Barriers to Hepatitis C Virus Care and How Federally Qualified Health Centers Can Improve Patient Access to Treatment
Bibliographic record
Abstract
Background: Despite the availability of direct-acting antiviral agents (DAAs) for hepatitis C virus (HCV) treatment, disparities in HCV care and treatment persist for underserved populations due to demographic-based and insurance-based barriers. We aim to examine the effect of barriers on HCV treatment access for a federally qualified health center (FQHC) population. Methods: We retrospectively evaluated medical records of adults diagnosed with chronic HCV at an FQHC clinic from 2016 to 2020 with follow-up through 2021. Univariate and bivariate analyses were used to describe the patient population and significant associations between predictors of linkage to HCV care and treatment access. Adjusted multivariate logistic regression analyses were used to identify predictors of starting HCV treatment. Results: Of 279 total patients with chronic HCV, 162 patients started treatment (58%), 138 patients (50%) completed treatment, and 99 patients (35%) achieved sustained virological response (SVR). Of the total patients, 145 (52%) were seen by their primary care physician (PCP) for their HCV care and treatment, and 134 (48%) were seen by a provider that specializes in management and treatment of HCV (HCV provider). Patients seen by an HCV provider in addition to their PCP were more likely to have had their prior authorization requests for HCV treatment denied by their insurance providers than patients seen only by their PCP for HCV care (30% vs. 14%, P = 0.001). We believe that this discrepancy stems from two issues. One, prior authorizations are reviewed by insurance providers who are not specially trained in HCV management, so the verbiage used perplexes these reviewers, possibly causing them to issue denials. Two, insurance providers often require HCV genotype testing for DAA medication eligibility, and HCV providers order genotype tests for patients only when HCV treatments have failed to cure patients, so this requirement becomes another barrier to DAA medications. Patients who spoke a non-English language, lived in the USA for less than 10 years, and showed inability to pay for treatment had received treatment despite these characteristics being common barriers to HCV treatment. On multivariate regression, factors independently associated with patients starting treatment included prior denial for DAA medication (odds ratio (OR), 8.88; 95% confidence interval (CI), 3.22 - 24.6; P < 0.001) and being seen by an HCV provider (OR, 24.8; 95% CI, 11.7 - 52.5; P < 0.001). However, the most significant barrier to HCV treatment access for the FQHC population was eligibility restrictions from insurance providers. Conclusions: Demographic-based barriers (e.g., age, race, and income) often impede HCV care and treatment, but insurance-based barriers are the greatest challenge currently that affects treatment outcomes in our study population. Removing these restrictions would, in our opinion, help to increase treatment levels to underserved populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".