Background: Heterogeneity in temperature-mortality relationships across locations may partly result from differences in the demographic structure of populations and their cause-specific vulnerabilities. Here we conduct the largest epidemiological study to date on the association between ambient temperature and mortality by age and cause using data from 532 cities in 33 countries. Methods: We collected daily temperature and mortality data from each country. Mortality data was provided as daily death counts within age groups from all, cardiovascular, respiratory, or noncardiorespiratory causes. We first fit quasi-Poisson regression models to estimate location-specific associations for each age-by-cause group. For each cause, we then pooled location-specific results in a dose-response multivariate meta-regression model that enabled us to estimate overall temperature-mortality curves at any age. The age analysis was limited to adults. Results: We observed high temperature effects on mortality from both cardiovascular and respiratory causes compared to noncardiorespiratory causes, with the highest cold-related risks from cardiovascular causes and the highest heat-related risks from respiratory causes. Risks generally increased with age, a pattern most consistent for cold and for nonrespiratory causes. For every cause group, risks at both temperature extremes were strongest at the oldest age (age 85 years). Excess mortality fractions were highest for cold at the oldest ages. Conclusions: There is a differential pattern of risk associated with heat and cold by cause and age; cardiorespiratory causes show stronger effects than noncardiorespiratory causes, and older adults have higher risks than younger adults.

Background: Air pollution is a recognized risk factor for cardiovascular disease (CVD). Temperature is also linked to CVD, with a primary focus on acute effects. Despite the close relationship between air pollution and temperature, their health effects are often examined separately, potentially overlooking their synergistic effects. Moreover, fewer studies have performed mixture analysis for multiple co-exposures, essential for adjusting confounding effects among them and assessing both cumulative and individual effects. Methods: We obtained hospitalization records for residents of 14 U.S. states, spanning 2000-2016, from the Health Cost and Utilization Project State Inpatient Databases. We used a grouped weighted quantile sum regression, a novel approach for mixture analysis, to simultaneously evaluate cumulative and individual associations of annual exposures to four grouped mixtures: air pollutants (elemental carbon, ammonium, nitrate, organic carbon, sulfate, nitrogen dioxide, ozone), differences between summer and winter temperature means and their long-term averages during the entire study period (i.e., summer and winter temperature mean anomalies), differences between summer and winter temperature standard deviations (SD) and their long-term averages during the entire study period (i.e., summer and winter temperature SD anomalies), and interaction terms between air pollutants and summer and winter temperature mean anomalies. The outcomes are hospitalization rates for four prevalent CVD subtypes: ischemic heart disease, cerebrovascular disease, heart failure, and arrhythmia. Results: Chronic exposure to air pollutant mixtures was associated with increased hospitalization rates for all CVD subtypes, with heart failure being the most susceptible subtype. Sulfate, nitrate, nitrogen dioxide, and organic carbon posed the highest risks. Mixtures of the interaction terms between air pollutants and temperature mean anomalies were associated with increased hospitalization rates for all CVD subtypes. Conclusions: Our findings identified critical pollutants for targeted emission controls and suggested that abnormal temperature changes chronically affected cardiovascular health by interacting with air pollution, not directly.

BACKGROUND AND AIM: Although emerging evidence suggests that climate change negatively impacts cardiovascular disease (CVD) through pathways involving worsening air quality and abnormal temperature patterns, the joint impacts and relative importance of related exposures are largely lacking. Our goal is to assess mixture effects of chronic exposures to air pollutants and seasonal temperature anomalies on hospitalization risk for CVD among all-age residents in 15 U.S. states during 2000–2016. METHOD: Using the Health Cost and Utilization Project State Inpatient Databases, we investigated associations between annual exposures to mixtures of major PM2.5 components (sulfate, nitrate, ammonium, organic carbon, and elemental carbon), ozone, nitrogen dioxide (NO2), and fluctuations of seasonal temperature averages and standard deviations on the hospitalization risks for ischemic heart disease, cerebrovascular disease, heart failure, cardiomyopathy, arrhythmia, valvular heart disease, and all of these CVDs combined. For each outcome, we used a generalized weighted quantile sum regression with quasi-Poisson link to examine the cumulative association between the mixture of exposures and hospitalization risk and the contribution of each single exposure to the cumulative association, adjusting for individual- and community-level characteristics as confounders. RESULTS: For the combined CVD, we found that each decile increase in the levels of the mixtures increased hospitalization risk by 7.8% (95% confidence interval: 7.3%, 8.2%). Sulfate, NO2, and nitrate contributed the most to the cumulative association. For individual CVDs, a decile increase in the mixtures was significantly associated with increased hospitalization risks, with point estimates ranging between 6.7%–10.0%. In addition to sulfate, NO2, and nitrate, warmer-than-average summer temperature was another important contributor for most individual CVDs. CONCLUSIONS: Our findings suggested that air pollution and warmer climate jointly increased the risk of cardiovascular hospitalization. Identified air pollutants suggested that combustion of fossil fuels, traffic, and agriculture were the most detrimental sources to cardiovascular health.

BACKGROUND AND AIM: More and more studies are documenting that air pollutants such as PM2.5 and ozone may have adverse effects on neurological disorders. However, few studies have investigated the long-term exposure of particle components in conjunction with nitrogen dioxide and ozone to assess their mixture effects on Parkinson's disease. We aim to utilize weighted quantile sum regression to assess the cumulative effects of five major particle components including organic carbon (OC), elemental carbon (EC), nitrate, sulfate, and ammonium, along with nitrogen dioxide and ozone, on counts of inpatient Parkinson's hospitalizations for adults ages 40 years and up. METHOD: Inpatient records for Parkinson's hospitalizations were collected from the State Inpatient Databases which included hospitals from 12 U.S. states ranging in years from 2000 through 2016. We also included temperature from Daymet and variables from the U.S. census to control for socio-economic status. All variables were aggregated to the annual level. RESULTS: We observed an increase of 7.2% (95%CI: 6.4%,8.1%) in the number of Parkinson's inpatient hospitalizations each year for each decile increase of the pollutant mixture in adults ages 40 years and up. Ozone contributed the most weight to the pollutant mixture while the other 6 pollutants carried the same relatively small weights. CONCLUSIONS: Our results emphasize the significance of the effects of ozone on Parkinson's disease while also contributing to the growing body of literature on neurological disorders.

Background:Epidemiologic evidence on the relationships between air pollution and the risks of primary cancers other than lung cancer remained largely lacking. We aimed to examine associations of 10-year exposures to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with risks of breast, prostate, colorectal, and endometrial cancers. Methods:For each cancer, we constructed a separate cohort among the national Medicare beneficiaries during 2000 to 2016. We simultaneously examined the additive associations of six exposures, namely, moving average exposures to PM2.5 and NO2 over the year of diagnosis and previous 2 years, previous 3 to 5 years, and previous 6 to 10 years, with the risk of first cancer diagnosis after 10 years of follow-up, during which there was no cancer diagnosis. Results:The cohorts included 2.2 to 6.5 million subjects for different cancers. Exposures to PM2.5 and NO2 were associated with increased risks of colorectal and prostate cancers but were not associated with endometrial cancer risk. NO2 was associated with a decreased risk of breast cancer, while the association for PM2.5 remained inconclusive. At exposure levels below the newly updated World Health Organization Air Quality Guideline, we observed substantially larger associations between most exposures and the risks of all cancers, which were translated to hundreds to thousands new cancer cases per year within the cohort per unit increase in each exposure. Conclusions:These findings suggested substantial cancer burden was associated with exposures to PM2.5 and NO2, emphasizing the urgent need for strategies to mitigate air pollution levels.

Background In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the “ Lancet Countdown: Tracking Progress on Health and Climate Change”, an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. Discussion This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change’s impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators’ relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. Conclusions Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive.

BACKGROUND: Particulate matter has been documented to adversely affect asthma exacerbation. However, few studies have investigated the long-term exposure of particle components in conjunction with PM2.5 and ozone to assess their individual and additive effects. AIM: We aim to utilize a Bayesian Kernel machine regression (BKMR) to assess the individual and join effects of air pollutants including 15 different particle components such as organic carbon (OC), elemental carbon (EC), copper (Cu), and zinc (Z), along with PM2.5 and ozone, on counts of inpatient asthma hospitalizations for children ages 0 to 18 and adults ages 19 to 64 years. METHODS: Inpatient records were collected from the State Inpatient Databases which included hospitals from 12 U.S. states ranging in years from 2000 through 2016. We also included temperature from Daymet and variables from the U.S. census to control for socio-economic status. All variables were aggregated to the annual level. RESULTS: We observed an increase of 0.44 (95%CI: 0.28,0.59), 1.24 (95%CI: 1.07,1.40), and 2.35 (95%CI: 2.17,2.52) in the number of children asthma inpatient hospitalizations each year at the 25th, 50th, and 75th percentiles of pollutant mixture, respectively. In adults, we observed an increase of 0.84 (95%CI: 0.63,1.04), 1.98 (95%CI: 1.78,2.19), and 3.27 (95%CI: 3.06,3.48) in the number of asthma inpatient hospitalizations each year at the 25th, 50th, and 75th percentiles of the pollutant mixture, respectively. CONCLUSIONS: Our results indicate that long-term exposure to pollutant mixtures result in increased asthma hospitalizations in both children and adults, and daily measurements of particle components data is needed to assess short-term exposure. KEYWORDS: Asthma, PM Components, PM2.5, Ozone, BKMR

BACKGROUND: Recent studies have shown that air pollutants may have adverse effects on neurological disorders. However, few studies have investigated the long-term exposure of particle components in conjunction with PM2.5 and ozone to assess their individual and additive effects on Parkinson's disease. AIM: We aim to utilize a Bayesian Kernel machine regression (BKMR) to assess the individual and join effects of air pollutants including 15 different particle components such as organic carbon (OC), elemental carbon (EC), copper (Cu), and zinc (Z), along with PM2.5 and ozone, on counts of inpatient Parkinson's hospitalizations for adults ages 40 years and up. METHODS: Inpatient records were collected from the State Inpatient Databases which included hospitals from 12 U.S. states ranging in years from 2000 through 2016. We also included temperature from Daymet and variables from the U.S. census to control for socio-economic status. All variables were aggregated to the annual level. RESULTS: We observed a decrease of 0.05 (95%CI: 0.03,-0.14), 0.04 (95%CI: 0.05,-0.14), and an increase of 0.03 (95%CI: -0.07,0.12) in the number of Parkinson's inpatient hospitalizations each year at the 25th, 50th, and 75th percentiles of pollutant mixture, respectively. At the 90th and 95th percentile, there is a significant increase of 0.12 (95%CI: 0.01,0.22) and 0.17 (95%CI: 0.06,0.28) annual Parkinson's cases, respectively. CONCLUSIONS: Our results contribute to the growing body of literature on air pollution and neurological disorders. KEYWORDS: Parkinson's Disease, PM Components, PM2.5, Ozone, BKMR

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

BACKGROUND: Air pollutants, including PM2.5, have been shown to adversely affect health; however, few studies have investigated the long-term exposure of particle components in conjunction with PM2.5 and ozone to assess their individual and additive effects on cerebrovascular incidents such as stroke. AIM: We aim to utilize a Bayesian Kernel machine regression (BKMR) to assess the individual and join effects of air pollutants including 15 different particle components such as organic carbon (OC), elemental carbon (EC), copper (Cu), and zinc (Z), along with PM2.5 and ozone, on counts of inpatient Parkinson's hospitalizations for adults ages 40 years and up. METHODS: Inpatient records were collected from the State Inpatient Databases which included hospitals from 12 U.S. states ranging in years from 2000 through 2016. We also included temperature from Daymet and variables from the U.S. census to control for socio-economic status. All variables were aggregated to the annual level. RESULTS We observed a significant increase of 2.00 (95%CI: 1.72,2.29), 5.87 (95%CI: 5.57,6.16), and 9.81 (95%CI: 9.51,10.12) in the number of inpatient stroke hospitalizations each year at the 25th, 50th, and 75th percentiles of pollutant mixture, respectively. CONCLUSIONS: Our results indicate that the mixture of pollutants greatly contribute to the increase in the number of stroke hospitalizations each year and that the effects of short-term exposures of particle components on stroke hospitalizations should be assessed next. KEYWORDS: Stroke, PM Components, PM2.5, Ozone, BKMR

Background: We have previously documented an inverse relationship between PM2.5 in Lima, Peru, and reproductive outcomes. Here, we investigate the effect of temperature on birth weight, birth weight-Z-score adjusted for gestational age, low birth weight, and preterm birth. We also explore interactions between PM2.5 and temperature. Methods: We studied 123,034 singleton births in three public hospitals of Lima with temperature and PM2.5 during gestation between 2012 and 2016. We used linear, logistic, and Cox regression to estimate associations between temperature during gestation and birth outcomes and explored possible modification of the temperature effect by PM2.5. Results: Exposure to maximum temperature in the last trimester was inversely associated with both birth weight [β: −23.7; 95% confidence interval [CI]: −28.0, −19.5] and z-score weight-for-gestational-age (β: −0.024; 95% CI: −0.029, −0.020) with an interquartile range of 5.32 °C. There was also an increased risk of preterm birth with higher temperature (interquartile range) in the first trimester (hazard ratio: 1.04; 95% CI: 1.001, 1.070). The effect of temperature on birthweight was primarily seen at higher PM2.5 levels. There were no statistically significant associations between temperature exposure with low birth weight. Conclusions: Exposition to maximum temperature was associated with lower birth weight and z-score weight-for-gestational-age and higher risk of preterm birth, in accordance with much of the literature. The effects on birth weight were seen only in the third trimester.

The health effects of fine particulate matter (PM2.5) may be worse at higher temperatures. To investigate temperature’s effect on PM2.5-mortality/morbidity associations in Lima, Peru. Time-series regressions relating PM2.5 and temperature to mortality and emergency room (ER) visits during 2010–2016. Daily PM2.5 levels (assigned to 40 Lima districts) and daily maximum temperature (Lima-wide) were estimated based on ground monitors, remote sensing, and modeling. We analyzed all-cause, cardiovascular (ICD codes I00-I99), and respiratory (ICD codes J00-J99) mortality, and cardiovascular and respiratory causes for ER visits. The average PM2.5 concentration was 20.9 µg/m3 (IQR 17.5–23.5). The mean daily maximum temperature was 23.8 °C (IQR 20.8–26.9). PM2.5’s effect on all-cause, respiratory, and circulatory disease mortality was significantly (p < 0.05) stronger at temperatures above the maximum temperature median. The rate ratios per increase of 10 µg/m3 of PM2.5 for all cause, respiratory, and circulatory mortality respectively were 1.03 (1.00–1.06), 1.04 (0.98–1.10), and 1.04 (0.98–1.10) at temperatures below the median, vs. 1.08 (1.04–1.12), 1.11 (1.03–1.19), and 1.14 (1.05–1.25) when temperatures were above the median. Results were analogous for ER visits for respiratory but not circulatory disease. Results strengthen the evidence that air pollution may be more dangerous when temperatures are higher. Our data contribute to a growing body of literature which indicates that the damaging effects of PM2.5 may be worse at higher temperature, adding new evidence from Lima, Peru.

The Lancet Countdown is an international collaboration that independently monitors the health consequences of a changing climate. Publishing updated, new, and improved indicators each year, the Lancet Countdown represents the consensus of leading researchers from 43 academic institutions and UN agencies. The 44 indicators of this report expose an unabated rise in the health impacts of climate change and the current health consequences of the delayed and inconsistent response of countries around the globe—providing a clear imperative for accelerated action that puts the health of people and planet above all else.

Background: Asthma affects millions of people worldwide. Lima, Peru is one of the most polluted cities in the Americas but has insufficient ground PM2.5 (particulate matter that are 2.5 mu m or less in diameter) measurements to conduct epidemiologic studies regarding air pollution. PM2.5 estimates from a satellite-driven model have recently been made, enabling a study between asthma and PM2.5. Objective: We conducted a daily time-series analysis to determine the association between asthma emergency department (ED) visits and estimated ambient PM2.5 levels in Lima, Peru from 2010 to 2016. Methods: We used Poisson generalized linear models to regress aggregated counts of asthma on district-level population weighted PM2.5. Indicator variables for hospitals, districts, and day of week were included to account for spatial and temporal autocorrelation while assessing same day, previous day, day before previous and average across all 3-day exposures. We also included temperature and humidity to account for meteorology and used dichotomous percent poverty and gender variables to assess effect modification. Results: There were 103,974 cases of asthma ED visits during the study period across 39 districts in Lima. We found a 3.7% (95% CI: 1.7%-5.8%) increase in ED visits for every interquartile range (IQR, 6.02 mu g/m(3)) increase in PM2.5 same day exposure with no age stratification. For the 0-18 years age group, we found a 4.5% (95% CI: 2.2%-6.8%) increase in ED visits for every IQR increase in PM2.5 same day exposure. For the 19-64 years age group, we found a 6.0% (95% CI: 1.0%-11.0%) increase in ED visits for every IQR in average 3-day exposure. For the 65 years and up age group, we found a 16.0% (95% CI: 7.0%-24.0%) decrease in ED visits for every IQR increase in PM2.5 average 3-day exposure, although the number of visits in this age group was low (4,488). We found no effect modification by SES or gender. Discussion: Results from this study provide additional literature on use of satellite-driven exposure estimates in time-series analyses and evidence for the association between PM2.5 and asthma in a low-and middle-income (LMIC) country.

Background There have been no studies of air pollution and mortality in Lima, Peru. We evaluate whether daily environmental PM 2.5 exposure is associated to respiratory and cardiovascular mortality in Lima during 2010 to 2016. Methods We analyzed 86,970 deaths from respiratory and cardiovascular diseases in Lima from 2010 to 2016. Estimated daily PM 2.5 was assigned based on district of residence. Poisson regression was used to estimate associations between daily district-level PM 2.5 exposures and daily counts of deaths. Results An increase in 10 μg/m 3 PM 2.5 on the day before was significantly associated with daily cardiorespiratory mortality (RR 1.029; 95% CI: 1.01–1.05) across all ages and in the age group over 65 (RR 1.04; 95% CI: 1.005–1.09) which included 74% of all deaths. We also observed associations with circulatory deaths for all age groups (RR 1.06; 95% CI: 1.01–1.11), and those over 65 (RR 1.06; 95% CI 1.00–1.12). A borderline significant trend was seen (RR 1.05; 95% CI 0.99–1.06; p = 0.10) for respiratory deaths in persons aged over 65. Trends were driven by the highest quintile of exposure. Conclusions PM 2.5 exposure is associated with daily cardiorespiratory mortality in Lima, especially for older people. Our data suggest that the existing limits on air pollution exposure are too high.