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

Journal of Exposure Science & Environmental Epidemiology (2025)Cite this article

Abstract

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.

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Fig. 1
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Fig. 5: Bivariate LISA cluster maps and scatter plots showing the spatial clustering of groundwater physicochemical properties and contaminants with hypertension in India.
Fig. 6
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Fig. 8
Fig. 9: Machine learning-based hypertension risk zonation in India using different predictive models.

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Data availability

The dataset analysed during the current study are available in the Demographic and Health Surveys (DHS) repository, https://www.dhsprogram.com/data/available-datasets.cfm.

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Acknowledgements

This study is part of Sourav Biswas’s Ph.D. research, and he gratefully acknowledges the support and academic environment provided by the International Institute for Population Sciences (IIPS), Mumbai, which made this work possible. This paper was presented at the 35th International Geographical Congress (IGC) 2024, held in Dublin, Ireland. The authors acknowledge the financial support provided by the Anusandhan National Research Foundation (ANRF), Government of India, which enabled attendance at the conference through travel funding. The authors are also thankful to the reviewers for their excellent comments and constructive suggestions that helped improve the quality of this manuscript. We extend our sincere thanks to the editor for the guidance throughout the review process.

Funding

This study did not receive any specific grant from any funding agencies.

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Authors and Affiliations

  1. Department of Population & Development, International Institute for Population Sciences, Mumbai, Maharashtra, 400088, India

    Sourav Biswas & Aparajita Chattopadhyay

  2. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 650 West 168th Street, New York, NY, USA

    Kathrin Schilling

  3. Department of Survey Research & Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, 400088, India

    Ayushi Das

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Contributions

Sourav Biswas is the guarantor of this work. Sourav Biswas: Conceptualisation, methodology, formal analysis, resource acquisition, preparation of maps and tables, writing—original draft, writing—review and editing. Aparajita Chattopadhyay: Conceptualisation, resource acquisition, writing—original draft, writing—review and editing, and supervision. Kathrin Schilling: Conceptualisation, writing—original draft, writing—review and editing, and supervision. Ayushi Das: Methodology, formal analysis, writing—review and editing.

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Correspondence to Aparajita Chattopadhyay.

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Ethics approval and consent to participate

This study is based on secondary analysis of publicly available, anonymized data from the National Family Health Survey (NFHS-5), which is the Indian version of the Demographic and Health Survey (DHS). The survey protocol, including procedures for data collection and informed consent, received ethical approval from the Ministry of Health and Family Welfare (MoHFW), Government of India. All methods were performed in accordance with relevant institutional and national guidelines and regulations. The NFHS-5 data were collected following strict ethical standards, including obtaining verbal and written informed consent from all participants prior to their inclusion in the survey. In cases involving minors, informed consent was obtained from a parent or legal guardian. As the study involved secondary, de-identified data, no additional ethical approval or anonymization was required. The dataset is publicly available and can be accessed at: http://www.dhsprogram.com/data/available-datasets.cfm. No identifiable images or personal details of participants were used; hence, consent for publication is not applicable.

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Biswas, S., Chattopadhyay, A., Schilling, K. et al. Investigating the association between groundwater contaminants and hypertension risk in India: a machine learning-based analysis. J Expo Sci Environ Epidemiol (2025). https://doi.org/10.1038/s41370-025-00776-0

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