Spillovers and Risk Substitution in Public Health Interventions: A case study of Deep Tubewell Interventions and Diarrheal Disease in Rural Bangladesh

Authors: Varun Goel*, UNC Chapel Hill, Michael Emch, UNC Chapel Hill
Topics: Medical and Health Geography, Spatial Analysis & Modeling, Quantitative Methods
Keywords: Disease, Health, Bangladesh, Water, Bayesian
Session Type: Virtual Paper
Day: 4/10/2021
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 8
Presentation File: No File Uploaded


Public health interventions aimed at specific populations to mitigate specific health outcomes can often have unintended consequences. First, they may impact not only direct recipients but also people living in close physical or social proximity resulting in spillover effects. Second, while decreasing risk of one health outcome, they may exacerbate risk of another outcome, resulting in risk substitution. For example, in Rural Bangladesh, deep tubwells are an important drinking-water source as they reduce harmful exposure to arsenic. However, there is concern that these health benefits may be substituted by increased risk of microbial contamination, and subsequent diarrheal disease burden, especially among infants and young children. Additionally, spatial proximity to other water sources may result in spillover of risk among non-deep tubwell households. Using a large geo-referenced panel survey, I combine bayesian geospatial modeling methods with a quasi-experimental difference-in-difference approach to quantify the direction and magnitude of spillover effects, and the spatially heterogenous impact of deep tubwells on diarrheal diseases in rural Bangladesh. Using comparative models, I show that there may be both positive and negative spillover effects of deep tubewells, and that the magnitude and direction of those effects is spatially heterogenous and modified by social and spatial factors such as tubewell density, distance, tubewell ownership, and socio-political capital. This study highlights the importance of incorporating geospatial methods in traditional impact evaluation frameworks to better measure health impacts and make spatially tailored and representative policy considerations.

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