Evidence map›Paper›PMID 38476083›Full record

SynthesisPerspectives in public health2026

Impact of the COVID-19 pandemic on employment and inequalities: a systematic review of international evidence and critical appraisal of statistical methods.

A Abugamza, D Kaskirbayeva, A Charlwood, S Nikolova, A Martin

Abstract readSystematic Review
In one paragraph

Synthesis in Perspectives in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

A AbugamzaAcademic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds, UK; Department of Health Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia.ORCID 0000-0001-6408-7772
D KaskirbayevaKAZGUU University, Kazakhstan.
A CharlwoodLeeds University Business School, University of Leeds, UK.
S NikolovaAcademic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds, UK; Real World Methods and Evidence Generation, IQVIA, UK.
A MartinAcademic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsTo assess the impact of the COVID-19 pandemic on individual labour market outcomes and how these vary over time and between different groups of individuals.

methodsSearches were conducted using Medline, Scopus and EconLit. Grey literature searches used Google Scholar and Econpapers. Study quality was assessed using the risk of bias in non-randomised studies of exposure tool (ROBINS-E), accompanied by a directed acyclic graph (DAG) to identify relevant mediators, moderators and confounders.

resultsA total of 85 studies (77 peer-reviewed articles, 8 working papers) were included. The ROBINS-E showed that the overall risk of bias varied between studies from low (

conclusionsThe COVID-19 health crisis caused unanticipated and unprecedented changes to employment opportunities around the world, with potential long-term health consequences. Further research should investigate the longer-term impact of COVID-19, with greater attention given to low- and middle-income countries. Our study provides guidance on the design and critical appraisal of future studies.

Indexed as

COVID-19EmploymentHealth Status DisparitiesFemaleHumansPandemicsSARS-CoV-2Socioeconomic FactorsCOVID-19employmentinequalitiespublic healthsystematic reviewwork

Identifiers

PMID38476083
PMCPMC13091924

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.