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Record W4402677288 · doi:10.2196/48047

Inequality in Mortality and Cardiovascular Risk Among Young, Low-Income, Self-Employed Workers: Nationwide Retrospective Cohort Study

2024· article· en· W4402677288 on OpenAlexvenueno aff
Byungyoon Yun, Heejoo Park, Jaesung Choi, Juyeon Oh, Juho Sim, Yangwook Kim, Jin‐Ha Yoon

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHazard ratioProportional hazards modelDemographyContext (archaeology)Retrospective cohort studyGerontologyCohortConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

Background: Self-employment is a significant component of South Korea's labor force; yet, it remains relatively understudied in the context of occupational safety and health. Owing to different guidelines for health checkup participation among economically active individuals, disparities in health maintenance may occur across varying employment statuses. Objective: This study aims to address such disparities by comparing the risk of all-cause mortality and comorbidities between the self-employed and employee populations in South Korea, using nationwide data. We sought to provide insights relevant to other countries with similar cultural, social, and economic contexts. Methods: This nationwide retrospective study used data from the Korean National Health Insurance Service database. Participants (aged 20-59 y) who maintained the same insurance type (self-employed or employee insurance) for ≥3 years (at least 2008-2010) were recruited for this study and monitored until death or December 2021-whichever occurred first. The primary outcome was all-cause mortality. The secondary outcomes were ischemic heart disease, ischemic stroke, cancer, and hospitalization with a mental illness. Age-standardized cumulative incidence rates were estimated through an indirect method involving 5-unit age standardization. A multivariable Cox proportional hazards model was used to estimate the adjusted hazard ratio (HR) and 95% CI for each sex stratum. Subgroup analyses and an analysis of the effect modification of health checkup participation were also performed. Results: A total of 11,652,716 participants were analyzed (follow-up: median 10.92, IQR 10.92-10.92 y; age: median 42, IQR 35-50 y; male: n=7,975,116, 68.44%); all-cause mortality occurred in 1.27% (99,542/7,851,282) of employees and 3.29% (124,963/3,801,434) of self-employed individuals (P<.001). The 10-year cumulative incidence rates of all-cause mortality differed significantly by employment status (1.1% for employees and 2.8% for self-employed individuals; P<.001). The risk of all-cause mortality was significantly higher among the self-employed individuals when compared with that among employees, especially among female individuals, according to the final model (male: adjusted HR 1.44, 95% CI 1.42-1.45; female: adjusted HR 1.89, 95% CI 1.84-1.94; P<.001). The risk of the secondary outcomes, except all types of malignancies, was significantly higher among the self-employed individuals (all P values were <.001). According to subgroup analyses, this association was prominent in younger individuals with lower incomes who formed a part of the nonparticipation groups. Furthermore, health checkup participation acted as an effect modifier for the association between employment status and all-cause mortality in both sexes (male: relative excess risk due to interaction [RERI] 0.76, 95% CI 0.74-0.79; female: RERI 1.13, 95% CI 1.05-1.21). Conclusions: This study revealed that self-employed individuals face higher risks of all-cause mortality, cardio-cerebrovascular diseases, and mental illnesses when compared to employees. The mortality risk is particularly elevated in younger, lower-income individuals who do not engage in health checkups, with health checkup nonparticipation acting as an effect modifier for this association.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.334
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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