How COVID-19 is affecting vulnerable groups

By Yu-shan Chiu and Bonnie Chiu

Data has suggested an emerging trend that COVID-19 puts a disproportionate burden on vulnerable groups such as the elderly, women, and ethnic minorities. These groups are not vulnerable in and of themselves, but are rather made vulnerable by the unequal systems and structures. As a firm deeply committed to diversity and inclusion, we wanted to consolidate the evidence currently scattered across many sources, to bring a holistic picture on how COVID-19 is affecting vulnerable groups. We hope that the emerging evidence base can generate some actionable insights, so that equity and inclusion will become integral to COVID-19 responses.

You will find the interactive database here, as a Google Sheets.

In the first launch of the database, we gathered a list of selected sources that demonstrate the five common effects of COVID-19 on seven frequently mentioned vulnerable groups across the globe. The database does not mean to be exhaustive, but is our attempt of consolidating popular sources. We want to add more country data and quantitative data as time goes on, but this will require further releases from official sources, and more granular disaggregation. If you have any sources you’d like to add, please request for editing rights via the Google Sheets or e-mail [email protected].

Understanding the database

Among the sources we listed, some provide quantitative analysis with raw data for further manipulation; some offer insights through qualitative data such as case and expert interviews; others describe the situation with summary statistics and graphs from official sources. We thus categorised data types into:

  • Quantitative: mostly quantitative data in the source
  • Qualitative: mostly qualitative data in the source
  • Both: balance between quantitative and qualitative data

We also remarked if the sources visualise the data, as categorised below:

  • Raw data: For quantitative data, the source usually will provide its own visualisation. But we think it is important to mark the source as “raw data”, viewers can access them and conduct their analysis. As for qualitative data, raw data means that the source provides first-hand interviews with vulnerable groups.
  • Infographic: It means that the source mainly uses infographics to explain the situation, which usually will include second-hand data.
  • Descriptive data: It means that the source is purely descriptive, drawing figures and quotations from other sources to form insights and opinions.

We then gave a brief summary of the sources and marked when the data were published. Please note that some quantitative data update regularly (usually weekly). We also included the countries mentioned in the sources. Following this basic information, we mapped the sources against vulnerable groups and the effects on them.

Seven frequently mentioned vulnerable groups are selected:

  • Gender: Data is disaggregated for male and female.
  • Age: Data is disaggregated for different age groups, especially youth and the elderly.
  • Class: Data describes the effects on different income groups.
  • Ethnicity: Data describes the effects on ethnic minorities in different countries.
  • Disability: Data describes effects on people that have a limitation in their physical or mental conditions.
  • LGBTIQ: Data describes effects on lesbian, gay, bisexual, transgender/transsexual, intersex and queer/questioning.
  • Pregnancy: Data mentions specific effects on pregnant women. 

Five common effects are identified: 

  • Cases: People infected with COVID-19.
  • Death: Mortality due to COVID-19.
  • Job loss: Unemployment due to COVID-19.
  • Income: Income is affected such as job loss, salary cut, temporary layoffs, and furlough.
  • Violence: All type of violence such as domestic abuse and xenophobia.

Below is an infographic that summarises the number of data sources across the categories mentioned above.

Top Findings

Among vulnerable groups, data for gender and age is most disaggregated and quantitative, especially in health-related areas. Granularity for other groups is required to inform solutions to prevent adverse, long-lasting impact.

In terms of the effects of COVID-19 on these groups, here are some selected findings from the sources we compiled:

  • Work circumstances have put vulnerable groups at higher risks in many countries because they tend to work at the frontline and in hard-hit sectors. 
  • The previous economic vulnerability has already given some groups poor health and lack of healthcare. Situations are worsened due to diseases and job loss.
  • A surge of domestic violence was observed everywhere due to lockdown.

Recognising that some groups are also hit hard if they face multiple disadvantages (the concept of intersectionality) when using the database, one can filter by vulnerable groups and effects to pinpoint sources that describe complicated effects on segments in overlapping groups. For instance, one can select “gender” and “ethnicity”, and then filter out an article articulating how COVID-19 affected black women’s health and economy in the United States. We found that 12 out of 27 data sources (44%) have captured the intersectionality dimension of COVID-19.

We hope that you will find the compilation of the data sources useful in guiding your work, through our meta-analysis of the databases. We welcome any feedback on what more we can do to ensure an equitable response to COVID-19. In the meantime, we will update sources frequently to track continual analysis in this area, so check back from time to time!

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