- Email: [email protected]
Factors affecting the quality of life among Chinese rural general residents: a cross-sectional study
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Original Research
Factors affecting the quality of life among Chinese rural general residents: a cross-sectional study Y. Chen, G. Sun, X. Guo, S. Chen, Y. Chang, Y. Li, Y. Sun* First Hospital of China Medical University, Shenyang, China
article info
abstract
Article history:
Objectives: The brief version of the World Health Organization's Quality of Life Instrument
Received 19 July 2016
(WHOQOL-BREF) is widely used for evaluating the personal subjective quality of life (QOL)
Received in revised form
of patients and particular populations. However, in the absence of sufficient studies among
25 November 2016
the general population, normative data for WHOQOL-BREF remain scarce. To fill this gap,
Accepted 24 January 2017
the present study explored more sociodemographic and health-related factors affecting the QOL. Study design: In total, 11,351 participants aged 35 years in rural areas of Liaoning Province
Keywords:
were screened with a stratified cluster multistage sampling scheme in 2012e2013.
Quality of life
Anthropometric measurements, laboratory examinations, and self-reported information
Depression
on disease history were collected by trained personnel. Depression symptoms were
WHOQOL-BREF
assessed using the Patient Health Questionnaire-9.
General population
Methods: Stepwise multiple linear regression was used to explore the association between multiple factors and QOL. Results: Females and single/widowed subjects had lower QOL scores than males and married/cohabiting subjects, respectively. Total QOL scores and scores for each domain decreased as age increased, but a positive correlation was found between age and the environmental domain score. Participants with higher annual incomes, education levels, and activity levels had higher QOL scores. In the regression model, the coefficient for stroke was 2.17 (95% confidence interval [CI] 2.64, 1.71) for the total QOL score. For a one-level increase in depression level, the total QOL score decreased by 5.62 (95% CI 5.83 to 5.42), physical domain score decreased by 1.63 (95% CI 1.69 to 1.58), and psychological domain score decreased by 1.81 (95% CI 1.87 to 1.75). Conclusions: Socio-economic status including marital status is highly related to QOL. Regarding chronic diseases, stroke is an important factor of QOL and depressive symptoms have a strong negative relationship with QOL. © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. E-mail address: [email protected] (Y. Sun). http://dx.doi.org/10.1016/j.puhe.2017.01.023 0033-3506/© 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
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Introduction The World Health Organization (WHO) defines health as ‘a state of complete physical, mental and social well-being, not merely the absence of disease or infirmity’.1 A really healthy person has a high quality of life (QOL) level. The QOL is affected by comprehensive factors in many aspects including physical health, psychological state, ability to be independent, social interpersonal relationships, and environmental adaptability, which reflects the individual's evaluation of themselves in the context of the culture and value systems, and in relation to personal goals, attitude to life, and values.2 A QOL measure is needed for the appropriate evaluation of population health or healthcare interventions. The World Health Organization Quality of Life (WHOQOL) Group designed an instrument to measure QOL (WHOQOL-100) and an abbreviated version (WHOQOL-BREF), but the specific questions have developed over the past three decades to be suitable for respondents with diverse cultural contexts; therefore, it has been promoted to all over the world.3,4 There are 26 items in the WHOQOL-BREF, which is more convenient to use and valid across cultures.3 Thus, many large-scale epidemiological investigations use the WHOQOL-BREF instrument to evaluate QOL. Since 2000, Chinese researchers have used the Chinese version of WHOQOL-100 and WHOQOL-BREF.5 Although many studies on QOL have been conducted around the world, few studies have evaluated QOL among large Chinese populations using WHOQOL-BREF, especially among the general population in mainland China. Previous Chinese studies have evaluated QOL in more vulnerable groups, patients and particular minority populations.5e11 No uniform cut-off value to define a high level of QOL has been set to date. To compare different populations around the world, there is a need for reference values from general populations.12 However, in the absence of sufficient studies among general populations, normative data for WHOQOL-BREF remain scarce. There is a need to study the QOL of the general population using WHOQOL-BREF to provide useful reference values to compare the results of diverse groups.12 Another Chinese study focused on an urban population, but the sample only included 1052 subjects.5 To fill this gap, the present study investigated a large sample of the general rural population in mainland China to provide more results regarding the sociodemographic and health-related factors affecting QOL. In addition to the QOL of patients and particular populations, there is a need to study QOL among the general population to help the national public health departments with the formulation and implementation of culture-specific policies and support.
Methods Study population Between July 2012 and August 2013, a representative general sample aged 35 years was selected from the rural areas of Liaoning Province in Northeast China. The study adopted a multistage, stratified random cluster sampling scheme. In the first stage, three counties (Dawa, Zhangwu, and Liaoyang)
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were selected from the eastern, southern, and northern regions of Liaoning Province, which has 44 counties in total. Dawa, Zhangwu, and Liaoyang have 15, eight, and 14 towns, respectively. In the second stage, one town was selected at random from each county (total of three towns), with 13, eight, and 20 rural villages, respectively. In the third stage, eight to 10 rural villages from each town were selected at random (total of 26 rural villages). All the eligible permanent residents aged 35 years from each village were invited to participate in the study (total of 14,016 participants). In total, 11,956 subjects agreed to participate and completed the present study (response rate 85.3%). The study was approved by the Ethics Committee of China Medical University (Shenyang, China). All procedures were performed in accordance with ethical standards. Written consent was obtained from all participants after they had been informed of the objectives, benefits, medical items, and confidentiality regarding their personal information. If the participants were illiterate, written informed consent was obtained from their proxies. Only participants with a complete set of data for the variables analyzed in the study were included in this report, making a final sample size of 11,351 (5264 men and 6087 women).
Data collection and measurements Data were collected by cardiologists and trained nurses during a single clinic visit using a standard questionnaire and a face-toface interview. Before the survey was performed, all eligible investigators were invited to attend organized training. The training covered the purpose of this study, how to administer the questionnaire, the standard method of measurement, the importance of standardization, and the study procedures. A strict test was given after training, and only those with a perfect score on the test became investigators. During data collection, the inspectors received further instructions and support. Data on demographic characteristics, lifestyle risk factors, family income, disease history, and evaluation of psychological status [Patient Health Questionnaire-9 (PHQ-9)], and quality of life (WHOQOL-BREF) were obtained by interview with a standardized questionnaire. There was a central steering committee with a subcommittee for quality control. Ethnicity was classified as Han or other (including ethnic minorities in China, such as Mongol and Manchu). Educational level was divided into primary school or below, middle school, and high school or above. Family income was classified as 5000, 5000e20,000, and >20,000 China Yuan (CNY)/year according to the mean annual income of rural residents in Liaoning Province. Smoking and alcohol consumption statuses were also surveyed. Current smokers were defined as those who had smoked more than one cigarette every day for 6 months and those who had quit smoking for <6 months. Current drinkers were defined as those who had consumed alcohol twice or more each week for 1 year least and those who had quit drinking for <6 months. Physical activity included occupational and leisure-time physical activity. Occupational and leisure-time physical activity were merged and regrouped into the following three categories: low, subjects who reported light levels of both occupational and leisure-time physical activity (i.e. most working time
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was spent sitting or standing and other time for moving activities, and no regular physical exercise); moderate, subjects who reported moderate or high levels of either occupational or leisure-time physical activity (i.e. half of working time was spent sitting or standing); and high, subjects who reported a moderate or high level of both occupational and leisure-time physical activity (e.g. non-mechanized farm work, mining, steelmaking etc.). Dietary patterns were assessed by asking participants to recall the foods they had eaten over the previous year. The questionnaire included questions regarding average consumption of several food items per week. The reported consumption was quantified approximately in terms of grams per week. Vegetable consumption was assessed on the following scale: rarely ¼ 3, <1000 g ¼ 2, 1000e2000 g ¼ 1, and 2000 g ¼ 0. Meat consumption, including red meat, fish, and poultry, was assessed on the following scale: rarely ¼ 0, <250 g ¼ 1, 250e500 g ¼ 2, and 500 g ¼ 3. A special diet score (vegetable consumption score plus meat consumption score) was calculated for each participant (range 0e6). Similar methods for calculating a diet score can be found in the ATTICA study.13 Higher values for diet score indicate higher meat consumption, lower vegetable consumption, and greater adherence to a Westernized diet, whereas lower values indicate adherence to the Chinese diet. According to the protocol of the American Heart Association, blood pressure was measured three times at 2-min intervals after at least 5 min of rest using a standardized automatic electronic sphygmomanometer (HEM-741C; Omron, Tokyo, Japan). The calibration of the Omron device was checked by two doctors every month using a standard mercury sphygmomanometer, according to the protocol of the British Hypertension Society.14 The participants were advised to avoid caffeinated beverages and exercise for at least 30 min before their blood pressure was measured. During blood pressure measurement, the participants were seated with their arm supported at the level of the heart. The mean of three blood pressure measurements was calculated and used in all analyses. Weight and height were measured to the nearest 0.5 kg and 0.1 cm, respectively, with the participants in lightweight clothing and without shoes. Waist circumference was measured at the midpoint between the lower rib and the upper margin of the iliac crest using a non-elastic tape (to the nearest 0.1 cm), with the participants standing, at the end of a normal expiration. Fasting blood samples were collected in the morning after at least 12 h of fasting for all participants. Blood samples were collected from the antecubital vein into vacutainer tubes containing EDTA. Blood chemical analyses were performed at a central, certified laboratory. Fasting plasma glucose and other routine blood biochemical indexes were analyzed enzymatically on an autoanalyzer (Olympus, Kobe, Japan). All laboratory equipments were calibrated and blinded duplicate samples were used. Serum creatinine was measured enzymatically on an autoanalyzer.
Diagnosis of depressive symptoms Depressive symptoms were assessed with the PHQ-9, which is widely used in primary health centers for screening for
depression.15,16 Each of the nine PHQ depression items corresponds to one of the DSM-IV diagnostic criteria for symptoms for major depressive disorders.17 The subjects were asked how often, over the past 2 weeks, they had been bothered by each of the depressive symptoms. The response options were ‘not at all’, ‘several days’, ‘more than half of the days’ and ‘nearly every day’, and were scored as 0, 1, 2, and 3, respectively. The responses were summed to create a score between 0 and 27 points. PHQ-9 scores range from 0 to 27, with scores of 5, 10, and 15 representing mild, moderate, and severe levels of depression, respectively.18 The psychometric properties of the PHQ-9 are well documented.19 A PHQ-9 score 10 indicates the symptoms of depression.20,21
Quality of life QOL was measured using WHOQOL-BREF22 which is a selfreport inventory with 26 original items. The items fall into four domains: physical health (seven items), psychological health (six items), social relationships (three items), and environment (eight items). In addition, two items measure overall QOL and general health.23 The scale has demonstrated good internal consistency, with Cronbach's alpha ranging from 0.67 to 0.81 for each domain. Each item is answered on a fivepoint response scale and scores range from 4 to 20; higher scores indicate better QOL. Two commonly used cut-off standards for low QOL were used in this study, namely ‘70% of the maximum score’ and ‘one SD below the population mean’.24,25
Diagnosis of chronic diseases Glomerular filtration rate (GFR) was estimated using the equation originating from the Chronic kidney disease (CKD) Epidemiology Collaboration (CKD-EPI) equation,26 which is more appropriate than the Modification of Diet in Renal Disease Study Group equation.27 Severely decreased estimated GFR (eGFR) was defined as eGFR <60 ml/min/1.73 m2, which was considered as CKD in the present study. Hypertension was defined as systolic blood pressure 140 mmHg, diastolic blood pressure 90 mmHg, or self-reported current treatment for hypertension with antihypertensive medication.28 Diabetes mellitus was diagnosed according to the WHO criteria: fasting plasma glucose 7 mmol/l (126 mg/dl) and/or being on treatment for diabetes.29 Body mass index, calculated as weight (in kg) divided by height (in m) squared, was used to define obesity (28 kg/m2).30 Information on history of stroke was collected with an epidemiological questionnaire. Ischemic stroke and hemorrhagic stroke were defined as history of a cerebrovascular event, diagnosed by doctors in hospital or verified by a cranial computed tomography or magnetic resonance scan.
Statistical analysis Continuous variables were expressed as mean values and standard deviations (SD), and categorical variables were described as frequencies and percentages. Differences between confounding factors for each WHOQOL-BREF domain were evaluated using Student's t-test or one-way analysis of variance and least significant difference method test, as appropriate. Associations between variables and QOL were
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tested using stepwise multiple linear regression analyses, and coefficient and 95% confidence intervals (CIs) were calculated; variables included in the regression model are listed in Table 3. All statistical analyses were performed using SPSS Version 22.0 (IBM Corp., Armonk, NY, USA) and P < 0.05 indicated statistical significance.
Results The main characteristics of the participants are presented in Table 1. Mean age was 53.7 (SD 10.5) years, 53.6% were women, 94.7% were of Han ethnicity, 9.5% had a high school education or above, 33.2% had an annual income exceeding 20,000 CNY, and 35.3% were current smokers. The prevalence of depression among the general population was 5.9%. Chronic diseases included hypertension (50.8%), diabetes (10.2%), CKD (2.1%), and stroke (8.8%). Table 2 shows the mean scores for the QOL domains based on sociodemographic and health-related factor categories.
Table 1 e General characteristics of the general population in rural northeastern China. Characteristics Total sample Age (years) Diet scores SBP (mmHg) DBP (mmHg) Fasting plasma glucose (mmol/l) Estimated GFR (ml/min 1.73 m2) Body mass index (kg/m2) Gender (female) Married/cohabiting (%) Han ethnicity (%) Current smoking (n, %) Current drinking (n, %) Education (%) Primary school or below Middle school High school or above Annual income (CNY/year) (%) 5000 5000e20,000 >20,000 Physical activity (%) Light Moderate Severe Obesity Depressiona Hypertension Diabetes Chronic kidney disease Stroke
n/mean
%/SD
11,351 53.7 2.3 141.7 82.0 5.9 93 24.8 6087 10,291 10,754 4009 2585
10.5 1.1 23.4 11.7 1.6 15.8 3.7 53.6 92.5 94.7 35.3 22.8
5612 4660 1079
49.4 41.1 9.5
1402 6182 3767
12.4 54.5 33.2
3339 7365 647 886 668 5770 1163 233 994
29.4 64.9 5.7 7.8 5.9 50.8 10.2 2.1 8.8
SBP, systolic blood pressure; DBP, diastolic blood pressure; GFR, glomerular filtration rate; CNY, China Yuan; SD, standard deviation. Continuous variables were expressed as mean values and SD, and categorical variables were described as frequencies and percentages. a PHQ-9 sum score of 10.
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Total QOL scores and scores for each domain decreased as age increased. Compared with males and married/cohabiting subjects, females and single/widowed subjects had lower QOL scores. Participants with higher annual incomes, education levels, and activity levels had higher QOL scores. Participants with hypertension, diabetes, depression, CKD, and stroke had lower QOL scores. In particular, an increase in depression level led to a significant decrease in mean QOL score. Under the two commonly used cut-off standards for low QOL (<70% of the maximum score, or more than one SD below the mean), the prevalence of low QOL increased with age in both men and women (Fig. 1). The prevalence of low social domain scores had the largest fluctuation with increasing age. The impact of age on low environmental domain scores was weak, except in females using the ‘<70% of the maximum score’ criterion. The results of multivariate linear regression are presented in Table 3. Overall, the multivariate analysis showed that higher diet scores, married/cohabiting, current drinker, higher educational level, higher physical activity level, and higher annual income were positively associated with QOL; the coefficient for income level was 2.18 (95% CI 1.97e2.39). However, increasing age and presence of diseases were negatively associated with QOL. Gender was only associated with the psychological and social domains. Similarly, ethnicity and hypertension were only associated with the psychological domain. Diabetes was associated with the psychological and physical domains, and CKD was associated with the physical and social domains. Another important disease was stroke; the coefficient of stroke was 2.17 (95% CI 2.64 to 1.71) in total QOL score. For a one-level increase in depression level, the total QOL score decreased 5.62 (95% CI 5.83 to 5.42), physical domain score decreased 1.63 (95% CI 1.69 to 1.58), and psychological domain score decreased 1.81 (95% CI 1.87 to 1.75).
Discussion This study used the standard WHOQOL-BREF instrument to evaluate QOL including four domains. The mean scores for each domain were close to previous data for Chinese populations, but lower than the scores for Western countries.21 As studies of the general population can provide normative values, the data from this study may be used as a reference to compare populations in terms of different diseases or living conditions.12 Using the two cut-off criteria, the difference in the prevalence of low QOL is significant in each age group. The prevalence rate using the ‘<70% maximum score cut-off point’ criterion is unreasonably high. Thus, some researchers considered the ‘one SD below mean estimate’ criterion to be more reasonable.5 In agreement with other studies,5,31 this study found that males had higher mean QOL scores than females. However, with the stepwise multiple linear regression model, gender differences did not affect the total QOL score. This may reflect social progress, as the social status of Chinese women has shown unprecedented improvement. Regarding age, for each additional 10 years, the total QOL score decreased. Increased age mainly reduced the physical and social domains. The
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Table 2 e Mean scores of quality of life domains based on sociodemographic and health-related factor categories. Variables
Total sample Age (years) 35e44 45e54 55e64 65e74 75 Gender Male Female Marital status Married/cohabiting Single/widowed Ethnicity Han Other Current smoker Yes No Current drinker Yes No Education Primary school or below Middle school High school or above Annual income (CNY/year) 5000 5000e20,000 >20,000 Physical activity Light Moderate Severe Depression level <5 Mild (5e9) Moderate (10e14) Severe (15) Hypertension Yes No Diabetes Yes No Chronic kidney disease Yes No Stroke Yes No
Total score 64.5 ± 8.3 67.0 65.2 63.3 61.9 60.0
± 7.8 ± 8.0 ± 8.4 ± 8.3 ± 8.9
Dimension Physical
Psychological
Social
Environmental
15.2 ± 2.3
14.6 ± 2.4
14.6 ± 2.1
13.5 ± 2.1
15.9 15.4 14.8 14.3 13.6
± 2.0 ± 2.2 ± 2.4 ± 2.4 ± 2.7
15.2 14.7 14.3 14.0 13.7
± 2.2 ± 2.4 ± 2.5 ± 2.5 ± 2.5
15.3 14.9 14.4 13.8 13.2
± 1.9 ± 2.0 ± 2.0 ± 2.1 ± 2.3
13.7 13.5 13.3 13.3 12.9
± 2.1 ± 2.1 ± 2.1a ± 2.0a ± 2.1
65.5 ± 8.0 63.6 ± 8.5
15.5 ± 2.2 14.9 ± 2.4
14.9 ± 2.2 14.3 ± 2.5
14.8 ± 2.1 14.6 ± 2.1
13.6 ± 2.1 13.4 ± 2.1
64.9 ± 8.2 59.9 ± 8.4
15.2 ± 2.3 14.2 ± 2.5
14.6 ± 2.4 13.6 ± 2.6
14.8 ± 2.0 12.9 ± 2.2
13.5 ± 2.1 12.8 ± 2.1
64.5 ± 8.4 65.3 ± 8.0
15.2 ± 2.3 15.4 ± 2.2
14.5 ± 2.4 15.0 ± 2.1
14.6 ± 2.1a 14.8 ± 2.0a
13.5 ± 2.1a 13.4 ± 2.1a
65.0 ± 7.9 64.2 ± 8.6
15.4 ± 2.2 15.0 ± 2.4
14.7 ± 2.3 14.5 ± 2.5
14.7 ± 2.0 14.6 ± 2.1
13.5 ± 2.1a 13.5 ± 2.1a
66.6 ± 7.4 63.9 ± 8.5
15.8 ± 1.9 15.0 ± 2.4
15.1 ± 2.1 14.4 ± 2.5
15.0 ± 2.0 14.6 ± 2.1
13.7 ± 2.0 13.4 ± 2.1
62.8 ± 8.4 65.9 ± 7.9 67.8 ± 7.9
14.7 ± 2.4 15.6 ± 2.2 15.9 ± 2.0
14.1 ± 2.5 14.9 ± 2.3 15.4 ± 2.3
14.3 ± 2.1 15.0 ± 2.0 15.3 ± 1.9
13.2 ± 2.1 13.7 ± 2.0 14.2 ± 2.1
60.0 ± 8.5 63.8 ± 8.1 67.4 ± 7.8
14.1 ± 2.5 15.0 ± 2.3 15.8 ± 2.0
13.4 ± 2.5 14.4 ± 2.4 15.2 ± 2.3
13.7 ± 2.1 14.5 ± 2.1 15.2 ± 1.9
12.5 ± 1.9 13.3 ± 2.0 14.2 ± 2.0
62.5 ± 9.4 65.2 ± 7.7 67.1 ± 7.3
14.3 ± 2.6 15.5 ± 2.1 15.7 ± 2.0
14.2 ± 2.7 14.7 ± 2.3 15.0 ± 2.2
14.3 ± 2.2 14.8 ± 2.0 15.1 ± 2.0
13.3 ± 2.2 13.5 ± 2.0 14.3 ± 1.9
66.7 59.7 53.8 47.8
± 7.0 ± 7.9 ± 7.8 ± 8.5
15.8 13.8 12.0 10.2
± 1.8 ± 2.2 ± 2.4 ± 2.5
15.2 13.1 11.3 9.4
± 1.9 ± 2.4 ± 2.5 ± 2.6
14.9 14.0 13.2 12.6
± 1.9 ± 2.1 ± 2.3 ± 2.5
13.8 12.7 12.0 10.9
± 2.0 ± 2.1 ± 2.0 ± 2.2
63.9 ± 8.4 65.2 ± 8.2
14.9 ± 2.4 15.4 ± 2.2
14.5 ± 2.5 14.6 ± 2.4
14.5 ± 2.1 14.8 ± 2.1
13.4 ± 2.1 13.5 ± 2.1
62.6 ± 9.0 64.7 ± 8.2
14.4 ± 2.6 15.2 ± 2.3
14.1 ± 2.6 14.6 ± 2.4
14.3 ± 2.2 14.7 ± 2.1
13.4 ± 2.1a 13.5 ± 2.1a
58.4 ± 9.1 64.7 ± 8.3
13.1 ± 2.7 15.2 ± 2.3
13.2 ± 2.7 14.6 ± 2.4
13.2 ± 2.2 14.7 ± 2.1
12.9 ± 2.1 13.5 ± 2.1
59.8 ± 8.8 65.0 ± 8.2
13.5 ± 2.6 15.3 ± 2.2
13.5 ± 2.6 14.7 ± 2.4
13.8 ± 2.1 14.7 ± 2.1
12.9 ± 2.1 13.5 ± 2.1
CNY, China Yuan. Values are mean ± standard deviation. a P > 0.05. The remaining pairwise comparisons between groups were statistically significant (P < 0.05).
prevalence of low QOL is higher in older participants. However, age (per 10 years) was positively correlated with the environmental domain, which was also in line with the urban community residents.5 A positive relationship was also found between socioeconomic status and QOL in the Chinese rural residents.
Xia et al. found that education level had the strongest relationship with the four QOL domains.5 As indicated by the non-standardized and standardized coefficients, the present results show that both annual income and education level had a significant positive association with QOL. Furthermore, in comparison with single/widowed
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Table 3 e Stepwise multiple linear regression analyses of factors associated with quality of life. Variables
Intercept Gender (female/male) Age (per 10 years) Diet scores BMI Ethnicity (Han/other) Married/cohabiting Current smoker (yes/no) Current drinker (yes/no) Education Annual income Physical activity Depression levelb Hypertension (yes/no) Diabetes (yes/no) Decreased eGFR (yes/no) Stroke (yes/no)
Total score
Physical
Psychological
Social
Environmental
Coef
SE
Beta
Coef
SE
Beta
Coef
SE
Beta
Coef
SE
Beta
Coef
SE
Beta
62.29 e 0.38 0.36 0.04a e 1.50 e 0.95 1.10 2.18 0.99 5.62 e 0.74a 1.40a 2.17
0.86 e 0.07 0.06 0.02 e 0.26 e 0.16 0.11 0.11 0.12 0.11 e 0.22 0.46 0.24
e e 0.05 0.05 0.02 e 0.05 e 0.05 0.09 0.17 0.06 0.43 e 0.03 0.02 0.07
16.47 e 0.25 0.09 e e e 0.11a 0.32 0.20 0.36 0.45 1.63 e 0.31 0.63 0.87
0.19 e 0.02 0.02 e e e 0.04 0.05 0.03 0.03 0.03 0.03 e 0.06 0.12 0.06
e e 0.12 0.04 e e e 0.02 0.06 0.06 0.10 0.11 0.45 e 0.04 0.04 0.11
15.62 0.10a 0.09 0.10 0.15a 0.36 0.22a e 0.18a 0.27 0.47 e 1.81 0.15 0.19a e 0.47
0.29 0.05 0.02 0.02 0.01 0.09 0.08 e 0.05 0.03 0.03 e 0.03 0.04 0.06 e 0.07
e 0.02 0.04 0.05 0.02 0.03 0.02 e 0.03 0.07 0.12 e 0.47 0.03 0.02 e 0.05
13.59 0.10a 0.25 0.08 0.01a e 1.15 e 0.21 0.21 0.32 0.15 0.65 e e 0.36a 0.33
0.27 0.04 0.02 0.02 0.00 e 0.07 e 0.05 0.03 0.03 0.03 0.03 e e 0.13 0.07
e 0.02 0.12 0.04 0.02 e 0.14 e 0.04 0.06 0.10 0.04 0.20 e e 0.02 0.04
10.99 e 0.11 0.04a 0.02a e 0.25a e e 0.29 0.71 0.17 0.84 e e e 0.22a
0.24 e 0.02 0.02 0.01 e 0.07 e e 0.03 0.03 0.17 0.03 e e e 0.07
e e 0.06 0.03 0.03 e 0.03 e e 0.09 0.22 0.05 0.25 e e e 0.03
BMI, body mass index; eGFR, estimated glomerular filtration rate; coef, unstandardized coefficient; SE, standard error; beta, standardized beta. The short dashes mean that the variable was removed by the stepwise process. The diet score and BMI coefficients are for a one-unit increase in score and BMI, respectively, and the education, income, and physical activity coefficients are for a one-category increase. a P < 0.05, other P < 0.001. b Patient Health Questionnaire-9 scores <5, 5 and <10, 10 and <15, and 15.
Fig. 1 e Prevalence of low quality of life (QOL). Two cut-off criteria were used to estimate the prevalence of low QOL among a rural Chinese population. The prevalence of low QOL was higher among females than males in all age groups. The ‘70% maximum score cut-off point’ criterion indicted a prevalence of low QOL of 56.9e90.1%, and the ‘one SD below the population mean’ criterion indicated a prevalence of low QOL of 8.3e36.2%. participants, QOL was significantly higher in married/ cohabiting participants. Marital status as a criterion of social support and socio-economic status was another important variable shown to positively predict QOL in the
psychological, social, and environmental domains. This study also found that daily activities increased not only the physical domain, but also the social and environmental domains.
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It has been reported that participants with chronic diseases including CKD, diabetes and hypertension had lower QOL.32,33 In the present study, stroke was shown to be an important chronic disease factor affecting the four domains of QOL, especially the physical domain. It may be that because stroke has a high disability rate, it affects both physical ability and psychological status. Previous studies have shown that depression affects every facet of the WHOQOL-BREF measurement in healthy workers, patients, and older people.34e36 Also, the QOL of patients with major depression has been shown to improve following effective antidepressant treatment.37 However, the studies did not reveal how depression affects the QOL domains and facets in the general population. In the general rural population examined in the present study, depressive symptoms also had a major impact on QOL. QOL scores decreased sharply with a one-level increase in depression level. In addition to affecting the total QOL score, depression symptoms affect different facets of each QOL domain. Researchers have suggested that QOL can be a good measure of the outcome of depression interventions.38 Depression not only reflects the patient's emotional and mental state, but sometimes derives from physical diseases. Chronic diseases can cause depressive symptoms, and depression associated with chronic diseases can have a negative effect on QOL.33 Therefore, the goal for treatment of depression in the general population may improve QOL, and if depression could be properly managed by targeted interventions, other efforts to improve QOL would be more effective. This study has several limitations. First, this study is a cross-sectional analysis; therefore, assessment of associations is possible, but inferences cannot be made with regard to causality. Second, the diagnosis of stroke was acquired from participants' self-reported disease history, and the diagnosis of hypertension in some participants, who were not aware they had it, was based on blood pressure measurement on a single occasion. The strengths of this study include its population-based design, large sample size, and extensive information on confounders. To the authors' knowledge, this is the first large-scale general population-based crosssectional survey to report factors influencing QOL in rural China.
Conclusions In conclusion, the mainland Chinese version of the WHOQOL-BREF questionnaire is a convenient, useful method to describe the QOL profile of the population. Among the study sample, older participants were found to have significantly lower physical QOL but similar environmental QOL compared with younger participants. Socio-economic status, including marital status, education level, and income level, was highly related to QOL. Among chronic diseases, stroke was found to be a remarkable factor of QOL, and a strong negative relationship was found between depressive symptoms and QOL. Therefore, more attention should be paid to the psychological situation and socio-economic status of rural populations. Effective treatment of depression may play an important role in improving QOL among the general population.
Author statements Ethical approval This research project was approved by the Ethics Committee of the First Hospital of China Medical University. All participants signed informed consent forms.
Funding This study was supported by grants from the ‘Twelfth FiveYear’ project funds (National Science and Technology Support Program of China, Grant #2012BAJ18B02) and Liaoning Research Center for Translational Medicine of Cardiovascular Disease (2014225017).
Competing interests None declared.
references
1. World Health Organization. WHO constitution. Geneva: WHO; 1948. 2. The World Health Organization Quality of Life assessment (WHOQOL). Position paper from the World Health Organization. Soc Sci Med 1995;41:1403e9. 3. Skevington SM, Sartorius N, Amir M. Developing methods for assessing quality of life in different cultural settings. The history of the WHOQOL instruments. Soc Psychiatry Psychiatr Epidemiol 2004;39:1e8. 4. Skevington SM. Advancing cross-cultural research on quality of life: observations drawn from the WHOQOL development. World Health Organisation Quality of Life Assessment. Qual Life Res 2002;11:135e44. 5. Xia P, Li N, Hau KT, Liu C, Lu Y. Quality of life of Chinese urban community residents: a psychometric study of the mainland Chinese version of the WHOQOL-BREF. BMC Med Res Methodol 2012;12:37. 6. Wang H, Zhou J, Huang L, Li X, Fennie KP, Williams AB. Effects of nurse-delivered home visits combined with telephone calls on medication adherence and quality of life in HIV-infected heroin users in Hunan of China. J Clin Nurs 2010;19:380e8. 7. Mkangara OB, Wang C, Xiang H, Xu Y, Nie S, Liu L, et al. The univariate and bivariate impact of HIV/AIDS on the quality of life: a cross sectional study in the Hubei Province-Central China. J Huazhong Univ Sci Technol Med Sci 2009;29:260e4. 8. Yu HM, Ren XW, Chen Q, Zhao JY, Zhu TJ, Guo ZX. Quality of life of coal dust workers without pneumoconiosis in mainland China. J Occup Health 2008;50:505e11. 9. Wang X, Gao L, Zhang H, Zhao C, Shen Y, Shinfuku N. Postearthquake quality of life and psychological well-being: longitudinal evaluation in a rural community sample in northern China. Psychiat Clin Neurosci 2000;54:427e33. 10. Chen X, Li Y, Zheng H, Hu K, Zhang H, Zhao L, et al. A randomized controlled trial of acupuncture and moxibustion to treat Bell's palsy according to different stages: design and protocol. Contemp Clin Trials 2009;30:347e53. 11. Zhang J, Li X, Fang X, Xiong Q. Discrimination experience and quality of life among rural-to-urban migrants in China: the mediation effect of expectation-reality discrepancy. Qual Life Res 2009;18:291e300.
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12. Cruz LN, Polanczyk CA, Camey SA, Hoffmann JF, Fleck MP. Quality of life in Brazil: normative values for the WHOQOLbref in a southern general population sample. Qual Life Res 2011;20:1123e9. 13. Hu G, Tuomilehto J, Silventoinen K, Barengo N, Jousilahti P. Joint effects of physical activity, body mass index, waist circumference and waist-to-hip ratio with the risk of cardiovascular disease among middle-aged Finnish men and women. Eur Heart J 2004;25:2212e9. 14. O'Brien E, Petrie J, Littler W, de Swiet M, Padfield PL, Altman DG, et al. An outline of the revised British Hypertension Society protocol for the evaluation of blood pressure measuring devices. J Hypertens 1993;11:677e9. 15. Lowe B, Kroenke K, Herzog W, Grafe K. Measuring depression outcome with a brief self-report instrument: sensitivity to change of the Patient Health Questionnaire (PHQ-9). J Affect Disord 2004;81:61e6. 16. Pignone MP, Gaynes BN, Rushton JL, Burchell CM, Orleans CT, Mulrow CD, et al. Screening for depression in adults: a summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 2002;136:765e76. 17. Cooper J. Diagnostic and statistical manual of mental disorders (4th edn, text revision) (DsM-IV-TR). Br J Psychiat 2001;179:85. 18. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16:606e13. 19. Kroenke K, Spitzer RL, Williams JB, Lowe B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen Hosp Psychiat 2010;32:345e59. 20. Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ 2012;184:E191e6. 21. Lowe B, Grafe K, Zipfel S, Witte S, Loerch B, Herzog W. Diagnosing ICD-10 depressive episodes: superior criterion validity of the Patient Health Questionnaire. Psychother Psychosom 2004;73:386e90. 22. Skevington SM, Lotfy M, O'Connell KA. The World Health Organization's WHOQOL-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHOQOL group. Qual Life Res 2004;13:299e310. 23. Development of the World Health Organization WHOQOLBREF quality of life assessment. The WHOQOL Group. Psychol Med 1998;28:551e8. 24. Al-Fayez GA, Ohaeri JU. Profile of subjective quality of life and its correlates in a nation-wide sample of high school students in an Arab setting using the WHOQOL-Bref. BMC Psychiat 2011;11:71. 25. Cummins RA. On the trail of the gold standard for subjective well-being. Soc Indic Res 1995;35:179e200. 26. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro 3rd AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604e12.
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27. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461e70. 28. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 2003;289:2560e72. 29. World Health. International diabetes fedaration. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: report of a WHO/IDF consultation. Geneva: World Health Organization; 2006. 30. World Health Organization. International association for the study of obesity, international obesity task force. The Asia-Pacific perspective: redefining obesity and its treatment. Melbourne: Health Communications; 2000. 31. Baumann C, Erpelding ML, Regat S, Collin JF, Briancon S. The WHOQOL-BREF questionnaire: French adult population norms for the physical health, psychological health and social relationship dimensions. Rev Epidemiol Sante Publ 2010;58:33e9. 32. Kuo YT, Chiu KM, Tsang YM, Chiu CM, Chien MY. Influence of chronic kidney disease on physical function and quality of life in patients after coronary artery bypass grafting. Cardiorenal Med 2015;5:237e45. 33. Tuzun H, Aycan S, Ilhan MN. Impact of comorbidity and socioeconomic status on quality of life in patients with chronic diseases who attend primary health care centres. Cent Eur J Publ Health 2015;23:188e94. 34. Lu IC, Yen Jean MC, Lei SM, Cheng HH, Wang JD. BSRS-5 (5item Brief Symptom Rating Scale) scores affect every aspect of quality of life measured by WHOQOL-BREF in healthy workers. Qual Life Res 2011;20:1469e75. 35. Chang YC, Ouyang WC, Lu MC, Wang JD, Hu SC. Levels of depressive symptoms may modify the relationship between the WHOQOL-BREF and its determining factors in community-dwelling older adults. Int Psychogeriatr 2015:1e11. 36. Derakhshanpour F, Vakili MA, Farsinia M, Mirkarimi K. Depression and quality of life in patients with type 2 diabetes. Iran Red Cresc Med J 2015;17:e27676. 37. Berlim MT, Pargendler J, Brenner J, Fleck MP. Significant improvement in the quality of life of Brazilian depressed outpatients 12 weeks following the start of antidepressants. Psychiat Res 2007;153:253e9. 38. Ishak WW, Greenberg JM, Balayan K, Kapitanski N, Jeffrey J, Fathy H, et al. Quality of life: the ultimate outcome measure of interventions in major depressive disorder. Harv Rev Psychiat 2011;19:229e39.
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Original Research
Factors affecting the quality of life among Chinese rural general residents: a cross-sectional study Y. Chen, G. Sun, X. Guo, S. Chen, Y. Chang, Y. Li, Y. Sun* First Hospital of China Medical University, Shenyang, China
article info
abstract
Article history:
Objectives: The brief version of the World Health Organization's Quality of Life Instrument
Received 19 July 2016
(WHOQOL-BREF) is widely used for evaluating the personal subjective quality of life (QOL)
Received in revised form
of patients and particular populations. However, in the absence of sufficient studies among
25 November 2016
the general population, normative data for WHOQOL-BREF remain scarce. To fill this gap,
Accepted 24 January 2017
the present study explored more sociodemographic and health-related factors affecting the QOL. Study design: In total, 11,351 participants aged 35 years in rural areas of Liaoning Province
Keywords:
were screened with a stratified cluster multistage sampling scheme in 2012e2013.
Quality of life
Anthropometric measurements, laboratory examinations, and self-reported information
Depression
on disease history were collected by trained personnel. Depression symptoms were
WHOQOL-BREF
assessed using the Patient Health Questionnaire-9.
General population
Methods: Stepwise multiple linear regression was used to explore the association between multiple factors and QOL. Results: Females and single/widowed subjects had lower QOL scores than males and married/cohabiting subjects, respectively. Total QOL scores and scores for each domain decreased as age increased, but a positive correlation was found between age and the environmental domain score. Participants with higher annual incomes, education levels, and activity levels had higher QOL scores. In the regression model, the coefficient for stroke was 2.17 (95% confidence interval [CI] 2.64, 1.71) for the total QOL score. For a one-level increase in depression level, the total QOL score decreased by 5.62 (95% CI 5.83 to 5.42), physical domain score decreased by 1.63 (95% CI 1.69 to 1.58), and psychological domain score decreased by 1.81 (95% CI 1.87 to 1.75). Conclusions: Socio-economic status including marital status is highly related to QOL. Regarding chronic diseases, stroke is an important factor of QOL and depressive symptoms have a strong negative relationship with QOL. © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. E-mail address: [email protected] (Y. Sun). http://dx.doi.org/10.1016/j.puhe.2017.01.023 0033-3506/© 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
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Introduction The World Health Organization (WHO) defines health as ‘a state of complete physical, mental and social well-being, not merely the absence of disease or infirmity’.1 A really healthy person has a high quality of life (QOL) level. The QOL is affected by comprehensive factors in many aspects including physical health, psychological state, ability to be independent, social interpersonal relationships, and environmental adaptability, which reflects the individual's evaluation of themselves in the context of the culture and value systems, and in relation to personal goals, attitude to life, and values.2 A QOL measure is needed for the appropriate evaluation of population health or healthcare interventions. The World Health Organization Quality of Life (WHOQOL) Group designed an instrument to measure QOL (WHOQOL-100) and an abbreviated version (WHOQOL-BREF), but the specific questions have developed over the past three decades to be suitable for respondents with diverse cultural contexts; therefore, it has been promoted to all over the world.3,4 There are 26 items in the WHOQOL-BREF, which is more convenient to use and valid across cultures.3 Thus, many large-scale epidemiological investigations use the WHOQOL-BREF instrument to evaluate QOL. Since 2000, Chinese researchers have used the Chinese version of WHOQOL-100 and WHOQOL-BREF.5 Although many studies on QOL have been conducted around the world, few studies have evaluated QOL among large Chinese populations using WHOQOL-BREF, especially among the general population in mainland China. Previous Chinese studies have evaluated QOL in more vulnerable groups, patients and particular minority populations.5e11 No uniform cut-off value to define a high level of QOL has been set to date. To compare different populations around the world, there is a need for reference values from general populations.12 However, in the absence of sufficient studies among general populations, normative data for WHOQOL-BREF remain scarce. There is a need to study the QOL of the general population using WHOQOL-BREF to provide useful reference values to compare the results of diverse groups.12 Another Chinese study focused on an urban population, but the sample only included 1052 subjects.5 To fill this gap, the present study investigated a large sample of the general rural population in mainland China to provide more results regarding the sociodemographic and health-related factors affecting QOL. In addition to the QOL of patients and particular populations, there is a need to study QOL among the general population to help the national public health departments with the formulation and implementation of culture-specific policies and support.
Methods Study population Between July 2012 and August 2013, a representative general sample aged 35 years was selected from the rural areas of Liaoning Province in Northeast China. The study adopted a multistage, stratified random cluster sampling scheme. In the first stage, three counties (Dawa, Zhangwu, and Liaoyang)
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were selected from the eastern, southern, and northern regions of Liaoning Province, which has 44 counties in total. Dawa, Zhangwu, and Liaoyang have 15, eight, and 14 towns, respectively. In the second stage, one town was selected at random from each county (total of three towns), with 13, eight, and 20 rural villages, respectively. In the third stage, eight to 10 rural villages from each town were selected at random (total of 26 rural villages). All the eligible permanent residents aged 35 years from each village were invited to participate in the study (total of 14,016 participants). In total, 11,956 subjects agreed to participate and completed the present study (response rate 85.3%). The study was approved by the Ethics Committee of China Medical University (Shenyang, China). All procedures were performed in accordance with ethical standards. Written consent was obtained from all participants after they had been informed of the objectives, benefits, medical items, and confidentiality regarding their personal information. If the participants were illiterate, written informed consent was obtained from their proxies. Only participants with a complete set of data for the variables analyzed in the study were included in this report, making a final sample size of 11,351 (5264 men and 6087 women).
Data collection and measurements Data were collected by cardiologists and trained nurses during a single clinic visit using a standard questionnaire and a face-toface interview. Before the survey was performed, all eligible investigators were invited to attend organized training. The training covered the purpose of this study, how to administer the questionnaire, the standard method of measurement, the importance of standardization, and the study procedures. A strict test was given after training, and only those with a perfect score on the test became investigators. During data collection, the inspectors received further instructions and support. Data on demographic characteristics, lifestyle risk factors, family income, disease history, and evaluation of psychological status [Patient Health Questionnaire-9 (PHQ-9)], and quality of life (WHOQOL-BREF) were obtained by interview with a standardized questionnaire. There was a central steering committee with a subcommittee for quality control. Ethnicity was classified as Han or other (including ethnic minorities in China, such as Mongol and Manchu). Educational level was divided into primary school or below, middle school, and high school or above. Family income was classified as 5000, 5000e20,000, and >20,000 China Yuan (CNY)/year according to the mean annual income of rural residents in Liaoning Province. Smoking and alcohol consumption statuses were also surveyed. Current smokers were defined as those who had smoked more than one cigarette every day for 6 months and those who had quit smoking for <6 months. Current drinkers were defined as those who had consumed alcohol twice or more each week for 1 year least and those who had quit drinking for <6 months. Physical activity included occupational and leisure-time physical activity. Occupational and leisure-time physical activity were merged and regrouped into the following three categories: low, subjects who reported light levels of both occupational and leisure-time physical activity (i.e. most working time
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was spent sitting or standing and other time for moving activities, and no regular physical exercise); moderate, subjects who reported moderate or high levels of either occupational or leisure-time physical activity (i.e. half of working time was spent sitting or standing); and high, subjects who reported a moderate or high level of both occupational and leisure-time physical activity (e.g. non-mechanized farm work, mining, steelmaking etc.). Dietary patterns were assessed by asking participants to recall the foods they had eaten over the previous year. The questionnaire included questions regarding average consumption of several food items per week. The reported consumption was quantified approximately in terms of grams per week. Vegetable consumption was assessed on the following scale: rarely ¼ 3, <1000 g ¼ 2, 1000e2000 g ¼ 1, and 2000 g ¼ 0. Meat consumption, including red meat, fish, and poultry, was assessed on the following scale: rarely ¼ 0, <250 g ¼ 1, 250e500 g ¼ 2, and 500 g ¼ 3. A special diet score (vegetable consumption score plus meat consumption score) was calculated for each participant (range 0e6). Similar methods for calculating a diet score can be found in the ATTICA study.13 Higher values for diet score indicate higher meat consumption, lower vegetable consumption, and greater adherence to a Westernized diet, whereas lower values indicate adherence to the Chinese diet. According to the protocol of the American Heart Association, blood pressure was measured three times at 2-min intervals after at least 5 min of rest using a standardized automatic electronic sphygmomanometer (HEM-741C; Omron, Tokyo, Japan). The calibration of the Omron device was checked by two doctors every month using a standard mercury sphygmomanometer, according to the protocol of the British Hypertension Society.14 The participants were advised to avoid caffeinated beverages and exercise for at least 30 min before their blood pressure was measured. During blood pressure measurement, the participants were seated with their arm supported at the level of the heart. The mean of three blood pressure measurements was calculated and used in all analyses. Weight and height were measured to the nearest 0.5 kg and 0.1 cm, respectively, with the participants in lightweight clothing and without shoes. Waist circumference was measured at the midpoint between the lower rib and the upper margin of the iliac crest using a non-elastic tape (to the nearest 0.1 cm), with the participants standing, at the end of a normal expiration. Fasting blood samples were collected in the morning after at least 12 h of fasting for all participants. Blood samples were collected from the antecubital vein into vacutainer tubes containing EDTA. Blood chemical analyses were performed at a central, certified laboratory. Fasting plasma glucose and other routine blood biochemical indexes were analyzed enzymatically on an autoanalyzer (Olympus, Kobe, Japan). All laboratory equipments were calibrated and blinded duplicate samples were used. Serum creatinine was measured enzymatically on an autoanalyzer.
Diagnosis of depressive symptoms Depressive symptoms were assessed with the PHQ-9, which is widely used in primary health centers for screening for
depression.15,16 Each of the nine PHQ depression items corresponds to one of the DSM-IV diagnostic criteria for symptoms for major depressive disorders.17 The subjects were asked how often, over the past 2 weeks, they had been bothered by each of the depressive symptoms. The response options were ‘not at all’, ‘several days’, ‘more than half of the days’ and ‘nearly every day’, and were scored as 0, 1, 2, and 3, respectively. The responses were summed to create a score between 0 and 27 points. PHQ-9 scores range from 0 to 27, with scores of 5, 10, and 15 representing mild, moderate, and severe levels of depression, respectively.18 The psychometric properties of the PHQ-9 are well documented.19 A PHQ-9 score 10 indicates the symptoms of depression.20,21
Quality of life QOL was measured using WHOQOL-BREF22 which is a selfreport inventory with 26 original items. The items fall into four domains: physical health (seven items), psychological health (six items), social relationships (three items), and environment (eight items). In addition, two items measure overall QOL and general health.23 The scale has demonstrated good internal consistency, with Cronbach's alpha ranging from 0.67 to 0.81 for each domain. Each item is answered on a fivepoint response scale and scores range from 4 to 20; higher scores indicate better QOL. Two commonly used cut-off standards for low QOL were used in this study, namely ‘70% of the maximum score’ and ‘one SD below the population mean’.24,25
Diagnosis of chronic diseases Glomerular filtration rate (GFR) was estimated using the equation originating from the Chronic kidney disease (CKD) Epidemiology Collaboration (CKD-EPI) equation,26 which is more appropriate than the Modification of Diet in Renal Disease Study Group equation.27 Severely decreased estimated GFR (eGFR) was defined as eGFR <60 ml/min/1.73 m2, which was considered as CKD in the present study. Hypertension was defined as systolic blood pressure 140 mmHg, diastolic blood pressure 90 mmHg, or self-reported current treatment for hypertension with antihypertensive medication.28 Diabetes mellitus was diagnosed according to the WHO criteria: fasting plasma glucose 7 mmol/l (126 mg/dl) and/or being on treatment for diabetes.29 Body mass index, calculated as weight (in kg) divided by height (in m) squared, was used to define obesity (28 kg/m2).30 Information on history of stroke was collected with an epidemiological questionnaire. Ischemic stroke and hemorrhagic stroke were defined as history of a cerebrovascular event, diagnosed by doctors in hospital or verified by a cranial computed tomography or magnetic resonance scan.
Statistical analysis Continuous variables were expressed as mean values and standard deviations (SD), and categorical variables were described as frequencies and percentages. Differences between confounding factors for each WHOQOL-BREF domain were evaluated using Student's t-test or one-way analysis of variance and least significant difference method test, as appropriate. Associations between variables and QOL were
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tested using stepwise multiple linear regression analyses, and coefficient and 95% confidence intervals (CIs) were calculated; variables included in the regression model are listed in Table 3. All statistical analyses were performed using SPSS Version 22.0 (IBM Corp., Armonk, NY, USA) and P < 0.05 indicated statistical significance.
Results The main characteristics of the participants are presented in Table 1. Mean age was 53.7 (SD 10.5) years, 53.6% were women, 94.7% were of Han ethnicity, 9.5% had a high school education or above, 33.2% had an annual income exceeding 20,000 CNY, and 35.3% were current smokers. The prevalence of depression among the general population was 5.9%. Chronic diseases included hypertension (50.8%), diabetes (10.2%), CKD (2.1%), and stroke (8.8%). Table 2 shows the mean scores for the QOL domains based on sociodemographic and health-related factor categories.
Table 1 e General characteristics of the general population in rural northeastern China. Characteristics Total sample Age (years) Diet scores SBP (mmHg) DBP (mmHg) Fasting plasma glucose (mmol/l) Estimated GFR (ml/min 1.73 m2) Body mass index (kg/m2) Gender (female) Married/cohabiting (%) Han ethnicity (%) Current smoking (n, %) Current drinking (n, %) Education (%) Primary school or below Middle school High school or above Annual income (CNY/year) (%) 5000 5000e20,000 >20,000 Physical activity (%) Light Moderate Severe Obesity Depressiona Hypertension Diabetes Chronic kidney disease Stroke
n/mean
%/SD
11,351 53.7 2.3 141.7 82.0 5.9 93 24.8 6087 10,291 10,754 4009 2585
10.5 1.1 23.4 11.7 1.6 15.8 3.7 53.6 92.5 94.7 35.3 22.8
5612 4660 1079
49.4 41.1 9.5
1402 6182 3767
12.4 54.5 33.2
3339 7365 647 886 668 5770 1163 233 994
29.4 64.9 5.7 7.8 5.9 50.8 10.2 2.1 8.8
SBP, systolic blood pressure; DBP, diastolic blood pressure; GFR, glomerular filtration rate; CNY, China Yuan; SD, standard deviation. Continuous variables were expressed as mean values and SD, and categorical variables were described as frequencies and percentages. a PHQ-9 sum score of 10.
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Total QOL scores and scores for each domain decreased as age increased. Compared with males and married/cohabiting subjects, females and single/widowed subjects had lower QOL scores. Participants with higher annual incomes, education levels, and activity levels had higher QOL scores. Participants with hypertension, diabetes, depression, CKD, and stroke had lower QOL scores. In particular, an increase in depression level led to a significant decrease in mean QOL score. Under the two commonly used cut-off standards for low QOL (<70% of the maximum score, or more than one SD below the mean), the prevalence of low QOL increased with age in both men and women (Fig. 1). The prevalence of low social domain scores had the largest fluctuation with increasing age. The impact of age on low environmental domain scores was weak, except in females using the ‘<70% of the maximum score’ criterion. The results of multivariate linear regression are presented in Table 3. Overall, the multivariate analysis showed that higher diet scores, married/cohabiting, current drinker, higher educational level, higher physical activity level, and higher annual income were positively associated with QOL; the coefficient for income level was 2.18 (95% CI 1.97e2.39). However, increasing age and presence of diseases were negatively associated with QOL. Gender was only associated with the psychological and social domains. Similarly, ethnicity and hypertension were only associated with the psychological domain. Diabetes was associated with the psychological and physical domains, and CKD was associated with the physical and social domains. Another important disease was stroke; the coefficient of stroke was 2.17 (95% CI 2.64 to 1.71) in total QOL score. For a one-level increase in depression level, the total QOL score decreased 5.62 (95% CI 5.83 to 5.42), physical domain score decreased 1.63 (95% CI 1.69 to 1.58), and psychological domain score decreased 1.81 (95% CI 1.87 to 1.75).
Discussion This study used the standard WHOQOL-BREF instrument to evaluate QOL including four domains. The mean scores for each domain were close to previous data for Chinese populations, but lower than the scores for Western countries.21 As studies of the general population can provide normative values, the data from this study may be used as a reference to compare populations in terms of different diseases or living conditions.12 Using the two cut-off criteria, the difference in the prevalence of low QOL is significant in each age group. The prevalence rate using the ‘<70% maximum score cut-off point’ criterion is unreasonably high. Thus, some researchers considered the ‘one SD below mean estimate’ criterion to be more reasonable.5 In agreement with other studies,5,31 this study found that males had higher mean QOL scores than females. However, with the stepwise multiple linear regression model, gender differences did not affect the total QOL score. This may reflect social progress, as the social status of Chinese women has shown unprecedented improvement. Regarding age, for each additional 10 years, the total QOL score decreased. Increased age mainly reduced the physical and social domains. The
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Table 2 e Mean scores of quality of life domains based on sociodemographic and health-related factor categories. Variables
Total sample Age (years) 35e44 45e54 55e64 65e74 75 Gender Male Female Marital status Married/cohabiting Single/widowed Ethnicity Han Other Current smoker Yes No Current drinker Yes No Education Primary school or below Middle school High school or above Annual income (CNY/year) 5000 5000e20,000 >20,000 Physical activity Light Moderate Severe Depression level <5 Mild (5e9) Moderate (10e14) Severe (15) Hypertension Yes No Diabetes Yes No Chronic kidney disease Yes No Stroke Yes No
Total score 64.5 ± 8.3 67.0 65.2 63.3 61.9 60.0
± 7.8 ± 8.0 ± 8.4 ± 8.3 ± 8.9
Dimension Physical
Psychological
Social
Environmental
15.2 ± 2.3
14.6 ± 2.4
14.6 ± 2.1
13.5 ± 2.1
15.9 15.4 14.8 14.3 13.6
± 2.0 ± 2.2 ± 2.4 ± 2.4 ± 2.7
15.2 14.7 14.3 14.0 13.7
± 2.2 ± 2.4 ± 2.5 ± 2.5 ± 2.5
15.3 14.9 14.4 13.8 13.2
± 1.9 ± 2.0 ± 2.0 ± 2.1 ± 2.3
13.7 13.5 13.3 13.3 12.9
± 2.1 ± 2.1 ± 2.1a ± 2.0a ± 2.1
65.5 ± 8.0 63.6 ± 8.5
15.5 ± 2.2 14.9 ± 2.4
14.9 ± 2.2 14.3 ± 2.5
14.8 ± 2.1 14.6 ± 2.1
13.6 ± 2.1 13.4 ± 2.1
64.9 ± 8.2 59.9 ± 8.4
15.2 ± 2.3 14.2 ± 2.5
14.6 ± 2.4 13.6 ± 2.6
14.8 ± 2.0 12.9 ± 2.2
13.5 ± 2.1 12.8 ± 2.1
64.5 ± 8.4 65.3 ± 8.0
15.2 ± 2.3 15.4 ± 2.2
14.5 ± 2.4 15.0 ± 2.1
14.6 ± 2.1a 14.8 ± 2.0a
13.5 ± 2.1a 13.4 ± 2.1a
65.0 ± 7.9 64.2 ± 8.6
15.4 ± 2.2 15.0 ± 2.4
14.7 ± 2.3 14.5 ± 2.5
14.7 ± 2.0 14.6 ± 2.1
13.5 ± 2.1a 13.5 ± 2.1a
66.6 ± 7.4 63.9 ± 8.5
15.8 ± 1.9 15.0 ± 2.4
15.1 ± 2.1 14.4 ± 2.5
15.0 ± 2.0 14.6 ± 2.1
13.7 ± 2.0 13.4 ± 2.1
62.8 ± 8.4 65.9 ± 7.9 67.8 ± 7.9
14.7 ± 2.4 15.6 ± 2.2 15.9 ± 2.0
14.1 ± 2.5 14.9 ± 2.3 15.4 ± 2.3
14.3 ± 2.1 15.0 ± 2.0 15.3 ± 1.9
13.2 ± 2.1 13.7 ± 2.0 14.2 ± 2.1
60.0 ± 8.5 63.8 ± 8.1 67.4 ± 7.8
14.1 ± 2.5 15.0 ± 2.3 15.8 ± 2.0
13.4 ± 2.5 14.4 ± 2.4 15.2 ± 2.3
13.7 ± 2.1 14.5 ± 2.1 15.2 ± 1.9
12.5 ± 1.9 13.3 ± 2.0 14.2 ± 2.0
62.5 ± 9.4 65.2 ± 7.7 67.1 ± 7.3
14.3 ± 2.6 15.5 ± 2.1 15.7 ± 2.0
14.2 ± 2.7 14.7 ± 2.3 15.0 ± 2.2
14.3 ± 2.2 14.8 ± 2.0 15.1 ± 2.0
13.3 ± 2.2 13.5 ± 2.0 14.3 ± 1.9
66.7 59.7 53.8 47.8
± 7.0 ± 7.9 ± 7.8 ± 8.5
15.8 13.8 12.0 10.2
± 1.8 ± 2.2 ± 2.4 ± 2.5
15.2 13.1 11.3 9.4
± 1.9 ± 2.4 ± 2.5 ± 2.6
14.9 14.0 13.2 12.6
± 1.9 ± 2.1 ± 2.3 ± 2.5
13.8 12.7 12.0 10.9
± 2.0 ± 2.1 ± 2.0 ± 2.2
63.9 ± 8.4 65.2 ± 8.2
14.9 ± 2.4 15.4 ± 2.2
14.5 ± 2.5 14.6 ± 2.4
14.5 ± 2.1 14.8 ± 2.1
13.4 ± 2.1 13.5 ± 2.1
62.6 ± 9.0 64.7 ± 8.2
14.4 ± 2.6 15.2 ± 2.3
14.1 ± 2.6 14.6 ± 2.4
14.3 ± 2.2 14.7 ± 2.1
13.4 ± 2.1a 13.5 ± 2.1a
58.4 ± 9.1 64.7 ± 8.3
13.1 ± 2.7 15.2 ± 2.3
13.2 ± 2.7 14.6 ± 2.4
13.2 ± 2.2 14.7 ± 2.1
12.9 ± 2.1 13.5 ± 2.1
59.8 ± 8.8 65.0 ± 8.2
13.5 ± 2.6 15.3 ± 2.2
13.5 ± 2.6 14.7 ± 2.4
13.8 ± 2.1 14.7 ± 2.1
12.9 ± 2.1 13.5 ± 2.1
CNY, China Yuan. Values are mean ± standard deviation. a P > 0.05. The remaining pairwise comparisons between groups were statistically significant (P < 0.05).
prevalence of low QOL is higher in older participants. However, age (per 10 years) was positively correlated with the environmental domain, which was also in line with the urban community residents.5 A positive relationship was also found between socioeconomic status and QOL in the Chinese rural residents.
Xia et al. found that education level had the strongest relationship with the four QOL domains.5 As indicated by the non-standardized and standardized coefficients, the present results show that both annual income and education level had a significant positive association with QOL. Furthermore, in comparison with single/widowed
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Table 3 e Stepwise multiple linear regression analyses of factors associated with quality of life. Variables
Intercept Gender (female/male) Age (per 10 years) Diet scores BMI Ethnicity (Han/other) Married/cohabiting Current smoker (yes/no) Current drinker (yes/no) Education Annual income Physical activity Depression levelb Hypertension (yes/no) Diabetes (yes/no) Decreased eGFR (yes/no) Stroke (yes/no)
Total score
Physical
Psychological
Social
Environmental
Coef
SE
Beta
Coef
SE
Beta
Coef
SE
Beta
Coef
SE
Beta
Coef
SE
Beta
62.29 e 0.38 0.36 0.04a e 1.50 e 0.95 1.10 2.18 0.99 5.62 e 0.74a 1.40a 2.17
0.86 e 0.07 0.06 0.02 e 0.26 e 0.16 0.11 0.11 0.12 0.11 e 0.22 0.46 0.24
e e 0.05 0.05 0.02 e 0.05 e 0.05 0.09 0.17 0.06 0.43 e 0.03 0.02 0.07
16.47 e 0.25 0.09 e e e 0.11a 0.32 0.20 0.36 0.45 1.63 e 0.31 0.63 0.87
0.19 e 0.02 0.02 e e e 0.04 0.05 0.03 0.03 0.03 0.03 e 0.06 0.12 0.06
e e 0.12 0.04 e e e 0.02 0.06 0.06 0.10 0.11 0.45 e 0.04 0.04 0.11
15.62 0.10a 0.09 0.10 0.15a 0.36 0.22a e 0.18a 0.27 0.47 e 1.81 0.15 0.19a e 0.47
0.29 0.05 0.02 0.02 0.01 0.09 0.08 e 0.05 0.03 0.03 e 0.03 0.04 0.06 e 0.07
e 0.02 0.04 0.05 0.02 0.03 0.02 e 0.03 0.07 0.12 e 0.47 0.03 0.02 e 0.05
13.59 0.10a 0.25 0.08 0.01a e 1.15 e 0.21 0.21 0.32 0.15 0.65 e e 0.36a 0.33
0.27 0.04 0.02 0.02 0.00 e 0.07 e 0.05 0.03 0.03 0.03 0.03 e e 0.13 0.07
e 0.02 0.12 0.04 0.02 e 0.14 e 0.04 0.06 0.10 0.04 0.20 e e 0.02 0.04
10.99 e 0.11 0.04a 0.02a e 0.25a e e 0.29 0.71 0.17 0.84 e e e 0.22a
0.24 e 0.02 0.02 0.01 e 0.07 e e 0.03 0.03 0.17 0.03 e e e 0.07
e e 0.06 0.03 0.03 e 0.03 e e 0.09 0.22 0.05 0.25 e e e 0.03
BMI, body mass index; eGFR, estimated glomerular filtration rate; coef, unstandardized coefficient; SE, standard error; beta, standardized beta. The short dashes mean that the variable was removed by the stepwise process. The diet score and BMI coefficients are for a one-unit increase in score and BMI, respectively, and the education, income, and physical activity coefficients are for a one-category increase. a P < 0.05, other P < 0.001. b Patient Health Questionnaire-9 scores <5, 5 and <10, 10 and <15, and 15.
Fig. 1 e Prevalence of low quality of life (QOL). Two cut-off criteria were used to estimate the prevalence of low QOL among a rural Chinese population. The prevalence of low QOL was higher among females than males in all age groups. The ‘70% maximum score cut-off point’ criterion indicted a prevalence of low QOL of 56.9e90.1%, and the ‘one SD below the population mean’ criterion indicated a prevalence of low QOL of 8.3e36.2%. participants, QOL was significantly higher in married/ cohabiting participants. Marital status as a criterion of social support and socio-economic status was another important variable shown to positively predict QOL in the
psychological, social, and environmental domains. This study also found that daily activities increased not only the physical domain, but also the social and environmental domains.
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It has been reported that participants with chronic diseases including CKD, diabetes and hypertension had lower QOL.32,33 In the present study, stroke was shown to be an important chronic disease factor affecting the four domains of QOL, especially the physical domain. It may be that because stroke has a high disability rate, it affects both physical ability and psychological status. Previous studies have shown that depression affects every facet of the WHOQOL-BREF measurement in healthy workers, patients, and older people.34e36 Also, the QOL of patients with major depression has been shown to improve following effective antidepressant treatment.37 However, the studies did not reveal how depression affects the QOL domains and facets in the general population. In the general rural population examined in the present study, depressive symptoms also had a major impact on QOL. QOL scores decreased sharply with a one-level increase in depression level. In addition to affecting the total QOL score, depression symptoms affect different facets of each QOL domain. Researchers have suggested that QOL can be a good measure of the outcome of depression interventions.38 Depression not only reflects the patient's emotional and mental state, but sometimes derives from physical diseases. Chronic diseases can cause depressive symptoms, and depression associated with chronic diseases can have a negative effect on QOL.33 Therefore, the goal for treatment of depression in the general population may improve QOL, and if depression could be properly managed by targeted interventions, other efforts to improve QOL would be more effective. This study has several limitations. First, this study is a cross-sectional analysis; therefore, assessment of associations is possible, but inferences cannot be made with regard to causality. Second, the diagnosis of stroke was acquired from participants' self-reported disease history, and the diagnosis of hypertension in some participants, who were not aware they had it, was based on blood pressure measurement on a single occasion. The strengths of this study include its population-based design, large sample size, and extensive information on confounders. To the authors' knowledge, this is the first large-scale general population-based crosssectional survey to report factors influencing QOL in rural China.
Conclusions In conclusion, the mainland Chinese version of the WHOQOL-BREF questionnaire is a convenient, useful method to describe the QOL profile of the population. Among the study sample, older participants were found to have significantly lower physical QOL but similar environmental QOL compared with younger participants. Socio-economic status, including marital status, education level, and income level, was highly related to QOL. Among chronic diseases, stroke was found to be a remarkable factor of QOL, and a strong negative relationship was found between depressive symptoms and QOL. Therefore, more attention should be paid to the psychological situation and socio-economic status of rural populations. Effective treatment of depression may play an important role in improving QOL among the general population.
Author statements Ethical approval This research project was approved by the Ethics Committee of the First Hospital of China Medical University. All participants signed informed consent forms.
Funding This study was supported by grants from the ‘Twelfth FiveYear’ project funds (National Science and Technology Support Program of China, Grant #2012BAJ18B02) and Liaoning Research Center for Translational Medicine of Cardiovascular Disease (2014225017).
Competing interests None declared.
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