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University settings have demonstrated potential for coronavirus disease (COVID-19) outbreaks; they combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin-Madison, Madison, Wisconsin, USA. During August-October 2020, a total of 3,485 students, including 856/6,162 students living in dormitories, tested positive. Case counts began rising during move-in week, August 25-31, 2020, then rose rapidly during September 1-11, 2020. The university initiated multiple prevention efforts, including quarantining 2 dormitories; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, in which the university is located, did not find evidence of transmission from a large cluster of cases in the 2 quarantined dorms during the outbreak. Coordinated implementation of prevention measures can reduce COVID-19 spread in university settings and may limit spillover to the surrounding community.

University settings have demonstrated potential for COVID-19 outbreaks, as they can combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin (UW)–Madison. During August – October 2020, 3,485 students tested positive, including 856/6,162 students living in residence halls. Case counts began rising during move-in week for on-campus students (August 25-31, 2020), then rose rapidly during September 1-11, 2020. UW-Madison initiated multiple prevention efforts, including quarantining two residence halls; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, where UW-Madison is located, did not find evidence of transmission from a large cluster of cases in the two residence halls quarantined during the outbreak. Coordinated implementation of prevention measures can effectively reduce SARS-CoV-2 spread in university settings and may limit spillover to the community surrounding the university.

Introduction The objective of this observational study was to examine the key contributors to health outcomes and to better understand the health disparities between Delta and non-Delta counties in 8 states in the Mississippi River Delta Region. We hypothesized that a unique set of contributors to health outcomes in the Delta counties could explain the disparities between Delta and non-Delta counties. Methods Data were from the 2014 County Health Rankings for counties in 8 states (Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, and Tennessee). We used the Delta Regional Authority definition to identify the 252 Delta counties and 468 non-Delta counties or county equivalents. Information on health factors (eg, health behaviors, clinical care) and outcomes (eg, mortality) were derived from 38 measures from the 2014 County Health Rankings. The contributions of health factors to health outcomes in Delta and non-Delta counties were examined using path analysis. Results We found similarities between Delta counties and non-Delta counties in the health factors (eg, tobacco use, diet and exercise) that significantly predicted the health outcomes of self-rated health and low birthweight. The most variation was seen in predictors of mortality; however, Delta counties shared 2 of the 3 significant predictors (ie, community safety and income) of mortality with non-Delta counties. On average across all measures, values in the Delta were 16% worse than in the non-Delta and 22% worse than in the rest of the United States. Conclusion The health status of Delta counties is poorer than that of non-Delta counties because the health factors that contribute to health outcomes in the entire region are worse in the Delta counties, not because of a unique set of health predictors.

United Health Foundation’s America’s Health Rankings, which ranks the states from “least healthy” to “healthiest,” receives wide press coverage and promotes discussion of public health issues. The University of Wisconsin Population Health Institute used the United Health Foundation’s model to develop the Wisconsin County Health Rankings (“Health Rankings”) from existing county-level data. The institute first released the rankings in 2004. A survey of the Wisconsin county health officers indicated that they intend to use the rankings for needs assessment, program planning, and discussion with county health boards. The institute implemented many of the health officers’ suggestions for improvement of the rankings in subsequent editions. The methods employed to create the rankings should be applicable in other states.

We sought to develop a county-level measure to evaluate residents’ access to exercise opportunities. Data were acquired from Esri, DeLorme World Vector (MapMart), and OneSource Global Business Browser (Avention). Using ArcGIS (Esri), we considered census blocks to have access to exercise opportunities if the census block fell within a buffer area around at least 1 park or recreational facility. The percentage of county residents with access to exercise opportunities was reported. Measure validity was examined through correlations with other County Health Rankings & Roadmaps’ measures. Included were 3,114 of 3,141 US counties. The average population with access to exercise opportunities was 52% (range, 0%–100%) with large regional variation. Access to exercise opportunities was most notably associated with no leisure-time physical activity (r = −0.47), premature death (r = −0.38), and obesity (r = −0.36). The measure uses multiple sources to create a valid county-level measure of exercise access. We highlight geographic disparities in access to exercise opportunities and call for improved data. Background

Although many researchers agree that multiple determinants impact health, there is no consensus regarding the magnitude of the relative contributions of individual health factors to health outcomes. This study presents a method to empirically estimate the relative contributions of health behaviors, clinical care, social and economic factors, and the physical environment to health outcomes using nationally representative county-level data and statistical approaches that account for potential sources of bias. The analyses for this study were conducted in 2014. Data were from the 2010–2013 County Health Rankings & Roadmaps. Data covered 2,996 of 3,141 U.S. counties. Ordinary least squares modeling was used as a baseline model. Multilevel latent growth curve modeling was used to estimate the relative contributions of health factors to health outcomes while accounting for measurement errors and state-specific characteristics. Almost half of the variance of health outcomes was due to state-level variation rather than county-level variation. When adjusted for measurement errors and state-level variation using multilevel latent growth curve modeling, the relative contribution of clinical care decreased and that of social and economic factors increased compared with the baseline model. This study presents how potential sources of bias affected the estimates of the relative contributions of a set of modifiable health factors to health outcomes at the county level. Further verification of these approaches with other data sources could lead to a better understanding of the impact of specific health determinants to health outcomes, and will provide useful information on policy interventions.