Studies have shown that long-term exposure to air pollution increases mortality. However, evidence is limited for air-pollution levels below the most recent National Ambient Air Quality Standards. Previous studies involved predominantly urban populations and did not have the statistical power to estimate the health effects in underrepresented groups. We constructed an open cohort of all Medicare beneficiaries (60,925,443 persons) in the continental United States from the years 2000 through 2012, with 460,310,521 person-years of follow-up. Annual averages of fine particulate matter (particles with a mass median aerodynamic diameter of less than 2.5 μm [PM_2.5]) and ozone were estimated according to the ZIP Code of residence for each enrollee with the use of previously validated prediction models. We estimated the risk of death associated with exposure to increases of 10 μg per cubic meter for PM_2.5 and 10 parts per billion (ppb) for ozone using a two-pollutant Cox proportional-hazards model that controlled for demographic characteristics, Medicaid eligibility, and area-level covariates. Increases of 10 μg per cubic meter in PM_2.5 and of 10 ppb in ozone were associated with increases in all-cause mortality of 7.3% (95% confidence interval [CI], 7.1 to 7.5) and 1.1% (95% CI, 1.0 to 1.2), respectively. When the analysis was restricted to person-years with exposure to PM_2.5 of less than 12 μg per cubic meter and ozone of less than 50 ppb, the same increases in PM_2.5 and ozone were associated with increases in the risk of death of 13.6% (95% CI, 13.1 to 14.1) and 1.0% (95% CI, 0.9 to 1.1), respectively. For PM_2.5, the risk of death among men, blacks, and people with Medicaid eligibility was higher than that in the rest of the population. In the entire Medicare population, there was significant evidence of adverse effects related to exposure to PM_2.5 and ozone at concentrations below current national standards. This effect was most pronounced among self-identified racial minorities and people with low income. (Supported by the Health Effects Institute and others.)

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

Importance The US Environmental Protection Agency is required to reexamine its National Ambient Air Quality Standards (NAAQS) every 5 years, but evidence of mortality risk is lacking at air pollution levels below the current daily NAAQS in unmonitored areas and for sensitive subgroups. Objective To estimate the association between short-term exposures to ambient fine particulate matter (PM_2.5) and ozone, and at levels below the current daily NAAQS, and mortality in the continental United States. Design, Setting, and Participants Case-crossover design and conditional logistic regression to estimate the association between short-term exposures to PM_2.5and ozone (mean of daily exposure on the same day of death and 1 day prior) and mortality in 2-pollutant models. The study included the entire Medicare population from January 1, 2000, to December 31, 2012, residing in 39 182 zip codes. Exposures Daily PM_2.5and ozone levels in a 1-km × 1-km grid were estimated using published and validated air pollution prediction models based on land use, chemical transport modeling, and satellite remote sensing data. From these gridded exposures, daily exposures were calculated for every zip code in the United States. Warm-season ozone was defined as ozone levels for the months April to September of each year. Main Outcomes and Measures All-cause mortality in the entire Medicare population from 2000 to 2012. Results During the study period, there were 22 433 862 million case days and 76 143 209 control days. Of all case and control days, 93.6% had PM_2.5levels below 25 μg/m³, during which 95.2% of deaths occurred (21 353 817 of 22 433 862), and 91.1% of days had ozone levels below 60 parts per billion, during which 93.4% of deaths occurred (20 955 387 of 22 433 862). The baseline daily mortality rates were 137.33 and 129.44 (per 1 million persons at risk per day) for the entire year and for the warm season, respectively. Each short-term increase of 10 μg/m³in PM_2.5(adjusted by ozone) and 10 parts per billion (10⁻⁹) in warm-season ozone (adjusted by PM_2.5) were statistically significantly associated with a relative increase of 1.05% (95% CI, 0.95%-1.15%) and 0.51% (95% CI, 0.41%-0.61%) in daily mortality rate, respectively. Absolute risk differences in daily mortality rate were 1.42 (95% CI, 1.29-1.56) and 0.66 (95% CI, 0.53-0.78) per 1 million persons at risk per day. There was no evidence of a threshold in the exposure-response relationship. Conclusions and Relevance In the US Medicare population from 2000 to 2012, short-term exposures to PM_2.5and warm-season ozone were significantly associated with increased risk of mortality. This risk occurred at levels below current national air quality standards, suggesting that these standards may need to be reevaluated.

(1) Background: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. Vast resources have been allocated to control the pandemic and treat patients. However, little attention has been paid to the adverse impact on mental health or effective mitigation strategies to improve mental health. (2) Purpose: The aim of this study was to assess the adverse impact of the COVID-19 outbreak on Chinese college students’ mental health, understand the underlying mechanisms, and explore feasible mitigation strategies. (3) Methods: During the peak time of the COVID-19 outbreak in China, we conducted longitudinal surveys of sixty-six college students. Structured questionnaires collected information on demographics, physical activity, negative emotions, sleep quality, and aggressiveness level. A mixed-effect model was used to evaluate associations between variables, and the mediating effect of sleep quality was further explored. A generalized additive model was used to determine the dose-response relationships between the COVID-19 death count, physical activity, and negative emotions. (4) Results: The COVID-19 death count showed a direct negative impact on general sleep quality (β = 1.37, 95% confidence interval [95% CI]: 0.55, 2.19) and reduced aggressiveness (β = −6.57, 95% CI: −12.78, −0.36). In contrast, the COVID-19 death count imposed not a direct but an indirect impact on general negative emotions (indirect effect (IE) = 0.81, p = 0.012), stress (IE = 0.40, p < 0.001), and anxiety (IE = 0.27, p = 0.004) with sleep quality as a mediator. Moreover, physical activity directly alleviated general negative emotions (β = −0.12, 95% CI: −0.22, −0.01), and the maximal mitigation effect occurred when weekly physical activity was about 2500 METs. (5) Conclusions: (a) The severity of the COVID-19 outbreak has an indirect effect on negative emotions by affecting sleep quality. (b) A possible mitigation strategy for improving mental health includes taking suitable amounts of daily physical activity and sleeping well. (c) The COVID-19 outbreak has reduced people’s aggressiveness, probably by making people realize the fragility and preciousness of life.