Observational studyJMIR public health and surveillance2025
Health Indicators as Measures of Individual Health Status, Their Perceived Importance, and Associated Factors: Cross-Sectional Observational Study.
Observational study in JMIR public health and surveillance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
10 authors.
Funding
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Abstract
Background: Health indicators can facilitate the assessment of individual and population health status and are valuable in health care and public health settings. However, it is challenging to collect all measures at once. There is no single, objective, comprehensive, accurate, easy-to-use measure for individual health status yet, and we aim to develop one. We identified 29 health indicators via literature review and ranked their importance through public ratings to start the effort. Objective: This study aims to explore the effects of 4 demographic variables (age, sex, professional group, and educational level) on the importance ratings of the 29 indicators. Methods: This was a cross-sectional study. Online surveys were administered on 2 US college campuses and through ResearchMatch, an online platform specialized in finding clinical research participants. Only individuals 18 years or older were considered for inclusion as respondents. A 4-way multivariate analysis of variance (MANOVA) with post hoc testing was conducted to determine the effects of 4 demographic factors on the 29 health indicators. Descriptive statistics and bivariate correlation analysis were reported, along with the results of MANOVA with post hoc testing. Results: The study included 1153 participants from the United States of America with 1144 complete responses. Our study found significant correlations among the 29 health indicators. Age, education, sex, and professional groups showed correlations with multiple health indicators. Correlation analysis indicated that age was correlated with 16 of the 29 health indicators, followed by education (9 indicators), sex (8 indicators), and professional group (7 indicators). MANOVA modeling revealed that age (partial eta squared=0.058; P<.001), sex (partial eta squared=0.05; P<.001), professional group (partial eta squared=0.049; P<.001), and educational levels (partial eta squared=0.047; P<.001), as well as the interactions between sex and professional group (partial eta squared=0.044; P<.001) and sex and age (partial eta squared=0.041; P<.001), showed significant effects on the combination of the 29 health indicators. In general, female participants provided significantly higher ratings on 10 health indicators than male participants (P<.05), whereas older participants provided significantly higher ratings on 20 health indicators than younger participants (P<.05). Conclusions: Age emerged as a critical factor influencing the ratings of the 29 health indicators and showed the largest effect size along with sex, professional group, and educational level. These findings can be used as a quantitative foundation for the development of an objective measure for individual health status, for grouping health indicators, for conducting demographic factor adjustments during data analysis, for designing customized behavioral intervention studies based on demographic factors, or for health policy-making or program development.
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