Evidence map›Paper›PMID 28765128›Full record

ArticleBMJ open2017

Insurance status, inhospital mortality and length of stay in hospitalised patients in Shanxi, China: a cross-sectional study.

Xiaojun Lin, Miao Cai, Hongbing Tao, Echu Liu, Zhaohui Cheng, Chang Xu, Manli Wang, Shuxu Xia, Tianyu Jiang

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
8.1field-weighted citation impact, top 2% of its field
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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Xiaojun LinDepartment of Health Administration, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Miao CaiDepartment of Health Administration, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Hongbing TaoDepartment of Health Administration, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Echu LiuDepartment of Health Management and Policy, College for Public Health and Social Justice, Saint Louis University, St Louis, Missouri, USA.
Zhaohui ChengDepartment of Health Administration, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Chang XuDepartment of Health Administration, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Manli WangDepartment of Health Administration, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Shuxu XiaDepartment of Health Administration, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Tianyu JiangDepartment of Health Administration, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Huazhong University of Science and Technology · CNSaint Louis University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo determine insurance-related disparities in hospital care for patients with acute myocardial infarction (AMI), heart failure (HF) and pneumonia. SETTING AND

participantsA total of 22 392 patients with AMI, 8056 patients with HF and 17 161 patients with pneumonia were selected from 31 tertiary hospitals in Shanxi, China, from 2014 to 2015 using the International Classification of Diseases, Tenth Revision codes. Patients were stratified by health insurance status, namely, urban employee-based basic medical insurance (UEBMI), urban resident-based basic medical insurance (URBMI), new cooperative medical scheme (NCMS) and self-payment. OUTCOME MEASURES: Inhospital mortality and length of stay (LOS).

resultsThe highest unadjusted inhospital mortality rate was detected in NCMS patients independent of medical conditions (4.7%, 4.4% and 11.1% for AMI, HF and pneumonia, respectively). The lowest unadjusted inhospital mortality rate and the longest LOS were observed in UEBMI patients. After controlling patient-level and hospital-level covariates, the adjusted inhospital mortality was significantly higher for NCMS and self-payment among patients with AMI, for NCMS among patients with HF and for URBMI, NCMS and self-payment among patients with pneumonia compared with UEBMI. The LOS of the URBMI, NCMS and self-payment groups was significantly shorter than that of the UEBMI group.

conclusionInsurance-related disparities in hospital care for patients with three common medical conditions were observed in this study. NCMS patients had significantly higher adjusted inhospital mortality and shorter LOS compared with UEBMI patients. Policies on minimising the disparities among different insurance schemes should be established by the government.

Indexed as

Hospital MortalityInsurance CoverageInsurance, HealthLength of StayAgedCardiovascular DiseasesChinaCross-Sectional StudiesDelivery of Health CareFemaleHealthcare DisparitiesHealth ExpendituresHeart FailureHumansMaleMyocardial Infarctionacute myocardial infarctioncross-sectionalheart failureinsurance statuslength of staymortalitypneumonia

Identifiers

PMID28765128
PMCPMC5642755
OpenAlexW2739925938

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.