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Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index
BACKGROUND: Interpretation of health-related quality of life (QOL) outcomes requires improved methods to control for the effects of multiple chronic conditions (MCC). This study systematically compared legacy and improved method effects of aggregating MCC on the accuracy of predictions of QOL outcom...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277868/ https://www.ncbi.nlm.nih.gov/pubmed/35820890 http://dx.doi.org/10.1186/s12955-022-02016-7 |
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author | McEntee, Mindy L. Gandek, Barbara Ware, John E. |
author_facet | McEntee, Mindy L. Gandek, Barbara Ware, John E. |
author_sort | McEntee, Mindy L. |
collection | PubMed |
description | BACKGROUND: Interpretation of health-related quality of life (QOL) outcomes requires improved methods to control for the effects of multiple chronic conditions (MCC). This study systematically compared legacy and improved method effects of aggregating MCC on the accuracy of predictions of QOL outcomes. METHODS: Online surveys administered generic physical (PCS) and mental (MCS) QOL outcome measures, the Charlson Comorbidity Index (CCI), an expanded chronic condition checklist (CCC), and individualized QOL Disease-specific Impact Scale (QDIS) ratings in a developmental sample (N = 5490) of US adults. Controlling for sociodemographic variables, regression models compared 12- and 35-condition checklists, mortality vs. population QOL-weighting, and population vs. individualized QOL weighting methods. Analyses were cross-validated in an independent sample (N = 1220) representing the adult general population. Models compared estimates of variance explained (adjusted R(2)) and model fit (AIC) for generic PCS and MCS across aggregation methods at baseline and nine-month follow-up. RESULTS: In comparison with sociodemographic-only regression models (MCS R(2) = 0.08, PCS = 0.09) and Charlson CCI models (MCS R(2) = 0.12, PCS = 0.16), increased variance was accounted for using the 35-item CCC (MCS R(2) = 0.22, PCS = 0.31), population MCS/PCS QOL weighting (R(2) = 0.31–0.38, respectively) and individualized QDIS weighting (R(2) = 0.33 & 0.42). Model R(2) and fit were replicated upon cross-validation. CONCLUSIONS: Physical and mental outcomes were more accurately predicted using an expanded MCC checklist, population QOL rather than mortality CCI weighting, and individualized rather than population QOL weighting for each reported condition. The 3-min combination of CCC and QDIS ratings (QDIS-MCC) warrant further testing for purposes of predicting and interpreting QOL outcomes affected by MCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12955-022-02016-7. |
format | Online Article Text |
id | pubmed-9277868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-92778682022-07-14 Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index McEntee, Mindy L. Gandek, Barbara Ware, John E. Health Qual Life Outcomes Research BACKGROUND: Interpretation of health-related quality of life (QOL) outcomes requires improved methods to control for the effects of multiple chronic conditions (MCC). This study systematically compared legacy and improved method effects of aggregating MCC on the accuracy of predictions of QOL outcomes. METHODS: Online surveys administered generic physical (PCS) and mental (MCS) QOL outcome measures, the Charlson Comorbidity Index (CCI), an expanded chronic condition checklist (CCC), and individualized QOL Disease-specific Impact Scale (QDIS) ratings in a developmental sample (N = 5490) of US adults. Controlling for sociodemographic variables, regression models compared 12- and 35-condition checklists, mortality vs. population QOL-weighting, and population vs. individualized QOL weighting methods. Analyses were cross-validated in an independent sample (N = 1220) representing the adult general population. Models compared estimates of variance explained (adjusted R(2)) and model fit (AIC) for generic PCS and MCS across aggregation methods at baseline and nine-month follow-up. RESULTS: In comparison with sociodemographic-only regression models (MCS R(2) = 0.08, PCS = 0.09) and Charlson CCI models (MCS R(2) = 0.12, PCS = 0.16), increased variance was accounted for using the 35-item CCC (MCS R(2) = 0.22, PCS = 0.31), population MCS/PCS QOL weighting (R(2) = 0.31–0.38, respectively) and individualized QDIS weighting (R(2) = 0.33 & 0.42). Model R(2) and fit were replicated upon cross-validation. CONCLUSIONS: Physical and mental outcomes were more accurately predicted using an expanded MCC checklist, population QOL rather than mortality CCI weighting, and individualized rather than population QOL weighting for each reported condition. The 3-min combination of CCC and QDIS ratings (QDIS-MCC) warrant further testing for purposes of predicting and interpreting QOL outcomes affected by MCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12955-022-02016-7. BioMed Central 2022-07-12 /pmc/articles/PMC9277868/ /pubmed/35820890 http://dx.doi.org/10.1186/s12955-022-02016-7 Text en © The Author(s) 2022 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 McEntee, Mindy L. Gandek, Barbara Ware, John E. Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index |
title | Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index |
title_full | Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index |
title_fullStr | Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index |
title_full_unstemmed | Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index |
title_short | Improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index |
title_sort | improving multimorbidity measurement using individualized disease-specific quality of life impact assessments: predictive validity of a new comorbidity index |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277868/ https://www.ncbi.nlm.nih.gov/pubmed/35820890 http://dx.doi.org/10.1186/s12955-022-02016-7 |
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