Despite a growing body of evidence suggesting that discrimination harms health, the association between appearance discrimination and health has been understudied. Our study investigated the association between perceived appearance discrimination and self-rated health among emerging adults using a nationally representative cohort study in South Korea. We analyzed the 2nd-10th (2005–2013) waves of cohort data from the Korean Education Employment Panel (KEEP). KEEP consists of two groups of individuals who were 15 (group I) and 18 (group II) years old at the 1st wave of the survey (2004) and were followed annually. Appearance discrimination was assessed at baseline (19 years old: 5th wave for group I, 2nd wave for group II) and at follow-up (24 years old: 10th wave for group I, 7th wave for group II). Responses of appearance discrimination at the two-time points were classified into four groups: 1) never (no discrimination at both baseline and follow-up); 2) repeated (discrimination at both baseline and follow-up); 3) incident (discrimination only at follow-up); and 4) in error (discrimination only at baseline). Multivariate logistic regression was applied to examine the association between reporting patterns of appearance discrimination and poor self-rated health, adjusting for potential confounders. Compared to those who did not experience appearance discrimination, ‘repeated’ (OR: 3.70; 95% CI: 2.19–6.27) and ‘incident’ (OR: 3.10; 95% CI: 1.99–4.83) groups had a higher odds ratio of poor self-rated health after adjusting for potential confounders including respondents’ body mass index change and baseline self-rated health. However, no significant association was observed among those who reported appearance discrimination ‘in error’. These results suggest that perceived appearance discrimination is associated with the health of Korean emerging adults considering participants’ reporting patterns of appearance discrimination.

Objective: This study examined whether perceived job insecurity was associated with absenteeism and presenteeism, and how these associations varied when the differential cutoff was applied to define absenteeism and presenteeism. Methods: We analyzed a nationally representative dataset of 19 720 full-time waged workers from the 4th Korean Working Conditions Survey (2014). As an independent variable, perceived job insecurity was assessed. As dependent variables, absenteeism and presenteeism were measured. Seven differential cutoffs (from “1 day” to “7 days”) were used when defining absenteeism and presenteeism. A Poisson regression model with a robust error variance was applied for the analysis. Results: When “1 day” was cutoff to define absenteeism and presenteeism, job insecurity was not associated with both absenteeism (PR: 1.07, 95% CI: 0.97, 1.16) and presenteeism (PR: 1.05, 95% CI: 1.00, 1.10). When the higher cutoff was used, the association between job insecurity and absenteeism was attenuated and remained statistically nonsignificant. However, statistically significant associations between job insecurity and presenteeism were observed when the differential cutoff was used: “2 days” (PR: 1.07, 95% CI: 1.01, 1.13), “3 days” (PR: 1.14, 95% CI: 1.06, 1.22), “4 days” (PR: 1.15, 95% CI: 1.05, 1.26), “5 days” (PR: 1.18, 95% CI: 1.06, 1.30), “6 days” (PR: 1.17, 95% CI: 1.02, 1.33), and “7 days” (PR: 1.17, 95% CI: 1.02, 1.34). Conclusion: This study found that perceived job insecurity was associated with presenteeism, but not absenteeism. Furthermore, the association differed by cutoff applied to define presenteeism among full-time waged workers in South Korea.

Sohn DG, et al. J Korean Med Sci. 2023 Apr;38(20):e147. https://doi.org/10.3346/jkms.2023.38.e147

OBJECTIVES: This study examined the association between lack of compensation for COVID-19-related overtime work (LCCOW) and burnout among emergency medical service (EMS) providers in Seoul, Korea.METHODS: We conducted a cross-sectional survey of 693 EMS providers in Seoul, Korea. Participants were classified into 3 groups according to their experience of coronavirus disease 2019 (COVID-19)-related overtime work and LCCOW: (1) “did not experience,” (2) “experienced and was compensated,” and (3) “experienced and was not compensated.” Burnout was measured using the Korean version of the Copenhagen Burnout Inventory, which has 3 subdomains: personal burnout (PB), work-related burnout (WRB), and citizen-related burnout (CRB). Multiple linear regression was applied to examine whether LCCOW was associated with burnout after adjusting for potential confounders.RESULTS: In total, 74.2% of participants experienced COVID-19-related overtime work, and 14.6% of those who worked overtime experienced LCCOW. COVID-19-related overtime work showed a statistically non-significant association with burnout. However, the association differed by LCCOW. Compared to the “did not experience” group, the “experienced and was not compensated” group was associated with PB (β=10.519; 95% confidence interval [CI], 3.455 to 17.584), WRB (β=10.339; 95% CI, 3.398 to 17.280), and CRB (β=12.290; 95% CI, 6.900 to 17.680), whereas no association was observed for the “experienced and was compensated” group. Furthermore, an analysis restricted to EMS providers who worked overtime due to COVID-19 showed that LCCOW was associated with PB (β=7.970; 95% CI, 1.064 to 14.876), WRB (β=7.276; 95% CI, 0.270 to 14.283), and CRB (β=10.000; 95% CI, 3.435 to 16.565).CONCLUSIONS: This study suggests that LCCOW could be critical in worsening burnout among EMS providers who worked overtime due to COVID-19.

OBJECTIVES: This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded “not applicable (NA)” to a question about hiring discrimination despite being eligible to answer.METHODS: Using data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using “yes” or “no” responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate the prevalence of experiencing hiring discrimination among those who answered “NA.” Under-reporting of hiring discrimination was calculated by comparing the prevalence of hiring discrimination between the “yes” or “no” group and the “NA” group.RESULTS: Based on the predictions from the random forest model, we found that 58.8% of the “NA” group were predicted to have experienced hiring discrimination, while 19.7% of the “yes” or “no” group reported hiring discrimination. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.CONCLUSIONS: This study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.