Evidence map›Paper›PMID 26733569›Full record

ArticleBMJ open2016

Analysis of health service amenable and non-amenable mortality before and since China's expansion of health coverage in 2009.

Xiaoqi Feng, Yunning Liu, Thomas Astell-Burt, Peng Yin, Andrew Page, Shiwei Liu, Jiangmei Liu, Lijun Wang, Maigeng Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Efficiency and productivity of health systems in prevention and control of non-communicable diseases in China, 2008-2015.The European journal of health economics : HEPAC : health economics in prevention and care · 2021
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xiaoqi FengEarly Start Research Institute, University of Wollongong, Wollongong, New South Wales, Australia School of Health and Society, University of Wollongong, Wollongong, New South Wales, Australia.
Yunning LiuNational Center for Noncommunicable and Chronic Disease Control and Prevention, China Center for Disease Control and Prevention, Beijing, China.
Thomas Astell-BurtSchool of Science and Health, Western Sydney University, Sydney, New South Wales, Australia.
Peng YinNational Center for Noncommunicable and Chronic Disease Control and Prevention, China Center for Disease Control and Prevention, Beijing, China.
Andrew PageCentre for Health Research, School of Medicine, Western Sydney University, Sydney, New South Wales, Australia.
Shiwei LiuNational Center for Noncommunicable and Chronic Disease Control and Prevention, China Center for Disease Control and Prevention, Beijing, China.
Jiangmei LiuNational Center for Noncommunicable and Chronic Disease Control and Prevention, China Center for Disease Control and Prevention, Beijing, China.
Lijun WangNational Center for Noncommunicable and Chronic Disease Control and Prevention, China Center for Disease Control and Prevention, Beijing, China.
Maigeng ZhouNational Center for Noncommunicable and Chronic Disease Control and Prevention, China Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore early impacts of China's health reforms in 2009 on mortality.

methodsAnnual mortality counts were obtained from 161 counties across all 31 provinces of mainland China between 2006 and 2012. We examined time-series of health service amenable mortality counts, including separate analyses for deaths from stroke and ischaemic heart diseases (IHD). Non-amenable mortality counts, including separate models for oesophageal and pancreatic cancers, were also analysed as part of a negative-outcome strategy to provide stronger foundations for falsification. Deaths due to amenable causes were hypothesised to decrease, whereas non-amenable causes of mortality would remain uninfluenced. All analyses were conducted using multilevel negative binomial regression.

resultsGeographical variation was observed for each mortality indicator, especially for IHD, oesophageal and pancreatic cancers. Negative covariances in all models indicated slight degrees of convergence in these geographic variations over time (but not significantly for deaths from oesophageal and pancreatic cancers). Linear and square functions of time indicated a curvilinear inverted parabolic trend between 2006 and 2012 for stroke and IHD mortality. Reduction in health-service amenable mortality over time was observed, but also for health service non-amenable mortality, including deaths from oesophageal cancer. Pancreatic cancer was found to increase across the study period. In counties where residents had more years of education, mortality from stroke was lower and reducing faster over time. A similar spatiotemporal patterning was observed for deaths from oesophageal cancer, and health service amenable and non-amenable causes. Counties with higher mean education years had higher mortality from IHD and pancreatic cancer, but also larger reductions in mortality were evident in areas with greater years of education.

conclusionsAlthough there was no clear evidence of an early impact of China's health reform on mortality, this does not rule out potentially important contributions to reducing the burden of disease in the longer term.

Indexed as

Cause of DeathHealth Care ReformHealth ServicesHealth StatusAgedAged, 80 and overChinaEducational StatusEsophageal NeoplasmsFemaleHumansMaleMiddle AgedPancreatic NeoplasmsResidence CharacteristicsSocioeconomic FactorsPREVENTIVE MEDICINEPUBLIC HEALTHSOCIAL MEDICINE

Identifiers

PMID26733569
PMCPMC4716146

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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.