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Record W7074029947

Maternal Deaths, Near misses and Great saves: Severe Maternal Outcomes in Metro East, Western Cape Province, South Africa

2023· dissertation· en· W7074029947 on OpenAlexaboutno aff

Bibliographic record

VenuePure Amsterdam UMC · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
Fundersnot available
KeywordsNear missIncidence (geometry)CapeMiddle EastCase fatality ratePregnancyQuarter (Canadian coin)Maternal deathStandardized mortality ratio
DOInot available

Abstract

fetched live from OpenAlex

Surveillance of Severe Maternal Outcome (SMO), which is the combination of Maternal Near Miss (MNM) and Maternal Mortality, in the Metro East health district, Western Cape province, South Africa, has given useful insight into its incidence and contributing factors. The MNM-ratio in Metro East was 8.6 per 1,000 livebirths in 2014-2015, and the Maternal Mortality Ratio 49.7 per 100,000 live births, resulting in a SMO-ratio of 9.1 per 1,000 live births. This MNM-ratio identified for Metro East is comparable to the median MNM-ratio reported for middle-income countries of 9.6 per 1,000 livebirths, with a median MNM-ratio of 15.9 per 1,000 in lower-middle and 7.8 per 1,000 in upper-middle income countries. The MNM-ratio in Metro East was slightly higher than the ratio in other regions in South Africa, but case fatality among women with MNM was lower, possibly illustrating a relatively higher quality of care. The main causes of SMO in Metro East were hypertensive disorders of pregnancy and major obstetric haemorrhage, which are similar to the commonest causes of SMO in other middle-income countries, and worldwide. Associated factors were a positive HIV-serostatus, birth by caesarean section, preeclampsia and obesity. The relatively large differences in the incidence of SMO between different regions in South Africa, and between different middle-income countries appear to be at least partly explained by difficulties in applying the MNM-tool as proposed by the World Health Organization. We suggest that the tool is currently mostly valuable for assessments of SMO in the local setting, rather than for comparisons between regions or countries. Analyses of SMO in Metro East provided useful insights into local causes: obstetric haemorhage relatively frequently due to placental abruption, often in combination with intrauterine fetal death and hypertension. In contrast with most other settings, hysterectomy for maternal sepsis was as common as for peripartum haemorrhage, both associated with cesarean section. Severe complications of hypertensive disorders of pregnancy were frequent, with pulmonary edema being a particularly common complication leading to maternal death. Early detection and management of preeclampsia, monitoring of fluid balance and cardiac evaluation when pulmonary edema persists, were preventive suggestions. Audit helped identify lessons learned: for all women with SMO, not attending antenatal care was a woman-related factor and missing the diagnosis or not starting adequate management in time were factors at the level of the health worker. In almost a quarter of cases, different management could have prevented SMO. Life style changes, understanding maternal behavior, but also recognizing SMO in an early stage and adequate multidisciplinary management in a critical care setting could reduce SMO and improve its outcomes. Health workers found the identification and analysis of MNM a valuable addition to the already existing confidential enquiry into maternal deaths in South Africa. A national MNM audit was recommended to address causes and improve maternal outcomes. A list of diagnosis additional to those mentioned in the existing MNM tool would help identifying local causes of MNM more precisely. Simplification of the tool would be welcomed in this setting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.235
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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