p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Available online at www.sciencedirect.com
Public Health journal homepage: www.elsevier.com/puhe
Original Research
Prevalence and influencing factors of depressive symptoms among women of reproductive age in the rural areas of Hubei, China B. Cao a,1, H. Jiang a,1, H. Xiang b, B. Lin c, Q. Qin a, F. Zhang a, W. Kong a, S. Wei a, L. Liu a, W. Yan a,**, S. Nie a,* a
Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China b Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China c Futian District Shenzhen City Center for Disease Control and Prevention, Shenzhen, Guangdong, China
article info
abstract
Article history:
Objectives: Depression is one of the most common mental disorders and a major public
Received 18 July 2013
health problem in the Chinese population, especially among women. The current study
Received in revised form
aims to understand prevalence of depression symptoms and provide detailed epidemio-
14 January 2015
logical factors associated with depression among reproductive women in rural areas which
Accepted 20 January 2015
was paid less attention in previous surveys.
Available online 25 March 2015
Study design: Cross-sectional study. Methods: Face-to-face household interviews were conducted on 1058 women (age: 15e49
Keywords:
years) in rural areas from July 2012 to August 2012. Questionnaires were used to investigate
Depression
the influencing factors of depression among women. Pearson's c2, logistic regression analysis
Risk factors
and structural equation modelling (SEM) were applied to analyze the related factors.
Rural areas
Results: The prevalence of depression among women was 30.7% [95% confidence interval
Social support
(CI): 27.9%e33.5%]. Compared with non-depressed individuals, those with depression were
Reproductive age
more likely to be short of social support [odd ratio (OR): 0.940, P < 0.001) and have no one to talk with (OR: 0.366, P < 0.001), to be dissatisfied with the house (OR: 2.673, P < 0.001) and economy (OR: 2.268, P < 0.01) of their family, and to have great pressure (OR: 2.099, P < 0.01), negative life events (OR: 1.485, P < 0.05) and physical diseases (OR: 1.364, P < 0.05). Pressure status, social support assessment, and socio-economic status were negatively related to depression (correlation coefficient: 0.57, 0.27 and 0.17). Conclusions: The high prevalence of depression among reproductive women in rural areas is of particular concern. Factors associated with depression may assist health care administrations to identify and assess high-risk women and target strategies accordingly. © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ86 027 83693763. ** Corresponding author. Tel.: þ86 027 83650713. E-mail addresses: [email protected] (W. Yan), [email protected] (S. Nie). 1 B. Cao and H. Jiang contribute equally to this work. http://dx.doi.org/10.1016/j.puhe.2015.01.020 0033-3506/© 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
466
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Introduction
Methods
The population of the rural area in China is 67.4 million, representing 50.3% of the total population.1 In the past ten years, with China's economy rapidly transforming, large numbers of the male workforce who previously worked in the countryside have moved into the cities for work; thus, agricultural production is dominated by women also who have to take the responsibilities of looking after children and feeding families.2 Because of differences of coping styles of emotion and physiological factors such as oestrogen and progesterone, women suffer depression easier than men.3 It has been reported that both the point and lifetime prevalence of major depressive disorder across cultures are approximately twice as high in females as in males.4e7 Major depression is one of the causes of disability in association with diabetes,8 stroke,9 and coronary heart disease,10,11 which could reduce health-related quality of life scores.12,13 Researches from China and abroad concerned with women's mental health14e17 showed that the prevalence of major depression disorder for non-pregnant women aged 18e50 years ranges from 8% to 16%,18,19 and may be higher in low-income districts, where prevalence of depression (defined as minor depression/dysthymia or major depression) could be as high as 29%.14 Depression is one of the highest burdens of diseases in China, with a cost of 51,370 million RMB at 2002 prices. 8090 million RMB among it were direct costs, accounting for about 16% of the total cost of depression.20 To the knowledge of the authors, there has been no survey of the prevalence of depression and risk factors among reproductive women conducted in the rural areas of China, although one national survey in urban areas using the same methodology as this survey revealed a close prevalence of 33.3% for depression measured by CES-D among total population including men and women.21 A survey in Malay women revealed that the point prevalence of depression measured by the same scale was 34.5% with three potential factors: stressful events, health status and education level.22 Nonetheless, apart from stressful life events (median odds ratio of 1.6), further analysis of the epidemiological profile of depression is not available from that study.17 Another survey conducted on 800 respondents in Anhui has suggested that education level and having a debt was closely linked to depression.23 Again, the survey has not provided further epidemiological information for reproductive women such as the social support, persons they talked with, the number of friends and so on. There is a shortage of information on the prevalence and the risk factors of depression among the reproductive women in the rural areas of China and a detailed analysis of the epidemiological profile of depression is desirable. The data presented in this paper should aid in calculating the prevalence of reproductive women in the rural areas with a high risk of depressive disorders and identifying related risk factors. Results from the study on potential risk factors associated with depression symptoms were used to from the framework from Structural Equation Model (SEM).
The study was a community-based cross-sectional survey of women in reproductive age. Data were collected from July 2012 to August 2012. Participants came from three rural areas of Hubei Province in central China. The inclusion criteria were as follows: (1) Residents living in rural areas for at least five years; (2) Aged between 15 and 49 years old. The exclusion criteria were as follows: hearing loss and muteness (due to a small number of six and difficulty in communication owing to lower education without formal sign language training). Informed consent was obtained from all final participants and the study was approved by the institutional review board of Tongji Medical College of Huazhong University of Science and Technology. The sampling method used in this survey was cluster random sampling of the entire group and three different sites (Suizhou, Huangmei and Qichun located at north, east south of Hubei province respectively) were selected. Agriculture was the backbone of their economy, accounting for over 85% of the total income. In each area, two rural villages were selected randomly with the method of random numbers among all the numbered villages. All women who met the requirements were selected. Meetings were conducted with village leaders in order to get permission to invite the women. All participants were interviewed with the uniformed questionnaire. Before the formal implementation, a pilot in villages was conducted. Finally, 1296 persons followed the inclusion, of whom 68 (5.2%) were working outside and 60 (4.6%) refused to participate in the survey. Thus, 1168 of the 1296 sampled population were interviewed by trained interviewers. A total of 1058 usable questionnaires remained, with the overall response rate of 81.6% (238 questionnaires with missing items accounting of 5% or logic errors were excluded). The precision with such a sample size was 0.027. To ensure the validity of results, interviewers were trained before the survey and the whole survey process was under close supervision. Thirty experienced investigators were recruited as data collectors with training for two weeks. During the training, privacy and confidentiality were given high emphasis. A quality assurance scheme was introduced both in the fieldwork and at data entry. Quality control in the field included supervising and ensuring the completeness and consistency of the responses on a daily basis.
Measures The study questionnaire was comprised of six parts: (1) Baseline demographic: areas, age, marital status, registered permanent residence, insurance status, educational status (years of schooling) and religion; (2) Socio-economic status: economic satisfactory status (their feelings about their own personal/family economic status), the average income, housing types and housing satisfactory status; (3) Health status: physical diseases; (4) Pressure status (Perceived pressure): negative life events and self-reported pressure status; (5) Social support assessment: social support rating scales including number of friends, neighbour relationship, persons they talk with when they met problems, activities and so on;
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
(6) Depression assessment: the Center for Epidemiologic Studies Depression Scale (CES-D). The variables were categorized into several groups. For example, educational status was categorized into three groups as ‘illiterate or primary school (6 years)’, ‘junior high school or high school (6e12 years)’, ‘college and above (12 years)’. Economic satisfactory status and housing satisfactory status were recorded on a three-point scale ranging from ‘dissatisfaction’ to ‘satisfaction’. The CES-D, a self-reporting depression scale, has been widely applied in population surveys worldwide, with satisfactory levels of reliability and validity.24e27 It contains 20 items that are scored by respondents to indicate the frequency of symptoms during the previous week, using a scale of 0-less than a day, 1e2 days, 3e4 days, and 5e7 days, which also identified four factors used to describe depressed affect, positive affect, somatic symptoms and retarded activity, and interpersonal relations. Radloff recommended a threshold of 16 for indicating the likely presence of clinically significant depressive symptoms.27 In this report, the cutoff point is introduced at a score of 16 or above, since this score could most effectively detect and cover ‘probable’ depression symptoms. The reliability and validity of the CES-D have been demonstrated before.28 The CES-D has been translated into Chinese and the reliability and validity of the Chinese version have also been confirmed. Cronbach's alpha for the CES-D in Chinese people was 0.74e0.90.21,29,30 The national norm with the sample of 16,047 among Chinese people was conducted with Cronbach's alpha of 0.90.21 Social support status was assessed by a well-validated social support rating scale designed by Xiao.31,32 The scale is a 10-item instrument consisting of three dimensions of social support: objective support (behaviour that directly helps the person in need), subjective support (provision of empathy, caring, love, and trust), and degree of social support utility (support actually delivered and received from the social network). A higher score means higher social support received by the people.
Statistical analysis Descriptive analysis was performed to estimate the prevalence of depression. Pearson's c2 and logistic regression analysis were used to examine the statistical significance of deviations and analyze risk factors (depression status as the dependent variable and the other variables as independent variables). For all comparisons, differences were tested using the two-tailed test and P-value less than 0.05 was considered statistically significant. Structural equations were conducted by AMOS 5.0 for student version (SPSS Inc., Chicago, IL), using the Maximum Likelihood Method. Standardized path coefficients were presented. Chi-squared test (c2) coupled with the degrees of freedom (df), the normed fit index, the relative fit index, the incremental fit index (IFI), the TuckereLewis coefficient (TLI), the comparative fit index (CFI), as well as the root mean square error of approximation (RMSEA) were shown to measure the model fit globally and incrementally. All analyses (except structural equations) were performed using SPSS, version 12.0 (SPSS Inc, Chicago, IL, USA).
467
Results A total of 1058 eligible women participated in the survey. The prevalence of depressive symptoms was 30.7%. Cronbach's alpha for the CES-D in the study was 0.851. The descriptive statistics for the demographic variables in the study were provided in Table 1. The age of the sample ranged from 15 to 49 years old (M ¼ 35.19, SD ¼ 8.55). For the level of education, 207 participants (19.57%) were illiterate or primary school (6 years). The prevalence of depression was 30.7% (95% CI: 27.9%e33.5%). The internal consistency of the CES-D assessed by Cronbach's alpha was 0.85. The average score of the depressive women was 12.52 ± 7.91, and the CES-D result showed that P25, P50 and P75 were 6, 12, and 17, respectively. Of the total women, the minimum score was 0, and the maximum score was 46. Table 1 shows 14 variables. The prevalence of depression in three study sites were similar (c2 ¼ 4.957, P ¼ 0.084) and Pearson's c2 analysis revealed no significant association between the variables of age, registered permanent residence, marital status, education, insurance and depressive symptoms. Respondents who were satisfied with their house and economic status were less likely to be depressive than those who were dissatisfied (c2 ¼ 70.785, P < 0.001; c2 ¼ 71.584, P < 0.001; respectively). For the number of friends, the prevalence of depression clearly reduced with an increase in the number of friends (47.1%, 33.9%, 21.0%, 19.4% with 0, 1e2, 3e5, 6 or more, respectively, c2 ¼ 35.764, P < 0.001). As for income, respondents having low-average exhibited higher prevalence (37.6%, c2 ¼ 21.666, P < 0.001) of depression in total population. In addition, results of c2 tests showed there was significant association between depression and self-reported pressure status (c2 ¼ 71.112, P < 0.001). Respondents with depression had a lower social support score compared with the women without depression. There were significant differences between the two groups in four aspects (social support: 35.63 ± 7.25 vs 39.91 ± 6.79, P < 0.001; objective social support: 7.63 ± 2.58 vs 8.95 ± 2.71, P < 0.001; subjective social support: 21.74 ± 4.78 vs 24.00 ± 4.50, P < 0.001; social support utility: 6.26 ± 1.96 vs 6.96 ± 1.80, P < 0.001) (Table 2). The correlation between depression score and social support score by linear regression are examined and a negative correlation between them is found. Table 3 shows the results of stepwise multiple logistic regression model. A stepwise multiple regression analysis was conducted to determine the best predictors of depression by adjusting for potential confounding variables and obtained the final multivariable model including all significant variables. The final model included seven variables (economic satisfactory status, negative life events, pressure status, housing satisfactory status, social support score and persons they talked with). Compared with the women who were satisfied with the house, the group that was dissatisfied with the housing was more likely to be depressed (OR: 2.203, 95% CI: 1.399e3.469, P < 0.01). Regarding physical health status, participants with physical diseases were more likely to experience depression, compare with the group without physical diseases (OR: 1.364, 95%CI: 1.004e1.853, P < 0.05). Specifically, dissatisfaction with economy (OR: 2.268, 95%CI: 1.372e3.749,
468
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Table 1 e Comparison of baseline demographic and clinical factors between the depressed and non-depressed participants. Variables Baseline demographic Areas Huangmei Qichun Suizhou Age (years), n (%)a 25 26e35 36e49 Registered permanent residence, n (%)a Non-farmer Farmer Marital status, n (%)a Never married/widowed/divorced Married/remarried Religion, n (%)a Yes No Education, n (%)a Illiteracy or primary school High school College and above Insurance, n (%)a Yes No Socio-economic status The average income ￥, n (%)a 1000 1000e2000 2000 Housing type, n (%)a Rent Purchase Employee dormitory Others Economic satisfaction, n (%)a Satisfaction Commonly satisfaction Non-satisfaction Housing satisfaction, n (%)a Non-satisfaction Commonly satisfaction Satisfaction Health status Physical diseases, n (%)a Yes No Pressure status Negative life events, n (%)a Yes No Self-reported pressure status, n (%)a Grate General A little or none Social support assessment Number of friends, n (%)a 0 1e2 3e5 6 and above Neighbour relationship, n (%)a Never care
Overall
Depression (%)
Non-depression (%)
c2
288 369 401
74 (25.7) 117 (31.7) 134 (33.4)
214 (74.3) 252 (68.3) 267 (66.6)
4.957
0.084
174 320 561
42 (24.1) 97 (30.3) 183 (32.6)
132 (75.9) 223 (69.7) 378 (67.4)
4.516
0.105
226 827
64 (28.3) 260 (31.4)
162 (71.7) 567 (68.6)
0.811
0.368
88 969
22 (25.0) 303 (31.3)
66 (75.0) 666 (68.7)
1.489
0.222
784 274
247 (31.5) 78 (28.5)
537 (68.5) 196 (71.5)
0.880
0.348
207 734 115
70 (33.8) 225 (30.7) 29 (25.2)
137 (66.2) 509 (69.3) 86 (74.8)
2.571
0.276
960 96
299 (31.1) 26 (27.1)
661 (68.9) 70 (72.9)
0.676
0.411
460 477 119
173 (37.6) 129 (27.0) 22 (18.5)
287 (62.4) 348 (73.0) 97 (81.5)
21.666
71 958 21 8
26 291 6 2
45 667 15 6
259 468 331
0.000***
(63.4) (69.6) (71.4) (75.0)
1.392
35 (13.5) 139 (29.7) 151 (45.6)
224 (86.5) 329 (70.3) 180 (54.4)
70.785
0.000***
205 454 399
90 (43.9) 173 (38.1) 62 (15.5)
115 (56.1) 281 (61.9) 337 (84.5)
71.584
0.000***
391 667
155 (39.6) 170 (25.5)
236 (60.4) 497 (74.5)
23.206
0.000***
378 680
155 (41.0) 170 (25.0)
223 (59.0) 510 (75.0)
29.243
0.000***
131 294 633
73 (55.7) 114 (38.8) 138 (21.8)
58 (44.3) 180 (61.2) 495 (78.2)
71.112
0.000***
119 569 262 108
56 193 55 21
63 376 207 87
(52.9) (66.1) (79.0) (80.6)
35.764
0.000***
68 (51.9)
22.016
0.000***
131
(36.6) (30.4) (28.6) (25.0)
P
(47.1) (33.9) (21.0) (19.4)
63 (48.1)
0.731
469
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Table 1 e (continued ) Variables A little care Some care Much care Persons they talked with, n (%)a No one Best friends Some friends All Activities n (%)a Yes No Social support (mean ± SD)b Social support Objective social support Subjective social support Social support utility
c2
P
(43.7) (74.1) (75.0) (72.0)
57.457
0.000***
127 (26.4) 197 (34.2)
354 (73.6) 379 (65.8)
7.498
0.006**
35.63 ± 7.25 7.63 ± 2.58 21.74 ± 4.78 6.26 ± 1.96
39.91 ± 6.79 8.95 ± 2.71 24.00 ± 4.50 6.96 ± 1.80
9.246 7.412 7.408 5.428
0.000*** 0.000*** 0.000*** 0.000***
Overall
Depression (%)
Non-depression (%)
327 209 391
98 (30.0) 59 (28.2) 105 (26.9)
229 (70.0) 150 (71.8) 286 (73.1)
158 637 80 182
89 165 20 51
69 472 60 131
481 576 38.59 ± 7.21 8.54 ± 2.74 23.30 ± 4.70 6.74 ± 1.88
(56.3) (25.9) (25.0) (28.0)
*P < 0.05; **P < 0.01; ***P < 0.001. a Chi-squared test. b Independent-samples t-test.
P < 0.01) was associated with depression. Having negative life events in the recent year and physical diseases were also associated with depression (OR: 1.485, 95%CI: 1.088e2.027, P < 0.05; OR: 1.364, 95%CI: 1.004e1.853, P < 0.05). Respondents who experienced greater pressure were more likely to have depression (OR: 2.099, 95%CI: 1.280e3.441, P < 0.05), yet those receiving more social support were less likely to be depressive (OR: 0.940, 95%CI: 0.919e0.962, P < 0.001). The Correlation coefficient of the depression and the variables were showed on Table 3. There were significant associations between depression and the variables. A structural equation model was established as shown in Fig. 1, using the Maximum Likelihood Method. Standardized path coefficients were presented on each arrow in Fig. 1 with standard error (S.E.). All of the coefficients were significant at the level of P < 0.001. Pressure status and social support assessment were negatively related to depression with the correlation coefficient being 0.57 and 0.27 respectively. That means greater pressure and less social support might affect the depressive symptoms. The correlation coefficient between socio-economic status and depression was 0.17. The goodness-of-fit test yielded a Chi-squared test of 171.571 (df ¼ 39, P < 0.001). The NFI is 0.931. The IFI is 0.946. The TLI is 0.907. The CFI is 0.945. And the RMSEA is 0.057. These indexes and the acceptable measures of them are given in Table 4.
Table 2 e Correlation of CES-D score and social support score. Items Social support Total score Objective social support score Subjective score Degree of social support utility score *P < 0.05; **P < 0.01; ***P < 0.001.
CES-D score (r) P-value 0.330 0.280 0.249 0.235
0.000*** 0.000*** 0.000*** 0.000***
Discussion The percentage of depressive women in subjects was 30.7%, which was much higher than the level found in the western countries (14.3%)33,34 using CES-D. This difference could be caused by the difference of the culture and socio-economic factors. Using other scales of depressive symptoms, researchers had found that the prevalence of the depression was 35.8%35e54.3%23 in women and 20% in the total population36 in China. This difference could be caused by the differences of the screening instruments, sampling and sites of survey. The results indicated that the seven variables were most critical in the structural model after logistic regression. In contrast to results from Qiu's study37 indicating that socioeconomic status has a distal association with depressive symptoms, this research found that socio-economic status was direct factor, similar to the results of studies.38 According to the model, socio-economic factors including economic satisfactory status and housing satisfactory status affected the depression directly with correlation coefficient of 0.17, and also affect pressure status with correlation coefficient of 0.87. Previous studies39e41 had examined risk factors for depression, including social factors such as socio-economic status and the average income. Several studies in high-income countries had shown that the economic status can affect women's depression.42 In a meta-analysis of 51 prevalence studies, Lorant found economic inequality was a determining factor for depression among people.43 Erick and his colleagues also brought up the similar results.44 In their ecological analysis, they discovered a significant positive association between income and depression prevalence across state. The population that was poorer than urban population were studied. In a study among Chinese Americans that had higher household income than the study, a lower prevalence of depression (4.2%) was measured by using the measure tool of CES-D.45 In addition to the differences of geographical and
470
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Table 3 e Unadjusted and adjusted associations between factors and depression (N ¼ 1058). Overall (%) Baseline demographic Areas Huangmei Qichun Suizhou Age (years), n (%)a 25 26e35 36e49 Marital status, n (%)a Never married/widowed/divorced Married/remarried Religion, n (%)a No Yes Education, n (%)a Illiteracy or primary school High school College and above Insurance, n (%)a No Yes Socio-economic status The average income ￥, n (%)a 1000 1000e2000 2000 Housing type, n (%)a Rent Purchase Employee dormitory Others Economic satisfactory status Satisfaction Commonly satisfaction Non-satisfaction Housing satisfactory status Satisfaction Commonly satisfaction Non-satisfaction Health status Physical diseases, n (%)a No Yes Pressure status Negative life events No Yes Self-reported pressure state A little or none General Grate Social support assessment Number of friends n (%)a 6 and above 3e5 1e2 0 Neighbour relationship Much care Some care A little care Never care Persons they talked with
Crude OR (95%CI)
Adjusted OR (95%CI)a
Adjusted OR (95%CI)b
288 369 401
1 1.343 (0.952e1.893) 1.451 (1.037e2.031)*
174 320 561
1 1.367 (0.897e2.083) 1.522 (1.031e2.246)*
88 969
1 1.365 (0.827e2.253)
274 784
1 0.865 (0.639e1.171)
207 734 115
1 0.865 (0.623e1.201) 0.660 (0.396e1.099)
96 960
1 0.821 (0.513e1.314)
460 477 119
1 0.615 (0.466e0.811)** 0.376 (0.228e0.620)***
1 0.617 (0.463e0.823)** 0.357 (0.214e0.598)***
71 958 21 8
1 1.733 (0.326e9.222) 1.309 (0.263e6.523) 1.200 (0.187e7.704)
1 0.664 (0.397e1.110) 0.792 (0.269e2.330) 0.604 (0.107e3.395)
259 468 331
1 2.704 (1.799e4.065)*** 5.369 (3.539e8.145)***
1 5.274 (3.440e8.085)*** 2.667 (1.757e4.051)***
1 1.726 (1.096e2.717)* 2.268 (1.372e3.749)**
399 454 205
1 3.346 (2.405e4.657)*** 4.254 (2.890e6.261)***
1 4.428 (2.981e6.579)*** 3.607 (2.562e5.078)***
1 2.203 (1.399e3.469)** 2.673 (1.845e3.872)***
667 391
1 0.916 (0.898e0.935)***
1 1.859 (1.399e2.471)***
1 1.364 (1.004e1.853)*
680 378
1 2.085 (1.594e2.728)***
1 2.029 (1.543e2.666)***
1 1.485 (1.088e2.027)*
633 294 131
1 2.272 (1.681e3.070)*** 4.515 (3.047e6.688)***
1 2.208 (1.627e2.997)*** 4.380 (2.943e6.518)***
1 1.608 (1.119e2.309)** 2.099 (1.280e3.441)*
108 262 569 119
1 1.101 (0.628e1.930) 2.127 (1.281e3.531)** 3.683 (2.027e6.691)***
1 0.983 (0.556e1.737) 1.947 (1.157e3.275)* 3.476 (1.880e6.426)***
391 209 327 131
1 1.071 (0.736e1.559) 1.166 (0.842e1.614) 2.524 (1.676e3.800)***
1 0.986 (0.669e1.454) 1.210 (0.865e1.692) 2.705 (1.765e4.145)***
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
471
Table 3 e (continued )
No one Best friends Some friends All Activities Yes No Social support score
Overall (%)
Crude OR (95%CI)
Adjusted OR (95%CI)a
Adjusted OR (95%CI)b
158 637 80 182
1 0.271 (0.189e0.389)*** 0.258 (0.142e0.469)*** 0.302 (0.192e0.474)***
1 0.274 (0.190e0.395)*** 0.273 (0.149e0.500)*** 0.306 (0.194e0.485)***
1 0.366 (0.240e0.559)*** 0.412 (0.211e0.807)* 0.568 (0.331e0.974)*
481 576
1 1.449 (1.111e1.890)*** 0.916 (0.898e0.935)***
1 1.437 (1.078e1.917)* 0.918 (0.899e0.937)***
0.940 (0.919e0.962)***
*P < 0.05; **P < 0.01; ***P < 0.001. a Adjusting for baseline demographic (including areas, age, marital status, education, religion, insurance). b Final multivariate model including all significant variables.
culture, economic factors were also a very important reason. In this study, 31.3% of the participants rated their economy as dissatisfactory. Within this population, economic satisfactory status was associated with depression in a doseeresponse manner, suggesting the importance of economic satisfactory. Among the reproductive age women, the status of housing satisfaction could have a tremendous impact on their health. However, few studies had shown that the women who were more dissatisfied with the housing environment had more depressive symptoms. In China, with the growth of population, the price of the houses has been soaring up in the past decade. Some Chinese families live in very small areas or in very dirty environments. House status has become one of the indicators to measure happiness of Chinese people and housing environment plays an important role in women's daily life. The study also suggested physical diseases to be risk factors of depressive symptoms. This finding was consistent with
several previous studies.46,47 For example, Boing and his colleagues figured out that people with chronic diseases were significantly more likely to suffer from depression than those without chronic diseases.48 One reason of this phenomenon was that depression was associated with disability and declines in health-related quality of life. Besides, diseases would increase the burden of the family and then result in depression. In this study, physical diseases had no direct effect on depression, but had an indirect impact by increasing pressure status instead with correlation coefficient of 0.19. Physical diseases were reported by 37.0% of participants and associations existed between depression and chronic diseases, such as hypertension, arthritis, heart diseases and diabetes. In rural China, the primary health care system is very fragile and mainly relies on minimally trained medical personnel (‘barefoot doctors’).41 The new rural cooperative medical care is the medical insurance for rural residents, but it only covers hospitalization fees. Since farmers still have to pay the outpatient
Fig. 1 e The structural equation model of depression and the influencing factors among reproductive women. Standardized path coefficients with standard errors are presented on each arrow. SS ¼ socio-economic status, HS ¼ health status, SSA ¼ social support assessment, PS ¼ pressure status, PD ¼ physical diseases, DA ¼ depressed affect, PA ¼ positive affect, SSR ¼ somatic symptoms and retarded activity, IR ¼ interpersonal relations, Economic ¼ economic satisfactory status, House ¼ housing satisfactory status, Physical ¼ physical diseases, Nega ¼ negative life events, Pressure ¼ self-reported pressure state, Talk ¼ talking with, Activities ¼ activities, SOS ¼ social support score.
472
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Table 4 e Measures of fit for the depression model of reproductive women. c2 Thresholds for acceptable fit The paper results
171.571
df
c2/df
NFI
IFI
TLI
CFI
RMSEA
39
Available online at www.sciencedirect.com
Public Health journal homepage: www.elsevier.com/puhe
Original Research
Prevalence and influencing factors of depressive symptoms among women of reproductive age in the rural areas of Hubei, China B. Cao a,1, H. Jiang a,1, H. Xiang b, B. Lin c, Q. Qin a, F. Zhang a, W. Kong a, S. Wei a, L. Liu a, W. Yan a,**, S. Nie a,* a
Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China b Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China c Futian District Shenzhen City Center for Disease Control and Prevention, Shenzhen, Guangdong, China
article info
abstract
Article history:
Objectives: Depression is one of the most common mental disorders and a major public
Received 18 July 2013
health problem in the Chinese population, especially among women. The current study
Received in revised form
aims to understand prevalence of depression symptoms and provide detailed epidemio-
14 January 2015
logical factors associated with depression among reproductive women in rural areas which
Accepted 20 January 2015
was paid less attention in previous surveys.
Available online 25 March 2015
Study design: Cross-sectional study. Methods: Face-to-face household interviews were conducted on 1058 women (age: 15e49
Keywords:
years) in rural areas from July 2012 to August 2012. Questionnaires were used to investigate
Depression
the influencing factors of depression among women. Pearson's c2, logistic regression analysis
Risk factors
and structural equation modelling (SEM) were applied to analyze the related factors.
Rural areas
Results: The prevalence of depression among women was 30.7% [95% confidence interval
Social support
(CI): 27.9%e33.5%]. Compared with non-depressed individuals, those with depression were
Reproductive age
more likely to be short of social support [odd ratio (OR): 0.940, P < 0.001) and have no one to talk with (OR: 0.366, P < 0.001), to be dissatisfied with the house (OR: 2.673, P < 0.001) and economy (OR: 2.268, P < 0.01) of their family, and to have great pressure (OR: 2.099, P < 0.01), negative life events (OR: 1.485, P < 0.05) and physical diseases (OR: 1.364, P < 0.05). Pressure status, social support assessment, and socio-economic status were negatively related to depression (correlation coefficient: 0.57, 0.27 and 0.17). Conclusions: The high prevalence of depression among reproductive women in rural areas is of particular concern. Factors associated with depression may assist health care administrations to identify and assess high-risk women and target strategies accordingly. © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ86 027 83693763. ** Corresponding author. Tel.: þ86 027 83650713. E-mail addresses: [email protected] (W. Yan), [email protected] (S. Nie). 1 B. Cao and H. Jiang contribute equally to this work. http://dx.doi.org/10.1016/j.puhe.2015.01.020 0033-3506/© 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
466
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Introduction
Methods
The population of the rural area in China is 67.4 million, representing 50.3% of the total population.1 In the past ten years, with China's economy rapidly transforming, large numbers of the male workforce who previously worked in the countryside have moved into the cities for work; thus, agricultural production is dominated by women also who have to take the responsibilities of looking after children and feeding families.2 Because of differences of coping styles of emotion and physiological factors such as oestrogen and progesterone, women suffer depression easier than men.3 It has been reported that both the point and lifetime prevalence of major depressive disorder across cultures are approximately twice as high in females as in males.4e7 Major depression is one of the causes of disability in association with diabetes,8 stroke,9 and coronary heart disease,10,11 which could reduce health-related quality of life scores.12,13 Researches from China and abroad concerned with women's mental health14e17 showed that the prevalence of major depression disorder for non-pregnant women aged 18e50 years ranges from 8% to 16%,18,19 and may be higher in low-income districts, where prevalence of depression (defined as minor depression/dysthymia or major depression) could be as high as 29%.14 Depression is one of the highest burdens of diseases in China, with a cost of 51,370 million RMB at 2002 prices. 8090 million RMB among it were direct costs, accounting for about 16% of the total cost of depression.20 To the knowledge of the authors, there has been no survey of the prevalence of depression and risk factors among reproductive women conducted in the rural areas of China, although one national survey in urban areas using the same methodology as this survey revealed a close prevalence of 33.3% for depression measured by CES-D among total population including men and women.21 A survey in Malay women revealed that the point prevalence of depression measured by the same scale was 34.5% with three potential factors: stressful events, health status and education level.22 Nonetheless, apart from stressful life events (median odds ratio of 1.6), further analysis of the epidemiological profile of depression is not available from that study.17 Another survey conducted on 800 respondents in Anhui has suggested that education level and having a debt was closely linked to depression.23 Again, the survey has not provided further epidemiological information for reproductive women such as the social support, persons they talked with, the number of friends and so on. There is a shortage of information on the prevalence and the risk factors of depression among the reproductive women in the rural areas of China and a detailed analysis of the epidemiological profile of depression is desirable. The data presented in this paper should aid in calculating the prevalence of reproductive women in the rural areas with a high risk of depressive disorders and identifying related risk factors. Results from the study on potential risk factors associated with depression symptoms were used to from the framework from Structural Equation Model (SEM).
The study was a community-based cross-sectional survey of women in reproductive age. Data were collected from July 2012 to August 2012. Participants came from three rural areas of Hubei Province in central China. The inclusion criteria were as follows: (1) Residents living in rural areas for at least five years; (2) Aged between 15 and 49 years old. The exclusion criteria were as follows: hearing loss and muteness (due to a small number of six and difficulty in communication owing to lower education without formal sign language training). Informed consent was obtained from all final participants and the study was approved by the institutional review board of Tongji Medical College of Huazhong University of Science and Technology. The sampling method used in this survey was cluster random sampling of the entire group and three different sites (Suizhou, Huangmei and Qichun located at north, east south of Hubei province respectively) were selected. Agriculture was the backbone of their economy, accounting for over 85% of the total income. In each area, two rural villages were selected randomly with the method of random numbers among all the numbered villages. All women who met the requirements were selected. Meetings were conducted with village leaders in order to get permission to invite the women. All participants were interviewed with the uniformed questionnaire. Before the formal implementation, a pilot in villages was conducted. Finally, 1296 persons followed the inclusion, of whom 68 (5.2%) were working outside and 60 (4.6%) refused to participate in the survey. Thus, 1168 of the 1296 sampled population were interviewed by trained interviewers. A total of 1058 usable questionnaires remained, with the overall response rate of 81.6% (238 questionnaires with missing items accounting of 5% or logic errors were excluded). The precision with such a sample size was 0.027. To ensure the validity of results, interviewers were trained before the survey and the whole survey process was under close supervision. Thirty experienced investigators were recruited as data collectors with training for two weeks. During the training, privacy and confidentiality were given high emphasis. A quality assurance scheme was introduced both in the fieldwork and at data entry. Quality control in the field included supervising and ensuring the completeness and consistency of the responses on a daily basis.
Measures The study questionnaire was comprised of six parts: (1) Baseline demographic: areas, age, marital status, registered permanent residence, insurance status, educational status (years of schooling) and religion; (2) Socio-economic status: economic satisfactory status (their feelings about their own personal/family economic status), the average income, housing types and housing satisfactory status; (3) Health status: physical diseases; (4) Pressure status (Perceived pressure): negative life events and self-reported pressure status; (5) Social support assessment: social support rating scales including number of friends, neighbour relationship, persons they talk with when they met problems, activities and so on;
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
(6) Depression assessment: the Center for Epidemiologic Studies Depression Scale (CES-D). The variables were categorized into several groups. For example, educational status was categorized into three groups as ‘illiterate or primary school (6 years)’, ‘junior high school or high school (6e12 years)’, ‘college and above (12 years)’. Economic satisfactory status and housing satisfactory status were recorded on a three-point scale ranging from ‘dissatisfaction’ to ‘satisfaction’. The CES-D, a self-reporting depression scale, has been widely applied in population surveys worldwide, with satisfactory levels of reliability and validity.24e27 It contains 20 items that are scored by respondents to indicate the frequency of symptoms during the previous week, using a scale of 0-less than a day, 1e2 days, 3e4 days, and 5e7 days, which also identified four factors used to describe depressed affect, positive affect, somatic symptoms and retarded activity, and interpersonal relations. Radloff recommended a threshold of 16 for indicating the likely presence of clinically significant depressive symptoms.27 In this report, the cutoff point is introduced at a score of 16 or above, since this score could most effectively detect and cover ‘probable’ depression symptoms. The reliability and validity of the CES-D have been demonstrated before.28 The CES-D has been translated into Chinese and the reliability and validity of the Chinese version have also been confirmed. Cronbach's alpha for the CES-D in Chinese people was 0.74e0.90.21,29,30 The national norm with the sample of 16,047 among Chinese people was conducted with Cronbach's alpha of 0.90.21 Social support status was assessed by a well-validated social support rating scale designed by Xiao.31,32 The scale is a 10-item instrument consisting of three dimensions of social support: objective support (behaviour that directly helps the person in need), subjective support (provision of empathy, caring, love, and trust), and degree of social support utility (support actually delivered and received from the social network). A higher score means higher social support received by the people.
Statistical analysis Descriptive analysis was performed to estimate the prevalence of depression. Pearson's c2 and logistic regression analysis were used to examine the statistical significance of deviations and analyze risk factors (depression status as the dependent variable and the other variables as independent variables). For all comparisons, differences were tested using the two-tailed test and P-value less than 0.05 was considered statistically significant. Structural equations were conducted by AMOS 5.0 for student version (SPSS Inc., Chicago, IL), using the Maximum Likelihood Method. Standardized path coefficients were presented. Chi-squared test (c2) coupled with the degrees of freedom (df), the normed fit index, the relative fit index, the incremental fit index (IFI), the TuckereLewis coefficient (TLI), the comparative fit index (CFI), as well as the root mean square error of approximation (RMSEA) were shown to measure the model fit globally and incrementally. All analyses (except structural equations) were performed using SPSS, version 12.0 (SPSS Inc, Chicago, IL, USA).
467
Results A total of 1058 eligible women participated in the survey. The prevalence of depressive symptoms was 30.7%. Cronbach's alpha for the CES-D in the study was 0.851. The descriptive statistics for the demographic variables in the study were provided in Table 1. The age of the sample ranged from 15 to 49 years old (M ¼ 35.19, SD ¼ 8.55). For the level of education, 207 participants (19.57%) were illiterate or primary school (6 years). The prevalence of depression was 30.7% (95% CI: 27.9%e33.5%). The internal consistency of the CES-D assessed by Cronbach's alpha was 0.85. The average score of the depressive women was 12.52 ± 7.91, and the CES-D result showed that P25, P50 and P75 were 6, 12, and 17, respectively. Of the total women, the minimum score was 0, and the maximum score was 46. Table 1 shows 14 variables. The prevalence of depression in three study sites were similar (c2 ¼ 4.957, P ¼ 0.084) and Pearson's c2 analysis revealed no significant association between the variables of age, registered permanent residence, marital status, education, insurance and depressive symptoms. Respondents who were satisfied with their house and economic status were less likely to be depressive than those who were dissatisfied (c2 ¼ 70.785, P < 0.001; c2 ¼ 71.584, P < 0.001; respectively). For the number of friends, the prevalence of depression clearly reduced with an increase in the number of friends (47.1%, 33.9%, 21.0%, 19.4% with 0, 1e2, 3e5, 6 or more, respectively, c2 ¼ 35.764, P < 0.001). As for income, respondents having low-average exhibited higher prevalence (37.6%, c2 ¼ 21.666, P < 0.001) of depression in total population. In addition, results of c2 tests showed there was significant association between depression and self-reported pressure status (c2 ¼ 71.112, P < 0.001). Respondents with depression had a lower social support score compared with the women without depression. There were significant differences between the two groups in four aspects (social support: 35.63 ± 7.25 vs 39.91 ± 6.79, P < 0.001; objective social support: 7.63 ± 2.58 vs 8.95 ± 2.71, P < 0.001; subjective social support: 21.74 ± 4.78 vs 24.00 ± 4.50, P < 0.001; social support utility: 6.26 ± 1.96 vs 6.96 ± 1.80, P < 0.001) (Table 2). The correlation between depression score and social support score by linear regression are examined and a negative correlation between them is found. Table 3 shows the results of stepwise multiple logistic regression model. A stepwise multiple regression analysis was conducted to determine the best predictors of depression by adjusting for potential confounding variables and obtained the final multivariable model including all significant variables. The final model included seven variables (economic satisfactory status, negative life events, pressure status, housing satisfactory status, social support score and persons they talked with). Compared with the women who were satisfied with the house, the group that was dissatisfied with the housing was more likely to be depressed (OR: 2.203, 95% CI: 1.399e3.469, P < 0.01). Regarding physical health status, participants with physical diseases were more likely to experience depression, compare with the group without physical diseases (OR: 1.364, 95%CI: 1.004e1.853, P < 0.05). Specifically, dissatisfaction with economy (OR: 2.268, 95%CI: 1.372e3.749,
468
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Table 1 e Comparison of baseline demographic and clinical factors between the depressed and non-depressed participants. Variables Baseline demographic Areas Huangmei Qichun Suizhou Age (years), n (%)a 25 26e35 36e49 Registered permanent residence, n (%)a Non-farmer Farmer Marital status, n (%)a Never married/widowed/divorced Married/remarried Religion, n (%)a Yes No Education, n (%)a Illiteracy or primary school High school College and above Insurance, n (%)a Yes No Socio-economic status The average income ￥, n (%)a 1000 1000e2000 2000 Housing type, n (%)a Rent Purchase Employee dormitory Others Economic satisfaction, n (%)a Satisfaction Commonly satisfaction Non-satisfaction Housing satisfaction, n (%)a Non-satisfaction Commonly satisfaction Satisfaction Health status Physical diseases, n (%)a Yes No Pressure status Negative life events, n (%)a Yes No Self-reported pressure status, n (%)a Grate General A little or none Social support assessment Number of friends, n (%)a 0 1e2 3e5 6 and above Neighbour relationship, n (%)a Never care
Overall
Depression (%)
Non-depression (%)
c2
288 369 401
74 (25.7) 117 (31.7) 134 (33.4)
214 (74.3) 252 (68.3) 267 (66.6)
4.957
0.084
174 320 561
42 (24.1) 97 (30.3) 183 (32.6)
132 (75.9) 223 (69.7) 378 (67.4)
4.516
0.105
226 827
64 (28.3) 260 (31.4)
162 (71.7) 567 (68.6)
0.811
0.368
88 969
22 (25.0) 303 (31.3)
66 (75.0) 666 (68.7)
1.489
0.222
784 274
247 (31.5) 78 (28.5)
537 (68.5) 196 (71.5)
0.880
0.348
207 734 115
70 (33.8) 225 (30.7) 29 (25.2)
137 (66.2) 509 (69.3) 86 (74.8)
2.571
0.276
960 96
299 (31.1) 26 (27.1)
661 (68.9) 70 (72.9)
0.676
0.411
460 477 119
173 (37.6) 129 (27.0) 22 (18.5)
287 (62.4) 348 (73.0) 97 (81.5)
21.666
71 958 21 8
26 291 6 2
45 667 15 6
259 468 331
0.000***
(63.4) (69.6) (71.4) (75.0)
1.392
35 (13.5) 139 (29.7) 151 (45.6)
224 (86.5) 329 (70.3) 180 (54.4)
70.785
0.000***
205 454 399
90 (43.9) 173 (38.1) 62 (15.5)
115 (56.1) 281 (61.9) 337 (84.5)
71.584
0.000***
391 667
155 (39.6) 170 (25.5)
236 (60.4) 497 (74.5)
23.206
0.000***
378 680
155 (41.0) 170 (25.0)
223 (59.0) 510 (75.0)
29.243
0.000***
131 294 633
73 (55.7) 114 (38.8) 138 (21.8)
58 (44.3) 180 (61.2) 495 (78.2)
71.112
0.000***
119 569 262 108
56 193 55 21
63 376 207 87
(52.9) (66.1) (79.0) (80.6)
35.764
0.000***
68 (51.9)
22.016
0.000***
131
(36.6) (30.4) (28.6) (25.0)
P
(47.1) (33.9) (21.0) (19.4)
63 (48.1)
0.731
469
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Table 1 e (continued ) Variables A little care Some care Much care Persons they talked with, n (%)a No one Best friends Some friends All Activities n (%)a Yes No Social support (mean ± SD)b Social support Objective social support Subjective social support Social support utility
c2
P
(43.7) (74.1) (75.0) (72.0)
57.457
0.000***
127 (26.4) 197 (34.2)
354 (73.6) 379 (65.8)
7.498
0.006**
35.63 ± 7.25 7.63 ± 2.58 21.74 ± 4.78 6.26 ± 1.96
39.91 ± 6.79 8.95 ± 2.71 24.00 ± 4.50 6.96 ± 1.80
9.246 7.412 7.408 5.428
0.000*** 0.000*** 0.000*** 0.000***
Overall
Depression (%)
Non-depression (%)
327 209 391
98 (30.0) 59 (28.2) 105 (26.9)
229 (70.0) 150 (71.8) 286 (73.1)
158 637 80 182
89 165 20 51
69 472 60 131
481 576 38.59 ± 7.21 8.54 ± 2.74 23.30 ± 4.70 6.74 ± 1.88
(56.3) (25.9) (25.0) (28.0)
*P < 0.05; **P < 0.01; ***P < 0.001. a Chi-squared test. b Independent-samples t-test.
P < 0.01) was associated with depression. Having negative life events in the recent year and physical diseases were also associated with depression (OR: 1.485, 95%CI: 1.088e2.027, P < 0.05; OR: 1.364, 95%CI: 1.004e1.853, P < 0.05). Respondents who experienced greater pressure were more likely to have depression (OR: 2.099, 95%CI: 1.280e3.441, P < 0.05), yet those receiving more social support were less likely to be depressive (OR: 0.940, 95%CI: 0.919e0.962, P < 0.001). The Correlation coefficient of the depression and the variables were showed on Table 3. There were significant associations between depression and the variables. A structural equation model was established as shown in Fig. 1, using the Maximum Likelihood Method. Standardized path coefficients were presented on each arrow in Fig. 1 with standard error (S.E.). All of the coefficients were significant at the level of P < 0.001. Pressure status and social support assessment were negatively related to depression with the correlation coefficient being 0.57 and 0.27 respectively. That means greater pressure and less social support might affect the depressive symptoms. The correlation coefficient between socio-economic status and depression was 0.17. The goodness-of-fit test yielded a Chi-squared test of 171.571 (df ¼ 39, P < 0.001). The NFI is 0.931. The IFI is 0.946. The TLI is 0.907. The CFI is 0.945. And the RMSEA is 0.057. These indexes and the acceptable measures of them are given in Table 4.
Table 2 e Correlation of CES-D score and social support score. Items Social support Total score Objective social support score Subjective score Degree of social support utility score *P < 0.05; **P < 0.01; ***P < 0.001.
CES-D score (r) P-value 0.330 0.280 0.249 0.235
0.000*** 0.000*** 0.000*** 0.000***
Discussion The percentage of depressive women in subjects was 30.7%, which was much higher than the level found in the western countries (14.3%)33,34 using CES-D. This difference could be caused by the difference of the culture and socio-economic factors. Using other scales of depressive symptoms, researchers had found that the prevalence of the depression was 35.8%35e54.3%23 in women and 20% in the total population36 in China. This difference could be caused by the differences of the screening instruments, sampling and sites of survey. The results indicated that the seven variables were most critical in the structural model after logistic regression. In contrast to results from Qiu's study37 indicating that socioeconomic status has a distal association with depressive symptoms, this research found that socio-economic status was direct factor, similar to the results of studies.38 According to the model, socio-economic factors including economic satisfactory status and housing satisfactory status affected the depression directly with correlation coefficient of 0.17, and also affect pressure status with correlation coefficient of 0.87. Previous studies39e41 had examined risk factors for depression, including social factors such as socio-economic status and the average income. Several studies in high-income countries had shown that the economic status can affect women's depression.42 In a meta-analysis of 51 prevalence studies, Lorant found economic inequality was a determining factor for depression among people.43 Erick and his colleagues also brought up the similar results.44 In their ecological analysis, they discovered a significant positive association between income and depression prevalence across state. The population that was poorer than urban population were studied. In a study among Chinese Americans that had higher household income than the study, a lower prevalence of depression (4.2%) was measured by using the measure tool of CES-D.45 In addition to the differences of geographical and
470
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Table 3 e Unadjusted and adjusted associations between factors and depression (N ¼ 1058). Overall (%) Baseline demographic Areas Huangmei Qichun Suizhou Age (years), n (%)a 25 26e35 36e49 Marital status, n (%)a Never married/widowed/divorced Married/remarried Religion, n (%)a No Yes Education, n (%)a Illiteracy or primary school High school College and above Insurance, n (%)a No Yes Socio-economic status The average income ￥, n (%)a 1000 1000e2000 2000 Housing type, n (%)a Rent Purchase Employee dormitory Others Economic satisfactory status Satisfaction Commonly satisfaction Non-satisfaction Housing satisfactory status Satisfaction Commonly satisfaction Non-satisfaction Health status Physical diseases, n (%)a No Yes Pressure status Negative life events No Yes Self-reported pressure state A little or none General Grate Social support assessment Number of friends n (%)a 6 and above 3e5 1e2 0 Neighbour relationship Much care Some care A little care Never care Persons they talked with
Crude OR (95%CI)
Adjusted OR (95%CI)a
Adjusted OR (95%CI)b
288 369 401
1 1.343 (0.952e1.893) 1.451 (1.037e2.031)*
174 320 561
1 1.367 (0.897e2.083) 1.522 (1.031e2.246)*
88 969
1 1.365 (0.827e2.253)
274 784
1 0.865 (0.639e1.171)
207 734 115
1 0.865 (0.623e1.201) 0.660 (0.396e1.099)
96 960
1 0.821 (0.513e1.314)
460 477 119
1 0.615 (0.466e0.811)** 0.376 (0.228e0.620)***
1 0.617 (0.463e0.823)** 0.357 (0.214e0.598)***
71 958 21 8
1 1.733 (0.326e9.222) 1.309 (0.263e6.523) 1.200 (0.187e7.704)
1 0.664 (0.397e1.110) 0.792 (0.269e2.330) 0.604 (0.107e3.395)
259 468 331
1 2.704 (1.799e4.065)*** 5.369 (3.539e8.145)***
1 5.274 (3.440e8.085)*** 2.667 (1.757e4.051)***
1 1.726 (1.096e2.717)* 2.268 (1.372e3.749)**
399 454 205
1 3.346 (2.405e4.657)*** 4.254 (2.890e6.261)***
1 4.428 (2.981e6.579)*** 3.607 (2.562e5.078)***
1 2.203 (1.399e3.469)** 2.673 (1.845e3.872)***
667 391
1 0.916 (0.898e0.935)***
1 1.859 (1.399e2.471)***
1 1.364 (1.004e1.853)*
680 378
1 2.085 (1.594e2.728)***
1 2.029 (1.543e2.666)***
1 1.485 (1.088e2.027)*
633 294 131
1 2.272 (1.681e3.070)*** 4.515 (3.047e6.688)***
1 2.208 (1.627e2.997)*** 4.380 (2.943e6.518)***
1 1.608 (1.119e2.309)** 2.099 (1.280e3.441)*
108 262 569 119
1 1.101 (0.628e1.930) 2.127 (1.281e3.531)** 3.683 (2.027e6.691)***
1 0.983 (0.556e1.737) 1.947 (1.157e3.275)* 3.476 (1.880e6.426)***
391 209 327 131
1 1.071 (0.736e1.559) 1.166 (0.842e1.614) 2.524 (1.676e3.800)***
1 0.986 (0.669e1.454) 1.210 (0.865e1.692) 2.705 (1.765e4.145)***
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
471
Table 3 e (continued )
No one Best friends Some friends All Activities Yes No Social support score
Overall (%)
Crude OR (95%CI)
Adjusted OR (95%CI)a
Adjusted OR (95%CI)b
158 637 80 182
1 0.271 (0.189e0.389)*** 0.258 (0.142e0.469)*** 0.302 (0.192e0.474)***
1 0.274 (0.190e0.395)*** 0.273 (0.149e0.500)*** 0.306 (0.194e0.485)***
1 0.366 (0.240e0.559)*** 0.412 (0.211e0.807)* 0.568 (0.331e0.974)*
481 576
1 1.449 (1.111e1.890)*** 0.916 (0.898e0.935)***
1 1.437 (1.078e1.917)* 0.918 (0.899e0.937)***
0.940 (0.919e0.962)***
*P < 0.05; **P < 0.01; ***P < 0.001. a Adjusting for baseline demographic (including areas, age, marital status, education, religion, insurance). b Final multivariate model including all significant variables.
culture, economic factors were also a very important reason. In this study, 31.3% of the participants rated their economy as dissatisfactory. Within this population, economic satisfactory status was associated with depression in a doseeresponse manner, suggesting the importance of economic satisfactory. Among the reproductive age women, the status of housing satisfaction could have a tremendous impact on their health. However, few studies had shown that the women who were more dissatisfied with the housing environment had more depressive symptoms. In China, with the growth of population, the price of the houses has been soaring up in the past decade. Some Chinese families live in very small areas or in very dirty environments. House status has become one of the indicators to measure happiness of Chinese people and housing environment plays an important role in women's daily life. The study also suggested physical diseases to be risk factors of depressive symptoms. This finding was consistent with
several previous studies.46,47 For example, Boing and his colleagues figured out that people with chronic diseases were significantly more likely to suffer from depression than those without chronic diseases.48 One reason of this phenomenon was that depression was associated with disability and declines in health-related quality of life. Besides, diseases would increase the burden of the family and then result in depression. In this study, physical diseases had no direct effect on depression, but had an indirect impact by increasing pressure status instead with correlation coefficient of 0.19. Physical diseases were reported by 37.0% of participants and associations existed between depression and chronic diseases, such as hypertension, arthritis, heart diseases and diabetes. In rural China, the primary health care system is very fragile and mainly relies on minimally trained medical personnel (‘barefoot doctors’).41 The new rural cooperative medical care is the medical insurance for rural residents, but it only covers hospitalization fees. Since farmers still have to pay the outpatient
Fig. 1 e The structural equation model of depression and the influencing factors among reproductive women. Standardized path coefficients with standard errors are presented on each arrow. SS ¼ socio-economic status, HS ¼ health status, SSA ¼ social support assessment, PS ¼ pressure status, PD ¼ physical diseases, DA ¼ depressed affect, PA ¼ positive affect, SSR ¼ somatic symptoms and retarded activity, IR ¼ interpersonal relations, Economic ¼ economic satisfactory status, House ¼ housing satisfactory status, Physical ¼ physical diseases, Nega ¼ negative life events, Pressure ¼ self-reported pressure state, Talk ¼ talking with, Activities ¼ activities, SOS ¼ social support score.
472
p u b l i c h e a l t h 1 2 9 ( 2 0 1 5 ) 4 6 5 e4 7 4
Table 4 e Measures of fit for the depression model of reproductive women. c2 Thresholds for acceptable fit The paper results
171.571
df
c2/df
NFI
IFI
TLI
CFI
RMSEA
39
