A minimum data set and empirical model for population health status assessment

Abstract

Background: The need to assess the health status of American communities in a comprehensive and systematic manner has been widely acknowledged. This study attempts to empirically derive a minimum core data set of indicators, in order to produce a uniform parsimonious model for population health status monitoring. Methods: Five years of secondary data (1992–1996) for 113 indicators of community health for each of Florida’s 67 counties were organized into 11 conceptual groups. Principal component analysis with orthogonal rotation was conducted separately on each group of indicators for each year. The component scores were converted to standard scores to further study the relationships among the conceptual groups measuring community health. A causal model was hypothesized and tested using ordinary least-squares path analysis. Results: Nineteen principal components composed of 78 indicators were identified. The model demonstrated a large difference in the ability to explain variance in adult mortality (56%) compared with variance associated with adverse birth outcomes (13%). Both demographic and socioenvironmental factors have a direct effect on adult mortality. Socioeconomic factors, on the other hand, influence adult mortality indirectly through adequacy of primary care and other available resources. Conclusions: Minimum core data sets of indicators drawn from extant databases can be used to uniformly describe and explain variation in adult mortality. This research suggests caution in regard to the creation of integrated indices that combine mortality, morbidity, and other concepts such as quality of life into a single measure of community health. Further validation research employing a national sample of counties is recommended.