Late Maternal Deaths and Deaths from Sequelae of Obstetric Causes in the Americas from 1999 to 2013: A Trend Analysis
Bibliographic record
Abstract
BACKGROUND: Data on maternal deaths occurring after the 42 days postpartum reference time is scarce; the objective of this analysis is to explore the trend and magnitude of late maternal deaths and deaths from sequelae of obstetric causes in the Americas between 1999 and 2013, and to recommend including these deaths in the monitoring of the Sustainable Development Goals (SDGs). METHODS: Exploratory data analysis enabled analyzing the magnitude and trend of late maternal deaths and deaths from sequelae of obstetric causes for seven countries of the Americas: Argentina, Brazil, Canada, Colombia, Cuba, Mexico and the United States. A Poisson regression model was developed to compare trends of late maternal deaths and deaths from sequelae of obstetric causes between two periods of time: 1999 to 2005 and 2006 to 2013; and to estimate the relative increase of these deaths in the two periods of time. FINDINGS: The proportion of late maternal deaths and deaths from sequelae of obstetric causes ranged between 2.40% (CI 0.85% - 5.48%) and 18.68% (CI 17.06% - 20.47%) in the seven countries. The ratio of late maternal deaths and deaths from sequelae of obstetric causes per 100,000 live births has increased by two times in the region of the Americas in the period 2006-2013 compared to the period 1999-2005. The regional relative increase of late maternal death was 2.46 (p<0.0001) times higher in the second period compared to the first. INTERPRETATION: Ascertainment of late maternal deaths and deaths from sequelae of obstetric causes has improved in the Americas since the early 2000's due to improvements in the quality of information and the obstetric transition. Late and obstetric sequelae maternal deaths should be included in the monitoring of the SDGs as well as in the revision of the International Classification of Diseases' 11th version (ICD-11).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".