Does distance from a clinic and poverty impact visit adherence for noncommunicable diseases? A retrospective cohort study using electronic medical records in rural Haiti
Bibliographic record
Abstract
BACKGROUND: Adherence to regular outpatient visits is vital to managing noncommunicable diseases (NCDs), a growing burden in low and middle-income countries. We characterized visit adherence among patients with NCDs in rural Haiti, hypothesizing higher poverty and distance from the clinic were associated with lower adherence. METHODS: We analyzed electronic medical records from a cohort of adults in an NCD clinic in Mirebalais, Haiti (April 2013 to June 2016). Visit adherence was: 1) visit constancy (≥1 visit every 3 months), 2) no gaps in care (> 60 days between visits), 3) ≥1 visit in the last quarter, and 4) ≥6 visits per year. We incorporated an adapted measure of intensity of multidimensional poverty. We calculated distance from clinic as Euclidean distance or self-reported transit time. We used multivariable logistic regressions to assess the association between poverty, distance, and visit adherence. RESULTS: We included 463 adult patients, mean age 57.8 years (SE 2.2), and 72.4% women. Over half of patients had at least one visit per quarter (58.1%), but a minority (19.6%) had no gaps between visits. Seventy percent of patients had a visit in the last quarter, and 73.9% made at least 6 visits per year. Only 9.9% of patients met all adherence criteria. In regression models, poverty was not associated with any adherence measures, and distance was only associated with visit in the last quarter (OR 0.87, 95% CI [0.78 to 0.98], p = 0.03) after adjusting for age, sex, and hardship financing. CONCLUSIONS: Visit adherence was low in this sample of adult patients presenting to a NCD Clinic in Haiti. Multidimensional poverty and distance from clinic were not associated with visit adherence measures among patients seen in the clinic, except for visit in the last quarter. Future research should focus on identifying and addressing barriers to visit adherence.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".