JMIR Publications

ABSTRACT

Background:

Health indicators, which facilitate the assessment of individual and population health status, have been a valuable tool in healthcare and public health settings. However, existing measures, such as self-rated health status, lack objectivity and clear criteria, leading to subjective ratings that hinder comparisons across population groups.

Objective:

To address this gap, more objective, comprehensive, and accurate measures of individual health status are required. Building upon our previous research that identified 29 health indicators and ranked their priority through public ratings, this study explored the effects of four demographic variables (age, gender, professional group, and educational level) on the importance ratings of the mentioned indicators based on public perception. Utilizing online surveys, we examined the main and interaction effects of these demographic variables on the perceived importance of the 29 health indicators.

Methods:

Methods:

A four-way multivariate analysis of variance (MANOVA) with post hoc testing was conducted to determine the effects of four 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. Design: Cross-sectional study. Setting: Online surveys were administered on two United States of America 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. Participants: 1,153 participants from the United States of America with 1,144 complete responses.

Results:

Our study found significant correlations among the 29 health indicators. Age, education, gender, and professional groups showed correlations with multiple health indicators. Correlation analysis indicated that age was correlated with most of the 29 health indicators (16 indicators), followed by education (9 indicators), gender (8 indicators), and professional group (7 indicators). MANOVA modeling revealed that gender, age, professional group, and educational levels, as well as the interactions between gender and age and gender and professional group, showed significant effects on the combination of the 29 health indicators. In general, female participants provided significantly higher ratings on 19 health indicators than did male participants, whereas older participants provided significantly higher ratings on 22 health indicators than did younger participants.

Conclusions:

Age emerged as a critical factor influencing the ratings of the 29 health indicators and showed the largest effect size among all demographic factors, with decreasing effect sizes having been observed for other demographic factors such as gender, professional group, educational level, and the interaction between gender and professional group and gender and age. These findings provide valuable insights into the likely influence of demographic characteristics on perceptions of health indicators and offer a preliminary framework for the development of objective measures of individual health status in future research. They also serve as a foundation for conducting demographic factor adjustments during data analysis and for designing future studies for behavioral interventions.