men who have sex with men (MSM) and transgenders (hijra) in Bangalore, India.

OBJECTIVE: The accuracy of self-reporting sensitive sexual risk behaviours is highly susceptible to misreporting. Informal confidential voting interviews (ICVIs) may minimise social desirability bias by increasing the privacy of the interview setting. The objective was to investigate determinants of risky behaviour among men who have sex with men (MSM) and 'hijra' (transgenders) reported through two interviewing tools: ICVIs and face-to-face interviews (FTFIs). METHODS: Cluster random sampling was used to recruit MSM in 85 cruising sites in Bangalore, including eight hammams (bath houses) and 77 public locations where MSM and hijra cruise for sex. Individuals were randomly allocated to one of the data collection methods(5:2 FTFI : ICVI). Data were analysed using standard regression and a profile regression approach that associates clusters of behaviours with our outcome (FTFI vs ICVI). RESULTS: A total of 372 MSM and hijra were interviewed for the FTFIs and 153 respondents completed ICVIs. Participants were more likely to report injecting drug use (4% vs 1%; p=0.008) and paying to have sex with a female sex worker (FSW) in the last year (28% vs 8%; p=0.001) in the ICVIs. There were no differences to questions on sociodemographics, sexual debut with another male, non-condom use (12% vs 14%), ever selling sex to men (58% vs 56%), current female partner (26% vs 20%) and non-condom use with a main female partner (17% vs 19%). CONCLUSIONS: The significant differences between interview modes for certain outcomes, such as intravenous drug use and sex with a FSW, demonstrate how certain behaviour is stigmatised among the MSM community. Nevertheless, the lack of effect of the interviewing tool in other outcomes may indicate either less reporting bias in reporting this behaviour or environmental factors such as the interviewers not adequately screening themselves from the respondent or a potential disadvantage of using other MSM as interviewers.

Association between urban green space and postpartum depression, and the role of physical activity: a retrospective cohort study in Southern California.

Background: Little research exists regarding the relationships between green space and postpartum depression (PPD). We aimed to investigate the relationships between PPD and green space exposure, and the mediating role of physical activity (PA). Methods: Clinical data were obtained from Kaiser Permanente Southern California electronic health records in 2008-2018. PPD ascertainment was based on both diagnostic codes and prescription medications. Maternal residential green space exposures were assessed using street view-based measures and vegetation types (i.e., street tree, low-lying vegetation, and grass), satellite-based measures [i.e., Normalized Difference Vegetation Index (NDVI), land-cover green space, and tree canopy cover], and proximity to the nearest park. Multilevel logistic regression was applied to estimate the association between green space and PPD. A causal mediation analysis was performed to estimate the proportion mediated by PA during pregnancy in the total effects of green space on PPD. Findings: In total, we included 415,020 participants (30.2 ± 5.8 years) with 43,399 (10.5%) PPD cases. Hispanic mothers accounted for about half of the total population. A reduced risk for PPD was associated with total green space exposure based on street-view measure [500 m buffer, adjusted odds ratio (OR) per interquartile range: 0.98, 95% CI: 0.97-0.99], but not NDVI, land-cover greenness, or proximity to a park. Compared to other types of green space, tree coverage showed stronger protective effects (500 m buffer, OR = 0.98, 95% CI: 0.97-0.99). The proportions of mediation effects attributable to PA during pregnancy ranged from 2.7% to 7.2% across green space indicators. Interpretation: Street view-based green space and tree coverage were associated with a decreased risk of PPD. The observed association was primarily due to increased tree coverage, rather than low-lying vegetation or grass. Increased PA was a plausible pathway linking green space to lower risk for PPD. Funding: National Institute of Environmental Health Sciences (NIEHS; R01ES030353).

Maternal exposure to ambient air pollution mixture and premature rupture of membranes: Evidence from a large cohort in Southern California (2008-2018).

BACKGROUND: There is minimal evidence of relationships between maternal air pollution exposure and spontaneous premature rupture of membranes (SPROM), a critical obstetrical problem that can significantly increase maternal and fetal mortality and morbidity. No prior study has explored the PROM risk related to specific components of particulate matter with aerodynamic diameters of ≤ 2.5 µm (PM2.5). We examined associations between maternal exposure to nitrogen dioxide (NO2), ozone (O3), PM2.5, PM10, and PM2.5 constituents and SPROM. METHODS: A large retrospective cohort study was conducted and included 427,870 singleton live births from Kaiser Permanente Southern California during 2008-2018. Monthly averages of NO2, O3 (8-h daily maximum), PM2.5, and PM10 were measured using empirical Bayesian kriging based on measurements from monitoring stations. Data on PM2.5 sulfate, nitrate, ammonium, organic matter, and black carbon were obtained from a fine-resolution model. A discrete time approach with pooled logistic regressions was used to estimate associations throughout the pregnancy and based on trimesters and gestational months. The quantile-based g-computation models were fitted to examine the effects of 1) the air pollution mixture of four pollutants of interest and 2) the mixture of PM2.5 components. RESULTS: There were 37,857 SPROM cases (8.8%) in our study population. We observed relationships between SPROM and maternal exposure to NO2, O3, and PM2.5. PM2.5 sulfate, nitrate, ammonium, and organic matter were associated with higher SPROM risks in the single-pollutant model. Mixture analyses demonstrated that the overall effects of the air pollution mixture and PM2.5 mixture in this study were mainly driven by O3 and PM2.5 nitrate, respectively. Underweight mothers had a significantly higher risk of SPROM associated with NO2. CONCLUSION: Our findings add to the literature on associations between air pollution exposure and SPROM. This is the first study reporting the impact of PM2.5 constituents on SPROM.

Examining the Relationship Between Extreme Temperature, Microclimate Indicators, and Gestational Diabetes Mellitus in Pregnant Women Living in Southern California.

Few studies have assessed extreme temperatures' impact on gestational diabetes mellitus (GDM). We examined the relation between GDM risk with weekly exposure to extreme high and low temperatures during the first 24 weeks of gestation and assessed potential effect modification by microclimate indicators. Methods: We utilized 2008-2018 data for pregnant women from Kaiser Permanente Southern California electronic health records. GDM screening occurred between 24 and 28 gestational weeks for most women using the Carpenter-Coustan criteria or the International Association of Diabetes and Pregnancy Study Groups criteria. Daily maximum, minimum, and mean temperature data were linked to participants' residential address. We utilized distributed lag models, which assessed the lag from the first to the corresponding week, with logistic regression models to examine the exposure-lag-response associations between the 12 weekly extreme temperature exposures and GDM risk. We used the relative risk due to interaction (RERI) to estimate the additive modification of microclimate indicators on the relation between extreme temperature and GDM risk. Results: GDM risks increased with extreme low temperature during gestational weeks 20--24 and with extreme high temperature at weeks 11-16. Microclimate indicators modified the influence of extreme temperatures on GDM risk. For example, there were positive RERIs for high-temperature extremes and less greenness, and a negative RERI for low-temperature extremes and increased impervious surface percentage. Discussion: Susceptibility windows to extreme temperatures during pregnancy were observed. Modifiable microclimate indicators were identified that may attenuate temperature exposures during these windows, which could in turn reduce the health burden from GDM.

The role of extreme heat exposure on premature rupture of membranes in Southern California: A study from a large pregnancy cohort.

BACKGROUND: Significant mortality and morbidity in pregnant women and their offspring are linked to premature rupture of membranes (PROM). Epidemiological evidence for heat-related PROM risk is extremely limited. We investigated associations between acute heatwave exposure and spontaneous PROM. METHODS: We conducted this retrospective cohort study among mothers in Kaiser Permanente Southern California who experienced membrane ruptures during the warm season (May-September) from 2008 to 2018. Twelve definitions of heatwaves with different cut-off percentiles (75th, 90th, 95th, and 98th) and durations (≥ 2, 3, and 4 consecutive days) were developed using the daily maximum heat index, which incorporates both daily maximum temperature and minimum relative humidity in the last gestational week. Cox proportional hazards models were fitted separately for spontaneous PROM, term PROM (TPROM), and preterm PROM (PPROM) with zip codes as the random effect and gestational week as the temporal unit. Effect modification by air pollution (i.e., PM2.5 and NO2), climate adaptation measures (i.e., green space and air conditioning [AC] penetration), sociodemographic factors, and smoking behavior was examined. RESULTS: In total, we included 190,767 subjects with 16,490 (8.6%) spontaneous PROMs. We identified a 9-14% increase in PROM risks associated with less intense heatwaves. Similar patterns as PROM were found for TPROM and PPROM. The heat-related PROM risks were greater among mothers exposed to a higher level of PM2.5 during pregnancy, under 25 years old, with lower education and household income level, and who smoked. Even though climate adaptation factors were not statistically significant effect modifiers, mothers living with lower green space or lower AC penetration were at consistently higher heat-related PROM risks compared to their counterparts. CONCLUSION: Using a rich and high-quality clinical database, we detected harmful heat exposure for spontaneous PROM in preterm and term deliveries. Some subgroups with specific characteristics were more susceptible to heat-related PROM risk.

Analysis of Heat Exposure During Pregnancy and Severe Maternal Morbidity.

Importance: The rate of severe maternal morbidity (SMM) is continuously increasing in the US. Evidence regarding the associations of climate-related exposure, such as environmental heat, with SMM is lacking. Objective: To examine associations between long- and short-term maternal heat exposure and SMM. Design, Setting, and Participants: This retrospective population-based epidemiological cohort study took place at a large integrated health care organization, Kaiser Permanente Southern California, between January 1, 2008, and December 31, 2018. Data were analyzed from February to April 2023. Singleton pregnancies with data on SMM diagnosis status were included. Exposures: Moderate, high, and extreme heat days, defined as daily maximum temperatures exceeding the 75th, 90th, and 95th percentiles of the time series data from May through September 2007 to 2018 in Southern California, respectively. Long-term exposures were measured by the proportions of different heat days during pregnancy and by trimester. Short-term exposures were represented by binary variables of heatwaves with 9 different definitions (combining percentile thresholds with 3 durations; ie, ≥2, ≥3, and ≥4 consecutive days) during the last gestational week. Main Outcomes and Measures: The primary outcome was SMM during delivery hospitalization, measured by 20 subconditions excluding blood transfusion. Discrete-time logistic regression was used to estimate associations with long- and short-term heat exposure. Effect modification by maternal characteristics and green space exposure was examined using interaction terms. Results: There were 3446 SMM cases (0.9%) among 403 602 pregnancies (mean [SD] age, 30.3 [5.7] years). Significant associations were observed with long-term heat exposure during pregnancy and during the third trimester. High exposure (≥80th percentile of the proportions) to extreme heat days during pregnancy and during the third trimester were associated with a 27% (95% CI, 17%-37%; P < .001) and 28% (95% CI, 17%-41%; P < .001) increase in risk of SMM, respectively. Elevated SMM risks were significantly associated with short-term heatwave exposure under all heatwave definitions. The magnitude of associations generally increased from the least severe (HWD1: daily maximum temperature >75th percentile lasting for ≥2 days; odds ratio [OR], 1.32; 95% CI, 1.17-1.48; P < .001) to the most severe heatwave exposure (HWD9: daily maximum temperature >95th percentile lasting for ≥4 days; OR, 2.39; 95% CI, 1.62-3.54; P < .001). Greater associations were observed among mothers with lower educational attainment (OR for high exposure to extreme heat days during pregnancy, 1.43; 95% CI, 1.26-1.63; P < .001) or whose pregnancies started in the cold season (November through April; OR, 1.37; 95% CI, 1.24-1.53; P < .001). Conclusions and Relevance: In this retrospective cohort study, long- and short-term heat exposure during pregnancy was associated with higher risk of SMM. These results might have important implications for SMM prevention, particularly in a changing climate.

Association of Antepartum and Postpartum Air Pollution Exposure With Postpartum Depression in Southern California.

Importance: Women are especially vulnerable to mental health matters post partum because of biological, emotional, and social changes during this period. However, epidemiologic evidence of an association between air pollution exposure and postpartum depression (PPD) is limited. Objective: To examine the associations between antepartum and postpartum maternal air pollution exposure and PPD. Design, Setting, and Participants: This retrospective cohort study used data from Kaiser Permanente Southern California (KPSC) electronic health records and included women who had singleton live births at KPSC facilities between January 1, 2008, and December 31, 2016. Data were analyzed between January 1 and May 10, 2023. Exposures: Ambient air pollution exposures were assessed based on maternal residential addresses using monthly averages of particulate matter less than or equal to 2.5 µm (PM2.5), particulate matter less than or equal to 10 µm (PM10), nitrogen dioxide (NO2), and ozone (O3) from spatial interpolation of monitoring station measurements. Constituents of PM2.5 (sulfate, nitrate, ammonium, organic matter, and black carbon) were obtained from fine-resolution geoscience-derived models based on satellite, ground-based monitor, and chemical transport modeling data. Main Outcomes and Measures: Participants with an Edinburgh Postnatal Depression Scale score of 10 or higher during the 6 months after giving birth were referred to a clinical interview for further assessment and diagnosis. Ascertainment of PPD was defined using a combination of diagnostic codes and prescription medications. Results: The study included 340 679 participants (mean [SD] age, 30.05 [5.81] years), with 25 674 having PPD (7.54%). Increased risks for PPD were observed to be associated with per-IQR increases in antepartum and postpartum exposures to O3 (adjusted odds ratio [AOR], 1.09; 95% CI, 1.06-1.12), PM10 (AOR, 1.02; 95% CI, 1.00-1.04), and PM2.5 (AOR, 1.02; 95% CI, 1. 00-1.03) but not with NO2; PPD risks were mainly associated with PM2.5 organic matter and black carbon. Overall, a higher risk of PPD was associated with O3 during the entire pregnancy and postpartum periods and with PM exposure during the late pregnancy and postpartum periods. Conclusions and Relevance: The study findings suggest that long-term exposure to antepartum and postpartum air pollution was associated with higher PPD risks. Identifying the modifiable environmental risk factors and developing interventions are important public health issues to improve maternal mental health and alleviate the disease burden of PPD.

Invited commentary: Lost in estimation--searching for alternatives to markov chains to fit complex Bayesian models.

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

Genetic determinants of height growth assessed longitudinally from infancy to adulthood in the northern Finland birth cohort 1966.

Recent genome-wide association (GWA) studies have identified dozens of common variants associated with adult height. However, it is unknown how these variants influence height growth during childhood. We derived peak height velocity in infancy (PHV1) and puberty (PHV2) and timing of pubertal height growth spurt from parametric growth curves fitted to longitudinal height growth data to test their association with known height variants. The study consisted of N = 3,538 singletons from the prospective Northern Finland Birth Cohort 1966 with genotype data and frequent height measurements (on average 20 measurements per person) from 0-20 years. Twenty-six of the 48 variants tested associated with adult height (p<0.05, adjusted for sex and principal components) in this sample, all in the same direction as in previous GWA scans. Seven SNPs in or near the genes HHIP, DLEU7, UQCC, SF3B4/SV2A, LCORL, and HIST1H1D associated with PHV1 and five SNPs in or near SOCS2, SF3B4/SV2A, C17orf67, CABLES1, and DOT1L with PHV2 (p<0.05). We formally tested variants for interaction with age (infancy versus puberty) and found biologically meaningful evidence for an age-dependent effect for the SNP in SOCS2 (p = 0.0030) and for the SNP in HHIP (p = 0.045). We did not have similar prior evidence for the association between height variants and timing of pubertal height growth spurt as we had for PHVs, and none of the associations were statistically significant after correction for multiple testing. The fact that in this sample, less than half of the variants associated with adult height had a measurable effect on PHV1 or PHV2 is likely to reflect limited power to detect these associations in this dataset. Our study is the first genetic association analysis on longitudinal height growth in a prospective cohort from birth to adulthood and gives grounding for future research on the genetic regulation of human height during different periods of growth.