Who is accessing public-sector anti-retroviral treatment in the Free State, South Africa? An exploratory study of the first three years of programme implementation
Bibliographic record
Abstract
BACKGROUND: Although South Africa has the largest public-sector anti-retroviral treatment (ART) programme in the world, anti-retroviral coverage in adults was only 40.2% in 2008. However, longitudinal studies of who is accessing the South African public-sector ART programme are scarce. This study therefore had one main research question: who is accessing public-sector ART in the Free State Province, South Africa? The study aimed to extend the current literature by investigating, in a quantitative manner and using a longitudinal study design, the participants enrolled in the public-sector ART programme in the period 2004-2006 in the Free State Province of South Africa. METHODS: Differences in the demographic (age, sex, population group and marital status) socio-economic (education, income, neo-material indicators), geographic (travel costs, relocation for ART), and medical characteristics (CD4, viral load, time since first diagnosis, treatment status) among 912 patients enrolled in the Free State public-sector ART programme between 2004 and 2006 were assessed with one-way analysis of variance, Bonferroni post-hoc analysis, and cross tabulations with the chi square test. RESULTS: The patients accessing treatment tended to be female (71.1%) and unemployed (83.4%). However, although relatively poor, those most likely to access ART services were not the most impoverished patients. The proportion of female patients increased (P < 0.05) and their socio-economic situation improved between 2004 and 2006 (P < 0.05). The increasing mean transport cost (P < 0.05) to visit the facility is worrying, because this cost is an important barrier to ART uptake and adherence. Encouragingly, the study results revealed that the interval between the first HIV-positive diagnosis and ART initiation decreased steadily over time (P < 0.05). This was also reflected in the increasing baseline CD4 cell count at ART initiation (P < 0.05). CONCLUSIONS: Our analysis showed significant changes in the demographic, socio-economic, geographic, and medical characteristics of the patients during the first three years of the programme. Knowledge of the characteristics of these patients can assist policy makers in developing measures to retain them in care. The information reported here can also be usefully applied to target patient groups that are currently not reached in the implementation of the ART programme.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".