Background One-fourth of Indians are hypertensive, and the majority relies on groundwater for drinking. But the role of groundwater physicochemical properties and contamination in hypertension remains understudied. Objective The study investigates the association between physicochemical groundwater characteristics andcontaminants and hypertension risk in India. Data This study used data from the fifth round of the National Family Health Survey (NFHS-5 collected 2019–2021), including health, socio-demographics, and food and dietary information (n = 712,666 individuals). The physicochemical characteristics of groundwater data were derived from the Central Groundwater Board (CGWB, 2019–2021). This groundwater data from raster maps was linked to NFHS-5 records using cluster shapefiles and merging them with individual records via cluster IDs. Methods Bivariate and multivariable regressions were used to identify factors associated with hypertension at the individual level. Moran’s I statistics, Local Indicator of Spatial Association (LISA) cluster maps, and the Spatial Error Model (SEM) were used at district levels to investigate the spatial association. Machine learning models, including Artificial Neural Networks (ANN), Random Forest and Extreme Gradient Boosting (XGBoost), were used to predict hypertension risk zones. Results Physicochemical drinking water composition is a key factor in hypertension risk. Elevated groundwater pH (>8.5, Adjusted Odds Ratio (AOR): 2.12), electrical conductivity (>300 μS/cm, AOR: 1.06), sulphate (>200 mg/L,  AOR: 1.16), arsenic (>0.01 mg/L, AOR: 1.09), nitrate (>45 mg/L, AOR: 1.07), and magnesium (>30 mg/L, AOR: 1.03) are associated to higher odds of hypertension. The Random Forest model demonstrated the highest predictive performance, with a coefficient of determination (R²) of 0.9970, mean absolute error (MAE) of 0.0012, and mean squared error (MSE) of 0.0077. It effectively identified high-risk zones in the northwestern (Delhi, Punjab, Haryana, and Rajasthan) and eastern (West Bengal and Bihar) regions of India. Impact <UnorderedList Mark="Bullet"> <ItemContent> This study highlights how important groundwater quality is in determining the incidence of hypertension, pointing to groundwater physicochemical properties and contaminants such as electrical conductivity, sulphate, arsenic, nitrate, and magnesium as essential factors. Our research is the first of its kind to comprehensively map hypertension risk zones using machine learning models and geospatial analysis. The findings highlight that water quality is a modifiable risk factor, reinforcing the need for improved drinking water supply systems, regular water quality testing, and targeted interventions in high-risk regions. This study emphasizes the importance of intersectoral collaborations to enhance public health outcomes. </ItemContent> </UnorderedList>

Investigating the association between groundwater contaminants and hypertension risk in India: a machine learning-based analysis

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发表日期：

2025年6月4日

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Sourav Biswas,
Aparajita Chattopadhyay,
Kathrin Schilling,
Ayushi Das

摘要

Background

One-fourth of Indians are hypertensive, and the majority relies on groundwater for drinking. But the role of groundwater physicochemical properties and contamination in hypertension remains understudied.

Objective

The study investigates the association between physicochemical groundwater characteristics andcontaminants and hypertension risk in India.

Data

This study used data from the fifth round of the National Family Health Survey (NFHS-5 collected 2019–2021), including health, socio-demographics, and food and dietary information (n = 712,666 individuals). The physicochemical characteristics of groundwater data were derived from the Central Groundwater Board (CGWB, 2019–2021). This groundwater data from raster maps was linked to NFHS-5 records using cluster shapefiles and merging them with individual records via cluster IDs.

Methods

Bivariate and multivariable regressions were used to identify factors associated with hypertension at the individual level. Moran’s I statistics, Local Indicator of Spatial Association (LISA) cluster maps, and the Spatial Error Model (SEM) were used at district levels to investigate the spatial association. Machine learning models, including Artificial Neural Networks (ANN), Random Forest and Extreme Gradient Boosting (XGBoost), were used to predict hypertension risk zones.

Results

Physicochemical drinking water composition is a key factor in hypertension risk. Elevated groundwater pH (>8.5, Adjusted Odds Ratio (AOR): 2.12), electrical conductivity (>300 μS/cm, AOR: 1.06), sulphate (>200 mg/L, AOR: 1.16), arsenic (>0.01 mg/L, AOR: 1.09), nitrate (>45 mg/L, AOR: 1.07), and magnesium (>30 mg/L, AOR: 1.03) are associated to higher odds of hypertension. The Random Forest model demonstrated the highest predictive performance, with a coefficient of determination (R²) of 0.9970, mean absolute error (MAE) of 0.0012, and mean squared error (MSE) of 0.0077. It effectively identified high-risk zones in the northwestern (Delhi, Punjab, Haryana, and Rajasthan) and eastern (West Bengal and Bihar) regions of India.

Impact

This study highlights how important groundwater quality is in determining the incidence of hypertension, pointing to groundwater physicochemical properties and contaminants such as electrical conductivity, sulphate, arsenic, nitrate, and magnesium as essential factors. Our research is the first of its kind to comprehensively map hypertension risk zones using machine learning models and geospatial analysis. The findings highlight that water quality is a modifiable risk factor, reinforcing the need for improved drinking water supply systems, regular water quality testing, and targeted interventions in high-risk regions. This study emphasizes the importance of intersectoral collaborations to enhance public health outcomes.