Real-Time Crowdsourcing of Health Data in a Low-Income country: A case study of Human Data Supply on Malaria first-line treatment policy tracking in Nigeria (by Olubayo Adekanmbi, Wuraola Fisayo Oyewusi and Ezekiel Ogundepo)

Malaria is one of the leading causes of high morbidity and mortality in Nigeria despite various policy interventions to frontally address the menace. While a national malaria policy agenda exists on the use of Artemisinin-based antimalarial as the first-line drug of choice for the treatment, there have been challenges in the implementation monitoring across various drug distribution layers, particularly the informal channels that dominate over eighty percent of the antimalarial drug distribution value chain. The lack of sustained policy monitoring through a structured and systematic surveillance system can encourage irrational drug usage, trigger antimalarial drug resistance, and worsen the disease burden in an economy where over ninety percent of the population live below the poverty line. We explored the use of real-time data collection through ordinary local residents, who leverages low-cost smartphones with an on-device app to run quick mystery shopping at drug outlets to check recommended malaria treatment drugs in four (4) states across the country. The instant survey data is collected via guided mystery shopping, which requires the volunteer participants to answer three basic questions after a 5 - 10 minutes in-store observation. Each submission is verified with the drug store picture and auto-generated location co-ordinates. The antimalarial policy compliance level is immediately determined and can be anonymously aggregated into a national map for onward sharing with pharmaceutical trade groups, government agencies and non-profits for immediate intervention via requisite stakeholder education. This crowd-sourcing effort provides an affordable option that can be scaled up to support healthcare surveillance and effective policy compliance tracking in developing nations, where there is a paucity of data as a result of high illiteracy and infrastructural inadequacy.