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Background. Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors.

Air pollution may increase risk of Alzheimer’s disease and related dementias (ADRD) in the U.S., but the extent of this relationship is unclear. Here, we constructed two national U.S. population-based cohorts of those aged ≥65 from the Medicare Chronic Conditions Warehouse (2000–2018), combined with high-resolution air pollution datasets, to investigate the association of long-term exposure to ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) with dementia and AD incidence, respectively. We identified ~2.0 million incident dementia cases (N = 12,233,371; dementia cohort) and ~0.8 million incident AD cases (N = 12,456,447; AD cohort). Per interquartile range (IQR) increase in the 5-year average PM2.5 (3.2 µg/m3), NO2 (11.6 ppb), and warm-season O3 (5.3 ppb) over the past 5 years prior to diagnosis, the hazard ratios (HRs) were 1.060 (95% confidence interval [CI]: 1.054, 1.066), 1.019 (95% CI: 1.012, 1.026), and 0.990 (95% CI: 0.987, 0.993) for incident dementias, and 1.078 (95% CI: 1.070, 1.086), 1.031 (95% CI: 1.023, 1.039), and 0.982 (95%CI: 0.977, 0.986) for incident AD, respectively, for the three pollutants. For both outcomes, concentration-response relationships for PM2.5 and NO2 were approximately linear. Our study suggests that exposures to PM2.5 and NO2 are associated with incidence of dementia and AD.

• On average, the Brazilian municipalities will have a deficit of approximately 17 beds. • Bed exceedances still occur for at least 2,119 municipalities in the most effective intervention scenario. • If there is a 50% increase in the hospital services, there will be an oversupply of 27 beds.

In this paper, we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate the daily maximum 8 h O 3 with high resolution over both space (at 1 km × 1 km grid cells covering the contiguous United States) and time (daily estimates between 2000 and 2016). We further quantify monthly model uncertainty for our 1 km × 1 km gridded domain. The results demonstrate high overall model performance with an average cross-validated R 2 (coefficient of determination) against observations of 0.90 and 0.86 for annual averages. Overall, the model performance of the three machine learning algorithms was quite similar. The overall model performance from the ensemble model outperformed those from any single algorithm. The East North Central region of the United States had the highest R 2 , 0.93, and performance was weakest for the western mountainous regions (R 2 of 0.86) and New England (R 2 of 0.87). For the cross validation by season, our model had the best performance during summer with an R 2 of 0.88. This study can be useful for the environmental health community to more accurately estimate the health impacts of O 3 over space and time, especially in health studies at an intra-urban scale.

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Background Long-term exposure to air pollution has been linked with an increase in risk of mortality. Whether existing US Environmental Protection Agency standards are sufficient to protect health is unclear. Our study aimed to examine the relationship between exposure to lower concentrations of air pollution and the risk of mortality.Methods Our nationwide cohort study investigated the effect of annual average exposure to air pollutants on all-cause mortality among Medicare enrolees from the beginning of 2000 to the end of 2016. Patients entered the cohort in the month of January following enrolment and were followed up until the end of the study period in 2016 or death. We restricted our analyses to participants who had only been exposed to lower concentrations of pollutants over the study period, specifically particulate matter less than 2•5 µg/m³ in diameter (PM 2•5 ) at a concentration of up to 12 µg/m³, nitrogen dioxide (NO 2 ) at a concentration of up to 53 parts per billion (ppb), and summer ozone (O 3 ) at concentrations of up to 50 ppb. We adjusted for two types of covariates, which were individual level and postal code-level variables. We used a doubly-robust additive model to estimate the change in risk. We further looked at effect-measure modification by stratification on the basis of demographic and socioeconomic characteristics. Findings We found an increased risk of mortality with all three pollutants. Each 1 µg/m³ increase in annual PM 2•5 concentrations increased the absolute annual risk of death by 0•073% (95% CI 0•071-0•076). Each 1 ppb increase in annual NO 2 concentrations increased the annual risk of death by 0•003% (0•003-0•004), and each 1 ppb increase in summer O 3 concentrations increased the annual risk of death by 0•081% (0•080-0•083). This increase translated to approximately 11 540 attributable deaths (95% CI 11 087-11 992) for PM 2•5 , 1176 attributable deaths (998-1353) for NO 2 , and 15 115 attributable deaths (14 896-15 333) for O 3 per year for each unit increase in pollution concentrations. The effects were higher in certain subgroups, including individuals living in areas of low socioeconomic status. Longterm exposure to permissible concentrations of air pollutants increases the risk of mortality.

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