Perceived Discrimination and Self-Rated Health in South Korea: A Nationally Representative Survey

Abstract

BackgroundThere is mounting evidence that discriminatory experiences can harm health. However, previous research has mainly focused on the health effects of racial discrimination in U.S. or European countries although there is pervasive discrimination by gender, age, education and other factors in Asian countries.MethodsWe analyzed the data from the 7th wave of Korean Labor and Income Panel Study to investigate the association between perceived discriminatory experience and poor self-rated health in South Korea. Perceived discriminatory experiences were measured in eight situations through a modified Experience of Discrimination questionnaire. In each of eight situations, the lifetime prevalence of perceived discriminatory experience was compared between men and women and the main causes of those experiences were identified separately by gender. After adjusting for potential confounders, we examined the association between perceived discriminatory experience and poor self-rated health in each of eight social situations and also checked the association using the number of situations of perceived discriminatory experiences.ResultsFor both men and women, education level and age were the main sources of work-related perceived discriminatory experiences. Gender was one of the main causes among women across eight situations and more than 90% of women reported their gender as a main cause of discriminatory experience in getting higher education and at home. Discriminatory experiences in four situations were positively associated with poor self-rated health. The odds ratio for poor self-rated health for those exposed to one, two, three or four or more social situations of perceived discrimination were respectively 1.06 (95% CI : 0.87–1.29), 1.15 (95% CI : 0.96–1.55), 1.59 (95% CI : 1.19–2.14), and 1.78 (95% CI :1.26–2.51).ConclusionThere is consistent association between perceived discriminatory experience and poor self-rated health across eight social situations in South Korea.

Discovering complex interrelationships between socioeconomic status and health in Europe: A case study applying Bayesian Networks

“…Self-Perceived discrimination (SPD): this variable was introduced into the model since a relevant part of the literature has highlighted the effect of self-perceived discrimination (based on race or ethnicity, age, gender, sexuality, etc.) on mental and physical health outcomes (Kim and Williams, 2012). 7.…”

“…In this study, following the Koivisto and Sood approach (Koivisto and Sood, 2004), we use a direct acyclic graph (DAG), a particular type of BN where no cycles are permitted, to find the model with the best goodness of fit to the observed data. The model considered in this study is an additive Bayesian network (ABN), which is essentially different from standard BN for binary or categorical data and based on contingency table parameterization.…”

“…Literature on this topic has mainly focused on studying the impact of various socioeconomic factors such as the direct determinants of individuals' states of health Eikemo et al, 2008a,b;Kunst et al, 2005). Studies demonstrate that SES affects health outcomes and thus, traditionally, it is assumed that health is determined by socioeconomic conditions.…”

“…The association between socioeconomic status and health is a well-grounded research finding (Costa-Font and Hernandez-Quevedo, 2012). Individuals with higher income, education, social class or occupational status tend to report better health, and thus lower mortality, compared to those individuals with lower SES (Wilkinson, 1996(Wilkinson, , 1997Nummela et al, 2007;Costa-Font and Hernandez-Quevedo, 2012;Eikemo et al, 2008a,b;Kunst et al, 2005).…”

“…Thus, in order to explain the complex interrelationship between health and SES determinants, an Additive Bayesian Networks (ABN) model (Koivisto and Sood, 2004) has been performed to find the most probable structure in a network that, being formally depicted as a directed acyclic graph (DAG), identifies the most probable ties between these factors. The resulting networks have been contextualized in five welfare state regimes, selecting the European context as a case study.…”