Audit-identified avoidable factors in maternal and perinatal deaths in low resource settings: a systematic review
Bibliographic record
Abstract
BACKGROUND: Audits provide a rational framework for quality improvement by systematically assessing clinical practices against accepted standards with the aim to develop recommendations and interventions that target modifiable deficiencies in care. Most childbirth-associated mortality audits in developing countries are focused on a single facility and, up to now, the avoidable factors in maternal and perinatal deaths cataloged in these reports have not been pooled and analyzed. We sought to identity the most frequent avoidable factors in childbirth-related deaths globally through a systematic review of all published mortality audits in low and lower-middle income countries. METHODS: We performed a systematic review of published literature from 1965 to November 2011 in Pubmed, Embase, CINAHL, POPLINE, LILACS and African Index Medicus. Inclusion criteria were audits from low and lower-middle income countries that identified at least one avoidable factor in maternal or perinatal mortality. Each study included in the analysis was assigned a quality score using a previously published instrument. A meta-analysis was performed for each avoidable factor taking into account the sample sizes and quality score from each individual audit. The study was conducted and reported according to PRISMA guidelines for systematic reviews. RESULTS: Thirty-nine studies comprising 44 datasets and a total of 6,205 audited deaths met inclusion criteria. The analysis yielded 42 different avoidable factors, which fell into four categories: health worker-oriented factors, patient-oriented factors, transport/referral factors, and administrative/supply factors. The top three factors by attributable deaths were substandard care by a health worker, patient delay, and deficiencies in blood transfusion capacity (accounting for 688, 665, and 634 deaths attributable, respectively). Health worker-oriented factors accounted for two-thirds of the avoidable factors identified. CONCLUSIONS: Audits provide insight into where systematic deficiencies in clinical care occur and can therefore provide crucial direction for the targeting of interventions to mitigate or eliminate health system failures. Given that the main causes of maternal and perinatal deaths are generally consistent across low resource settings, the specific avoidable factors identified in this review can help to inform the rational design of health systems with the aim of achieving continued progress towards Millennium Development Goals Four and Five.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.001 |
| 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".