This blog series accompanies the Mortality Estimation Systems Innovation Partnership (SIP), supported by UKHIH-Elrha, which brings together diverse partners to strengthen how mortality data is collected, interpreted, and used across humanitarian crises. Earlier blogs in this series highlighted why excess mortality measurement is critical for understanding crisis severity, as well as exploring how to maximise local and national actors' leadership in the mortality estimation ecosystem.
In this third blog, we turn to Eastern Democratic Republic of the Congo, where Rebuild Hope for Africa and the Johns Hopkins Center for Humanitarian Health share how their work is making mortality estimation more accurate, accessible, and feasible for national actors best placed to do this work, even in the most challenging settings.
The Public Health Aspects of Complex Emergencies and Refugee Situations, 1997, Michael Toole, Ronald Waldman“As an indicator, a mortality rate tries to evaluate the size and scale of a crisis in a single metric.”
In 2023, the Humanitarian Congress in Vienna released a statement saying,
"The humanitarian imperative is an absolute moral obligation to save lives and alleviate human suffering on the basis of need, without discrimination”.
Yet, when resources are constrained, allocation is often based on geopolitical interests, media coverage, or how relatable a population may be to high-income donor countries. In short, human lives are valued differentially.
The disconnect is not theoretical. In 2022, Rebuild Hope for Africa (RHA) led a nationwide mortality survey in the Central African Republic which estimated up to one fifth of the population had died during the previous year. Despite the scale of these findings, the study received little media attention and did not lead to meaningful changes in donor policy.
In conflict-affected settings, various, often compounding, factors make primary data collection difficult or impossible. These include forced displacement, insecurity, system failures, poor infrastructure, limited capacity, and restricted access. In practice, mortality is often not measured at all. And as threats to healthcare workers grow, international agencies have become understandably risk averse, collecting data only safer, accessible areas, where death rates are usually lowest. Without reliable data, decision makers and responders depend on fragmented sources and non-robust estimates. The result is a biased and misleading picture of crisis severity, that often portrays crises as less severe than they are.
The magnitude of these biases and their effects on decisions by humanitarian actors, governments, and donors who rely on such data, remain largely unexamined. Our partnership between Rebuild Hope for Africa (RHA) and the Johns Hopkins Center for Humanitarian Health (CHH) is working to change this.
Eastern Democratic Republic of the Congo - An Unquantified Crisis
Few places demonstrate the challenges of mortality estimation more than the Democratic Republic of the Congo (DRC), one of the world’s most enduring humanitarian crises. The crisis worsened drastically in January 2025 when the country suffered a devastating double shock: the abrupt withdrawal of USAID funding and a violent military offensive by the Rwandan-backed rebel group M23. The M23 seized large swathes of territory, killing and displacing an unknown number of people in the process. With the departure of many international agencies and a vacuum in humanitarian response, the population has been left vulnerable to the worst effects of the conflict. A year later, the true human cost remains unknown. We recognise that without reliable data, it becomes even harder to mobilise the support that people living in Eastern DRC urgently need.
Placing Data and Decision-Making in Congolese hands
Augustin Gang Karume, one of the authors of this blog, was born and raised in Eastern DRC, where he still lives and works today. In 2008, he founded RHA to place data and decision-making back in Congolese hands. He understood then that national actors are the future of sustainable humanitarian response. Rooted in the community and living with the long-term consequences of decision-making, national actors have a strong incentive to prioritise community needs over institutional agendas. Using local networks and knowledge, they are the best equipped to conduct primary data collection in insecure settings. While international actors have scaled back amid funding austerity, national organisations like RHA have remained in place, continuing to work for and within their communities. These actors are also proving to be far more cost-effective and efficient. Without international overhead, they can often deliver results at a fraction of the cost of international organisations. As an example, RHA’s 2022 nationwide mortality survey in the Central African Republic, cost a total of 50,000 USD, whereas a single district SMART survey may cost upwards of 15,000 USD*.
National actors are the first responders in nearly all crises and remain present long after international attention and funding fade.
Bridging Local Leadership with Technical Expertise
With funding from the UK Humanitarian Innovation Hub’s Systems Innovation Partnership, we are bridging RHA’s local leadership with technical expertise from the CHH, combining community trust with advanced epidemiological and statistical training. Together RHA and CHH are collaborating on a study to assess potential biases in mortality estimation through both primary data collection and innovative use of statistical approaches. We’re working to make mortality estimation more accurate, credible, and efficient, with the intent to apply the findings across humanitarian settings.
In the primary data collection component, our study is comparing three different methods of mortality estimation: a retrospective household survey, rapid key informant listing, and a full census. Using a common reference population and recall period, the study aims to identify where biases arise, quantify which deaths are missed, and assess relative performance of a light-, medium- and resource-intensive approach to mortality measurement.
In the statistical component, we are applying innovative use of established causal and design-based methods to assess biases. We are testing the utility and feasibility of these methods to answer questions like: to what extent are hard to capture deaths, such as neonatal and violent deaths, systematically missed; can fewer survey clusters still provide estimates precise enough for decision making; and can analytical adjustments be used to address known biases?
We are also supporting localisation by building field-ready guidance tools designed to make mortality estimation more accessible to operational actors. These tools include an algorithm to help teams choose a method, an operational readiness checklist, and a guide to data validation, triangulation, interpretation. Our aim is to make mortality estimation practicable in even the most challenging settings, without compromising quality. As the best-placed actors to assess mortality, we hope to pilot the guidance with national actors in the DRC and elsewhere to ensure it is user-friendly, actionable, and scalable for use in any crisis.
Looking Ahead: Making Mortality Count
Without credible mortality data, humanitarian response risks being inefficient, inequitable, and disconnected from reality. We cannot respond appropriately to crises we do not understand. When those with the greatest capacity to measure mortality have the least stake in the results, the system fails. The best way to ensure efficiency and effectiveness is to place local organisations at the centre. Connecting local expertise with technical knowledge offers a path toward a fairer humanitarian sector, where the reality of a crisis is described by those living through it.
*2017 estimate adjusted for inflation