A Robust Estimator of Malaria Incidence from Routine Health Facility Data

Area of Science:

• Public Health
• Epidemiology
• Infectious Disease Surveillance

Background:

• Routine malaria case data are crucial for surveillance in sub-Saharan Africa.
• Crude incidence data are susceptible to biases from care-seeking, testing, and reporting variations.
• Accurate malaria burden estimation is vital for effective control strategies.

Purpose of the Study:

• To develop a method for correcting routine malaria incidence data for biases.
• To provide a more accurate estimation of malaria burden across sub-Saharan Africa.
• To quantify the unmet need for malaria diagnostics and treatments.

Main Methods:

• Derived a simple algebraic formula to adjust crude malaria incidence rates.
• Applied the correction formula to data from Guinea, Mozambique, and the World Malaria Report.
• Calculated continent-wide needs for malaria tests and treatments based on corrected incidence and current care-seeking rates.

Main Results:

• Corrected incidence maps showed better alignment with community survey prevalence than crude data.
• Estimated unmet need for malaria tests: 160 million (IQR: 139-188 million).
• Estimated unmet need for malaria treatments: 37 million (IQR: 29-51 million).

Conclusions:

• Crude malaria incidence data require careful interpretation due to suboptimal testing and care-seeking.
• The developed correction method offers improved insight into spatiotemporal malaria trends.
• Accurate malaria burden assessment is essential for addressing significant unmet needs in diagnostics and treatment.