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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 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. Yi Sun and colleagues investigate the association between hypertensive disorders in pregnancy and particulate air pollution. Author summary: Why was this study done?: Although previous studies have explored the associations of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP), earlier findings are inconclusive. Several unaddressed issues remain, such as lack of simultaneous consideration of PM2.5 constituents, mixture effects, windows of susceptibility, and levels of disease severity. What did the researchers do and find?: A retrospective cohort study (N = 386,361) was conducted using electronic health records from Kaiser Permanente Southern California (2008 to 2017) to examine the associations between HDP (mild HDP: gestational hypertension (GH) versus severe HDP: preeclampsia and eclampsia (PE-E)) and exposure to PM2.5, and identify windows of susceptibility to PM2.5 during pregnancy. We found higher risks for PE-E associated with exposures to PM2.5 total mass, organic matter, and black carbon, but not for GH. The main effect of increased PE-E risk was driven by PM2.5 black carbon (71%) and PM2.5 organic matter (26%). The most influential time windows of PM2.5 exposure associated with HDP are the late first trimester and second trimester. Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods may be more vulnerable to air pollution on HDP risk. What do these findings mean?: Our findings suggest that different PM2.5 chemical constituents and critical exposure windows may affect GH and PE-E differently. Our work highlights the need for further research to investigate different components of air pollutants, levels of disease severity, and various susceptible windows, which is important to better understand the underlying mechanisms between air pollution and HDP and develop corresponding interventions. [ABSTRACT FROM AUTHOR]

Abstract: Little is known about the impacts of green spaces on pregnancy outcomes. The relationship between green space exposure and preeclampsia has never been studied. We used a hospital-based perinatal database including more than 80,000 births to study the relationships between greenness exposure and three pregnancy outcomes: birth weight in term born infants, preterm deliveries and preeclampsia. Greenness was characterized using the normalized difference vegetation index (NDVI) within circular buffers surrounding maternal homes. Analyses were conducted using generalized estimating equations, adjusted for potential confounders. We observed an increase in birth weight in term born infants and a reduced risk of preterm births associated with an increase in NDVI. No significant association was observed between greenness and preeclampsia. This study provides modest support for beneficial effects of greenness exposure on pregnancy outcomes and calls for confirmation in other study settings. [Copyright &y& Elsevier]

Abstract: Land-use regression (LUR) models have been developed to estimate spatial distributions of traffic-related pollutants. Several studies have examined spatial autocorrelation among residuals in LUR models, but few utilized spatial residual information in model prediction, or examined the impact of modeling methods, monitoring site selection, or traffic data quality on LUR performance. This study aims to improve spatial models for traffic-related pollutants using generalized additive models (GAM) combined with cokriging of spatial residuals. Specifically, we developed spatial models for nitrogen dioxide (NO2) and nitrogen oxides (NOx) concentrations in Southern California separately for two seasons (summer and winter) based on over 240 sampling locations. Pollutant concentrations were disaggregated into three components: local means, spatial residuals, and normal random residuals. Local means were modeled by GAM. Spatial residuals were cokriged with global residuals at nearby sampling locations that were spatially auto-correlated. We compared this two-stage approach with four commonly-used spatial models: universal kriging, multiple linear LUR and GAM with and without a spatial smoothing term. Leave-one-out cross validation was conducted for model validation and comparison purposes. The results show that our GAM plus cokriging models predicted summer and winter NO2 and NOx concentration surfaces well, with cross validation R 2 values ranging from 0.88 to 0.92. While local covariates accounted for partial variance of the measured NO2 and NOx concentrations, spatial autocorrelation accounted for about 20% of the variance. Our spatial GAM model improved R 2 considerably compared to the other four approaches. Conclusively, our two-stage model captured summer and winter differences in NO2 and NOx spatial distributions in Southern California well. When sampling location selection cannot be optimized for the intended model and fewer covariates are available as predictors for the model, the two-stage model is more robust compared to multiple linear regression models. [Copyright &y& Elsevier]

Structural inequalities provide an important context for understanding and responding to the impact of high traffic densities on disadvantaged neighborhoods. Emerging atmospheric science and epidemiological research indicates hazardous vehicle-related pollutants(e.g., diesel exhaust) are highly concentrated near major roadways, and the prevalence of respiratory ailments and mortality are heightened in these high-traffic corridors. This article builds on recent findings that low-income and minority children in California disproportionately reside in high-traffic areas by demonstrating how the urban structure provides a critical framework for evaluating the causes, characteristics, and magnitude of traffic, particularly for disadvantaged neighborhoods. We find minority and high-poverty neighborhoods bear over two times the level of traffic density compared to the rest of the Southern California region, which may associate them with a higher risk of exposure to vehicle-related pollutants. Furthermore, these areas have older and more multifamily housing, which is associated with higher rates of indoor exposure to outdoor pollutants, including intrusion of motor vehicle exhaust. We discuss the implications of these patterns on future planning and policy strategies for mitigating the serious health consequences of exposure to vehicle-related air pollutants. [ABSTRACT FROM AUTHOR]

Background The 2-dose recombinant zoster vaccine (RZV) series is recommended for prevention of herpes zoster (HZ) in adults aged ≥50 years, but data are limited on the impact of concomitant administration with other vaccines on subsequent HZ risk. Methods This cohort study included Kaiser Permanente Southern California members aged ≥50 years who received 2 doses of RZV 4 weeks to ≤6 months apart during 1 April 2018–30 September 2019. RZV recipients with and without same-day concomitant vaccination for either RZV dose were followed up for incident HZ beginning 31 days after the second RZV dose until 30 September 2020. The hazard ratio (HR) for HZ comparing RZV recipients with and without concomitant vaccination was estimated using Cox proportional hazards regression, adjusting for confounders. Results RZV with and without concomitant vaccination was received by 12 898 and 28 353 individuals, respectively. HZ occurred among 41 individuals with concomitant vaccination (incidence rate, 2.2 [95% confidence interval {CI}, 1.6–3.0] per 1000 person-years) and 136 without concomitant vaccination (3.4 [95% CI, 2.9–4.0] per 1000 person-years). The adjusted HR for HZ comparing RZV recipients with and without concomitant vaccination was 0.75 (95% CI,.53–1.08). Conclusions HZ risk was not significantly different between RZV recipients with and without concomitant vaccination, supporting recommendations allowing for concomitant administration of RZV with other vaccines. [ABSTRACT FROM AUTHOR]

Air pollution is a major public health threat that is associated with asthma, cardiovascular disease, respiratory disease and all-cause mortality. Among the most important acute air pollution events occurring each year are celebrations involving fireworks, such as the 4th of July holiday in the United States. In this community-engaged study, academic partners and residents collaborated to collect indoor and outdoor PM2.5 concentration measurements in the disadvantaged city of Santa Ana, California, using low-cost AtmoTube sensor devices before, during and after the July 4th firework celebration, while also examining July 4th data extracted from the PurpleAir sensor network across over a hundred other cities in southern California. Average outdoor PM2.5 concentrations on July 4th were found to be three-to-five times higher than baseline, with hourly concentrations exceeding 160 μg/m3. Outdoor averages were roughly 30% to 100% higher than indoor levels. The most polluted cities exhibited 15-times higher PM2.5 levels compared with the least contaminated cities and were often those where household-level fireworks were legal for sale and use. Race/ethnicity was found to be the leading predictor of July 4th-related air pollution across three counties in southern California, with greater PM2.5 being associated with higher proportions of Hispanic residents and lower proportions of White residents. The findings from this study underscore the importance of environmental justice as it relates to firework-related air pollution exposure, and the critical role city- and county-level firework policies play in determining exposure. [ABSTRACT FROM AUTHOR]

• RZV series completion was suboptimal in the early stages of implementation. • Lower completion was associated with Black and Hispanic race/ethnicity and some comorbidities. • Increased completion was associated with greater outpatient and decreased inpatient utilization. • Education, reminders, and reducing missed opportunities to vaccinate may increase completion. In 2017, a new adjuvanted recombinant zoster vaccine (RZV) was recommended for adults ≥50 years as two-dose series 2–6 months apart. We evaluated two-dose RZV completion and factors associated with completion. The study included Kaiser Permanente Southern California members ≥50 years who received an RZV dose during April-November 2018 and had continuous membership 12 months before to 9 months after the first RZV dose. Completion was defined as receipt of the second dose ≥4 weeks to 9 months after the first dose (allowing 3-month grace period). Characteristics including age, sex, race/ethnicity, Medicaid status, neighborhood level income and education, comorbidities, history of zoster, health care utilization before and after the first dose, receipt of influenza vaccine, vaccination month (supply shortage proxy), concomitant vaccine, medical center, and medically attended reactions, pain, or gout after the first dose were compared between completers and non-completers. Adjusted odds ratios and 95% confidence intervals for factors associated with completion were estimated by multivariable logistic regression. Among 31,120 first dose recipients, 67.2% completed the series within 9 months. In adjusted analyses, higher completion was associated with White compared with Black or Hispanic race/ethnicity, higher neighborhood income and education, no chronic pulmonary disease, diabetes, or dementia, more outpatient visits and fewer emergency department visits before or after the first dose, no hospitalizations after the first dose, receipt of influenza vaccine, receipt of the first dose in June-November rather than April-May 2018, and no concomitant vaccine with the first dose. Systemic reactions or pain after the first dose was not associated with completion. Completion of RZV series appears suboptimal in the early phase of implementation. Despite similar accessibility in a health care system, completion varied by race/ethnicity, socioeconomic status, health status, and care seeking behavior, suggesting areas to target for improvement. [ABSTRACT FROM AUTHOR]

Background: The role of omega-3 fatty acid in multiple sclerosis (MS) susceptibility is unclear. Objective: To determine whether fish/seafood intake or genetic factors that regulate omega-3 fatty acids levels are associated with MS risk. Methods: We examined the association of fish and shrimp consumption and 13 tag single nucleotide polymorphisms (SNPs) in FADS1, FADS2, and ELOV2 with risk of MS in 1153 individuals from the MS Sunshine Study, a case-control study of incident MS or clinically isolated syndrome (CIS), recruited from Kaiser Permanente Southern California. Results: Consuming fish/seafood at least once a week or at least once a month with regular fish oil use was associated with 44% reduced odds of MS/CIS (adjusted OR = 0.56; 95% CI = 0.41–0.76; p = 0.0002) compared with consuming fish/seafood less than once a month and no fish oil supplementation. Two FADS2 SNPs (rs174611 and rs174618) were independently associated with a lower risk of MS (adjusted ORs = 0.74, 0.79, p = 0.0056, 0.0090, respectively). Association of FADS2 SNPs with MS risk was confirmed in an independent dataset. Conclusion: These findings suggest that omega-3 fatty acid intake may be an important modifiable risk factor for MS. This is consistent with the other known health benefits of fish consumption and complementary genetic studies supporting a key role for omega-3 regulation. [ABSTRACT FROM AUTHOR]

• NO 2 , O 3 , and PM 2.5 exposure can increase premature rupture of membranes (PROM) risk. • Maternal PM 2.5 nitrate, sulfate, and organic matter exposure can increase PROM risk. • The effect of air pollution mixture on PROM may be mainly driven by O 3 exposure. • Underweight mothers can be more vulnerable to PROM associated with air pollution. 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 (PM 2.5). We examined associations between maternal exposure to nitrogen dioxide (NO 2), ozone (O 3), PM 2.5 , PM 10 , and PM 2.5 constituents and SPROM. 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 NO 2 , O 3 (8-h daily maximum), PM 2.5 , and PM 10 were measured using empirical Bayesian kriging based on measurements from monitoring stations. Data on PM 2.5 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 PM 2.5 components. There were 37,857 SPROM cases (8.8%) in our study population. We observed relationships between SPROM and maternal exposure to NO 2 , O 3 , and PM 2.5. PM 2.5 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 PM 2.5 mixture in this study were mainly driven by O 3 and PM 2.5 nitrate, respectively. Underweight mothers had a significantly higher risk of SPROM associated with NO 2. Our findings add to the literature on associations between air pollution exposure and SPROM. This is the first study reporting the impact of PM 2.5 constituents on SPROM. [ABSTRACT FROM AUTHOR]

The article discusses a research study that investigates the correlation of asthma with school commuting time in Southern California. Researchers studied school children aged five to seven years old who commuted to school and found that rising commuting time was associated with the severe wheezing of children with asthma. Because of their discovery, they suggested to hold further investigation on the impact of on-road pollutant exposure.

Airborne exposures to polycyclic aromatic hydrocarbons (PAHs) are associated with adverse health outcomes. Because personal air measurements of PAHs are labor intensive and costly, spatial PAH exposure models are useful for epidemiological studies. However, few studies provide adequate spatial coverage to reflect intra-urban variability of ambient PAHs. In this study, we collected 39–40 weekly gas-phase PAH samples in southern California twice in summer and twice in winter, 2009, in order to characterize PAH source contributions and develop spatial models that can estimate gas-phase PAH concentrations at a high resolution. A spatial mixed regression model was constructed, including such variables as roadway, traffic, land-use, vegetation index, commercial cooking facilities, meteorology, and population density. Cross validation of the model resulted in an R 2 of 0.66 for summer and 0.77 for winter. Results showed higher total PAH concentrations in winter. Pyrogenic sources, such as fossil fuels and diesel exhaust, were the most dominant contributors to total PAHs. PAH sources varied by season, with a higher fossil fuel and wood burning contribution in winter. Spatial autocorrelation accounted for a substantial amount of the variance in total PAH concentrations for both winter (56%) and summer (19%). In summer, other key variables explaining the variance included meteorological factors (9%), population density (15%), and roadway length (21%). In winter, the variance was also explained by traffic density (16%). In this study, source characterization confirmed the dominance of traffic and other fossil fuel sources to total measured gas-phase PAH concentrations while a spatial exposure model identified key predictors of PAH concentrations. Gas-phase PAH source characterization and exposure estimation is of high utility to epidemiologist and policy makers interested in understanding the health impacts of gas-phase PAHs and strategies to reduce emissions. [ABSTRACT FROM AUTHOR]

• Acute heatwave exposure can increase the premature rupture of membranes (PROM) risk. • Younger mothers with lower socioeconomic status have higher heat-related PROM risks. • Air pollution during pregnancy can be a positive effect modifier of heat effects. • More residential green space may mitigate adverse heat effects on PROM. 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. 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., PM 2.5 and NO 2), climate adaptation measures (i.e., green space and air conditioning [AC] penetration), sociodemographic factors, and smoking behavior was examined. 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 PM 2.5 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. 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. [ABSTRACT FROM AUTHOR]

Background: Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Methods: Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Results: Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. Conclusions: The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations. [ABSTRACT FROM AUTHOR]

[Display omitted] • PM 2.5 , PM 10 , NO 2 , and PM 2.5 constituents was associated with an increased GDM risk. • NO 2 and black carbon PM 2.5 contributed most to GDM among air pollution mixtures. • GDM associated with air pollution were higher among Hispanic or overweight mothers. Epidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents. To assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents. Detailed clinical data were obtained for 395,927 pregnancies in southern California (2008–2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter < 2.5 μm (PM 2.5), <10 μm (PM 10), nitrogen dioxide (NO 2), and ozone (O 3) were estimated using kriging interpolation of Environmental Protection Agency's routine monitoring station data, while PM 2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon) were estimated using a fine-resolution geoscience-derived model. A multilevel logistic regression was used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of air pollution and PM component mixtures. Main analyses adjusted for maternal age, race/ethnicity, education, median family household income, pre-pregnancy BMI, smoking during pregnancy, insurance type, season of conception and year of delivery. The incidence of GDM was 10.9% in the study population. In single-pollutant models, we observed an increased odds for GDM associated with exposures to PM 2.5 , PM 10 , NO 2 and PM 2.5 constituents. The association was strongest for NO 2 [adjusted odds ratio (OR) per interquartile range: 1.176, 95% confidence interval (CI): 1.147–1.205)]. In multi-pollutant models, increased ORs for GDM in association with one quartile increase in air pollution mixtures were found for both kriging-based regional air pollutants (NO 2 , PM 2.5 , and PM 10 , OR = 1.095, 95% CI: 1.082–1.108) and PM 2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon, OR = 1.258, 95% CI: 1.206–1.314); NO 2 (78%) and black carbon (48%) contributed the most to the overall mixture effects among all krigged air pollutants and all PM 2.5 constituents, respectively. The risk of GDM associated with air pollution exposure were significantly higher among Hispanic mothers, and overweight/obese mothers. This study found that exposure to a mixture of ambient PM 2.5 , PM 10 , NO 2 , and PM 2.5 chemical constituents was associated with an increased risk of GDM. NO 2 and black carbon PM 2.5 contributed most to GDM risk. [ABSTRACT FROM AUTHOR]