Interventi
10/03/2025

Estimating and interpreting mortality data in conflicts: Challenges, controversies, and significance

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Introduction

Assessing death tolls in conflict is fraught with political, methodological, and contextual challenges, which make these estimates susceptible to media misinterpretation and political manipulation. Estimates of death tolls bear, however, high political and humanitarian weight, as they can have far-reaching implications, by shaping policy priorities and political debates, prompting diplomatic action, and guiding humanitarian responses. The present article complements the editorial in this issue of E&P (see pp. 9-11) by exploring the politicisation of numbers in armed conflicts and examining the rationale, methods, and challenges involved in assessing mortality.
The main goal of humanitarian assistance is to reduce excess mortality, morbidity, and suffering due to armed conflicts and natural disasters. Just as clinicians use a thermometer to assess the severity of certain diseases, humanitarian workers rely on the assessment of increased mortality, combined with few selected indicators, to gauge the nature and severity of a crisis. Measuring mortality can inform decisions on the most appropriate life-saving interventions. It can guide resource allocation and advocate for additional ones. It can contribute to assessing the overall effectiveness of the humanitarian response, even if a direct causal pathway between certain interventions and health outcomes is difficult or impossible to demonstrate. It can aid diplomatic initiatives in peace negotiations. Finally, it can document violations of international humanitarian law, supporting the efforts of transnational justice to hold perpetrators of violence accountable.
Contextual factors are important: armed conflicts often occur in dangerous and hard-to-reach areas, making field data collection difficult, perilous, and, at times, impossible. At times, access to certain areas requires political negotiation with warring parties. Some areas are inaccessible, due to insecurity or political barriers. Further, killings of civilians and other atrocities usually involve low visibility behaviours and perpetrators have strong incentives to conceal their actions. In inaccessible areas, mortality rates may be higher than in accessible sites, leading to potential underestimation of the overall death tolls. The urgency of a humanitarian response often requires rapid field data collection, sometimes driven by the need to ensure that data collectors can safely return to a secure base. However, this time pressure can undermine data quality.

The politics of numbers in armed conflicts

The politicization of numbers in armed conflicts is a longstanding issue. Data on deceased combatants, civilians, health workers, and humanitarians, as well as the number of displaced persons, of people in need of humanitarian assistance, and of attacks on aid convoys, healthcare facilities, ambulances and health personnel have been frequently manipulated –inflated, downplayed, or simply fabricated – to advance political agendas or to mobilise more aid funding.
The incentives to manipulate and politicise war mortality data in the service of political goals can be strong. Some examples are given below.

  • In the Vietnam War, “the statistics regarding body counts were notoriously unreliable”,1 as American field commanders had interest to exaggerate enemy casualties to boost troop morale.
  • In 2004, the World Health Organization conducted a mortality survey in Darfur,2 a region grappling with a violent armed conflict still going on. The political context was highly charged: the International Criminal Court had accused then-President Omar al-Bashir of war crimes, crimes against humanity, and genocide. The survey revealed alarming mortality levels, exceeding emergency thresholds. While the study protocol adhered to international standards and had received government approval, Sudan’s Ministry of Health dismissed the findings on political grounds.
    An analysis of 67 mortality surveys conducted in Darfur estimated the number of excess deaths between 2004 and 2008 at around 300,000 (95%CI 178,258-461,520).3 Sudan’s then-President al-Bashir claimed, however, that the number was much lower, closer to 9,000.4
  • A similar political reaction occurred in 2005 with a WHO mortality survey in conflict-affected northern Uganda.5 Also in this case, the findings, revealing very high mortality rates, were rejected by the government. In both Sudan and Uganda instances, researchers were prohibited from publishing their final reports.
  • The release of findings from two mortality surveys conducted in Iraq after the 2003 invasion prompted a heated and high-profile debate among U.S. and British politicians. The two surveys, conducted by prestigious American academic institutions, were published in The Lancet.6,7 An excess of 655,000 deaths (95%CI 392,979-942,636) was estimated between 2003 and 2006. U.S. President George Bush dismissed the findings of the surveys, stating: “I don’t consider it a credible report”8, while the British Prime Minister Tony Blair’s spokesman argued that the study’s result “was not one we believe to be anywhere near accurate”9, contradicting the Ministry of Defence’s chief scientific adviser who acknowledged that the studies had adhered to the established methods of this type of survey.10

Methods for assessing war mortality

In general, three approaches can be used to assess mortality: demographic methods, passive surveillance/body count, and epidemiological methods adapted for humanitarian crises. 
Demographic methods are used to reconstruct the demography of a country and estimate excess deaths by comparing the sex and age distribution of the population at two different censuses.11 Censuses, however, are not usually conducted during wartime and data collection may exclude insecure areas. Census data become quickly obsolete and irrelevant in humanitarian crises. Moreover, these methods are not suitable in situations where a large number of people flee the country, as is currently the case in Sudan. Census data can also be suppressed for political reasons. For example, Stalin banned the publication of the 1937 Soviet census, which would have revealed that millions of citizens were ‘missing’, due to the Ukrainian famine of 1932-33, an outcome of his agricultural collectivisation policies. An excess of 3.9 million deaths was estimated to have occurred, out of a population of approximately 31 million, making it one of the deadliest famines in recorded history.12
Large household surveys – the USAID-supported
Demographic and Health Surveys13 (DHS) and the UNICEF-supported Multiple Indicator Cluster Surveys14 (MICS) – are the main sources of national-level data of maternal, newborn, and child health indicators. These surveys provide the primary data that are used by the UN for building country estimates of several indicators, albeit with some adjustments. DHS and MICS are expensive and time consuming, involving the sampling of several thousand households; therefore, they cannot be replicated every year. In countries affected by violent conflicts, once data become available, they may already be obsolete. 
The second approach consists of passive surveillance – body counts – and involves collating reports of war deaths from a variety of sources (morgues, media, eyewitness reports, health facilities, and so forth). These data are the main sources for government estimates of deaths, although they are limited to direct, violent deaths and tend to undercount the death toll, particularly in isolated incidents and in remote or dangerous areas.15,16 Research institutes have been compiling conflict-death data from passive surveillance on an ongoing basis.17 Their statistics are widely cited despite their limitations.
The third approach, commonly employed by UN agencies and NGOs in conflict settings, relies on epidemiological methods adapted to humanitarian crises. Table 1 summarises key epidemiological methods for estimating mortality in emergencies, along with their respective advantages and disadvantages. Readers interested in understanding in detail the methods, their uses, and limitations are encouraged to refer to two key readings in the bibliography.18,19 

Table 1. Key epidemiological methods for estimating mortality in humanitarian crises.
 

Estimating indirect and excess mortality: two thorny methodological issues

Indirect causes of death

Civilians can die from the direct consequences of violence, i.e., traumas and injuries. These causes of death represent an important share of the total death toll in high intensity conflicts, i.e., during the 1994 Rwandan genocide, in Iraq in the post 2003 invasion, and in the current wars in Sudan and Gaza. Survivors of direct violence may also succumb later to indirect causes, including infectious diseases due to poor sanitation and hygiene in overcrowded and unsanitary refugee camps and interruptions of immunisation services, malnutrition due to impeded food access, often interacting with infectious diseases, maternal and neonatal issues due to lack of access to skilled birth attendants and to health centres for delivery, untreated chronic illnesses – cardiovascular diseases, cancers, diabetes, etc. – caused by the collapse of health services, or from exposure to extreme weather conditions. These deaths often occur long after the conflict has ended. Estimating indirect deaths due to the conflict requires subtracting from the total estimated number of indirect deaths those expected during the same period, had the conflict not occurred – a baseline which is often unavailable. Alternatively, researchers have used an estimated ratio of indirect to direct deaths from previous surveys, ranging from 1 to 15.25,26 For example, Khatib et al. used a ‘conservative’ ratio of four indirect to direct deaths for the war in Gaza, which, applied to the reported direct deaths as of June 2024, led to an estimated “186,000 or even more deaths attributable to the current conflict”.27 There is, however, no solid evidence basis for choosing a decontextualised ratio, given the variation in contexts, pre-war burden of disease, health systems’ performance, and level of humanitarian assistance. It can be assumed, however, that in many conflict contexts, especially prolonged ones, indirect deaths outnumber direct ones. 

Excess mortality

Beyond the severity of the crisis, measured by mortality rates against emergency benchmarks, it is important to consider the magnitude of its impact, which requires taking the duration of the rate elevation and the size of the population affected during this period into account. Together, these three parameters determine the excess mortality, that is, the number of deaths that would not have occurred in the absence of conflict. In other words, excess deaths express the number of deaths that are attributable to the crisis. However, in most emergencies in low-resource settings, pre-crisis mortality estimates are often unavailable, and a counterfactual scenario is used, i.e., the estimated mortality level that would have existed prior to or in the absence of the conflict. The choice of a specific counterfactual scenario carries an element of subjectivity and is consequently often a topic of debate. For example, the International Rescue Committee carried out five mortality surveys in the Democratic Republic of the Congo between 1998 and 2007, a period marked by war and upheaval in the country.28 The authors estimated that 5.4 million excess deaths, an alarmingly very high number, occurred in the studied period, assuming as a baseline rate the average mortality rate of 0.44 per 10,000 per day in sub-Saharan Africa.29 The baseline rate choice faced severe criticism from some researchers who deemed it too low, arguing it led to an overestimation of excess mortality.30,31 

Mortality indicators from selected conflicts

Table 2 shows key mortality indicators from selected studies in conflict settings from the 1990s to the present. The studies were selected to provide a broad picture of countries, contexts, affected populations, periods, and methods. All the published studies were peer-reviewed, except for certain WHO reports that were withheld from publication due to political reasons (see above).

Table 2. Key mortality indicators from selected studies in conflict settings.
 

Some caveats are warranted:

  • direct comparisons of mortality indicators across the table are not meaningful, due to variations in the context, the intensity of violence, the mortality patterns, the methods and their limitations, the populations studied (internally displaced persons, refugees, other conflict-affected groups, or the general population), their age structure, and the pre-conflict burden of disease;
  • some studies focused on small populations within camps, while others studied entire populations in specific regions of a country or across entire nations;
  • while most studies express mortality rates as deaths per 10,000 people per day (traditionally the most employed), other studies express the rates as deaths per 1,000 people per month or per 1,000 people per year;
  • bolded rates indicate that death rates have surpassed emergency thresholds.32

The figures are based on data from areas where measuring mortality was feasible. In regions inaccessible due to insecurity, political barriers, or resource constraints, mortality rates may have been higher. 
The crude and under-five death rates for the first three emergencies listed in the table (Zaire, Liberia, and Southern Sudan) are catastrophically high, surpassing up to 40 times the emergency threshold for the crude death rate (CDR) and 30 times for the under-five death rate (U5DR). 
Since then, there has been a significant decline in mortality rates recorded during conflicts. Several factors, or a combination of them, might explain this trend: reduced impact of conflicts on civilian populations, the introduction of effective public health interventions, increased humanitarian aid, and the growing professionalisation of humanitarian workers. However, it is also possible that high mortality persists in low-visibility areas, remaining hidden from humanitarian organizations.
The leading causes of death vary widely, ranging from injuries and trauma to outbreaks of communicable diseases, malnutrition, or a combination of the latter two. However, it is important to note that, in humanitarian crises, causes of death are primarily ascertained through verbal autopsy – a time-consuming and challenging method. Its reliability is limited, allowing causes of death to be classified only into broad categories.
The interpretation of excess mortality, when estimated, requires reference to the population, which has been omitted here for brevity. For the critical issue of baseline data – essential for calculating excess deaths – see the previous chapter.
Retrospective household surveys based on cluster sampling are the most prevalent method used. This preference can be explained by the availability of guidelines and software, the relatively short implementation time and low cost, and the extensive experience accumulated by numerous humanitarian workers.

Conclusions

Humanitarian agencies have an “ethical imperative to collect good data”52 in emergencies, to guide appropriate resource allocation and the delivery of effective interventions. However, it would be disingenuous to believe that evidence by itself is sufficient to counter decisions driven by political partisanship. 
This article has examined some of the challenges that the NGOs, UN agencies, ministries of health of affected countries, and researchers face in estimating the death tolls in conflicts and measuring other indicators. Conflict zones are unstable, unpredictable, insecure, often inaccessible. In these environments, “the circumstances that make information collection so important are precisely those that make it extremely difficult to do”.53 As a result, the epidemiological methods used in humanitarian crises produce inherently imperfect data, which may be unsatisfactory to conventional epidemiologists accustomed to applying rigorous methods in controlled settings.
Interpreting mortality data and communicating them to end-users, who are often innumerate, requires transparency and honesty from those who produce them, to minimise confusion and intentional manipulation. For instance, the picture provided by the contrasting findings of surveys in Darfur raised doubts about the quality of measurements, leading the Washington Post to refer to mortality data in the region as ‘statistical anarchy’.54
Past reviews of the quality of mortality surveys in humanitarian crises have raised concerns about the poor standardisation of measurement methods and issues in the design and implementation of studies.55 With the growing professionalisation of humanitarian workers, it can be assumed that progress has been made since then. However, public health information remains an underfunded and under-prioritised aspect of humanitarian assistance. Indeed, a shortage of trained ‘humanitarian epidemiologists’ has been highlighted,56 despite the growing emphasis by donor governments on the importance of quantitative evidence to inform decision-making and justify aid budgets. The interest in the field epidemiology course, organised by the Italian Association of Epidemiology in 2024, shows that the ground is fertile for more investment in this area. 
Finally, there is a risk that the daily repetition of death tolls by the media, as in the case of Gaza, may lead to increased tolerance and desensitisation to the tragedy those numbers represent: “In counting we somehow lost track of the mountain of dead bodies piling up beneath our numbers”.57

Conflicts of interest: none declared.

References and notes

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