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Background: Heatwaves are expected to increase with climate change, posing a significant threat to population health. In India, with the world’s largest population, heatwaves occur annually but have not been comprehensively studied. Accordingly, we evaluated the association between heatwaves and all-cause mortality and quantifying the attributable mortality fraction in India. Methods: We obtained all-cause mortality counts for ten cities in India (2008–2019) and estimated daily mean temperatures from satellite data. Our main extreme heatwave was defined as two-consecutive days with an intensity above the 97th annual percentile. We estimated city-specific heatwave associations through generalised additive Poisson regression models, and meta-analysed the associations. We reported effects as the percentage change in daily mortality, with 95% confidence intervals (CI), comparing heatwave vs non-heatwave days. We further evaluated heatwaves using different percentiles (95th, 97th, 99th) for one, two, three and five-consecutive days. We also evaluated the influence of heatwave duration, intensity and timing in the summer season on heatwave mortality, and estimated the number of heatwave-related deaths. Findings: Among ∼ 3.6 million deaths, we observed that temperatures above 97th percentile for 2-consecutive days was associated with a 14.7 % (95 %CI, 10.3; 19.3) increase in daily mortality. Alternative heatwave definitions with higher percentiles and longer duration resulted in stronger relative risks. Furthermore, we observed stronger associations between heatwaves and mortality with higher heatwave intensity. We estimated that around 1116 deaths annually (95 %CI, 861; 1361) were attributed to heatwaves. Shorter and less intense definitions of heatwaves resulted in a higher estimated burden of heatwave-related deaths. Conclusions: We found strong evidence of heatwave impacts on daily mortality. Longer and more intense heatwaves were linked to an increased mortality risk, however, resulted in a lower burden of heatwave-related deaths. Both definitions and the burden associated with each heatwave definition should be incorporated into planning and decision-making processes for policymakers.

Hormones play critical roles in facilitating pregnancy progression and the onset of parturition. Several classes of environmental contaminants, including fine particulate matter (PM2.5) and ambient temperature, have been shown to alter hormone biosynthesis or activity. However, epidemiologic research has not considered PM2.5 in relation to a broader range of steroid hormones, particularly in pregnant women. Using metabolomics data collected within 20–40 weeks of gestation in an ethnically diverse pregnancy cohort study, we identified 42 steroid hormones that we grouped into five classes (pregnenolone, androgens, estrogens, progestin, and corticosteroids) based on their biosynthesis type. We found that exposure to PM2.5 during the pre-conception and early prenatal periods was associated with higher maternal androgen concentrations in late pregnancy. We also detected a positive association between early pregnancy PM2.5 exposure and maternal pregnenolone levels and a marginal positive association between early pregnancy PM2.5 exposure and progestin levels. When considering each hormone metabolite individually, we found positive associations between early pregnancy PM2.5 exposure and five steroids, two of which survived multiple comparison testing: 11beta-hydroxyandrosterone glucuronide (a pregnenolone steroid) and adrosteroneglucuronide (a progestin steroid). None of the steroid classes were statistically significant associated with ambient temperature. In sex-stratified analyses, we did not detect any sex differences in our associations. This is the first study showing that exposure to fine particulate matter during the pre-conception and early prenatal periods can lead to altered steroid adaptation during the state of pregnancy, which has been shown to have potential consequences on maternal and child health.

Background: Air pollution has been linked to obesity while higher ambient temperatures typically reduce metabolic demand in a compensatory manner. Both relationships may impact glucose metabolism, thus we examined the association between intermediate- and long-term exposure to fine particulate matter (PM2.5) and ambient temperature and glycated hemoglobin (HbA1c), a longer-term marker of glucose control. Methods: We assessed 3-month, 6-month, and 12-month average air pollution and ambient temperature at 1-km2 spatial resolution via satellite remote sensing models (2013–2019), and assessed HbA1c at four, six, and eight years postpartum in women enrolled in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) cohort based in Mexico City. PM2.5 and ambient temperature were matched to participants’ addresses and confirmed by GPS tracker. Using linear mixed-effects models, we examined the association between 3-month, 6-month, and 12-month average PM2.5 and ambient temperature with repeated log-transformed HbA1c values. All models included a random intercept for each woman and were adjusted for calendar year, season, and individual-level confounders (age, marital status, smoking, alcohol consumption level, and education level). Results: We analyzed 1,265 HbA1c measurements of 484 women. Per 1 µg/m3 increase in 3-month and 6-month PM2.5, HbA1c levels increased by 0.28% (95% confidence interval (95 %CI): 0.14, 0.42%) and 0.28% (95 %CI: 0.04, 0.52%) respectively. No association was seen for 12-month average PM2.5. Per 1 °C increase in ambient temperature, HbA1c levels decreased by 0.63% (95 %CI: −1.06, −0.21%) and 0.61% (95 %CI: −1.08, −0.13%), while the 12-month average again is not associated with HbA1c. Conclusions: Intermediate-term exposure to PM2.5 and ambient temperature are associated with opposing changes in HbA1c levels, in this region of high PM2.5 and moderate temperature fluctuation. These effects, measurable in mid-adult life, may portend future risk of type 2 diabetes and possible heart disease.

Background: Previous findings concerning the risk for preeclampsia following exposure to particulate matter are inconclusive. Methods: We used data from all singleton pregnancies of women insured by the “Clalit health services” (CHS) maintenance organization in southern Israel that resulted in delivery or perinatal mortality at Soroka Medical Center (SMC). Daily PM2.5 concentrations were estimated by a hybrid satellite-based model at one-squared kilometer spatial resolution. We used Cox proportional hazard models coupled with distributed lag models to examine the association between the mean exposure to PM2.5 in every gestational week and the diagnosis of preeclampsia, adjusting for maternal age, parity, year of birth, season of birth and socio-economic status. Hazard Ratios (HR) and 95% Confidence Intervals (CI) were calculated for individual gestational weeks and for cumulative exposure until the 25th gestational week. Results: A total of 133,197 pregnancies ended at SMC during the study period, of which 68,126 (51.1%) were Jewish and 65,071 (48.9%) were Bedouin. For pregnancies of Jewish women, exposure to PM2.5 from the 7th until the 14st gestational week was significantly associated with preeclampsia (maximal HR = 1.06; 95%CI: 1.01 – 1.11 during the 10th gestational week per 10 μg/m3 increase in PM2.5). Cumulative exposure to PM2.5 during the first 25th gestational weeks was also significantly associated with preeclampsia (HR = 2.08; 95%CI: 1.10 – 3.94 per 10 μg/m3 increase in PM2.5). We observed no association for pregnancies of Bedouin women. Conclusions: Exposure to PM2.5 between the 7th and the 14st gestational weeks was associated with preeclampsia among Jewish women but not among Bedouin women.

Background: Birthweight is a strong predictor of normal growth, healthy development, and survival. Several studies have found associations between temperature, fine particulate matter (PM2.5), and birth weight. However, the relevant timing of exposures varies between studies and is yet unclear. Therefore, we assessed the difference in term birthweight (TBW) associated with weekly exposure to temperature and PM2.5 throughout 37 weeks of gestation. Methods: We included all singleton live term births in Massachusetts, U.S between 2004 and 2015 (n = 712,438). Weekly PM2.5 and temperature predictions were estimated on a 1 km grid from satellite-based models. We utilized a distributed lag nonlinear model (DLNM) to estimate the difference in TBW associated with weekly exposures from the last menstrual period to 37 weeks of gestation. Results: We found a nonlinear association with prenatal temperature exposure. Larger effects were observed in warmer temperatures, where higher temperatures were negatively associated with TBW. Temperature effects were larger in the first and final weeks of gestation. We observed a negative difference in TBW associated with PM2.5 exposure. Overall, a 1 µg/m3 increase in prenatal exposure was associated with 3.9 g lower TBW (95% CI −5.0 g; −2.9 g). PM2.5 effects were larger in the final weeks of gestation. Conclusion: We found heat and PM2.5 exposure to be related to lower TBW. Our findings suggest that women are more susceptible to both exposures towards the end of pregnancy. Susceptibility to heat was higher in the initial weeks of pregnancy as well. These critical windows of susceptibility can be communicated to pregnant women during routine prenatal visits to increase awareness and target interventions to reduce exposures.

Rationale: PM2.5-induced adverse effects on respiratory health may be driven by epigenetic modifications in airway cells. The potential impact of exposure duration on epigenetic alterations in the airways is not yet known. Objectives: We aimed to study associations of fine particulate matter PM2.5 exposure with DNA methylation in nasal cells. Methods: We conducted nasal epigenome-wide association analyses within 503 children from Project Viva (mean age 12.9 y), and examined various exposure durations (1-day, 1-week, 1-month, 3-months and 1-year) prior to nasal sampling. We used residential addresses to estimate average daily PM2.5 at 1 km resolution. We collected nasal swabs from the anterior nares and measured DNA methylation (DNAm) using the Illumina MethylationEPIC BeadChip. We tested 719,075 high quality autosomal CpGs using CpG-by-CpG and regional DNAm analyses controlling for multiple comparisons, and adjusted for maternal education, household smokers, child sex, race/ethnicity, BMI z-score, age, season at sample collection and cell-type heterogeneity. We further corrected for bias and genomic inflation. We tested for replication in a cohort from the Netherlands (PIAMA). Results: In adjusted analyses, we found 362 CpGs associated with 1-year PM2.5 (FDR

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM

Background: The programming of sleep architecture begins in pregnancy and depends upon optimal in utero formation and maturation of the neural connectivity of the brain. Particulate air pollution exposure can disrupt fetal brain development but associations between fine particulate matter (PM2.5) exposure during pregnancy and child sleep outcomes have not been previously explored. Methods: Analyses included 397 mother-child pairs enrolled in a pregnancy cohort in Mexico City. Daily ambient prenatal PM2.5 exposure was estimated using a validated satellite-based spatio-temporally resolved prediction model. Child sleep periods were estimated objectively using wrist-worn, continuous actigraphy over a 1-week period at age 4–5 years. Data-driven advanced statistical methods (distributed lag models (DLMs)) were employed to identify sensitive windows whereby PM2.5 exposure during gestation was significantly associated with changes in sleep duration or efficiency. Models were adjusted for maternal education, season, child's age, sex, and BMI z-score. Results: Mother's average age was 27.7 years, with 59% having at least a high school education. Children slept an average of 7.7 h at night, with mean 80.1% efficiency. The adjusted DLM identified windows of PM2.5 exposure between 31 and 35 weeks gestation that were significantly associated with decreased sleep duration in children. In addition, increased PM2.5 during weeks 1–8 was associated with decreased sleep efficiency. In other exposure windows (weeks 39–40), PM2.5 was associated with increased sleep duration. Conclusion: Prenatal PM2.5 exposure is associated with altered sleep in preschool-aged children in Mexico City. Pollutant exposure during sensitive windows of pregnancy may have critical influence upon sleep programming. Keywords: Particulate matter, Air pollution, Prenatal, Sleep, Child, Preschool-aged

Summary: Background: Pregnancy is associated with deteriorations in maternal bone strength and heightened susceptibility to bone fractures. We aimed to investigate whether ambient particulate matter (PM)2·5 concentrations were associated with bone strength during pregnancy. Methods: In this longitudinal cohort study, we analysed longitudinal data from women participating in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) cohort in Mexico City, Mexico. Eligible women were aged 18 years or older, at less than 20 weeks' gestation at the time of recruitment, planning to stay in Mexico City for the next 3 years, without heart or kidney disease, did not use steroids or anti-epileptic drugs, were not daily consumers of alcohol, and had access to a telephone. Daily ambient PM2·5 concentrations were estimated from a spatio-temporal model that was based on the individual's address. Trabecular bone strength was measured using quantitative ultrasound from the radius of the middle finger and cortical bone strength from the proximal phalanx of the middle finger, during the second trimester, third trimester, and 1 and 6 months post partum. Bone strength T scores were modelled with PM2·5 concentrations using linear mixed models and distributed lag models. Findings: Adjusting for multiple exposure windows, each 10 ug/m3 increase in PM2·5 exposure concentrations in the first trimester was associated with a 0·18 SD decrease (95% CI −0·35 to −0·01; p=0·033) in ultrasound speed-of-sound (SOS) T score of trabecular bone strength from the second trimester until 6 months post partum. Similarly, each 10 μg/m3 increase in third trimester PM2·5 exposure was associated with a 0·18 SD decrease (–0·36 to −0·01; p=0·044) in the SOS T score of trabecular bone strength from the third trimester until 6 months post partum. PM2·5 exposure in the first month post partum was associated with a 0·20 SD decline (–0·39 to −0·01; p=0·043) in cortical bone strength until 6 months post partum. Interpretation: Ambient PM2·5 exposure during and after pregnancy was associated with diminished trabecular and cortical bone strength. Early pregnancy PM2·5 exposure was associated with a greater decline in bone strength later during pregnancy. Late pregnancy and early post-partum exposures adversely affected the post-partum bone strength recovery. Technological and policy solutions to reduce PM2·5 pollution could improve public health by reducing bone fracture risk. Funding: US National Institute of Environmental Health Sciences.

Background: Heat stress during pregnancy may limit fetal growth, with ramifications throughout the life course. However, critical exposure windows are unknown, and effects of meteorological variability have not been investigated. Objectives: We aimed to identify sensitive windows for the associations of mean and variability of temperature and humidity with term birthweight. Methods: We analyzed data from two French mother–child cohorts, EDEN and PELAGIE (n = 4771), recruited in 2002–2006. Temperature exposure was assessed using a satellite-based model with daily 1-km2 resolution, and relative humidity exposure data were obtained from Météo France monitors. Distributed lag models were constructed using weekly means and standard deviation (SD, to quantify variability) from the first 37 gestational weeks. Analyses were then stratified by sex. Results for each exposure were adjusted for the other exposures, gestational age at birth, season and year of conception, cohort and recruitment center, and individual confounders. Results: There was no evidence of association between term birthweight and mean temperature. We identified a critical window in weeks 6–20 for temperature variability (cumulative change in term birthweight of −54.2 g [95% CI: −102, −6] for a 1 °C increase in SD of temperature for each week in that window). Upon stratification by sex of the infant, the relationship remained for boys (weeks 1–21, cumulative change: −125 g [95% CI: −228, −21]). For mean humidity, there was a critical window in weeks 26–37, with a cumulative change of −28 g (95% CI: −49, −7) associated with a 5% increase in humidity for each week. The critical window was longer and had a stronger association in boys (weeks 29–37; −37 g, 95% CI: −63, −11) than girls (week 14; −1.8 g, 95% CI: −3.6, −0.1). Discussion: Weekly temperature variability and mean humidity during critical exposure windows were associated with decreased term birthweight, especially in boys.

Background: Preterm birth is a major determinant of adverse health consequences, and early term births are also associated with increased risk of various outcomes. In light of climate change, the effect of ambient temperature on earlier delivery is an important factor to consider. Several studies have focused on associations of ambient air temperature (Ta) on preterm birth, but few have examined associations with early term births. Aims: To investigate the association of prenatal exposure to Ta with preterm birth (

Background: Postpartum depression (PPD), which affects up to 1 in 5 mothers globally, negatively impacts the health of both mothers and children. Exposure to ambient air pollution has been linked to depressive symptoms in animal models and human studies, but the relationship between air pollution and PPD has not been widely studied. Methods: In a birth cohort in Mexico City (509 mothers with available data), we examined the association between exposure to particulate matter ≤2.5 μm in diameter (PM2.5) with symptoms of psychological dysfunction at 1 and 6 months postpartum. Daily PM2.5 estimates were derived from a hybrid satellite-based spatio-temporally resolved model and averaged over pregnancy and the first year postpartum. Edinburgh Postnatal Depression Scale (EPDS) scores at 1 and 6 months were used to assess the relationship between PM2.5 exposure and probable PPD (EPDS score ≥13) using relative risk regression and symptoms of anhedonia, depression, and anxiety (derived from EPDS subscales) using negative binomial regression. Results: A 5-μg/m3 increase in average PM2.5 exposure during pregnancy was associated with an increased risk of PPD at 6 months (RR = 1.59; 95% CI: 1.11 to 2.28) and of late-onset PPD (no PPD at 1 month, PPD at 6 months) (RR = 2.58; 95% CI: 1.40 to 4.73) in covariate-adjusted models. No association was observed between PM2.5 exposure in the first year postpartum and PPD. Average PM2.5 exposure during pregnancy was also associated with increased 6-month EPDS subscale symptom scores for anhedonia (p = 0.03) and depression (p = 0.04). Conclusion: Our results suggest that in women in Mexico City, particulate matter exposure during pregnancy is positively associated with PPD and symptoms of anhedonia and depression at 6 months postpartum. Future studies should examine mechanisms linking air pollution and other environmental exposures during pregnancy with postpartum psychological functioning.

Background: Radon is an inert gas formed from the decay of naturally-occurring materials in the earth's crust. It infiltrates into homes from soil, water, and construction materials. Its decay products are radionuclides, which attach to ambient particles. Residential radon is one of the leading risk factors for lung cancer. The scarce evidence for associations with other mortality causes originates mostly from occupational studies. Methods: In a cohort study with 14 years of follow-up (2000−2013), we evaluated the association between chronic radon exposure and all-cause mortality, and explored whether there are subpopulations who are more vulnerable to radon effects. We included 87,296,195 person-years of follow-up from all Medicare beneficiaries in the Mid-Atlantic and Northeastern U.S. states. We examined the association between the logarithm of county-averaged radon (ln(Rn)) and mortality and assessed effect modification by chronic conditions. Results: An interquartile range increase in the ln(Rn) was associated with a 2·62% increase (95% CI 2·52%; 2·73%) in mortality, independent of PM2.5 exposure. Larger mortality risks were observed among individuals with respiratory, cardiovascular and metabolic diseases, with the highest associations observed among those with diabetes (4·98% increase), heart failure (4·58% increase), and chronic obstructive pulmonary disease (4·49% increase). Conclusion: We found an increased risk for all-cause mortality associated with increased radon exposure. The risk was enhanced among susceptible individuals with chronic conditions. We believe this is the first cohort study to identify populations at higher risk for non-malignant health consequences of radon exposure. Due to the limitations in exposure assessment and availability of individual confounders, these findings should be interpreted with caution. Keywords: Radon, Radiation, Mortality, PM2.5

Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R2 of 0.86 for daily PM2.5 predictions. For annual PM2.5 estimates, the cross-validated R2 was 0.89. Our model demonstrated good performance up to 60 μg/m3. Using trained PM2.5 model and predictor variables, we predicted daily PM2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM2.5 for every 1 km × 1 km grid cell. This PM2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM2.5. Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance. Keywords: Fine particulate matter (PM2.5), Ensemble model, Neural network, Gradient boosting, Random forest

Summary: Background: Air particulate matter is a ubiquitous environmental exposure associated with oxidation, inflammation, and age-related chronic disease. Whether particulate matter is associated with loss of bone mineral density and risk of bone fractures is undetermined. We did two independent studies with complementary designs, objectives, and measures to determine the relationship between ambient concentrations of particulate matter and bone health. Methods: In the first study, we examined the association of long-term concentrations of particulate matter less than 2·5 μm (PM2·5) and osteoporosis-related fracture hospital admissions among 9·2 million Medicare enrollees (aged ≥65 years) of the northeast-mid-Atlantic USA between January, 2003, and December, 2010. In the second study, we examined the association of long-term black carbon and PM2·5 concentrations with serum calcium homoeostasis biomarkers (parathyroid hormone, calcium, and 25-hydroxyvitamin [25(OH)D]) and annualised bone mineral density over 8 years (baseline, November, 2002–July, 2005; follow-up, June, 2010–October, 2012) of 692 middle-aged (46·7 years [SD12·3]), low-income men from the Boston Area Community Health/Bone Survey (BACH/Bone study) cohort. PM2·5 concentrations were estimated using spatiotemporal hybrid modelling including Aerosol Optical Depth data, spatial smoothing, and local predictors. Black carbon concentrations were estimated using spatiotemporal land-use regression models. Findings: In the Medicare analysis, risk of bone fracture admissions at osteoporosis-related sites was greater in areas with higher PM2·5 concentrations (risk ratio [RR] 1·041, 95% CI 1·030 to 1·051). This risk was particularly high among low-income communities (RR 1·076, 95% CI 1·052 to 1·100). In the longitudinal BACH/Bone study, baseline black carbon and PM2·5 concentrations were associated with lower serum parathyroid hormone (β=–1·16, 95% CI −1·93 to −0·38, p=0·004, for 1 IQR increase [0·106 μg/m3] in the 1-year average of black carbon concentrations; β=–7·39, 95% CI −14·17 to −0·61, p=0·03, for 1 IQR increase [2·18 μg/m3] in the 1-year average of PM2·5 concentrations). Black carbon concentration was associated with higher bone mineral density loss over time at multiple anatomical sites, including femoral neck (−0·08% per year for 1 IQR increase, 95% CI −0·14 to −0·02) and ultradistal radius (−0·06% per year for 1 IQR increase, −0·12 to −0·01). Black carbon and PM2·5 concentrations were not associated with serum calcium or serum 25(OH)D concentrations. Interpretation: Our results suggest that poor air quality is a modifiable risk factor for bone fractures and osteoporosis, especially in low-income communities. Funding: National Institutes of Health, Institute on Aging, National Institute of Environmental Health, the US Environmental Protection Agency, Consejo Nacional de Ciencia y Tecnología, and the Fundación México en Harvard.

Background: Increasing concentrations of atmospheric carbon dioxide (CO2) lower the content of zinc and other nutrients in important food crops. Zinc deficiency is currently responsible for large burdens of disease globally, and the populations who are at highest risk of zinc deficiency also receive most of their dietary zinc from crops. By modelling dietary intake of bioavailable zinc for the populations of 188 countries under both an ambient CO2 and elevated CO2 scenario, we sought to estimate the effect of anthropogenic CO2 emissions on the global risk of zinc deficiency. Methods: We estimated per capita per day bioavailable intake of zinc for the populations of 188 countries at ambient CO2 concentrations (375–384 ppm) using food balance sheet data for 2003–07 from the Food and Agriculture Organization. We then used previously published data from free air CO2 enrichment and open-top chamber experiments to model zinc intake at elevated CO2 concentrations (550 ppm, which is the concentration expected by 2050). Estimates developed by the International Zinc Nutrition Consultative Group were used for country-specific theoretical mean daily per-capita physiological requirements for zinc. Finally, we used these data on zinc bioavailability and population-weighted estimated average zinc requirements to estimate the risk of inadequate zinc intake among the populations of the different nations under the two scenarios (ambient and elevated CO2). The difference between the population at risk at elevated and ambient CO2 concentrations (ie, population at new risk of zinc deficiency) was our measure of impact. Findings: The total number of people estimated to be placed at new risk of zinc deficiency by 2050 was 138 million (95% CI 120–156). The people likely to be most affected live in Africa and South Asia, with nearly 48 million (32–63) residing in India alone. Global maps of increased risk show significant heterogeneity. Interpretation: Our results indicate that one heretofore unquantified human health effect associated with anthropogenic CO2 emissions will be a significant increase in the human population at risk of zinc deficiency. Our country-specific findings can be used to help guide interventions aimed at reducing this vulnerability. Funding: Bill & Melinda Gates Foundation, Winslow Foundation.