19 Feb Understanding Malaria Prevalence At The Last Mile
Fabrice visits a remote health center in Ifanadiana District to receive a malaria test.
To fight infectious disease, most governments and NGOs rely on data from patients diagnosed at health facilities in order to identify disease outbreaks and allocate resources such as drugs, bed nets, etc. This is commonly known as passive surveillance. Although passive surveillance is cheap and provides essential information for planning and implementing control activities, it has a fundamental flaw: it misses all the people sick who do not (or cannot) access the health system.
Importantly, many rural areas of sub-Saharan Africa with the highest rates of infectious diseases in the world are also those with the lowest rates of access to health care. Active surveillance, where health workers go door to door to diagnose and treat cases, can help find those missed cases. But this strategy is mostly restricted to areas targeted for disease elimination and are too expensive to implement in high-transmission areas across the developing world.
As a cost-effective alternative, innovations to improve the data from passive surveillance systems could help inform the implementation of disease control interventions by local health actors.
In a recent article by Elizabeth Hyde and collaborators, published in the International Journal of Health Geographics, researchers have developed a new method to estimate how many cases of malaria – the most deadly infectious diseases in Madagascar – were missed by passive surveillance systems.
The goal of this study was to help produce more realistic estimates of malaria incidence for every community within a health district over time, across all levels of accessibility to health care. To do this, Hyde and team used a dataset with patients’ residences from nearly 300,000 people who access care in one of Ifanadiana District’s health centers over four years, of which 75,000 were diagnosed with malaria. Using statistical models of geographic and financial access to health care, they adjusted malaria incidence rates for every community in the district. The study found that passive surveillance missed about 4 out of every 5 cases of malaria, and failed to detect areas of high transmission in several parts of the district that were now visible for the first time.
A figure from Hyde’s article shows a comparison between the number of new malaria cases per 1000 people in teal, and the most plausible adjusted dataset of undetected cases in orange.
“If integrated into e-health platforms that are being deployed for disease surveillance, this method could be easily scaled-up to better understand local disease dynamics in other settings across the developing world” said Dr. Andres Garchitorena, Associate Scientific Director at PIVOT and senior author of the study.
This work represents the first step towards a broader research agenda to understand local malaria dynamics in Ifanadiana District. Funded by the French National Research Agency and conducted in partnership between PIVOT and Institut de recherche pour le développement, ongoing efforts involve the use of malaria prevalence surveys, high-resolution environmental monitoring via remote sensing, and mathematical models of malaria transmission to generate information that could help optimize malaria control activities in the district.
Altogether, this study could change the game when it comes to managing the burden of disease in regions like rural Madagascar – including cases such as COVID-19 – enabling public health actors to better manage the distribution of resources and reduce the threat of disease upon those at the last mile.