Cost-effectiveness of community health systems strengthening: quality improvement interventions at community level to realise maternal and child health gains in Kenya
Bibliographic record
Abstract
INTRODUCTION: Improvements in maternal and infant health outcomes are policy priorities in Kenya. Achieving these outcomes depends on early identification of pregnancy and quality of primary healthcare. Quality improvement interventions have been shown to contribute to increases in identification, referral and follow-up of pregnant women by community health workers. In this study, we evaluate the cost-effectiveness of using quality improvement at community level to reduce maternal and infant mortality in Kenya. METHODS: We estimated the cost-effectiveness of quality improvement compared with standard of care treatment for antenatal and delivering mothers using a decision tree model and taking a health system perspective. We used both process (antenatal initiation in first trimester and skilled delivery) and health outcomes (maternal and infant deaths averted, as well as disability-adjusted life years (DALYs)) as our effectiveness measures and actual implementation costs, discounting costs only. We conducted deterministic and probabilistic sensitivity analyses. RESULTS: We found that the community quality improvement intervention was more cost-effective compared with standard community healthcare, with incremental cost per DALY averted of $249 under the deterministic analysis and 76% likelihood of cost-effectiveness under the probabilistic sensitivity analysis using a standard threshold. The deterministic estimate of incremental cost per additional skilled delivery was US$10, per additional early antenatal care presentation US$155, per maternal death averted US$5654 and per infant death averted US$37 536 (2017 dollars). CONCLUSIONS: This analysis shows that the community quality improvement intervention was cost-effective compared with the standard community healthcare in Kenya due to improvements in antenatal care uptake and skilled delivery. It is likely that quality improvement interventions are a good investment and may also yield benefits in other health areas.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".