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Background Numerous studies have linked fine particulate matter (PM2.5) to increased cardiovascular mortality. Less is known how the PM2.5-cardiovascular mortality association varies by use of cardiovascular medications. This study sought to quantify effect modification by statin use status on the associations between long-term exposure to PM2.5 and mortality from any cardiovascular cause, coronary heart disease (CHD), and stroke. Methods In this nested case-control study, we followed 1.2 million community-dwelling adults aged ≥66 years who lived in Ontario, Canada from 2000 through 2018. Cases were patients who died from the three causes. Each case was individually matched to up to 30 randomly selected controls using incidence density sampling. Conditional logistic regression models were used to estimate odds ratios (ORs) for the associations between PM2.5 and mortality. We evaluated the presence of effect modification considering both multiplicative (ratio of ORs) and additive scales (the relative excess risk due to interaction, RERI). Results Exposure to PM2.5 increased the risks for cardiovascular, CHD, and stroke mortality. For all three causes of death, compared with statin users, stronger PM2.5-mortality associations were observed among non-users [e.g. for cardiovascular mortality corresponding to each interquartile range increase in PM2.5, OR = 1.042 (95% CI, 1.032–1.053) vs OR = 1.009 (95% CI, 0.996–1.022) in users, ratio of ORs = 1.033 (95% CI, 1.019–1.047), RERI = 0.039 (95% CI, 0.025–0.050)]. Among users, partially adherent users exhibited a higher risk of PM2.5-associated mortality than fully adherent users. Conclusions The associations of chronic exposure to PM2.5 with cardiovascular and CHD mortality were stronger among statin non-users compared to users. [ABSTRACT FROM AUTHOR]

Summary: Many studies suggest a relationship between excessive daytime sleepiness (EDS) and dementia incidence, but the underlying mechanisms remain uncertain. The study aimed to investigate the role of cardiovascular burden in the relationship between EDS and dementia incidence over a 12‐year follow‐up in community‐dwelling older adults. We performed analyses on 6171 subjects (aged ≥65 years) free of dementia and vascular disease at baseline. Participants self‐reported EDS at baseline and an expert committee validated both prevalent and incident dementia. We defined cardiovascular burden by a low Cardiovascular Health score, constructed using the American Heart Association metrics, and incident vascular events. To explore the potential role of the cardiovascular burden in the relationship between EDS and dementia, we conducted mediation analyses with inverse odds ratio‐weighted estimation, using multivariable‐adjusted proportional hazard Cox and logistic regression models. Subjects with EDS had a higher risk of all‐cause dementia (hazard ratio [HR] 1.39, 95% confidence interval [CI] 1.13–1.69) and dementia with vascular component (DVC) (HR 2.14, 95% CI 1.30–3.51), but not Alzheimer's disease (HR 1.18, 95% CI 0.93–1.51). Cardiovascular burden explained 5% (95% CI 4.1–5.2) and 11% (95% CI 9.7–11.3) of the relationship between EDS and all‐cause dementia and DVC, respectively. These findings confirm that EDS may be implicated in the development of dementia and indicate a weaker than expected role of cardiovascular burden in the relationship between EDS and DVC. [ABSTRACT FROM AUTHOR]

Background: Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy. Methods and findings: A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochran's Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP. Conclusions: Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently. Yi Sun and colleagues investigate the association between hypertensive disorders in pregnancy and particulate air pollution. Author summary: Why was this study done?: Although previous studies have explored the associations of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP), earlier findings are inconclusive. Several unaddressed issues remain, such as lack of simultaneous consideration of PM2.5 constituents, mixture effects, windows of susceptibility, and levels of disease severity. What did the researchers do and find?: A retrospective cohort study (N = 386,361) was conducted using electronic health records from Kaiser Permanente Southern California (2008 to 2017) to examine the associations between HDP (mild HDP: gestational hypertension (GH) versus severe HDP: preeclampsia and eclampsia (PE-E)) and exposure to PM2.5, and identify windows of susceptibility to PM2.5 during pregnancy. We found higher risks for PE-E associated with exposures to PM2.5 total mass, organic matter, and black carbon, but not for GH. The main effect of increased PE-E risk was driven by PM2.5 black carbon (71%) and PM2.5 organic matter (26%). The most influential time windows of PM2.5 exposure associated with HDP are the late first trimester and second trimester. Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods may be more vulnerable to air pollution on HDP risk. What do these findings mean?: Our findings suggest that different PM2.5 chemical constituents and critical exposure windows may affect GH and PE-E differently. Our work highlights the need for further research to investigate different components of air pollutants, levels of disease severity, and various susceptible windows, which is important to better understand the underlying mechanisms between air pollution and HDP and develop corresponding interventions. [ABSTRACT FROM AUTHOR]

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 world's 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. Household water, sanitation, and handwashing (WASH) interventions can reduce diarrhoea-related morbidity in young children. Here, the authors report findings from a pre-specified secondary analysis of a cluster-randomised trial assessing how WASH impacts vary by socioeconomic position and season. [ABSTRACT FROM AUTHOR]

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermittent and extreme daily wildfire PM2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM2.5 > 5 µg/m3; 2) days with non-zero wildfire PM2.5; 3) mean wildfire PM2.5 during peak exposure week; 4) smoke waves (= 2 consecutive days with > 15 µg/m3 wildfire PM2.5); and 5) mean annual wildfire PM2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM2.5 exposure for several racial/ethnic groups. [ABSTRACT FROM AUTHOR]

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 children's 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. [ABSTRACT FROM AUTHOR]

Wildfires constitute a growing source of extremely high levels of particulate matter that is less than 2.5 microns in diameter (PM2.5). Recently, toxicologic and epidemiologic studies have shown that PM2.5 generated from wildfires may have a greater health burden than PM2.5 generated from other pollutant sources. This study examined the impact of PM2.5 on hospitalizations for respiratory diseases in California between 2006 and 2019 using a health impact assessment approach that considers differential concentration‐response functions (CRF) for PM2.5 from wildfire and non‐wildfire sources of emissions. We quantified the burden of respiratory hospitalizations related to PM2.5 exposure at the zip code level through two different approaches: (a) naïve (considering the same CRF for all PM2.5 emissions) and (b) nuanced (considering different CRFs for PM2.5 from wildfires and from other sources). We conducted a Geographically Weighted Regression to analyze spatially varying relationships between the delta (i.e., the difference between the naïve and nuanced approaches) and the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). A higher attributable number of respiratory hospitalizations was found when accounting for the larger health burden of wildfire PM2.5. We found that, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13% as a result of not accounting for the higher CRF of wildfire‐related PM2.5 throughout California. This underestimation was higher in northern California and areas with higher SVI rankings. The relationship between delta and SVI varied spatially across California. These findings can be useful for updating future air pollution guideline recommendations. Plain Language Summary: Climate change is leading to an increase in the frequency and intensity of extreme weather events, including wildfires. Wildfire smoke is a large source of particulate matter (i.e., fine inhalable particles) that could have a greater health burden than particulate matter from other sources (e.g., vehicle or industrial emissions). We aimed to quantify the burden of respiratory hospitalizations related to all particulate matter exposure in California for the years 2006–2019 with and without considering the differential health burden of wildfire smoke. We then compared these two approaches to quantify the number of respiratory hospitalizations that may have been underestimated in the past by not taking into account the higher health burden of wildfire smoke. We found that throughout California, between 2006 and 2019, we may have underestimated the number of respiratory hospitalizations attributable to particulate matter exposure by approximately 410,000 (13%). This underestimation was higher in northern California and in vulnerable areas. These results may be helpful for updating recommendations in upcoming air pollution guidelines and for protecting communities at higher risk from wildfire smoke. Key Points: By not considering the differential health burden of wildfire PM2.5, we underestimate the number of hospitalizations attributable to PM2.5In California, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13%Underestimation of PM2.5‐attributable hospitalizations was higher in vulnerable areas [ABSTRACT FROM AUTHOR]

Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000–2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-μg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors. [ABSTRACT FROM AUTHOR]

Background As the interest in and use of quasi-experimental methods to evaluate impacts of health policies have dramatically increased in the epidemiological literature, we set out this study to (i) systematically compare several quasi-experimental methods that use data before and after an intervention and contrast their performance within a simulation framework while providing a brief overview of the methods; and (ii) discuss challenges that could arise from using these methods as well as directions for future research in the context of epidemiological applications. Methods We considered single-group designs [pre-post and interrupted time series (ITS)] and multiple-group designs [controlled interrupted time series/difference-in-differences, synthetic control methods (SCMs): traditional SCMs and generalized SCMs]. We assessed performance based on bias and root mean squared error. Results We identified settings in which each method failed to provide unbiased estimates. We found that, among the methods investigated, when data for multiple time points and for multiple control groups are available (multiple-group designs), data-adaptive methods such as the generalized SCM were generally less biased than other methods evaluated in our study. In addition, when all of the included units have been exposed to treatment (single-group designs) and data for a sufficiently long pre-intervention period are available, then the ITS performs very well, provided the underlying model is correctly specified. Conclusions When using a quasi-experimental method using data before and after an intervention, epidemiologists should strive to use, whenever feasible, data-adaptive methods that nest alternative identifying assumptions including relaxing the parallel trend assumption (e.g. generalized SCMs). [ABSTRACT FROM AUTHOR]

Purpose: It remains unclear why individuals living in disadvantaged neighborhoods have shorter non-small cell lung cancer (NSCLC) survival. It is possible that living in these deprived areas is linked with increased risk of developing aggressive NSCLC biology. Here, we explored the association of somatic KRAS mutations, which are associated with shorter survival in NSCLC patients, and 11 definitions of neighborhood disadvantage spanning socioeconomic and structural environmental elements. Methods: We analyzed data from 429 NSCLC patients treated at a Comprehensive Cancer Center from 2015 to 2018. Data were abstracted from medical records and each patient's home address was used to assign publicly available indices of neighborhood disadvantage. Prevalence Ratios (PRs) for the presence of somatic KRAS mutations were estimated using modified Poisson regression models adjusted for age, sex, smoking status, race/ethnicity, educational attainment, cancer stage, and histology. Results: In the NSCLC cohort, 29% had KRAS mutation-positive tumors. We found that five deprivation indices of socioeconomic disadvantage were associated with KRAS mutation. A one decile increase in several of these socioeconomic disadvantage indices was associated with a 1.06 to 1.14 increased risk of KRAS mutation. Measures of built structural environment were not associated with KRAS mutation status. Conclusion: Socioeconomic disadvantage at the neighborhood level is associated with higher risk of KRAS mutation while disadvantage related to built environmental structural measures was inversely associated. Our results indicate not only that neighborhood disadvantage may contribute to aggressive NSCLC biology, but the pathways linking biology to disadvantage are likely operating through socioeconomic-related stress. [ABSTRACT FROM AUTHOR]

In 2011, Chile added 12 mandatory extra weeks of maternity leave (ML). In January 2015, a pay-for-performance (P4P) strategy was included in the primary healthcare system, incorporating exclusive breastfeeding (EBF) promotion actions. The COVID-19 pandemic led to healthcare access difficulties and augmented household workloads. Our aim was to evaluate the effect of a 24-week ML, the P4P strategy, and COVID-19 on EBF prevalence, at 3 and 6 months in Chile. Aggregated EBF prevalence data from public healthcare users nationwide (80% of the Chilean population) was collected by month. Interrupted time series analyses were used to quantify changes in EBF trends from 2009 to 2020. The heterogeneity of EBF changes was assessed by urban/setting and across geographic settings. We found no effect of ML on EBF; the P4P strategy increased EBF at 3 months by 3.1% and 5.7% at 6 months. COVID-19 reduced EBF at 3 months by − 4.5%. Geographical heterogeneity in the impact of the two policies and COVID-19 on EBF was identified. The null effect of ML on EBF in the public healthcare system could be explained by low access from public healthcare users to ML (20% had access to ML) and by an insufficient ML duration (five and a half months). The negative impact of COVID-19 on EBF should alert policy makers about the crisis's effect on health promotion activities. [ABSTRACT FROM AUTHOR]

Heat and tropospheric ozone have acute impacts on rates of premature death. Warm temperatures affect the photochemical processes in ozone formation, suggesting ozone as a mediator of the acute health effect of heat on mortality. We assembled a summertime daily time-series data set of 15 French urban areas during 2000–2015 to decompose the acute total effect of heat waves on mortality into natural direct and indirect effects using a regression-based product method under the potential outcomes framework. For each area, we estimated the effect of heat waves on mortality using a quasi-Poisson model with adjustment for covariates such as lagged nitrogen dioxide concentration, and we modeled ozone with a linear regression of heat waves and the same set of covariates. We pooled estimates across areas using random-effects models. We also provide R software code (R Foundation for Statistical Computing, Vienna, Austria) with which to reproduce or replicate our analysis. Most areas demonstrated evidence of mediation by ozone, with the pooled relative risks for natural indirect effects being 1.03 (95% confidence interval (CI): 1.02, 1.05), 1.03 (95% CI: 1.01, 1.04), and 1.04 (95% CI: 1.00, 1.07) for nonaccidental, cardiovascular, and respiratory mortality, respectively. We found evidence of a mediation effect by ozone in the association between heat waves and mortality in France which varied by geographic location and cause of mortality. [ABSTRACT FROM AUTHOR]

Background: Policies 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. Methods: Utilizing 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. Results: We 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. Conclusions: This 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. [ABSTRACT FROM AUTHOR]

BACKGROUND: As 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 heatrelated health risks limit confidence in selecting which interventions are effective in reducing health impacts and in projecting future heat-related health risks. OBJECTIVES: Via 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. DISCUSSION: We 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. [ABSTRACT FROM AUTHOR]

Purpose: Understanding the relationship between an environmental determinant and a given health outcome is key to inform public health policies. The short-term mortality and morbidity responses to outdoor air pollutants are traditionally assessed as a log-linear relationship, but few studies suggest a possible deviation from linearity. This paper investigates the shape of the relationship between ozone, NO2 and fine particulate matter (PM10 and PM2.5), mortality and hospital admissions in 18 French cities between 2000 and 2017. Method: A multi-centric time series design, using quasi-Poisson generalized additive models, was used. Four approaches were compared to model concentration–response curves (log-linear, piecewise-linear with a priori defined breakpoints, piecewise-linear with no a priori breakpoint and cubic spline). Results: All the models indicated evidence of supra-linearity between PM10, PM2.5, NO2, mortality and hospital admissions. For instance, with a log-linear model, a 10 µg/m3 increase in PM2.5 was associated with a 0.4% [95% CI 0.2; 0.7] increase in non-accidental mortality. When using a piecewise model with a priori set breakpoint at 10 µg/m3, the mortality increase was 3.8% [4.4; 6.3] below 10 µg/m3, and 0.3% [0; 0.6] above. Non-significant impacts of ozone were found for concentrations below 90 µg/m3 to 120 µg/m3, with some variability in the identified threshold across the heath indicator studied. Conclusion: The supra-linearity of the relationship between PM10, PM2.5, NO2, mortality and hospital admissions supports the need to further reduce air pollution concentrations well below regulatory values. [ABSTRACT FROM AUTHOR]

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 women's 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 women's 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). Women's involvement in marital choice may facilitate couples' engagement related to family planning, possibly via the establishment of better communication between partners. [ABSTRACT FROM AUTHOR]

The gut microbiome has been shown to play a role in the relationship between diet and cardiometabolic health. We sought to examine the degree to which key microbial lignan metabolites are involved in the relationship between diet quality and cardiometabolic health using a multidimensional framework. This analysis was undertaken using cross-sectional data from 4685 US adults (age 43.6 ± 16.5 years; 50.4% female) participating in the National Health and Nutrition Examination Survey for 1999–2010. Dietary data were collected from one to two separate 24-hour dietary recalls and diet quality was characterized using the 2015 Healthy Eating Index. Cardiometabolic health markers included blood lipid profile, glycemic control, adiposity, and blood pressure. Microbial lignan metabolites considered were urinary concentrations of enterolignans, including enterolactone and enterodiol, with higher levels indicating a healthier gut microbial environment. Models were visually examined using a multidimensional approach and statistically analyzed using three-dimensional generalized additive models. There was a significant interactive association between diet quality and microbial lignan metabolites for triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, insulin, oral glucose tolerance, adiposity, systolic blood pressure, and diastolic blood pressure (all p < 0.05). Each of these cardiometabolic health markers displayed an association such that optimal cardiometabolic health was only observed in individuals with both high diet quality and elevated urinary enterolignans. When comparing effect sizes on the multidimensional response surfaces and model selection criteria, the strongest support for a potential moderating relationship of the gut microbiome was observed for fasting triglycerides and oral glucose tolerance. In this study, we revealed interactive associations of diet quality and microbial lignan metabolites with cardiometabolic health markers. These findings suggest that the overall association of diet quality on cardiometabolic health may be affected by the gut microbiome. [ABSTRACT FROM AUTHOR]

BACKGROUND: Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5 μm 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. METHOD: We 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. RESULTS: All 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. DISCUSSION: We 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. [ABSTRACT FROM AUTHOR]

Background: Precipitation 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. Methods: Using 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. Results: Rainfall 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. Conclusion: Precipitation 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. [ABSTRACT FROM AUTHOR]

Using the parametric g-formula, we estimated the 27-year risk of all-cause and specific causes of mortality under different potential interventions for blood lead (BLLs) and urinary cadmium (UCd) levels. We used data on 14,311 adults aged ≥20 years enrolled in the NHANES-III between 1988 and 1994 and followed up through 31 Dec 31 2015. Time and cause of death were determined from the National Death Index records. We used the parametric g-formula with pooled logistic regression models to estimate the relative and absolute risk of all-cause, cardiovascular, and cancer mortality under different potential threshold interventions for BLLs and UCd concentrations. Median follow-up was 22.5 years. A total of 5167 (36%) participants died by the end of the study, including 1550 from cardiovascular diseases and 1135 from cancer. Increases in BLLs and creatinine-corrected UCd levels from the 5th to the 95th percentiles were associated with risk differences of 4.17% (1.54 to 8.77) and 6.22% (4.51 to 12.00) for all-cause mortality, 1.52% (0.09 to 3.74) and 1.06% (−0.57 to 3.50) for cardiovascular disease mortality, and 1.32% (−0.09 to 3.67) and 0.64% (−0.98 to 2.80) for cancer mortality, respectively. Interventions to reduce historical exposures to lead and cadmium may have prevented premature deaths, especially from cardiovascular disease. [ABSTRACT FROM AUTHOR]

Background: Globally, access to life-saving vaccines has improved considerably in the past 5 decades. However, progress has started to slow down and even reverse in recent years. Understanding subnational heterogeneities in essential child immunization will be critical for closing the global vaccination gap. Methods and findings: We use vaccination information for over 220,000 children across 1,366 administrative regions in 43 low- and middle-income countries (LMICs) from the most recent Demographic and Health Surveys. We estimate essential immunization coverage at the national and subnational levels and quantify socioeconomic inequalities in such coverage using adjusted concentration indices. Within- and between-country variations are summarized via the Theil index. We use local indicator of spatial association (LISA) statistics to identify clusters of administrative regions with high or low values. Finally, we estimate the number of missed vaccinations among children aged 15 to 35 months across all 43 countries and the types of vaccines most often missed. We show that national-level vaccination rates can conceal wide subnational heterogeneities. Large gaps in child immunization are found across West and Central Africa and in South Asia, particularly in regions of Angola, Chad, Nigeria, Guinea, and Afghanistan, where less than 10% of children are fully immunized. Furthermore, children living in these countries consistently lack all 4 basic vaccines included in the WHO's recommended schedule for young children. Across most countries, children from poorer households are less likely to be fully immunized. The main limitations include subnational estimates based on large administrative divisions for some countries and different periods of survey data collection. Conclusions: The identified heterogeneities in essential childhood immunization, especially given that some regions consistently are underserved for all basic vaccines, can be used to inform the design and implementation of localized intervention programs aimed at eliminating child suffering and deaths from existing and novel vaccine-preventable diseases. Dr. Anna Dimitrova and colleagues analyse spatial trends and socioeconomic inequalities in vaccine coverage for essential childhood immunization across 43 low- and middle-income countries. Author summary: Why was this study done?: Despite global efforts to improve child immunization rates in low- and middle-income countries (LMICs), progress has slowed down and even reversed in recent years. Identifying hard-to-reach populations will be critical for closing the vaccination gap. Socioeconomic disparities in child immunization coverage have been mostly studied at the national level. What did the researchers do and find?: We analyzed survey data from 43 LMICs and investigated disparities in child vaccination coverage at the subnational level and across socioeconomic groups. We identified geographical regions in Africa and Asia where levels of childhood vaccination are particularly low. Across most countries, children from poorer households are less likely to be fully immunized and a large number of children miss all 4 essential vaccines recommended by the World Health Organization. What do these findings mean?: Large gaps in child immunization are found across and within countries, and among socioeconomic groups. More efforts are needed to ensure equitable access to essential vaccines in LMICs, where infectious diseases are among the leading causes of child death. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the association of short‐term exposure to overall fine particulate matter of <2.5 μm (PM2.5) and wildfire‐specific PM2.5 with emergency department (ED) visits for headache. Background: Studies have reported associations between PM2.5 exposure and headache risk. As climate change drives longer and more intense wildfire seasons, wildfire PM2.5 may contribute to more frequent headaches. Methods: Our study included adult Californian members (aged ≥18 years) of a large de‐identified commercial and Medicare Advantage claims database from 2006 to 2020. We identified ED visits for primary headache disorders (subtypes: tension‐type headache, migraine headache, cluster headache, and "other" primary headache). Claims included member age, sex, and residential zip code. We linked daily overall and wildfire‐specific PM2.5 to residential zip code and conducted a time‐stratified case‐crossover analysis considering 7‐day average PM2.5 concentrations, first for primary headache disorders combined, and then by headache subtype. Results: Among 9898 unique individuals we identified 13,623 ED encounters for primary headache disorders. Migraine was the most frequently diagnosed headache (N = 5534/13,623 [47.6%]) followed by "other" primary headache (N = 6489/13,623 [40.6%]). For all primary headache ED diagnoses, we observed an association of 7‐day average wildfire PM2.5 (odds ratio [OR] 1.17, 95% confidence interval [CI] 0.95–1.44 per 10 μg/m3 increase) and by subtype we observed increased odds of ED visits associated with 7‐day average wildfire PM2.5 for tension‐type headache (OR 1.42, 95% CI 0.91–2.22), "other" primary headache (OR 1.40, 95% CI 0.96–2.05), and cluster headache (OR 1.29, 95% CI 0.71–2.35), although these findings were not statistically significant under traditional null hypothesis testing. Overall PM2.5 was associated with tension‐type headache (OR 1.29, 95% CI 1.03–1.62), but not migraine, cluster, or "other" primary headaches. Conclusions: Although imprecise, these results suggest short‐term wildfire PM2.5 exposure may be associated with ED visits for headache. Patients, healthcare providers, and systems may need to respond to increased headache‐related healthcare needs in the wake of wildfires and on poor air quality days. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Reducing the mortality burden associated with urban air pollution constitutes a public health priority, and evidence of unequal exposure and susceptibility across population subgroups is growing. Many European countries have implemented low emission zones (LEZs) in densely populated city centers. Although LEZs decrease air pollution exposure and health impacts, evidence is lacking on their impact across neighborhoods and socio-economic groups. Objectives: The aim of this study was to evaluate the most equitable approach to implementing the second phase of the LEZ in Paris, France. We also present a literature review of the studies evaluating the benefits associated with LEZs in Europe. Methods: A health impact assessment (HIA) was conducted to quantify changes in air pollution exposure and expected health benefits by socioeconomic group and neighborhood related to four hypothetical scenarios for the second phase of the LEZ based on French Deprivation Index scores. The study focused on NO2 and PM2.5 as air pollutants and evaluated the impact of the LEZ on the inequitable burden of childhood asthma and all-cause premature adult mortality. We also conducted an economic evaluation associated with the LEZ benefits on prevented deaths and asthma cases. Results: The scenario with the largest LEZ perimeter and the most stringent vehicle standards prevented the highest number of cases and produced the most equitable distribution of health benefits, especially childhood asthma. It is expected that 810 deaths and 3200 cases of asthma could be prevented from the LEZ extension in this scenario. These results were distributed heterogeneously across three socioeconomic (SES) groups, most noticeably with asthma cases as 230, 180, and 210 cases were avoided per 100,000 inhabitants in high, medium, and low SES groups, respectively. We found substantial economic benefits associated with LEZ, with estimates ranging from €0.76 billion to €2.36 billion for prevented deaths. The benefits associated with asthma reduction ranged from €2.3 million to €8.3 million. Discussion: Conducting HIAs with a focus on equity will further inform policy makers of the impact of LEZ models on air pollution, health, and environmental justice. Developing these systematic methods and applying them to future LEZs and other air pollution policies will increase their effectiveness to reduce the burden of ambient air pollution on society and the environment. [ABSTRACT FROM AUTHOR]

In the Amazon Region of Peru, occupational activities are important drivers of human mobility and may increase the individual risk of being infected while contributing to increasing malaria community-level transmission. Even though out-of-village working activities and other mobility patterns have been identified as determinants of malaria transmission, no studies have quantified the effect of out-of-village working activities on recent malaria exposure and proposed plausible intervention scenarios. Using two population-based cross-sectional studies in the Loreto Department in Peru, and the parametric g-formula method, we simulated various hypothetical scenarios intervening in out-of-village working activities to reflect their potential health benefits. This study estimated that the standardized mean outcome (malaria seroprevalence) in the unexposed population (no out-of-village workers) was 44.6% (95% CI: 41.7%–47.5%) and 66.7% (95% CI: 61.6%–71.8%) in the exposed population resulting in a risk difference of 22.1% (95% CI: 16.3%–27.9%). However, heterogeneous patterns in the effects of interest were observed between peri-urban and rural areas (Cochran's Q test = 15.5, p < 0.001). Heterogeneous patterns were also observed in scenarios of increased prevalence of out-of-village working activities and restriction scenarios by gender (male vs. female) and age (18 and under vs. 19 and older) that inform possible occupational interventions targetting population subgroups. The findings of this study support the hypothesis that targeting out-of-village workers will considerably benefit current malaria elimination strategies in the Amazon Region. Particularly, males and adult populations that carried out out-of-village working activities in rural areas contribute the most to the malaria seropositivity (recent exposure to the parasite) in the Peruvian Amazon. [ABSTRACT FROM AUTHOR]

Background: Previous studies reported associations between high blood lead levels (BLLs) and urinary cadmium (UCd) concentrations and all-cause and cause-specific mortality. It is hypothesized that these associations are mediated by inflammation; therefore, adherence to an anti-inflammatory diet may mitigate these effects. We sought to estimate the potential effects of joint hypothetical interventions on metals levels and adherence to an anti-inflammatory diet or fruits and vegetables (FV) intake on the expected mortality distributions.Methods: We used data on 14,311 adults aged ≥ 20 years enrolled in the NHANES-III between 1988 and 1994 and followed up through Dec 31, 2015. We estimated daily FV servings and adherence to the dietary inflammatory index at baseline using 24-hour dietary recalls. Mortality was determined from the National Death Index records. We used the parametric g-formula with pooled logistic regression models to estimate the absolute risk of all-cause, cardiovascular, and cancer mortality under different hypothetical interventions compared to the natural course (no intervention).Results: Overall, we observed a decreased mortality risk when intervening to lower metals levels or increasing adherence to an anti-inflammatory diet or the daily FV servings. The joint intervention to lower BLLs and UCd and increase the adherence to the anti-inflammatory diet had the strongest impact on cancer mortality risk (risk difference [RD] = -1.50% (-2.52% to -0.62%)) compared to the joint intervention only on metals levels RD= -0.97% (-1.89 to 0.70). The same pattern of associations was observed for the joint intervention to lower both metals and increased daily FV servings and cardiovascular diseases mortality risk.Conclusion: Higher diet quality may constitute a complementary approach to the interventions to reduce exposures to cadmium and lead to further minimize their effects on mortality. A paradigm shift is required from a pollutant-focused only to a combination with a human-focused approach for primary prevention against these metals. [ABSTRACT FROM AUTHOR]

Lower respiratory tract infections disproportionately affect children and are one of the main causes of hospital referral and admission. COVID‐19 stay‐at‐home orders in early 2020 led to substantial reductions in hospital admissions, but the specific contribution of changes in air quality through this natural experiment has not been examined. Capitalizing on the timing of the stay‐at‐home order, we quantified the specific contribution of fine‐scale changes in PM2.5 concentrations to reduced respiratory emergency department (ED) visits in the pediatric population of San Diego County, California. We analyzed data on pediatric ED visits (n = 72,333) at the ZIP‐code level for respiratory complaints obtained from the ED at Rady Children's Hospital in San Diego County (2015–2020) and ZIP‐code level PM2.5 from an ensemble model integrating multiple machine learning algorithms. We examined the decrease in respiratory visits in the pediatric population attributable to the stay‐at‐home order and quantified the contribution of changes in PM2.5 exposure using mediation analysis (inverse of odds ratio weighting). Pediatric respiratory ED visits dropped during the stay‐at‐home order (starting on 19 March 2020). Immediately after this period, PM2.5 concentrations, relative to the counterfactual values based in the 4‐year baseline period, also decreased with important spatial variability across ZIP codes in San Diego County. Overall, we found that decreases in PM2.5 attributed to the stay‐at‐home order contributed to explain 4% of the decrease in pediatric respiratory ED visits. We identified important spatial inequalities in the decreased incidence of pediatric respiratory illness and found that brief decline in air pollution levels contributed to a decrease in respiratory ED visits. Plain Language Summary: Children are commonly affected by respiratory tract infections, which can lead to hospitalization in some cases. During the COVID‐19 lockdown in early 2020, pediatric cases of respiratory illnesses decreased substantially. We examined the contribution in the lower levels of fine particle pollution observed in San Diego County, over the lockdown period, on the decreased number of visits to pediatric Emergency Department facilities. Key Points: We assessed changes in pediatric respiratory emergency department (ED) visits and PM2.5 in 2020 with respect to the preceding 4 yearsWe observed a sustained decline in pediatric respiratory ED visits, while decreases in PM2.5 concentrations were short‐livedWe estimated a 4% contribution of PM2.5 changes on the reduction in pediatric respiratory ED visits during the stay‐at‐home period [ABSTRACT FROM AUTHOR]

Background: 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.Methods: 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 < 1 mile, 1-2 miles, or  > 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.Discussion: 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.Trial Registration: 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. [ABSTRACT FROM AUTHOR]

BACKGROUND: Both chronic and acute heat result in a substantial health burden globally, causing particular concern for at-risk populations, such as older adults. Outdoor temperatures are often assessed as the exposure and are used for heat warning systems despite individuals spending most of their time indoors. Many studies use ecological designs, with death or hospitalizations rates. Individual-level outcomes that are directly related to heat-symptoms should also be considered to refine prevention efforts. OBJECTIVES: In this longitudinal study, we assessed the association between indoor temperature and proximal symptoms in individuals ≥60 years of age living in non-air-conditioned households in Montérégie, Quebec, during the 2017-2018 summer months. METHODS: We gathered continuously measured indoor temperature and humidity from HOBO sensors and repeated health-related questionnaires about health-related symptoms administered across three periods of increasing outdoor temperatures, where the reference measurement (T1) occurred during a cool period with a target temperature of 18–22°C and two measurements (T2 and T3) occurred during warmer periods with target temperatures of 28–30°C and 30–33°C, respectively. We used generalized estimating equations with Poisson regression models and estimated risk ratios (RRs) between temperature, humidity, and each heat-related symptom. RESULTS: Participants (푛=277) had an average age (mean±standard deviation) of 72.8±7.02 y. Higher indoor temperatures were associated with increased risk of dry mouth (T3 RR=2.5; 95% CI: 1.8, 3.5), fatigue (RR=2.3; 95% CI: 1.8, 3.0), thirst (RR=3.4; 95% CI: 2.5, 4.5), less frequent urination (RR=3.7; 95% CI: 1.8, 7.3), and trouble sleeping (RR=2.2; 95% CI: 1.6, 3.2) compared with T1. We identified a nonlinear relationship with indoor temperatures across most symptoms of interest. DISCUSSION: This study identified that increasing indoor temperatures were associated with various health symptoms. By considering the prevalence of these early stage outcomes and indoor temperature exposures, adaptation strategies may be improved to minimize the burden of heat among vulnerable communities. [ABSTRACT FROM AUTHOR]

Background: Older adults are the least active population in the U.S. Low-income communities have fewer physical activity (PA) resources, contributing to less PA and increased chronic disease risk. This study assessed the effect of the multilevel, peer-led, Peer Empowerment Program 4 Physical Activity (PEP4PA) on moderate-to-vigorous PA (MVPA) and health outcomes, over 2 years of follow up. Methods: In a cluster-randomized controlled trial, 12 senior or community centers serving low-income older adults were assigned to a PA intervention (n = 6) or usual programming (n = 6) condition. PEP4PA included self-monitoring, health coaching, group walks, social support, and community advocacy to improve walking conditions. The primary outcome was daily minutes of MVPA (7-day accelerometer). Secondary outcomes included Perceived Quality of Life (PQoL), 6-Minute Walk Test (6-MWT), blood pressure (BP), and depressive symptoms at baseline, 6, 12, 18 and 24 months. Mixed effects regression models estimated the effects on outcomes between groups over time and included random effects for repeated measures and center clustering. Effect modification by sex and income status was assessed. We calculated the incremental cost per daily minute of MVPA gained in the intervention group relative to the control group to assess cost effectiveness. Results: We enrolled 476 older adults (50 + years). Participants were on average 71 years old, 76% female, 60% low income, and 38% identified as racial or ethnic minorities. Compared to the control group, intervention participants sustained roughly a 10 min/day increase in MVPA from baseline at all time points and increased mean PQoL scores from unsatisfied at baseline to satisfied at 12, 18 and 24 months. Males and higher-income groups had greater improvements in MVPA. No significant effects were observed for 6-MWT or depressive symptoms, and BP results were mixed. The incremental cost per minute MVPA gained per person was $0.25, $0.09, $0.06, and $0.05 at 6, 12, 18 and 24 months, respectively. Conclusions: PEP4PA achieved increases in MVPA and PQoL in low-income older adults, over 2 years of follow up. The peer-led, community-based intervention provides a sustainable and cost-effective model to improve health behaviors in underserved, aging populations. Trial registration: ClinicalTrials.gov (NCT02405325) March 20, 2015. [ABSTRACT FROM AUTHOR]

Background: The tremendous global health burden related to COVID-19 means that identifying determinants of COVID-19 severity is important for prevention and intervention. We aimed to explore long-term exposure to ambient air pollution as a potential contributor to COVID-19 severity, given its known impact on the respiratory system.Methods: We used a cohort of all people with confirmed SARS-CoV-2 infection, aged 20 years and older and not residing in a long-term care facility in Ontario, Canada, during 2020. We evaluated the association between long-term exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ground-level ozone (O3), and risk of COVID-19-related hospital admission, intensive care unit (ICU) admission and death. We ascertained individuals' long-term exposures to each air pollutant based on their residence from 2015 to 2019. We used logistic regression and adjusted for confounders and selection bias using various individual and contextual covariates obtained through data linkage.Results: Among the 151 105 people with confirmed SARS-CoV-2 infection in Ontario in 2020, we observed 8630 hospital admissions, 1912 ICU admissions and 2137 deaths related to COVID-19. For each interquartile range increase in exposure to PM2.5 (1.70 μg/m3), we estimated odds ratios of 1.06 (95% confidence interval [CI] 1.01-1.12), 1.09 (95% CI 0.98-1.21) and 1.00 (95% CI 0.90-1.11) for hospital admission, ICU admission and death, respectively. Estimates were smaller for NO2. We also estimated odds ratios of 1.15 (95% CI 1.06-1.23), 1.30 (95% CI 1.12-1.50) and 1.18 (95% CI 1.02-1.36) per interquartile range increase of 5.14 ppb in O3 for hospital admission, ICU admission and death, respectively.Interpretation: Chronic exposure to air pollution may contribute to severe outcomes after SARS-CoV-2 infection, particularly exposure to O3. [ABSTRACT FROM AUTHOR]

A growing literature in economics and epidemiology has exploited changes in wind patterns as a source of exogenous variation to better measure the acute health effects of air pollution. Since the distribution of wind components is not randomly distributed over time and related to other weather parameters, multivariate regression models are used to adjust for these confounding factors. However, this type of analysis relies on its ability to correctly adjust for all confounding factors and extrapolate to units without empirical counterfactuals. As an alternative to current practices and to gauge the extent of these issues, we propose to implement a causal inference pipeline to embed this type of observational study within an hypothetical randomized experiment. We illustrate this approach using daily data from Paris, France, over the 2008–2018 period. Using the Neyman–Rubin potential outcomes framework, we first define the treatment of interest as the effect of North-East winds on particulate matter concentrations compared to the effects of other wind directions. We then implement a matching algorithm to approximate a pairwise randomized experiment. It adjusts nonparametrically for observed confounders while avoiding model extrapolation by discarding treated days without similar control days. We find that the effective sample size for which treated and control units are comparable is surprisingly small. It is however reassuring that results on the matched sample are consistent with a standard regression analysis of the initial data. We finally carry out a quantitative bias analysis to check whether our results could be altered by an unmeasured confounder: estimated effects seem robust to a relatively large hidden bias. Our causal inference pipeline is a principled approach to improve the design of air pollution studies based on wind patterns. [ABSTRACT FROM AUTHOR]

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Background: Since climate change, pandemics and population mobility are challenging healthcare systems, an empirical and integrative research to studying and help improving the health systems resilience is needed. We present an interdisciplinary and mixed-methods research protocol, ClimHB, focusing on vulnerable localities in Bangladesh and Haiti, two countries highly sensitive to global changes. We develop a protocol studying the resilience of the healthcare system at multiple levels in the context of climate change and variability, population mobility and the Covid-19 pandemic, both from an institutional and community perspective.Methods: The conceptual framework designed is based on a combination of Levesque's Health Access Framework and the Foreign, Commonwealth and Development Office's Resilience Framework to address both outputs and the processes of resilience of healthcare systems. It uses a mixed-method sequential exploratory research design combining multi-sites and longitudinal approaches. Forty clusters spread over four sites will be studied to understand the importance of context, involving more than 40 healthcare service providers and 2000 households to be surveyed. We will collect primary data through questionnaires, in-depth and semi-structured interviews, focus groups and participatory filming. We will also use secondary data on environmental events sensitive to climate change and potential health risks, healthcare providers' functioning and organisation. Statistical analyses will include event-history analyses, development of composite indices, multilevel modelling and spatial analyses.Discussion: This research will generate inter-disciplinary evidence and thus, through knowledge transfer activities, contribute to research on low and middle-income countries (LMIC) health systems and global changes and will better inform decision-makers and populations. [ABSTRACT FROM AUTHOR]

Objectives. To determine the effect of heat waves on emergency department (ED) visits for individuals experiencing homelessness and explore vulnerability factors. Methods. We used a unique highly detailed data set on sociodemographics of ED visits in San Diego, California, 2012 to 2019. We applied a time-stratified case–crossover design to study the association between various heat wave definitions and ED visits. We compared associations with a similar population not experiencing homelessness using coarsened exact matching. Results. Of the 24 688 individuals identified as experiencing homelessness who visited an ED, most were younger than 65 years (94%) and of non-Hispanic ethnicity (84%), and 14% indicated the need for a psychiatric consultation. Results indicated a positive association, with the strongest risk of ED visits during daytime (e.g., 99th percentile, 2 days) heat waves (odds ratio = 1.29; 95% confidence interval = 1.02, 1.64). Patients experiencing homelessness who were younger or elderly and who required a psychiatric consultation were particularly vulnerable to heat waves. Odds of ED visits were higher for individuals experiencing homelessness after matching to nonhomeless individuals based on age, gender, and race/ethnicity. Conclusions. It is important to prioritize individuals experiencing homelessness in heat action plans and consider vulnerability factors to reduce their burden. (Am J Public Health. 2022;112(1):98–106. https://doi.org/10.2105/AJPH.2021.306557) [ABSTRACT FROM AUTHOR]