Evidence map›Paper›PMID 38857074›Full record

ArticleJMIR public health and surveillance2024

Multimorbidity and its Associated Factors in Korean Shift Workers: Population-Based Cross-Sectional Study.

Hye Chong Hong, Young Man Kim

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Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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0cells of the map it votes in
6citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Hye Chong HongDepartment of Nursing, Chung-Ang University, Seoul, Republic of Korea.ORCID 0000-0003-0501-8456
Young Man KimCollege of Nursing, Jeonbuk National University, Jeonju, Republic of Korea.ORCID 0000-0003-4042-8346

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultimorbidity is a crucial factor that influences premature death rates, poor health, depression, quality of life, and use of health care. Approximately one-fifth of the global workforce is involved in shift work, which is associated with increased risk for several chronic diseases and multimorbidity. About 12% to 14% of wage workers in Korea are shift workers. However, the prevalence of multimorbidity and its associated factors in Korean shift workers are rarely reported.

objectiveThis study aimed to assess multimorbidity prevalence, examine the factors associated with multimorbidity, and identify multimorbidity patterns among shift workers in Korea.

methodsThis study is a population-based cross-sectional study using Korea National Health and Nutrition Examination Survey data from 2016 to 2020. The study included 1704 (weighted n=2,697,228) Korean shift workers aged 19 years and older. Multimorbidity was defined as participants having 2 or more chronic diseases. Demographic and job-related variables, including regular work status, average working hours per week, and shift work type, as well as health behaviors, including BMI, smoking status, alcohol use, physical activity, and sleep duration, were included in the analysis. A survey-corrected logistic regression analysis was performed to identify factors influencing multimorbidity among the workers, and multimorbidity patterns were identified with a network analysis.

resultsThe overall prevalence of multimorbidity was 13.7% (302/1704). Logistic regression indicated that age, income, regular work, and obesity were significant factors influencing multimorbidity. Network analysis results revealed that chronic diseases clustered into three groups: (1) cardiometabolic multimorbidity (hypertension, dyslipidemia, diabetes, coronary heart disease, and stroke), (2) musculoskeletal multimorbidity (arthritis and osteoporosis), and (3) unclassified diseases (depression, chronic liver disease, thyroid disease, asthma, cancer, and chronic kidney disease).

conclusionsThe findings revealed that several socioeconomic and behavioral factors were associated with multimorbidity among shift workers, indicating the need for policy development related to work schedule modification. Further organization-level screening and intervention programs are needed to prevent and manage multimorbidity among shift workers. We also recommend longitudinal studies to confirm the effects of job-related factors and health behaviors on multimorbidity among shift workers in the future.

Indexed as

MultimorbidityAdultCross-Sectional StudiesFemaleHumansMaleMiddle AgedNutrition SurveysPrevalenceRepublic of KoreaRisk FactorsShift Work ScheduleYoung Adultchronic diseasecross-sectional studyKorealogistic regressionmultimorbiditynetwork analysispopulation-based studypublic healthshift workersshift work schedule

Identifiers

PMID38857074
PMCPMC11196912

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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.