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The cross-interaction between global and age-comparative self-rated health on depressive symptoms–considering both the individual and combined effects

  • Jaeyong Shin1, 2,
  • Eun-Cheol Park1, 2,
  • Sang Gyu Lee3,
  • Young Choi4,
  • Jae-Hyun Kim4, 5, 6 and
  • Tae Hyun Kim3Email author
BMC PsychiatryBMC series – open, inclusive and trusted201616:433

https://doi.org/10.1186/s12888-016-1098-9

Received: 8 January 2016

Accepted: 30 October 2016

Published: 5 December 2016

Abstract

Background

Numerous studies suggesting the relation between self-rated health (SRH) and depression have been reported using different measures. Therefore, we attempted to determine the difference in a depressive scale based on the different ways of measuring health between global SRH (SRH-global) and age-comparative SRH (SRH-age). Then, the combined effect of SRH-global and SRH-age on depressive symptoms was further investigated.

Methods

Data from the Korean Longitudinal Study of Ageing (KLoSA) from 2008 to 2012 were analyzed. We divided the SRH-global and SRH-age into three levels—high, middle, and low—and combined each into nine new categories (SRH-combi). The Center for Epidemiologic Studies Depression Scale-10 Korean edition was used as the dependent variable.

Results

A total of 8621 participant were enrolled at baseline. Individuals with lower SRHs-age compared to SRH-global tended to be more vulnerable to depressive symptoms. Low SRH-global with low (b = 0.654, p < 0.001) and middle SRH-age (b = 0.210, p = 0.003) showed association with higher CESD scores. Participants with high SRH-global × low SRH-age also had higher scores (b = 0.536, p < 0.001) compared to the “middle SRH-global × middle SRH-age” reference group. In contrast, among the middle (b = −0.696, p < 0.001) and high SRH-global (b = −0.545, p < 0.001) groups, participants with superior SRH-age had statistically lower CESD scores than the reference group.

Conclusions

Although a sole general SRH has historically been widely used, it has been suggested that use of both general and age-comparative SRH would be more powerful and easy when we consider analyzing depression in old age.

Keywords

Self-rated health Combined Age comparative Depression Old aged

Background

Major depressive disorder (MDD) contributes the significant burden of diseases in developed countries, and there will be further increases [13]. According to the Korean Statistical Informational Service (KOSIS, 2011), approximately 27.6% of the general population in Korea suffers from mental disorders during a lifetime [4]. While it is more prevalent than men, 12.0 and 9.1% were observed to have anxiety disorder and MDD, respectively, among women during their lifetimes.

In addition, the Republic of Korea has become a rapidly aging society. As a result, geriatric depression has also emerged as a major social issue. It is widespread and affects at least one in six patients treated in general medical practice and an even higher percentage in hospitals and nursing homes. Depression later in life has serious consequences, including distress among patients and caregivers, which is amplified by disability associated with medical and cognitive disorders of later life, increased health care costs, and increased mortality related to suicide and medical illness. In fact, in 2011, the age-standardized suicide mortality rate in Korea was 33.3 per 100,000 individuals, the highest among all OECD countries [5].

Thus, it is important to identify the determinants associated with depressive symptoms as early as possible. In fact, numerous studies suggesting the relation between self-rated health (SRH) and depression have been reported [2, 68]. SRH is able to measure one’s perception of one’s general health status. This has been widely used and recognized as a validated indicator of health in a variety of populations. It also allows for comparisons across different conditions and populations [912].

The questions for measuring SRH could be classified according to three main categories. The first is a non-comparative SRH, which is usually measured by asking respondents whether they would rate their health as excellent, good, fair, poor, or very poor. The next is an age-comparative SRH measured by asking respondents whether they would rate their health status as better, the same, or worse if compared to that of other people their age. The last is a time-comparative SRH, in which respondents are asked to rate their health compared to how it was at a given time in the past. [13] The three different SRH measurements seem to represent parallel assessments of subjective health. However, there is a possibility of difference among the measurements. For example, people tend to overestimate their health in relation to others with increasing age [11, 13]. A recent study reported significantly positive linear trends between age-comparative SRH and physical health problems, such as respiratory diseases, musculoskeletal diseases, any active chronic diseases, functional disability, depressive symptoms, taking medication regularly, and admission to hospital last year [14]. However, those who rated their time-comparative SRH as “normal” had the smallest odds ratios in all of the physical health problems mentioned above than those who rated it as “better” or “worse”. Thus, it is necessary to compare the differences in depression using a Center for Epidemiologic Studies Depression Scale (CESD) scale based on the different ways of measuring SRH. Because studies regarding the combined effect of global and age-comparative SRH on depressive symptoms in Korea are rare, it will be valuable to investigate the differences among SRHs and the association between combined SRH and depression.

Methods

Study population

We used data from the Korean Longitudinal Study of Ageing (KLoSA) from the second panel survey in 2008 to the fourth in 2012. A basic survey for KLoSA has been conducted every even-numbered year starting in 2006, mainly using the same survey categories regarding social, financial, and health. The population of KLoSA includes, in principle, all adults aged 45 and over. Many other surveys of elderly people in other countries only include the population aged 50 and over. In contrast, Korea experienced a financial crisis in the late 1990s, and career changes in the population of middle-aged individuals in their late 40s became an important social issue. Therefore, KLoSA decided to extend the population to include those aged between 45 and 49. The KLoSA used the Computer Assisted Personal Interviewing (CAPI) survey method. Because age-comparative SRH was not included in the first survey conducted in 2006, we defined the starting point as the second wave in 2008.

Ethics statement

The institutional review board from the Graduate School of Public Health, Yonsei University, approved this research (IRB approval No. 2-1040939-AB-N-01-2016-149). Since we used the national public opened data with de-identification and designed retrospective cross-sectional study, we did not seek informed consent for participation. However, KLoSA initially explained the aim of survey and collected informed consents from participants at baseline.

Measurement on depressive symptoms

The CESD was created in 1977 by Laurie Radloff [15] and revised in 2004 by William Eaton and others [16]. The CESD has been the workhorse of depression epidemiology since its first use in the Community Mental Health Assessment Surveys in the 1970s [17, 18] and is used in the National Health and Nutrition Examination Surveys [19]. It has survived transition to the telephone as well as a self-administered version and is usable with typically undercounted populations such as the elderly and the economically disadvantaged. The scale is well known and remains one of the most widely used instruments in the field of psychiatric epidemiology [2022]. We used the CESD-10 Korean edition for measuring depressive symptoms defined by the American Psychiatric Association’ Diagnostic and Statistical Manual (DSM-IV).

Socio-demographic factors

These factors include age, marital status, living area, education, and economic situation. Age group was carefully classified according to four categories: 54 or below, 55 to 64, 65 to 74, and 75 or above. Living area was divided into three levels: rural area, small to medium city, and metropolitan. Educational level was classified according to four levels from elementary school to college or over. The household heads provided their annual household income level. We then divided the income level into four categories based on the quartile results: Low, Low-Middle, Middle-High, High.

Health-related factors

These factors included whether they performed regular exercise, the number of chronic diseases, cancer history, the proportion of health expenditure in household income, type of medical security system, and whether they joined private health insurance. The number of chronic diseases was divided into four levels: none, one, two, three or more. Excessive health expenditure was defined as the proportion of health expenditures in household income and classified according to four levels from 5 % or below to 20 % or over.

We also included the type of medical security system and whether they joined private health insurance to adjust the effect of security for health on depression. In Korea, the medical security system is classified as national health insurance (NHI) or medical aid. People can qualify for medical aid if their single-family household income is < $600 per month; otherwise, they have mandatory NHI. Those who have NHI based on employment pay a monthly insurance premium according to their annual salary, and people who are self-employed pay for their premium based on the value of their property.

There are two types of private health insurance in Korea [23, 24]. The first is fixed benefit insurance, which pays a fixed amount defined in accordance with the PHI contract. Another is indemnity health insurance, which fully covers services uninsured by the NHI program and out-of-pocket payments for services covered by the NHI program. According to the national statistics [25], 76.8% of household had any kind of private health insurance in 2011.

Measurement on SRH

In KLoSA, SRH was measured using the two following questions.
  1. I.

    “How would you rate your general health status?” Reply alternatives were Excellent, Quite good, Neither good nor poor, Quite poor, and Poor (referred to subsequently as SRH-global).

     
  2. II.

    “Now I’m going to ask you about life satisfaction. Please answer how satisfied you are with the following compared to people of your own age. How satisfied are you with your health?” The answer was measured by a continuous variable from 0 to 100 by units of ten. In other words, Zero meant absolutely dissatisfied and 100 meant absolutely satisfied. To make a comparable study design, we classified this according to five categories as Excellent (90 to 100), Quite good (70 to 80), Neither good nor poor (40 to 60), Quite poor (20 to 30), and Poor (0 to 10) (referred to subsequently as SRH-age).

     
  3. III.

    To measure the combined effect between SRH-global and SRH-age, we re-categorized both health status variables as follows: the health status of participants was defined as High (who answer Excellent and Quite good), Middle (Neither good nor poor), or Low (Quite poor and Poor). We then made another health status variable for the combined analysis (referred to subsequently as SRH-combined). (Additional file 1: Figure S1)

     

Statistical analysis

We evaluated both the separate effects of SRH-global and SRH-age and the combined effect of both variables. For analysis of combined effect, we selected the “middle SRH-global × middle SRH-age” group as reference. Differences in CESD-mean by each variable were tested using a t-test and ANOVA. Associations between CESD and the variables included in three different health status variables (SRH-global, SRH-age, and SRH-combined) and other covariates (socio-demographic and health-related) were initially analyzed with product-moment correlation. As a second step, multiple linear regression analyses (PROC GENMOD; SAS procedure) were used separately for the different SRHs with CESD as the dependent variable, and the variables were included in the factors as independent variables. To compare the goodness of fit among three different SRHs, Quasi-Akaike Information Criterion (QIC) was also applied. In general, the lower value was relatively better than the others. Statistical analyses were performed using SAS, version 9.3 (SAS Institute Inc., Cary, NC, US). Significant differences are indicated according to the following: * P < 0.05 and ** P < 0.001.

Results

General characteristics of participants

Table 1 summarizes the general characteristics of the population. Among 8688 participants of KLoSA in 2008, 67 (0.8%) were excluded due to lack of information. Thus, a total of 8621 participants were initially enrolled at baseline. Among them, 3734 men and 4887 women were included in this study. There were 3998 participants (46.4%) aged 65 or over, and 4603 (53.4%) had one or more chronic diseases such as hypertension, diabetes, hypercholesterolemia, or osteoarthritis. Five hundred and fifteen enrolled participants (6.0%) were covered by the medical aid program while the remaining participants were covered by national medical insurance; 2742 participants (31.8%) had private health insurance at baseline. The mean CESD score at baseline was 3.77 with a standard deviation (SD) of 2.96. Except for gender, there were statistical differences in all covariates. Thus, we put all of these independent variables together for analysis of the CESD scores using multivariate analysis.
Table 1

General characteristics and CESD 10 among subjects at the baseline

 

General characteristics

CESD 10 score

p-value

N

%

Mean ± SD

Gender

   

0.387

 Male

3734

43.3

3.78 ± 2.93

 

 Female

4887

56.7

3.75 ± 2.97

 

Age

   

<0.001

 −54

2241

26.0

3.48 ± 2.84

 

 55–64

2382

27.6

3.63 ± 2.90

 

 65–74

2485

28.8

3.92 ± 3.00

 

 75−

1513

17.6

4.16 ± 3.09

 

Marital status

   

<0.001

 Single

1954

22.7

4.94 ± 2.99

 

 Married

6667

77.3

3.42 ± 2.85

 

Region

   

<0.001

 Metropolitan

3738

43.4

3.50 ± 2.90

 

 Small to Medium city

2763

32.0

3.78 ± 3.00

 

 Rural area

2120

24.6

4.21 ± 2.93

 

Regular exercise

   

<0.001

 No

5555

64.4

4.13 ± 3.02

 

 Yes

3066

35.6

3.11 ± 2.72

 

Employment

 

0.0

 

<0.001

 Employed

3616

41.9

2.92 ± 2.63

 

 Unemployed

5005

58.1

4.37 ± 3.03

 

Educational level

   

<0.001

 Elementary

3942

45.7

4.60 ± 2.99

 

 Middle

1474

17.1

3.61 ± 2.89

 

 High

2291

26.6

2.92 ± 2.68

<0.001

 College or above

914

10.6

2.55 ± 2.44

 

Income level

 

0.0

  

 Lowest

2170

25.2

5.02 ± 3.00

 

 Lower

2428

28.2

3.85 ± 2.91

 

 Higher

2146

24.9

3.19 ± 2.78

<0.001

 Highest

1877

21.8

2.86 ± 2.63

 

Number of chronic diseases

    

 None

4018

46.6

3.05 ± 2.72

 

 One

2602

30.2

3.93 ± 2.94

 

 Two

1329

15.4

4.70 ± 2.95

<0.001

 Three or more

672

7.8

5.62 ± 2.97

 

Cancer history

    

 Yes

8388

97.3

2.95 ± 0.03

 

 No

233

2.7

3.01 ± 0.20

 

Excessive health expenditure

   

<0.001

 < 5.0 %

5166

59.9

3.38 ± 2.84

 

 5.0–9.9 %

1372

15.9

3.86 ± 2.94

 

 10.0–19.9 %

950

11.0

4.26 ± 3.05

<0.001

 20.0 % −

1133

13.1

5.01 ± 2.99

 

Type of medical guarantee

 

0.0

  

 Insurance

8106

94.0

3.65 ± 2.93

 

 Medical aid

515

6.0

5.52 ± 2.87

 

Co-coverage from private health insurance

   

<0.001

 Yes

2742

31.8

2.82 ± 2.58

 

 No

5879

68.2

4.20 ± 3.02

 

Total

8621

100.0

3.77 ± 2.96

 

Distribution of health status variables (SRHs) among participants

According to the SRH-global, there were 2537 participants (29.4%) with ratings of Low, 3119 (36.2%) with ratings of Middle, and 2965 (34.4%) with ratings of High at baseline (Table 2). The distribution of SRH-age was as follows: 1725 participants (20.0%) with ratings of Low, 3695 (42.9%) with ratings of Middle, and 3201 (37.1%) with ratings of High in 2008. The differences in distribution between two SRH variables were small. The distributions of SRH-combined categories are summarized in Table 2. The number of reference categories with the middle SRH-global × middle SRH-age was 1700 (19.7%) at baseline. Interestingly, the largest category was High SRH-global × High SRH-age with 1912 participants (22.0%).
Table 2

The distribution of health status variables regarding SRH by year and CESD 10

  

2008

2010

2012

N

%

CESD 10 score

N

%

CESD 10 score

N

%

CESD 10 score

Mean SD

p-value

Mean SD

p-value

Mean SD

p-value

SRH-global

   

<0.001

   

<0.001

   

<0.001

 Low

2537

29.4

4.10 ± 3.06

 

2372

30.0

4.00 ± 3.10

 

2226

29.8

5.22 ± 3.07

 

 Middle

3119

36.2

3.85 ± 2.93

 

2945

37.3

3.67 ± 2.95

 

3093

41.4

3.17 ± 2.72

 

 High

2965

34.4

3.39 ± 2.85

 

2583

32.7

3.57 ± 2.97

 

2153

28.8

2.64 ± 2.47

 

SRH-age

   

<0.001

   

<0.001

   

<0.001

 Low

1725

20.0

4.25 ± 2.99

 

1525

19.3

4.18 ± 3.04

 

1340

17.9

5.67 ± 2.87

 

 Middle

3695

42.9

3.94 ± 2.94

 

3399

43.0

3.78 ± 2.97

 

3292

44.1

3.87 ± 2.91

 

 High

3201

37.1

3.30 ± 2.89

 

2976

37.7

3.46 ± 2.99

 

2840

38.0

2.38 ± 2.40

 

SRH-Combi (SRH-global and SRH-age)

   

<0.001

   

<0.001

   

<0.001

 LL [Low-Low]

1218

14.1

4.23 ± 3.04

 

1095

13.9

4.17 ± 3.09

 

977

13.1

6.16 ± 2.77

 

 LM [Low-Middle]

1088

12.6

4.06 ± 3.05

 

1061

13.4

3.89 ± 3.09

 

1025

13.7

4.66 ± 3.10

 

 LH [Low-High]

231

2.7

3.55 ± 3.11

 

216

2.7

3.70 ± 3.16

 

224

3.0

3.76 ± 3.00

 

 ML [Middle-Low]

351

4.1

4.46 ± 2.89

 

287

3.6

4.06 ± 3.00

 

260

3.5

4.31 ± 2.82

 

 MM [Middle-Middle]

1700

19.7

4.06 ± 2.92

 

1596

20.2

3.79 ± 2.91

 

1611

21.6

3.57 ± 2.75

 

 MH [Middle-High]

1068

12.4

3.32 ± 2.88

 

1062

13.4

3.38 ± 2.97

 

1222

16.4

2.40 ± 2.47

 

 HL [High-Low]

156

1.8

3.90 ± 2.77

 

143

1.8

4.50 ± 2.77

 

103

1.4

4.58 ± 2.59

 

 HM [High-Middle]

907

10.5

3.58 ± 2.83

 

742

9.4

3.60 ± 2.93

 

656

8.8

3.39 ± 2.75

 

 HH [High-High]

1902

22.1

3.26 ± 2.86

 

1698

21.5

3.48 ± 2.98

 

1394

18.7

2.14 ± 2.14

 

Total

8621

100.0

3.77 ± 2.96

 

7900

100.0

3.74 ± 3.00

 

7472

100.0

3.62 ± 2.96

 

Multivariate analysis using SRH-global and SRH-age

Before the multivariate analysis using SRH-combined, we first performed multivariate analyses using SRH-global and SRH-age with three levels (Table 3). According to the results, it appears that CESD-10 scores were increased with statistically significant difference when both the SRH-global and SRH-age were low (SRH-global; b = 0.51, p < 0.001, SRH-age; b = 0.52, p < 0.001). In contrast, the estimates for CESD-10 scores were decreased when both SRH variables were high, although there was statistical difference only in SRH-age (b = −0.59, p < 0.001) and not SRH-global (b = −0.07, p = 0.130).
Table 3

Multivariate analysis among all subjects, without the interaction between SRH-global and SRH-age

 

CESD on SRH-global

CESD on SRH-age

beta

SE

p-value

beta

SE

p-value

SRH

 Low

0.51

0.05

<.001

0.52

0.06

<.001

 Middle

ref

  

ref

  

 High

−0.07

0.05

0.130

−0.59

0.05

<.001

The other covariates including gender, age, marital status, living area, regular exercise, emplyment, educational level, income level, number of chronic diseases, cancer history, excessive health expenditure, type of medical guarantee and co-coverage from private health insurance were adjusted

To determine the combined effect of SRH-global and SRH-age on depressive symptoms, we used another health status variable, SRH-combined (Table 4). According to the result, the low SRH-global with low (b = 0.654, p < 0.001) and middle (b = 0.210, p = 0.003) SRH-age showed association with the increased CESD score with statistically significant difference. Interestingly, participants with high SRH-global × low SRH-age also had a higher CESD score (b = 0.536, p < 0.001) compared to the reference group “middle SRH-global × middle SRH-age.” In contrast, participants whose SRH-age was superior to their SRH-global had significantly lower CESD than the reference group among the middle (b = −0.696, p < 0.001) and high SRH-global (b = −0.545, p < 0.001) groups Fig. 1.
Table 4

Multivariate analysis among all subjects, with the combined effect (SRH-combined) between general SRH (SRH-global) and age-comparative SRH (SRH-age)

 

CESD

beta

SE

p-value

Combined effect beetween current health status and expected health status in the aged

 LL [Low-Low]

0.654

0.075

<.0001

 LM [Low-Middle]

0.210

0.071

0.003

 LH [Low-High]

−0.216

0.128

0.093

 ML [Middle-Low]

0.360

0.106

0.001

 MM [Middle-Middle]

ref

  

 MH [Middle-High]

−0.696

0.066

<.0001

 HL [High-Low]

0.536

0.143

<.0001

 HM [High-Middle]

−0.117

0.074

0.115

 HH [High-High]

−0.545

0.064

<.0001

Year

 2008

ref

  

 2010

−0.046

0.036

0.204

 2012

−0.158

0.037

<.0001

Sex

 Male

0.131

0.044

0.003

 Female

ref

  

Age

 −54

ref

  

 55–64

−0.092

0.057

0.108

 65–74

−0.051

0.070

0.471

 75−

0.203

0.083

0.015

Marital status

 Married

ref

  

 Single

0.119

0.055

0.029

Region

 Metropolitan

−0.369

0.063

<.0001

 Small to Medium city

−0.092

0.066

0.162

 Rural area

ref

  

Regular exercise

 No

0.149

0.044

<.0001

 Yes

ref

  

Employment

 Employed

−0.240

0.048

<.0001

 Unemployed

ref

  

Educational level

 Elementary

ref

  

 Middle

0.045

0.060

0.453

 High

−0.078

0.058

0.179

 College or above

−0.230

0.078

0.003

Income level

 Lowest

ref

  

 Lower

−0.007

0.059

0.903

 Higher

−0.079

0.064

0.219

 Highest

−0.084

0.072

0.245

Number of chronic diseases

 None

ref

  

 One

−0.035

0.048

0.467

 Two

0.040

0.062

0.519

 Three or more

0.088

0.080

0.276

Cancer history

 Yes

ref

  

 No

−0.030

0.112

0.788

Excessive health expenditure

 < 5.0 %

ref

  

 5.0–9.9 %

0.091

0.055

0.096

 10.0–19.9 %

0.122

0.066

0.066

 20.0 %−

0.288

0.066

<.0001

Type of medical guarantee

 Insurance

−0.448

0.095

<.0001

 Medical aid

ref

  

Co-coverage from private health insurance

 Yes

0.019

0.048

0.701

 No

ref

  

Regarding the other covariates, singles (b = 0.119, p = 0.029) had a slightly increased CESD score compared to married participants. In terms of living area, participants living in metropolitan areas (b = −0.369, p < 0.001) had significantly lower CESD scores than those in rural areas. In educational level, participants with college education or over (b = −0.230, p = 0.003) only showed decreased CESD scores compared to those with elementary school education. The employees (b = −0.230, p = 0.003) among the participants demonstrated significantly negative association with CESD.

Compared to those with medical aid, participants with medical insurance (b = −0.448, p < 0.001) showed association with low CESD scores, and participants in households in which medical expense/total income was 20 % or above presented significantly increased CESD scores (b = 0.288, p = 0.003) compared to the reference participants with household expenses for medical cost less than five percent of total house income.

We also performed subgroup analysis by income group for both SRH-global and SRH-age (Additional file 2: Figure S2). We observed increasing CESD scores according to the increased income groups among low SRH-global and SRH-age. In contrast, there was no definite statistical tendency to decrease CESD scores in both higher SRHs. However, the magnitudes of CESD scores between low and high SRHs were both the greatest in the highest quartile income group.

Subgroup analysis by gender using SRH-combined

Male participants rating low SRH-age matched with all kinds of SRH-global (low; b = 0.825, p < 0.001, middle; b = 0.472, p = 0.006, high; b = 0.696, p = 0.001) had high CESD scores compared to the reference “middle SRH-global × middle SRH-age” group (Additional file 3: Figure S3). Similarly, women with low SRH-global × low SRH-age (b = 0.553, p < 0.001) and middle SRH-global × low SRH-age (b = 0.286, p = 0.037) indicated that association with higher CESD scores. However, there was no statistical difference in women in the “high SRH-global × low SRH-age” group regardless of the positive estimate (b = 0.377, p = 0.064).

Subgroup analysis by quartile income groups using SRH-combined

In the low “SRH-global × low SRH-age” category [LL], estimates for CESD scores in all quartile income groups had positive value compared to the reference [MM], and the beta estimates increased as the income increased (low; beta = 0.433, p < 0.001, middle-low; beta = 0.788, p < 0.001, middle-high; beta = 0.823, p < 0.001, high; beta = 1.011, p < 0.001). In the “middle SRH-global × the high SRH-age” group [MH], all quartile groups had lower CESD scores compared to the reference group (low; beta = −0.680, p < 0.001, middle-low; beta = −0.542, p < 0.001, middle-high; beta = −0.785, p < 0.001, high; beta = −0.837, p < 0.001). Interestingly, only the middle-low quartile group in the “middle SRH-global × the low SRH-age” ([ML], b = 0.478, p = 0.013) and “high SRH-global × the low SRH age” ([HL], b = 0.946, p = 0.001) categories showed statistical differences (Fig. 2).
Fig. 1

CESD by each nine SRH-combined categories. CESD means the center for epidemiologic studies depression scale, which is a screening test for depression and depressive disorder. In all SRH-global levels including low, middle, and high, the low SRH-age showed a statistically increase in CESD. In contrast, in middle and high SRH-global levels, relatively high SRH-age was statistically associated with the increase in CESD. *P < 0.05, **P < 0.001

Fig. 2

CESD by each nine SRH-combined categories, by income quartile groups from low to high consequence. CESD means the center for epidemiologic studies depression scale, which is a screening test for depression and depressive disorder. Among the low SRH-global * low SRH-age group [LL], all estimates of CESD had positive value compared to the reference group with statistical difference and the beta estimates were increased as the income was increased (low; beta = 0.433, p < 0.001, middle-low; beta = 0.788, p < 0.001, middle-high; beta = 0.823, p < 0.001, high; beta = 1.011, p <0.001). Interestingly, only the middle-low quartile group in the middle SRH-global * the low SRH-age ([ML], b = 0.478, p = 0.013) and the high SRH-global * the low SRH age ([HL], b = 0.946, p = 0.001) categories showed statistically differences. *P < 0.05, **P < 0.001

Discussion

According to the results, there are statistically different variances among the estimates for CESD scores within the same SRH-global levels among different SRH-age levels. Even though the SRH-global level was high, the estimate for CESD was statistically increased in low SRH-age. Conversely, when the SRH-global level was low, there was no significant difference from the reference “middle SRH-global × middle SRH-age” group. Other covariates of marital status, regular exercise, employment, educational level, and excessive health expenditure presented similar results to those of previous studies.

Review of previous papers regarding depressive symptoms was difficult using SRH-age and not SRH-global [26, 27]. As we performed the same methods as those used in the previous studies using SRH-global and SRH-age alone, the results from both health status variables showed similar trends to the CESD. The QIC values, which are indicators of goodness of fit, were almost the same between the two SRH variables (SRH-global; QIC = 23,911.3, QICu = 23,905/SRH-age; QIC = 23,911.2, QICu = 23,905). In this sense, it is necessary to measure the effect of the combined SRH on depressive symptoms in another way. To make the interpretation easier, we reduced the five levels of the original SRH-global and SRH-age into three and made another nine SRH-combined categories by three methods. Despite the fact that we could not verify statistical significance, we observed the reduction of power in SRH-combined (SRH-combined; QIC = 23,917.5, QICu = 23,911).

Based on the subgroup analysis by gender, it appeared that men had a higher magnitude by the change of SRH-combined categories. In other words, men might be more sensitive to self-related health. However, in another study from Hong Kong, men were more likely to report “better” and less likely to report “worse” SRH than were women [14]. Thus, in order to determine this difference between genders, further investigation is needed.

In addition, according to the subgroup analysis by the quartile income groups, the highest income group was the most sensitive to changes in SRH-combined. Similar or opposite trends were also observed in other SRH-related research [28, 29]. Burstrom and Fredlund investigated the relationship between SRH and subsequent mortality across individual’s socioeconomic classes [28]. Similarly, the association between less than good SRH and mortality rate appeared stronger in higher than in lower socioeconomic individuals. However, according to another study in the United Kingdom [29], there was no interaction between SRH and socioeconomic classes in their effect on risk of deaths. Although there are reasonable hypotheses to explain why the effect of SRH on health outcomes might differ across socioeconomic classes in both directions, it is not clear why SRH would have a weaker effect in high socioeconomic individuals in some countries or stronger in others. However, in our study, we cautiously suggest that depressive symptoms might be largely associated with economic situation among lower socioeconomic groups, especially in Korea [30, 31]. Thus, SRH in the higher socioeconomic group might have greater association with depressive symptoms compared to SRH in the lower socioeconomic group.

When we looked inside the categories in which SRH-age was inferior to SRH-global, such as middle SRH-global × low SRH-age [ML], high SRH-global × low SRH-age [HL], and high SRH-global × middle SRH-age [HM], the middle-low income group was the most vulnerable to the change of SRH-combined. In fact, it is well known that income changes and the time dimension of income are important for SRH [3234]. SRH responds to decreases in absolute income and lowered rank position in the income distribution to a greater extent than it does to income gains over time. However, it is very interesting that the group most vulnerable to depressive symptoms by the change of SRH-combined is the second lowest group, not the lowest one. We suggested that Medical Aid, a special medical security system for the poor in Korea, covered a large portion of these vulnerable participants in the lowest quartile groups while it could not in the second lowest group.

SRH is one of the most frequent measurements, assessing health perceptions in many epidemiological studies. Several previous studies suggested that even though other important covariates, including physical, socio-demographic, and psycho-social health characteristics, were adjusted, the individual’s self-assessment for his or her own global health could be a powerful indicator for further morbidity and mortality [3538]. Several hypotheses have explained these results as follows. First, SRH might be associated with some illness that could not be detected by medical science. Another theory is that SRH is able to reflect one’s lifestyle behaviors as well as psycho-social and socio-demographic conditions known to be related to health outcomes [39]. Finally, excessive anxiety regarding health has been proved to have important association with poor SRH [4042]. In summary, SRH could be an indicator for the instability of the masked physical and mental sickness in one’s general health.

In this context, SRH could offer an easy and efficient way to identify patients at risk for poor long-term depression outcomes [2]. According to one previous study from Australia [2], cross-sectional analysis of baseline data showed that participants reporting poor or fair SRH had greater odds of chronic illness, MDD, and lower socioeconomic status than those reporting good to excellent SRH. For participants rating their health as poor to fair compared with those rating it good to excellent, risk ratios of MDD were 2.10 (95% CI, 1.60–2.76), 2.38 (95% CI, 1.77–3.20), 2.22 (95% CI, 1.70–2.89), 1.73 (95% CI, 1.30–2.28), and 2.15 (95% CI, 1.59–2.90) at 1, 2, 3, 4, and 5 years, respectively, after accounting for missing data us19pt?>after the adjustment for other covariates (Additional file 4: Table S1). In another study [6], a decrease in depressive symptoms was associated with increased odds for having better SRH (OR, 1.15, 95% CI; 1.04–1.27). Interestingly, it can be expanded to all age groups even though the target population was patients with type 2 diabetes [7]. To put all the things together, the SRH was a strong direct predictor of depressive symptoms and patients’ functional health.

In this study, SRH was measured as follows: Participants were asked to estimate their SRH on a scale ranging from 0 (“very poor”) to 10 (“very good”). Although this scoring system was different from that used in the two previous studies, it was the same method to our measurement of SRH-age. Therefore, when we put all these results together, it is clear that SRH could clearly reflect depressive symptoms or be useful and efficient indicators for the depressive disorder.

A cross-sectional survey of a nationally representative sample of Israeli persons aged 45 years or older showed that individuals aged 65 years or older were more likely to give a more favorable rating of their health when asked to compare themselves with people of the same age and sex than when the rating was made without a comparison instruction [43]. Heckhausen insisted that self-enhancement is a more important motive in later life because of the need to stabilize the self-amidst increasing difficulties in controlling events in life such as a major health problem [44]. As a result, the effect of self-enhancement on SRH should be greater for older than for younger people. In line with this thinking, we hypothesize that SRH is a stronger function of social comparison in the physical domain for older than for younger adults and that such comparisons serve as a buffer for older people against the threat to SRH due to increasing physical problems. Thus, greater consideration of the SRH-combined, which is the summary of SRH-global and SRH-age, as an important factor is needed when using the SRH in analysis of depression among older people.

There are several limitations in this study.
  • First, we could not expand this result to all age groups. Since this KLoSA panel was designed to determine the characteristics of families with older persons aged 65 or over, the participants here could be left-truncated at the baseline.

  • Second, we could not evaluate whether there is a real effect of SRH-age changes with age. As previous studies conducted in other countries mentioned that SRH-age had greater association with older than with younger people, it was necessary to evaluate this using other national data in Korea. However, it was impossible that KLoSA is the only Korean national survey using SRH-age until now.

  • Third, SRH could differ from one culture to another, even if the questions are the same [45, 46]. For example, self-enhancement may be a less salient motive for Asians [47, 48]. Additionally, SRH might have different values across ethnicity. Among African Americans, SRH does not have predictability for long-term predictive power to mortality compared to the Caucasians in America [49]. Thus, it should be carefully interpreted when this result is applied in other sociocultural backgrounds.

  • Fourth, we could not generalize the association of SRH and various health outcomes. Although SRH is a good indicator for predicting mortality rate, mortality is not as same as depressive symptom in this research. Thus, we clearly mention that it should be differentiated when you compare the results to other health outcomes like comorbidity or mortality.

  • Finally, SRH-age is not exactly the same as in other studies. Other studies [26, 27] measured the comparative SRH as follows: “How would you assess your general health condition compared to persons of your own age?” with the alternatives “Better,” “Worse,” or “Similar.” However, our indicator also measured age-comparative satisfaction with health reflecting overall health and well-being as the same as others, and we operationally defined SRH-age. Regardless of the similarity, you carefully interpreted the outcome compared to others in different studies.

Conclusions

Although global SRH is a well-known indicator for estimating depressive disorder, it is suggested that use of both general SRH and age-comparative SRH would be more powerful when considering analyzing depression. In conclusion, individuals with lower SRHs-age compared to SRH-global tend to be more vulnerable to depressive symptoms.

Declarations

Acknowledgements

All authors agreed to submit this paper to BMC Psychiatry. The Nurisco checked and revised English.

Funding

None.

Availability of data and materials

Since this study used public opened national data with de-identification, anyone can access them at the following website:

http://survey.keis.or.kr/eng/index.jsp

Since we used the national public opened data with de-identification and designed retrospective cross-sectional study, we did not seek informed consent for participation. However, KLoSA initially explained the aim of survey and collected informed consents from participants at baseline. The raw data files are available in http://survey.keis.or.kr/eng/index.jsp.

Authors’ contributions

All authors have contributed significantly, and all authors are in agreement with the content of the manuscript. JS designed the study and analyzed the obtained data as the first author. Moreover, JS wrote this paper from beginning to end. YC and JHK contributed to the statistical analysis. They had a lot of experience handling the data and gave important comments for the statistical methods. ECP and SGL are public health professionals and were responsible for interpreting the results. Last, THK finalized all the results and comments and revised the first draft with JS.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

KLoSA initially explained the aim of survey and collected informed consents from participants at baseline.

Ethics approval and consent to participate

The institutional review board from the Graduate School of Public Health, Yonsei University, approved this research (IRB approval No. 2-1040939-AB-N-01-2016-149).

The source(s) of financial support for the research

None.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Authors’ Affiliations

(1)
Department of Preventive Medicine, Yonsei University, College of Medicine
(2)
Institute of Health Services Research, Yonsei University, College of Medicine
(3)
Department of Hospital Administration, Graduate School of Public Health, Yonsei University
(4)
Department of Public Health, Graduate School, Yonsei University
(5)
Department of Preventive Medicine and Public Health, Ajou University School of Medicine
(6)
Institute on Aging, Ajou University Medical Center

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Copyright

© The Author(s). 2016