Establishment of a Hypertension Predictive Model and Analysis of Its Influencing Factors Among Residents in Tibet Autonomous Region, China: A Health Ecological Model (HEM)-Based Study

Background : This study aimed to analyze the status and influencing factors of hypertension among residents in the high-altitude areas of Tibet based on the Health Ecological Model, to provide a reference for improving hypertension prevention and control strategies in the region. Methods : Data were obtained from the Seventh National Health Service Survey in Tibet (2023), including 9,480 participants aged ≥18 years. Based on self-reported hypertension status, they were categorized into hypertensive and non-hypertensive groups. Participants were randomly divided into a training set and a validation set at a 7:3 ratio using a random number table. The Chi-square test or Fisher’s exact test was used to compare categorical variables. Significant predictors were selected via LASSO regression, and a nomogram was developed using logistic regression. The model's predictive performance was evaluated. Results : Through univariate analysis, LASSO selection, and logistic regression, nine key variables were identified from the initial 30 for nomogram construction: age group, BMI classification, self-rated health, alcohol consumption, self-treatment for illness, prefecture-level city, drinking water type, annual household income, and family doctor service utilization. The model demonstrated an area under the curve (AUC) of 0.899 (95% CI: 0.889–0.909) in the training set and 0.873 (95% CI: 0.856–0.891) in the validation set. The calibration curve indicated good agreement between predicted and observed outcomes. Decision curve analysis (DCA) confirmed the clinical utility of the model. Conclusion : Integrating the Health Ecological Model, this study developed a risk prediction nomogram for hypertension. This tool can assist primary healthcare providers in identifying high-risk individuals, thereby facilitating early intervention and prevention.