Abstract
BackgroundHealth literacy is a strong predictor of health status. This study develops and tests a structural equation model to explore the factors that are associated with the health literacy level of rural residents in Central China.MethodsThe participants were recruited from a county-level city in Central China (N = 1164). Face-to-face interviews were conducted to complete the self-designed questionnaire of each participant. The questionnaire included items for the (1) demographic information, (2) socioeconomic status, and (3) health literacy of the participants. Mplus analyses were performed to evaluate the proposed model.ResultsThe final model showed good fit for the data, and both demographic characteristics (i.e., age, BMI, and residence) and socioeconomic status (i.e., monthly income, occupation, and education level) were significantly associated with health literacy level. The effects of these two variables were − 0.277 (P < 0.05) and 0.615 (P < 0.001), respectively, and the model explained 70.2% of the variance in health literacy.ConclusionsHealth literacy was significantly associated with age, BMI, distance between residence and nearest medical institution, monthly income, occupation, and education level, whereas socioeconomic status was a dominant predictor of health literacy level. Targeting these factors might be helpful in allocating health resources rationally when performing health promotion work.
Highlights
Health literacy is a strong predictor of health status
Some studies revealed that the age, region, occupation, education, annual household income, and self-reported health status of individuals were associated with their Health literacy (HL) [11,12,13]
Health behavior, and health skill were all highly correlated with age and the observed variables of socio-economic status (p < 0.01)
Summary
This study develops and tests a structural equation model to explore the factors that are associated with the health literacy level of rural residents in Central China. Compared with traditional sociodemographic factors (e.g., age, race, education, income, and employment status), HL is as a stronger predictor of an individual’s health status [2] and is a significant component of his/her health behaviors, health quality, and access to healthcare. Some studies revealed that the age, region (urban vs rural), occupation, education, annual household income, and self-reported health status of individuals were associated with their HL [11,12,13]. As for occupational population, age, education levels, and type of industry show a significant relationship with HL [15]
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