Unlocking value: a comprehensive costing study of primary health care service delivery in Tanzania
Bibliographic record
Abstract
BACKGROUND: Tanzania has long prioritized primary health care (PHC) as the pathway to achieving universal health coverage. However, greater, and more effective investments are needed to expand access to quality PHC services and further improve population health outcomes. Furthermore, as Tanzania graduated to lower-middle-income country status, the Government is expected to move towards full domestic financing of health services. To support this aim, there is a need to estimate the current expenditure of PHC services, the resources needed to deliver quality PHC services according to nationally defined standards, and the gap between the two. METHODS: A top-down approach was used to understand the costs incurred by the government to provide PHC services in public health facilities. All facility and community-level expenditures incurred by the government and development partners on human resources, medicines, medical supplies, and facility operations were collected and included in the costing. The total funding gap was calculated as the difference between actual expenditure and estimated normative cost. The gap analysis was undertaken by input categories and level of facility. RESULTS: Government expenditure on PHC substantially increased between fiscal year (FY) 2021/22 and 2022/23. Nevertheless, the spending level is significantly lower than global benchmarks, and the resources required to deliver quality PHC services according to the basic service standards. Moreover, the analysis revealed there are important differences in the levels of spending per capita across regions and health service delivery productivity. CONCLUSIONS: The Government of Tanzania's PHC spending increased significantly over the two years, raising the per capita PHC expenditure and the expenditure per outpatient visit. As the Government of Tanzania increasingly finances health services from domestic sources, a key consideration for long-term planning in the context of declining partner funding is the total amount of funding required to provide quality PHC services equitably to the population. At the same time, a more detailed understanding of current PHC expenditure informs the calculation and estimation of the funding gap.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".