On this page you can explore a variety of demographic variables and investigate which countries were reporting them as part of their COVID-19 surveillance data. For each variable and country you can follow the hyperlinks to the original data sources.
Data disaggregated by demographic characteristics is essential to understand who is left behind and therefore will be one of the factors that determine if the Sustainable Development Goals are on track.
In target SDG 17.18 countries committed to “enhance capacity-building support to developing countries [...] to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.”
The COVID-19 pandemic allowed us to measure progress on this goal. While SDG 17.18 focused on “least developed countries and small island developing states”, the data published on these pages indicates that the majority of countries have a long way to go to meet this SDG target.
Disaggregated data is crucial because it allows us to identify inequities in health outcomes. By collecting and analysing data on factors such as race, ethnicity, income, and disability, we can identify which groups are at a disadvantage and work towards addressing the root causes of these inequities. This data is essential for developing targeted interventions to reduce health inequities . Without disaggregated data, we risk perpetuating systemic injustices.In October 2022 we looked at whether countries were reporting COVID-19 data disaggregated by demographic characteristics (also called demographic variables) other than gender/sex. We explored national reporting systems for their COVID-19 data disaggregated by variables including race or ethnicity, disability, place of residence (urban/rural), pregnancy status, refugee status, and reporting the presence of other clinical conditions (illnesses) in people with COVID-19. This research provides a snapshot of nationally reported surveillance data available at one point in time - October 2022 - rather than a summary of all data on demographic characteristics ever reported on during the pandemic. If you think we have missed something, we would be very pleased to hear from you. Please contact us at: info@globalhealth5050.org
| Sex/gender | Age | People with clinical risk | Nationality | Race or ethnicity | Living with disability | Socio-economic status or poverty status | Pregnant and/or breastfeeding women | Rural/urban | Internally Displaced Persons (IDP) or Refugees | |
|---|---|---|---|---|---|---|---|---|---|---|
| TOTAL countries reporting any data for this characteristic |
186
|
84
|
16
|
8
|
5
|
5
|
4
|
4
|
4
| 0 |
| Countries reporting data for tests |
18
|
6
| 0 | 0 |
1
| 0 | 0 | 0 | 0 | 0 |
| Countries reporting data for cases |
183
|
70
|
1
|
6
|
3
|
3
|
2
|
2
|
2
| 0 |
| Countries reporting data for hospitalisations |
77
|
14
|
4
|
1
|
2
|
1
|
1
|
1
|
1
| 0 |
| Countries reporting data for intensive care admissions |
20
|
9
|
4
|
1
|
4
| 0 |
2
|
2
|
1
| 0 |
| Countries reporting data for deaths |
140
|
44
|
8
| 0 |
2
|
4
|
2
|
1
|
1
| 0 |
| Countries reporting data for vaccinations |
114
|
33
|
4
|
1
|
3
|
2
| 0 |
1
|
1
| 0 |
Data disaggregated by demographic characteristics enhances the ability of countries to promote equitable access to prevention and care interventions, monitor the possible inequitable impacts of the pandemic on different groups of people, and ensure that no-one is left behind in responses to the pandemic.
We reviewed the availability of data from 205 countries, comprising 29 classified as low-income (LICs), 96 middle-income (both lower-middle and upper-middle; MICs) and 80 high-income (HICs) [World Bank classifications].
Here is what we found when looking at the availability of disaggregated data across these 205 countries:
85 countries - 11 LICs, 30 MICs and 43 HICs - reported any COVID-19 data disaggregated by age groups, such as cases, deaths, vaccinations etc.. There is a strong association between age and risk of poor outcomes, but only 44 countries reported age-disaggregated COVID-19 mortality data - a missed opportunity to better understand how COVID-19 has been impacting the health of older people.
A handful of countries reported data disaggregated by demographic characteristics other than age or sex/gender. We found minimal amounts of disaggregated data on race or ethnicity (reported in 5 HICs), people living with disability (reported in 5 HICs), socio-economic status (4 HICs), or the impact of COVID-19 in people with pre-existing illnesses/comorbidities (1 LIC, 3 MICs and 12 HICs).
No countries report disaggregated data on COVID-19 among internally displaced persons and/or refugees, despite the known vulnerabilities of these populations to both adverse risk environments (e.g. over-crowded living conditions) and inequitable access to health care interventions.
Low-income countries report the smallest number of disaggregated data variables (beyond sex-disaggregation). No LICs or MICs reported disaggregated data on socio-economic status, race or ethnicity, disability, rural/urban status or pregnant women.
No countries have reported COVID-19 data disaggregated by all ten of the characteristics we reviewed of which eight are recommended by SDG 17.18. The lack of data disaggregated by demographics severely limits our understanding of the impact of the disease on different population groups and thus the ability of countries to prioritise vulnerable groups and ensure no one is left behind. Only four countries (England, Wales, and Northern Ireland and the United States of America) have reported COVID-19 data disaggregated by six or more characteristics. Most other countries reporting disaggregated data (n=79) reported on two or three characteristics - generally sex/gender and age. Nineteen countries have not reported COVID-19 data disaggregated by ANY of the ten characteristics.
Good data starts at collection. Countries should collect health data disaggregated by as many of the recommended demographic characteristics outlined in SDG 17.18 as possible. Good data collection is a collaborative process requiring inputs from a range of stakeholders, including communities and people affected.
Define the terms. Some demographic characteristics will vary by region or context and some demographic classifications will contain binaries and hierarchies, such as the historical classification of races or ethnicities. Variables should be carefully considered for potential inequities, clearly defined (with the participation of those affected) and standardised to the extent possible for anyone accessing or using the data.
Support all stages of data collection and collation. Governments need to invest in disaggregated data. Collecting, reporting, interpreting and acting on disaggregated data requires financial and human resource capacity at all stages.
Use disaggregated data to shape and inform policy. Disaggregated data is vital for decision making (including policy decisions, resource allocation, programme evaluation) to address the needs of those groups of people being left behind.
Disaggregated data should be open access. Data disaggregated by demographic characteristics should be published in accessible or widely-used formats and wherever possible should be freely available for use by decision-makers, researchers, and the public. Governments should explain why they do not publish data on particularly affected communities.
