Health inequalities in stroke outcomes across the COVID-19 pandemic: a meta-analysis

This meta-analysis examined the association between health disparities and stroke outcomes across the pre-COVID-19 and during-COVID-19 periods. A systematic search of PsycINFO, PubMed, Scopus, and Web of Science identified relevant studies published between 1 January 2019 and 31 December 2024. Fourteen studies met the inclusion criteria. Random-effects meta-analysis demonstrated a significant moderate positive association between health disparities and stroke outcomes ( r = 0.34, 95%CI [0.22, 0.45]). Subgroup analyses showed significant associations across study design, COVID-19 period, and country setting categories, although subgroup differences were not statistically significant. In contrast, significant between-group differences were observed according to disparity type, with the strongest associations identified for healthcare access, racial, socioeconomic, and healthcare utilization disparities. Meta-regression analyses indicated that publication year and country economic group did not significantly explain between-study variability. Overall, the findings suggest that health disparities were consistently associated with poorer stroke outcomes across diverse healthcare contexts, highlighting the importance of addressing structural and socioeconomic inequities in stroke care and public health preparedness.

Authors

Institutions

Publication Details

Journal
Discover Social Science and Health
Published
2026-06-30
DOI
https://doi.org/10.1007/s44155-026-00449-9
Primary Topic
COVID-19 and healthcare impacts
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Health inequalities in stroke outcomes across the COVID-19 pandemic: a meta-analysis

Chatchai Mingmalairak, Debra Hain, Jinpitcha Sathiyamas, Bunyong Rungreungduayboon et al.
Discover Social Science and Health
COVID-19 and healthcare impacts
article

Health inequalities in stroke outcomes across the COVID-19 pandemic: a meta-analysis

Chatchai Mingmalairak, Debra Hain, Jinpitcha Sathiyamas, Bunyong Rungreungduayboon, Benyaporn Bannasan
article en

Abstract

This meta-analysis examined the association between health disparities and stroke outcomes across the pre-COVID-19 and during-COVID-19 periods. A systematic search of PsycINFO, PubMed, Scopus, and Web of Science identified relevant studies published between 1 January 2019 and 31 December 2024. Fourteen studies met the inclusion criteria. Random-effects meta-analysis demonstrated a significant moderate positive association between health disparities and stroke outcomes ( r = 0.34, 95%CI [0.22, 0.45]). Subgroup analyses showed significant associations across study design, COVID-19 period, and country setting categories, although subgroup differences were not statistically significant. In contrast, significant between-group differences were observed according to disparity type, with the strongest associations identified for healthcare access, racial, socioeconomic, and healthcare utilization disparities. Meta-regression analyses indicated that publication year and country economic group did not significantly explain between-study variability. Overall, the findings suggest that health disparities were consistently associated with poorer stroke outcomes across diverse healthcare contexts, highlighting the importance of addressing structural and socioeconomic inequities in stroke care and public health preparedness.

Discover Social Science and Health
Thammasat University (TH), Kasetsart University (TH), Florida Atlantic University (US)
Thammasat University
Openalex Percentile: Top 8%
COVID-19 and healthcare impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.