Using equitable impact sensitive tool (EQUIST) to promote implementation of evidence informed policymaking to improve maternal and child health outcomes: a focus on six West African Countries
Bibliographic record
Abstract
BACKGROUND: United Nations Children's Fund (UNICEF) designed EQUitable Impact Sensitive Tool (EQUIST) to enable global health community address the issue of equity in maternal, newborn and child health (MNCH) and minimize health disparities between the most marginalized population and the better-off. The purpose of this study was to use EQUIST to provide reliable evidence, based on demographic health surveys (DHS) on cost-effectiveness and equitable impact of interventions that can be implemented to improve MNCH outcomes in Benin, Burkina Faso, Ghana, Mali, Nigeria and Senegal. METHODS: Using the latest available DHS data sets, we conducted EQUIST Situation Analysis of maternal and child health outcomes in the six countries by sub-national categorization, wealth and by residence. We then identified the poorest population class within each country with the highest maternal and child mortality and performed EQUIST Scenario Analysis of this population to identify intervention package, bottlenecks and strategies to address them, cost of the intervention and strategies as well as the number of deaths avertible. RESULTS: Under-five mortality was highest in Atlantique (Benin), Sahel (Burkina Faso), Northern (Ghana), Sikasso (Mali), North-West (Nigeria), and Diourbel (Senegal). The number of under-five deaths was considerably higher among the poorest and rural population. Neonatal causes, malaria, pneumonia and diarrhoea were responsible for most of the under-five deaths. Ante-partum, intra-partum, and post-partum haemorrhages, and hypertensive disorder, were responsible for highest maternal deaths. The national average for improved water source was highest in Ghana (82%). Insecticide treated nets ownership percentage national average was highest in Benin (73%). Delivery by skilled professional is capable of averting the highest number of under-five and maternal deaths in the six countries. Redeployment/relocation of existing staff was the strategy with highest costs in Burkina Faso, Nigeria and Senegal. Ghana recorded the least cost per capita ($0.39) while the highest cost per capita was recorded in Benin ($4.0). CONCLUSION: EQUIST highlights the most vulnerable and deprived children and women needing urgent health interventions as a matter of priority. It will continue to serve as a tool for maximizing the number of lives saved; decreasing health disparities and improving overall cost effectiveness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".