Background: United States government scientists estimate that COVID-19 may kill between 100,000 and 240,000 Americans. The majority of the pre-existing conditions that increase the risk of death for COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigate whether long-term average exposure to fine particulate matter (PM 2.5 ) increases the risk of COVID-19 deaths in the United States.Methods: Data was collected for approximately 3,000 counties in the United States (98% of the population) up to April 04, 2020. We fit zero-inflated negative binomial mixed models using county level COVID-19 deaths as the outcome and county level long-term average of PM 2.5 as the exposure. We adjust by population size, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables including, but not limited to obesity and smoking. We include a random intercept by state to account for potential correlation in counties within the same state. Results:We found that an increase of only 1 ߤ g/m 3 in PM 2.5 is associated with a 15% increase in the COVID-19 death rate, 95% confidence interval (CI) (5%, 25%). Results are statistically significant and robust to secondary and sensitivity analyses. Conclusions:A small increase in long-term exposure to PM 2.5 leads to a large increase in COVID-19 death rate, with the magnitude of increase 20 times that observed for PM 2.5 and allcause mortality. The study results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available.

Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

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.

Numerous models have been developed to quantify the combined effect of various risk factors to predict either risk of developing breast cancer, risk of carrying a high-risk germline genetic mutation, specifically in the BRCA1 and BRCA2 genes, or the risk of both. These breast cancer risk models can be separated into those that utilize mainly hormonal and environmental factors and those that focus more on hereditary risk. Given the wide range of models from which to choose, understanding what each model predicts, the populations for which each is best suited to provide risk estimations, the current validation and comparative studies that have been performed for each model, and how to apply them practically is important for clinicians and researchers seeking to utilize risk models in their practice. This review provides a comprehensive guide for those seeking to understand and apply breast cancer risk models by summarizing the majority of existing breast cancer risk prediction models including the risk factors they incorporate, the basic methodology in their development, the information each provides, their strengths and limitations, relevant validation studies, and how to access each for clinical or investigative purposes.