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S1333 Acceptability and Feasibility of Hepatitis B Screening Using Dried Blood Spots in a Free Community Health Fair Setting

2022· article· en· W4316079854 on OpenAlexaboutno aff
Sirisha Gaddipati, Kristin N Gmunder, Joseph Deshan, Osmay Cardoso, Amar R. Deshpande, Patricia D. Jones

Bibliographic record

VenueThe American Journal of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFamily medicineHepatitis BHepatitis B virusCommunity healthPublic healthInternal medicineNursingImmunologyVirus

Abstract

fetched live from OpenAlex

Introduction: Chronic hepatitis B virus (HBV) infection is a major global health problem. Rates of HBV in Florida are higher than the national rate, reflecting immigration patterns. There are pervasive barriers to screening for HBV. In this study, we aimed to assess the feasibility of using dried blood spot (DBS) tests to screen for HBV at medical-student run, free community health fairs and to understand factors influencing acceptability of such testing. Methods: Participants attended two of the University of Miami’s Mitchell Wolfson Sr. Department of Community Service (DOCS) health fairs in December 2021 and April 2022. At these USPSTF-based screening health fairs, patients were invited to participate and complete a questionnaire assessing HBV screening acceptability. Those who agreed to screening had fingerstick performed and DBS testing; DBS cards were shipped to Molecular Testing Labs (Vancouver, WA). Samples were processed for hepatitis B surface antigen and results were returned to DOCS student personnel, who then provided results via letter and/or telephone call according to a pre-specified schema over 3 to 4 weeks. (Figure) Results: There were 82 participants in this pilot study. While all participants agreed to receive DBS testing, 28.0% reported that aspects made them feel uncomforTable, citing the long turnaround time for results, 78.2%, and lack of financial resources should treatment be necessary, 69.9%. The majority, 72.0%, found the proposed screening acceptable, but proposed that other community members might be uncomforTable with screening due to the lack of immediate results, 33.9%, and lack of resources for treatment, 30.5%. In total, 73 patients had negative tests, 8 patients had insufficient samples for adequate screening; 1 patient received preliminary positive findings for HBV infection. Conclusion: In a community health fair setting, we found that using DBS to screen for HBV was acceptable among most participants receiving similar health screenings. Offering screening tests with faster turnaround time for results could improve acceptability. The finding of insufficient samples highlights the need for quality improvement training to obtain adequate blood samples. Participant concern regarding lack of financial resources if treatment is needed has been reported in other studies. Future studies will examine acceptability at other health fairs with different demographics and a cost-benefit analysis of routine screening for HBV amongst vulnerable patients.Figure 1.: Figure A shows the geographic distribution of the countries of origin of patients seen at the community health fairs. Figure B displays proportion of patients who have been vaccinated for HBV as well as those who have not been vaccinated and those who are unaware of their vaccination status. Figure C shows testing results and the schema utilized to return results to participants and patients Table 1. - Demographics of patients who underwent HBV Screening Median age (years) 49.5 Female sex (%) 50 Race/Ethnicity (%) Black (African American) 2.4 Black (Hispanic) 4.9 White (Hispanic) 85.4 White (Non-Hispanic) 3.7 Prefer not to answer 3.7 Language Preferences (%) Spanish 78 English 22 Country of Origin: United States (%) 12.2 Insurance Type (%) Uninsured 58.5 Private 28 Medicare/Medicaid 6.1 Other/Do not know/Refused 7.3

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.331
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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