Ensuring social data's reliability is essential in accurately evaluating social and economic impacts across geographical locations, economic sectors and stakeholder categories. Yet, the MRIO model utilized in our research (EXIOBASE) was hindered by out-of-date or significantly proxy fatality statistics, causing potential inaccuracies in our findings. We have comprehensively revised EXIOBASE fatality data to address this shortcoming, incorporating detailed, nation-specific, and up-to-date data. The update includes work-related fatal occupational injuries as well as fatalities associated with occupational exposure to a variety of 17 hazardous substances and conditions such as asbestos, arsenic, benzene, beryllium, cadmium, chromium, diesel engine exhaust, formaldehyde, nickel, polycyclic aromatic hydrocarbons, silica, sulfuric acid, trichloroethylene, asthmagens, particulate matter, gases and fumes, noise and ergonomic factors. Our methodological process is built on three pillars: data acquisition, raw data processing, and computation of fatal injuries by country, gender, year, and EXIOBASE economic sector.

Data were sourced from the World Health Organization (WHO) (Pega et al., 2021) and Eurostat databases (Publications Office of the European Union, 2013). The WHO data was carefully screened based on specific criteria such as age above 15 years, gender, and fatal injuries only. Eurostat data provided granular information on work-related fatalities, classified by economic activities in the European Community (or NACE Rev.2 (Eurostat, 2008)). The WHO provided aggregate fatality data for 2010 and 2016. The strategy for allocating these deaths across Eurostat categories depended on the countries' geographical location, with different methods applied to European and non-European nations.

For European nations, fluctuations in fatality numbers within a NACE Rev.2 sector mirrored the changes registered by Eurostat. For non-European countries, fatality figures were proportionally allocated across economic sectors split according to the NACE Rev.2 classification, reflecting the workforce size associated with each economic sector. Due to the scarcity of data for nations within Asia, America, or Africa, we adopted a regional approach, computing fatality ratios over each NACE Rev.2 category for each region by integrating data for available countries over a reference year. For 2010 and 2016, the aggregate fatality figures for nations within these three zones were established. Due to the temporal proximity of both reference years, we postulated a linear trend in the fatality count between these two years. The number of fatalities for a specific country, year, and per NACE Rev.2 activity was then calculated by applying the previously mentioned fatality ratio to the total number of deaths for that nation. Last, we applied the European annual ratios to their total mortality figures for the few countries that could not be classified as European or belonging to one of the aforementioned zones.

The result is a comprehensive database that includes the number of fatalities (expressed in the number of deaths for work-related fatal occupational injuries and in Disability-adjusted life years (DALYs), for fatalities associated with occupational exposure to a specific risk factor), detailed at the country, gender, and NACE Rev.2 sector levels from 2008 to 2019, providing insights into work-related fatal injuries across different health effects and geographical regions.

Nomenclature

Archives:

• Concordance_ISIC_Exiobase.xlsx : Concordance between the International Standard Industrial Classification (ISIC) and the exiobase sectors
• Concordance_ISO3_EXIO3.xlsx - Concordance between the ISO3 code and the Exiobase regions
• Workforce_by_ISO3.csv - Number of active persons per Country (ISO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)
• Workforce_by_EXIO3.csv - Number of active persons per Exiobase region (EXIO3 code), per Statistical Classification of Economic Activities in the European Community (NACE), Sex, Year (from 1991 to 2021)
• Death_ISO3.csv - Number of death per Country (ISO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)
• Death_EXIO3.csv - Number of death per Exiobase Region (EXIO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Year (from 2009 to 2019)
• Injuries_ISO3.zip - Archive of DALY per Country (ISO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)
• Injuries_EXIO3.zip - Archive of DALY per Exiobase Region (EXIO3 code), Exiobase Sector, Sex, Estimate (point, lower, upper), Type of Exposure, Year (from 2009 to 2019)
  

Content of Injuries_*.zip:

• arsenic_*.csv
• asbestos_*.csv
• asthmagens_*.csv
• benzene_*.csv
• beryllium_*.csv
• cadmium_*.csv
• chromium_*.csv
• diesel_*.csv
• ergonomic_*.csv
• formaldehyde_*.csv
• gases_*.csv
• nickel_*.csv
• noise_*.csv
• polycyclic_*.csv
• silica_*.csv
• sulfuric_*.csv
• trichloro_*.csv

In 2019, the occupational injury death rate for workers in South Korea was approximately **** deaths per every hundred thousand workers. Comparatively, the occupational injury death rate for workers in Sri Lanka was approximately *** death per ******************** workers in 2019.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

This horizontal bar chart displays death rate (per 1,000 people) by country using the aggregation average, weighted by population. The data is about countries.

This scatter chart displays self-employed workers (% of total employment) against death rate (per 1,000 people). The data is about countries.

This scatter chart displays vulnerable employment (% of total employment) against death rate (per 1,000 people) in Caribbean. The data is about countries.

This dataset is about countries. It has 194 rows. It features 2 columns including death rate. It is 100% filled with non-null values.

This dataset is about countries per year in South America. It has 768 rows. It features 4 columns: country, vulnerable employment, and death rate.

This dataset is about countries per year in Europe. It has 2,816 rows. It features 4 columns: country, country full name, and death rate.

This scatter chart displays death rate (per 1,000 people) against vulnerable employment (% of total employment) in Oceania. The data is about countries.

This dataset is about countries per year in Czech Republic. It has 64 rows. It features 4 columns: country, country full name, and death rate.

This scatter chart displays unemployment (% of total labor force) against death rate (per 1,000 people) in Europe. The data is about countries.

This scatter chart displays death rate (per 1,000 people) against vulnerable employment (% of total employment) in South America. The data is about countries per year.

This scatter chart displays death rate (per 1,000 people) against self-employed workers (% of total employment) in Caribbean. The data is about countries.

This dataset is about countries per year in Ireland. It has 64 rows. It features 4 columns: country, self-employed workers, and death rate.

This dataset is about countries in Oceania. It has 14 rows. It features 3 columns: vulnerable employment, and death rate.

This dataset is about countries per year in the Americas. It has 2,240 rows. It features 4 columns: country, GDP, and death rate.

This dataset is about countries per year in St. Lucia. It has 64 rows. It features 4 columns: country, country full name, and death rate.

This dataset is about countries per year in Somalia. It has 64 rows. It features 4 columns: country, unemployment, and death rate.

This bar chart displays death rate (per 1,000 people) by country full name using the aggregation average, weighted by population. The data is about countries.