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Background: Annually since 2010, the University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation have produced the County Health Rankings--a "population health checkup" for the nation's over 3,000 counties. The purpose of this paper is to review the background and rationale for the Rankings, explain in detail the methods we use to create the health rankings in each state, and discuss the strengths and limitations associated with ranking the health of communities. Methods: We base the Rankings on a conceptual model of population health that includes both health outcomes (mortality and morbidity) and health factors (health behaviors, clinical care, social and economic factors, and the physical environment). Data for over 30 measures available at the county level are assembled from a number of national sources. Z-scores are calculated for each measure, multiplied by their assigned weights, and summed to create composite measure scores. Composite scores are then ordered and counties are ranked from best to worst health within each state. Results: Health outcomes and related health factors vary significantly within states, with over two-fold differences between the least healthy counties versus the healthiest counties for measures such as premature mortality, teen birth rates, and percent of children living in poverty. Ranking within each state depicts disparities that are not apparent when counties are ranked across the entire nation. Discussion: The County Health Rankings can be used to clearly demonstrate differences in health by place, raise awareness of the many factors that influence health, and stimulate community health improvement efforts. The Rankings draws upon the human instinct to compete by facilitating comparisons between neighboring or peer counties within states. Since no population health model, or rankings based off such models, will ever perfectly describe the health of its population, we encourage users to look to local sources of data to understand more about the health of their community. [ABSTRACT FROM AUTHOR]

Background: Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. Methods: Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. Results: Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p <.0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. Conclusions: Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes. [ABSTRACT FROM AUTHOR]

Background: We examine the association between family structure and children's health care utilization, barriers to health care access, health, and schooling and cognitive outcomes and assess whether socioeconomic status (SES) accounts for those family structure differences. We advance prior research by focusing on understudied but increasingly common family structures including single father families and five different family structures that include grandparents. Methods: Our data on United States children aged birth through 17 (unweighted N = 198,864) come from the 1997-2013 waves of the National Health Interview Survey, a nationally representative, publicly available, household-based sample. We examine 17 outcomes across nine family structures, including married couple, cohabiting couple, single mother, and single father families, with and without grandparents, and skipped-generation families that include children and grandparents but not parents. The SES measures include family income, home ownership, and parents' or grandparents' (depending on who is in the household) employment and education. Results: Compared to children living with married couples, children in single mother, extended single mother, and cohabiting couple families average poorer outcomes, but children in single father families sometimes average better health outcomes. The presence of grandparents in single parent, cohabiting, or married couple families does not buffer children from adverse outcomes. SES only partially explains family structure disparities in children's well-being. Conclusions: All non-married couple family structures are associated with some adverse outcomes among children, but the degree of disadvantage varies across family structures. Efforts to understand and improve child well-being might be most effective if they recognize the increasing diversity in children's living arrangements. [ABSTRACT FROM AUTHOR]