Introduction
Health-related quality of life (HRQoL) is a multi-dimensional concept that includes domains related to physical, mental, emotional, and social functioning and the social context in which people live [
1]. HRQoL is an important outcome used in a variety of medical research disciplines to ascertain aspects of well-being in settings of health and disease. Since 1949, the World Health Organization (WHO) has emphasized the importance of HRQoL [
2]. The WHO’s ‘Healthy People 2020’ initiative emphasizes HRQoL as one of its four overarching goals [
3].
As factors affecting HRQoL vary, HRQoL has been analyzed as an outcome in a variety of populations and settings [
4]. Previous studies to clarify the factors affecting HRQoL have generally considered physical functioning (e.g., overall physical health, physical functioning, pain, fatigue), disease-specific (e.g., cancer, chronic disease), health care service use (e.g., unmet healthcare needs) factors as relevant [
5‐
9]. In particular, not only burden for clinical status but also socio-economic burden can also affect HRQoL. For example, financial hardship was associated with degenerated physical and psychological heath, thereby exacerbating HRQoL [
10]. In addition, financial hardship that occurs after receiving hematopoietic cell transplantation was associated with worse quality of life and exacerbated perceived stress [
11]. Studies on this topic have focused primarily on specific disease with high medical expenditures such as cancer. On the other hands, in line with the increasing trend of socio-economic burden of chronic disease such as non-communicable disease [
12], study reported that high medical expenditure including out-of-pocket expenditure in type 2 diabetic patients was associated to poor health-related quality of life [
13]. Especially, household members with chronic illness are the major factors affecting financial catastrophe, financial hardship of healthcare was greater for subjects affected by chronic disease than those unaffected [
14‐
16]. Hence, catastrophic healthcare expenditure which implies a financial hardship due to medical expenditure may impact on health-related quality of life. Only a few studies have addressed this issue. This topic is especially relevant to countries with concerns about health-related life satisfaction and health service utilization.
In Korea, subjective health satisfaction is the lowest among all OECD countries. According to an OECD report, only 35.1% of Koreans ≥15 years of age believe their health condition to be “good”. This value is approximately one-half the OECD average of 69.2%. Hence, it is necessary to examine health-related life satisfaction issues in terms of promotion and identification of the factors that affect HRQoL. In addition, a national health insurance system provides universal healthcare coverage in Korea, but there are barriers to medical care access because high out-of-pocket payments (OOP) cause catastrophic health expenditures (CHEs). Overall, the South Korean OOP payment for healthcare is the highest among OECD countries (Korea: 4.7%; OECD average: 2.8%) [
17]. Korea also has a relatively greater proportion of households with catastrophic expenditures [
17,
18].
Therefore, the present study used longitudinal data and analysed the effects of catastrophic health expenditure on HRQoL in the general population. In addition, we examined the relationship between CHE and HRQoL by number of chronic disease.
Results
In our study, 8850 subjects were included to assess the association between catastrophic health expenditure and health-related quality of life. Table
1 shows the baseline characteristics of the study population. Among the 8850 subjects, 4.5% (
n = 398) experienced catastrophic health expenditure. The mean baseline EQ-VAS score was 70.76 ± 15.39. The EQ-VAS score value was lower for those who experienced CHE (64.46 ± 17.56) compared with those who did not experience CHE (71.06 ± 15.21). Lower scores indicated more severe status in HRQoL.
Table 1
General characteristics of study population at baseline in 2012
Catastrophic expenditure | −7.37 | <.0001 |
Yes | 398 | (4.5) | 64.46 | ± | 17.56 | | |
No | 8452 | (95.5) | 71.06 | ± | 15.21 | | |
Sex | 8.42 | <.0001 |
Male | 3834 | (43.3) | 72.32 | ± | 14.72 | | |
Female | 5016 | (56.7) | 69.57 | ± | 15.78 | | |
Age | 104.34 | <.0001 |
19~ 29 | 733 | (8.3) | 75.19 | ± | 14.60 | | |
30~ 39 | 1421 | (16.1) | 73.56 | ± | 14.55 | | |
40~ 49 | 2057 | (23.2) | 73.65 | ± | 13.37 | | |
50~ 59 | 1693 | (19.1) | 70.86 | ± | 14.55 | | |
60~ 69 | 1503 | (17.0) | 68.36 | ± | 16.03 | | |
70+ | 1443 | (16.3) | 64.03 | ± | 16.88 | | |
Education level | 281.25 | <.0001 |
Elementary or below | 1912 | (21.6) | 64.16 | ± | 16.87 | | |
Middle/high school | 3955 | (44.7) | 71.14 | ± | 14.85 | | |
College or above | 2983 | (33.7) | 74.49 | ± | 13.63 | | |
Economic status | 10.29 | <.0001 |
Employed | 5498 | (62.1) | 72.11 | ± | 14.46 | | |
Unemployed | 3352 | (37.9) | 68.55 | ± | 16.56 | | |
Income | 117.73 | <.0001 |
Low | 1243 | (14.0) | 63.41 | ± | 17.67 | | |
Middle-low | 1687 | (19.1) | 69.08 | ± | 15.39 | | |
Middle | 1866 | (21.1) | 71.25 | ± | 14.82 | | |
Middle-high | 1987 | (22.4) | 72.56 | ± | 14.56 | | |
High | 2067 | (23.4) | 74.38 | ± | 13.41 | | |
Health insurance type | 12.17 | <.0001 |
Health insurance | 8448 | (95.5) | 71.30 | ± | 14.96 | | |
Medical aid | 402 | (4.5) | 59.39 | ± | 19.34 | | |
Family constitution | 79.65 | <.0001 |
Living alone | 667 | (7.5) | 66.46 | ± | 17.12 | | |
Couple | 1792 | (20.3) | 68.06 | ± | 16.37 | | |
Couple with children | 4803 | (54.3) | 73.00 | ± | 14.11 | | |
More | 1588 | (17.9) | 68.87 | ± | 16.06 | | |
Number of chronic disease | 225.56 | <.0001 |
0 | 3180 | (35.9) | 74.92 | ± | 13.73 | | |
1 | 1794 | (20.3) | 72.41 | ± | 14.19 | | |
2 | 1252 | (14.1) | 70.00 | ± | 15.11 | | |
3+ | 2624 | (29.7) | 64.96 | ± | 16.34 | | |
Disability | 12.77 | <.0001 |
Absent | 8266 | (93.4) | 71.38 | ± | 15.04 | | |
Present | 584 | (6.6) | 61.95 | ± | 17.40 | | |
Perceive health status | 40.78 | <.0001 |
Good | 7478 | (84.5) | 73.72 | ± | 13.20 | | |
Bad | 1372 | (15.5) | 54.62 | ± | 16.41 | | |
Depression mood | −18.53 | <.0001 |
Present | 632 | (7.4) | 58.11 | ± | 18.03 | | |
Absent | 8218 | (92.9) | 71.74 | ± | 14.72 | | |
Year | |
2012 | 8850 | 100.0 | 70.76 | ± | 15.39 | | |
Table
2 shows the association between CHE experiencing and HRQoL while adjusting for all independent variables. Those with CHE experiencing tended to have lower EQ-VAS index values compared with those without CHE (β: − 1.34,
p = 0.013). A more detailed examination of the relationship between experiencing CHE and HRQoL revealed that respondents ≥70 years of age tended to have lower EQ-VAS index values compared with respondents 19~29 years of age (β: − 1.72,
p = 0.010). An examination based on income revealed that EQ-VAS values increased as income increased (i.e., low < middle-low < middle < middle-high < high; low: − 3.26, middle-low: − 1.69, middle: − 1.75, middle-high: − 0.94). Respondents with ≥3 chronic diseases had lower EQ-VAS scores compared with those without any chronic diseases (β: − 3.11,
p < 0.001).
Table 2
Results of the GEE analyzing for the effect of catastrophic health expenditure on EQ-VAS
Catastrophic health expenditure |
Yes | −1.34 | 0.54 | 0.013 |
No | Ref. | | |
Sex |
Male | Ref. | | |
Female | −0.99 | 0.25 | 0.000 |
Age |
19~ 29 | Ref. | | |
30~ 39 | − 0.46 | 0.53 | 0.384 |
40~ 49 | 0.44 | 0.51 | 0.387 |
50~ 59 | −0.26 | 0.56 | 0.642 |
60~ 69 | −0.45 | 0.61 | 0.459 |
70+ | −1.72 | 0.67 | 0.010 |
Education level |
Elementary or below | −2.40 | 0.45 | <.001 |
Middle/high school | −0.75 | 0.29 | 0.010 |
College or above | Ref. | | |
Economic status |
Employed | Ref. | | |
Unemployed | 0.44 | 0.26 | 0.099 |
Income |
Low | −3.26 | 0.48 | <.001 |
Middle-low | −1.69 | 0.36 | <.001 |
Middle | −1.75 | 0.34 | <.001 |
Middle-high | −0.94 | 0.31 | 0.002 |
High | Ref. | | |
Health insurance type |
Health insurance | Ref. | | |
Medical aid | −2.03 | 0.67 | 0.002 |
Family constitution |
Living alone | 0.91 | 0.51 | 0.073 |
Couple | Ref. | | |
Couple with children | −1.19 | 0.36 | 0.001 |
More | −1.80 | 0.40 | <.001 |
Number of chronic disease |
0 | Ref. | | |
1 | −1.16 | 0.32 | <.001 |
2 | −1.82 | 0.38 | <.001 |
3+ | −3.11 | 0.36 | <.001 |
Disability |
Absent | Ref. | | |
Present | −1.96 | 0.52 | <.001 |
Perceive health status |
Good | Ref. | | |
Bad | −14.90 | 0.37 | <.001 |
Depression mood |
Present | −7.07 | 0.46 | <.001 |
Absent | Ref. | | |
Year |
2012 | Ref. | | |
2013 | −0.83 | 0.18 | <.001 |
The subgroup analysis results are shown in Table
3. Subjects with CHE and greater chronic disease (3 or more) exhibited a drastic decrease in HRQoL.
Table 3
Results of the GEE analyzing for the effect of catastrophic health expenditure on EQ-VAS by number of chronic disease
Number of chronic disease | Catastrophic expenditure |
0 | No | Ref. | | |
Yes | 0.53 | 1.35 | 0.696 |
Number of chronic disease | Catastrophic expenditure |
1 | No | Ref. | | |
Yes | −0.54 | 1.50 | 0.719 |
Number of chronic disease | Catastrophic expenditure |
2 | No | Ref. | | |
Yes | −2.17 | 1.23 | 0.077 |
Number of chronic disease | Catastrophic expenditure |
3+ | No | Ref. | | |
Yes | −1.85 | 0.75 | 0.014 |
Discussion
We found that after adjustment for multiple variables, CHE was significantly associated with degenerated HRQoL in the general population. The results of our subgroup analysis indicated that the association between CHE and HRQoL was stronger in individuals with chronic disease.
These findings can be explained by the associations between financial burden and life satisfaction. Previous studies have examined the associations between economic hardship and life satisfaction and have found that financial burden has adverse consequences on life satisfaction characteristics [
23‐
25]. Studies of catastrophic expenditure revealed that there is a robust association between excessive expenditure for healthcare and financial strain (e.g., onset of poverty). Hence, experiencing CHE may increase financial strain and result in a deteriorating HRQoL.
Consistent with previous studies on financial hardship, cancer survivors in the USA who have financial burdens (e.g., borrowed money) are more likely to have low Physical Component and Mental Component scores and are therefore more likely to experience a depressed mood [
26]. Patients in the UK who have head and neck cancer that has resulted in serious effects on household finances have poor HRQoL [
27].
Populations who suffer from a chronic disease are more likely to experience CHE because medical expenditures are likely to continue for a long period. As expenditures for chronic disease treatment accumulate, individuals or households are more likely to compromise healthy lifestyle choices. For example, they cannot afford fresh fruits and vegetables, which are more expensive than processed foods [
28]. Therefore, chronic disease has the potential to negatively affect health-related life satisfaction characteristics [
29]. This phenomenon has been found in developing [
30] countries and in the wealthiest countries in Europe [
31].
Our study revealed that 4.5% of households in Korea experienced CHE. This estimate is similar to the 3.0% that the OECD reported for 2012 using Korea national statistics. This value is also the highest among OECD countries [
32]. Among developed countries, only Portugal, Greece, Switzerland, and the United States have 0.5% or more of households with catastrophic-level health spending. OOP payments for healthcare can cause households to incur catastrophic expenditures [
33,
34]. Therefore, this result for Korea is expected because OOP spending as a share of total health expenditure is relatively high (Korea = 36%; OECD average = 19%) [
35]. High OOP payments may create barriers to medical utilization that cause delays in care, low screening rates among vulnerable people, and exacerbate inequities in health status and in health-related life satisfaction characteristics.
Even when we excluded households that experienced CHE in the most recent year, the mean EQ-VAS score at baseline was 70.8; this value was less than that of the general population of China (80.1) [
36] and of the mean overall score of six European countries (77.1) [
37]. South Korea currently has serious life satisfaction issues. Koreans are substantially less satisfied with their lives compared with residents of OECD countries. The ‘Better Life Index’ report presents results for eleven parameters (e.g., income, jobs, health and work-life balance); Korea ranked 29th among OECD countries in 2014 (Korea’s score: 5.8/10; OECD average: 6.6/10). The results for the self-reported health measure of health-related life satisfaction indicated that individual South Korean citizens have the least confidence in their own health condition level. Taken together, these findings indicate that effective strategies to manage HRQoL among households with CHE should be designed and implemented.
Our findings suggested that programs (e.g., medical expense assistance) that support populations who experience CHE are needed to improving the quality of life. The Korean government recently implemented the pilot catastrophic healthcare expenditure aid program. This public assistance program targets poor individuals who experience catastrophic healthcare expenditure due to major severe diseases (e.g., cancer, cardiovascular disease, rare diseases).
We suggest that countries with low financial assistance levels for healthcare should aim to reduce the barriers within the healthcare system and allocate resources to strengthen healthcare coverage and increase healthcare equity. These efforts should emphasize guarantee of healthcare services for people who suffer from excessive health expenditures and chronic disease.
This study had some limitations. First, the EQ-VAS measures current health status and CHE was measured using yearly health expenditure data. Therefore, the effects from external events might have moderated or reinforced the HRQoL results. Second, we used the EQ-VAS to measure HRQoL, which depends on the participant’s subjective perception. However, the EQ-VAS is widely used for HRQoL studies. Third, due to limitations of our data, we measured short-term effects (i.e., 2 years). Further studies of longer-term effects of CHE are needed.
Despite the limitations, this study is the first to investigate associations between CHE and HRQoL among the general Korean population. Given the high values for incidence of catastrophic healthcare expenditure and the low health-related satisfaction levels in Korea, our findings are important for health policy makers to identify solutions aimed at control of HRQoL characteristics.