Subnational variation for care at birth in Tanzania: is this explained by place, people, money or drugs?
Bibliographic record
Abstract
BACKGROUND: Tanzania achieved the Millennium Development Goal for child survival, yet made insufficient progress for maternal and neonatal survival and stillbirths, due to low coverage and quality of services for care at birth, with rural women left behind. Our study aimed to evaluate Tanzania's subnational (regional-level) variations for rural care at birth outcomes, i.e., rural women giving birth in a facility and by Caesarean section (C-section), and associations with health systems inputs (financing, health workforce, facilities, and commodities), outputs (readiness and quality of care) and context (education and GDP). METHODS: We undertook correlation analyses of subnational-level associations between health system inputs, outputs, context, and rural care at birth outcomes; and constructed implementation readiness barometers using benchmarks for each health system input indicator. We used geographical information system (GIS) mapping to visualise subnational variations in care at birth for rural women, with a focus on service availability and readiness, and collected qualitative data to investigate financial flows from national to council level to understand variation in financing inputs. RESULTS: We found wide subnational variation for rural care at birth outcomes, health systems inputs, and contextual indicators. There was a positive association between rural women giving birth in a facility and by C-section; maternal education; workforce and facility density; and quality of care. There was a negative association between these outcomes and proportion of all births to rural women, total fertility rate, and availability of essential commodities at facilities. Per capita recurrent expenditure was positively associated with facility births (correlation coefficient = 0.43; p = 0.05) but not with C-section. Qualitative results showed that the health financing system is complex and insufficient for providing care at birth services. Bottlenecks for care at birth included low density of health workers, poor availability of essential commodities, and low health financing in Lake and Western Zones. CONCLUSIONS: No region meets the benchmarks for the four health systems building blocks including health finance, health workforce, health facilities, and commodities. Strategies for addressing health system inequities, including overall increases in health expenditure, are needed in rural populations and areas of highest unmet need for family planning to improve coverage of care at birth for rural women in Tanzania.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".