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Available online at www.sciencedirect.com

Public Health journal homepage: www.elsevier.com/puhe

Original Research

Linking health states to subjective well-being: an empirical study of 5854 rural residents in China X. Wang a, X. Jia b, M. Zhu b, J. Chen b,* a b

School of Public Health, Nanjing Medical University, Nanjing, Jiangsu Province, PR China School of Health Policy & Management, Nanjing Medical University, Nanjing, Jiangsu Province, PR China

article info

abstract

Article history:

Objectives: Despite a maturing literature on the association between subjective well-being

Received 25 April 2014

(SWB) and health status of the general population in Western countries, little is known

Received in revised form

regarding the happinessehealth relation in China, and rural populations in particular. This

9 March 2015

study was aimed to explore the correlation between SWB and health states of China's rural

Accepted 16 March 2015

residents.

Available online 27 April 2015

Study design: Cross-sectional survey. Methods: Data derived from a household survey conducted in 2010 with 5854 rural residents

Keywords:

included. The single-item self-reported happiness measure used in the World Values

Subjective well-being

Survey was employed to measure SWB. EQ-5D dimensions and visual analogue scale (VAS)

Health status

were applied to measure subjective health status. The number of chronic diseases was

Chronic diseases

used as proxy of objective health status. OLS regressions were performed to estimate the

EQ-5D

variation in SWB by health status and b coefficients were employed as effect size measures.

Rural area

Results: Among EQ-5D dimensions, anxiety/depression had the strongest negative effect on SWB. Having severe anxiety/depression problems could reduce SWB by 1.65 on a scale 1e4. Reporting severe problems in pain/discomfort could also reduce SWB by 0.41, while the impact of other dimensions was insignificant. The coefficient on VAS implied a difference in SWB of 1.60 between the worst health state and the best health state. And suffering from three chronic diseases could reduce SWB by 0.62, but the effect turned insignificant when all measures of subjective health status were entered in the regression. Conclusions: The results from this study verify the strongly negative effect of the mental health dimension on SWB in the context of rural China. And suffering from chronic diseases has substantial negative effect on SWB even after subjective health status is controlled for. But the impact of chronic diseases on SWB could be fully captured when all measures of subjective health status are taken into account. © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

* Corresponding author. School of Health Policy & Management, Nanjing Medical University, 140 Hanzhong Road, Nanjing, Jiangsu Province 210029, PR China. Tel.: þ86 25 868 629 50; fax: þ86 25 868 629 53. E-mail address: [email protected] (J. Chen). http://dx.doi.org/10.1016/j.puhe.2015.03.014 0033-3506/© 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

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Introduction Subjective well-being (SWB) is defined as people's emotional and cognitive evaluations of their lives.1 These evaluations include people's emotional reactions to events, their moods, and judgments about their life satisfaction.2 Happiness is used interchangeably with life satisfaction in literature as synonyms of SWB.3 But researchers have pointed out that happiness can mean a global evaluation of life satisfaction, living a good life, or a general positive mood.4 Life satisfaction is thus subsumed within happiness according to the definitions aforementioned, which implies that happiness is more akin to SWB.5 As a result, happiness was chose as SWB proxy in this study. Psychologists have conducted numerous studies on the relationship between health states and happiness.6 But most of them were small in scale and had problems in generalizability.7 Despite the growing interest in the use of SWB for public policy purposes to gauge social progress and sustainable development,8,9 the attempts to relate SWB and health states using large-sample national datasets are far from enough,10 and the role health states play in individuals' SWB remains indefinite as yet.11 Health states can be indicated by either subjective health status (evaluated by respondents themselves) or objective health status (assessed by medical personnel). Studies that examined the association between subjective health status and SWB usually used answers to the self-rated health question as the health variable.12 The typical question is ‘All in all, how would you describe your state of health these days?’ with answers ranging from very poor to very good on a five-point scale. There have been population-based researches in Sweden, US, Latin America and Russia, and the results all suggested a strongly positive health-happiness correlation.13,14 The conclusion that self-rated health is a significant predictor for SWB has also been reached in Hsieh's study of older adults15 and in a nationally representative sample of Australian rural residents.16 Blanchflower & Oswald have made comparisons of the health-happiness relation in 16 European countries and found that happier nations report fewer bloodpressure problems.17 Since literature on the relationship between SWB and selfrated health has become maturing, researchers start to examine the association between SWB and different dimensions of subjective health status measured by health related quality-of-life (HRQoL) instruments. HRQoL is multifaceted and emphasizes the subjective evaluation of physical and mental health as well as functional capacity.18 By applying HRQoL measures, the effect of different dimensions on SWB could be prioritized. Michalos employed SF-36 in his research and found that good mental health made a substantial contribution to happiness.19 EQ-5D has been included in surveys of the general population in Latin America and the older residents in the United States. Results of both studies showed that anxiety/depression, pain/discomfort and difficulties with usual activities had substantially negative effect on SWB, while the negative effect of mobility and self-care was not significant.20,21 In a patient sample in UK, the mental health dimension (anxiety/depression) of EQ-5D had a

significantly negative association with happiness, pain was less so, and the physical health dimension (mobility) had no association.22 Despite the growing interest in determining the relationship between different dimensions of subjective health status and SWB in Western countries, hardly any literature has dealt with that issue among the Chinese population so far. In terms of the association between objective health status and SWB, few studies have ever been done. Uppal found that happiness of Canadians decreased with severity of disability and was independent of type of physical disability and mental € ckerman et al. found disability had the strongest effect.23 Bo that among the Finnish population, psychiatric disorders had the largest negative impact on SWB, while other types of chronic conditions all had a significantly negative effect but were less so. Those conditions included in the study were pulmonary, cardiovascular, musculoskeletal, neurological, hearing and visual problems and other disorders.24 Under the context of China, no research has reported the association between objective health status and SWB yet. The past decade has witnessed an emerging volume of literature exploring SWB of Chinese people. But most were limited to urban areas,25,26 centred on the elderly,27 and generally investigated the relationship between SWB and socio-economic status.28,29 In 2010, there were an estimated 674 million rural residents in China, comprising 50.3% of the population.30 Yet, the SWB literature provides few data regarding this group of people. As to the health-happiness relation among Chinese people, two studies have been conducted, one in urban areas31 and the other in rural regions.32 Researchers in both studies used the typical self-rated health question with five answer options to measure health status and confirmed the result found in Western countries that there was a significantly positive correlation between happiness and self-rated health. To better understand the contribution of health to SWB of Chinese people, it is important to examine the effect of both subjective health status and objective health status and it would be more reliable to compare the impact of different dimensions of subjective health status. By far, no such research has been carried out with representative samples of China's rural population. To fill these gaps in current literature, the correlation was examined between health status and happiness of the general population in rural China, with special attention paid to the effect of objective health status and the multidimensional characteristics of subjective health status. Through empirical studies, the authors aimed to testify which dimension(s) of subjective health status had stronger effect on SWB than the others and whether subjective health status could adequately capture the full impact of objective health status on SWB in the context of rural China.

Methods Data were drawn from ‘The Household Health Survey of Health-Related Quality of Life of China's General Population’, which was conducted from July to August in 2010. The questionnaire mainly included questions on socio-economic conditions, chronic and other diseases, hospitalization,

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health-related behaviour, SWB and EQ-5D. In the survey, 1800 households were sampled through the multistage stratified sampling, and the selection criteria were in line with those applied in China's 2008 National Health Services Survey (NHSS).33 In the first sample stage, three provinces were selected based on geographic location and economic characteristics from the eastern, central, and western parts of China, respectively. In the next stage, one county in each province was selected from the sample county list in 2008 NHSS, based on socio-economic, health care and population structure. In the third stage, 15 towns were sampled, with five towns in each county based on population size and income per capita. In the fourth stage, 30 villages were sampled, with two villages in each town using the same criteria as in the third stage. In each village, 60 households were randomly selected, and all family members in a sampled household were interviewed individually and faceto-face by trained interviewers. All in all, 7039 rural residents were included in the survey. Since EQ-5D questions should only be asked among persons aged 15 years and over, respondents aged below 15 years and those who did not answer the questions by themselves were excluded. If missing answers appeared on age, sex, SWB or EQ-5D questions, the corresponding cases were excluded. After applying the exclusion criteria, 5854 respondents were finally included in analyses. Respondents were divided into three age groups: 15e44 years, 45e64 years, and 65þ. The highest education level accomplished was sorted into: primary school and below, middle school, high school, and college and above. A respondent's annual income was calculated by dividing the household annual income by the number of people living in the family for at least half a year. Ranked from lowest to the highest by their annual income, respondents were then distributed into five income groups of equal size. As to the SWB measure, there is no gold standard yet. But a number of large social surveys such as Euro-barometer Survey Series, United States General Social Survey and World Value Surveys (WVS) that provide national data all have used singleitem, self-report measures of happiness since last century.34 The typical item is: ‘Taking all things together, would you say you are …’ with possible answers ranging from 1 (very happy) to 4 (not at all happy), which has been used in all six waves of WVS.35 Despite the brevity, results of external validation of such happiness measure with ratings by friends and family members have been consistent36 and the test-retest reliability has also been proven to be high.37 In view of such wide usage and high reliability, the WVS happiness question was used and recoded the answers from 1 (not at all happy) to 4 (very happy) for data analyses. In this study, EQ-5D was applied to measure subjective health status. The EQ-5D instrument was a standardized measure of health status developed by the EuroQol Group in 1990.38 It consists of the EQ-5D descriptive system and the EQ visual analogue scale (VAS). The EQ-5D descriptive system contains five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Each dimension has three levels: no problems, moderate problems and severe problems. Respondents are required to indicate their health status by selecting the most appropriate statement in each of

657

the five dimensions.39 VAS records respondents' self-rated health status on a horizontal scale, where the endpoint 0 is labelled ‘worst imaginable health state’ and 100 is labelled ‘best imaginable health state’. Respondents are asked to comprehensively assess their health status on the day of the survey and then mark a point on the scale.38 In this study, the number of chronic diseases diagnosed during the past six months was used as proxy of objective health status. In the survey, respondents were asked ‘Have you been diagnosed with chronic diseases during the past 6 months?’ For those answering ‘yes’, further questions on the number and specific type of chronic diseases were asked. Interviewers then identified the corresponding codes of respondents' chronic diseases on a list of disease categories, which included chronic diseases in endocrine, circulatory, respiratory, digestive, urogenital, musculoskeletal and nervous systems and the other systems. To avoid recall bias, a time frame of six months was set. This sequence of questions, the disease category list and the time frame have been employed in all waves of NHSS, which has been organized by the Chinese Ministry of Health every five year since 1993.33 Descriptive analyses on respondents' SWB (Mean þ SD) by subjective health status and by objective health status were performed, stratified by sex and age. To estimate how SWB varied with health status, OLS regression analyses were performed. In the empirical studies of happiness, some researchers treat responses to happiness questions as interpersonally cardinally comparable and generally run OLS regressions. Some researchers, on the other hand, assume the ordinal comparability across respondents and mainly use ordered logit or probit models.7 Empirical studies have been carried out to compare those different approaches and the findings suggest that it makes virtually no difference whether one assumes ordinality or cardinality of happiness answers, and any effect of time-invariant unobservables will drop out in linear specifications.40 Besides, under cardinality assumption, OLS models allow for a more intuitive interpretation. Using OLS regressions, one can simply first-difference the changes in happiness and relate them to changes in observables.11 OLS regressions, therefore, prove to be a more appropriate approach in happiness studies. And it was chose as the major statistical method in this study. b coefficients were taken as effect size measures, and controls for socioeconomic status were included in each model. To check robustness, the sample was stratified by gender and performed the same sets of regressions. To test whether the negative effect of subjective health was moderated by suffering from chronic diseases, interaction terms were built and entered into regressions. For data analyses, the EQ-5D dimension variables were recoded into continuous measures, with 1 ¼ no problems, 2 ¼ moderate problems and 3 ¼ severe problems; and in the same vein, the objective health status variable was recoded into a continuous measure, with 1 ¼ no chronic diseases, 2 ¼ one chronic disease, 3 ¼ two chronic diseases, and 4 ¼ three chronic diseases. Dummy variables were created for age group, education level, and income level. To keep the observation number the same in all models, a dummy variable for missing values in income level (n ¼ 7) was entered. And 5%, 1% and 0.1% significance levels were used for regression analyses.

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Results Over 20% of respondents in this study felt very happy, 68.8% reported rather happy, while only 9.4% and less than 1% reported not very happy and not at all happy respectively. The mean SWB was 3.1 in general (Table 1).

Table 1 e Characteristics of respondents (n ¼ 5854). Category Subjective well-being Not at all happy Not very happy Rather happy Very happy Subjective well-being (Mean þ SD) Socio-economic status Sex Men Women Age group (years) 15e44 45e64 65þ Education level Primary school and below Middle school High school College and above Income group First group Second group Third group Fourth group Fifth group Missing Subjective health status EQ-5D dimension Mobility No problems Moderate problems Severe problems Self-care No problems Moderate problems Severe problems Usual activities No problems Moderate problems Severe problems Pain/discomfort No problems Moderate problems Severe problems Anxiety/depression No problems Moderate problems Severe problems VAS (Mean þ SD) Objective health status No chronic diseases One chronic disease Two chronic diseases Three chronic diseases

N

%

40 550 4027 1237 3.10

0.7 9.4 68.8 21.1 0.57

2828 3026

48.3 51.7

2908 2095 851

49.7 35.8 14.5

3286 1979 490 99

56.1 33.8 8.4 1.7

1154 1135 1165 1223 1170 7

19.7 19.4 19.9 20.9 20.0 0.1

5496 331 27

93.9 5.7 0.5

5637 185 32

96.3 3.2 0.5

5474 315 65

93.5 5.4 1.1

5159 664 31

88.1 11.3 0.5

5260 548 46 79.24

89.9 9.4 0.8 15.21

4735 913 163 43

80.9 15.6 2.8 0.7

The proportion of male respondents was around 50%. Respondents in the age group of 15e44 years occupied 49.7%. And the educational level of primary school and below was reported by 56.1% of the respondents. The distribution of basic socio-economic statistics in this study were consistent with those of rural respondents in 2008 NHSS, i.e. 49.0% were male respondents, 50.3% were in the age group of 15e44 years, and 51.9% received education of primary school level and below.41 In this study 19.1% respondents had been diagnosed with chronic diseases. The corresponding figure in 2008 NHSS was 14.0%.42 Among patients with chronic diseases, most suffered from one chronic condition, quadruple the number of patients with multiple chronic conditions. The top six systems with the highest prevalence were circulatory, musculoskeletal, digestive, endocrine, respiratory and urogenital systems, altogether covering 90.8% of the chronic diseases patients. The corresponding figure in 2008 NHSS was 87.0%, and the endocrine system ranked 6th then.42 Moderate and severe problems were mostly reported in pain/discomfort, accounting for 11.8% of respondents, followed in sequence by anxiety/depression (10.2%), usual activities (6.5%), mobility (6.2%) and self-care (3.7%). This sequence of proportion of reporting problems in EQ-5D dimensions was similar to that of rural residents in 2008 NHSS,41 i.e. pain/discomfort (9.8%), anxiety/depression (7.0%), mobility (5.3%), usual activities (5.2%) and self-care (3.5%). The mean VAS score was 79.2 in this study, which was also in line with that of rural residents in 2008 NHSS (79.7).41 The distribution of socio-economic characteristics and health status in this study was generally in consistency with that in the national survey, which suggested the national representativeness of the study sample. Alongside the increase of the severity of problems in each EQ-5D dimension, mean SWB of both men and women in all age groups exhibited a clear downward trend (Table 2), except for men of 35e64 years in the self-care dimension where the mean SWB of those reporting severe problems was slightly higher than those reporting moderate ones. This might be due to the small number of respondents reporting severe problems. Compared to the other four EQ-5D dimensions, the decline of mean SWB in anxiety/depression was much steeper. Along with the increase of the number of chronic diseases, mean SWB of both men and women trended down (Table 3). In all age groups, respondents with one chronic disease had lower SWB than those without. But the pattern was inconsistent among those suffering from chronic complications. For men older than 65, the mean SWB of those having three chronic conditions was higher than those having two. For women of 35e64 years, the mean SWB of those suffering from three chronic conditions was the same to those suffering from two. This cross-age variation of mean SWB might be due to the small number of respondents with three chronic diseases. Regression analyses of variation in SWB by subjective health status were presented in Table 4, with socio-economic variables controlled for in all four models. Model 1e2 presented the corresponding effect of EQ-5D dimensions and VAS. Model 3 included both EQ-5D dimensions and VAS in the regression, and objective health status was added in Model 4.

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Table 2 e SWB (Mean þ SD) by subjective health status, stratified by sex and age (n ¼ 5854). Category

Men EQ-5D dimension Mobility No problems Moderate problems Severe problems Self-care No problems Moderate problems Severe problems Usual activities No problems Moderate problems Severe problems Pain/discomfort No problems Moderate problems Severe problems Anxiety/depression No problems Moderate problems Severe problems Women EQ-5D dimension Mobility No problems Moderate problems Severe problems Self-care No problems Moderate problems Severe problems Usual activities No problems Moderate problems Severe problems Pain/discomfort No problems Moderate problems Severe problems Anxiety/depression No problems Moderate problems Severe problems

15e34 years

35e64 years

65þ years

Total

N

Mean

SD

N

Mean

SD

N

Mean

SD

N

Mean

SD

1367 29 5

3.23 2.69 2.20

0.53 0.85 0.45

956 50 3

3.10 2.74 2.00

0.51 0.60 0.00

353 62 3

2.98 2.76 1.67

0.60 0.56 0.58

2676 141 11

3.15 2.74 2.00

0.54 0.64 0.45

1375 20 6

3.22 2.90 2.33

0.54 0.55 1.03

976 29 4

3.09 2.66 3.00

0.51 0.67 0.82

380 32 6

2.98 2.66 2.00

0.58 0.70 0.63

2731 81 16

3.14 2.72 2.38

0.54 0.66 0.89

1365 26 10

3.23 2.77 2.40

0.54 0.65 0.84

950 55 4

3.11 2.62 2.50

0.51 0.56 0.58

355 52 11

3.00 2.65 2.18

0.56 0.71 0.60

2670 133 25

3.16 2.66 2.32

0.54 0.64 0.69

1352 47 2

3.24 2.53 2.00

0.53 0.69 0.00

895 112 2

3.13 2.67 2.50

0.50 0.54 0.71

334 81 3

3.04 2.54 2.00

0.53 0.69 1.00

2581 240 7

3.18 2.60 2.14

0.52 0.63 0.69

1330 63 8

3.26 2.51 1.75

0.51 0.62 0.46

907 97 5

3.15 2.47 2.00

0.47 0.54 0.00

346 66 6

3.08 2.35 1.50

0.49 0.62 0.55

2583 226 19

3.20 2.45 1.74

0.50 0.59 0.45

1473 31 3

3.19 2.87 2.33

0.55 0.56 1.16

1016 65 5

3.05 2.57 1.80

0.54 0.64 0.84

331 94 8

3.02 2.57 1.88

0.54 0.66 0.64

2820 190 16

3.12 2.62 1.94

0.55 0.65 0.77

1481 22 4

3.19 2.91 2.50

0.55 0.61 1.00

1050 31 5

3.04 2.52 2.00

0.55 0.72 0.71

375 51 7

2.97 2.49 2.00

0.55 0.76 0.82

2906 104 16

3.11 2.59 2.13

0.56 0.73 0.81

1465 37 5

3.20 2.73 2.20

0.54 0.65 1.10

1000 77 9

3.05 2.61 2.11

0.54 0.61 0.78

339 68 26

3.00 2.63 2.27

0.54 0.73 0.67

2804 182 40

3.12 2.64 2.22

0.55 0.66 0.73

1413 88 6

3.22 2.70 2.50

0.53 0.66 0.84

876 202 8

3.09 2.71 2.00

0.50 0.64 0.93

289 134 10

3.06 2.63 2.00

0.51 0.66 0.82

2578 424 24

3.16 2.68 2.12

0.52 0.65 0.85

1408 93 6

3.23 2.51 1.83

0.51 0.60 0.98

941 136 9

3.11 2.49 1.56

0.49 0.62 0.73

328 93 12

3.06 2.49 1.58

0.50 0.60 0.67

2677 322 27

3.17 2.50 1.63

0.51 0.61 0.74

Table 3 e SWB (Mean þ SD) by objective health status, stratified by sex and age (n ¼ 5854). Category

Men No chronic diseases One chronic diseases Two chronic diseases Three chronic diseases Women No chronic diseases One chronic diseases Two chronic diseases Three chronic diseases

15e34

35e64

65þ

Total

N

Mean

SD

N

Mean

SD

N

Mean

SD

N

Mean

SD

1325 71 3 2

3.24 2.80 3.00 2.00

0.53 0.62 0.00 1.41

780 191 31 7

3.11 3.02 2.87 2.71

0.51 0.55 0.50 0.49

262 124 25 7

3.01 2.82 2.76 2.86

0.56 0.64 0.72 0.90

2367 386 59 16

3.17 2.91 2.83 2.69

0.54 0.60 0.59 0.79

1384 109 13 1

3.22 2.81 2.69 3.00

0.54 0.62 0.48 e

768 245 56 17

3.08 2.89 2.71 2.71

0.54 0.57 0.62 0.59

216 173 35 9

3.01 2.82 2.71 2.33

0.57 0.63 0.62 0.71

2368 527 104 27

3.16 2.85 2.71 2.59

0.55 0.60 0.60 0.64

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Table 4 e OLS regression analyses on SWB by subjective health status, controlling for socio-economic status. Model 1 b Constant Socio-economic status Sexa Age group (years)b 35e64 65þ Education levelc Middle school High school College and above Income leveld Second group Third group Fourth group Fifth group Missing Subjective health status EQ-5D dimensione Mobility Self-care Usual activities Pain/discomfort Anxiety/depression VASf Objective health statusg Observations Adjusted R2

Model 2 b

SE

3.694

0.039

0.008

0.013

0.076 0.060

0.015 0.021

0.133 0.194 0.323

***

Model 3 b

SE

1.746

0.042

0.006 *** **

0.016 0.025 0.051

0.042 0.120 0.224 0.305 0.025

0.020 0.021 0.020 0.021 0.185

0.003 0.015 0.010 0.136 0.549 e e 5854 0.263

0.038 0.040 0.035 0.025 0.023 e e

2.562

0.069

0.013

0.012

0.017 0.021

0.016 0.022

*** *** ***

0.076 0.127 0.205

0.016 0.025 0.051

* *** *** ***

0.010 0.060 0.114 0.175 0.168

0.021 0.021 0.021 0.022 0.186

e e e e e 0.016 e 5854 0.257

e e e e e 0.001 e

*** ***

***

Model 4 b

SE

2.581

0.071

0.013

0.013

0.013

0.022 0.021

0.015 0.021

0.019 0.025

0.015 0.021

*** *** ***

0.082 0.134 0.227

0.015 0.024 0.049

*** *** ***

0.082 0.134 0.227

0.015 0.024 0.049

*** *** ***

** *** ***

0.009 0.057 0.123 0.185 0.168

0.020 0.020 0.020 0.021 0.179

** *** ***

0.010 0.057 0.123 0.186 0.164

0.020 0.020 0.020 0.021 0.179

** *** ***

0.046 0.012 0.050 0.052 0.438 0.011 e 5854 0.309

0.037 0.039 0.034 0.024 0.023 0.001 e

* *** ***

0.048 0.015 0.052 0.047 0.438 0.011 0.015 5854 0.309

0.037 0.039 0.034 0.025 0.023 0.001 0.013

***

***

SE ***

*** ***

*P < 0.05; **P < 0.01; ***P < 0.001. a Reference category: men. b Reference category: age group 15e34 years. c Reference category: primary school and below. d Reference category: first income group. e Continuous variable 1e3. f Continuous variable 0e100. g Continuous variable 1e4.

Model 1 showed that significantly negative coefficients were only tracked in anxiety/depression and pain/discomfort. Having severe problems in anxiety/depression and pain/ discomfort could reduce SWB by 1.65 and 0.41 respectively. In model 2, VAS was substituted for EQ-5D dimensions as a measure of subjective health status. The coefficient on VAS implied a difference in SWB of 1.60 between the worst health state and the best health state. In model 3, the patterns of variation in SWB by EQ-5D dimensions were robust to controlling for VAS, i.e. among five dimensions only coefficients of pain/discomfort and anxiety/depression were significant. But the effect sizes of those two dimensions and VAS declined. This model had the highest adjusted R2 of 0.309. In model 4, when objective health status was entered, the effect sizes of anxiety/depression and VAS remained stable and the adjusted R2 was the same to that of model 3, whereas the effect of pain/ discomfort and objective health status was insignificant. In all four models with subjective health status controlled for, the effect of gender was insignificant, and the difference between age groups turned insignificant when VAS was entered. Education had substantial positive effect on SWB, with a difference in SWB of 0.32 between the highest education level and the lowest one in model 1, but the effect size

became smaller when VAS was controlled for. A larger positive coefficient for a higher level of income was tracked in all four models, with a difference in SWB of 0.31 between the highest and the lowest group in model 1, but the difference between the lowest and the second group became insignificant when VAS was controlled for and the effect sizes of the other income levels also turned smaller. Besides, in model 3 with all subjective health measures controlled for, the effect size of reporting severe problems in anxiety/depression (1.31) was still much larger than that of achieving the highest education level (0.23) and being in the highest income level (0.19). For robustness check, the variation in SWB by subjective health status was also examined within sub-groups of male and female respondents respectively (Table 5). Most patterns of variation remained constant, while for male respondents the effect of pain/discomfort was significant even when objective health status was controlled for, and for female respondents the effect of pain/discomfort became insignificant when VAS was controlled for. Regression analyses of variation in SWB by objective health status were presented in Table 6, with socio-economic variables controlled for in all three models. Model 1 displayed the

661

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Table 5 e OLS regression analyses on SWB by subjective health status, stratified by sex, controlling for socio-economic status. Model 1 b Men Constant Socio-economic status Age group (years)a 35e64 65þ Education levelb Middle school High school College and above Income levelc Second group Third group Fourth group Fifth group Missing Subjective health status EQ-5D dimensiond Mobility Self-care Usual activities Pain/discomfort Anxiety/depression VASe Objective health statusf Observations Adjusted R2 Women Constant Socio-economic status Age groupa 35e64 65þ Education levelb Middle school High school College and above Income levelc Second group Third group Fourth group Fifth group Missing Subjective health status EQ-5D dimensiond Mobility Self-care Usual activities Pain/discomfort Anxiety/depression VASe Objective health statusf Observations Adjusted R2

Model 2 b

SE

Model 3

3.726

0.057

***

1.797

0.059

0.087 0.088

0.021 0.030

*** **

0.037 0.014

0.022 0.030

0.125 0.140 0.324

0.022 0.033 0.066

*** *** ***

0.076 0.091 0.201

0.022 0.033 0.067

0.040 0.124 0.227 0.315 0.082

0.029 0.029 0.029 0.030 0.278

*** *** ***

0.005 0.068 0.123 0.198 0.309

0.029 0.030 0.030 0.031 0.279

0.071 0.024 0.019 0.174 0.559 e e 2828 0.260

0.058 0.059 0.055 0.040 0.035 e e

e e e e e 0.015 e 2828 0.253

e e e e e 0.001 e

3.683

0.053

***

1.704

0.056

0.063 0.030

0.022 0.031

**

0.005 0.056

0.023 0.031

0.138 0.266 0.310

0.023 0.038 0.079

*** *** ***

0.075 0.177 0.206

0.024 0.039 0.080

0.046 0.118 0.223 0.299 0.011

0.029 0.029 0.029 0.029 0.249

*** *** ***

0.015 0.053 0.107 0.158 0.052

0.029 0.029 0.030 0.031 0.249

0.046 0.013 0.030 0.118 0.540 e e 3026 0.263

0.050 0.055 0.047 0.031 0.032 e e

e e e e e 0.016 e 3026 0.258

e e e e e 0.001 e

*P < 0.05; **P < 0.01; ***P < 0.001. a Reference category: age group 15e34 years. b Reference category: primary school and below. c Reference category: first income group. d Continuous variable 1e3. e Continuous variable 0e100. f Continuous variable 1e4.

*** ***

*** ***

b

SE ***

Model 4

2.603

0.098

0.036 0.014

0.021 0.029

** ** **

0.078 0.089 0.220

0.021 0.032 0.064

* *** ***

0.008 0.060 0.127 0.195 0.282

0.028 0.029 0.029 0.030 0.269

0.109 0.021 0.035 0.092 0.451 0.011 e 2828 0.306

0.056 0.057 0.053 0.040 0.035 0.001 e

2.549

0.096

0.005 0.057

0.022 0.030

0.084 0.193 0.225

0.023 0.037 0.077

0.012 0.054 0.122 0.178 0.088

0.028 0.028 0.029 0.030 0.241

0.002 0.009 0.061 0.032 0.425 0.011 e 5854 0.308

0.049 0.053 0.045 0.031 0.032 0.001 e

***

***

** *** *

*** ***

***

b

SE ***

SE

2.603

0.102

0.036 0.014

0.021 0.030

*** ** **

0.078 0.089 0.220

0.021 0.032 0.064

*** ** **

* *** ***

0.008 0.060 0.127 0.195 0.282

0.028 0.029 0.029 0.030 0.269

* *** ***

* *** ***

0.109 0.021 0.035 0.092 0.451 0.011 0.000 2828 0.306

0.056 0.057 0.053 0.040 0.035 0.001 0.020

***

2.586

0.099

***

0.001 0.064

0.022 0.031

*

0.083 0.193 0.224

0.023 0.037 0.077

*** *** **

0.013 0.055 0.122 0.179 0.076

0.028 0.028 0.029 0.030 0.241

0.005 0.018 0.066 0.024 0.424 0.011 0.026 5854 0.308

0.049 0.054 0.045 0.031 0.032 0.001 0.018

*** *** **

*** ***

*** ***

***

* *** ***

*** ***

*** ***

662

p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 6 5 5 e6 6 6

Table 6 e OLS regression analyses on SWB by objective health status, controlling for socio-economic status. Model 1

Model 2

b Constant Socio-economic status Sexa Age group (years)b 35e64 65þ Education levelc Middle school High school College and above Income leveld Second group Third group Fourth group Fifth group Missing Objective health statuse Subjective health status EQ-5D dimensionf Mobility Self-care Usual activities Pain/discomfort Anxiety/depression VASg Observations Adjusted R2

3.081

0.026

0.003

0.014

0.083 0.093

0.017 0.023

0.154 0.226 0.344 0.075 0.175 0.279 0.379 0.082 0.156

e e e e e e 5854 0.151

b

SE ***

Model 3

3.728

0.040

0.010

0.013

*** ***

0.065 0.040

0.015 0.022

0.017 0.027 0.055

*** *** ***

0.132 0.195 0.320

0.022 0.022 0.022 0.022 0.198 0.014

** *** *** *** ***

e e e e e e

b

SE ***

SE

1.824

0.049

0.008

0.013

***

0.010 0.033

0.016 0.022

0.016 0.025 0.051

*** *** ***

0.077 0.130 0.207

0.016 0.025 0.051

0.043 0.119 0.220 0.303 0.016 0.060

0.020 0.020 0.020 0.021 0.185 0.014

* *** *** ***

0.011 0.061 0.115 0.178 0.155 0.039

0.021 0.021 0.021 0.022 0.185 0.014

0.011 0.030 0.020 0.113 0.546 e 5854 0.265

0.038 0.040 0.035 0.025 0.023 e

e e e e e 0.015 5854 0.258

e e e e e 0.001

***

*** ***

***

*** *** ***

** *** *** **

***

*P < 0.05; **P < 0.01; ***P < 0.001. a Reference category: men. b Reference category: age group 15e34 years. c Reference category: primary school and below. d Reference category: first income group. e Continuous variable 1e4. f Continuous variable 1e3. g Continuous variable 0e100.

impact of objective health status, and Model 2e3 included EQ5D dimensions and VAS in the regression respectively. Model 1 showed that having three chronic diseases could reduce SWB by 0.62. In model 2, when EQ-5D dimensions were controlled for, the negative effect of chronic diseases remained significant, but the effect size decreased. Compared with model 1 in Table 4, the coefficient of pain/discomfort slightly dropped from 0.14 to 0.11, while the coefficient of anxiety/depression remained stable (0.55). In model 3, when VAS was entered to replace EQ-5D dimensions, the negative effect of chronic conditions remained significant, but the effect size declined. Compared to model 2 in Table 4, the effect size of VAS also had a slight drop. In all three models with objective health status controlled for, the effect of gender was insignificant. The difference between different age groups was significant only in model 1, and when all subjective health measures were entered in model 3 the effect of age turned insignificant. Education had strongly positive effect on SWB, with a difference in SWB of 0.34 between the highest education level and the lowest one in model 1, but the effect size became smaller when variables of subjective health status were controlled for. Income also had

substantial positive effect on SWB, with a difference in SWB of 0.38 between the highest and the lowest group in model 1, but the effect size turned smaller when subjective health status was controlled for. Furthermore, compared to the effect size of the highest education level and the highest income group in model 1, the effect size of having three chronic diseases was much larger (0.62). For robustness check, the variation in SWB by objective health status was examined within sub-groups of male and female respondents respectively (Table 7). Most patterns of variation remained constant except that among male respondents the effect of objective health status became insignificant when VAS was controlled for. In Table 8, the interaction effect of subjective and objective health status was displayed. Model 1e2 examined the interaction effect of pain/discomfort and number of chronic diseases. Although these two variables were significant when both were included in the model, their interaction effect was insignificant. Model 3e4 examined the interaction effect of anxiety/depression and number of chronic diseases. Similarly, the main effect of each variable was significant, but the interaction effect turned to be insignificant.

663

p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 6 5 5 e6 6 6

Table 7 e OLS regression analyses on SWB by objective health status, stratified by sex, controlling for socio-economic status. Model 1

Model 2

b Men Constant Socio-economic status Age group (years)a 35e64 65þ Education levelb Middle school High school College and above Income levelc Second group Third group Fourth group Fifth group Missing Objective health statusd Subjective health status EQ-5D dimensione Mobility Self-care Usual activities Pain/discomfort Anxiety/depression VASf Observations Adjusted R2 Women Constant Socio-economic status Age groupa 35e64 65þ Education levelb Middle school High school College and above Income levelc Second group Third group Fourth group Fifth group Missing Objective health statusd Subjective health status EQ-5D dimensione Mobility Self-care Usual activities Pain/discomfort Anxiety/depression VASf Observations Adjusted R2

b

SE

Model 3 b

SE

SE

3.066

0.035

***

3.753

0.058

***

1.835

0.070

0.099 0.112

0.023 0.033

*** **

0.077 0.071

0.022 0.031

*** *

0.033 0.008

0.022 0.031

0.151 0.177 0.351

0.023 0.035 0.071

*** *** ***

0.126 0.142 0.322

0.022 0.033 0.066

*** *** ***

0.077 0.093 0.203

0.022 0.033 0.067

0.068 0.183 0.285 0.402 0.006 0.137

0.031 0.031 0.031 0.032 0.298 0.021

* *** *** ***

0.040 0.124 0.224 0.314 0.088 0.052

0.029 0.029 0.029 0.030 0.278 0.021

0.006 0.068 0.123 0.200 0.309 0.020

0.029 0.030 0.030 0.031 0.279 0.021

0.077 0.029 0.014 0.153 0.558 e 2828 0.261

0.058 0.059 0.055 0.041 0.035 e

e e e e e 0.015 2828 0.253

e e e e e 0.001

***

e e e e e e 2828 0.149

***

e e e e e e

*** *** *** *

*** ***

***

*** ** **

* *** ***

3.088

0.034

***

3.726

0.054

***

1.817

0.068

***

0.066 0.074

0.024 0.033

** *

0.049 0.007

0.022 0.031

*

0.014 0.073

0.023 0.032

*

0.154 0.290 0.326

0.025 0.041 0.085

*** *** ***

0.135 0.264 0.304

0.023 0.038 0.079

*** *** ***

0.075 0.178 0.205

0.024 0.039 0.080

** *** *

0.081 0.168 0.274 0.361 0.168 0.170

0.031 0.031 0.031 0.031 0.267 0.018

** *** *** ***

0.047 0.117 0.218 0.296 0.037 0.067

0.029 0.029 0.029 0.029 0.248 0.018

0.017 0.055 0.108 0.160 0.024 0.054

0.029 0.029 0.030 0.031 0.249 0.018

*** ***

0.036 0.037 0.044 0.093 0.533 e 3026 0.266

0.050 0.055 0.047 0.032 0.032 e

e e e e e 0.016 3026 0.260

e e e e e 0.001

e e e e e e 3026 0.149

e e e e e e

*P < 0.05; **P < 0.01; ***P < 0.001. a Reference category: age group 15e34 years. b Reference category: primary school and below. c Reference category: first income group. d Continuous variable 1e4. e Continuous variable 1e3. f Continuous variable 0e100.

***

*** *** *** ***

** ***

**

***

664

p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 6 5 5 e6 6 6

Table 8 e OLS regression analyses on SWB by interaction effects of subjective and objective health status. Model 1 b Constant Pain/discomforta Anxiety/depressionb Objective health statusc Interaction term Pain*OHSd Anxiety*OHS Observations Adjusted R2

3.752 0.443 e 0.122 e e 5854 0.109

Model 2 b

SE 0.025 0.022 e 0.014 e e

*** *** ***

Model 3 b

SE

3.752 0.443 e 0.163

0.025 0.022 e 0.039

0.030 e 5854 0.109

0.026 e

*** *** ***

3.998 e 0.668 0.124 e e 5854 0.193

Model4 b

SE 0.025 e 0.020 0.013 e e

*** *** ***

SE

4.072 e 0.730 0.174

0.055 e 0.046 0.035

e 0.039 5854 0.194

e 0.026

*** *** ***

*P < 0.05; **P < 0.01; ***P < 0.001. a Continuous variable 1e3. b Continuous variable 1e3. c Continuous variable 1e4. d OHS: Objective health status.

Discussion Through a unique dataset containing rich and relatively new information, the health-happiness correlation of Chinese rural residents was analysed. It is the first population-based analysis of the association between SWB and health states under the context of rural China. And it is also the first study that applies EQ-5D instead of the traditional single-item selfrated health question to examine the effect of different dimensions of subjective health status on SWB. These results showed that among EQ-5D dimensions, having moderate or severe problems in anxiety/depression and pain/discomfort had substantial negative effect on SWB, and anxiety/depression had the largest negative effect, which could reduce SWB by 1.65 point on a scale 1e4. This effect size is considerable, for the observations on SWB are bunched towards the higher end of the scale. In addition, through comparing coefficients of standard socio-economic status and anxiety/depression, researchers found that the effect size of having severe problems in anxiety/depression was much larger than that of achieving the highest education level and being in the highest income group. Respondents having severe problems in anxiety/ depression in the highest education group were highly likely to have lower level of SWB than those reporting no problems in anxiety/depression in the lowest education group, when holding all the other variables constant. From that perspective, the negative effect of anxiety/depression was rather huge among Chinese rural population. Although these findings have already been reached among the general population in Latin America and US,20,21 it is for the first time that close conclusion could be made for China's rural population, i.e. the mental health dimension of EQ-5D has a much larger impact on SWB, pain is less on, and physical health dimensions have no significant association. In this study, when VAS was entered in model 3 of Table 4, the pattern of variation in SWB by EQ-5D dimensions remained, but the effect sizes of EQ-5D dimensions decreased. Including EQ-5D dimensions and VAS into this same model yielded the best result, with adjusted R2 equal to 0.31. And the

addition of objective health status in the next model did not affect the effect of EQ-5D dimensions and VAS on SWB. Besides, the impact of socio-economic status also decreased when VAS was controlled for. These results have novel contribution to current literature concerning the Chinese rural population in that the negative effect of objective health status on SWB could be completely captured by subjective heath status when both EQ-5D dimensions and VAS are employed as subjective health measures. Furthermore, another interpretation of the results could be inspired is that VAS has better explanatory power of SWB. VAS is the global assessment of the individual's own health status rather than the evaluation of the five set domains. The impact of aspects that cannot be captured by EQ-5D dimensions might be reflected through VAS.43 In this study, suffering from chronic diseases was associated with reduced SWB, but the effect size decreased or even became insignificant when controlling for different measures of subjective health status. As discussed above, it indicates that reporting problems in EQ-5D dimensions and lower VAS score better capture the impact of ill health on SWB than chronic diseases do. However, a critical conclusion could also be drawn is that suffering from chronic diseases has substantial negative effect on SWB even after controlling for VAS, a more powerful measure for subjective health status. Moreover, by comparing the coefficients of socio-economic status and objective health status, researchers found that the effect size of having three chronic diseases was much larger than that of achieving the highest education level and being in the highest income group. With other factors held constant, respondents suffering from three chronic diseases in the highest education level were more likely to have lower level of SWB than those with no chronic diseases in the lowest education group. In view of that, despite the strong link between subjective health and SWB, the negative impact of chronic diseases should never be underestimated. This study also provides evidence that the substantial negative effect of anxiety/depression and pain/discomfort is not moderated by suffering from chronic diseases, for the interaction effect turns out to be statistically insignificant. The

p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 6 5 5 e6 6 6

importance of anxiety and depression as principal predicting factors of SWB is a valued finding from the public health perspective, because about 50% of outpatient visits in China were due to depression;44 nevertheless, mental-health professionals fell short at the same time. In China's new round of health reform, national funding has been appropriated to cover registration and following up of patients with mental illness at the community level, but general physicians in community health centers still lack basic knowledge and skills for these tasks.45 Given the cross-sectional nature of the data, researchers could only examine the association between SWB and health states, while the causality between SWB and health states and the potential causal mechanisms through which SWB and health states are linked cannot be determined this time. Chinese off-farm migrants would be a potential population for future SWB research. Current studies have examined the SWB of this group of people,46,47 and the results show that they had even lower SWB than that of rural residents. Their health outcomes measured by EQ-5D, their prevalence of different types of chronic diseases, and the relationship between their SWB and health states are all potential field worth exploring in future studies.

Author statements Acknowledgements The authors would like to thank Yunqiu Dong for her helpful comments, the two anonymous reviewers for their enlightening suggestions, the investigators for their contribution to the household survey, and the National Natural Science Foundation of China for the funding support (70873064).

Ethical approval Informed consent was obtained from each respondent before the corresponding interview was conducted. No private information was leaked during data analyses or used for nonacademic purposes.

Funding This research was supported by the National Natural Science Foundation of China (70873064).

Competing interests None declared.

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