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Evidence linking traffic noise to insulin resistance and diabetes is limited and unanswered questions remain regarding the potential effect modification by neighborhood socioeconomic status (nSES). We aimed to assess socioeconomic inequalities in noise exposure, whether road and aircraft noise exposures were associated with insulin resistance or diabetes, and whether nSES modified these relationships. Among the Community of Mine Study in San Diego County, road and aircraft noise exposure at enrollment was calculated based on the static (participant's administrative boundary, and circular buffer around participant homes), and dynamic (mobility data by global positioning system, GPS) spatio-temporal aggregation methods. Associations of noise with insulin resistance (HOMA-IR) or type 2 diabetes (T2DM) were quantified using generalized estimating equation models adjusted for sex, age, ethnicity, individual income, and air pollution (nitrogen dioxide) exposure. Additive interaction between noise and nSES was assessed. Among 573 participants (mean age 58.7 y), participants living in low nSES were exposed to higher levels of aircraft and road noise using noise level at the census tract, circular buffer, or Kernel Density Estimation (KDE) of GPS data. Participants exposed to road noise greater or equal to the median (53 dB(A)) at the census tract and living in low nSES had an increased level of insulin resistance (β = 0.15, 95%CI: −0.04, 0.34) and higher odds of T2DM (Odds Ratio = 2.34, 95%CI: 1.12, 4.90). A positive additive interaction was found as participants living in low nSES had higher odds of T2DM. The impact of noise exposure on insulin resistance and T2DM differs substantially by nSES. Public health benefits of reducing exposure to road or aircraft noise would be larger in individuals living in low nSES. [Display omitted] • We used static (census tract and buffer around home) and dynamic (GPS data) noise exposures. • Higher noise levels were found in low neighborhood socioeconomic status (nSES). • The impact of noise exposure on insulin resistance and diabetes differs by nSES. • Health benefits of intervention would be larger in participants living in low nSES. [ABSTRACT FROM AUTHOR]

Extreme cold weather alert programs have been implemented in some areas to address the significant health impacts of exposure to cold. One such program is the Toronto Cold Weather Program (TCWP) that was implemented in the City of Toronto since 1996 to protect the public from extreme weather conditions. In this paper, we aim to evaluate the effectiveness of the TCWP in reducing mortality and morbidity outcomes related to cold temperatures. We applied a quasi-experimental study design using the Difference-in-Differences method coupled with propensity-score-matching to determine the effect of the TCMP on daily hospitalizations and deaths due to cardiovascular disease (CVD), coronary heart disease (CHD) or cerebrovascular disease, using two complementary analytical approaches. Overall, the analysis did not detect an impact on reduced mortality/morbidity in the City of Toronto from the TCMP. For example, we obtained a Risk Difference (RD) of −0.88 (per 1,000,000 people) (95% CI: −3.27 to 1.51) and a Risk Ratio (RR) of 0.98 (95% CI: 0.91 to 1.05) people for CVD hospitalizations. The TCWP was not found to be effective in reducing cold related mortality and morbidity which demonstrates the importance of improving existing policies related to cold in Canada and other countries. • Exposure to cold is substantially increases an individual's risk for injury and death, and worsen pre-existing chronic conditions • Little is known about existing cold warning programs impact on reducing cold-related mortality and morbidity. • We observed that the Toronto Cold Weather Program was not effective in reducing cold-related mortality/morbidity. [ABSTRACT FROM AUTHOR]

Abstract Ambient air pollution has been linked to small for gestational age (SGA); however, the relationship with large for gestational age (LGA) is unclear and very few studies have investigated seasonal effects on the association between air pollution and SGA or LGA. Using birth registry data of 506,000 singleton live births from 11 districts in Guangzhou, China between January 2015 and July 2017, we examined associations between ambient air pollutants (PM 2.5 , PM 10 , NO 2 , SO 2 , and O 3) and SGA/LGA, and further assessed the modification effect of season. Daily concentrations of air pollutants from 11 monitoring stations were used to estimate district-specific exposures for each participant based on their district of residence during pregnancy. Two-level binary logistic regression models were used to evaluate associations between air pollution and SGA/LGA. Stratified analyses by season and a Cochran Q test were performed to assess the modification of season. Exposure to PM 2.5 , NO 2 , SO 2 , and O 3 was significantly associated with increased risk of SGA, especially for exposure during the second and third trimester. For an interquartile range (IQR) increase in PM 2.5 (6.5 μg/m3), NO 2 (12.7 μg/m3), SO 2 (2.8 μg/m3) and O 3 (20.8 μg/m3) during the entire pregnancy, SGA risk increased by 2% (OR = 1.02, 95% CI: 1.00–1.04), 8% (OR = 1.08, 95% CI: 1.04–1.12), 2% (OR = 1.02, 95% CI: 1.01–1.03), and 14% (1.14, 1.11–1.17), respectively. A decreased risk of LGA was found for PM 2.5 , PM 10 , SO 2 , and O 3 during the first trimester or entire pregnancy. When examined by season, significant associations between air pollutants and SGA were observed for women who conceived during summer or fall, and the patterns were consistent for all pollutants. Our study suggests that conception during different seasons might modify the association between ambient air pollution and SGA. Graphical abstract Unlabelled Image Highlights • Prenatal PM 2.5 , NO 2 , SO 2 , and O 3 exposures increased the risk of SGA. • Prenatal exposure to PM 2.5 , PM 10 , SO 2 , and O 3 decreased the risk of LGA. • Effects of prenatal air pollution exposure on SGA may be modified by season. • Effects of air pollution on SGA were observed for pregnancies conceived in summer or fall. [ABSTRACT FROM AUTHOR]

Abstract Maternal exposure to ambient air pollution has been associated with preterm birth (PTB), however, entire pregnancy or trimester-specific associations were generally reported, which may not sufficiently identify windows of susceptibility. Using birth registry data from Guangzhou, a megacity of southern China (population ~14.5 million), including 469,975 singleton live births between January 2015 and July 2017, we assessed the association between weekly air pollution exposure and PTB in a retrospective cohort study. Daily average concentrations of PM 2.5 , PM 10 , NO 2 , SO 2 , and O 3 from 11 monitoring stations were used to estimate district-specific exposures for each participant based on their district residency during pregnancy. Distributed lag models (DLMs) incorporating Cox proportional hazard models were applied to estimate the association between weekly maternal exposure to air pollutant and PTB risk (as a time-to-event outcome), after controlling for temperature, seasonality, and individual-level covariates. We also considered moderate PTB (32–36 gestational weeks) and very PTB (28–31 gestational weeks) as outcomes of interest. Hazard ratios (HRs) and 95% confidential intervals (95% CIs) were calculated for an interquartile range (IQR) increase in air pollutants during the study period. An IQR increase in PM 2.5 exposure during the 20th to 28th gestational weeks (27.0 μg/m3) was significantly associated with PTB risk, with the strongest effect in the 25th week (HR = 1.034, 95% CI:1.010–1.059). The significant exposure windows were the 19th–28th weeks for PM 10 , the 18th–31st weeks for NO 2 , and the 23rd–31st weeks for O 3 , respectively. The strongest associations were observed in the 25th week for PM 10 (IQR = 37.0 μg/m3; HR = 1.048, 95% CI:1.034–1.062), the 26th week for NO 2 (IQR = 29.0 μg/m3; HR = 1.060, 95% CI:1.028–1.094), and in the 28th week for O 3 (IQR = 90.0 μg/m3; HR = 1.063, 95% CI:1.046–1.081). Similar patterns were observed for moderate PTB (32–36 gestational weeks) and very PTB (28–31 gestational weeks) for PM 2.5 , PM 10 , NO 2 exposure, but the effects were greater for very PTB. We did not observe any association between pregnancy SO 2 exposure and the risk of PTB. Our results suggest that middle to late pregnancy is the most susceptible air pollution exposure window for air pollution and PTB among women in Guangzhou, China. Highlights • Limited studies identified susceptible windows for prenatal air pollution and preterm birth at a weekly level. • Prenatal PM 2.5 , PM 10 , NO 2 , and O 3 exposure is associated with preterm birth. • The most susceptible exposure window for each pollutant is gestational weeks 18–31. • Distributed lag models are applicable for identifying the susceptible windows. [ABSTRACT FROM AUTHOR]

Background Heat-waves have a substantial public health burden. Understanding spatial heterogeneity at a fine spatial scale in relation to heat and related mortality is central to target interventions towards vulnerable communities. Objectives To determine the spatial variability of heat-wave-related mortality risk among elderly in Paris, France at the census block level. We also aimed to assess area-level social and environmental determinants of high mortality risk within Paris. Methods We used daily mortality data from 2004 to 2009 among people aged > 65 at the French census block level within Paris. We used two heat wave days' definitions that were compared to non-heat wave days. A Bernoulli cluster analysis method was applied to identify high risk clusters of mortality during heat waves. We performed random effects meta-regression analyses to investigate factors associated with the magnitude of the mortality risk. Results The spatial approach revealed a spatial aggregation of death cases during heat wave days. We found that small scale chronic PM 10 exposure was associated with a 0.02 (95% CI: 0.001; 0.045) increase of the risk of dying during a heat wave episode. We also found a positive association with the percentage of foreigners and the percentage of labor force, while the proportion of elderly people living in the neighborhood was negatively associated. We also found that green space density had a protective effect and inversely that the density of constructed feature increased the risk of dying during a heat wave episode. Conclusion We showed that a spatial variation in terms of heat-related vulnerability exists within Paris and that it can be explained by some contextual factors. This study can be useful for designing interventions targeting more vulnerable areas and reduce the burden of heat waves. [ABSTRACT FROM AUTHOR]

Few studies have assessed social inequalities in exposure to drinking water contaminants. This study explores this issue in 593 rural municipalities of Québec, Canada. Quartiles of an ecological composite deprivation index were used as a proxy of socioeconomic status. Total trihalomethanes (TTHMs) and lead were chosen as proxies of chemical drinking water quality. The results show that the majority of deprived rural municipalities apply no treatment to their water (26%) or use a basic treatment (51%), whereas a relative majority of the wealthiest municipalities (40%) use advanced treatment. The proportion of municipalities having important lead (>5 μg/L) levels is highest in most deprived municipalities. Moreover, most deprived municipalities have a higher risk of high tap lead levels (RR = 1.33; 95%CI: 1.30, 1.36). Conversely, most deprived municipalities have a lower risk of high TTHMs levels (RR = 0.78; 95%CI: 0.69, 0.86). These findings suggest an environmental inequality in drinking water contaminants distribution in rural municipalities. [ABSTRACT FROM AUTHOR]

Cash transfer programs have grown increasingly popular and are now used as interventions to target a wide array of health outcomes across many diverse settings. However, cash transfer experiments have yielded mixed results, highlighting gaps in our understanding of how these programs work. In particular, we do not yet know whether cash transfers are more effective for certain health outcomes compared to others, or are more effective for some population subgroups compared to others. Here, we ask whether the effects of cash transfers on health outcomes differ across study subgroups. We reviewed the literature on cash transfer experiments conducted in low and middle income countries, published in English between 1985 and 2015. We documented whether the investigators reported either i) stratum-specific estimates or ii) the interaction term between subgroups and exposure to the intervention. For studies that presented stratum-specific estimates without statistical tests for heterogeneity, we assessed heterogeneity across subgroups with a Cochran Q test. Of the 56 studies we reviewed, 40 reported effects on study subgroups. The majority of the cash transfer interventions had different magnitudes of effects on health across subgroups. This heterogeneity was often underreported or not formally analyzed. We find substantial heterogeneity of cash transfers on child health and on adult health yet little heterogeneity of cash transfers on sexual and reproductive health. Accounting for the heterogeneous impacts of cash transfers during program design and evaluation is necessary to better target cash transfer programs and generate more precise data on their effects. • Across subgroups, most cash transfers had heterogeneous effects on health outcomes. • These heterogeneous effects are often under-reported. • Cash transfer interventions had the most heterogeneity on child health. • In contrast, the least the heterogeneity was on sexual and reproductive health. [ABSTRACT FROM AUTHOR]

Precipitation in California is projected to become more volatile: less frequent but more extreme as global warming pushes midlatitude frontal cyclones further poleward while bolstering the atmospheric rivers (ARs), which tend to produce the region's extreme rainfall. Pollutant accumulation and delivery to coastal waters can be expected to increase, as lengthening dry spells will be increasingly punctuated by more extreme precipitation events. Coastal pollution exposes human populations to high levels of fecal bacteria and associated pathogens, which can cause a variety of health impacts. Consequently, studying the impact of atmospheric rivers as the mechanism generating pulses of water pollution in coastal areas is relevant for public health and in the context of climate change. We aimed to quantify the links between precipitation events and water quality in order to explore meteorological causes as first steps toward effective early warning systems for the benefit of population health in California and beyond. We used historical gridded daily precipitation and weekly multiple fecal bacteria indicators at ~500 monitoring locations in California's coastal waters to identify weekly associations between precipitation and water quality during 2003–09 using canonical correlation analysis to account for the nested/clustered nature of longitudinal data. We then quantified, using a recently published catalog of atmospheric rivers, the proportion of coastal pollution events attributable to ARs. Association between precipitation and fecal bacteria was strongest in Southern California. Over two-thirds of coastal water pollution spikes exceeding one standard deviation were associated with ARs. This work highlights the importance of skillful AR landfall predictions in reducing vulnerability to extreme weather improving resilience of human populations in a varying and changing climate. Quantifying the impacts of ARs on waterborne diseases is important for planning effective preventive strategies for public health. Canonical Correlation Analysis (CCA) – Precipitation and Total Coliform Association between Precipitation CC1 and Atmospheric Rivers' Integrated Water Vapor Unlabelled Image • Atmospheric rivers (ARs) are the meteorological mechanism driving coastal water pollution spikes. • About 75% of water pollution spikes were associated with ARs via extreme precipitation. • The above association was strongest in densely populated Southern California. • Impairment of coastal waters could further increase as ARs are projected to intensify. [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]

• Maternal exposure to temperature extremes and variability can increase the risk of GDM. • The susceptible time window for the effect of non-optimal temperature on GDM appeared in the second trimester. • No interaction between ambient temperature and air pollution on GDM were observed. Previous observational studies have shown that exposure to ambient temperature and air pollution were associated with the incidence of gestational diabetes mellitus (GDM). However, the susceptible time window of non-optimal temperature on GDM is still unknown, and the interaction with air pollution has not been examined. We conducted a prospective cohort study in Guangzhou, China to investigate the windows of susceptibility of temperature extremes and variability on the risk of GDM and to explore any interaction effect with air pollution. Daily maximum (T max), minimum temperature (T min) and diurnal temperature range (DTR) were obtained from Guangdong Meteorological Service. Distributed lag non-linear models with a logistic regression were applied to assess the effect of temperature extremes and DTR in different weeks of gestation on GDM. To examine the interaction effect, relative excess risk due to interaction index, attributable proportion and synergy index were calculated. There were 5,165 pregnant women enrolled, of which 604 were diagnosed with GDM (11.7%). Compared with a reference temperature (50th percentile of T max), we found that extreme high temperature (99th percentile of T max) exposure during 21st and 22nd gestational weeks was associated with an increased risk of GDM. Extreme low temperature (1st percentile of T max) exposure during 14th to 17th weeks increased the risk of GDM. We observed that per 1 °C increment of DTR during 21st to 24th weeks was associated with an elevated GDM risk. No interaction effect of temperature extremes or variability with air pollution on GDM were observed. Our results suggested that non-optimal temperature is an independent risk factor of GDM. The time window of susceptibility for extreme temperatures and DTR exposure on the risk of GDM generally occurred in second trimester of pregnancy. In the context of climate change, our study has important implications for reproductive health and justifies more research in different climate zones. [ABSTRACT FROM AUTHOR]

Previous work reports an inverse association between neighborhood greenness and obesity. Limitations of this work, which relies largely on cross-sectional data, include that studies often lack control for unmeasured genetic and sociodemographic factors that may confound associations, and cannot disentangle temporal order between neighborhood greenness and obesity. We move beyond a cross-sectional approach and leverage a longitudinal sibling-linked dataset with health, residential, and demographic information on women with two births in California between 2007 and 2015 (N = 552,929). We used a sibling comparison design to control for unmeasured stable characteristics of women and tested whether a positive change in neighborhood greenness (i.e., "upward green mobility") precedes a reduction in obesity risk. Models also adjusted for baseline obesity risk and time-varying individual- and neighborhood-level socioeconomic factors. As hypothesized, we find that upward green mobility varies inversely with the odds of obesity. Results indicate that small decreases in neighborhood greenness may also show protective associations with obesity risk. Our findings, if replicated, suggest that changing levels (particularly increases) of greenness in the residential environment may combat the rise of obesity. [ABSTRACT FROM AUTHOR]

Short-term exposure to PM 2.5 has been shown to be associated with changes in blood pressure. However, most of the evidence is based on PM 2.5 measurements from fixed stations and resting blood pressure measured at a regular time. To evaluate the short-term daily and hourly effects of real-time personal PM 2.5 exposure on ambulatory blood pressure, and to compare the effects with those of PM 2.5 exposure from fixed stations. Between April 2017 and December 2017, 37 young adults were recruited in a panel study from a central urban area and a suburban area, to measure personal hourly PM 2.5 and ambulatory systolic blood pressure (SBP) as well as diastolic blood pressure (DBP) for three consecutive days. Hourly PM 2.5 concentrations were also obtained from the nearest monitoring station operated by Guangdong Environmental Monitoring Center. Generalized additive mixed model was employed to evaluate the effects of PM 2.5 on ambulatory blood pressure. During the study period, the mean concentration of personal PM 2.5 exposure was 60.30 ± 52.14 μg/m3, while the value of PM 2.5 from fixed stations was 36.77 ± 21.52 μg/m3. Both personal PM 2.5 exposure and exposure from fixed stations averaged over the previous 1 to 3 days decreased blood pressure. During daytime, a 10 μg/m3 increase in 1-day moving average of personal PM 2.5 was associated with a 0.54 mmHg (95% CI: −1.03, −0.05) and 0.22 mmHg (95% CI: −0.59, 0.15) decrease in SBP and DBP, respectively. When using PM 2.5 exposures from fixed stations, the decrease in SBP and DBP were 0.95 mmHg (95% CI: −1.82, −0.07) and 0.74 mmHg (95% CI: −1.46, −0.03). Stratified analysis showed stronger effects in the central urban area and among males. Both personal PM 2.5 exposure and exposure from fixed stations averaged over the previous 1 to 3 days decreased blood pressure. Stronger effects were found in a central urban area and among males. Unlabelled Image • Personal real-time PM 2.5 exposure was measured by wearable aerosol monitors. • Wearable monitors were used to measure ambulatory blood pressure for 3 days. • 1–3 days moving averages of personal PM 2.5 exposure decreased SBP and DBP. • Similar decreased effects were found by using PM 2.5 exposure from fixed stations. • Stronger effects were found in a central urban area and among males. [ABSTRACT FROM AUTHOR]

• Evidence suggests exposure to green space benefits human health. • Commonly used non-native plants may have negative consequences for biodiversity. • We developed four native-plants policy scenarios in collaboration with stakeholders. • If 30% of the study area were native plants, 88 premature deaths could be prevented. Cities 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. We aim to estimate premature deaths that would be prevented by the implementation of native-plants policy scenarios in the City of Denver, Colorado, USA. After 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. In 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). Using 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. [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]

• 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]

Time-weighted spatial averaging approaches (TWSA) are an increasingly utilized method for calculating exposure using global positioning system (GPS) mobility data for health-related research. They can provide a time-weighted measure of exposure, or dose, to various environments or health hazards. However, little work has been done to compare existing methodologies, nor to assess how sensitive these methods are to mobility data inputs (e.g., walking vs driving), the type of environmental data being assessed as the exposure (e.g., continuous surfaces vs points of interest), and underlying point-pattern clustering of participants (e.g., if a person is highly mobile vs predominantly stationary). Here we contrast three TWSA approaches that have been previously used or recently introduced in the literature: Kernel Density Estimation (KDE), Density Ranking (DR), and Point Overlay (PO). We feed GPS and accelerometer data from 602 participants through each method to derive time-weighted activity spaces, comparing four mobility behaviors: all movement, stationary time, walking time, and in-vehicle time. We then calculate exposure values derived from the various TWSA activity spaces with four environmental layer data types (point, line, area, surface). Similarities and differences across TWSA derived exposures for the sample and between individuals are explored, and we discuss interpretation of TWSA outputs providing recommendations for researchers seeking to apply these methods to health-related studies. • Time-weighted spatial averaging exposures vary with mobility and environmental data inputs. • Kernel Density Estimation and Point Overlay methods result in similar mean and standard deviation exposure estimates. • Density Ranking results in larger mean and lower standard deviation estimates compared to other methods. • Density Ranking generally provides more reliable estimates of exposure within individuals. • Between individual variability is consistent between methods; clustering of individual movement does not impact results. [ABSTRACT FROM AUTHOR]

Though fine particulate matter (PM 2.5) 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 PM 2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM 2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM 2.5 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 PM 2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM 2.5 presents a challenge since measurements of the smoke contribution to PM 2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM 2.5 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 PM 2.5 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 PM 2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM 2.5 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 PM 2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Association between socioeconomic position (SEP) and pregnancy and child health outcome disparities is often mediated by differential environmental exposures. • Better understanding of the socio-exposome relationship is needed to unravel potential etiological mechanisms. • Different construct of SEP indicators revealed different social patterning of pregnancy exposures. • Household income and paternal education were more strongly and consistently related to pregnancy exposures than maternal education. • Women with lower educated spouse (secondary school qualifications or below) were associated with higher adiposity measures and lower micronutrient levels such as folate, regardless of their own education level. • Ethnicity and nativity differences were found for environmental contaminants and micronutrients associations in our context. While socioeconomic position (SEP) is consistently related to pregnancy and birth outcome disparities, relevant biological mechanisms are manifold, thus necessitating more comprehensive characterization of SEP-exposome associations during pregnancy. We implemented an exposomic approach to systematically characterize the socioeconomic landscape of prenatal exposures in a setting where social segregation was less distinct in a hypotheses-generating manner. We described the correlation structure of 134 prenatal exogenous and endogenous sources (e.g. , micronutrients, hormones, immunomodulatory metabolites, environmental pollutants) collected in a diverse, population-representative, urban, high-income longitudinal mother-offspring cohort (N = 1341; 2009–2011). We examined the associations between maternal, paternal, household, and areal level SEP indicators and 134 exposures using multiple regressions adjusted for precision variables, as well as potential effect measure modification by ethnicity and nativity. Finally, we generated summary SEP indices using Multiple Correspondence Analysis to further explore possible curved relationships. Individual and household SEP were associated with anthropometric/adiposity measures, folate, omega-3 fatty acids, insulin-like growth factor-II, fasting glucose, and neopterin, an inflammatory marker. We observed paternal education was more strongly and consistently related to maternal exposures than maternal education. This was most apparent amongst couples discordant on education. Analyses revealed additional non-linear associations between areal composite SEP and particulate matter. Environmental contaminants (e.g., per- and polyfluoroalkyl substances) and micronutrients (e.g., folate and copper) showed opposing associations by ethnicity and nativity, respectively. SEP-exposome relationships are complex, non-linear, and context specific. Our findings reinforce the potential role of paternal contributions and context-specific modifiers of associations, such as between ethnicity and maternal diet-related exposures. Despite weak presumed areal clustering of individual exposures in our context, our approach reinforces subtle non-linearities in areal-level exposures. [ABSTRACT FROM AUTHOR]

Compared with other health areas, the mental health impacts of climate change have received less research attention. The literature on climate change and mental health is growing rapidly but is characterised by several limitations and research gaps. In a field where the need for designing evidence-based adaptation strategies is urgent, and research gaps are vast, implementing a broad, all-encompassing research agenda will require some strategic focus. We followed a structured approach to prioritise future climate change and mental health research. We consulted with experts working across mental health and climate change, both within and outside of research and working in high, middle, and low-income countries, to garner consensus about the future research priorities for mental health and climate change. Experts were identified based on whether they had published work on climate change and mental health, worked in governmental and non-governmental organisations on climate change and mental health, and from the professional networks of the authors who have been active in the mental health and climate change space. Twenty-two experts participated from across low- and middle-income countries (n = 4) and high-income countries (n = 18). Our process identified ten key priorities for progressing research on mental health and climate change. While climate change is considered the biggest threat to global mental health in the coming century, tackling this threat could be the most significant opportunity to shape our mental health for centuries to come because of health co-benefits of transitioning to more sustainable ways of living. Research on the impacts of climate change on mental health and mental health-related systems will assist decision-makers to develop robust evidence-based mitigation and adaptation policies and plans with the potential for broad benefits to society and the environment. [ABSTRACT FROM AUTHOR]

• We examined the acute relationship between preterm birth and stillbirth in 14 LMICs. • Women exposed to extreme heat were at higher risk for adverse birth outcomes. • Found different critical windows of vulnerability for preterm birth and stillbirth. Stillbirths and complications from preterm birth are two of the leading causes of neonatal deaths across the globe. Lower- to middle-income countries (LMICs) are experiencing some of the highest rates of these adverse birth outcomes. Research has suggested that environmental determinants, such as extreme heat, can increase the risk of preterm birth and stillbirth. Under climate change, extreme heat events have become more severe and frequent and are occurring in differential seasonal patterns. Little is known about how extreme heat affects the risk of preterm birth and stillbirth in LMICs. Thus, it is imperative to examine how exposure to extreme heat affects adverse birth outcomes in regions with some of the highest rates of preterm and stillbirths. Most of the evidence linking extreme heat and adverse birth outcomes has been generated from high-income countries (HICs) notably because measuring temperature in LMICs has proven challenging due to the scarcity of ground monitors. The paucity of health data has been an additional obstacle to study this relationship in LMICs. In this study, globally gridded meteorological data was linked with spatially and temporally resolved Demographic and Health Surveys (DHS) data on adverse birth outcomes. A global analysis of 14 LMICs was conducted per a pooled time-stratified case-crossover design with distributed-lag nonlinear models to ascertain the relationship between acute exposure to extreme heat and PTB and stillbirths. We notably found that experiencing higher maximum temperatures and smaller diurnal temperature range during the last week before birth increased the risk of preterm birth and stillbirth. This study is the first global assessment of extreme heat events and adverse birth outcomes and builds the evidence base for LMICs. [ABSTRACT FROM AUTHOR]

• Air Quality Alerts (AQA) may not have systematic benefits for population health. • Difference-in-differences approach coupled with propensity-score matching. • No evidence of an effect on mortality of the PM 10 AQA with initial thresholds. • Reduction in cardiovascular mortality when thresholds were revised. • Still no change in overall respiratory mortality with revised threshold levels. Daily exposure to air pollution has been shown to increase cardiovascular and respiratory mortality. While increases in short-term exposure to air pollutants at any daily concentrations has been shown to be associated to adverse health outcomes, days with extreme levels, also known as air pollution peaks based on specific thresholds, have been used to implement air quality alerts in various cities across the globe. We aimed at evaluating the potential effects of the Air Quality Alerts (AQA) system on different causes of premature mortality in Paris, France. Air quality alerts (AQA) based on particulate matter (PM 10) levels and related interventions were implemented in the region of Paris in 2008 and were revised to be more stringent in 2011. In this study, we applied a difference-in-differences (DID) approach coupled with propensity-score matching (PSM) to daily mortality data for the period 2000 to 2015 to evaluate the effects of the Paris AQA program on different causes of premature mortality for the entire population and for adults > 75 years old. Overall, results did not show evidence of a reduction in mortality of the PM 10 AQA program when first implemented in 2008 with initial thresholds (80 µg/m3); DID estimates were slightly above 1 for cardiovascular and respiratory mortality. However, when evaluating the drastic reduction in revised thresholds in 2011 (50 µg/m3) to trigger interventions, we identified a reduction in cardiovascular (DID = 0.84, 95% CI: 0.755 to 0.930) mortality, but no change in respiratory mortality was detected (DID = 0.97, 95% CI: 0.796, 1.191). Our study suggests that AQA may not have health benefits for the population when thresholds are set at high daily PM 10 levels. Given that such policies are implemented in many other metropolitan areas across the globe, evaluating the effectiveness of AQA is important to provide public authorities and researchers a rationale for defining specific thresholds and extending the scope of these policies to lower air pollution levels. [ABSTRACT FROM AUTHOR]

This study employed novel GPS methods to assess the effect of a multilevel physical activity (PA) intervention on device-measured walking locations in 305 community dwelling older adults, ages 65+ (mean age = 83, 73% women). Retirement communities were randomized to a 1-year PA intervention that encouraged neighborhood walking, or to a healthy aging control condition. Total time and time spent walking in four life-space domains were assessed using GPS and accelerometer devices. The intervention increased the time spent walking as a proportion of total time spent in the Campus, Neighborhood and Beyond Neighborhood domains. Intervention effects on walking location were observed in both genders and across physical and cognitive functioning groups. Results demonstrate that an intervention providing individual, social and environmental support for walking can increase PA in larger life-space domains for a broad spectrum of older adults. [ABSTRACT FROM AUTHOR]

• Exploring the effects of long-term exposure to air pollution on dementia incidence. • PM2.5 level was associated with Alzheimer's disease and vascular dementia incidence. • No association was detected between NO 2 or black carbon exposure and dementia risk. • PM2.5 might be a modifiable risk factor of the main forms of dementia. Emerging epidemiological evidence suggests a relationship between exposure to air pollution and dementia. However, most of the existing studies relied on health administrative databases for the diagnosis of dementia. In a large French population-based cohort (the 3C Study), we assessed the effects of particulate matter ≤2.5 µm (PM2.5), nitrogen dioxide (NO 2) and black carbon (BC) on the risk of dementia diagnosed with reliable tools. Participants aged ≥65 years were recruited between 1999 and 2001 and followed for 12 years. At baseline and every 2 years, dementia was suspected on the basis of the neuropsychological and neurological examination and confirmed by an independent committee of clinicians. Exposure to NO 2 , BC and PM2.5 at the participants' residential address was estimated using land use regression models. For each pollutant and year of follow-up, the 10-year moving average of past exposure was estimated. Multilevel spatial random-effects Cox proportional hazards models were used in which exposure was included as a time-varying variable. Analyses were adjusted for individual (age, sex, education, APOE4 genotype, health behaviours) and contextual (neighbourhood deprivation index) confounders. At baseline, the median age of the 7066 participants was 73.4 years, and 62% were women. The median follow-up duration was 10.0 years during which 791 participants developed dementia (n = 541 Alzheimer's disease (AD) and n = 155 vascular/mixed dementia (VaD)). The 10-year moving average of PM2.5 concentrations ranged from 14.6 to 31.3 µg/m3. PM2.5 concentration was positively associated with dementia risk: HR = 1.20, 95% CI (1.08–1.32) for all-cause dementia, 1.20 (1.09–1.32) for AD, and 1.33 (1.05–1.68) for VaD per 5 µg/m3 PM2.5 increase. No association was detected between NO 2 or BC exposure and dementia risk. In this large cohort of older adults, long-term PM2.5 exposure was associated with increased dementia incidence. Reducing PM2.5 emissions might lessen the burden of dementia in aging populations. [ABSTRACT FROM AUTHOR]

• Maternal exposure to ozone could increase the risk of term low birth weight. • Gestational weeks 15th- 26th was the particularly susceptible period. • The effects were present among women ≥ 35 or < 25 years old and those with female babies. Ozone pollution keeps deteriorating in the context of climate change. Maternal ozone exposure may be associated with low birth weight (LBW), but the results are still inconsistent. The identification of the critical exposure windows, a specific period of particular susceptibility during pregnancy, remains unresolved. We aimed to evaluate whether ozone exposure was associated with term LBW and further identify the susceptible exposure windows. A retrospective cohort study was conducted in Guangzhou, a megacity in the most populous and economically developed city clusters in China. We included 444,096 singleton live births between January 2015 and July 2017. From 11 fixed stations, we collected daily 1-h maximum and 8-h maximum moving average ozone level (O 3 -1 h and O 3 -8 h) and calculated exposures for each participant based on their district of residence during pregnancy. We used traditional Logistic regression to estimate the trimester-specific association between ozone exposure and term LBW, and further estimated monthly- and weekly association by distributed lag models (DLMs) with Logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) of term LBW were calculated for an interquartile range (IQR) increase in ozone exposure. Stratified analyses and heterogeneity tests were conducted by maternal age and infant sex. The incidence of term LBW was 1.9%. During the study period, the mean O 3 -1 h and O 3 -8 h levels were 112.6 µg/m3 and 84.5 µg/m3, respectively. Increased O 3 -1 h (IQR: 90 µg/m3) and O 3 -8 h (73 µg/m3) exposure during the second trimester were associated with increased risk of term LBW. At a monthly level, the term LBW risk was associated with O 3 -1 h exposure during the 4th-6th month and O 3 -8 h exposure during the 6th month. By estimating the weekly-specific association, we observed that critical exposure windows were the 15th- 26th gestational weeks for O 3 -1 h, and the 20th-26th weeks for O 3 -8 h, respectively. Estimated ORs and 95% CIs ranged from 1.012 (1.000, 1.024) to 1.023 (1.007, 1.039). When examined by subgroups, the effects were present among women ≥ 35 years or < 25 years old and those with female babies. This study provides compelling evidence that exposure to O 3 was associated with increased risk of term LBW, and gestational weeks 15th- 26th was found to be particularly susceptible. These findings provide a research basis for further mechanism examination, public health interventions, and targeted environmental policy-making. [ABSTRACT FROM AUTHOR]

• Exposure to residential green space is associated with decreased risk of PTB. • Green space has stronger protective associations with PTB in the second trimester. • There are positive interactions between green space and air pollution on PTB. Recent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born < 37 completed gestational weeks). In addition, windows of susceptibility during pregnancy have not been explored and potential interactions of green space with air pollution exposures during pregnancy are still unclear. To evaluate the relationships between green space and PTB, identify windows of susceptibility, and explore potential interactions between green space and air pollution. Birth certificate records for all births in California (2001–2008) were obtained. The Normalized Difference Vegetation Index (NDVI) was used to characterized green space exposure. Gestational age was treated as a time-to-event outcome; Cox proportional hazard models were applied to estimate the association between green space exposure and PTB, moderately PTB (MPTB, gestational age < 35 weeks), and very PTB (VPTB, gestational age < 30 weeks), after controlling for maternal age, race/ethnicity, education, and median household income. Month-specific green space exposure was used to identify potential windows of susceptibility. Potential interactions between green space and air pollution [fine particulate matter < 2.5 µm (PM 2.5), nitrogen dioxide (NO 2), and ozone (O 3)] were examined on both additive and multiplicative scales. In total, 3,753,799 eligible births were identified, including 341,123 (9.09%) PTBs, 124,631 (3.32%) MPTBs, and 22,313 (0.59%) VPTBs. A reduced risk of PTB was associated with increases in residential NDVI exposure in 250 m , 500 m , 1000 m , and 2000 m buffers. In the 2000 m buffer, the association was strongest for VPTB [adjusted hazard ratio (HR) per interquartile range increase in NDVI: 0.959, 95% confidence interval (CI): 0.942–0.976)], followed by MPTB (HR = 0.970, 95% CI: 0.962–0.978) and overall PTB (HR = 0.972, 95% CI: 0.966–0.978). For PTB, green space during the 3rd − 5th gestational months had stronger associations than those in the other time periods, especially during the 4th gestational month (NDVI 2000 m : HR = 0.970, 95% CI: 0.965–0.975). We identified consistent positive additive and multiplicative interactions between decreasing green space and higher air pollution. This large study found that maternal exposure to residential green space was associated with decreased risk of PTB, MPTB, and VPTB, especially in the second trimester. There is a synergistic effect between low green space and high air pollution levels on PTB, indicating that increasing exposure to green space may be more beneficial for women with higher air pollution exposures during pregnancy. [ABSTRACT FROM AUTHOR]

• This study examines acute exposure to extreme heat and risk of preterm birth. • We expand on previous research by considering multiple definitions of extreme heat. • Extreme heat during the final weeks of gestation may increase risk of preterm birth. Preterm birth is a leading cause of infant morbidity and mortality. Identifying potentially modifiable triggers toward the end of gestation, such as extreme heat, can improve understanding of the role of acute stress on early deliveries and inform warning systems. In this study we examined the association between extreme heat, variously defined during the last week of gestation, and risk of preterm birth among mothers in California. We created a population-based cohort comprised of 1,967,300 mothers who had live, singleton births in California, from May through September 2005–2013. Daily temperature data estimated at the maternal zip code of residence was used to create 12 definitions of extreme heat with varying relative temperatures (75th, 90th, 95th, and 98th percentiles) and durations (at least 2, 3, or 4 consecutive days). We estimated risk of preterm birth (<37 gestational weeks) in relation to exposure to extreme heat during the last week of gestation with multi-level Cox proportional hazard regression models, adjusting for maternal characteristics, sex of neonate, and seasonality. We also included randomly generated data, SAS code, and estimates for reproducibility purposes. Approximately 7% of the cohort had a preterm birth. For all definitions of extreme heat, the risk of preterm birth was consistently higher among mothers who experienced an extreme heat episode during their last week of gestation. Hazard ratios ranged from 1.008 (95% CI: 0.997, 1.021) to 1.128 (95% CI: 1.052, 1.210), with increasing associations as the relative temperature and duration of extreme heat episode increased. This study adds to the previous literature by considering multiple definitions of extreme heat and applying a time-to-event framework. Findings suggest that acute exposure to extreme heat during the last week of gestation may trigger an earlier delivery. Implementing heat warning systems targeted toward pregnant women may improve birth outcomes. [ABSTRACT FROM AUTHOR]