Comparing antiretroviral treatment outcomes between a prospective community-based and hospital-based cohort of HIV patients in rural Uganda
Bibliographic record
Abstract
BACKGROUND: Improved availability of antiretroviral therapy in sub-Saharan Africa is intended to benefit all eligible HIV-infected patients; however in reality antiretroviral services are mainly offered in urban hospitals. Poor rural patients have difficulty accessing the drugs, making the provision of antiretroviral therapy inequitable. Initial tests of community-based treatment programs in Uganda suggest that home-based treatment of HIV/AIDS may equal hospital-based treatment; however the literature reveals limited experiences with such programs. THE RESEARCH: This intervention study aimed to; 1) assess the effectiveness of a rural community-based ART program in a subcounty (Rwimi) of Uganda; and 2) compare treatment outcomes and mortality in a rural community-based antiretroviral therapy program with a well-established hospital-based program. Ethics approvals were obtained in Canada and Uganda. RESULTS AND OUTCOMES: Successful treatment outcomes after two years in both the community and hospital cohorts were high. All-cause mortality was similar in both cohorts. However, community-based patients were more likely to achieve viral suppression and had good adherence to treatment. The community-based program was slightly more cost-effective. Per capita costs in both settings were unsustainable, representing more than Uganda's Primary Health Care Services current expenditures per person per year for all health services. The unpaid community volunteers showed high participation and low attrition rates for the two years that this program was evaluated. CHALLENGES AND SUCCESSES: Key successes of this study include the demonstration that antiretroviral therapy can be provided in a rural setting, the creation of a research infrastructure and culture within Kabarole's health system, and the establishment of a research collaboration capable of enriching the global health graduate program at the University of Alberta. Challenging questions about the long-term feasibility and sustainability of a community-based ARV program in Uganda still remain. THE PARTNERSHIP: This project is a continuation of previous successful collaborations between the School of Public Health of Makerere University, the School of Public Health of University of Alberta, the Kabarole District Administration and the Kabarole Research and Resource Center.
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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 | 0.001 | 0.001 |
| 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".