Evidence map›Paper›PMID 30189602›Full record

ArticleInternational journal of environmental research and public health2018

Rural Versus Urban Patients: Benchmarking the Outcomes of Patients with Acute Myocardial Infarction in Shanxi, China from 2013 to 2017.

Miao Cai, Echu Liu, Wei Li

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.9field-weighted citation impact, top 13% 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

11 citing papers in PubMed, 20 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

3 authors at 2 institutions in 2 countries.

Miao CaiDepartment of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, Saint Louis, MO 63103, USA. miao.cai@slu.edu.ORCID 0000-0003-0170-6905
Echu LiuDepartment of Health Management and Policy, College for Public Health and Social Justice, Saint Louis University, Saint Louis, MO 63103, USA. echu.liu@slu.edu.
Wei LiDongfang College, Zhejiang University of Finance and Economics, Hangzhou 314408, China. appleliwei0220@163.com.ORCID 0000-0002-4438-8903
Saint Louis University · USZhejiang University of Finance and Economics · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rural-urban disparity in China attracts special international attention in view of the imbalance of economic development between rural and urban areas. However, few studies used patient level data to explore the disparity of health outcomes between rural and urban patients. This study aims to evaluate the trend of health outcomes between rural and urban patients hospitalized with acute myocardial infarction (AMI) in China. Using an electronic medical records (EMRs) database in Shanxi, China, we identified 87,219 AMI patients hospitalized between 2013 and 2017. We used multivariable binary logistic regressions and two-part models to estimate the association between region of origin (rural/urban) and two outcomes, in-hospital mortality and out-of-pocket (OOP) expenses. Rural patients were associated with lower in-hospital mortality and the adjusted Odds Ratios (ORs) were 0.173, 0.34, 0.605, 0.522, 0.556 (

Indexed as

AdolescentAdultAgedBenchmarkingChinaElectronic Health RecordsFemaleHealth ExpendituresHospital MortalityHumansLogistic ModelsMaleMiddle AgedMyocardial InfarctionOdds RatioOutcome and Process Assessment, Health Careacute myocardial infarctionelectronic medical recordsin-hospital mortalityout-of-pocket expenserural-urban disparity

Identifiers

PMID30189602
PMCPMC6165441
OpenAlexW2891777981

What OpenQuestion holds

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LicenceCC BY
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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.