Opportunities to Improve Long COVID Care: Implications from Semi-structured Interviews with Black Patients
Rachel S. Bergmans (),
Keiyana Chambers-Peeple,
Deena Aboul-Hassan,
Samantha Dell’Imperio,
Allie Martin,
Riley Wegryn-Jones,
Lillian Z. Xiao,
Christine Yu,
David A. Williams,
Daniel J. Clauw and
Melissa DeJonckheere
Additional contact information
Rachel S. Bergmans: University of Michigan, Medical School
Keiyana Chambers-Peeple: University of Michigan, Medical School
Deena Aboul-Hassan: University of Michigan
Samantha Dell’Imperio: University of Michigan, Medical School
Allie Martin: University of Michigan, Medical School
Riley Wegryn-Jones: University of Michigan
Lillian Z. Xiao: University of Michigan
Christine Yu: University of Michigan
David A. Williams: University of Michigan, Medical School
Daniel J. Clauw: University of Michigan, Medical School
Melissa DeJonckheere: University of Michigan, Medical School
The Patient: Patient-Centered Outcomes Research, 2022, vol. 15, issue 6, No 12, 715-728
Abstract:
Abstract Background Long coronavirus disease (COVID) is an emerging condition that could considerably burden healthcare systems. Prior qualitative studies characterize the experience of having long COVID, which is valuable for informing care strategies. However, evidence comes from predominantly White samples. This is a concern because underrepresentation of Black patients in research and intervention development contribute to racial inequities. Objective To facilitate racial equity in long COVID care, the purpose of this qualitative study was to inform the development of care strategies that are responsive to the experiences and perspectives of Black patients with long COVID in the United States of America. Methods Using convenience sampling, we conducted race-concordant, semi-structured, and open-ended interviews with Black adults (80% female, mean age = 39) who had long COVID. We transcribed and anonymized the recorded interviews. We analyzed the transcripts using inductive, thematic analysis. Theme development focused on who can help or hinder strategies for reducing health inequities, what should be done to change care policies or treatment strategies, and when are the critical timepoints for intervention. Results We developed four main themes. Participants reported challenges before and after COVID testing. Many participants contacted primary care physicians as a first step for long COVID treatment. However, not all respondents had positive experiences and at times felt dismissed. Without a qualifying diagnosis, participants could not obtain disability benefits, which negatively influenced their employment and increased financial hardship. Conclusions There are possible targets for improving long COVID care, from COVID testing through to long-term treatment plans. There is a need to increase long COVID awareness among physicians. Diagnosis and a standardized treatment plan could help patients avoid unnecessary healthcare utilization and obtain comprehensive support.
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s40271-022-00594-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:patien:v:15:y:2022:i:6:d:10.1007_s40271-022-00594-8
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40271
DOI: 10.1007/s40271-022-00594-8
Access Statistics for this article
The Patient: Patient-Centered Outcomes Research is currently edited by Christopher I. Carswell
More articles in The Patient: Patient-Centered Outcomes Research from Springer, International Academy of Health Preference Research
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().