https://orcid.org/0000-0002-5639-7209
Abstract
In this research note, we used excess deaths among young males to estimate the number of Russian fatalities in the Russo-Ukrainian war in 2022–2023. We based our calculations on the official mortality statistics, split by age and sex. To separate excess deaths due to war from those due to COVID-19, we relied on the ratio of male to female deaths and extrapolated the 2015–2019 trend to get the baseline value for 2022–2023. We found noticeable excess male mortality in all age groups between 15 and 49, with 58,500 ± 2,500 excess male deaths in 2022–2023 (20,600 ± 1,400 in 2022 and 37,900 ± 1,500 in 2023). These estimates were obtained after excluding all HIV-related deaths that showed complex dynamics unrelated to the war. Depending on the modeling assumptions, the estimated number of deaths over the two years varied from about 46,600 to about 64,100, with 58,500 corresponding to our preferred model. Our estimate should be treated as a lower bound on the true number of deaths because the data do not include either the Russian military personnel missing in action and not officially declared dead or the deaths registered in the Ukrainian territories annexed in 2022.
Introduction
Russia launched an invasion of Ukraine in February 2022, and at the time of writing, the war still continues. Neither Russia nor Ukraine normally release official data on their casualties. The aim of this research note is to use statistical analysis of the Russian all-cause mortality data in 2022 and 2023 to estimate war fatalities via excess mortality.
Excess mortality is defined as the increase in all-cause mortality over mortality otherwise expected based on historic trends. Subtracting the counterfactual expected number of deaths from the observed number of deaths allows us to estimate the death toll of epidemics, natural disasters, and armed conflicts. Excess mortality has been used to estimate the number of deaths caused by plague (Boka and Wainer 2020), influenza epidemics (e.g., Housworth and Langmuir 1974; Murray et al. 2006; Simonsen et al. 2013), the COVID-19 pandemic (e.g., Islam et al. 2021; Karlinsky and Kobak 2021; Kontis et al. 2020; Msemburi et al. 2023), hurricanes (e.g., Rivera and Rolke 2019), and heat waves (e.g., Robine et al. 2008). It can also be used to estimate war mortality (Karlinsky and Torrisi 2023).
Lacina and Gleditsch (2005) made a distinction between three different estimates related to war mortality: (1) combatant deaths, (2) battle deaths (all people, soldiers or civilians, killed in battle), and (3) war deaths (all deaths caused by war). In most cases, excess mortality can be understood as an estimate of all war deaths. However, in the context of the Russian losses in the Russo-Ukrainian war in 2022–2023, all three estimates are similar because the hostilities were conducted in the territory of Ukraine and few civilians in Russia were affected.
The estimation of combatant deaths is often fairly straightforward, as governments customarily release these data. For example, the U.S. Department of Defense regularly published statistics on combatant deaths in Iraq and Afghanistan (U.S. Department of Defense 2024); a more detailed analysis of the risk of death by age group, race and ethnicity, state, and combat unit is also available (Buzzell and Preston 2007; Kriner and Shen 2010). Tabeau and Bijak (2005) used the official administrative data, among other sources, for their estimates of combatant mortality in the Bosnian War. However, in our case, the conflict is ongoing, and unsurprisingly the official estimates are either not available or unreliable.
The challenge is usually to estimate the number of civilian rather than military deaths, and this is when the excess mortality method is often applied. Sometimes it is based on comparison of the population estimates from the censuses before and after the war (the two-census method; see Preston et al. (2001: chap. 11.5)), although then it depends on rather strong assumptions about migration. In other cases, civil registration records can be used, when available and of sufficiently high quality. Other methods for estimating war mortality include passive surveillance (i.e., the analysis of published reports); relying on statistics from mortuaries, gravesites, and health facilities (which are often incomplete); and, retrospectively, use of household survey data, including on sibling mortality (Obermeyer et al. 2008). The latter method was used for a widely discussed estimate of civilian mortality in the Iraq War (Hagopian et al. 2013; Spagat and Van Weezel 2017). Recently, Karlinsky and Torrisi (2023) used excess mortality among young males to estimate war fatalities in Armenia and Azerbaijan during the 2020 Nagorno-Karabakh War, reasoning that it was mostly young males who contributed to war casualties. In other examples, Hacker (2011) applied the excess mortality method to provide an estimate of the Civil War deaths in the United States, and Heuveline (1998) used it to estimate mortality during the Khmer regime in Cambodia.
In the present work, we use the official all-cause number of deaths in Russia in 2022 and 2023, split by age, sex, and region, to estimate excess male mortality, which we interpret as war deaths. Our analysis is complicated by the COVID-19 pandemic, which strongly increased the number of deaths in Russia in 2020, 2021, and 2022 (Karlinsky and Kobak 2021; Kobak 2021). Indeed, according to Karlinsky and Kobak (2021), Russia had 1.3 million excess deaths due to COVID, including 190,000 in 2022. To separate the effect of war from the effect of COVID, we rely on the ratio of male to female deaths (M/F ratio) to compute excess male mortality on top of what could be expected based on observed female mortality (a similar method was used in Hacker (2011)). We take excess male deaths as an estimate of all war-related deaths because known war fatalities in Russia are almost exclusively male (see below).
Multiple estimates of the Russian war fatalities already exist and are reviewed in the next section. Most of them are either based on unverifiable intelligence sources, or are incomplete by design, or rely on multiple assumptions with a high level of uncertainty. Our estimate is the first to be computed using the official Rosstat data following a transparent methodology and relying on fewer assumptions. Therefore, we believe it may be more reliable than most existing estimates.
Our approach and initial analysis for 2022 was first presented in Meduza and Mediazona (2023); this research note is an extended and more detailed analysis covering 2022 and 2023. All our data and analysis are openly available at https://github.com/dkobak/excess-mortality-war.
Previous Estimates of Russian War Fatalities
This is not the first estimate of the Russian military fatalities in Ukraine (Table 1). Radford et al. (2023) used published reports on military deaths from both Russian and Ukrainian sources as well as a Bayesian statistical model to provide an estimate of the Russian losses of about 77,000 in the first year of war (up to February 2023). Although their model discounts for potential bias, it is still based on data that are not reliable. Other estimates are nonacademic and often come from United Kingdom and U.S. intelligence sources (The Economist 2024a, 2024b). For example, in April 2023, a Pentagon leak estimated the number of Russian deaths between 35,500 and 43,000 (Kirby 2023).
Other examples of estimates based on published casualty reports include data from the Uppsala Conflict Data Program (UCDP) and the Armed Conflict Location and Event Data Project (ACLED). According to Uppsala data (Davies et al. 2023), the total number of Russian military fatalities was 35,000 in 2022 and 50,000 in 2023 (data accessed in July 2024 and rounded to thousands). According to ACLED (Raleigh et al. 2010), the number of war fatalities was 34,000 in 2022 and 32,000 in 2023 (data accessed in July 2024 and rounded to thousands). However, this estimate includes both Russian and Ukrainian military losses as well as civilian deaths. The Uppsala authors emphasize event-based methodology, which implies that aggregate fatalities provided by sources such as hospitals and government agencies are not included in the dataset, which may explain the discrepancy in the estimates between the two projects.
For the lowest estimate of the number of fatalities, it is reasonable to consider the total number of killed Russian military personnel whose names and circumstances of death are known from obituaries, publications on social media, or information from the burial sites. Since the beginning of the war, journalists from Mediazona and BBC News Russian have been conducting a volunteer project to collect those records. At the time of writing (February 2025), project volunteers have identified 54,760 named military deaths in 2022–2023 (17,877 in 2022 and 36,883 in 2023) (BBC News Russian and Mediazona 2023). More than 99.9% of these individuals were male.
Another estimate of the Russian military losses comes from probate records available online. In Russia, to receive an inheritance, relatives of a deceased person must in most cases open a probate case. The analysis of the data on the number of probate applications in 2022 shows a sharp increase of the number of dead young men compared with women of the same age (Meduza and Mediazona 2023). Journalists have calculated the number of excess probate cases for men and, after matching probate data with the BBC News Russian/Mediazona data of known fatalities and making some adjustments, estimated the number of excess male deaths in 2022 as 24,000 and in 2023 as 51,000, totaling 75,000 to the end of 2023 (Meduza and Mediazona 2024).
Finally, using the number of external deaths in 2022 split by sex, Nikolaeva and Marokhovskaya (2023) estimated the number of Russian war fatalities in 2022 as 18,000.
Data and Methods
Data Sources
We obtained all-cause mortality data for the period from 1990 to 2023, split by year, sex, age group (in five-year age brackets), and federal region, directly from the Federal State Statistics Service of Russia (Rosstat). The dataset covers the Russian Federation as well as Crimea and Sevastopol (annexed by Russia in 2014), but does not cover the Ukrainian regions annexed in 2022 (for which Rosstat provides no data). These data are openly available online at https://www.fedstat.ru/indicator/58775, but a Russian IP address may be required for access.
Rosstat mortality data are sourced directly from the administrative IT system used to register deaths by local civil acts registration offices. Without a death certificate, it is officially not possible in Russia to organize a burial or to open a probate case. We are not aware of any evidence that military fatalities were not registered or that official mortality data were directly falsified. That said, it is possible that some military deaths could be registered in the regions of Ukraine annexed in 2022 (parts of the Donetsk, Kherson, Luhansk, and Zaporizhzhia regions). If so, such deaths were not included in the Rosstat mortality statistics for 2022–2023.
Furthermore, we obtained directly from Rosstat the data on deaths from external causes, split by sex, from 2000 to 2023. These data are openly available at https://rosstat.gov.ru/folder/210/document/13215 (currently only through 2021). We also used the openly available data on the number of external deaths from 2000 to 2023 split by the cause of death (but without a split by sex), available at https://www.fedstat.ru/indicator/31620. External deaths include murders, suicides, car accidents, accidental deaths, and alcohol poisonings, as well as deaths from military action and terrorist attacks. However, the numbers for the latter two categories are not available. Rosstat likely keeps track of the number of military deaths, as it does for any other cause of death, but did not provide this information upon our request. Finally, we obtained from Rosstat the data on HIV deaths split by age (in one-year bands) and sex, from 2006 to 2023.
In all cases, deaths were grouped by the date of death (as opposed to the date of registration): for example, a death that happened in December 2022 but was registered in January 2023 is part of the 2022 data.
Excess Mortality Calculations
Excess deaths are defined as the difference between the observed number of deaths and the counterfactual number of deaths that could have been expected on the basis of prior trends. To compute this counterfactual baseline, we used extrapolation from pre-2020 data to 2022–2023. The data from 2020–2021 were not used because the number of deaths during that time was higher owing to COVID-19 excess mortality (Karlinsky and Kobak 2021; Kobak 2021), so these two years cannot provide a robust basis for comparison.
As explained earlier, we are interested in excess male mortality. However, it is not possible to obtain the counterfactual number of male deaths in 2022–2023 from the male data alone. This is because Russia experienced another COVID wave in early 2022, meaning that 2022 also showed some COVID-related excess mortality and so not the entire 2022 excess was due to war. To circumvent this problem, we based all our calculations on the ratio of the number of male deaths to the number of female deaths (M/F ratio). Once we obtained the counterfactual M/F ratio for 2022, we multiplied it by the observed number of female deaths in 2022 to get the counterfactual number of male deaths in 2022 (and similarly for 2023).
We performed all calculations for each age group separately, conducting M/F ratio extrapolations and obtaining estimates of excess male mortality in each age group. To get the total estimate of excess male deaths, we summed age-specific estimates over all age groups showing excess deaths (nine age groups from 20–24 to 45–49, see below).
We considered five ways of computing the baseline number of 2022 and 2023 male deaths (our preferred way is the second one listed here):
Using linear extrapolation of M/F ratios from 2015–2019 to 2022–2023, based on the raw number of male and female deaths.
Using linear extrapolation of M/F ratios from 2015–2019 to 2022–2023, but after subtracting the number of HIV deaths from all deaths for each sex, age group, and year. In Russia, male and female HIV-related deaths show peaks in different cohorts (see below) and hence can affect M/F ratios in a time-dependent way, potentially biasing our estimates because HIV-related deaths are unrelated to war fatalities.
Using linear extrapolation of M/F ratios from 2010–2019 (10 years instead of 5 years) to 2022–2023 (after subtracting HIV-related deaths).
Using exponential extrapolation of M/F ratios from 2015–2019 to 2022–2023. We used linear regression to predict log(M/F – 1) from year Y, assuming that M/F = 1 + exp(aY + b). This model assumes that the M/F ratio should eventually converge to 1. Here we also subtracted HIV-related deaths.
Using the 2019 M/F ratio without any extrapolation (after subtracting HIV-related deaths).
All these calculations were based on the number of all-cause (or non-HIV-related) deaths. Apart from that, we used the data on the number of deaths from external causes to compute two further estimates:
6. Using linear extrapolation of M/F ratios from 2015–2019 to 2022–2023, based on the number of external deaths. Note that the data were not split by the exact cause of death.
7. Using linear extrapolation of the number of uncategorized deaths from external causes, after subtracting suicides, alcohol poisonings, murders, and road fatalities. Note that we did not have these data split by sex, so this calculation was not based on the M/F ratios but directly on the number of uncategorized external deaths.
Uncertainty Calculations
To compute standard errors of the linear extrapolation, we followed the approach of Karlinsky and Kobak (2021) and used predictive uncertainty (Abramovich and Ritov 2022). If X is the matrix of predictors (e.g., a 5 × 2 matrix, one column for years from 2015 to 2019 and another column filled with 1s), y is the response vector (5 × 1 vector of M/F ratios in a given age group), and we want to extrapolate the model to x = [2022, 1], then
where n is the sample size (n = 5) and p is the number of predictors (p = 2). Here yˆ are fitted M/F ratios; σˆ2 is the unbiased estimate of the noise variance; and s2 is the predictive variance. To compute excess male mortality, we multiplied the extrapolated M/F ratio with the number of female deaths in 2022 (call it f), so the resulting standard error is given by sf. When summing estimated excess deaths over several age groups (or over several years), we summed their squared standard errors and took a square root.
When using the exponential trend, we applied linear regression to predict log(M/F – 1) from year. Then we computed the baseline number of male deaths as (exp(yˆ) + 1)f. We computed the uncertainty s as above, and took (exp(yˆ + s) – exp(yˆ))f as the uncertainty of the excess estimate.
Results
The raw all-cause number of deaths in nine age groups from 15 to 59 is shown in panel a of Figure 1 from 2015 onward. Comparison of the number of male deaths in the 20–24 age group in 2022–2023 and in the preceding years suggests an obvious excess of 3,000 deaths in 2022 and again in 2023. An even larger excess is visible in the 25–29 age group. However, estimating excess mortality in older groups using the male data alone is not possible owing to the effect of the COVID-19 pandemic in 2020–2022.
To get around this problem, we based our calculations on the ratio of the number of male deaths to the number of female deaths. This ratio was above 1 in all age groups between 15 and 59 (panel b of Figure 1). Prior to 2022, it was slowly decreasing in most age groups. This monotonic decrease has been happening for more than 20 years (Figures S1 and S2 in the online appendix) as a result of decreasing male mortality. In 2020 and 2021, during the height of the pandemic, the M/F ratio strongly decreased in older age groups as the number of deaths rose among both men and women (panel a of Figure 1), reducing the ratio. However, in 2022 the ratio increased above the prepandemic levels, suggesting male-specific excess mortality. To calculate the baseline M/F ratios for 2022–2023, we fitted a linear trend to the 2015–2019 values and extrapolated it to 2022–2023, assuming that the decrease of M/F ratios would have continued after the pandemic if not for the war.
We estimated the number of excess deaths for men in all age groups in 2022 and in 2023 (Table 2, column 1). The excess was clearly above zero in all age groups from 15–19 to 45–49, but barely statistically distinguishable from zero in the two older age groups. As expected, the age range with positive excess roughly corresponded to the combat age. The sum of excess deaths over all nine 15–49 age groups in 2022 was 23,590 ± 1,570 (estimate ± standard error), corresponding to the 95% confidence interval of [20.5, 26.7] thousand. In 2023, the sum of excess deaths over the same groups was 40,540 ± 1,740 (95% CI: [37.1, 44.0] thousand).
To make our estimates more precise, we subtracted the number of HIV-related deaths from the number of all deaths for each sex, age group, and year (Figure S3 in the online appendix). We found that male HIV-related deaths every year peaked in the cohort born in 1979 and female HIV-related deaths peaked in the cohort born in 1983 (Figure S4 in the online appendix) because the HIV epidemic in Russia has been mostly circulating within these cohorts. This affects M/F ratios in a time-dependent way, and hence can bias our estimates as HIV-related deaths are unrelated to war fatalities. When summing over the same 15–49 age groups as above, we obtained 20,620 ± 1,380 excess male non-HIV-related deaths in 2022, corresponding to the 95% confidence interval of [18.7, 22.6] thousand, and 37,860 ± 1,530 in 2023 (95% CI: [34.8, 40.9] thousand) (Table 2, column 2).
For sensitivity analysis, we considered several alternative modeling strategies (see Data and Methods), always subtracting HIV-related deaths. Using a longer 2010–2019 trend, we obtained 21,790 ± 1,320 and 39,280 ± 1,340 excess deaths over the same age groups (Table 2, column 3). Using a five-year exponential trend, we obtained 20,300 ± 1,640 and 37,410 ± 1,610 excess deaths over the same age groups (column 4). This showed that our results were robust with respect to the choice of the trend length or functional shape. Using the 2019 M/F ratios without any extrapolation, we obtained 15,380 and 31,440 excess deaths (column 5). We consider this a substantial underestimate because the M/F ratios have been decreasing in all age groups from 15–19 to 45–49 for more than 20 years (Figure 1 and Figure S1 in the online appendix); in 2022–2023, they should have been lower than in 2019, if not for the war.
We also obtained Rosstat data on the number of deaths from external causes (2000–2023) split by sex (Figure 2). Using linear extrapolation to obtain the baseline M/F ratio, we estimated 20,560 ± 1,080 excess male external deaths in 2022 and 35,670 ± 1,190 in 2023 (when using a five-year trend). This estimate of excess male external deaths was very close to our estimate of excess male non-HIV-related deaths, suggesting that most war fatalities have been correctly coded by Rosstat as deaths from external causes.
Finally, we used the number of uncategorized deaths from external causes, after subtracting suicides, alcohol poisonings, murders, and road fatalities, to compute the number of excess uncategorized external deaths in 2022–2023. We obtained 29,890 ± 2,800 in 2022 and 47,580 ± 3,190 in 2023 (Figure S5 in the online appendix). For this calculation, we did not have information on the split by sex, so using M/F ratios was not possible. Because part of this excess could be due to the indirect effects of the pandemic (or the war) affecting both males and females, we do not consider this to be a reliable estimate of the war fatalities.
Discussion
In this research note, we used the excess male mortality method to estimate Russian fatalities in the Russo-Ukrainian war in 2022–2023, obtaining 58,500 ± 2,500 excess male deaths in 2022–2023 (20,620 ± 1,370 in 2022 and 37,860 ± 1,530 in 2023), spread over the 15–49 age groups. As expected, our estimates are somewhat higher than in the named list of known Russian military losses in the BBC News Russian/Mediazona (2023) dataset that is by definition incomplete.
Our estimate is much lower than the one in Radford et al. (2023), who counted about 77,000 Russian deaths in the first year of war. While our data come from the official Russian mortality statistics, Radford et al. (2023) used the data from about 4,600 claims of losses reported in the news, on social media, and by governmental sources. We would argue that in the conditions of war there are strong incentives for the parties to report high enemy losses, and despite accounting for the possibly biased data, the estimate of Radford et al. (2023) may still be upwardly biased.
Data reliability is also the key issue for our analysis. As explained earlier, we have no reason to suspect that data were directly falsified by Rosstat, and given the patterns found in the data, we consider this unlikely. Nevertheless, our estimate is likely to be a lower bound on the true number of fatalities. First, Rosstat mortality data do not include military personnel missing in action and not yet officially declared dead. According to Russian legislation, soldiers missing in action can only be declared dead by a court order two years after the hostilities end (in April 2023, this period was changed to six months after going missing). Second, it is possible that some deaths could be registered in the regions of Ukraine annexed in 2022 (parts of the Donetsk, Kherson, Luhansk, and Zaporizhzhia regions). These deaths were not included in the Rosstat mortality statistics for 2022–2023. We do not have data on the number of missing Russian military personnel or the number of fatalities registered in the annexed parts of Ukraine (except Crimea and Sevastopol), so we cannot directly assess the size of the downward bias in our estimates.
We performed all calculations using the raw death numbers, not normalized by population size. We did this to avoid relying on population size estimates based on the much criticized 2021 Russian census. Note that our calculations relied on the male-to-female death ratios that implicitly accounted for changing population size (as long as the changes were the same for men and women). Indeed, the M/F ratio trend over the last 20 years was much smoother than the numbers of male and female deaths, because the latter were strongly affected by the dynamics of the population pyramid (Figures S1 and S2 in the online appendix). Our calculations also implicitly accounted for a possible civilian excess mortality in 2022–2023 owing to indirect war effects such as decreased investment in social and health services. Such excess mortality would affect both males and females and would not strongly influence the M/F ratio.
Similarly, our approach assumed that COVID-related excess deaths in 2022 increased male and female deaths (in each age group) by the same factor. There is evidence that COVID-19 in Russia in 2020 took an especially high toll on women (Aburto et al. 2022); if this were also true in 2022, then it could bias our 2022 estimate downward. In 2023, the effect of COVID in Russia was likely negligible.
Our analysis suggests that the crowdsourced dataset on Russian fatalities collected by BBC News Russian/Mediazona (2023), even though inherently incomplete, may eventually contain the records of more than 90% of all fatalities. However, it takes a long time to get to this level of coverage: back at the end of 2022, the project identified 10,711 deaths, much lower than the 17,877 deaths from 2022 identified there by now (February 2025), and closer to 50% of our estimate. Moreover, the number of 2022 deaths in the dataset kept increasing long after the end of 2022: it increased by around 1,000 people over the second half of 2024.
To put our estimates in perspective, the Soviet military fatalities in the Afghan war (1979–1989) were about 15,000, and the Russian losses in the two Chechen wars (1994–1996 and 1999–2000) were about 8,000 (Krivosheev 2001). The Russo-Ukrainian war is therefore by far the most devastating conflict for the Russian military since World War II. To place this in a broader context, the U.S. military lost about 47,000 lives in Vietnam (1965–1973), about 4,400 in Iraq (2003–2010), and about 2,300 in Afghanistan (2001–2014).
Although our data were split by federal region, we found that computing excess male mortality in individual regions was less reliable and resulted in noisy estimates. Furthermore, in the Rosstat data, deaths may not always be attributed to the individual's home region but can potentially refer to the region of death (e.g., hospitals in Rostov Oblast or in Moscow) or the region where the military unit was based. Using the BBC News Russian/Mediazona dataset, Bessudnov (2023) showed large regional inequalities in mortality, with particularly high fatality rates in Buryatia, Tuva, and other regions in Eastern Siberia and the Russian Far East and low fatality rates in Moscow and St. Petersburg.
Acknowledgments
Dmitry Kobak is supported by the Gemeinnützige Hertie-Stiftung and is a member of the University of Tübingen's Cluster of Excellence “Machine Learning—New Perspectives for Science” (EXC 390727645).
