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Analysis of Factors Contributing to Occupational Health Inequality In Noh and Khang Archives of Public Health (2021) 79:113 https://doi.org/10.1186/s13690-021-00638-9 RESEARCH Open Access Analysis of factors contributing to occupational health inequality in Korea: a cross-sectional study using nationally representative survey data Eunjeong Noh1 and Young-Ho Khang1,2* Abstract Background: Despite the consensus that higher occupational classes tend to have better health and lower mortality rates, one study has reported reversed occupational gradients in mortality rates among Korean men after the economic crisis in the late 2000s. To examine these patterns of health inequality in more detail, we investigated the tendency of occupational gradients in socioeconomic position and multiple pathway indicators known to affect mortality in Korea. Methods: We used data from 4176 men aged 35–64 in Korea derived from the 2007–2009 and 2013–2015 Korean National Health and Nutrition Examination Surveys. We compared the age-standardized prevalence and age- adjusted mean values of each contributing factor to health inequality among occupational groups, which are divided into upper non-manual workers, lower non-manual workers, manual workers, and others. Contributing factors included childhood and adulthood socioeconomic position indicators, biological risk factors, health behaviors, psychosocial factors, and work environment. Results: Upper non-manual workers had prominently higher levels of education, income, parental education, and economic activity than lower non-manual and manual workers. The rates of smoking and high-risk alcohol consumption were lower, and the rate of weight control activities was higher, in the non-manual classes. Further, the rates of depression and suicidal ideation were lower, and perceptions of the work environment were more favorable, among non-manual workers than among their manual counterparts. Conclusions: We detected occupational inequality in a wide range of socioeconomic positions and pathway indicators in Korea with consistently favorable patterns for upper non-manual workers. These occupational gradients do not support the previously reported reversed pattern of higher mortality rates in non-manual groups versus in the manual job class in Korea. * Correspondence: [email protected] 1Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, Republic of Korea 2Department of Health Policy and Management, College of Medicine, Seoul National University, Seoul, Republic of Korea © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Noh and Khang Archives of Public Health (2021) 79:113 Page 2 of 8 Keywords: Health inequalities, Mortality, Occupations, Psychology, Republic of Korea, Risk factors, Socioeconomic factors Background and manual workers, these findings could suggest that Several international studies have investigated health the reversed health inequality in Korea is likely due to gradients according to occupational status, with gener- other issues (e.g., use of unlinked data) rather than to ally concordant findings of better health and lower mor- changes in the status of contributing factors influenced tality among people with a higher occupational status. In by the economic crisis [8]. other words, people with non-manual jobs (e.g., profes- sional and office jobs) have lower mortality and morbid- Methods ity as well as better health behaviors than those who Data sources and population – work in jobs with high levels of manual labor [1 7]. We used the Korea National Health and Nutrition Examin- However, Tanaka et al. argued that occupational gradi- ation Survey (KNHANES) IV, which is a nationally represen- ent patterns in mortality in Korea have diverged from tative cross-sectional survey conducted by the Korea Centers European countries since the economic crisis of the for Disease Control and Prevention (KCDC). The survey in- 2000s [8]. They claimed that since this time, the all- cludes information on socioeconomic status, health-related cause mortality rate for people with upper non-manual behaviors, quality of life, healthcare utilization, anthropomet- jobs has increased dramatically, exceeding that of people ric measures, and biochemical and clinical profiles collected with lower non-manual and manual jobs. They proposed through three component surveys: a health interview, a that the economic crisis might have affected the rising health examination, and a nutrition survey [13, 14]. In par- prevalence of cancer and suicide among non-manual ticular, the 2007–2009 KNHANES IV contains information workers, thereby contributing to the reversed patterns of on participants’ work environment. Therefore, these data – health inequalities [8 10]. Mackenbach also stated in his constitute an appropriate source for investigating occupa- “ recent book that the main exception is Japan and South tional gradients in various pathway variables known to influ- Korea where occupational class inequalities in mortality ence health status and to contribute to mortality in Korea. ” do not follow the usual pattern (p. 47) [10]. The deteri- We also employed the more recent 2013–2015 KNHANES oration of factors contributing to health in response to data, which lacked work environment variables, for an add- the economic crisis might explain the anomalous occu- itional analysis of occupational inequalities in contributing pational gradients in Korea. However, Khang pointed factors to mortality in Korea. The Institutional Review Board out that these purportedly reversed patterns are likely to (IRB) of the KCDC approved these nationwide studies. Our be biased due to the use of unlinked data with popula- study was exempted from IRB approval by the Seoul Na- tion denominators from census data and mortality nu- tional University Hospital IRB (IRB number: E-2005-091- merators from death certificates [11]. Previous Korean 1123). studies have also shown that using unlinked data may The KNHANES IV was conducted among noninstitu- result in increased mortality estimates among non- tionalized Korean citizens residing in Korea from 2007 to manual workers and reduced mortality estimates among 2009. Of the 31,705 targeted individuals, 24,871 partici- “ manual workers [12]. Moreover, the category of an in- pated in the survey (participation rate = 78.4%). Because ” active or class-unknown group, which accounted for Tanaka et al.’s 2019 study considered the association be- – 44 51% of total deaths in the most recent decade in tween occupational class and mortality among men aged Korea, may be related to the reversed pattern that Ta- 35–64, we focused on the same population—men aged 35 naka et al. and Tanaka reported [8, 9]. to 64 years in Korea—to enable equivalent comparisons Although this finding of reversed health inequality pat- [8]. Ultimately, we included 4176 men in the analyses, ex- terns in Korea poses an important challenge to the uni- cluding all female participants as well as men outside the versality of health inequality by occupational class, little age range of 35–64. We extracted demographic informa- research has clearly identified the unusual patterns of tion (age) and variables of interest (e.g., occupational class, occupational inequality in mortality in Korea [10]. To socioeconomic position, and other pathway variables) shed further light on this controversy, we investigated from the dataset (see Additional file 1: Table S1). the tendencies of occupational inequality in socioeco- nomic environments and various pathway factors known to affect mortality. If the contributing factors to occupa- Occupational class and contributing factors to health tional health inequalities reveal better conditions among inequality the upper non-manual class than in lower non-manual We collected the data on occupational class and contrib- uting variables to health from the 2007–2009 KNHA Noh and Khang Archives of Public Health (2021) 79:113 Page 3 of 8 NES. We categorized occupational class into four groups Results using the same definition as in Tanaka et al.’s study: We examined whether gaps in SEP and pathway vari- upper non-manual workers, lower non-manual workers, ables existed depending on occupational status (Table 1 manual workers, and others (e.g., agricultural, forestry and Table 2). In terms of childhood SEP, 43.2% of upper and fishery workers, and unemployed people) [8]. We non-manual workers’ fathers had a primary education or also classified socioeconomic and pathway variables into below, compared to 52.2% of lower non-manual workers six categories—childhood
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