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Influence of health-related quality of life on health service utilization in Chinese rural-to-urban female migrant workers

Abstract

Background

Rural-to-urban migrant workers have been increasing rapidly in China over recent decades. Health related quality of life (HRQOL) may affect health service utilization. There is a lack of data on HRQOL in relation to health service utilization in Chinese rural-to-urban migrant workers. This study was aimed to explore the influence of HRQOL on health service utilization in Chinese rural-to-urban female migrant workers.

Methods

This was a cross-sectional survey of 1,438 female rural-to-urban migrant workers in Shenzhen-Dongguan economic zone, China in 2013. HRQOL was assessed by the 36-items Health Survey Short Form (SF-36). Health service utilization was measured by any physician visit over the recent two weeks and any hospitalization over the last 1-year (annual hospitalization). Clustered logistic regression was used to analyze the influence of HRQOL on health service utilization.

Results

Lower scores in three HRQOL domains (bodily pain, general health, role physical) were associated with more frequent health service utilization in female rural-to-urban migrant workers. Bodily pain and general health were associated with an independent influence of 15.6% on the risk of recent two-week physician visit, while role physical and general health were associated with an independent influence of 21.2% on the risk of annual hospitalization. The independent influence of HRQOL on health service utilization was smaller than that of socio-demographic and health-related variables.

Conclusions

HRQOL may have a modest influence on health service utilization in Chinese rural-to-urban female migrant workers - an underprivileged population in urban China.

Background

Surging numbers of rural-to-urban migrant workers are a unique phenomenon in China, a result of rapid economic development and urbanization over the last 3 decades. With the rapid development of economy, the migrant population continues to increase from rural to urban areas in China [1],[2]. Rural-to-urban migrant population increased from 70 millions in 1993 to 268.9 millions in 2013 [3]-[5]. Female migrant workers are in high demand as they are generally cheap labor, and particularly suitable for certain jobs requiring dexterity and attention to details (e.g. textile and assembling industries). Female migrant workers play an important role in economic development in China, but they face a variety of social disadvantages in urban areas [6]. The root cause of this phenomenon is the dual household registration system in China. Dual household registration system divides the population into rural and urban citizens. Employment opportunities and social welfare distribution differ according to household registration status. Most social welfare benefits in urban areas are available to registered urban citizens only, but not to registered rural citizens who live in urban areas [1],[2],[7],[8]. The health and quality of life of female migrant workers are an increasingly recognized social concern in China [9]. In general, females tend to have higher health service utilization than males [10]-[12]. Li H et al. reported that females had higher health service utilization (53.9% vs. 46.2%) than males [11]. Dai M et al. showed that females might have higher recent two-week morbidity (28.8% vs. 24.9%) and higher chronic disease morbidity (35.5% vs. 32.9%) than males [12]. Hence, optimizing the distribution of health service resources to improve the health of migrant female workers has become a priority for public health policy makers [13]. Rural-to-urban migrants in China have been studied in various aspects including socio-demographics, anthropology and management [14]-[17]. However, to the best of our knowledge (according to literature search in PubMed), there is a lack of data on health related quality of life (HRQOL) in relation to health service utilization in rural-to-urban female migrant workers. HRQOL is an essential aspect of human health embedded in an individual's physical health, psychological state, social relationships, personal beliefs and relationships to salient features of the environment [18]. Poor HRQOL has been strongly associated with reduced work performance and early retirement. HRQOL is an important issue in caring for the elderly [19],[20], acute and chronic disease patients [21],[22]. The associations between HRQOL and health service utilization have been reported in chronic disease patients [23],[24], aging patients with osteoarthritis [25] and primary care patients [13], and healthy individuals [26]. As an underprivileged population group in China, migrant workers suffer a variety of inequalities including long working hours, insecure employment, overcrowded and insalubrious living conditions [6],[27]. Zhu CY et al. founded that HRQOL in rural–urban female migrant workers was lower as compared to Chinese females in the general population [27]. Liu Y et al. showed that HRQOL among migrant workers was significantly lower than governmental civil employees [28]. However, the influence of HRQOL on health service utilization in Chinese female rural-to-urban migrant workers has not yet been reported. We carried out a study to explore the influence of HRQOL on health service utilization in rural-to-urban female migrant workers in Shenzhen-Dongguan economic zone, a leading urban economic development area in China. The findings may provide useful information for policy makers and health service providers in developing programs to optimize health service delivery to rural-to-urban female migrant workers - an underprivileged working population in urban China.

Methods

Study design and participants

A cross-sectional survey was carried out in 2013 in three factories in the Shenzhen-Dongguan economic zone in China. The study was aimed to evaluate HRQOL and health service utilization in rural-to-urban women migrant workers in light-duty large factories (>1000 employees) without strong occupational health risk hazards. With an estimated annual hospitalization rate (a primary outcome) of about 6.0%, allowing a maximal deviation of 1.2%, at an alpha error of 5%, a minimal sample size of 1204 subjects was required. The required sample sizes would have been smaller if the sample size calculations were based on other outcomes (2-week physician visit or HRQOL scores). Three factories (a textile manufacturer, a furniture assembler and a camera assembler) were approached and accepted to allow the implementation of the study protocol. Women in the three factories were all light-duty manual workers. The study subjects were selected by a cluster sampling method. We randomly sampled 20 of 61 workshops (about 1/3) in the three factories. All consented eligible subjects in the sampled workshops were recruited (n = 1438 rural-to-urban female migrant workers). Data were collected via face-to-face interview by trained study personnel. The Research Ethics Board of Guangzhou Medical University approved the study. Written informed consent was obtained from the study participant, or from a parent or legal guardian of the participant if her age was <18 years (a few participants of 16–17 years old).

Procedures

Well-trained interviewers (medical students from Guangzhou Medical University) collected research data through face-to-face interviews using structured study questionnaires. The interviewers received training and engaged in group discussions and simulated interviews to standardize data collection and recording procedures.

General study questionnaire

The general study questionnaire included information items on socio-demographic characteristics, health-related factors and health service utilization. Socio-demographic variables included age, marital status, education, medical insurance and duration of employment (years). Health-related variables included body mass index (BMI), any diagnosed chronic disease (yes/no), and recent-month self-reported morbidity (diagnosed disease, or self-perceived disease due to symptoms). BMI was calculated as weight/height2 (kg/m2), and categorized as "underweight" (<18.5), "normal weight" (18.5-23.9), "overweight" (24.0-27.9), and "obese" (≥28.0), according to the Chinese BMI reference standards [29]. Recent-month self-reported morbidity was obtained in the question "Have you been ill over the past month?", an indicator of diagnosed disease plus self-perceived disease (due to uncomfortable symptoms). Health service utilization variables included recent two-week physician visit and annual hospitalization. "Recent two-week physician visit" was obtained in the question "In the past two weeks, have you ever visited a doctor?". Annual hospitalization" was obtained in the question "In the last one year, have you been hospitalized?".

SF-36 questionnaire

The Chinese version of the Short Form (SF-36) Health Survey, which was translated from the standard English version of SF-36, showed satisfactory construct and clinical validity and internal consistency for measuring HRQOL [30]-[32]. The SF-36 was a generic measure of HRQOL in both physical and mental domains [33]-[37]. The questionnaire comprises 36 questions covering eight domains: four in the area of physical health including physical functioning (PF), role physical (RP), bodily pain (BP) and general health (GH), and four in the area of mental health including vitality (VT), social functioning (SF), role-emotional (RE) and mental health (MH). Both physical and mental health scores have been empirically validated [38],[39]. Total score in each of the eight domains can be converted into 0–100 scale; higher scores indicating better HRQOL. SF-36 has been widely accepted as a valid instrument for measuring HRQOL in the general population and in those with specific health conditions due to its sound psychometric properties, brevity and comprehensiveness [40]-[42].

Statistical analysis

Statistical analyses were performed using Statistical Package for Social Sciences (SPSS), version 13.0 (SPSS Inc., Chicago, IL). Mean and standard deviation (SD) are presented for continuous variables. Frequency and percentage are presented for categorical variables. Clustered logistic regression [43] was employed to explore the impacts of socio-demographic, health-related and HRQOL factors (three clusters) on health service utilization. The two dependent (outcome) variables were recent two-week physician visit ("no visit", "at least one visit") and annual hospitalization ("no hospitalization", "at least one hospitalization "). Significance level was set at P <0.05 for including predictor variables into the regression models.

We first explored the associations of the eight domains of SF-36 with health service utilization without adjustment for other factors. In subsequent clustered regression analyses, independent predictor variables were grouped into three clusters according to nature of the study variables: Cluster 1, socio-demographic variables; Cluster 2, health-related variables; Cluster 3, HRQOL variables. Health-related variables included BMI, self-reported morbidity and chronic illnesses. HRQOL variables included the standard 8 domains as measured by SF-36. Multidirectional associations may exist between the three clusters of independent variables and the dependent variables. Specifically, cluster 1 may affect cluster 2, cluster 3 and the two dependent variables of health service utilization. Similarly, cluster 2 may impact cluster 3 as well as the two dependent variables, while cluster 3 may affect the dependent variables. As a result, simultaneous consideration of variables from the clusters in a free multiple regression model (i.e. a free forward stepwise logistic regression model) might result in confounded inference. Therefore, clustered logistic regression [43] was adopted to analyze whether the addition of HRQOL variables to the models including socio-demographic and health-related variables could significantly increase the explanatory power of the risk-adjustment models. The two health service utilization variables were regressed on the three clusters of independent variables. The final regression model was determined in three steps: (1) A forward stepwise regression of health service utilization for the cluster 1 variables; (2) A forward stepwise regression for the cluster 2 variables with the equation derived from step 1 as a fixed part of the new regression model; (3) A forward stepwise regression for the cluster 3 variables with the equation derived from step 2 as a fixed part of the new regression model.

The independent effect of each cluster on the dependent variable was calculated by the difference in the corresponding R2 values between the two regression models (with vs. without the cluster). The independent contribution share of each cluster was calculated as individual R2 change/total R2 change in the final model × 100%. In logistic regression models, the R2 is the Nagelkerke `pseudo' R2 which is similar to the classical R2 in linear regression models for data interpretation [43].

Results for the pooled data in the whole study cohort are presented, because similar results were observed among subjects from the three factories in exploratory analyses.

Results

Participant characteristics

Descriptive statistics on study variables are presented in Table 1. Subjects aged between 16 and 59 years, with an average of 31.4±9.2 (SD) years. Most participants were 26–35 years of age, married, had worked less than 5 years (88.5%), had completed middle school education, had medical insurance (84.2%), normal weight (64.8%), and didn't report any chronic disease (91.7%). The recent-month self-reported morbidity was 67.7%. HRQOL scores in the eight domains varied from 70.6 ± 18.5 to 93.8±10.9. General health scored the lowest, while physical functioning scored the highest.

Table 1 Characteristics of study participants (n = 1,438 female rural-to-urban migrant workers) in Shenzhen-Dongguan economic zone, China 2013

Associations of HRQOL's eight domains with health service utilization

Higher HRQOL scores in BP and GH domains were associated with significantly lower odds of recent two-week physician visit (Table 2). That is, the risk of recent two-week physician visit increased significantly with decreasing HRQOL scores in BP and GH domains. The risk of hospitalization during the last 1-year increased significantly with decreasing HRQOL scores in RP and GH domains.

Table 2 Associations of HRQOL domains with health service utilization a

Determinants of health service utilization

In the first cluster (social demographic), all variables were not significantly associated with two-week physician visit (Table 3). In the second cluster (health related variables), recent-month self-reported morbidity and the presence of chronic disease were positively associated with the likelihood of recent two-week physician visit. The independent contribution of health-related variables was 84.4%. The risk of recent two-week physician visit decreased significantly with increasing HRQOL scores in BP (adjusted OR = 0.71 per SD increase, p = 0.001) and GH (adjusted OR = 0.76 per SD increase, p = 0.01) domains. The independent influence of BP and GH on the risk of recent two-week physician visit was 15.6%.

Table 3 Clustered logistic regression models explaining recent 2-week physician visit by socio-demographic characteristics (cluster 1), health-related factors (cluster 2) and HRQOL domains (cluster 3)

In the first cluster, social demographic variables, marital status and duration of employment were associated with annual hospitalization (Table 4). The independent contribution from socio-demographic variables was 78.8%. In the second cluster, surprisingly, health-related variables were not associated with annual hospitalization. The risk of annual hospitalization decreased significantly with increasing HRQOL scores in RP (adjusted OR = 0.75 per SD increase, p = 0.003) and GH (adjusted OR = 0.64 per SD increase, p< 0.001) domains. The independent influence of RP and GH on the risk of annual hospitalization was 21.2%.

Table 4 Clustered logistic regression models explaining annual hospitalization by socio-demographic characteristics (cluster 1), health-related factors (cluster 2) and HRQOL domains (cluster 3)

Discussion

Main findings

To the best of our knowledge, this is the first study demonstrating an association between HRQOL and health service utilization in Chinese rural-to-urban female migrant workers. The results revealed that health service utilization increased modestly and significantly with decreasing scores in certain HRQOL domains. Nevertheless, the independent influence of the HRQOL was smaller than that of socio-demographic variables on annual hospitalization, and than that of health-related variables on recent 2-weeks physician visit. The strong association of hospitalization with socio-demographic variables and the lack of association with health related variables suggest that affordability may be a major determinant in the use of hospitalization care for female migrant workers.

Comparisons with previous studies

We found that HRQOL was a significant independent predictor of health service utilization in Chinese female rural-to-urban migrant workers. This finding is consistent with the findings in previous reports on the impact of HRQOL on health service utilization in other study populations [13],[23]-[25],[44]. In the USA, Nelson and colleagues reported that physical functioning and mental health were two important predictors of both clinic consultation and hospitalization in patients with chronic diseases [23]. Dominick and colleagues reported that HRQOL, especially pain frequency, could be an invaluable tool for evaluation of future health service utilization among patients with osteoarthritis [25]. In northern Europe, Miilunpalo and colleagues found that HRQOL was associated with annual outpatient consultation in a working-age population in Finland [44]. In Hong Kong, Lam CL and colleagues reported that HRQOL was a more important determinant of outpatient consultation than chronic morbidity and socio-demographic factors [24]. In contrast, we found that the independent influence of HRQOL on health service utilization was smaller than that of social-demographic or health related variables. The differential findings may be due to different study context–Hong Kong is quite different from mainland China in socioeconomic environment and health care system. In China, Chen T and colleagues showed that the number of monthly outpatient consultations increased significantly with decreasing HRQOL scores in PF, BP and GH domains, while annual hospitalization rate increased significantly with decreasing scores in PF and GH domains in primary care patients [13]. In contrast, among female migrant workers, our study showed that the likelihood of two-week physician visit increased significantly with decreasing scores in BP and GH domains, while the risk of annual hospitalization increased significantly with decreasing scores in RP and GH domains. Therefore, our findings are consistent with those of Chen's study with respect to the impact of GH on health service utilization, but somewhat different with respect to the roles of PF and RP domains. The differential findings might be due to different study populations: primary care patients in Chen T's study vs. mostly healthy rural-to-urban migrant workers in our study. Besides, all the 4 domains - physical functioning (PF), role physical (RP), bodily pain (BP) and general health (GH) are the measures of physical health [38]. Taken together, it seems that physical health domains as measured by SF-36 are important predictors of health service utilization in Chinese population. In both studies, the independent influence of the SF-36 domains on health service utilization was observed to be smaller than that of other clusters of variables (social-demographic or health- related). Similar to the findings in Zhu CY's study [27], the observed HRQOL scores in most domains (PF, BP, GH, VT, SF) were lower in rural-to-urban migrant female workers in our study population as compared to the norms for Chinese women [32].

Limitations

Self-reported information is prone to inaccuracies. Nelson and colleagues reported that 5% of subjects over-reported clinic consultations over the last one month, and that self-reported health service utilization could not be taken as actual health service utilization, but only as an indicator of utilization pattern [23]. We expected that such errors in self-reports were random and would not affect the validity of the comparisons. We have data on education, but no data on income which is another important dimension of socioeconomic status associated with HRQOL [45]. Income reporting is a sensitive issue and highly unreliable in China. Nevertheless, it is known that rural-to-urban migrant light-duty manual workers are low-pay workers in urban areas. In addition, we have data on duration of employment which may partly reflect their relative income levels. Our findings are suggestive rather than definitive. We could not draw a firm causal inference from an observational study. HRQOL, health status and health service utilization are inter-correlated, and it is difficult to disentangle causes vs. effects. We do not have detailed information on the specific diseases the study subjects had at the time of investigation, reasons of hospitalization, types of insurance, cost for health service utilization and burdens from out-of-pocket payment. It would be of interest to have these variables in future studies for a more in-depth understanding on the association between HRQOL and health service utilization in Chinese female rural-to-urban migrant workers. The study was based on a sample of rural-to-urban migrant women workers in an affluent economic region. We could not assume that the findings are applicable to other regions without additional validation studies. Nevertheless, it is likely that similar associations may be observed in female rural-to-urban migrant workers in other urban areas in China as they share some similarities in much lower socioeconomic status as compared to local urban residents.

Conclusions

Lower HRQOL scores in bodily pain, role physical and general health domains were associated with more frequent health service utilization in Chinese female rural-to-urban migrant workers. The findings suggest that HRQOL may have a modest influence on health service utilization in this underprivileged population in urban China.

Authors' contributions

All authors contributed to the development of the study framework, interpretation of the results, revisions of successive drafts of the manuscript, and approved the version submitted for publication. CHL and PXW conducted the data analyses. CHL drafted the manuscript, PXW and ZC Luo finalized the manuscript with inputs from all authors.

Abbreviations

HRQOL:

Health-related quality of life

PF:

Physical functioning

RP:

Role-physical

BP:

Bodily pain

GH:

General health

VT:

Vitality

SF:

Social functioning

RE:

Role-emotional

MH:

Mental health

OR:

Odds ratio

CI:

Confidence interval

References

  1. 1.

    Yao HS, Xu XJ, Xue DS: Progress of the research on the floating population in China (Chinese). Urban Probl 2008, 6: 69–76.

  2. 2.

    Yuan XM: Empirical analysis on current trait and problems of urban floating population in Shanghai (Chinese). East China Econ Manage 2008, 7: 4–8.

  3. 3.

    Li C, Li XS: The utilization and influential factors of community health services in migrant population in China (Chinese). Chinese Health Serv Manage 2010, 6: 422–424.

  4. 4.

    National Bureau of Statistics China: National migrant workers monitoring report in 2013., retrieved May 12, 2014., [http://www.stats.gov.cn/tjsj/zxfb/201405/t20140512_551585.html]

  5. 5.

    National Bureau of Statistics China: National economy and society developed statistical bulletin of the People' Republic of China in 2013., retrieved February 24, 2014., [http://www.stats.gov.cn/tjsj/zxfb/201402/t20140224_514970.html]

  6. 6.

    Song WY: A Study of Health Status of Female Migrant Workers and Relevant Factors (Chinese). Master Thesis. Suzhou University, Applied Sociology Department, Suzhou, China; 2006.

  7. 7.

    Li RF: On the formation of the dual household registration system in China (Chinese). Xi Nan Ke Ji Univ J 2007, 6: 16–19.

  8. 8.

    Jiang MH, Wang GY: The social integration of rural female floating population with cities - investigations and considerations in Henan Province (Chinese). Chinese Women's Acad Shandong Col J 2009, 3: 22–25.

  9. 9.

    Mein G, Martikainen P, Stansfeld SA, Brunner EJ, Fuhrer R, Marmot MG: Predictors of early retirement in British civil servants. Age Ageing 2000, 29: 529–536. 10.1093/ageing/29.6.529

  10. 10.

    Li H, Xie JY, Yang XH, Lin RB, Zhou HB, Peng ZR, Peng J: Investigation on Health Service Requirements and Utilization in Yantian District of Shenzhen (Chinese). In Proceedings of the Seminar of Shenzhen Preventive Medicine Association, 1 December 2012. Chinese Academic Journal Electronic Publishing House, Shenzhen, China; 2012.

  11. 11.

    Chen MM, Lin JY, Lin BB, Li GX, Chen Q: Investigation on health service utilization in one district in Guangzhou City (Chinese). Pract Med J 2009, 14: 2364–2366.

  12. 12.

    Dai M: Study on Prevalence of Chronic Diseases, Demands of Health Services of Young and Middle age Civilians in Guangdong Province (Chinese). PhD Thesis. Southern Medical University, Epidemiology and Health Statistics Department, Guangzhou, China; 2008.

  13. 13.

    Chen T, Li L: Influence of health-related quality of Life on health service utilization in addition to socio-demographic and morbidity variables among primary care patients in China. Int J Public Health 2009, 54: 325–332. 10.1007/s00038-009-0057-3

  14. 14.

    Sun YJ, Qin T: The status of rural women migrants (Chinese). China Market 2009, 14: 77–78.

  15. 15.

    Hopman WM, Berger C, Joseph L, Barr SI, Gao Y, Prior JC, Poliquin S, Towheed T, Anastassiades T: The association between body mass index and health-related quality of life: data from CaMos, a stratified population study. Qual Life Res 2007, 16: 1595–1603. 10.1007/s11136-007-9273-6

  16. 16.

    Wang LH, Zheng J, Ren LP, Shu AQ: Health status of migrant women and improvement strategies (Chinese). Chin Community Doctors 2012, 12: 408–410.

  17. 17.

    Zhang ZF: Analysis of rural migrant women's psychological harmony (Chinese). J Shanxi Provincial Party School Communist Party China 2007, 30: 55–57.

  18. 18.

    Development of the World Health Organization WHOQOL-BREF quality of life assessment Psychol Med 1998, 28: 551–558. 10.1017/S0033291798006667

  19. 19.

    Hwang HF, Liang WM, Chiu YN, Lin MR: Suitability of the WHOQOL-BREF for community dwelling older people in Taiwan. Age Ageing 2003, 32: 593–600. 10.1093/ageing/afg102

  20. 20.

    Xiang YT, Wang CY, Wang Y, Chiu HF, Zhao JP, Chen Q, Chan SS, Lee EH, Ungvari GS: Socio-demographic and clinical determinants of quality of life in Chinese patients with schizophrenia: a prospective study. Qual Life Res 2010, 19: 317–322. 10.1007/s11136-010-9593-9

  21. 21.

    Gautam Y, Sharma A, Agarwal A, Bhatnagar M, Trehan RR: A cross-sectional study of QOL of diabetic patients at Tertiary Care Hospitals in Delhi. Indian J Community Med 2009, 34: 346–350. 10.4103/0970-0218.58397

  22. 22.

    Venekamp RP, Bonten MJ, Rovers MM, Verheij TJ, Sachs AP: Systemic corticosteroid monotherapy for clinically diagnosed acute rhinosinusitis: a randomized controlled trial. CMAJ 2012, 184: E751-E757. 10.1503/cmaj.120430

  23. 23.

    Nelson EC, McHorney CA, Manning WG Jr, Rogers WH, Zubkoff M, Greenfield S, Ware JE Jr, Tariov AR: A longitudinal study of hospitalization rates for patients with chronic disease: results from the Medical Outcomes Study. Health Serv Res 1998, 32: 759–774.

  24. 24.

    Lam CL, Fong DY, Lauder IJ, Lam TP: The effect of health-related quality of life (HRQOL) on health service utilisation of a Chinese population. Soc Sci Med 2002, 55: 1635–1646. 10.1016/S0277-9536(01)00296-9

  25. 25.

    Dominick KL, Ahern FM, Gold CH, Heller DA: Health-related quality of life and health service use among older adults with osteoarthritis. Arthritis Rheum 2004, 51: 326–331. 10.1002/art.20390

  26. 26.

    Sarna L, Bialous SA, Cooley ME, Jun HJ, Feskanich D: Impact of smoking and smoking cessation on health-related quality of life in women in the nurses' health study. Qual Life Res 2008, 17: 1217–1227. 10.1007/s11136-008-9404-8

  27. 27.

    Zhu CY, Wang JJ, Fu XH, Zhou ZH, Wang CX: Correlates of quality of life in China rural–urban female migrate workers. Qual Life Res 2012, 21: 495–503. 10.1007/s11136-011-9950-3

  28. 28.

    Liu Y, Liu L, Sun W, Shan GL, Wang ZZ: Survey on the quality of life and related factors among farmer workers in Hubei province (Chinese). Zhong Hua Liu Xing Bing Xue Za Zhi 2011, 32: 481–484.

  29. 29.

    Chen CM: Overview of obesity in Mainland China. Obes Rev 2008, 9(Suppl 1):14–21. 10.1111/j.1467-789X.2007.00433.x

  30. 30.

    Ware JE, Snow KK, Kosinski M, Gandek B: SF-36 Health Survey Manual and Interpretation Guide. New England Medical Center, Boston; 1993.

  31. 31.

    Liu C, Li N, Ren X, Li J, Zhang J, Sun D: Feasibility of using short form 36 in Chinese population (Chinese). Hua Xi Yi Ke Da Xue Xue Bao 2001, 32: 39–42.

  32. 32.

    Li NX, Liu CJ, Li J, Ren XH: The norms of SF-36 scale scores in urban and rural residents of Sichuan province (Chinese). Hua Xi Yi Ke Da Xue Xue Bao 2001, 32: 43–47.

  33. 33.

    Lam CL, Lauder IJ, Lam TP, Gandek B: Population based norm of the Chinese (HK) version of the SF-36 health survey. Hong Kong Practitioner 1999, 21: 460–470.

  34. 34.

    McHorney CA, Ware JE Jr, Lu JF, Sherbourne CD: The MOS 36-item short-form health survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med Care 1994, 32: 40–66. 10.1097/00005650-199401000-00004

  35. 35.

    McHorney CA, Ware JE Jr, Raczek AE: The MOS 36-item short-form health survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Med Care 1993, 31: 247–263. 10.1097/00005650-199303000-00006

  36. 36.

    Garber CE, Greaney ML, Riebe D, Nigg CR, Burbank PA, Clark PG: Physical and mental health-related correlates of physical function in community dwelling older adults: a cross sectional study. BMC Geriatr 2010, 10: 6. 10.1186/1471-2318-10-6

  37. 37.

    Lam CL, Gandek B, Ren XS, Chan MS: Tests of scaling assumptions and construct validity of the Chinese (HK) version of the SF-36 health survey. J Clin Epidemiol 1998, 51: 1139–1147. 10.1016/S0895-4356(98)00105-X

  38. 38.

    Manderbacka K: Examining what self-rated health question is understood to mean by respondents. Scand J Soc Med 1998, 26: 145–153.

  39. 39.

    Kaplan G, Baron-Epel O: What lies behind the subjective evaluation of health status? Soc Sci Med 2003, 56: 1669–1676. 10.1016/S0277-9536(02)00179-X

  40. 40.

    Martikainen P, Aromaa A, Heliövaara M, Klaukka T, Knekt P, Maatela J, Lahelma E: Reliability of perceived health by sex and age. Soc Sci Med 1999, 48: 1117–1122. 10.1016/S0277-9536(98)00416-X

  41. 41.

    Kartikainen P, Moustgaard H, Murphy M, Einiö E, Koskinen S, Martelin T, Noro A: Gender, living arrangements, and social circumstances as determinates of entry into and exit from long-term institutional care at older ages: A 6-year follow-up study of Finns. Gerontologist 2009, 49: 34–45. 10.1093/geront/gnp013

  42. 42.

    Cohen YC, Rubin HR, Freedman L, Mozes B: Use of a clustered model to identify factors affecting hospital length of stay. J Clin Epidemiol 1999, 52: 1031–1036. 10.1016/S0895-4356(99)00079-7

  43. 43.

    Nagelkerke NJD: A note on a general definition of the coefficient of determination. Biometrika 1991, 78: 691–692. 10.1093/biomet/78.3.691

  44. 44.

    Miilunpalo S, Vuori I, Oja P, Pasanen M, Urponen H: Self-rated health status as a health measure: the predictive value of Self-Reported health status on the use of physician services and on mortality in the Working-Age population. J Clin Epidemiol 1997, 50: 517–528. 10.1016/S0895-4356(97)00045-0

  45. 45.

    Ross NA, Garner R, Bernier J, Feeny DH, Kaplan MS, McFarland B, Orpana HM, Oderkirk J: Trajectories of health-related quality of life by socio-economic status in a nationally representative Canadian cohort. J Epidemiol Community Health 2012, 66: 593–598. 10.1136/jech.2010.115378

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Acknowledgements

This study was supported by a research grant from Liaobu Social Service Centre, Dongguang, and Baoan Xixiang Community Health Administration Centre, Shenzhen, China. We gratefully acknowledged Songtao Tang, Jianhu Zhong for their excellent work in study coordination, data collection and management, and Xin Wang, Weiquan Lin, Junhua Chen for their kind assistance in data collection.

Author information

Correspondence to Pei-Xi Wang or Zhong-Cheng Luo.

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The authors declare that they have no competing interests.

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Keywords

  • Health-related quality of life
  • Health service utilization
  • Female migrant worker
  • Chinese