Background and Objectives To characterize the association of ambient particulate matter (PM) pollution of different sizes (PM ≤1 µm in aerodynamic diameter [PM₁], PM_2.5, and PM₁₀) with in-hospital case fatality among patients with stroke in China. Methods We collected hospitalizations due to stroke in 4 provinces in China from 2013 to 2019. Seven-day and annual averages of PM prior to hospitalization were estimated using bilinear interpolation and residential addresses. Associations with in-hospital case fatality were estimated using random-effects logistic regression models. Potential reducible fraction and the number of fatalities attributed to PM were estimated using a counterfactual approach. Results Among 3,109,634 stroke hospitalizations (mean age 67.23 years [SD 12.22]; 1,765,644 [56.78%] male), we identified 32,140 in-hospital stroke fatalities (case fatality rate 1.03%). Each 10 µg/m³ increase in 7-day average (short-term) exposure to PM was associated with increased in-hospital case fatality: odds ratios (ORs) were 1.058 (95% CI 1.047–1.068) for PM₁, 1.037 (95% CI 1.031–1.043) for PM_2.5, and 1.025 (95% CI 1.021–1.029) for PM₁₀. Similar but larger ORs were observed for annual averages (long-term): 1.240 (95% CI 1.217–1.265) for PM₁, 1.105 (95% CI 1.094–1.116) for PM_2.5, and 1.090 (95% CI 1.082–1.099) for PM₁₀. In counterfactual analyses, PM₁₀ was associated with the largest potential reducible fraction in in-hospital case fatality (10% [95% CI 8.3–11.7] for short-term exposure and 21.1% [19.1%–23%] for long-term exposure), followed by PM₁ and PM_2.5. Discussion PM pollution is a risk factor for in-hospital stroke-related deaths. Strategies that target reducing PM pollution may improve the health outcomes of patients with stroke.

Background: The purpose of this study was to explore the association of sleep patterns with the development of first cardiovascular diseases (FCVD), progression to cardiovascular multimorbidity (CVM), and subsequently to mortality. Methods and Results: This prospective study included 381 179 participants without coronary heart disease, stroke, atrial fibrillation, or heart failure at baseline, and they were followed up until March 31, 2021. We generated sleep patterns by summing the scores for 5 sleep behaviors, whereby 8 hours/d of sleep, evening chronotype, frequent insomnia, snoring, and daytime dozing were defined as high‐risk groups. We used a multistate model to estimate the impacts of sleep patterns on the dynamic progression of cardiovascular diseases. Over a median follow‐up of 12.1 years, 41 910 participants developed FCVD, 7302 further developed CVM, and 20 707 died. We found that adverse sleep patterns were significantly associated with the transition from health to FCVD, from FCVD to CVM, and from health to death, with hazard ratio associated with 1‐factor increase in sleep scores being 1.08 (95% CI, 1.07–1.09), 1.04 (95% CI, 1.02–1.06), and 1.04 (95% CI, 1.02–1.05), respectively. When further dividing FCVD into coronary heart disease, stroke, atrial fibrillation, and heart failure, adverse sleep patterns showed a significant and persistent effect on the transition from health to each cardiovascular disease, and from heart failure or atrial fibrillation to CVM. Conclusions: Our study provides evidence that adverse sleep patterns might increase the risk for the progression from health to cardiovascular diseases and further to CVM. Our findings suggest that improving sleep behaviors might be helpful for the primary and secondary prevention of cardiovascular diseases.

Survey researchers have carefully modified their data collection operations for various reasons, including the rising costs of data collection and the ongoing Coronavirus disease (COVID-19) pandemic, both of which have made in-person interviewing difficult. For large national surveys that require household (HH) screening to determine survey eligibility, cost-efficient screening methods that do not include in-person visits need additional evaluation and testing. A new study, known as the American Family Health Study (AFHS), recently initiated data collection with a national probability sample, using a sequential mixed-mode mail/web protocol for push-to-web US HH screening (targeting persons aged 18–49 years). To better understand optimal approaches for this type of national screening effort, we embedded two randomized experiments in the AFHS data collection. The first tested the use of bilingual respondent materials where mailed invitations to the screener were sent in both English and Spanish to 50 percent of addresses with a high predicted likelihood of having a Spanish speaker and 10 percent of all other addresses. We found that the bilingual approach did not increase the response rate of high-likelihood Spanish-speaking addresses, but consistent with prior work, it increased the proportion of eligible Hispanic respondents identified among completed screeners, especially among addresses predicted to have a high likelihood of having Spanish speakers. The second tested a form of nonresponse follow-up, where a subsample of active sampled HHs that had not yet responded to the screening invitations was sent a priority mailing with a $5 incentive, adding to the $2 incentive provided for all sampled HHs in the initial screening invitation. We found this approach to be quite valuable for increasing the screening survey response rate.