Evidence map›Paper›PMID 30668580›Full record

ArticlePloS one2019

Can diabetes patients seeking a second hospital get better care? Results from nested case-control study.

Jae-Hyun Kim, Eun-Cheol Park

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Social determinants of health and type 2 diabetes in Asia.Journal of diabetes investigation · 2025
    Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Jae-Hyun KimDepartment of Health Administration, College of Health Science, Dankook University, Cheonan, Republic of Korea.
Eun-Cheol ParkInstitute of Health Services Research, Yonsei University, Seoul, Republic of Korea.ORCID 0000-0002-2306-5398

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the effects of the number of medical institutions visited on risk of death. This study conducted a nested case-control design using the National Health Insurance Service-Senior database from 2002 to 2013. Cases were defined as those with death among outpatients who had first diagnosis of diabetes mellitus (E10-E14) after entry into the base cohort and controls were selected by incidence density sampling and matched to cases based on age, and sex. Our main results were presented by conditional logistic regression for nested case-controls design. Of total 55,558 final study samples, there were 9,313 (16.8%) cases and 46,245 (83.2%) controls. With an increase by one point in the number of hospitals per medical utilization, risk of death significantly increased by 4.1% (odds ratio (OR): 1.041, 95% confidence interval [CI]: 1.039-1.043). In both medical utilization and number of hospitals, those with high medical utilization (OR: 1.065, 95% CI: 1.059-1.070) and number of hospitals (OR: 1.049, 95% CI: 1.041-1.058) for risk of death were significantly higher than those with low medical utilization (OR: 1.040, 95% CI: 1.037-1.043) and number of hospitals (OR: 1.029, 95% CI: 1.027-1.032), respectively. The number of medical institution visited was significantly associated with risk of death. Therefore, diabetics should be warned about the potential of risk of death incurred from excessive access to medical utilizations.

Indexed as

HospitalizationAgedAged, 80 and overCase-Control StudiesContinuity of Patient CareDiabetes MellitusHospitalsHumansLogistic ModelsMaleOdds RatioPatient Acceptance of Health CareRepublic of KoreaRetrospective StudiesRisk Factors

Identifiers

PMID30668580
PMCPMC6342308

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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.