Social Inequities in the Survival of Liver Cancer: A Nationwide Cohort Study in Korea, 2007-2017.

BACKGROUND: To analyze the effects of socioeconomic status (type of insurance and income level) and cancer stage on the survival of patients with liver cancer in Korea. METHODS: A retrospective cohort study was constructed using data from the Healthcare Big Data Platform project in Korea between January 1, 2007, and December 31, 2017. A total of 143,511 patients in Korea diagnosed with liver cancer (International Classification of Diseases, 10th Revision [ICD-10] codes C22, C220, and C221) were followed for an average of 11 years. Of these, 110,443 died. The patient's insurance type and income level were used as indicators of socioeconomic status. Unadjusted and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using a Cox proportional hazards regression model to analyze the relationship between the effects of sex, age, and cancer stage at first diagnosis (Surveillance, Epidemiology, and the End Results; SEER), type of insurance, and income level on the survival of patients with liver cancer. The interactive effects of the type of insurance, income level, and cancer stage on liver cancer death were also analyzed. RESULTS: The lowest income group (medical aid) showed a higher risk for mortality (HR (95% CI); 1.37 (1.27-1.47) for all patients, 1.44 (1.32-1.57) for men, and 1.16 (1.01-1.34) for women) compared to the highest income group (1-6) among liver cancer (ICD-10 code C22) patients. The risk of liver cancer death was also higher in the lowest income group with a distant cancer stage (SEER = 7) diagnosis than for any other group. CONCLUSION: Liver cancer patients with lower socioeconomic status and more severe cancer stages were at greater risk of death. Reducing social inequalities is needed to improve mortality rates among patients in lower social class groups who present with advanced cancer.

Gender Inequalities in Mental Health During the COVID-19 Pandemic: A Population-based Study in Korea.

OBJECTIVES: This study explored the effect of the coronavirus disease 2019 (COVID-19) pandemic on psychosocial stress in prime working-age individuals in Korea, focusing on gender inequalities. We hypothesized that the impact of COVID-19 on mental health would differ by age and gender, with younger women potentially demonstrating heightened vulnerability relative to men. METHODS: The study involved data from the Korea Community Health Survey and included 319 592 adults aged 30 years to 49 years. We employed log-binomial regression analysis, controlling for variables including age, education, employment status, marital status, and the presence of children. The study period included 3 phases: the period prior to the COVID-19 outbreak (pre-COVID-19), the early pandemic, and the period following the introduction of vaccinations (post-vaccination). RESULTS: The findings indicated that women were at a heightened risk of psychosocial stress during the early pandemic (relative risk [RR], 1.01; 95% confidence interval [CI], 0.98 to 1.05) and post-vaccination period (RR, 1.07; 95% CI, 1.04 to 1.10) compared to men. This pattern was prominent in urban women aged 30-34 years (pre-COVID-19: RR, 1.06; 95% CI, 1.02 to 1.10; early pandemic: RR, 1.16; 95% CI, 1.08 to 1.25; post-vaccination period, RR, 1.22; 95% CI, 1.14 to 1.31). CONCLUSIONS: The COVID-19 pandemic has exerted unequal impacts on psychosocial stress among prime working-age individuals in Korea, with women, particularly those in urban areas, experiencing a heightened risk. The findings highlight the importance of addressing gender-specific needs and implementing appropriate interventions to mitigate the psychosocial consequences of the pandemic.

Widening Social Inequalities in Cancer Mortality of Children Under 5 Years in Korea.

BACKGROUND: To investigate the effect of parental social class on cancer mortality in children under 5 in Korea, two birth cohorts were constructed by linking national birth data to under-5 death data from the Statistics Korea for 1995-1999 (3,323,613 births) and 2010-2014 (2,297,876 births). METHODS: The Cox proportional hazards model adjusted for covariates was used in this study. RESULTS: Social inequalities of under-5 cancer mortality risk in paternal education and paternal employment status were greater in 2010-2014 than in 1995-1999. The gap of hazard ratio (HR) of under-5 cancer mortality between lower (high school or below) and higher (university or higher) paternal education increased from 1.23 (95% confidence interval, 1.041.46) in 1995-1999 to 1.45 (1.11-1.97) in 2010-2014; the gap of HR between parents engaged in manual work and non-manual work increased from 1.32 (1.12-1.56) in 1995-1999 to 1.45 (1.12-1.89) in 2010-2014 for fathers, and from 1.18 (0.7-1.98) to 1.69 (1.03-2.79) for mothers. When the parental social class was lower, the risk of under-5 cancer mortality was higher in not only adverse but normal births. CONCLUSION: Social inequalities must be addressed to reduce the disparity in cancer mortality of children under 5 years old.

Socioeconomic Inequalities in COVID-19 Incidence During Different Epidemic Phases in South Korea.

Objective: Area-level socioeconomic status (SES) is associated with coronavirus disease 2019 (COVID-19) incidence. However, the underlying mechanism of the association is context-specific, and the choice of measure is still important. We aimed to evaluate the socioeconomic gradient regarding COVID-19 incidence in Korea based on several area-level SES measures. Methods: COVID-19 incidence and area-level SES measures across 229 Korean municipalities were derived from various administrative regional data collected between 2015 and 2020. The Bayesian negative binomial model with a spatial autocorrelation term was used to estimate the incidence rate ratio (IRR) and relative index of inequality (RII) of each SES factor, with adjustment for covariates. The magnitude of association was compared between two epidemic phases: a low phase (<100 daily cases, from May 6 to August 14, 2020) and a rebound phase (>100 daily cases, from August 15 to December 31, 2020). Results: Area-level socioeconomic inequalities in COVID-19 incidence between the most disadvantaged region and the least disadvantaged region were observed for nonemployment rates [RII = 1.40, 95% credible interval (Crl) = 1.01-1.95] and basic livelihood security recipients (RII = 2.66, 95% Crl = 1.12-5.97), but were not observed for other measures in the low phase. However, the magnitude of the inequalities of these SES variables diminished in the rebound phase. A higher area-level mobility showed a higher risk of COVID-19 incidence in both the low (IRR = 1.67, 95% Crl = 1.26-2.17) and rebound phases (IRR = 1.28, 95% Crl = 1.14-1.44). When SES and mobility measures were simultaneously adjusted, the association of SES with COVID-19 incidence remained significant but only in the low phase, indicating they were mutually independent in the low phase. Conclusion: The level of basic livelihood benefit recipients and nonemployment rate showed social stratification of COVID-19 incidence in Korea. Explanation of area-level inequalities in COVID-19 incidence may not be derived only from mobility differences in Korea but, instead, from the country's own context.

Financial hardship and suicide ideation: Age and gender difference in a Korean panel study.

BACKGROUNDS: Socioeconomic factors influence suicide risk but a systematic understanding of the role of financial hardship is unclear. We examined whether financial hardship had cumulative or contemporaneous impacts on suicide ideation and any gender and age differences in a large Korean sample. METHODS: Data of 67,728 observations from 14,321 individuals were extracted from seven waves of Korean Welfare Panel Study. The association of financial hardship at baseline and its change over two years with suicide ideation was investigated using generalized estimation equation to account for repeated measurements within an individual, adjusting for other socioeconomic factors. RESULTS: Financial hardship was associated with suicide ideation but the magnitude of association varied across age and gender groups. Specifically, the impact of financial hardship was persistent over two years presenting a cumulative effect among men aged 50-64 years and ≥65 years; e.g., adjusted OR (adjusted odds ratio) = 3.87, 95 % CI = 2.71-5.54 for emergent hardship group vs adjusted OR = 4.22, 95 %CI = 3.00-5.93 for persistent group in those aged ≥65 years. Financial hardship increased the risk of suicide ideation incrementally with age, although the pattern was less clear among women. LIMITATIONS: Financial hardship was identified as having changing nature, though it was assumed to occur over two years. CONCLUSION: In general, financial hardship plays a role in amplifying suicide ideation in a contemporaneous way but also in a cumulative way, predominantly among late-middle-aged and elderly men. Monitoring and intervention for financial hardship would be a promising strategy for suicide prevention.

Impact of insurance type on outcomes in cardiac arrest patients from 2004 to 2015: A nation-wide population-based study.

OBJECTIVES: There do not appear to be many studies which have examined the socio-economic burden and medical factors influencing the mortality and hospital costs incurred by patients with cardiac arrest in South Korea. We analyzed the differences in characteristics, medical factors, mortality, and costs between patients with national health insurance and those on a medical aid program. METHODS: We selected patients (≥20 years old) who experienced their first episode of cardiac arrest from 2004 to 2015 using data from the National Health Insurance Service database. We analyzed demographic characteristics, insurance type, urbanization of residential area, comorbidities, treatments, hospital costs, and mortality within 30 days and one year for each group. A multiple regression analysis was used to identify an association between insurance type and outcomes. RESULTS: Among the 487,442 patients with cardiac arrest, the medical aid group (13.3% of the total) had a higher proportion of females, rural residents, and patients treated in low-level hospitals. The patients in the medical aid group also reported a higher rate of non-shockable conditions; a high Charlson Comorbidity Index; and pre-existing comorbidities, such as hypertension, diabetes mellitus, and renal failure with a lower rate of providing a coronary angiography. The national health insurance group reported a lower one-year mortality rate (91.2%), compared to the medical aid group (94%), and a negative association with one-year mortality (Adjusted OR 0.74, 95% CI 0.71-0.76). While there was no significant difference in short-term costs between the two groups, the medical aid group reported lower long-term costs, despite a higher rate of readmission. CONCLUSIONS: Medical aid coverage was an associated factor for one-year mortality, and may be the result of an insufficient delivery of long-term services as reflected by the lower long-term costs and higher readmission rates. There were differences of characteristics, comorbidities, medical and hospital factors and treatments in two groups. These differences in medical and hospital factors may display discrepancies by type of insurance in the delivery of services, especially in chronic healthcare services.

Analysis of Characteristics and Mortality in Cardiac Arrest Patients by Hospital Level: a Nationwide Population-based Study.

BACKGROUND: Survival and post-cardiac arrest care vary considerably by hospital, region, and country. In the current study, we aimed to analyze mortality in patients who underwent cardiac arrest by hospital level, and to reveal differences in patient characteristics and hospital factors, including post-cardiac arrest care, hospital costs, and adherence to changes in resuscitation guidelines. METHODS: We enrolled adult patients (≥ 20 years) who suffered non-traumatic cardiac arrest from 2006 to 2015. Patient demographics, insurance type, admission route, comorbidities, treatments, and hospital costs were extracted from the National Health Insurance Service database. We categorized patients into tertiary hospital, general hospital, and hospital groups according to the level of the hospital where they were treated. We analyzed the patients' characteristics, hospital factors, and mortalities among the three groups. We also analyzed post-cardiac arrest care before and after the 2010 guideline changes. The primary end-point was 30 days and 1 year mortality rates. RESULTS: The tertiary hospital, general hospital, and hospital groups represented 32.6%, 49.6%, and 17.8% of 337,042 patients, respectively. The tertiary and general hospital groups were younger, had a lower proportion of medical aid coverage, and fewer comorbidities, compared to the hospital group. Post-cardiac arrest care, such as percutaneous coronary intervention, targeted temperature management, and extracorporeal membrane oxygenation, were provided more frequently in the tertiary and general hospital groups. After adjusting for age, sex, insurance type, urbanization level, admission route, comorbidities, defibrillation, resuscitation medications, angiography, and guideline changes, the tertiary and general hospital groups showed lower 1-year mortality (tertiary hospital vs. general hospital vs. hospital, adjusted odds ratios, 0.538 vs. 0.604 vs. 1; P < 0.001). After 2010 guideline changes, a marked decline in atropine use and an increase in post-cardiac arrest care were observed in the tertiary and general hospital groups. CONCLUSION: The tertiary and general hospital groups showed lower 30 days and 1 year mortality rates than the hospital group, after adjusting for patient characteristics and hospital factors. Higher-level hospitals provided more post-cardiac arrest care, which led to high hospital costs, and showed good adherence to the guideline change after 2010.

Small but protective social capital against suicide ideation in poor communities: A community-based cross-sectional study.

Coupled with the lowest level of social connectedness, South Korea has the highest suicide rate among the Organization for Economic Co-operation and Development countries. A possible link between community and suicide is social capital imprinted in social connectedness. This study explores whether social capital is protective against suicide ideation in relation to the poverty level of communities, and whether the associations are specific to certain elements of social capital.A total of 908 participants were included to assess cross-sectional association of social capital at individual level with suicide ideation by comparing between poor (government-leased apartments) and non-poor communities (nongovernment-leased apartments). Logistic regression analyses were performed to examine various social capital dimensions in relation to suicide ideation.Suicide ideation was far higher among those living in the poor communities (poor communities 12%; non poor communities 6.3%) and the level of social capital was lower in the poor communities. Nevertheless, the protective effect of social capital, in particular, the cognitive dimension against suicide ideation was demonstrated only in the poor communities (eg, odds ratio = 0.27, 95% confidence interval: 0.12-0.58 for trust in the poor communities). Low income was significantly associated with suicide ideation only in the poor communities, but depression and resilience were associated with suicide ideation both in the poor and non-poor communities.To increase the reliability of the results, established measures based on relevant literature were utilized, but measures on bridging social capital and social network might have relatively low reliability.As to protection against suicide ideation, the extent of reliance on social capital was higher in poor communities than in non-poor communities, in particular, the cognitive dimension was likely to activate in this regard.

An Assessment of Magnitudes and Patterns of Socioeconomic Inequalities across Various Health Problems: A Large National Cross-Sectional Survey in Korea.

Magnitudes of health inequalities present consequences of socioeconomic impact on each health problem. To provide knowledge on the size of health problems in terms of socioeconomic burden, we examined the magnitudes and patterns of health inequalities across 12 health problems. A total of 17,292 participants older than 30 years were drawn from the Korea National Health and Nutrition Examination Survey (KNHANES, 2010⁻2012). The age-adjusted prevalence ratios were compared across socioeconomic positions (SEPs) based on income, education, and occupation. The magnitudes of socioeconomic inequalities varied across 12 health problems and, in general, the patterns of socioeconomic inequalities were similar among groups of health problems (i.e., non-communicable diseases (NCDs), mental health, and subjective health states). Significant health inequalities across NCDs, such as diabetes, hypertension, ischemic heart disease, and arthritis, were observed mainly in women. Socioeconomic inequalities in mental health problems, such as depression, suicidal ideation, and suicide attempts, were profound for both genders and across SEP measures. Significant socioeconomic inequalities were also observed for subjective health. No or weak associations were observed for injury and HBV infection. The patterns of socioeconomic inequalities were similar among groups of health problems. Mental illnesses appeared to require prioritization of socioeconomic approaches for improvement in terms of absolute prevalence and relative socioeconomic distribution.

Socioeconomic inequalities in health in the context of multimorbidity: A Korean panel study.

Socioeconomic inequalities in health are commonly known to decrease at late age. Yet, it remains unclear whether socioeconomic inequalities in health at late age appear in relation to multimorbidity, particularly in Korea where social support remains unsatisfactory for older people. Using three waves of Korea Health Panel, data of 19,942 observations with repeated measure were constructed to ensure a temporal sequence between three socioeconomic measures (i.e., poverty, employment status, and education) and multimorbidity with a t to t+1 year transition. A multilevel multinomial model was applied to quantify the socioeconomic impact across different age, diseases and disease groups, both separately and in combination. There were associations between socioeconomic position (SEP) and multimorbidity, and increasing trends of socioeconomic inequalities not only with greater number of morbidity but also with age. The latter result was only observed with employment status through mid-to-early old age; i.e., between the 40s (odds ratio (OR) = 2.45, 95% confidence interval (CI):1.08-5.57) and 70s (OR = 3.48, 95%CI: 1.24-9.74). The patterns of socioeconomic inequalities in multimorbidity varied for particular pairs of diseases and were stronger in the disease pairs co-occurring with mental and cardiovascular diseases but weaker in the disease pairs co-occurring with cancer. Accumulation of adversity tended to intensify with increase in number of diseases and older age, though this finding was not consistently supported. The labour market should be encouraged to actively participate in actions to promote healthy aging needs to be complemented by the provision of more generous and universal income support to the elderly in Korea.

Working hours and self-rated health over 7 years: gender differences in a Korean longitudinal study.

BACKGROUND: To investigate the association between long working hours and self-rated health (SRH), examining the roles of potential confounding and mediating factors, such as job characteristics. METHODS: Data were pooled from seven waves (2005-2011) of the Korean Labour and Income Panel Study. A total of 1578 workers who consecutively participated in all seven study years were available for analysis. A generalized estimating equation for repeated measures with binary outcome was used to examine the association between working hours (five categories; 20-35, 36-40, 41-52, 53-68 and ≥ 69 h) and SRH (two categories; poor and good health), considering possible confounders and serial correlation. RESULTS: Associations between working hours and SRH were observed among women, but only for the category of the shortest working hours among men. The associations with the category of shortest working hours among men and women disappeared after adjustment for socioeconomic factors. Among women, though not men, working longer than standard hours (36-40 h) showed a linear association with poor health; OR = 1.41 (95% CI = 1.08-1.84) for 52-68 working hours and OR = 2.11 (95% CI = 1.42-3.12) for ≥ 69 working hours. This association persisted after serial adjustments. However, it was substantially attenuated with the addition of socioeconomic factors (e.g., OR = 1.66 (95% CI = 1.07-2.57)) but only slightly attenuated with further adjustment for behavioural factors (e.g., OR = 1.63 (95% CI = 1.05-2.53)). The associations with job satisfaction were significant for men and women. CONCLUSIONS: The worsening of SRH with increasing working hours only among women suggests that female workers are more vulnerable to long working hours because of family responsibilities in addition to their workload.

Poor health, employment transitions and gender: evidence from the British Household Panel Survey.

OBJECTIVES: We examined health selection in the context of transitions across employment statuses (employment, unemployment and inactivity), with attention to gender differences. METHODS: 60,536 transitions from 7,901 individuals were pooled from 17 waves of the British Household Panel Survey. Associations between self-rated health and transitions across employment statuses were examined using multilevel multinomial analysis. RESULTS: Health selective employment transitions between year t-1 and t were observed at entry to as well as exit from employment. Associations for poor health with the transitions were similar for men and women in transitions from employment to both unemployment and to inactivity, but with some differences in other transitions. When leaving employment, transitions from employment to unemployment (OR(adjusted)(adjusted odds ratio) = 1.51, 95 % CI = 1.21-1.89 for men and OR(adjusted) = 1.60, 95 % CI = 1.25-2.04 for women) and to inactivity (OR(adjusted) = 1.58, 95 % CI = 1.21-1.89 for men and OR(adjusted) = 1.63, 95 % CI = 1.35-1.96 for women) were affected by health status among both men and women. Similarly, poor health lowered the probability of transitions to employment from unemployment and inactivity; however, the negative impact of poor health was statistically significant only for women. CONCLUSIONS: There is a strong relationship between health and transitions both into and out of employment suggesting an independent role for poor health, and these associations were similar for men and women.

Health selection operating between classes and across employment statuses.

BACKGROUNDS: The debate on health selection which describes the influence of health on subsequent social mobility is highly contested. The authors set out to examine the effect of health selection by looking at the effect of previous health status on changes in socio-economic position (SEP) over two time periods. METHOD: Data were pooled from 13 waves (1991-2003) of the British Household Panel Survey (BHPS). Using a multilevel multinomial approach, the presence of health selection between classes and into/out of employment was concurrently tested and compared. RESULTS: In the descriptive analysis, poor health was consistently associated with moving downward, while the outcome was inverse for upward movement. After accounting for the data structure using multilevel analysis, health was a predictor for social mobility when leaving and entering employment, but the effect was minimal for transitions between classes for both men and women. CONCLUSION: The non-significant impact of health on mobility inside employment may reflect the presence of the significant impact of health on mobility between employment and non-employment. This implies that the effect of health was not evenly spread over all social mobility, but rather tends to concentrate on some types of mobility. The effect of each predictor on social mobility is more concentrated among specific transitions, and health and age were likely to be substantial in moving into/out of the labour force, whereas education was a relevant predictor for mobility into/out of upper classes, in particular, classes I/II.