Life expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities.
Our study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008–2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years.
Aggregation resulted in a 9.9% reduction in the number of areas. Tracts with < 2% of population living below the poverty level had a LE of 82.8 years, while tracts with a poverty level ≥ 25% had a LE of 75.5. We observed differences in LE in border areas, of up to 10.4 years, when excluding or including deaths of study area residents that occurred outside the study area. The range and standard deviation at the county level (77.5–82.8, SD = 1.2 years) were smaller than our final sub-county areas (64.7–92.0, SD = 3.3 years). The correlation between LE and poverty were similar and statistically significant (p < 0.0001) at the county (r = − 0.58) and sub-county level (r = − 0.58). The correlations between LE and percent African-American at the county level were (r = 0.11, p = 0.43) and at the sub-county level (r = − 0.25, p < 0.0001).
The proposed approach for geocoding and aggregation of mortality and population data provides a solution for health departments to produce stable empirically-derived LE estimates using data coded to the tract. Reliable estimates within sub-county areas are needed to aid public health officials in focusing preventive health programs in areas where health disparities would be masked by county level estimates.