Inequalities in Medical Care Utilization by South Korean Cancer Patients According to Income: A Retrospective Cohort Study

Abstract

This study explores income inequalities in the utilization of medical care by cancer patients in South Korea, according to type of medical facilities and survival duration. The five-year retrospective cohort study used data drawn from the Korean Cancer Registry, the National Health Insurance database, and the death database of the Korean National Statistical Office. The sample consisted of 43,433 patients diagnosed with cancer in 1999. The authors found significant quantitative inequalities as a function of income in the patients' utilization of medical care. Cancer patients from the highest income class used inpatient and outpatient care more frequently than did patients from the lowest income class. Those with higher incomes tended to use more inpatient and outpatient services at major tertiary hospitals, which were known as providing better medical care than other types of hospitals and clinics. Moreover, horizontal inequality in cancer-care expenditures favoring those with higher incomes was observed during earlier periods of treatment. In conclusion, income substantially affects the utilization of inpatient and outpatient services, amount of medical expenditures, and type of medical facilities.

Income related inequalities in New Cooperative Medical Scheme: a five-year empirical study of Junan County in China

“…First, as we mentioned above, annual net income per rural resident at the town level was used as a proxy income variable, which would possibly underestimate the inequality in this study although the income difference within the same town for such a poor county is not so distinct. Referring to employee-based health insurance, the contribution of employees is also a useful surrogate index for actual household income because it is calculated on the income, property and private auto taxes of the employee [23,24]. For NCMS, the contribution of enrollees is flat so town-level income is finally adopted, but still income data at individual level would be preferred in the analysis of income related inequality if possible.…”

A systematic review of barriers to optimal outpatient specialist services for individuals with prevalent chronic diseases: what are the unique and common barriers experienced by patients in high income countries?

“…However, the volume of articles differed between chronic diseases: 59 articles (79.7 %) included oncology samples [19, 32–40, 42–47, 50–55, 57–63, 65–72, 74, 76–96]; 12 articles (16.2 %) included depression [32, 33, 41, 62, 64, 75, 83, 93, 97–100]; 10 articles (13.5 %) included diabetes [18, 32, 33, 64, 74, 75, 83, 101–103]; 10 articles (13.5 %) included ischaemic heart disease [18, 32, 33, 48, 56, 64, 74, 75, 83, 102]; 7 articles (9.5 %) included COPD [32, 33, 49, 56, 74, 75, 102]; 7 articles (9.5 %) included asthma [18, 32, 33, 64, 74, 75, 83]; 6 articles (8.1 %) included arthritis [32, 33, 73–75, 83]; and 3 articles (4.1 %) included osteoporosis [73–75]. A total of 12 studies (16.2 %) included more than one disease of interest [18, 32, 33, 56, 62, 64, 73–75, 83, 93, 102].…”

“…As the most frequently described barrier to outpatient care, difference in service use, levels of need, or satisfaction according to demographic characteristics were reported across all diseases of interest [18, 19, 34, 38, 41, 45, 47, 51, 52, 54, 55, 57, 61, 64, 65, 68, 74, 75, 79, 84, 85, 87–89, 91, 92, 96, 97, 99–103]. Additional barriers resulting from sub-optimal interactions with healthcare teams or non-patient focussed health service organization were commonly reported.…”

“…This was primarily due to the focus on patients’ demographic characteristics as both a barrier to receiving optimal care and as a critical factor in mediating the magnitude of barriers experienced. Examples include examining disparities according to race [18, 19, 91, 102]; education [61, 71]; age [65, 71, 89, 92]; gender [51, 52, 65]; presence of comorbidities, disease severity or reduced health status [89, 92, 103]; and socioeconomic groupings [74, 96, 101]. For example, using the population-based Nord-Tondelag Health Survey (HUNT-3), Vikum et al explored the differential use of healthcare according to patients’ education levels and household income [74].…”

“…Approximately 17 articles explored a barrier resulting from the cost of healthcare [19, 32, 36, 39, 47, 49, 55, 57, 61, 69, 74, 75, 83, 87, 96, 98, 103]. These articles were conducted in a variety of healthcare systems, including those with publically-funded healthcare schemes designed to encourage universal access to services.…”