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Record W2598615815 · doi:10.1016/s2214-109x(17)30100-6

Progress and challenges in maternal health in western China: a Countdown to 2015 national case study

2017· article· en· W2598615815 on OpenAlexfundaboutno aff
Yanqiu Gao, Hong Zhou, Neha Singh, Timothy Powell‐Jackson, Stephen Nash, Min Yang, Sufang Guo, Hai Fang, Melisa Martínez-Álvarez, Xiaoyun Liu, Jay Pan, Yan Wang, Carine Ronsmans

Bibliographic record

VenueThe Lancet Global Health · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersGovernment of CanadaUNICEFMedical Research CouncilBill and Melinda Gates Foundation
KeywordsPopulationDemographyGeographyPer capitaStandardized mortality ratioHealth careHealth equityMedicineChinaEconomic growthEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: China is one of the few Countdown countries to have achieved Millennium Development Goal 5 (75% reduction in maternal mortality ratio between 1990 and 2015). We aimed to examine the health systems and contextual factors that might have contributed to the substantial decline in maternal mortality between 1997 and 2014. We chose to focus on western China because poverty, ethnic diversity, and geographical access represent particular challenges to ensuring universal access to maternal care in the region. METHODS: In this systematic assessment, we used data from national census reports, National Statistical Yearbooks, the National Maternal and Child Health Routine Reporting System, the China National Health Accounts report, and National Health Statistical Yearbooks to describe changes in policies, health financing, health workforce, health infrastructure, coverage of maternal care, and maternal mortality by region between 1997 and 2014. We used a multivariate linear regression model to examine which contextual and health systems factors contributed to the regional variation in maternal mortality ratio in the same period. Using data from a cross-sectional survey in 2011, we also examined equity in access to maternity care in 42 poor counties in western China. FINDINGS: Maternal mortality declined by 8·9% per year between 1997 and 2014 (geometric mean ratio for each year 0·91, 95% CI 0·91-0·92). After adjusting for GDP per capita, length of highways, female illiteracy, the number of licensed doctors per 1000 population, and the proportion of ethnic minorities, the maternal mortality ratio was 118% higher in the western region (2·18, 1·44-3·28) and 41% higher in the central region (1·41, 0·99-2·01) than in the eastern region. In the rural western region, the proportion of births in health facilities rose from 41·9% in 1997 to 98·4% in 2014. Underpinning such progress was the Government's strong commitment to long-term strategies to ensure access to delivery care in health facilities-eg, professionalisation of maternity care in large hospitals, effective referral systems for women medically or socially at high risk, and financial subsidies for antenatal and delivery care. However, in the poor western counties, substantial disparity by education level of the mother existed in access to health facility births (44% of illiterate women vs 100% of those with college or higher education), antenatal care (17% vs 69%) had at least four visits), and caesarean section (8% vs 44%). INTERPRETATION: Despite remarkable progress in maternal survival in China, substantial disparities remain, especially for the poor, less educated, and ethnic minority groups in remote areas in western China. Whether China's highly medicalised model of maternity care will be an answer for these populations is uncertain. A strategy modelled after China's immunisation programme, whereby care is provided close to the women's homes, might need to be explored, with township hospitals taking a more prominent role. FUNDING: Government of Canada, UNICEF, and the Bill & Melinda Gates Foundation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.158
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.098
GPT teacher head0.444
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations121
Published2017
Admission routes2
Has abstractyes

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