Patient-Centered Outcomes Associated With a Novel Office-Based Opioid Treatment Program in a District Health Department: Mixed Methods Pilot Study
Bibliographic record
Abstract
BACKGROUND: Granville and Vance counties have some of the highest opioid-related death rates in North Carolina, and have significant unmet needs with regard to opioid treatment. Medication for opioid use disorder (MOUD) is the most effective evidence-based approach to address opioid use disorder. Despite demonstrated efficacy and substantial need, access to MOUD is still insufficient in many parts of the United States. In order to connect patients with needed MOUD services, the district health department, Granville Vance Public Health (GVPH), established an office-based opioid treatment (OBOT) program. OBJECTIVE: In this formative pilot study, we sought to describe patients' goals and outcomes in a program delivered at a rural local health department using an integrated care approach. METHODS: We used a mixed methods concurrent nested research design. The primary method of investigation was one-on-one qualitative interviews with active OBOT patients (n=7) focused on patients' goals and perceived impacts of the program. Trained interviewers followed a semistructured interview guide developed iteratively by the study team. The secondary method was a descriptive quantitative analysis (79 patients; 1478 visits over 2.5 years) of treatment retention and patient-reported outcomes (anxiety and depression). RESULTS: Participants in the OBOT program were 39.6 years of age on average, and 25.3% (20/79) were uninsured. The average retention in the program was 18.4 months. The proportion of individuals in the program with moderate to severe depression (Patient Health Questionnaire-9 scores ≥10) decreased between program initiation (66%, 23/35) and at the most recent assessment (34%, 11/32). In qualitative interviews, participants credited the OBOT program for reducing or stopping the use of opioids and other substances (eg, marijuana, cocaine, and benzodiazepines). Many participants noted how the program helped them manage withdrawal symptoms and cravings, which helped them feel more in control of their use. Participants also attributed improvements in quality of life to the OBOT program, such as improved relationships with loved ones, improved mental and physical health, and improved financial stability. CONCLUSIONS: Initial data show promising patient outcomes for active GVPH OBOT participants, including reduction in opioid use and improvements in quality of life. As a pilot study, a limitation of this study is a lack of a comparison group. However, this formative project demonstrates promising patient-centered outcome improvements for GVPH OBOT participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".