Reduction in maternal mortality ratio varies by district in Sidama National Regional State, southern Ethiopia: Estimates by cross-sectional studies using the sisterhood method and a household survey of pregnancy and birth outcomes
Bibliographic record
Abstract
BACKGROUND: Few studies assess the magnitude, variations, and reduction of maternal mortality at a lower administrative level. This study was conducted to estimate the life time risk (LTR) of maternal death and the maternal mortality ratio (MMR) and assess the reduction in MMR. METHODS: This is a population-based cross-sectional study conducted in six districts of Sidama National Regional State, southern Ethiopia, from July 2019 to May 2020. The study was conducted with men and women aged 15-49 years. By creating a retrospective cohort of women of reproductive age, we calculated the LTR of maternal mortality and approximated the MMR using the total fertility of the rural Ethiopian population. Variations in maternal mortality was assessed based on characteristics of the respondents, like age, sex, and the districts where they lived. Reduction in MMR was examined using the estimates of the sisterhood method and the 5-year recall of pregnancy and birth outcome household survey. RESULTS: We analysed 17374 (99.6%) respondents: 8884 (51.1%) men and 8490 (48.9%) women. The 17,374 respondents reported 64,387 maternal sisters. 2,402 (3.7%) sisters had died; 776 (32.3%) were pregnancy-related deaths. The LTR of maternal death was 3.2%, and the MMR was 623 (95% CI: 573-658) per 100,000 live births (LB). The remote district (Aroresa) had a MMR of 1210 (95% CI: 1027-1318) per 100,000 LB. The estimates from male and female respondents were not different. A significant reduction in MMR was observed in districts located near the regional centre. However, no reduction was observed in districts located far from the regional centre. CONCLUSIONS: The high MMR with district-level variations and the lack of mortality reduction in districts located far from the centre highlight the need for instituting interventions tailored to the local context to save mothers and accelerate reductions in MMR.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".