Evidence map›Paper›PMID 32290367›Full record

ArticleInternational journal of environmental research and public health2020

Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel.

Yoonju Lee, Heejin Kim, Hyesun Jeong, Yunhwan Noh

Erratum issuedAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. 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 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

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

  1. Pooled it
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  16. Detecting Multimorbidity Patterns with Association Rule Mining in Patients with Alzheimer's Disease and Related Dementias.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Yoonju LeeCollege of Nursing, Pusan National University, Yangsan 50612, Korea.ORCID 0000-0001-9378-7022
Heejin KimDepartment of Nursing, The Graduate School, Pusan National University, Yangsan 50612, Korea.
Hyesun JeongDepartment of Nursing, The Graduate School, Pusan National University, Yangsan 50612, Korea.ORCID 0000-0003-3769-9591
Yunhwan NohDepartment of Statistics, The Graduate School, Pusan National University, Busan 46241, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to identify the prevalence and patterns of multimorbidity among Korean adults. A descriptive study design was used. Of 11,232 adults aged 18 and older extracted from the 2014 Korean Health Panel Survey, 7118 had one or more chronic conditions. The chronic conditions code uses the Korean Standard Classification of Diseases. Association rule analysis and network analysis were conducted to identify patterns of multimorbidity among 4922 participants with multimorbidity. The prevalence of multimorbidity in the overall population was 34.8%, with a higher prevalence among women (40.8%) than men (28.6%). Hypertension had the highest prevalence in both men and women. In men, diabetes mellitus and hypertension yielded the highest probability of comorbidity (10.04%). In women, polyarthrosis and hypertension yielded the highest probability of comorbidity (12.51%). The results of the network analysis in four groups divided according to gender and age showed different characteristics for each group. Public health practitioners should adopt an integrated approach to manage multimorbidity rather than an individual disease-specific approach, along with different strategies according to age and gender groups.

Indexed as

AdolescentAdultChronic DiseaseComorbidityCross-Sectional StudiesFemaleHumansMaleMultimorbidityPrevalenceRepublic of Koreaassociation rules analysismultimorbiditynetwork analysispattern

Identifiers

PMID32290367
PMCPMC7215522

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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.