Performance of a novel machine learning-based proxy means test in comparison to other methods for targeting pro-poor water subsidies in Ghana

Occurrence and Mitigation of Bacterial Regrowth in Stored Household Water in Eastern Coastal Madagascar

“…The difference between our results and those of the previous study could be caused by a difference in context: the regions of Madagascar studied by Poulin et al [37] are predominantly rural and agricultural, whereas Toamasina is urban and peri-urban. Water sources in the rural southeast are more likely to be impacted by animal feces [37]. Also, in the present study, for many of the samples from both source water and household water, E. coli were not detected, resulting in an apparent situation of "bacterial concentration stayed the same" in Figure 1 (i.e., the source water and the household water both appeared to be 0 CFU/(100 mL)).…”

“…These findings are particularly important in light of a recent study [37] that found that drinking water is an important route of exposure to fecal bacteria for children in southeastern Madagascar. Beyond Madagascar, household storage of gathered water is common in cities in many low-and middle-income countries [40].…”

“…The relatively infrequent occurrence of E. coli regrowth/recontamination (Figure 1) is interesting in light of a recent study in southeastern Madagascar [37] that found more than 60% of drinking water samples were contaminated by E. coli at relatively high concentrations. The difference between our results and those of the previous study could be caused by a difference in context: the regions of Madagascar studied by Poulin et al [37] are predominantly rural and agricultural, whereas Toamasina is urban and peri-urban. Water sources in the rural southeast are more likely to be impacted by animal feces [37].…”

“…These objectives respond to important research needs: determining the conditions under which regrowth/recontamination depends on the type of the storage vessel; determining the prevalence or ubiquity of biofilms as a source of recontamination; and finding an affordable, effective, locally available method for cleaning storage containers. For Toamasina, these needs are particularly acute in light of a recent report [37] that found drinking water to be an important route of exposure to fecal bacteria for children in southeastern Madagascar. Also, the quantification of bacterial contamination in water in Toamasina is important because such quantification is relatively rare in Madagascar, perhaps because of challenges associated with the scarcity of laboratory facilities capable of performing such analysis, lack of availability of necessary equipment and chemical reagents, and high costs of reagents when they are available; these are issues that frequently hinder microbiological research in many parts of sub-Saharan Africa [38,39].…”

Improving the effectiveness of financial education programs. A targeting approach

“…ML‐based targeting could also help improve programs directed at households and firms. For instance, Aiken et al (2023) use machine learning methods leveraging phone data to differentiate ultra‐poor households eligible for program benefits in Afghanistan; similarly, Poulin et al (2022) apply ML techniques to target poor households eligible for water subsidies in Ghana, thus improving public health. Regarding developed countries, Andini et al (2018) study the use of ML for targeting a tax bonus intended to spur consumption in Italy.…”