Predictors of loss to follow-up among HIV-infected patients in a rural South-Eastern Nigeria Hospital: A 5-year retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Patient attrition has been a challenge in managing HIV programs in resource-limited settings. AIM: This study reviews the predictors of loss to follow-up (LTFU) in our hospital and suggests the best practices for dealing with the issue. SUBJECTS AND METHODS: A 5-year retrospective cohort study of 1256 HIV-infected patients. Baseline CD4 counts, age, gender, year of enrolment, and antiretroviral therapy combination regimen were considered in this study. Kaplan-Meier models were used to estimate the univariate time-to-LTFU and Cox proportional hazards models to identify the multivariate predictors of LTFU. RESULTS: Twenty-four percent (23.9% [301/1256]) of patients were lost to follow-up. Baseline CD4 count, year of enrolment, and drug combination were significant predictors of LTFU. Patients enrolled earlier (2008/2009) were twice as likely to be LTFU compared with those enrolled later (2010-2013). Gender and age did not significantly predict LTFU nor confound other predictors. CONCLUSION: The program showed higher LTFU rates than most studies in Nigeria and Africa, maybe due to difficulties with the access to the hospital and possible treatment fatigue. This study recommends the provision of transportation subsidies and proactive patient follow-up with "peer-tracking" to reduce LTFU among HIV infected patients, especially in resource-limited settings.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".