Comparative Methodologic and Practical Considerations for Life Expectancy as a Public Health Mortality Measure.

INTRODUCTION: Life expectancy is a public health metric used to assess mortality. We describe life expectancy calculations for US counties and present methodologic considerations compared with years of potential life lost before age 75 (YPLL-75) and premature age-adjusted mortality (PAAM), 2 commonly used length-of-life metrics. METHODS: We used death data from the National Center for Health Statistics for 2015-2017 and other health measures from the 2019 County Health Rankings & Roadmaps. We calculated life expectancy from birth at the county level using an abridged life table and the Chiang method of variance. Studentized residuals identified counties with discordant life expectancy and YPLL-75 or PAAM values. Correlations tested associations of life expectancy with key health measures (eg, smoking, child poverty, uninsured). RESULTS: Among 3073 US counties, life expectancy ranged from 62.4 to 98.0 years, with a mean of 77.4 years. Life expectancy was strongly and negatively correlated with YPLL-75 (r = -0.91) and PAAM (r = -0.95) at the county level. Life expectancy was also associated with other key health metrics, such as smoking, employment, and education rates, where an improvement in the health factor indicated improvement in the respective length-of-life measure. Counties with discordant life expectancy and YPLL-75 or PAAM values had differing age structures. PRACTICE IMPLICATIONS: Commonly used length-of-life metrics in population health settings are differentiated by methodological matters, such as computation complexity, data availability, and differential risk among age groups, especially among the very old or very young. The choice of metric should consider these factors, in addition to practical concerns, such as the communication needs of the audience.

Life Expectancy At The US-Mexico Border: Evidence Of Disparities By Place, Race, And Ethnicity.

The mortality experience for the cluster of US counties in the US-Mexico border region has not been well described. We calculated 2016-18 life expectancy for the border region (counties within 100 kilometers of the border), making key comparisons to the US overall and to nonborder counties in border states. Life expectancy from birth for the border region was 81.1 years, which was greater than for the US and for the nonborder counties of border states. However, the disparity in life expectancy between racial/ethnic subgroups in the border region was also greater, within a range of more than thirteen years. Although White, Black, and Asian residents of the border region could expect to live significantly longer than residents of the US and nonborder counties of border states, Hispanic and American Indian residents could not. Understanding the mortality experience via life expectancy can help public health professionals and leaders prioritize efforts to ensure that all border residents have an equal opportunity to live a long, healthy life.

Deaths of Despair(ity) in Early 21st Century America: The Rise of Mortality and Racial/Ethnic Disparities.

INTRODUCTION: Recent media coverage and research have emphasized increasing mortality rates for middle-aged white Americans. A concern is that this has shifted focus away from the health burden of other population subgroups. This cross-sectional study compares the magnitude of racial/ethnic mortality disparities across age groups and investigates how changing mortality trends have affected these disparities. METHODS: Mortality data from 2007 to 2016 by race/ethnicity and age were obtained from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database in 2018‒2019. Absolute and relative racial/ethnic mortality disparities by age groups were determined by calculating between-group variance and mortality rate-adjusted between-group variance, respectively. Trends in disparities were analyzed using joinpoint regression modeling. Annual percentage change in rate-adjusted between-group variance was calculated for each trend segment as well as the relative contribution of each racial/ethnic group to the change. RESULTS: The largest relative and absolute disparities were found in the youngest and oldest age groups, respectively. Trend analysis detected an inflection point between 2009 and 2012 for most age groups where a period of decreasing disparities changed to one of increasing disparities. Three quarters of the decreasing disparities in Period 1 were resultant of lowering mortality among the black subgroup. During Period 2, the increase in child disparities were due to increased mortality among blacks, whereas increased adult disparities were due to increased mortality among whites shifting the overall mean away from subgroups with lower rates. CONCLUSIONS: Racial/ethnic mortality disparities persist and are widening for some age groups. It is imperative to maintain focus on the age groups where those with historically poorer health are contributing most to the increase.

Geographic Variability in Liver Disease-Related Mortality Rates in the United States.

PURPOSE: Liver disease is an important cause of morbidity and mortality in the United States. Geographic variations in the burden of chronic liver disease may have significant impact on public health policies but have not been explored at the national level. The objective of this study is to examine interstate variability in liver disease mortality in the United States. METHODS: We compared liver disease mortality from the 2010 National Vital Statistics Report on a state level. States in each quartile of liver disease mortality were compared with regard to viral hepatitis death rates, alcohol consumption, obesity, ethnic and racial composition, and household income. Race, ethnicity, and median household income data were derived from the 2010 US Census. Alcohol consumption and obesity data were obtained from the 2010 Behavioral Risk Factor Surveillance System Survey. RESULTS AND CONCLUSION: We found significant interstate variability in liver disease mortality, ranging from 6.4 to 17.0 per 100,000. The South and the West carry some of the highest rates of liver disease mortality. In addition to viral hepatitis death rates, there is a strong correlation between higher percentage of Hispanic population and a state's liver disease mortality rate (r = 0.538, P < .001). Lower household income (r = 0.405, P = .003) was also associated with the higher liver disease mortality. While there was a trend between higher obesity rates and higher liver disease mortality, the correlation was not strong and there was no clear association between alcohol consumption and liver disease mortality rates.

Relative Contributions of a Set of Health Factors to Selected Health Outcomes.

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.

Development of a nationally representative built environment measure of access to exercise opportunities.

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.