International Journal for Equity in Health (May 2023)

Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties

  • Maria Isabel Roldós,
  • John Orazem,
  • Talita Fortunato-Tavares

DOI
https://doi.org/10.1186/s12939-023-01902-w
Journal volume & issue
Vol. 22, no. 1
pp. 1 – 12

Abstract

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Abstract Background New York State (NYS) is the 27th largest state and the 4th most populous state in the U.S., with close to 20 million people in 62 counties. Territories with diverse populations present the best opportunity to study health outcomes and associated covariates, and how these differ across different populations and groups. The County Health Ranking and Roadmaps (CHR&R) ranks counties by linking the population’s characteristics and health outcomes and contextual factors in a synchronic approach. Methods The goal of this study is to analyze the longitudinal trends in NYS counties of age-adjusted premature mortality rate and years of potential life loss rate (YPLL) from 2011–2020 using (CHR&R) data to identify similarities and trends among the counties of the state. This study used a weighted mixed regression model to analyze the longitudinal trend in health outcomes as a function of the time-varying covariates and clustered the 62 counties according to the trend over time in the covariates. Results Four clusters of counties were identified. Cluster 1, which represents 33 of the 62 counties in NYS, contains the most rural counties and the least racially and ethnically diverse counties. Clusters 2 and 3 mirror each other in most covariates and Cluster 4 is comprised of 3 counties (Bronx, Kings/Brooklyn, Queens) representing the most urban and racial and ethnic diverse counties in the state. Conclusion The analysis clustered counties according to the longitudinal trends of the covariates, and by doing so identified clusters of counties that shared similar trends among the covariates, to later examine trends in the health outcomes through a regression model. The strength of this approach lies in the predictive feature of what is to come for the counties by understanding the covariates and setting prevention goals.

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