Understanding the factors associated with hypertension in the Western Himalaya: a GIS integrated analysis

This study investigates the prevalence and determinants of hypertension among the Gujjar Bakarwals tribe in multi-altitude zones (314-4,713 m) of the western Himalaya. The aim is to assess the association of geographic altitude along with key demographic, socioeconomic, environmental, and behavioral variables on hypertension prevalence using integrated Geographic Information Systems (GIS) and statistical methods.

Using stratified sampling, we selected a representative sample (n = 816) from 50 villages. A binary logistic regression model (BLRM) was used to identify hypertension-associated factors, assessing model fit using Pseudo R². GIS mapped hypertension prevalence and associated factors across the study region. Receiver operating characteristics (ROC or area under the curve) was used for validation of the BLRM accuracy.

Overall, 29.9% of the study region reported hypertension in the year 2022. Those at the highest altitude had significantly higher odds of hypertension (adjusted odds ratio [AOR]: 2.19, 95% CI: 1.28-3.74, p = 0.004). Males exhibited notably higher prevalence than females (AOR: 2.77, 95% CI: 1.70-4.51, p < 0.001), and so did married individuals (AOR: 8.79, 95% CI: 5.00-15.46, p < 0.001). The likelihood of hypertension increased significantly with age (p < 0.001). Smoking, reliance on unclean water sources, and certain sanitation practices were linked to increased odds of hypertension with AOR: 1.79, 2.72 and 1.90 respectively. High-risk areas were mainly found in both low and high-altitude zones of Poonch and Rajouri districts. The BLRM demonstrated good fit (Hosmer-Lemeshow p = 0.954, Nagelkerke R² = 0.63), validated by the ROC curve (AUC = 0.93).

This study identifies critical socio-environmental and behavioural determinants of hypertension within high-altitude tribal populations. Findings inform context-specific public health strategies that address the geographic and cultural uniqueness of vulnerable communities. Integrating GIS and epidemiological tools can help policymakers develop altitude-adaptive, equity-driven hypertension interventions to improve cardiovascular outcomes in underserved regions.