Beyond cost-effectiveness, morbidity and mortality: a comprehensive evaluation of priority setting for HIV programming in Uganda
Bibliographic record
Abstract
BACKGROUND: While there has been progress in controlling the HIV epidemic, HIV still remains a disease of global concern. Some of the progress has been attributed to increased public awareness and uptake of public health interventions, as well as increased access to anti- retroviral treatment and the prevention of vertical HIV transmission. These interventions would not have been possible without substantial investments in HIV programs. However, donor fatigue introduces the need for low income countries to maximize the benefits of the available resources. This necessitates identification of priorities that should be funded. Evaluating prioritization processes would enable decision makers to assess the effectiveness of their processes, thereby designing intervention strategies. To date most evaluations have focused on cost-benefit analyses, which overlooks additional critical impacts of priority setting decisions. Kapiriri & Martin (2010) developed and validated a comprehensive framework for evaluating PS in low income countries. The objective of this paper report findings from a comprehensive evaluation of priority setting for HIV in Uganda, using the framework; and to identify lessons of good practice and areas for improvement. METHODS: This was a qualitative study based on forty interviews with decision makers and policy document review. Data were analysed using INVIVO 10, and based on the parameters in Kapiriri et al's evaluation framework. RESULTS: We found that HIV enjoys political support, which contributes to the availability of resources, strong planning institutions, and participatory prioritization process based on some criteria. Some of the identified limitations included; undue donor and political influence, priorities not being publicized, and lack of mechanisms for appealing the decisions. HIV prioritization had both positive and negative impacts on the health system. CONCLUSIONS: The framework facilitated a more comprehensive evaluation of HIV priority setting. While there were successful areas, the process could be strengthened by minimizing undue influence of external actors, and support the legitimate institutions to set priorities and implement them. These should also institute mechanisms for publicizing the decisions, appeals and increased accountability. While this paper looked at HIV, the framework is flexible enough to be used in evaluating priority setting for other health programs within similar context.
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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 | 0.098 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".