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Record W2604373618 · doi:10.1016/s2214-109x(17)30139-0

Progress and inequities in maternal mortality in Afghanistan (RAMOS-II): a retrospective observational study

2017· article· en· W2604373618 on OpenAlexaff
Linda Bartlett, Amnesty LeFevre, Linnea Zimmerman, Sayed Ataullah Saeedzai, Sabera Turkmani, Weeda Zabih, Hannah Tappis, Stan Becker, Peter J. Winch, Marge Koblinsky, Ahmed Javed Rahmanzai

Bibliographic record

VenueThe Lancet Global Health · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsHospital for Sick ChildrenSickKids FoundationInstitute for Clinical Evaluative Sciences
FundersUnited States Agency for International Development
KeywordsVerbal autopsyMedicineDemographyObstetric transitionMaternal deathObservational studyEnvironmental healthStandardized mortality ratioMortality ratePopulationCause of deathMaternal healthDiseaseHealth services

Abstract

fetched live from OpenAlex

BACKGROUND: The risk of maternal death in Afghanistan is among the highest in the world; however, the risks within the country are poorly understood. Subnational maternal mortality estimates are needed along with a broader understanding of determinants to guide future maternal health programmes. Here we aimed to study maternal mortality risk and causes, care-seeking patterns, and costs within the country. METHODS: We did a household survey (RAMOS-II) in the urban area of Kabul city and the rural area of Ragh, Badakshan. Questionnaires were administered to senior female household members and data were collected by a team of female interviewers with secondary school education. Information was collected about all deaths, livebirths, stillbirths, health-care access and costs, household income, and assets. Births were documented using a pregnancy history. We investigated all deaths in women of reproductive age (12-49 years) since January, 2008, using verbal autopsy. Community members; service providers; and district, provincial, and national officials in each district were interviewed to elicit perceptions of changes in maternal mortality risk and health service provision, along with programme and policy documentation of maternal care coverage. FINDINGS: Data were collected between March 2, 2011, and Oct 16, 2011, from 130 688 participants: 63 329 in Kabul and 67 359 in Ragh. The maternal mortality ratio in Ragh was quadruple that in Kabul (713 per 100 000 livebirths, 95% CI 553-873 in Ragh vs 166, 63-270 in Kabul). We recorded similar patterns for all other maternal death indicators, including the maternal mortality rate (1·7 per 1000 women of reproductive age, 95% CI 1·3-2·1 in Ragh vs 0·2, 0·1-0·3 in Kabul). Infant mortality also differed significantly between the two areas (115·5 per 1000 livebirths, 95% CI 108·6-122·3 in Ragh vs 24·8, 20·5-29·0 in Kabul). In Kabul, 5594 (82%) of 6789 women reported a skilled attendant during recent deliveries compared with 381 (3%) of 11 366 women in Ragh. An estimated 85% of women in Kabul and 47% in Ragh incurred delivery costs (mean US$66·20, IQR $61·30 in Kabul and $9·89, $11·87 in Ragh). Maternal complications were the third leading cause of death in women of reproductive age in Kabul, and the leading cause in Ragh, and were mainly due to hypertensive diseases of pregnancy. The maternal mortality rate decreased significantly between 2002 and 2011 in both Kabul (by 71%) and Ragh (by 84%), plus all other maternal mortality indicators in Ragh. INTERPRETATION: Remarkable maternal and other mortality reductions have occurred in Afghanistan, but the disparity between urban and rural sites is alarming, with all maternal mortality indicators significantly higher in Ragh than in Kabul. Customised service delivery is needed to ensure parity for different geographic and security settings. FUNDING: United States Agency for International Development (USAID).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
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 head0.121
GPT teacher head0.443
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations36
Published2017
Admission routes1
Has abstractyes

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