The research team, led by Benyaporn Bannasan of the Department of Adult and the Aged Nursing at Thammasat University’s Faculty of Nursing, conducted a systematic search of four major bibliographic databases: PsycINFO, PubMed, Scopus, and Web of Science. Their search window deliberately straddled the pandemic, capturing studies published from the start of 2019, before the first reports of the novel coronavirus, through the end of 2024, well after vaccines had reshaped the epidemiological landscape. Fourteen studies met the team’s inclusion criteria, providing a body of evidence diverse enough to examine whether the relationship between disadvantage and stroke outcomes shifted when health systems came under extraordinary strain.

The statistical core of the analysis was a random-effects meta-analysis, the standard technique for combining effect estimates from studies that are expected to differ in their populations, settings, and methods. Rather than assuming a single true effect size shared by all studies, the random-effects model treats each study as drawing from a distribution of possible effects, which yields wider but more honest confidence intervals when heterogeneity is present. The pooled result showed a significant moderate positive association between health disparities and stroke outcomes, with a correlation coefficient of 0.34 and a 95 percent confidence interval spanning 0.22 to 0.45. In practical terms, populations experiencing greater health disadvantage were reliably more likely to experience poorer outcomes after stroke, and this relationship was unlikely to be a statistical fluke given the interval’s distance from zero.

To probe the robustness of that finding, the authors carried out subgroup analyses, splitting the included studies by design, by COVID-19 period, and by country setting. The association remained statistically significant within every one of these categories, a pattern that speaks to the pervasiveness of the underlying phenomenon. Notably, however, the differences between the subgroups themselves were not statistically significant. That absence of a detectable difference between pre-pandemic and during-pandemic studies is one of the most striking results in the analysis. It suggests that although the pandemic disrupted stroke systems of care in dramatic and well-documented ways, the fundamental gradient in outcomes between advantaged and disadvantaged populations persisted largely unchanged across the divide.

Where the researchers did find meaningful statistical separation was among types of disparity. When studies were grouped according to the kind of disadvantage they measured, significant between-group differences emerged. The strongest associations with poor stroke outcomes were identified for disparities in healthcare access, followed by racial disparities, socioeconomic disparities, and disparities in healthcare utilization. This ordering carries a certain logic. Access to care is the gateway through which every other intervention must pass; a patient who cannot reach a stroke-ready hospital within the treatment window cannot benefit from thrombolysis or thrombectomy regardless of any other factor. Racial and socioeconomic disadvantage, in turn, shape both the likelihood of reaching care promptly and the quality of care received once there, while utilization disparities capture the realized gap between the care that is available and the care that people actually receive.

The authors also employed meta-regression, a technique that asks whether study-level characteristics can statistically explain the variability in effect sizes observed across studies. Two candidate moderators were tested: the year of publication and the economic classification of the country in which the study was conducted. Neither emerged as a significant explanation of between-study variability. The failure of publication year to moderate the effect reinforces the subgroup finding that the disparity-outcome relationship was stable across the pandemic timeline. The failure of country economic group suggests that the association was not confined to wealthy nations or to low- and middle-income settings, but instead appeared across diverse healthcare contexts, albeit with the caveat that meta-regression in a pool of fourteen studies has limited statistical power to detect such moderators.

Stroke is a disease of minutes. Every thirty-minute delay in reperfusion therapy translates into measurable losses in neurological function, and the entire architecture of modern stroke care, from public awareness campaigns to thrombectomy-capable centers, is built around compressing the interval between symptom onset and definitive treatment. Against that backdrop, the meta-analytic finding that access and utilization disparities carry the strongest associations with poor outcomes is a pointed indictment of structural barriers. During the pandemic, those barriers were compounded by fear of infection in emergency departments, by triage protocols that deprioritized some stroke presentations, and by the diversion of rehabilitation resources, all of which fell hardest on populations already navigating fragile connections to the health system.

The study’s framing as an examination of outcomes across the pandemic gives it particular relevance for public health preparedness. The authors argue that their findings highlight the importance of addressing structural and socioeconomic inequities in stroke care and in planning for future health emergencies. If the disparity-outcome gradient held steady through a period when health systems were stretched to their limits, then resilience planning that does not explicitly account for disadvantaged populations risks reproducing the same pattern in the next crisis. The implication is that equity cannot be treated as a secondary consideration to be addressed once surge capacity is secured; it must be built into the design of emergency stroke pathways from the outset.

Like all meta-analyses, the study inherits the limitations of its constituent parts. Fourteen studies is a modest number for the range of subgroup and meta-regression analyses performed, and the authors’ own finding that subgroup differences were mostly not statistically significant underscores how much uncertainty remains about which moderators truly matter. The correlation of 0.34 describes an association, not a causal mechanism, and the underlying observational studies cannot fully disentangle the effects of disadvantage from confounding factors such as stroke severity at presentation, comorbidity burden, and measurement differences across countries and healthcare systems. The article was also published as a shared, citable version ahead of final editorial production, a mechanism Springer uses to accelerate access to peer-reviewed accepted research.

Nevertheless, the consistency of the signal is difficult to dismiss. A moderate pooled association, significant across study designs, pandemic periods, and country settings, and strongest precisely where the pathway into care is most vulnerable, converges with a broad literature on social determinants of health. The work was supported by the Thammasat University Research Fund, and the authors report no competing interests. As health systems digest the lessons of COVID-19, this analysis offers a quantitative reminder that the pandemic’s unequal toll was not an aberration but an amplification, and that the levers for change, access, utilization, and the socioeconomic conditions that shape both, lie largely outside the stroke unit itself, in the structural fabric of the societies it serves.

Subject of Research: Health disparities and stroke outcomes before and during the COVID-19 pandemic

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

Article References: Health inequalities in stroke outcomes across the COVID-19 pandemic: a meta-analysis. (n.d.). https://doi.org/10.1007/s44155-026-00449-9

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00449-9

Keywords: stroke, health disparities, COVID-19, meta-analysis, healthcare access, health inequality, socioeconomic status, healthcare utilization, stroke outcomes, public health preparedness, racial disparities, social determinants of health