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Ji-Hwan Kim 2 Articles
Health inequalities of 57,541 prisoners in Korea: a comparison with the general population
Seohyun Yoon, Young-Su Ju, Jaehong Yoon, Ji-Hwan Kim, Bokyoung Choi, Seung-Sup Kim
Epidemiol Health. 2021;43:e2021033.   Published online May 6, 2021
DOI: https://doi.org/10.4178/epih.e2021033
  • 6,986 View
  • 322 Download
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
This study aimed to examine health disparities between prisoners and the general population in Korea.
METHODS
We sought to estimate the prevalence of 17 physical and mental diseases using the nationwide medication prescription dataset among the total population of prisoners (n=57,541) in Korea. Age- and sex- standardized prevalence ratios (SPRs) were estimated to compare the disease prevalence between the prisoners and the general population. The disease prevalence for the general population was calculated from the prescription dataset for a representative of the Korean population (n=926,246) from the 2013 Korean National Health Insurance Service-National Sample Cohort. Furthermore, the prevalence of these diseases was compared between prisoners and a low-income segment of the general population (n=159,781).
RESULTS
Compared to the general population, prisoners had higher prevalence of almost all physical and mental diseases, including hyperlipidemia (SPR, 20.18; 95% confidence interval [CI], 19.43 to 20.94), pulmonary tuberculosis (SPR, 9.58; 95% CI, 7.91 to 11.50), diabetes (SPR, 6.13; 95% CI, 5.96 to 6.31), cancer (SPR, 2.36; 95% CI, 2.07 to 2.68), and depression (SPR, 46.73; 95% CI, 44.14 to 49.43). When compared with the low-income population segment, higher prevalence were still found among prisoners for most diseases, including pulmonary tuberculosis (SPR, 6.39; 95% CI, 5.27 to 7.67) and depression (SPR, 34.71; 95% CI, 32.79 to 36.72).
CONCLUSIONS
We found that prisoners were more likely to be unhealthy than the general population, even in comparison with a low-income segment of the general population in Korea.
Summary
Korean summary
이 연구는 대한민국 재소자 전수(전국 총 52개 정부운영 교정시설 재소자 57,541명)를 대상으로 법무부 전체 구금시설 의료실태 현황조사 자료를 이용해 신체적∙정신적 건강상태를 파악하고자 하였다. 그 결과를 성∙연령별 표준화율을 적용해 일반인구집단의 국민건강보험공단 표본 코호트 자료와 비교했을 때 재소자들은 고지혈증, 폐결핵, 우울증 등 대부분의 질병에서 일반인구집단보다 더 높은 유병률을 보였다. 이러한 결과는 저소득 일반인구집단과의 비교에서도 유사하게 나타났다.
Key Message
This study aimed to assess mental and physical health conditions among the total population of 57,541 prisoners in all 52 government correctional facilities. And we sought to estimate age- and sex- standardized prevalence ratios to compare the disease prevalence between the prisoners and the general population. Prisoners were more likely to have most of physical and mental diseases (including hyperlipidemia, pulmonary tuberculosis, and depression) than the general population in Korea.
Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach
Jaehong Yoon, Ji-Hwan Kim, Yeonseung Chung, Jinsu Park, Glorian Sorensen, Seung-Sup Kim
Epidemiol Health. 2021;43:e2021099.   Published online November 17, 2021
DOI: https://doi.org/10.4178/epih.e2021099
  • 3,750 View
  • 145 Download
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
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.
Summary
Korean summary
본 연구는 한국노동패널조사(7차년도)에 포함된 3576명의 임금근로자의 자료를 이용해 성별에 따른 구직 과정 경험한 차별에 대한 과소보고의 규모를 확인하고자 하였다. 질문에 “예” 또는 “아니요”라고 응답한 임금근로자 3479명 데이터를 이용하여 고용 시 차별경험을 예측하는 머신러닝 모형을 구축하였고, 이를 활용해 이미 직장에서 일하고 있는 상태임에도 “해당사항 없음”이라고 응답한 임금근로자 97명이 차별을 경험했는지 여부를 예측하였다. 분석결과, “해당사항 없음”이라고 답한남성 근로자 64명 중 29명(45.3%), 여성 근로자 33명 중 28명(84.8%)가 실제로 차별을 경험한 것으로 추정되었다.
Key Message
We examined gender differences in under-reporting hiring discrimination for wage workers who responded “not applicable(NA)” to a question about hiring discrimination despite being eligible to answer “yes” or “no.” Using data from 3,576 wage workers of the Korea Labor and Income Panel Study, we estimated the prevalence of hiring discrimination among those who answered “NA,” based on the best-performing machine learning prediction model for “yes” or “no” group. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively.

Epidemiol Health : Epidemiology and Health