An epidemiological assessment of the distribution and sociodemographic burden of chronic diseases: a focus on hypertension, diabetes, and cardiovascular conditions.

Abstract

Background: Chronic diseases remain a major contributor to morbidity and mortality worldwide. Understanding the sociodemographic and behavioral factors associated with these conditions, as well as potential effect modification across population subgroups, is essential for developing targeted prevention strategies.

Methods: We conducted a cross-sectional analysis of nationally representative survey data to examine the associations between sociodemographic and behavioral factors and hypertension, diabetes, and cardiovascular disease (CVD). Survey-weighted multivariable logistic regression models were fitted for each outcome. Restricted cubic splines were used to assess the non-linearity of age. An age × gender interaction term was evaluated, and predicted probabilities were estimated to visualize effect modification. The issue of missing data was addressed using multiple imputation (five imputations). Model discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots.

Results: Advancing age was strongly associated with higher odds of all three conditions. Significant age × gender interactions were observed for diabetes and CVD, indicating steeper age-related increases in risk among males compared with females. In contrast, age-related increases in hypertension were similar across genders. Sociodemographic and behavioral factors such as income and BMI were independently associated with cardiometabolic outcomes. Model discrimination was good for hypertension (AUC = 0.80), diabetes (AUC = 0.77), and CVD (AUC = 0.83), with adequate calibration across risk deciles. Sensitivity analyses yielded consistent findings.

Conclusions: Cardiometabolic risk is strongly associated with age and sociodemographic and behavioral factors, with important gender differences in age-related trajectories for diabetes and CVD. These findings underscore the importance of accounting for interaction effects in epidemiologic analyses and support age- and gender-tailored prevention strategies at the population level.