ArticleBMC public health2020
Anatomy of provincial level inequality in maternal mortality in China during 2004-2016: a new decomposition analysis.
Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of individual and ecological determinants of maternal mortality in the world based on the income level of countries.BMC public health · 2022Pooled it
- The association between china's primary health care reform and inequalities in primary care utilisation and maternal mortality: a quasi-experimental longitudinal study from 2010 to 2019.International journal for equity in health · 2025Article
- Evolving trends, regional differences, determinants, and disease sources of provincial-level health inequalities in china 1990-2019: a temporal convergence and novel triple decomposition analysis.International journal for equity in health · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundThe maternal mortality ratio (MMR) is an important indicator of maternal health and socioeconomic development. Although China has experienced a large decline in MMR, substantial disparities across regions are still apparent. This study aims to explore causes of socioeconomic related inequality in MMR at the province-level in China from 2004 to 2016.
methodsWe collected data from various issues of the China Health Statistics Yearbook, China Statistics Yearbook, and China Population and Employment Statistics Yearbook to construct a longitudinal sample of all provinces in China. We first examined determinants of the MMR using province fixed-effect models, accounted for socioeconomic condition, health resource allocation, and access to health care. We then used the concentration index (CI) to measure MMR inequality and employed the direct decomposition method to estimate the marginal impact of the determinants on the inequality index. Importance of the determinants were compared based on logworth values.
resultsDuring our study period, economically more deprived provinces experienced higher MMR than better-off ones. There was no evidence of improved socioeconomic related inequality in MMR. Illiteracy proportion was positively associated with the MMR (p < 0.01). In contrast, prenatal check-up rate (p = 0.05), hospital delivery rate (p < 0.01) and rate of delivery attended by professionals (p = 0.02) were negatively associated with the MMR. We also find that higher maternal health profile creation rate (p < 0.01) was associated with a pro-poor change of MMR inequality.
conclusionAccess to healthcare was the most important factor in explaining the persistent MMR inequality in China, followed by socioeconomic condition. We do not find evidence that health resource allocation was a contributing factor.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.