Improving health care facility birth rates in Rorya District, Tanzania: a multiple baseline trial
Bibliographic record
Abstract
BACKGROUND: Rates of maternal mortality and morbidity in Africa remain unacceptably high, as many women deliver at home, without access to skilled birth attendants and life-saving medications. In rural Tanzania, women face significant barriers accessing health care facilities for their deliveries. METHODS: From January 2017 to February 2019 we conducted a multiple baseline (interrupted time series) trial within the four divisions of Rorya District, Tanzania. We collected baseline data, then sequentially introduced a complex intervention in each of the divisions, in randomized order, over 3 month intervals. We allowed for a 6 month transition period to avoid contamination between the pre- and post-intervention periods. The intervention included using community health workers to educate about safe delivery, distribution of birth kits with misoprostol, and a transport subsidy for women living a distance from the health care facility. The primary outcome was the health facility birth rate, while the secondary outcomes were the rates of antenatal and postpartum care and postpartum hemorrhage. Outcomes were analyzed using fixed effects segmented logistic regression, adjusting for age, marital status, education, and parity. Maternal and baby morbidity/mortality were analyzed descriptively. RESULTS: We analyzed data from 9565 pregnant women (2634 before and 6913 after the intervention was implemented). Facility births increased from 1892 (71.8%) before to 5895 (85.1%) after implementation of the intervention. After accounting for the secular trend, the intervention was associated with an immediate increase in the odds of facility births (OR = 1.51, 95% CI 1.14 to 2.01, p = 0.0045) as well as a small gradual effect (OR = 1.03 per month, 95% CI 1.00 to 1.07, p = 0.0633). For the secondary outcomes, there were no statistically significant immediate changes associated with the intervention. Rates of maternal and baby morbidity/mortality were low and similar between the pre- and post-implementation periods. CONCLUSIONS: Access to health care facilities can be improved through implementation of education of the population by community health workers about the importance of a health care facility birth, provision of birth kits with misoprostol to women in late pregnancy, and access to a transport subsidy for delivery for women living at a distance from the health facility. CLINICAL TRIALS REGISTRATION: NCT03024905 19/01/2017.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".