Maternal and child health indicators in primary healthcare facilities: Findings in a health systems quasi-experimental study in western Kenya
Bibliographic record
Abstract
Background and purpose: Maternal and infant mortality are higher in low-income than in high-income countries due to weak health systems. The objective of this study was to improve access, utilization and quality of Maternal and Child Health care through a predesigned Enhanced Health Care System (EHC) that embodies the World Health Organization (WHO) pillars of the health system. Design and methodology: This study was conducted in two dispensaries in the Counties of Busia and Bungoma in Kenya as intervention sites and in four control clusters in Kakamega, Uasin Gishu, Trans Nzoia and Elgeyo Marakwet Counties. The study population was pregnant women and their children delivered over the study period in the intervention and control clusters.A quasi-experimental study design was used to conduct the study between 2015 and 2020 to compare the outcomes of the implementation of the EHC using the Find Link Treat and Retain (FLTR) strategy in one cluster, community owned initiatives in the other cluster and four control clusters at baseline and at the end of the study. A baseline survey was conducted in year one and an end line survey in the fifth year. Continuous data collection on maternal and childhood health indicators was done in all the six clusters and comparison made at the end of the study between the clusters. Results: We found a 26%, 10.3% and 0.8% increase in antenatal care (ANC) attendance in the intervention clusters of Obekai, Kabula and control clusters respectively. There was a 28.2%, 5.8% and 17.0% increase in attendance of 4+ ANC clinics of Obekai, Kabula and control clusters respectively. There was a 24% and 13% increase in Obekai and Kabula respectively in contraceptive use and a 2% decrease in contraceptive use in the control locations. There was a 38.2%, 25.6% and 34.7% increase in facility deliveries over the study period in Obekai, Kabula and control clusters respectively. There was a marked increase in immunization coverage in the intervention clusters of Obekai and Kabula compared to a significant decrease in control clusters for BCG, polio, pentavalent and measles. Conclusions and recommendations: In conclusion, use of the health systems approach in health care provision provides a holistic improvement in access and utilization of health services and in the improvement of health indicators.We do recommend that a systems approach be used in health services delivery to improve access, utilization and quality of health care provision at community and primary care levels.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".