Antenatal care coverage and early childhood mortality in Zimbabwe: new interpretations from nationally representative household surveys
Bibliographic record
Abstract
Zimbabwe has implemented universal antenatal care (ANC) policies since 1980 that have significantly contributed to improvements in ANC access and early childhood mortality rates. However, Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), two of Zimbabwe's main sources of health data and evidence, often provide seemingly different estimates of ANC coverage and under-five mortality rates. This creates confusion that can result in disparate policies and practices, with potential negative impacts on mother and child health in Zimbabwe. We conducted a comparability analysis of multiple DHS and MICS datasets to enhance the understanding of point estimates, temporal changes, rural-urban differences and reliability of estimates of ANC coverage and neonatal, infant and under-five mortality rates (NMR, IMR and U5MR, separately) from 2009 to 2019 in Zimbabwe. Our two samples z-tests revealed that both DHS and MICS indicated significant increases in ANC coverage and declines in IMR and U5MR but only from 2009 to 2015. NMR neither increased nor declined from 2009 to 2019. Rural-urban differences were significant for ANC coverage (2009-15 only) but not for NMR, IMR and U5MR. We found that there is a need for more precise DHS and MICS estimates of urban ANC coverage and all estimates of NMR, IMR and U5MR, and that shorter recall periods provide more reliable estimates of ANC coverage in Zimbabwe. Our findings represent new interpretations and clearer insights into progress and gaps around ANC coverage and under-five mortality rates that can inform the development, implementation, monitoring and evaluation of policy and practice responses and further research in Zimbabwe.
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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 | 0.000 | 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".