Abstract
Compared with people in other developed countries, Americans live shorter lives, have more disease and disability, and lag on most population health measures. Recent research suggests that this poor comparative performance is primarily driven by profound local place-based disparities. Several initiatives successfully used sub-county life expectancy estimates to identify geographic disparities, generate widespread interest, and catalyze multisector actions. To explore the feasibility of scaling these efforts, the Centers for Disease Control and Prevention and the Council of State and Territorial Epidemiologists initiated a multiphase project – the Sub-County Assessment of Life Expectancy. Phase I participants reviewed the literature, assessed and identified appropriate tools, calculated locally relevant estimates, and developed methodological guidance. Phase I results suggest that most state and local health departments will be able to calculate actionable sub-county life expectancy estimates despite varying resources, expertise, and population sizes, densities, and geographies. To accelerate widespread scaling, we describe several successful case examples, identify user-friendly validated tools, and provide practical tips that resulted from lessons learned.
Insights Results
Overview of article/project
This article explores the feasibility of scaling efforts to use sub-county life expectancy as a way to identify geographic disparities. This specific project focuses on the Sub-County Assessment of Life Expectancy (SCALE)
In an effort to identify and quantify local geographical disparities using life expectancy, the goal of SCALE Phase I was to identify appropriate methods for calculating actionable sub-county life expectancy and develop easy-to-use resources designed to assist other health departments
Methods & Results
Identifying and quantifying local disparities is a necessary first step for selecting, implementing, and documenting the impact of interventions. As exemplified through the intervention’s implementation in the Los Angeles County Department of Public Health (LACDPH), the Public Health-Seattle & King County (PHSKC), and other case studies, use sub-county life expectancy is a strategy to quantify geographical disparities
Case Example 1) 2009, LACDPH examined life expectancy (LE) disparities in the county. Although LE had increased steadily since 1991, large disparities were observed, including a nearly 18-year difference between black males (69.4 years) and Asian/Pacific Islander females (86.9 years). Partnerships with cities and unincorporated communities were established, and maps examining LE at matching geographic levels were created to increase engagement. Methodologically, there was a strong inverse relationship between the Economic Hardship Index (EHI), score and LE, which prompted the LACDPH to publish a report that ranks cities and communities by LE and economic hardship that was broadly disseminated. This report received much local, national and international press resulting in increased awareness of the connection between social issues and health, leading to reframed city and community engagement and motivation to act
Case Example 2) PHSKC staff calculated LE for King County using the adjusted Chiang II method and 2012 mortality data. LE in King County (81.2 years) was substantially longer than LE in the United
States. However, pronounced disparities across race/ethnicity and subregions were evident, so PHSKC staff examined census tract–level LE. Strikingly similar spatial patterns of disparities in LE and risk factors led to identification of potential communities for engagement; catalyzed an ongoing partnership between PHSKC, the Department of Community and Human Services, Seattle Foundation, and Living Cities; and led to formation of the Communities of Opportunity (COO), an organization that focuses on improving equity in communities through system and policy level solutions led by or engaging the local community. Desired results are that all people thrive economically; have quality, affordable housing; are healthy; and are connected to the community. To date, more than 90 community residents and 45 community organizations and their leaders have codesigned solutions
Methods & results
The goal of SCALE Phase I, which ended in June 2015, was to identify appropriate methods for calculating actionable sub-county LE and develop easy-to-use resources designed to assist other health departments. For LE to be considered actionable, the method needed to produce accurate estimates for most of the jurisdiction’s populations with standard errors and confidence intervals narrow enough to permit identification of areas with significantly higher or significantly lower LE values
Phase I participant activities included a literature review to identify feasible methods, successful case studies, and gold-standard parameters. After each jurisdiction independently tested various approaches, a consensus was reached to adopt the adjusted Chiang II method and associated software developed by the South East Public Health Observatory. Phase I participants also developed a draft guidance document clarifying methodological decision points and sharing lessons learned
To evaluate feasibility of generating sub-county LE, interviews with each jurisdiction were conducted using questions designed to answer the following questions: 1) What resources are required for health departments with varying resources and diverse populations to calculate actionable?; 2) What methodological and data challenges were encountered and how were they addressed?
All jurisdictions reported successful calculation of actionable LE for most sub-county areas in less than 1.5 years, with 7 of the 8 participating jurisdictions completing calculations in less than 1 year
Key takeaways/implications
Overall, findings suggest that researchers and policy makers can use life expectancy as an avenue to quantify local geographical disparities and warrants increased consideration to be tested at a larger scale
Characteristics that helped the SCALE project include adequate and engaged staffing, total population size, and total expenditure of the participating health department
Factors that may have impeded the success of the program include data challenges (e.g., unsuitable data platforms) and small sample sizes
Future areas of research should include: 1) Identifying key local social determinant and health indicators for co-release with life expectancy; 2) Assessing feasibility of generating summary population measures that better reflect overall health; 3) Identifying life expectancy visualization and messaging best practices; and 4) Evaluating the utility of local life expectancy for monitoring and evaluating the health effects of local policies and programs
