Tracking Cholera through Surveillance of Oral Rehydration Solution Sales at Pharmacies: Insights from Urban Bangladesh
In Bangladesh, pharmacy-purchased oral rehydration solution (ORS) is often used to treat diarrhea, including cholera. Over-the-counter sales have been used for epidemiologic surveillance in the past, but rarely, if ever, in low-income countries. With few early indicators for cholera outbreaks in endemic areas, diarrhea-related product sales may serve as a useful surveillance tool.
We tracked daily ORS sales at 50 pharmacies and drug-sellers in an urban Bangladesh community of 129,000 for 6-months while simultaneously conducting surveillance for diarrhea hospitalizations among residents. We developed a mobile phone based system to track the sales of ORS and deployed it in parallel with a paper-based system. Our objectives were to determine if the mobile phone system was practical and acceptable to pharmacists and drug sellers, whether data were reported accurately compared to a paper-based system, and whether ORS sales were associated with future incidence of cholera hospitalizations within the community. We recorded 47,215 customers purchasing ORS, and 315 hospitalized diarrhea cases, 22% of which had culture-confirmed cholera. ORS sales and diarrhea incidence were independently associated with the mean daily temperature; therefore both unadjusted and adjusted models were explored. Through unadjusted cross-correlation statistics and generalized linear models, we found increases in ORS sales were significantly associated with increases in hospitalized diarrhea cases up to 9-days later and hospitalized cholera cases up to one day later. After adjusting for mean daily temperature, ORS was significantly associated with hospitalized diarrhea two days later and hospitalized cholera one day later.
Pharmacy sales data may serve as a feasible and useful surveillance tool. Given the relatively short lagged correlation between ORS sales and diarrhea, rapid and accurate sales data are key. More work is needed in creating actionable algorithms that make use of this data and in understanding the generalizability of our findings to other settings.