A cost analysis of postpartum home visit programming in Kenya: estimates to aid policymakers
Bibliographic record
Abstract
Introduction: The World Health Organization (WHO) and UNICEF recommend at least two postnatal home visits by a health provider within the first two weeks of life to improve newborn survival. Kenya's Universal Health Coverage (UHC) initiative includes a home-visit strategy to advance Sustainable Development Goal (SDG) 3.2, which targets reducing neonatal mortality to below 12 per 1,000 live births. We estimated the costs of starting a postnatal home-visit program in a level three health facility in Kenya, based on recommendations from Kenya's Ministry of Health policymakers. Methods: An ingredients-based costing method was used to determine the actual costs of home visits incurred during a research project conducted in 2019, and to estimate program costs from the government's perspective as the payer. Per-visit costs were calculated for three staffing approaches: Community Health Promoter (CHP), Registered Nurse (RN), and a Combined model where two providers (RN + CHP) visited each home together. Results: Staff salaries and transportation costs were the main drivers of recurrent program expenses. The CHP approach had the lowest total cost at $27,302 ($24.46 per visit), followed by the RN-only approach at $36.45 per visit, while the Combined model (RN+CHP) was the most expensive at $52.10 per visit. Discussions with policymakers noted that the RN+CHP approach was least feasible and scalable. They proposed an alternative "Hybrid" model in line with current programs being scaled up: weekly RN visits during the first month of life (neonatal period), and quarterly CHP visits thereafter. Discussion: This study presents a costing tool and generalizable formula that policymakers can use to estimate program costs based on different facility characteristics and staffing needs. The findings can support Kenya's efforts to scale up postnatal home-visit programs to improve maternal and newborn health outcomes within the UHC framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".