A SYSTEMS VIEW OF ACCESSIBILITY TO ALTERNATIVE SAFE WATER SOURCES, AND ITS IMPLICATIONS ON PUBLIC HEALTH: A CASE STUDY OF GOALMARI, BANGLADESH

The objective of this research was to examine how key socio-economic driving forces influence households' choice of water for drinking and cooking in the Goalmari area of Bangladesh. Possible sources ranged from a piped water supply provided by Veolia to untreated sources contaminated with high levels of arsenic (As) and pathogens. A further aim was to identify potential leverage points required for effective interventions to reduce the use of the potentially contaminated water. Two data sets were considered resulting from household surveys conducted in 2009 and 2019 before and after implementing the Veolia piped water system. Cluster analysis showed that households mainly fall into three main cluster groups based on variations in socio-economic status and other causal factors, including physical, infrastructure, and institutional elements. The principal component analysis revealed about 54% of the variations within the dataset are related to differences in awareness, willingness to pay, and the ability to pay to get access to safe water sources. Veolia piped water supply authority provides services to collect treated water from the designated tap standpoints and alternatively allows an internal house connection option directly to their homes. Results indicated families who have relatively higher monthly income showed interest in accepting Veolia's internal house connection options resulting in the shutdown of a significant number of tap points and ultimately affecting the low-income households' accessibility to Veolia water. Statistical significant interactions were observed between the quality of drinking water and the types of water purification used with the number of diarrhoea cases at the household level. A causal loop diagram showed five feedback loops (4 reinforcing and one balancing) that directly or indirectly influence the choice of drinking high As contaminated shallow aquifer water. Bayesian Networks were constructed to explore factors influencing the probability of drinking As or pathogenically contaminated water. Both models were found to be sensitive to the ability to pay for safe water, accessibility to Veolia piped water, deep tube wells (DTW) installation and maintenance costs, DTW distributions, household income level, non-ownership of tube wells, and level of awareness.