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

ABSTRACT: BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSION: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.

SUBMITTER: Yap CW

PROVIDER: S-EPMC7540893 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Yap Chun Wei CW Ge Lixia L Ong Reuben R Li Ruijie R Heng Bee Hoon BH

PloS one 20201007 10

<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, Menta ...[more]

PMID: 33027291

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Project description:BackgroundThe increasing digitization of health care services with enhanced access to fast internet connections, along with wide use of smartphones, offers the opportunity to get health advice or treatment remotely. For service providers, it is important to consider how consumers can take full advantage of available services and how this can create an enabling environment. However, it is important to consider the digital context and the attributes of current and future users, such as their readiness (ie, knowledge, skills, and attitudes, including trust and motivation).ObjectiveThe objective of this study was to evaluate how the eHealth Literacy Questionnaire (eHLQ) combined with selected dimensions from the Health Education Impact Questionnaire (heiQ) and the Health Literacy Questionnaire (HLQ) can be used together as an instrument to characterize an individual's level of health technology readiness and explore how the generated data can be used to create health technology readiness profiles of potential users of health technologies and digital health services.MethodsWe administered the instrument and sociodemographic questions to a population of 305 patients with a recent cancer diagnosis referred to rehabilitation in a setting that plans to introduce various technologies to assist the individuals. We evaluated properties of the Readiness and Enablement Index for Health Technology (READHY) instrument using confirmatory factor analysis, convergent and discriminant validity analysis, and exploratory factor analysis. To identify different health technology readiness profiles in the population, we further analyzed the data using hierarchical and k-means cluster analysis.ResultsThe confirmatory factor analysis found a suitable fit for the 13 factors with only 1 cross-loading of 1 item between 2 dimensions. The convergent and discriminant validity analysis revealed many factor correlations, suggesting that, in this population, a more parsimonious model might be achieved. Exploratory factor analysis pointed to 5 to 6 constructs based on aggregates of the existing dimensions. The results were not satisfactory, so we performed an 8-factor confirmatory factor analysis, resulting in a good fit with only 1 item cross-loading between 2 dimensions. Cluster analysis showed that data from the READHY instrument can be clustered to create meaningful health technology readiness profiles of users.ConclusionsThe 13 dimensions from heiQ, HLQ, and eHLQ can be used in combination to describe a user's health technology readiness level and degree of enablement. Further studies in other populations are needed to understand whether the associations between dimensions are consistent and the number of dimensions can be reduced.

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

Publications

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

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