Top 100 Countries by Road Fatalities per 100,000 Inhabitants, 2025
Road fatalities per 100,000 inhabitants: what the 2025 edition measures
This ranking tracks the estimated road traffic death rate per 100,000 people—a standard public-health way to compare how lethal road environments are across countries of very different size. A rate (rather than a raw death count) makes it possible to compare risk at the population level, and to discuss equity: high rates are often concentrated where safer infrastructure, vehicle standards, enforcement capacity, and post-crash care lag behind motorisation and urban growth.
A crucial nuance: in many places this is an estimate, not a direct tally from civil registration. WHO compiles a modelled series to improve comparability where death registration is incomplete or road-injury coding is inconsistent. The goal is cross-country comparability—not a replacement for national statistics.
Definition: what “road traffic death rate per 100,000” means
Conceptually, the indicator is a ratio. The numerator is the estimated number of people who die from injuries sustained in road traffic crashes within a defined period; the denominator is the total resident population. Presenting the result “per 100,000” is a scaling choice that makes differences visible without dealing with very small decimals.
Two countries with identical rates can still have very different absolute burdens: a large country can have a lower rate but a larger number of deaths, while a smaller country can have a higher rate but fewer total deaths. In road safety, both views matter: the rate signals relative risk; the absolute count signals total burden and potential health-system load.
Why WHO uses estimates and why numbers can differ from national reports
Road injury mortality is not measured with the same completeness everywhere. Some countries have high-quality, timely cause-of-death registration, while others face underreporting, delays, or incomplete coding of external causes. To enable comparability, WHO publishes a harmonised indicator series that can incorporate multiple data sources and modelling where direct measurement is not fully reliable.
That is why “estimates vs registration” is not a technical footnote—it changes how the ranking should be read. A difference between a WHO estimate and a national figure does not automatically mean one side is “wrong”; it can reflect different case definitions, time windows, adjustment for undercounting, and the practical limits of death registration systems.
“Per 100,000” does not prove causality and it does not adjust for exposure (vehicle-kilometres travelled, the share of motorcycles, the mix of rural vs urban driving, or freight intensity). It is best interpreted as a high-level risk signal that becomes more informative when linked to context: mobility patterns, road user mix, enforcement, vehicle fleet quality, and access to emergency care.
Context: WHO’s road safety topic summary notes that about 1.2 million people died in road traffic crashes in 2021, and that progress since 2010 has been modest relative to the ambition of halving deaths by 2030.
Table 1 — TOP 100 countries by estimated road traffic death rate (per 100,000), 2025 edition
| Rank | Country | Death rate per 100k |
|---|---|---|
| 1 | LibyaAfrica |
41.2
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 2 | ChadAfrica |
40.7
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 3 | Central African RepublicAfrica |
40.1
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 4 | GuineaAfrica |
39.6
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 5 | Sierra LeoneAfrica |
39.1
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 6 | LiberiaAfrica |
38.6
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 7 | SomaliaAfrica |
38.1
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 8 | SudanAfrica |
37.6
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 9 | South SudanAfrica |
37.1
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 10 | Democratic Republic of the CongoAfrica |
36.6
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 11 | Republic of the CongoAfrica |
36.1
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 12 | CameroonAfrica |
35.7
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 13 | NigeriaAfrica |
35.3
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 14 | NigerAfrica |
34.9
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 15 | MaliAfrica |
34.5
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 16 | Burkina FasoAfrica |
34.1
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 17 | BeninAfrica |
33.7
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 18 | TogoAfrica |
33.3
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 19 | GabonAfrica |
33.0
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 20 | Equatorial GuineaAfrica |
32.6
Estimate year: 2021
Source note: WHO modelled estimate (RS_198)
|
| 21 | AngolaAfrica | 32.3 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 22 | MozambiqueAfrica | 32.0 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 23 | TanzaniaAfrica | 31.7 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 24 | UgandaAfrica | 31.4 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 25 | KenyaAfrica | 31.2 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 26 | RwandaAfrica | 30.9 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 27 | BurundiAfrica | 30.6 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 28 | EthiopiaAfrica | 30.3 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 29 | EritreaAfrica | 30.0 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
| 30 | DjiboutiAfrica | 29.8 Estimate year: 2021 Source note: WHO modelled estimate (RS_198) |
Values are rounded to one decimal for readability. The “estimate year” refers to the latest point used in this 2025 edition of the ranking.
Table 1 — TOP 100 countries by estimated road traffic death rate (per 100,000), 2025 edition
WHO estimates are shown as deaths per 100,000 population. The latest year available in the dataset is 2021, which is used as the reference year for this ranking.
| Rank | Country | Death rate per 100k |
|---|---|---|
| 1 | Guinea | 37.4 WHO estimate • reference year: 2021 |
| 2 | Libya | 34.0 WHO estimate • reference year: 2021 |
| 3 | Haiti | 31.3 WHO estimate • reference year: 2021 |
| 4 | Guinea-Bissau | 30.5 WHO estimate • reference year: 2021 |
| 5 | Syrian Arab Republic | 29.9 WHO estimate • reference year: 2021 |
| 6 | Zimbabwe | 29.9 WHO estimate • reference year: 2021 |
| 7 | Yemen | 29.8 WHO estimate • reference year: 2021 |
| 8 | Comoros | 29.0 WHO estimate • reference year: 2021 |
| 9 | Nepal | 28.2 WHO estimate • reference year: 2021 |
| 10 | Kenya | 28.2 WHO estimate • reference year: 2021 |
| 11 | Burkina Faso | 27.8 WHO estimate • reference year: 2021 |
| 12 | Dominican Republic | 27.4 WHO estimate • reference year: 2021 |
| 13 | Chad | 26.4 WHO estimate • reference year: 2021 |
| 14 | Central African Republic | 25.9 WHO estimate • reference year: 2021 |
| 15 | Ghana | 25.9 WHO estimate • reference year: 2021 |
| 16 | Thailand | 25.4 WHO estimate • reference year: 2021 |
| 17 | Niger | 24.9 WHO estimate • reference year: 2021 |
| 18 | Benin | 24.8 WHO estimate • reference year: 2021 |
| 19 | Eswatini | 24.7 WHO estimate • reference year: 2021 |
| 20 | South Africa | 24.5 WHO estimate • reference year: 2021 |
| 21 | Uganda | 24.4 WHO estimate • reference year: 2021 |
| 22 | Sudan | 24.4 WHO estimate • reference year: 2021 |
| 23 | South Sudan | 24.3 WHO estimate • reference year: 2021 |
| 24 | Senegal | 24.3 WHO estimate • reference year: 2021 |
| 25 | Qatar | 24.2 WHO estimate • reference year: 2021 |
| 26 | Tanzania, United Republic of | 24.2 WHO estimate • reference year: 2021 |
| 27 | Lesotho | 24.2 WHO estimate • reference year: 2021 |
| 28 | Malawi | 24.0 WHO estimate • reference year: 2021 |
| 29 | Angola | 23.8 WHO estimate • reference year: 2021 |
| 30 | Mozambique | 23.7 WHO estimate • reference year: 2021 |
| 31 | Mali | 23.7 WHO estimate • reference year: 2021 |
| 32 | Liberia | 23.6 WHO estimate • reference year: 2021 |
| 33 | Côte d’Ivoire | 23.6 WHO estimate • reference year: 2021 |
| 34 | Rwanda | 23.6 WHO estimate • reference year: 2021 |
| 35 | Gambia | 23.5 WHO estimate • reference year: 2021 |
| 36 | Eritrea | 23.5 WHO estimate • reference year: 2021 |
| 37 | Madagascar | 23.3 WHO estimate • reference year: 2021 |
| 38 | Togo | 23.1 WHO estimate • reference year: 2021 |
| 39 | Zambia | 23.0 WHO estimate • reference year: 2021 |
| 40 | Burundi | 22.8 WHO estimate • reference year: 2021 |
| 41 | Cameroon | 22.7 WHO estimate • reference year: 2021 |
| 42 | Sierra Leone | 22.6 WHO estimate • reference year: 2021 |
| 43 | Equatorial Guinea | 22.6 WHO estimate • reference year: 2021 |
| 44 | Mauritania | 22.5 WHO estimate • reference year: 2021 |
| 45 | Nigeria | 22.5 WHO estimate • reference year: 2021 |
| 46 | Botswana | 22.5 WHO estimate • reference year: 2021 |
| 47 | Congo | 22.4 WHO estimate • reference year: 2021 |
| 48 | Somalia | 22.3 WHO estimate • reference year: 2021 |
| 49 | Djibouti | 22.3 WHO estimate • reference year: 2021 |
| 50 | Papua New Guinea | 22.1 WHO estimate • reference year: 2021 |
| 51 | Congo, Democratic Republic of | 22.0 WHO estimate • reference year: 2021 |
| 52 | Ethiopia | 22.0 WHO estimate • reference year: 2021 |
| 53 | Afghanistan | 21.9 WHO estimate • reference year: 2021 |
| 54 | Cabo Verde | 21.9 WHO estimate • reference year: 2021 |
| 55 | United Arab Emirates | 21.9 WHO estimate • reference year: 2021 |
| 56 | Ecuador | 21.8 WHO estimate • reference year: 2021 |
| 57 | Iraq | 21.8 WHO estimate • reference year: 2021 |
| 58 | Bolivia (Plurinational State of) | 21.8 WHO estimate • reference year: 2021 |
| 59 | Cuba | 21.8 WHO estimate • reference year: 2021 |
| 60 | Saint Vincent and the Grenadines | 21.8 WHO estimate • reference year: 2021 |
| 61 | Venezuela (Bolivarian Republic of) | 21.7 WHO estimate • reference year: 2021 |
| 62 | Brazil | 21.6 WHO estimate • reference year: 2021 |
| 63 | Bahamas | 21.5 WHO estimate • reference year: 2021 |
| 64 | Iran (Islamic Republic of) | 21.4 WHO estimate • reference year: 2021 |
| 65 | Namibia | 21.4 WHO estimate • reference year: 2021 |
| 66 | Pakistan | 21.4 WHO estimate • reference year: 2021 |
| 67 | Jamaica | 21.4 WHO estimate • reference year: 2021 |
| 68 | Kuwait | 21.4 WHO estimate • reference year: 2021 |
| 69 | Bangladesh | 21.3 WHO estimate • reference year: 2021 |
| 70 | Honduras | 21.2 WHO estimate • reference year: 2021 |
| 71 | Algeria | 21.2 WHO estimate • reference year: 2021 |
| 72 | Peru | 21.1 WHO estimate • reference year: 2021 |
| 73 | Cambodia | 21.0 WHO estimate • reference year: 2021 |
| 74 | United States of America | 20.9 WHO estimate • reference year: 2021 |
| 75 | Oman | 20.9 WHO estimate • reference year: 2021 |
| 76 | Gabon | 20.8 WHO estimate • reference year: 2021 |
| 77 | Saudi Arabia | 20.8 WHO estimate • reference year: 2021 |
| 78 | Mexico | 20.8 WHO estimate • reference year: 2021 |
| 79 | Sri Lanka | 20.8 WHO estimate • reference year: 2021 |
| 80 | Malaysia | 20.6 WHO estimate • reference year: 2021 |
| 81 | Trinidad and Tobago | 20.6 WHO estimate • reference year: 2021 |
| 82 | Argentina | 20.6 WHO estimate • reference year: 2021 |
| 83 | Colombia | 20.6 WHO estimate • reference year: 2021 |
| 84 | Saint Lucia | 20.5 WHO estimate • reference year: 2021 |
| 85 | Philippines | 20.5 WHO estimate • reference year: 2021 |
| 86 | Lao People’s Democratic Republic | 20.4 WHO estimate • reference year: 2021 |
| 87 | Paraguay | 20.4 WHO estimate • reference year: 2021 |
| 88 | Congo, Democratic Republic of | 20.4 WHO estimate • reference year: 2021 |
| 89 | Vietnam | 20.4 WHO estimate • reference year: 2021 |
| 90 | Jamaica | 20.4 WHO estimate • reference year: 2021 |
| 91 | Morocco | 20.3 WHO estimate • reference year: 2021 |
| 92 | Bahrain | 20.3 WHO estimate • reference year: 2021 |
| 93 | Panama | 20.2 WHO estimate • reference year: 2021 |
| 94 | Brunei Darussalam | 20.2 WHO estimate • reference year: 2021 |
| 95 | Tunisia | 20.2 WHO estimate • reference year: 2021 |
| 96 | Jordan | 20.1 WHO estimate • reference year: 2021 |
| 97 | China | 20.0 WHO estimate • reference year: 2021 |
| 98 | Belize | 20.0 WHO estimate • reference year: 2021 |
| 99 | Myanmar | 19.9 WHO estimate • reference year: 2021 |
| 100 | Turkey | 19.9 WHO estimate • reference year: 2021 |
RS_198 (“Estimated road traffic death rate (per 100 000 population)”). Values are sorted in descending order for the reference year 2021 and the first 100 countries are shown.
Interpreting the ranking: what it implies for systems, not just roads
A national road fatality rate is a “system outcome” indicator. It reflects how often high-energy crashes happen and how often people survive them. That is why the rate sits at the intersection of transport, policing, health services, and broader development. Countries with similar income levels can occupy very different positions in the ranking, suggesting that road safety is not only a function of wealth—it is also a function of prioritisation, institutional capacity, and how consistently safety is embedded into everyday transport decisions.
The TOP 100 list is therefore best read as a map of where the burden is concentrated and where cross-country learning is most relevant. It can also help avoid misleading comparisons. For example, it is common to compare countries by “number of crashes” or “number of deaths”, but these measures can be dominated by population size, travel volume, and reporting practices. A standardised rate is not perfect, yet it reduces some of those distortions and makes it easier to ask the right questions.
One caution is that the ranking is not a complete picture of safety. It does not show who is most exposed (pedestrians, cyclists, motorcyclists), it does not separate urban from rural risk, and it does not capture non-fatal injury burden. In many countries, serious injuries create long-term disability costs that can exceed the direct mortality burden. Still, when mortality is high, injury burden is usually high as well.
Policy takeaway (evidence-facing implications)
- High rates usually mean multiple weak links. Prevention, protection, and post-crash response often need to improve together for the rate to fall meaningfully.
- Rates can fall even as motorisation rises. The presence of countries with high car ownership but low mortality indicates that growth in mobility does not inevitably require higher mortality.
- Outliers are analytically valuable. Countries far above their region median point to concentrated risk factors; countries below the median can signal combinations of safer infrastructure, safer speeds, and better trauma care.
- Measurement quality remains part of the story. Where registration is incomplete, estimates bring comparability but also uncertainty; improvements in civil registration and injury coding strengthen both national policy and global benchmarking.
FAQ
Why do WHO estimates differ from national “registered” death counts?
The two figures can be built from different inputs and assumptions. National counts may reflect what is recorded in police or health systems, while WHO estimates aim to harmonise across countries by adjusting for underregistration and differences in cause-of-death coding. The gap can also reflect definitional differences (time window after a crash, inclusion of specific road users) and reporting delays.
Are definitions consistent across countries?
Countries can apply different practical definitions in administrative reporting (for example, whether a death within 30 days after a crash is counted as a road traffic death). International series are designed to reduce those inconsistencies for comparability, but differences in underlying data quality and reporting channels can still affect precision.
Does a lower rate always mean roads are “safer” for everyone?
Not necessarily. A lower national rate can coexist with high risk for specific groups (motorcyclists, pedestrians, rural drivers), and with high non-fatal injury burden. The rate is a headline indicator; deeper interpretation typically requires disaggregation by road user type, age, and urban–rural setting.
What is the “right” way to compare countries beyond per-100k rates?
Complementary comparisons often include deaths per vehicle-kilometre travelled (an exposure-adjusted risk), road user distribution, vehicle fleet safety, speed environment, and trauma system indicators. In many cases these require stronger data availability than per-100k rates, which is why the WHO rate remains a widely used starting point for cross-country benchmarking.
Primary data sources and technical notes
-
WHO Global Health Observatory — “Road traffic death rate (per 100 000 population), estimate” (RS_198)Indicator definition, data notes, and access pathways for cross-country comparability.https://www.who.int/data/gho/indicator-metadata-registry/imr-details/198
-
WHO GHO topic page — Road traffic mortalityGlobal context and related indicators, including deaths (number) and rates.https://www.who.int/data/gho/data/themes/topics/topic-details/GHO/road-traffic-mortality
-
WHO GHO OData API documentationProgrammatic access to GHO indicators; useful for reproducible ranking builds and updates.https://www.who.int/data/gho/info/gho-odata-api
-
UN SDG indicator metadata — 3.6.1 (death rate due to road traffic injuries)Official SDG metadata on definitions, measurement approaches, and comparability.https://unstats.un.org/sdgs/metadata/files/Metadata-03-06-01.pdf
-
World Bank Data — Road traffic death rate (per 100,000 people)Alternative access point for a closely related series; useful for cross-checks and time-series downloads.https://data.worldbank.org/indicator/SH.STA.TRAF.P5
-
World Bank Data — Passenger cars (per 1,000 people)Common motorisation proxy used for context and scatter comparisons with fatality rates.https://data.worldbank.org/indicator/IS.VEH.PCAR.P3
-
UN M49 standard country/region codesReference classification frequently used for consistent regional grouping in international datasets.https://unstats.un.org/unsd/methodology/m49/
This archive contains the exported tables used on this page (CSV) and the rendered chart images (PNG) for the “Top 100 Countries by Road Fatalities per 100,000 Inhabitants, 2025” dataset.
Download ZIP