Improving maternal, newborn, and child health outcomes through a community-based women’s health education program ( <i>Chamas for Change</i> ): a cluster randomized controlled trial
Bibliographic record
Abstract
ABSTRACT Introduction Community-based women’s health education groups may improve maternal, newborn, and child health (MNCH); however, evidence from sub-Saharan Africa is lacking. Chamas for Change (Chamas) is a community health volunteer (CHV)-led health education program for pregnant and postpartum women in western Kenya. We evaluated Chamas ’ effect on facility-based deliveries and other MNCH outcomes. Methods We conducted a cluster randomized controlled trial involving 74 communities in Trans Nzoia County. We included pregnant women who presented to health facilities for their first antenatal care visits by 32 weeks gestation. We randomized community clusters 1:1 without stratification or matching; we masked data collectors, investigators, and analysts to allocation. Intervention clusters were invited to bimonthly, group-based, CHV-led health lessons ( Chamas ); control clusters had monthly CHV home-visits (standard of care). The primary outcome was facility-based delivery at 12-months follow-up. We conducted an intention-to-treat approach with multilevel logistic regression models using individual-level data. We prospectively registered this trial with ClinicalTrials.gov ( NCT03187873 ). Results Between November 27, 2017 and March 8, 2018, we enrolled 1920 participants from 37 intervention and 37 control clusters. A total of 1550 (80.7%) participants completed the study with 822 (82.5%) and 728 (78.8%) in the intervention and control arms, respectively. Facility-based deliveries improved in the intervention arm (80.9% vs 73.0%; Risk Difference (RD) 7.4%, 95% CI 3.0-12.5, OR=1.58, 95% CI 0.97-2.55, p=0.057). Chamas participants also demonstrated higher rates of 48-hour postpartum visits (RD 15.3%, 95% CI 12.0-19.6), exclusive breastfeeding (RD 11.9%, 95% CI 7.2-16.9), contraceptive adoption (RD 7.2%, 95% CI 2.6-12.9), and infant immunization completion (RD 15.6%, 95% CI 11.5-20.9). Conclusion Chamas participation was associated with significantly improved MNCH outcomes compared with the standard of care. This trial contributes robust data from sub-Saharan Africa to support community-based, women’s health education groups for MNCH in resource-limited settings. KEY QUESTIONS What is already known? Globally, maternal and infant deaths have declined over the last three decades; however, low and middle-income countries (LMICs), including Kenya, still disproportionately incur the highest morbidity and mortality. The World Health Organization recommends leveraging lay health workers (LHWs), including community health volunteers (CHVs), to promote maternal, newborn, and child health (MNCH) in resource-limited settings. Prior research suggests coupling strategies that promote community-based approaches (i.e. integrating LHWs) and women’s health education and support groups during pregnancy and postpartum may improve MNCH; however, robust evidence from sub-Saharan Africa is lacking. What are the new findings? Using a cluster randomized controlled trial design, we found that participation in Chamas for Change (Chamas) – a group-based women’s health education program led by CHVs – was associated with significantly improved MNCH outcomes, including facility-based deliveries, compared with the standard of care (i.e. monthly home-visits) in rural Kenya. This trial also demonstrated significant associations between program participation and receiving 48 hour postpartum home-visits, breastfeeding exclusively, adopting a contraceptive method postpartum, and immunizing infants fully by 12 months of life as compared to the standard of care. These findings support pilot data from a preceding evaluation of the Chamas program as well as the current literature on community-based interventions delivered by LHWs to promote MNCH in other resource-limited settings. What do the new findings imply? Effective community-based strategies that build upon existing infrastructure to promote MNCH are needed to continue to improve the health and well-being of women and infants in rural sub-Saharan Africa and other LMICs. Chamas offers an innovative approach to improve MNCH in resource-limited settings with significant health policy implications; collective evidence from this trial and preceding studies support community-based women’s health education groups as an effective strategy for improving uptake of facility-based deliveries and other life-saving MNCH practices.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".