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Convergence of four measures of multi-morbidity

OBJECTIVES: To compare the agreement between percentile ranks from 4 multi-morbidity scores. DESIGN: Population-based descriptive study. SETTING: Olmsted County, Minnesota (USA). PARTICIPANTS: We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochester...

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Autores principales: Grossardt, Brandon R., Chamberlain, Alanna M., Boyd, Cynthia M., Bobo, William V., St Sauver, Jennifer L., Rocca, Walter A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9813979/
https://www.ncbi.nlm.nih.gov/pubmed/36618107
http://dx.doi.org/10.1177/26335565221150124
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author Grossardt, Brandon R.
Chamberlain, Alanna M.
Boyd, Cynthia M.
Bobo, William V.
St Sauver, Jennifer L.
Rocca, Walter A.
author_facet Grossardt, Brandon R.
Chamberlain, Alanna M.
Boyd, Cynthia M.
Bobo, William V.
St Sauver, Jennifer L.
Rocca, Walter A.
author_sort Grossardt, Brandon R.
collection PubMed
description OBJECTIVES: To compare the agreement between percentile ranks from 4 multi-morbidity scores. DESIGN: Population-based descriptive study. SETTING: Olmsted County, Minnesota (USA). PARTICIPANTS: We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). METHODS: For each person, we calculated 4 multi-morbidity scores using readily available diagnostic code lists from the US Department of Health and Human Services, the Clinical Classifications Software, and the Elixhauser Comorbidity Index. We calculated scores using diagnostic codes received in the 5 years before the index birthday and fit quantile regression models across age and separately by sex to transform unweighted, simple counts of conditions into percentile ranks as compared to peers of same age and of same sex. We compared the percentile ranks of the 4 multi-morbidity scores using intra-class correlation coefficients (ICCs). RESULTS: We assessed agreement in 181,553 persons who reached a total of 1,075,433 birthdays at ages 18 years through 85 years during the study period. In general, the percentile ranks of the 4 multi-morbidity scores exhibited high levels of agreement in 6 score-to-score pairwise comparisons. The agreement increased with older age for all pairwise comparisons, and ICCs were consistently greater than 0.65 at ages 50 years and older. CONCLUSIONS: The assignment of percentile ranks may be a simple and intuitive way to assess the underlying trait of multi-morbidity across studies that use different measures.
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spelling pubmed-98139792023-01-06 Convergence of four measures of multi-morbidity Grossardt, Brandon R. Chamberlain, Alanna M. Boyd, Cynthia M. Bobo, William V. St Sauver, Jennifer L. Rocca, Walter A. J Multimorb Comorb Original Article OBJECTIVES: To compare the agreement between percentile ranks from 4 multi-morbidity scores. DESIGN: Population-based descriptive study. SETTING: Olmsted County, Minnesota (USA). PARTICIPANTS: We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). METHODS: For each person, we calculated 4 multi-morbidity scores using readily available diagnostic code lists from the US Department of Health and Human Services, the Clinical Classifications Software, and the Elixhauser Comorbidity Index. We calculated scores using diagnostic codes received in the 5 years before the index birthday and fit quantile regression models across age and separately by sex to transform unweighted, simple counts of conditions into percentile ranks as compared to peers of same age and of same sex. We compared the percentile ranks of the 4 multi-morbidity scores using intra-class correlation coefficients (ICCs). RESULTS: We assessed agreement in 181,553 persons who reached a total of 1,075,433 birthdays at ages 18 years through 85 years during the study period. In general, the percentile ranks of the 4 multi-morbidity scores exhibited high levels of agreement in 6 score-to-score pairwise comparisons. The agreement increased with older age for all pairwise comparisons, and ICCs were consistently greater than 0.65 at ages 50 years and older. CONCLUSIONS: The assignment of percentile ranks may be a simple and intuitive way to assess the underlying trait of multi-morbidity across studies that use different measures. SAGE Publications 2023-01-02 /pmc/articles/PMC9813979/ /pubmed/36618107 http://dx.doi.org/10.1177/26335565221150124 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Article
Grossardt, Brandon R.
Chamberlain, Alanna M.
Boyd, Cynthia M.
Bobo, William V.
St Sauver, Jennifer L.
Rocca, Walter A.
Convergence of four measures of multi-morbidity
title Convergence of four measures of multi-morbidity
title_full Convergence of four measures of multi-morbidity
title_fullStr Convergence of four measures of multi-morbidity
title_full_unstemmed Convergence of four measures of multi-morbidity
title_short Convergence of four measures of multi-morbidity
title_sort convergence of four measures of multi-morbidity
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9813979/
https://www.ncbi.nlm.nih.gov/pubmed/36618107
http://dx.doi.org/10.1177/26335565221150124
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