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Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties
BACKGROUND: New York State (NYS) is the 27(th) largest state and the 4(th) 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 acros...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188228/ https://www.ncbi.nlm.nih.gov/pubmed/37193975 http://dx.doi.org/10.1186/s12939-023-01902-w |
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author | Roldós, Maria Isabel Orazem, John Fortunato-Tavares, Talita |
author_facet | Roldós, Maria Isabel Orazem, John Fortunato-Tavares, Talita |
author_sort | Roldós, Maria Isabel |
collection | PubMed |
description | BACKGROUND: New York State (NYS) is the 27(th) largest state and the 4(th) 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12939-023-01902-w. |
format | Online Article Text |
id | pubmed-10188228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101882282023-05-17 Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties Roldós, Maria Isabel Orazem, John Fortunato-Tavares, Talita Int J Equity Health Research BACKGROUND: New York State (NYS) is the 27(th) largest state and the 4(th) 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12939-023-01902-w. BioMed Central 2023-05-16 /pmc/articles/PMC10188228/ /pubmed/37193975 http://dx.doi.org/10.1186/s12939-023-01902-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Roldós, Maria Isabel Orazem, John Fortunato-Tavares, Talita Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties |
title | Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties |
title_full | Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties |
title_fullStr | Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties |
title_full_unstemmed | Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties |
title_short | Longitudinal trends (2011–2020) of premature mortality and years of potential life loss (YPLL) and associated covariates of the 62 New York State counties |
title_sort | longitudinal trends (2011–2020) of premature mortality and years of potential life loss (ypll) and associated covariates of the 62 new york state counties |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188228/ https://www.ncbi.nlm.nih.gov/pubmed/37193975 http://dx.doi.org/10.1186/s12939-023-01902-w |
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