This report was commissioned by GiveWell and produced by Rethink Priorities from August to September 2023. We slightly revised the report for publication in May 2025. GiveWell does not necessarily endorse our conclusions, nor do the organizations represented by those who were interviewed.
The primary focus of the report is a landscape analysis to provide an overview of the major challenges, barriers, and improvement initiatives with regards to malaria-related data systems in sub-Saharan Africa. Our research involved reviewing the scientific and gray literature, and we spoke with three experts.
Since the time of writing, the malaria data systems landscape has evolved, particularly due to recent disruptions in United States Agency for International Development (USAID) funding. In early 2025, the U.S. government significantly reduced USAID’s budget, leading to the reduction or termination of several major initiatives, including the Demographic and Health Surveys (DHS) Program, which ended in February 2025. Many of the efforts described in this report, especially those focused on strengthening routine malaria data, list USAID as a major funder. While we have not systematically assessed the impact of these changes, it is likely that some programs have since been altered, scaled back, or discontinued. These developments are not reflected in the body of the report but are noted here to provide context.
We don’t intend this report to be Rethink Priorities’ final word on malaria-related data systems. We have tried to flag major sources of uncertainty in the report and are open to revising our views based on new information or further research.
Executive summary
Notes on the focus and scope of this report
This project is a landscape analysis of the main challenges and efforts to improve routine data collection systems for estimating the malaria burden in low-income countries. Its two main goals are to:
Provide an overview of the main challenges and constraints for collecting health data routinely.
Provide an overview of previous or current efforts or initiatives to improve the accuracy of disease burden data.
The focus of this project was guided by what we consider the most decision-relevant aspects for GiveWell:
We focus predominantly on malaria, rather than general disease burden, as this is a key funding area for GiveWell and allows the project to be more focused and actionable.
We focus on the data collection stage in the disease burden estimation pipeline, as our prior is that this stage has the largest scope for improvement.
Our core focus is annual and national disease burden estimates, rather than higher frequency or more disaggregated estimates.
Geographically, we focus our literature review predominantly on sub-Saharan Africa (SSA), as it is home to the vast majority of the malaria burden.
We focus a larger share of our time on reviewing the challenges and solutions with respect to malaria morbidity rather than mortality, as our prior is that malaria mortality estimates are more difficult to improve compared to incidence or prevalence estimates.
Key takeaways
The Institute for Health Metrics and Evaluation (IHME) and the World Health Organization (WHO) both use two malaria burden estimation approaches: Estimates for low-transmission countries are based on (adjusted) national routine data, whereas estimates for high-transmission countries are based on geospatial models by the Malaria Atlas Project (MAP) that rely predominantly on household survey data. Even though the geospatial modeling approach is currently relied on for most malaria cases/deaths, we have seen it heavily criticized. There is a growing reliance on routine data as national surveillance systems improve, though we are unsure about the pace at which this is happening. [more]
Challenges related to malaria morbidity: Routine surveillance systems in high-transmission countries have challenges at all stages of the malaria routine data flow in terms of coverage, completeness, and accuracy. While some countries’ surveillance systems have shown recent improvements, we are not sure whether and how similar improvements can be made in other countries. Some experts seem rather pessimistic. Household surveys and censuses are also used for modeling, but they have temporal and content-related gaps, which may be possible to resolve with additional funding. [more]
Challenges to malaria mortality: Malaria mortality estimates in high-transmission countries are based on cause of death fractions, informed by clinical records in vital registration systems and verbal autopsy studies. Clinical records are sporadic, fragmented, and prone to misclassification errors. Verbal autopsy is a rough interim method until vital registration methods are more reliable, but it is very imprecise at determining deaths due to malaria. Minimally invasive autopsy is a promising, emerging method in situations where full autopsy is not possible, but we are unsure whether and to what extent it can be introduced at scale. Case fatality rates are used by the WHO for mortality estimates in low-transmission countries, but they are based on relatively few, old studies, from few countries, and seem fairly noisy. [more]
A systematic review found that targeted interventions, such as training, data quality checks, and electronic health systems, can improve the accuracy and completeness of routine health data, though evidence is limited and drawn from a small number of studies. [more]
Multiple large-scale programs aim to strengthen routine and malaria-specific health data systems, but many are heavily reliant on the United States Agency for International Development (USAID) and thus face potential disruption due to recent funding cuts. [more]
We highlight two case studies of interventions to improve malaria-related data accuracy, based on limited available quantitative evidence. These include a data audit program in Zambia and a system integration project in Burkina Faso. While not necessarily the most impactful or representative examples, they are among the few with measurable outcomes. Additional initiatives are listed in our broader list of potential interventions.
Zambia data quality audits (DQAs): Between 2015-2021, Zambia conducted audits comparing paper data on health facility registers to digitized data in the health management information system (HMIS). Repeated audits were associated with better accuracy, but we have concerns about the analysis and do not find it to be particularly convincing. DQAs are likely transferable and easy to implement, and are supported by both the President’s Malaria Initiative (PMI) and WHO, among others. [more]
We wonder whether technology might be another way to achieve better accuracy for digitized data, but do not have enough information to be confident about an existing app. [more]
Burkina Faso Improving Malaria Care project: Between 2013-2020, Jhpiego led a USAID-funded project to integrate malaria data collection into health management information systems (HMIS), train healthcare staff, and set up data validation procedures. Accuracy of data collection is clearly shown to improve between 2014-2017, and may have reached the project’s target of 90% accuracy by 2020. It’s unclear how transferable this project would be without political buy-in and sustained investment. [more]
Read more
Click here to read the full report on Rethink Priorities' website.
Jenny Kudymowa, Carmen van Schoubroeck, and Aisling Leow jointly researched and wrote this report. Jenny also served as the project lead. Melanie Basnak supervised the report.
Special thanks to Melanie Basnak, Ruby Dickson, and Natalie Crispin (GiveWell) for helpful comments on drafts. Thanks also to Shane Coburn for copyediting and Sarina Wong for assistance with publishing the report online. Further thanks to Tom Churcher (Imperial College London), Bob Snow (University of Oxford), and Tasmin Symons (Malaria Atlas Project) for taking the time to speak with us.
GiveWell provided funding for this report, but it does not necessarily endorse our conclusions.
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