Geospatial Data Aggregation Methods for Novel Geographies: Validating Congressional District Life Expectancy Estimates

Background: Place is a critical determinant of health. Recent novel analyses have explored health outcome estimation for small geographies, such as census tracts, as well as health outcome aggregation to geopolitical geographies with accountable political representatives, such as congressional districts. In one such application, combining these approaches, researchers aggregated census tract estimates of life expectancy at the congressional district level to derive local estimates, but such an approach has not been validated. Methods: Here, we compared two sources and approaches to calculating life expectancy data for Pennsylvania congressional districts. We used 2010–2015 census tract life expectancy estimates from the US Small-area Life Expectancy Estimates Project and dasymetric methods to compute population-weighted life expectancy aggregated to the congressional district level. Using georeferenced Vital Statistics data, we aggregated age-specific census tract death and population counts to congressional districts and used abridged life tables to estimate life expectancy. To validate the dasymetric aggregated estimates we compared absolute differences, assessed the correlation, and created Bland–Altman plots to visualize the agreement between the two measures. Results: We found strong agreement between congressional district estimates of life expectancy at birth derived using the dasymetric Life Expectancy Estimates Project model–based approach and the Vital Statistics direct estimates approach, though life expectancy at older ages (75 years and older) showed weak correlations. Conclusions: This validation contributes to our understanding of geospatial aggregation methods for novel geographies including congressional districts. Health outcome data aggregated to the congressional district geography can support congressional policymaking aimed at improving population health outcomes.