Influence of Transportation Cost on Long-Term Retention in Clinic for HIV Patients in Rural Haiti
Bibliographic record
Abstract
BACKGROUND: With improved access to antiretroviral therapy in resource-constrained settings, long-term retention in HIV clinics has become an important means of reducing costs and improving outcomes. Published data on retention in HIV clinics beyond 24 months are, however, limited. In our clinic in rural Haiti, we hypothesized that individuals residing in locations with higher transportation costs to clinic would have poorer retention than those who had lower costs. METHODS: We used a retrospective cohort design to evaluate potential predictors of HIV clinic retention. Patient information was abstracted from the electronic medical records. Cox proportional hazards regression was used to identify independent predictors of 4-year clinic retention. RESULTS: There were 410 patients in our cohort, 266 (64.9%) females and 144 (35.1%) males. Forty-five (11%) patients lived in locations with transportation costs >$2. Males were 1.5 times more likely to live in municipalities with transportation costs to clinic of >$2. Multivariate analysis suggested that age <30 years, male gender, and transportation cost were independent predictors of loss to follow-up (LTFU): risk ratio of 2.98, 95% confidence interval (CI): 1.73 to 4.96, P < 0.001; 1.71, CI: 1.08 to 2.70, P = 0.02; and 1.91, CI: 1.08 to 3.36, P = 0.02, respectively. CONCLUSIONS: Patients with transportation costs greater than $2 were 1.9 times more likely to be lost to care compared with those who paid less for transportation. HIV treatment programs in resource-constrained settings may need to pay closer attention to issues related to transportation cost to improve patient retention.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".