an article from Kowal et al. (2023) In fact, it measures disparities in health outcomes. To do this, they use data from the American Community Survey (SCA), the National Vital Statistics System (NVSS) and the CDC Social Vulnerability Index (SVI) to estimate differences in life expectancy (LY), quality-adjusted life expectancy (QALE), and disability-free life expectancy (DFLY). The authors used a Bayesian approach to address missing (or suppressed) mortality data among racial/ethnic groups (i.e., non-Hispanic American Indian or Alaska Native). [AI/AN]non-Hispanic Asian/Pacific Islanders, non-Hispanic Black, non-Hispanic White, and Hispanic) at the county level. Mortality data comes from the NVSS (via the CDC WONDER tool). Specifically, the Bayesian approach relied on spatiotemporal models that used a binomial distribution for the number of deaths by county age group and a conditional autoregressive structure to account for county-specific effects.
Using this approach, they found that:
Life expectancy, disability-free life expectancy, and quality-adjusted life expectancy at birth decreased from 79.5, 69.4, and 64.3 years, respectively, among the 20% of least socially vulnerable counties (in better economic situation) at 76.8, 63.6 and 61.1 years. , respectively, among the 20% most socially vulnerable (worst-off) counties. Taking into account differences among racial and ethnic subgroups as well as geography, the gaps between the wealthiest (Asian and Pacific Islanders; least socially vulnerable 20% of counties) and the worst (American Indian/Alaska Native; 20% of most socially vulnerable counties) Subgroups were large (17.6 life years, 20.9 disability-free life years, and 18.0 quality-adjusted life years) and increased with age.
These results are particularly useful as they can be used to inform baseline health inequalities applied within a distributional cost-effectiveness analysis (DCEA) framework in the US. You can read the full article. here. Please note that a summary of the conference Slejko et al. (2024) also estimates US-based inequality aversion parameters that you could also use for your DCEA.
