ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2020
Health gap for multimorbidity: comparison of models combining uniconditional health gap.
Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Korean National Burden of Disease: The Importance of Diabetes Management.Diabetes & metabolism journal · 2024Review
- Opportunities to Reduce Potential Bias in Ophthalmic Cost-Utility Analysis.JAMA ophthalmology · 2021Article
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7 authors.
Funding
Abstract
purposeThe aim of this study is to identify the best-fitting model in predicting the health gap of multimorbid status based on the health gap of uniconditional status.
methodsThis study analyzed data of adults aged 50 years or older derived from the cross-sectional, nationally representative 6th Korean National Health and Nutrition Examination Survey (KNHANES). We translated the EQ-5D utility score assessed from the KNHANES using the Korean EQ-5D-3L into the health gap by subtracting the EQ-5D utility score from one. The predicted health gap of multimorbid status was calculated based on the health gap of uniconditional status using the additive, multiplicative, and maximum limit models. We assessed the performance of the multimorbidity adjustment models based on the root mean square error and mean absolute error. We also examined the impact of multimorbidity adjustment on the estimated disease burden in the best-fitting model.
resultsOf the three approaches, the multiplicative adjustment model had the smallest root mean square error between the predicted and observed health gap of multimorbid status. The total number of prevalence-based years lived with the disability after adjusting for multimorbid status using the multiplicative model decreased compared to that without adjustment for multimorbid status.
conclusionUsing the appropriate methodology to adjust for multimorbidity in estimations of population health is becoming more important as the prevalence of multimorbidity increases, particularly in older populations. Further empirical research is required to develop additional general adjustment approaches that consider the independent co-occurrence of multiple diseases, and to understand how multimorbidity influences health gap.
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