Background Evidence suggests that longer-term exposure to air pollutants over years confers higher risks of cardiovascular morbidity and mortality than shorter term exposure. One explanation is that cumulative adverse effects that develop over longer durations lead to the genesis of chronic disease. Preliminary epidemiological and clinical evidence suggest that air pollution may contribute to the development hypertension and type 2 diabetes. Methods and Results We used Cox proportional hazards models to assess incidence rate ratios (IRRs) and 95% confidence intervals (CI) for incident hypertension and diabetes associated with exposure to fine particulate matter (PM2.5) and nitrogen oxides (NOx) in a cohort of African American women living in Los Angeles. Pollutant levels were estimated at participant residential addresses with land use regression models (NOx) and interpolation from monitoring station measurements (PM2.5). Over follow-up from 1995-2005, 531 incident cases of hypertension and 183 incident cases of diabetes occurred. When pollutants were analyzed separately, the IRR for hypertension for a 10 μg/m3 increase in PM2.5 was 1.48 (95% CI 0.95-2.31) and the IRR for the interquartile range (12.4 parts per billion) of NOx was 1.14 (95% CI 1.03-1.25). The corresponding IRRs for diabetes were 1.63 (95% CI 0.78-3.44) and 1.25 (95% CI 1.07-1.46). When both pollutants were included in the same model, the IRRs for PM2.5 were attenuated and the IRRs for NOx were essentially unchanged for both outcomes. Conclusions Our results suggest that exposure to air pollutants, especially traffic-related pollutants, may increase the risk of type 2 diabetes and possibly of hypertension.

Four different endemic coronaviruses (eCoVs) are etiologic agents for the seasonal "common cold," and these eCoVs share extensive sequence homology with human severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Here, we show that individuals with as compared to without a relatively recent documented eCoV were tested at greater frequency for respiratory infections but had similar rate of SARS-CoV-2 acquisition. Importantly, the patients with a previously detected eCoV had less severe coronavirus disease-2019 (COVID-19) illness. Our observations suggest that pre-existing immune responses against endemic human coronaviruses can mitigate disease manifestations from SARS-CoV-2 infection.

Background  The United States was the second country to have a major outbreak of novel influenza A/H1N1 in what has become a new pandemic. Appropriate public health responses to this pandemic depend in part on early estimates of key epidemiological parameters of the virus in defined populations. Methods  We use a likelihood‐based method to estimate the basic reproductive number (R 0) and serial interval using individual level U.S. data from the Centers for Disease Control and Prevention (CDC). We adjust for missing dates of illness and changes in case ascertainment. Using prior estimates for the serial interval we also estimate the reproductive number only. Results  Using the raw CDC data, we estimate the reproductive number to be between 2·2 and 2·3 and the mean of the serial interval (μ) between 2·5 and 2·6 days. After adjustment for increased case ascertainment our estimates change to 1·7 to 1·8 for R 0 and 2·2 to 2·3 days for μ. In a sensitivity analysis making use of previous estimates of the mean of the serial interval, both for this epidemic (μ = 1·91 days) and for seasonal influenza (μ = 3·6 days), we estimate the reproductive number at 1·5 to 3·1. Conclusions  With adjustments for data imperfections we obtain useful estimates of key epidemiological parameters for the current influenza H1N1 outbreak in the United States. Estimates that adjust for suspected increases in reporting suggest that substantial reductions in the spread of this epidemic may be achievable with aggressive control measures, while sensitivity analyses suggest the possibility that even such measures would have limited effect in reducing total attack rates.