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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 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 Cochrans 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.

Many diarrhea-causing pathogens are climate-sensitive, and populations with the lowest socioeconomic position (SEP) are often most vulnerable to climate-related transmission. Household Water, Sanitation, and Handwashing (WASH) interventions constitute one potential effective strategy to reduce child diarrhea, especially among low-income households. Capitalizing on a cluster randomized trial population (360 clusters, 4941 children with 8440 measurements) in rural Bangladesh, one of the worlds most climate-sensitive regions, we show that improved WASH substantially reduces diarrhea risk with largest benefits among children with lowest SEP and during the monsoon season. We extrapolated trial results to rural Bangladesh regions using high-resolution geospatial layers to identify areas most likely to benefit. Scaling up a similar intervention could prevent an estimated 734 (95% CI 385, 1085) cases per 1000 children per month during the seasonal monsoon, with marked regional heterogeneities. Here, we show how to extend large-scale trials to inform WASH strategies among climate-sensitive and low-income populations.

Extreme heat and wildfire smoke events are increasingly co-occurring in the context of climate change, especially in California. Extreme heat and wildfire smoke may have synergistic effects on population health that vary over space. We leveraged high-resolution satellite and monitoring data to quantify spatially varying compound exposures to extreme heat and wildfire smoke in California (2006-2019) at ZIP Code Tabulation Area (ZCTA) level. We found synergistic effects between extreme heat and wildfire smoke on daily cardiorespiratory hospitalizations at the state level. We also found spatial heterogeneity in such synergistic effects across ZCTAs. Communities with lower education attainment, lower health insurance coverage, lower income, lower proportion of automobile ownership, lower tree canopy coverage, higher population density, and higher proportions of racial/ethnic minorities experienced higher synergistic effects. This study highlights the need to incorporate compound hazards and environmental justice considerations into evidence-based policy development to protect populations from increasingly prevalent compound hazards.

The Ganges-Brahmaputra-Meghna river basin, running through Tibet, Nepal, Bhutan, Bangladesh, and northern India, is home to more than 618 million people. Annual monsoons bring extensive flooding to the basin, with floods predicted to be more frequent and extreme due to climate change. Yet, evidence regarding the long-term impacts of floods on childrens health is lacking. In this analysis, we used high-resolution maps of recent large floods in Bangladesh to identify flood-prone areas over the country. We then used propensity score techniques to identify, among 58,945 mothers interviewed in six demographic population-based surveys throughout Bangladesh, matched cohorts of exposed and unexposed mothers and leverage data on 150,081 births to estimate that living in flood-prone areas was associated with an excess risk in infant mortality of 5.3 (95% CI 2.2 to 8.4) additional deaths per 1,000 births compared to living in non-flood-prone areas over the 30-y period between 1988 and 2017, with higher risk for children born during rainy (7.9, 95% CI: 3.3 to 12.5) vs. dry months (3.1, 95% CI: -1.1 to 7.2). Finally, drawing on national-scale, high-resolution estimates of flood risk and population distribution, we estimated an excess of 152,753 (64,120 to 241,386) infant deaths were attributable to living in flood-prone areas in Bangladesh over the past 30 y, with marked heterogeneity in attributable burden by subdistrict. Our approach demonstrates the importance of measuring longer-term health impacts from floods and provides a generalizable example for how to study climate-related exposures and long-term health effects.

BackgroundCities often use non-native plants such as turf grass to expand green space. Native plants, however, may require less water and maintenance and have co-benefits for local biodiversity, including pollinators. Previous studies estimating mortality averted by adding green space have not considered the provision of native plants as part of the greening policies.AimWe aim to estimate premature deaths that would be prevented by the implementation of native-plants policy scenarios in the City of Denver, Colorado, USA.MethodsAfter conducting interviews with local expert stakeholders, we designed four native-plants policy scenarios: (1) greening 30% of all city census-block groups to the greenness level of native plants, (2) adding 200-foot native-plants buffers around riparian areas, (3) constructing large water retention ponds landscaped with native plants, and (4) greening parking lots. We defined the normalized difference vegetation index (NDVI) corresponding to native plants by measuring the NDVI at locations with known native or highly diverse vegetation. Using a quantitative health-impact assessment approach, we estimated premature mortality averted under each scenario, comparing alternative NDVI with the baseline value.ResultsIn the most ambitious scenario, we estimated that 88 (95% uncertainty interval (UI): 20, 128) annual premature deaths would be prevented by greening 30% of the area of census block groups with native plants. We estimated that greening 30% of parking-lot surface with native plants would prevent 14 annual deaths (95% UI: 7, 18), adding the native buffers around riparian areas would prevent 13 annual deaths (95% UI: 2, 20), and adding the proposed stormwater retention ponds would prevent no annual deaths (95% UI: 0, 1).ConclusionUsing native plants to increase green spaces has the potential to prevent premature deaths in the City of Denver, but results were sensitive to the definition of native plants and the policy scenario.

ObjectiveTo assess the effectiveness of e-cigarettes in smoking cessation in the USA from 2017 to 2019, given the 2017 increase in high nicotine e-cigarette sales.MethodsIn 2017, the PATH Cohort Study included data on 3578 previous year smokers with a recent quit attempt and 1323 recent former smokers. Respondents reported e-cigarettes or other products used to quit cigarettes and many covariates associated with e-cigarette use. Study outcomes were 12+ months of cigarette abstinence and tobacco abstinence in 2019. We report weighted unadjusted estimates and use propensity score matched analyses with 1500 bootstrap samples to estimate adjusted risk differences (aRD).ResultsIn 2017, 12.6% (95% CI 11.3% to 13.9%) of recent quit attempters used e-cigarettes to help with their quit attempt, a decline from previous years. Cigarette abstinence for e-cigarette users (9.9%, 95% CI 6.6% to 13.2%) was lower than for no product use (18.6%, 95% CI 16.0% to 21.2%), and the aRD for e-cigarettes versus pharmaceutical aids was -7.3% (95% CI -14.4 to -0.4) and for e-cigarettes versus any other method was -7.7% (95% CI -12.2 to -3.2). Only 2.2% (95% CI 0.0% to 4.4%) of recent former smokers switched to a high nicotine e-cigarette. Subjects who switched to e-cigarettes appeared to have a higher relapse rate than those who did not switch to e-cigarettes or other tobacco, although the difference was not statistically significant.ConclusionsSales increases in high nicotine e-cigarettes in 2017 did not translate to more smokers using these e-cigarettes to quit smoking. On average, using e-cigarettes for cessation in 2017 did not improve successful quitting or prevent relapse.

When our brains are healthy, we can memorize, pay attention, reason, move, communicate, make decisions, and complete complex tasks. As some people get older, they can no longer do those things—they suffer from cognitive disorders that could indicate the start of dementia. Many factors might play a role in the development of dementia and some of those factors, like age and education level, have been identified. We know that there are additional, unidentified factors that play a role in the development of dementia, and scientists are discovering that where we live also matters. Our living environments include buildings and other structures, green spaces, and the level of air pollution. In this article, we explain the impact of the living environment on brain health decline during aging.

BACKGROUND: This study capitalized on coal and oil facility retirements to quantify their potential effects on fine particulate matter (PM2.5) concentrations and cardiorespiratory hospitalizations in affected areas using a generalized synthetic control method. METHODS: We identified 11 coal and oil facilities in California that retired between 2006 and 2013. We classified zip code tabulation areas (ZCTA) as exposed or unexposed to a facility retirement using emissions information, distance, and a dispersion model. We calculated weekly ZCTA-specific PM2.5 concentrations based on previously estimated daily time-series PM2.5 concentrations from an ensemble model, and weekly cardiorespiratory hospitalization rates based on hospitalization data collected by the California Department of Health Care Access and Information. We estimated the average differences in weekly average PM2.5 concentrations and cardiorespiratory hospitalization rates in four weeks after each facility retirement between the exposed ZCTAs and the synthetic control using all unexposed ZCTAs (i.e., the average treatment effect among the treated [ATT]) and pooled ATTs using meta-analysis. We conducted sensitivity analyses to consider different classification schemes to distinguish exposed from unexposed ZCTAs, including aggregating outcomes with different time intervals and including a subset of facilities with reported retirement date confirmed via emission record. RESULTS: The pooled ATTs were 0.02 μg/m3 (95% confidence interval (CI): -0.25 to 0.29 μg/m3) and 0.34 per 10,000 person-weeks (95%CI: -0.08 to 0.75 per 10,000 person-weeks) following the facility closure for weekly PM2.5 and cardiorespiratory hospitalization rates, respectively. Our inferences remained the same after conducting sensitivity analyses. CONCLUSIONS: We demonstrated a novel approach to study the potential benefits associated with industrial facility retirements. The declining contribution of industrial emissions to ambient air pollution in California may explain our null findings. We encourage future research to replicate this work in regions with different industrial activities.

Despite the occurrence of wildfires quadrupling over the past four decades, the health effects associated with wildfire smoke exposures during pregnancy remains unknown. Particulate matter less than 2.5 μms (PM2.5) is among the major pollutants emitted in wildfire smoke. Previous studies found PM2.5 associated with lower birthweight, however, the relationship between wildfire-specific PM2.5 and birthweight is uncertain. Our study of 7923 singleton births in San Francisco between January 1, 2017 and March 12, 2020 examines associations between wildfire smoke exposure during pregnancy and birthweight. We linked daily estimates of wildfire-specific PM2.5 to maternal residence at the ZIP code level. We used linear and log-binomial regression to examine the relationship between wildfire smoke exposure by trimester and birthweight and adjusted for gestational age, maternal age, race/ethnicity, and educational attainment. We stratified by infant sex to examine potential effect modification. Exposure to wildfire-specific PM2.5 during the second trimester of pregnancy was positively associated with increased risk of large for gestational age (OR = 1.13; 95% CI: 1.03, 1.24), as was the number of days of wildfire-specific PM2.5 above 5 μg m-3 in the second trimester (OR = 1.03; 95% CI: 1.01, 1.06). We found consistent results with wildfire smoke exposure in the second trimester and increased continuous birthweight-for-gestational age z-score. Differences by infant sex were not consistent. Counter to our hypothesis, results suggest that wildfire smoke exposures are associated with increased risk for higher birthweight. We observed strongest associations during the second trimester. These investigations should be expanded to other populations exposed to wildfire smoke and aim to identify vulnerable communities. Additional research is needed to clarify the biological mechanisms in this relationship between wildfire smoke exposure and adverse birth outcomes.

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.MethodsWe 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.ResultsGDM 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.DiscussionSusceptibility 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.

BackgroundAs atmospheric greenhouse gas concentrations continue to rise, temperature and humidity will increase further, causing potentially dire increases in human heat stress. On physiological and biophysical grounds, exposure to higher levels of humidity should worsen heat stress by decreasing sweat evaporation. However, population-scale epidemiological studies of heat exposure and response often do not detect associations between high levels of humidity and heat-related mortality or morbidity. These divergent, disciplinary views regarding the role of humidity in heat-related health risks limit confidence in selecting which interventions are effective in reducing health impacts and in projecting future heat-related health risks.ObjectivesVia our multidisciplinary perspective we seek to a) reconcile the competing realities concerning the role of humidity in heat-related health impacts and b) help ensure robust projections of heat-related health risks with climate change. These objectives are critical pathways to identify and communicate effective approaches to cope with present and future heat challenges.DiscussionWe hypothesize six key reasons epidemiological studies have found little impact of humidity on heat-health outcomes: a) At high temperatures, there may be limited influence of humidity on the health conditions that cause most heat-related deaths (i.e., cardiovascular collapse); b) epidemiological data sets have limited spatial extent, a bias toward extratropical (i.e., cooler and less humid), high-income nations, and tend to exist in places where temporal variations in temperature and humidity are positively correlated; c) analyses focus on older, vulnerable populations with sweating, and thus evaporative, impairments that may be further aggravated by dehydration; d) extremely high levels of temperature and humidity (seldom seen in the historical record) are necessary for humidity to substantially impact heat strain of sedentary individuals; e) relationships between temperature and humidity are improperly considered when interpreting epidemiological model results; and f) sub-daily meteorological phenomena, such as rain, occur at high temperatures and humidity, and may bias epidemiological studies based on daily data. Future research must robustly test these hypotheses to advance methods for more accurate incorporation of humidity in estimating heat-related health outcomes under present and projected future climates. https://doi.org/10.1289/EHP11807.

BackgroundLittle 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).MethodsClinical 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.FindingsIn 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.InterpretationStreet 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.FundingNational Institute of Environmental Health Sciences (NIEHS; R01ES030353).

In India, traditional social practices around marriage, such as non-involvement of prospective brides in choice of partner and timing of marriage, child/early marriage, dowry and purdah, compromise womens agency at the time of marriage and may also affect contraceptive practices in marriage. This paper examines the associations between traditional marital practices and contraceptive behaviours, including womens control over contraceptive decision-making, couples communication about contraception, and ever use of contraceptives, among married women aged 18-29 years (N = 1,200) and their husbands in rural Maharashtra, India. Multivariable logistic regression was used to examine the association between these marginalising social practices and family planning behavioural outcomes, adjusting for demographic and parity confounders. Wives who were the primary decision-makers on who to marry had higher odds of ever having communicated with their husband on pregnancy prevention (AOR 1.76, 95% CI 1.16-2.68), and ever using modern contraceptives (AOR 2.19, 95% CI 1.52-3.16). Wives who were the primary decision-makers on when to marry also had higher odds of ever having used modern contraceptives (AOR 1.86, 95% CI 1.21-2.93). Womens involvement in marital choice may facilitate couples engagement related to family planning, possibly via the establishment of better communication between partners.

ObjectivesTo estimate the effect of menthol use and transitions in use (switching to or from menthol) on short-term and long-term cessation from cigarette smoking and whether this differed across demographic groups (age, sex, race).MethodsWe compared the probability of 30+ day and 12-month abstinence from cigarette smoking by menthol use status using two cohorts of US adult cigarette smokers who attempted to quit smoking in the Population Assessment of Tobacco and Health (wave 1 to wave 3 and wave 2 to wave 4; n=5759), inverse probability of treatment weighting and adjusted risk ratios (aRRs).ResultsUsing menthol (vs non-menthol) prior to a quit attempt decreased the probability of 30+ day abstinence by 28% (aRR=0.78; 95% CI 0.67 to 0.91) and the probability of 12-month abstinence by 53% (aRR=0.65; 95% CI 0.47 to 0.88). Additionally, switching from menthol (vs maintaining menthol use) increased the probability of 30+ day abstinence by 58% (aRR=1.58; 95% CI 1.00 to 2.50) and the probability of 12-month abstinence by 97% (aRR=1.86; 95% CI 0.92 to 3.74). Switching to menthol (vs maintaining non-menthol use) was associated with a lower probability of 30+ day (aRR=0.70; 95% CI 0.42 to 1.16) and 12-month abstinence (aRR=0.64; 95% CI 0.30 to 1.36), but these associations were imprecise. The effects of menthol use on impaired quitting were slightly larger for non-Hispanic Black smokers, but not different for other demographic groups.ConclusionThese results demonstrate that menthol impaired menthol smokers' attempts to quit smoking but switching from menthol improved success. This suggests that removing menthol may improve menthol smokers' success during quit attempts.

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.

BackgroundNumerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention's complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges.MethodWe applied the parametric g-computation to estimate the cumulative risks of nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to PM2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold (8.8 μg/m3, 7.04 μg/m3, 5 μg/m3, and 4 μg/m3), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM2.5. We considered baseline and time-varying confounders, including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication.ResultsAll hypothetical intervention strategies explored led to lower 11-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of 8.8 μg/m3, and the largest reduction of 3.40 per 1,000 participants (95% CI: -0.23, 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those ≥65 years of age were greater with a similar pattern. Our estimates were robust to different model specifications.DiscussionWe found evidence that any intervention further reducing the long-term exposure to PM2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. The parametric g-computation used in this study provides flexibilities in simulating real-world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research. https://doi.org/10.1289/EHP11095.

Climate-sensitive infectious diseases are an issue of growing concern due to global warming and the related increase in the incidence of extreme weather and climate events. Diarrhea, which is strongly associated with climatic factors, remains among the leading causes of child death globally, disproportionately affecting populations in low- and middle-income countries (LMICs). We use survey data for 51 LMICs between 2000 and 2019 in combination with gridded climate data to estimate the association between precipitation shocks and reported symptoms of diarrheal illness in young children. We account for differences in exposure risk by climate type and explore the modifying role of various social factors. We find that droughts are positively associated with diarrhea in the tropical savanna regions, particularly during the dry season and dry-to-wet and wet-to-dry transition seasons. In the humid subtropical regions, we find that heavy precipitation events are associated with increased risk of diarrhea during the dry season and the transition from dry-to-wet season. Our analysis of effect modifiers highlights certain social vulnerabilities that exacerbate these associations in the two climate zones and present opportunities for public health intervention. For example, we show that stool disposal practices, child feeding practices, and immunizing against the rotavirus modify the association between drought and diarrhea in the tropical savanna regions. In the humid subtropical regions, household's source of water and water disinfection practices modify the association between heavy precipitation and diarrhea. The evidence of effect modification varies depending on the type and duration of the precipitation shock.

STUDY OBJECTIVES: Examining multiple dimensions of sleep health may better capture associations between sleep and health risks, including cardiometabolic disease (CMD). Hispanics have elevated risk for inadequate sleep and CMD biomarkers. Few studies have explored whether associations between sleep and CMD differ by Hispanic ethnicity. METHODS: Leveraging data from the Community of Mine (CoM) study, a cross-sectional investigation of 602 ethnically diverse participants, we derived accelerometer-measured sleep duration and efficiency, and self-reported sleep quality. Accelerometer-measured sleep exposures were analyzed both as continuous and categorical variables. Multivariate and quantile regression models were used to assess associations between sleep and CMD biomarkers (insulin resistance, systolic blood pressure, and low-density-lipoprotein cholesterol), controlling for age, sex, ethnicity, education, smoking status, and body mass index. We examined the potential effect modification of Hispanic ethnicity. RESULTS: We observed mixed results based on CMD biomarkers and sleep exposure. Increased sleep duration was significantly related to low-density lipoprotein cholesterol in adjusted models (estimate = 0.06; 95% CI: 0.02, 0.11). Poor sleep efficiency was associated with greater insulin resistance in the adjusted quantile (estimate = 0.20; 95% CI: 0.04, 0.36) model at the 90th percentile. Self-reported sleep quality was not associated with CMD outcomes. There was no evidence of effect modification by Hispanic ethnicity. CONCLUSIONS: In this cohort, sleep health measures were found to have mixed and at times opposing effects on CMD outcomes. These effects did not demonstrate an interaction with Hispanic ethnicity.

Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

BackgroundPolicies to restrict population mobility are a commonly used strategy to limit the transmission of contagious diseases. Among measures implemented during the COVID-19 pandemic were dynamic stay-at-home orders informed by real-time, regional-level data. California was the first state in the U.S. to implement this novel approach; however, the effectiveness of California's four-tier system on population mobility has not been quantified.MethodsUtilizing data from mobile devices and county-level demographic data, we evaluated the impact of policy changes on population mobility and explored whether demographic characteristics explained variability in responsiveness to policy changes. For each California county, we calculated the proportion of people staying home and the average number of daily trips taken per 100 persons, across different trip distances and compared this to pre-COVID-19 levels.ResultsWe found that overall mobility decreased when counties moved to a more restrictive tier and increased when moving to a less restrictive tier, as the policy intended. When placed in a more restrictive tier, the greatest decrease in mobility was observed for shorter and medium-range trips, while there was an unexpected increase in the longer trips. The mobility response varied by geographic region, as well as county-level median income, gross domestic product, economic, social, and educational contexts, the prevalence of farms, and recent election results.ConclusionsThis analysis provides evidence of the effectiveness of the tier-based system in decreasing overall population mobility to ultimately reduce COVID-19 transmission. Results demonstrate that socio-political demographic indicators drive important variability in such patterns across counties.

BackgroundPrecipitation anomalies are associated with a number of poor health outcomes. One potential consequence of precipitation extremes is human geographic mobility. We evaluated the associations between precipitation anomalies (droughts and heavy rains) and short-term mobility in 23 sub-Saharan African countries by linking satellite data on precipitation to cross-sectional representative surveys.MethodsUsing data from 23 Demographic and Health Surveys from 2011 to 2017, we estimated the associations between deviations in long-term rainfall trends and short-term mobility among 294,539 women and 136,415 men over 15 years of age. We fit multivariable logistic regression models to assess potential non-linear relationships between rainfall deviations and short-term mobility, adjusting for survey month and socio-demographic covariates, and stratified by participant gender. Furthermore, we assessed whether these associations differed by marital status.ResultsRainfall deviations were associated with short-term mobility among women, but not men. The relationship between rainfall deviations and mobility among women was U-shaped, such that women had increased marginal probabilities of mobility in instances of both lower and heavier precipitation. Differences between married and unmarried women were also revealed: among married women, we found positive associations between both rainfall deviation extremes (drought and heavy rains) and mobility; however, among unmarried women, there was only a positive association for heavy rains.ConclusionPrecipitation anomalies were associated with short-term mobility among women, which may be in turn associated with poor health outcomes. More research with longitudinal data is needed to elaborate the associations between weather shocks, mobility, and downstream health impacts.

Emissions of fine particulate matter (PM2.5) from human activities have been linked to substantial disease burdens, but evidence regarding how reducing PM2.5 at its sources would improve public health is sparse. We followed a population-based cohort of 2.7 million adults across Canada from 2007 through 2016. For each participant, we estimated annual mean concentrations of PM2.5 and the fractional contributions to PM2.5 from the five leading anthropogenic sources at their residential address using satellite observations in combination with a global atmospheric chemistry transport model. For each source, we estimated the causal effects of six hypothetical interventions on 10-y nonaccidental mortality risk using the parametric g-formula, a structural causal model. We conducted stratified analyses by age, sex, and income. This cohort would have experienced tangible health gains had contributions to PM2.5 from any of the five sources been reduced. Compared with no intervention, a 10% annual reduction in PM2.5 contributions from transportation and power generation, Canada's largest and fifth-largest anthropogenic sources, would have prevented approximately 175 (95%CI: 123-226) and 90 (95%CI: 63-117) deaths per million by 2016, respectively. A more intensive 50% reduction per year in PM2.5 contributions from the two sources would have averted 360 and 185 deaths per million, respectively, by 2016. The potential health benefits were greater among men, older adults, and low-income earners. In Canada, where PM2.5 levels are among the lowest worldwide, reducing PM2.5 contributions from anthropogenic sources by as little as 10% annually would yield meaningful health gains.

Accumulating evidence links cardiometabolic health with social and environmental neighborhood exposures, which may contribute to health inequities. We examined whether environmental characteristics were individually or jointly associated with insulin resistance, hypertension, obesity, type 2 diabetes, and metabolic syndrome in San Diego County, CA. As part of the Community of Mine Study, cardiometabolic outcomes of insulin resistance, hypertension, BMI, diabetes, and metabolic syndrome were collected in 570 participants. Seven census tract level characteristics of participants' residential environment were assessed and grouped as follows: economic, education, health care access, neighborhood conditions, social environment, transportation, and clean environment. Generalized estimating equation models were performed, to take into account the clustered nature of the data and to estimate β or relative risk (RR) and 95 % confidence intervals (CIs) between each of the seven environmental characteristics and cardiometabolic outcomes. Quantile g-computation was used to examine the association between the joint effect of a simultaneous increase in all environmental characteristics and cardiometabolic outcomes. Among 570 participants (mean age 58.8 ± 11 years), environmental economic, educational and health characteristics were individually associated with insulin resistance, diabetes, obesity, and metabolic syndrome. In the mixture analyses, a joint quartile increase in all environmental characteristics (i.e., improvement) was associated with decreasing insulin resistance (β, 95 %CI: -0.09, -0.18-0.01)), risk of diabetes (RR, 95 %CI: 0.59, 0.36-0.98) and obesity (RR, 95 %CI: 0.81, 0.64-1.02). Environmental characteristics synergistically contribute to cardiometabolic health and independent analysis of these determinants may not fully capture the potential health impact of social and environmental determinants of health.

The COVID-19 pandemic disrupted life in extraordinary ways impacting health and daily mobility. Public transit provides a strategy to improve individual and population health through increased active travel and reduced vehicle dependency, while ensuring equitable access to jobs, healthcare, education, and mitigating climate change. However, health safety concerns during the COVID-19 pandemic eroded ridership, which could have longstanding negative consequences. Research is needed to understand how mobility and health change as the pandemic recedes and how transit investments impact health and equity outcomes. The TROLLEY (TRansit Opportunities for HeaLth, Livability, Exercise and EquitY) study will prospectively investigate a diverse cohort of university employees after the opening of a new light rail transit (LRT) line and the easing of campus COVID-19 restrictions. Participants are current staff who live either  2 miles from LRT, with equal distribution across economic and racial/ethnic strata. The primary aim is to assess change in physical activity, travel mode, and vehicle miles travelled using accelerometer and GPS devices. Equity outcomes include household transportation and health-related expenditures. Change in health outcomes, including depressive symptoms, stress, quality of life, body mass index and behavior change constructs related to transit use will be assessed via self-report. Pre-pandemic variables will be retrospectively collected. Participants will be measured at 3 times over 2 years of follow up. Longitudinal changes in outcomes will be assessed using multilevel mixed effects models. Analyses will evaluate whether proximity to LRT, sociodemographic, and environmental factors modify change in outcomes over time. The TROLLEY study will utilize rigorous methods to advance our understanding of health, well-being, and equity-oriented outcomes of new LRT infrastructure through the COVID-19 recovery period, in a sample of demographically diverse adult workers whose employment location is accessed by new transit. Results will inform land use, transportation and health investments, and workplace interventions. Findings have the potential to elevate LRT as a public health priority and provide insight on how to ensure public transit meets the needs of vulnerable users and is more resilient in the face of future health pandemics. The TROLLEY study was registered at ClinicalTrials.gov ( NCT04940481 ) June 17, 2021, and OSF Registries ( https://doi.org/10.17605/OSF.IO/PGEHU ) June 24, 2021, prior to participant enrollment.