Summary Background Accumulating evidence links fine particulate matter (PM 2·5 ) to premature mortality, cardiovascular disease, and respiratory disease. However, less is known about the influence of PM 2·5 on neurological disorders. We aimed to investigate the effect of long-term PM 2·5 exposure on development of Parkinson’s disease or Alzheimer’s disease and related dementias. Methods We did a longitudinal cohort study in which we constructed a population-based nationwide open cohort including all fee-for-service Medicare beneficiaries (aged ≥65 years) in the contiguous United States (2000–16) with no exclusions. We assigned PM 2·5 postal code (ie, ZIP code) concentrations based on mean annual predictions from a high-resolution model. To accommodate our very large dataset, we applied Cox-equivalent Poisson models with parallel computing to estimate hazard ratios (HRs) for first hospital admission for Parkinson’s disease or Alzheimer’s disease and related dementias, adjusting for potential confounders in the health models. Findings Between Jan 1, 2000, and Dec 31, 2016, of 63 038 019 individuals who were aged 65 years or older during the study period, we identified 1·0 million cases of Parkinson’s disease and 3·4 million cases of Alzheimer’s disease and related dementias based on primary and secondary diagnosis billing codes. For each 5 μg/m 3 increase in annual PM 2·5 concentrations, the HR was 1·13 (95% CI 1·12–1·14) for first hospital admission for Parkinson’s disease and 1·13 (1·12–1·14) for first hospital admission for Alzheimer’s disease and related dementias. For both outcomes, there was strong evidence of linearity at PM 2·5 concentrations less than 16 μg/m 3 (95th percentile of the PM 2·5 distribution), followed by a plateaued association with increasingly larger confidence bands. Interpretation We provide evidence that exposure to annual mean PM 2·5 in the USA is significantly associated with an increased hazard of first hospital admission with Parkinson’s disease and Alzheimer’s disease and related dementias. For the ageing American population, improving air quality to reduce PM 2·5 concentrations to less than current national standards could yield substantial health benefits by reducing the burden of neurological disorders.

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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.

Background Long‐term air pollution exposure is a significant risk factor for inpatient hospital admissions in the general population. However, we lack information on whether long‐term air pollution exposure is a risk factor for hospital readmissions, particularly in individuals with elevated readmission rates. Methods and Results We determined the number of readmissions and total hospital visits (outpatient visits+emergency room visits+inpatient admissions) for 20 920 individuals with heart failure. We used quasi‐Poisson regression models to associate annual average fine particulate matter at the date of heart failure diagnosis with the number of hospital visits and 30‐day readmissions. We used inverse probability weights to balance the distribution of confounders and adjust for the competing risk of death. Models were adjusted for age, race, sex, smoking status, urbanicity, year of diagnosis, short‐term fine particulate matter exposure, comorbid disease, and socioeconomic status. A 1‐µg/m 3 increase in fine particulate matter was associated with a 9.31% increase (95% CI, 7.85%–10.8%) in total hospital visits, a 4.35% increase (95% CI, 1.12%–7.68%) in inpatient admissions, and a 14.2% increase (95% CI, 8.41%–20.2%) in 30‐day readmissions. Associations were robust to different modeling approaches. Conclusions These results highlight the potential for air pollution to play a role in hospital use, particularly hospital visits and readmissions. Given the elevated frequency of hospitalizations and readmissions among patients with heart failure, these results also represent an important insight into modifiable environmental risk factors that may improve outcomes and reduce hospital use among patients with heart failure.

Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km × 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.