Postnatal home visitation: Lessons from country programs operating at scale
Bibliographic record
Abstract
BACKGROUND: Newborn mortality remains unacceptably high in many countries. Postnatal home visits (PNHVs) have been endorsed as a strategy for delivery of postnatal care (PNC) to reduce newborn mortality as well as to improve maternal outcomes. This paper reports on a review of coverage-related performance of such programs implemented at scale through government health services in Bangladesh, Ethiopia, Ghana, India, Indonesia, Malawi, Myanmar, Nepal, Pakistan, Rwanda, Sri Lanka and Uganda. METHODS: We undertook a multi-country, mixed-method program review and used available survey and administrative data and key informant interviews to characterize performance of postnatal home visitation programs. In results presented in this paper, we have relied primarily on population-based surveys, notably Demographic and Health Surveys and Multi-Indicator Cluster Surveys. In addition, based on key informant interviews, we sought to understand the implementation challenges experienced delivering PNHVs, as well as responses to those challenges, in order to provide useful insights to countries to design home visitation programming when they can meet requirements for effective delivery at scale - and to identify other options when they cannot. RESULTS: Contact coverage of PNC within 48 hours of birth following home birth (the group most prioritized in these programs) is below 10% in most of the countries reviewed; in no country does it exceed 20%. Most country programs have been unable to achieve PNHV contact coverage that would have any meaningful impact on newborn or maternal mortality. Country responses to disappointing performance have varied: some continued programming unchanged, some suspended attempts to provide PNHVs, and others modified their strategies for providing postnatal care (PNC). CONCLUSIONS: Policymakers and program managers need to consider seriously context and local feasibility when determining whether and how to use a strategy like PNHVs. At the global level, we need more than evidence of effectiveness (as determined through proof-of-concept trials) as a basis for formulating recommendations for how governments should provide services. We must also give serious attention to what can be learned from experience implementing at scale and place greater importance on feasibility of implementation in the real world.
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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 | 0.028 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".