TOP 10 Countries with Highest Excess Mortality in 2020–2024
TOP 10 Countries with Highest Excess Mortality in 2020–2024
Excess mortality compares how many people actually died in a given period with how many deaths would have been expected if pre-pandemic trends had continued. It captures not only confirmed COVID-19 deaths, but also indirect effects of the crisis – missed treatment, overwhelmed hospitals, economic shocks and behavioural changes.
What do “excess deaths per 100k” actually measure?
In this article we focus on excess deaths per 100,000 population, accumulated over the period from early 2020 to the most recent data points in 2023–2024. Excess deaths are calculated as:
Excess deaths = Observed all-cause deaths − Expected deaths (based on 2015–2019 trends, adjusted for seasonality). Dividing by population and scaling to 100,000 people allows comparisons between countries of very different size.
The estimates used here are based on the World Mortality Dataset (WMD) and the Human Mortality Database (HMD), as processed by Our World in Data. For countries without detailed weekly data, modelled estimates from international organisations and statistical offices are used. The methodology is broadly similar across sources, but there is still uncertainty, especially in countries with incomplete death registration.
Throughout the text we refer to the period as “2020–2024”, but for many countries the latest complete data point is in late 2022 or 2023. The ranking therefore reflects the pandemic period to date, not a final tally.
Data sources, scope and limitations
The ranking uses several complementary sources:
- World Mortality Dataset (WMD) – weekly or monthly all-cause deaths for more than 120 countries, with a projection-based baseline for expected deaths.
- Human Mortality Database (HMD) – high-quality mortality series, especially for European and high-income countries.
- Our World in Data (OWID) excess mortality series – harmonised, downloadable indicators such as cumulative excess deaths per million and P-scores (% above baseline).
- National statistical offices and civil registration systems – for cross-checks where national series are more up to date than international compilations.
The metric for the Top 10 ranking is cumulative excess deaths per 100,000 population from 2020 to the latest available date (usually mid- or late-2023). Because countries differ in age structure, data completeness and pandemic timing, the figures should be interpreted as approximate comparative indicators, not exact death counts.
With these caveats in mind, the table below summarises the countries that experienced the highest excess mortality burden relative to their population during the COVID-19 era.
Top 10 countries by cumulative excess deaths per 100,000 people
The Top 10 list is dominated by Eastern Europe and the Western Balkans, with one Latin American country (Peru) also among the hardest-hit. Values are rounded and based primarily on WMD/OWID estimates and related modelled series for 2020–2023.
| Country | Cumulative excess deaths per 100k (2020–2023, approx.) |
Region |
|---|---|---|
| Lithuania | ≈1,206 | Eastern Europe |
| Russia | ≈1,067 | Eastern Europe |
| Bulgaria | ≈988 | Eastern Europe |
| Serbia | ≈872 | Western Balkans |
| Bosnia and Herzegovina | ≈860 | Western Balkans |
| North Macedonia | ≈860 | Western Balkans |
| Croatia | ≈753 | Eastern Europe |
| Georgia | ≈713 | Caucasus |
| Peru | ≈618 | Latin America |
| Albania | ≈618 | Western Balkans |
Values are approximate and rounded. Coverage typically runs from early 2020 to late 2022 or 2023, depending on the latest available data for each country. Rankings may change slightly as mortality statistics for 2023–2024 are revised and finalised.
Annual pattern: from shock waves in 2020–2021 to lingering excess deaths
Excess mortality did not unfold evenly over time. Most countries experienced sharp waves of deaths associated with infection surges in 2020 and 2021, followed by more moderate but still significant excess mortality in 2022–2023. The table below shows a stylised summary of annual excess mortality as a P-score – the percentage difference between observed and expected deaths.
The values are averaged across all countries with reliable data (“World”) and across the Top 10 high-mortality countries listed above (“Top 10 average”). They are meant to show the broad pattern rather than precise country-level estimates.
| Year | World excess mortality P-score (% above expected deaths, approx.) |
Top 10 average P-score (approx.) |
|---|---|---|
| 2020 | ≈11% | ≈18% |
| 2021 | ≈15% | ≈25% |
| 2022 | ≈8% | ≈19% |
| 2023 | ≈5% | ≈12% |
| 2024* | ≈3% | ≈6% |
*Values for 2024 are based on partial data and model projections. The broad pattern – a peak in 2021 followed by gradually declining but still positive excess mortality – is consistent across multiple data sources and recent peer-reviewed studies.
Why did some countries accumulate so many excess deaths?
Countries in Eastern Europe and the Western Balkans combined several risk factors: relatively older populations, high prevalence of cardiovascular and metabolic disease, limited ICU capacity and sometimes delayed or fragmented public-health responses. When major waves hit in late 2020 and 2021, systems were quickly overwhelmed, leading to large spikes in all-cause mortality.
In Latin America, Peru stands out because early waves struck before health systems could scale oxygen supply, testing and intensive care. Subsequent methodological revisions to the country’s vital statistics also revealed that the initial official COVID-19 death toll had substantially under-counted the true impact of the pandemic.
It is important to emphasise that “highest excess mortality” does not mean “worst policy in every dimension”. Some countries faced severe pre-existing vulnerabilities or structural constraints. Excess mortality provides a summary outcome metric, but understanding why it was high requires deeper analysis of health-system preparedness, socio-economic conditions and timing of both the virus waves and mitigation measures.
Visualising excess mortality: selected countries vs. world
The line chart below compares annual P-scores (% above expected deaths) for several of the hardest-hit countries – Lithuania, Russia, Bulgaria and Peru – with the global average. The pattern shows intense waves in 2020–2021, followed by lower but still elevated excess mortality in subsequent years.
Figure 1. Annual excess mortality P-score (%), selected countries vs. world, 2020–2024 (approx.)
Stylised values based on Our World in Data and World Mortality Dataset estimates. The curves highlight how Eastern Europe and Peru experienced much higher peaks than the global average.
The bar chart ranks the Top 10 countries by cumulative excess deaths per 100,000 people over the pandemic period. This visual emphasises just how far above typical mortality patterns these countries have been pushed.
Figure 2. Cumulative excess deaths per 100k, 2020–2023/24 – Top 10 countries (approx.)
Each bar shows approximate cumulative excess deaths per 100,000 population. Lithuania and Russia stand out, but several other Eastern European and Western Balkan countries are close behind.
Interpreting the Top 10: structural vulnerabilities and policy lessons
High excess mortality often reflects a combination of virus dynamics, population structure, health-system capacity and policy decisions. Eastern European and Balkan countries in the Top 10 typically entered the pandemic with lower life expectancy, higher rates of cardiovascular disease, fewer ICU beds per capita and constraints in primary care. When large waves arrived, these systems had little spare capacity.
In Peru, early community spread collided with structural inequalities and limited access to oxygen, intensive care and high-quality outpatient treatment. Later, improved surveillance and retrospective reconciliation of death certificates increased the measured excess mortality even further, revealing that the initial data had substantially underestimated the crisis.
At the same time, some high-income countries with strong hospital systems still recorded persistent excess mortality into 2022–2023, driven by delayed care for non-COVID conditions, disruption of chronic-disease management, and possible long-term effects of SARS-CoV-2 infections on cardiovascular and metabolic risk. Excess mortality is therefore best seen as a broad stress-test of health and social protection systems, not just a COVID-specific metric.
The key policy lessons include the need for robust surveillance and civil registration, surge-ready hospital and primary-care capacity, protection of vulnerable groups, and clear risk communication. Countries that invested in these capacities before the pandemic generally saw lower excess mortality, even when case numbers were high.
Caveats: why excess mortality estimates differ between sources
Different organisations – WHO, the World Mortality Dataset, national offices and academic groups – often publish different estimates for the same country. This is not necessarily an error: it reflects differences in how expected deaths are modelled, how missing weeks are imputed, and how reporting delays are treated.
In countries with incomplete registration or long reporting lags, models must extrapolate from limited data. As a result, uncertainty bands are wide, and rankings near the middle of the distribution are not very informative. The Top 10 presented here focuses on countries where multiple independent datasets converge on very high excess mortality.
For analytical work, it is essential to consult the underlying series directly and, where possible, cross-check against national statistical publications and metadata. The links below provide entry points to the main open datasets.
Data sources and further reading
- Our World in Data – Excess mortality during the Coronavirus pandemic (COVID-19)
- OWID grapher – Cumulative excess deaths per million (country-level series)
- World Mortality Dataset (Karlinsky & Kobak) – weekly all-cause mortality and P-scores
- Human Mortality Database – Short-term Mortality Fluctuations (STMF) series
- WHO – Global excess deaths associated with COVID-19 (methods and estimates for 2020–2021)
- Excess mortality analysis code and data (Karlinsky & Kobak, eLife 2021)
All CSV tables and PNG chart images used in the excess mortality ranking are available in a single ZIP archive:
excess_mortality_top10_2020_2024_assets.zip.
Download archive
