Predictors of Lost to Follow Up (LTFU) among HIV Positive Patients Enrolled in 70 PEPFAR Supported Treatment Facilities in Edo, Bayelsa and Lagos States, Nigeria
Bibliographic record
Abstract
This retrospective cross-sectional study examined demographic factors that predict Lost to Follow-up (LTFU) among HIV-positive patients on treatment based on patient-level data from 2000 to 2021 from 70 the President's Emergency Plan for AIDS Relief (PEPFAR)-supported facilities in Edo, Lagos and Bayelsa states of Nigeria. A total of 32,910 patients were identified for the descriptive analysis, although only 26,797 were included in the final model due to missing values for certain variables. Descriptive statistics describe the basic features of the data, while logistic regression identified patient characteristics at ART initiation that predicted LTFU. A stepwise forward and backward regression were used to select the variables to include in the model. Despite improving adherence in each cohort initiated since 2005, a large proportion of patients (72%) were LTFU between 2005 and 2015. However, thereafter (2016 to 2020) Anti-Retroviral Therapy (ART)’s adherence improved with the average retrospective cumulative LFTU dropping to 27% for the period. The predictive analysis suggests the following patient variables are significantly associated with LTFU at 95% CI: Patients initiated prior to 2018 were 57% more likely to become LTFU. HIV patients who reported post-secondary education as their highest education level were twice as likely to become LTFU in comparison to those with no education. Compared to their counterparts aged 25+, the patients’ ages 0-19 and 20-24 subset are less likely to become LTFU. HIV patients who were divorced or separated were about 1.3 times more likely to be LTFU compared to their married counterparts. The tendency to be LTFU increases at WHO stage 2 and decreases as the patient’s WHO clinical stage progresses from stage 3 to stage 4. Lastly, patients in Edo were 23 times more likely to become LTFU, while patients in Lagos were 4 times more likely to become LTFU compared to their Bayelsa counterparts.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".