Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.

<h4>Background</h4>For population health management, it is important to have health indices that can monitor prevailing health trends in the population. Traditional health indices are generally measurable at different geographical levels with varied number of health dimensions. The aim o...

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Main Authors: Chun Wei Yap, Lixia Ge, Reuben Ong, Ruijie Li, Bee Hoon Heng
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0240302
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spelling doaj-52baea86ee364cee81365c96f469899b2021-03-04T11:11:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-011510e024030210.1371/journal.pone.0240302Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.Chun Wei YapLixia GeReuben OngRuijie LiBee Hoon Heng<h4>Background</h4>For population health management, it is important to have health indices that can monitor prevailing health trends in the population. Traditional health indices are generally measurable at different geographical levels with varied number of health dimensions. The aim of this work was to develop and validate a scalable and extendable multi-dimensional health index based on individual data.<h4>Methods</h4>We defined health to be made up of five different domains: Physical, Mental, Social, Risk, and Healthcare utilization. Item response theory was used to develop models to compute domain scores and a health index. These were normalized to represent an individual's health percentile relative to the population (0 = worst health, 100 = best health). Data for the models came from a longitudinal health survey on 1,942 participants. The health index was validated using age, frailty, post-survey one-year healthcare utilization and one-year mortality.<h4>Results</h4>The Spearman rho between the health index and age, frailty and post-survey one-year healthcare utilization were -0.571, -0.561 and -0.435, respectively, with all p<0.001. The area under the Receiver Operating Characteristic curve (AUROC) for post-survey one-year mortality was 0.930. An advantage of the health index is that it can be calculated using different sets of questions and the number of questions can be easily expanded.<h4>Conclusion</h4>The health index can be used at the individual, program, local, regional or national level to track the state of health of the population. When used together with the domain scores, it can identify regions with poor health and deficiencies within each of the five health domains.https://doi.org/10.1371/journal.pone.0240302
collection DOAJ
language English
format Article
sources DOAJ
author Chun Wei Yap
Lixia Ge
Reuben Ong
Ruijie Li
Bee Hoon Heng
spellingShingle Chun Wei Yap
Lixia Ge
Reuben Ong
Ruijie Li
Bee Hoon Heng
Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.
PLoS ONE
author_facet Chun Wei Yap
Lixia Ge
Reuben Ong
Ruijie Li
Bee Hoon Heng
author_sort Chun Wei Yap
title Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.
title_short Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.
title_full Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.
title_fullStr Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.
title_full_unstemmed Development of a scalable and extendable multi-dimensional health index to measure the health of individuals.
title_sort development of a scalable and extendable multi-dimensional health index to measure the health of individuals.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2020-01-01
description <h4>Background</h4>For population health management, it is important to have health indices that can monitor prevailing health trends in the population. Traditional health indices are generally measurable at different geographical levels with varied number of health dimensions. The aim of this work was to develop and validate a scalable and extendable multi-dimensional health index based on individual data.<h4>Methods</h4>We defined health to be made up of five different domains: Physical, Mental, Social, Risk, and Healthcare utilization. Item response theory was used to develop models to compute domain scores and a health index. These were normalized to represent an individual's health percentile relative to the population (0 = worst health, 100 = best health). Data for the models came from a longitudinal health survey on 1,942 participants. The health index was validated using age, frailty, post-survey one-year healthcare utilization and one-year mortality.<h4>Results</h4>The Spearman rho between the health index and age, frailty and post-survey one-year healthcare utilization were -0.571, -0.561 and -0.435, respectively, with all p<0.001. The area under the Receiver Operating Characteristic curve (AUROC) for post-survey one-year mortality was 0.930. An advantage of the health index is that it can be calculated using different sets of questions and the number of questions can be easily expanded.<h4>Conclusion</h4>The health index can be used at the individual, program, local, regional or national level to track the state of health of the population. When used together with the domain scores, it can identify regions with poor health and deficiencies within each of the five health domains.
url https://doi.org/10.1371/journal.pone.0240302
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