Health indicators as measures of individual health status, perceived importance, and their associated factors—an observational study

Abstract

AbstractBackgroundSelf-rated health status, a subjective measure, is used broadly to describe an individual’s overall health status. Our long-term goal is to create a more objective, comprehensive, and accurate measure of individual health status. We selected 29 health indicators and prioritized them by conducting online surveys. Thirteen of these 29 indicators received relatively more consistent ratings across 3 samples.ObjectivesTo explore the main and interaction effects of 4 demographic factors as independent variables (age, gender, professional group, and educational level) in the importance ratings of the 13 health indicators.MethodsWe conducted a 4-way multivariate analysis of variance (MANOVA) with post-hoc testing to examine the effects of independent variables on all 13 dependent variables. Descriptive statistics and bivariate correlation analysis were also conducted.DesignCross-sectional study.SettingAn online survey (≥ 18 years).Participants791 participants in the USA.Results13 health indicators were significantly correlated with each other. Age correlated with most of the health indicators (8 of 13). The MANOVA modeling results indicated that gender, age, and education levels significantly affected the combination of the 13 health indicators. There was a significant interaction effect by age and professional group on 5 health indicators.ConclusionsAge is critical in rating the 13 health indicators. Among all the statistically significant main effects of demographic factors, the effect sizes descend regarding age, gender, educational level, and interaction between age and professional group. These results can provide a foundation for further studies to explore behavioral interventions for individual subgroups.Article SummaryStrengths and limitations of this studyThe work establishes the interactions and effects between demographic data (age, gender, education, and professional group) and the perceived importance of 13 health indicators via MANOVA analysisThe interactions and effects of demographic data on the importance ratings of the 13 health indicatorscan guide future study designs for behavioral interventionsDeep analysis of the demographic variables and their effects on and interactions with the rating results are helpful for thoroughly understanding the perspectivesThe study is an observational study despite with relatively large sample size and a robust analysisThe data are not racially representative