Study design
Hangzhou is a well-developed city in China, which had a population of 7 million in 2012, per capital and total gross domestic products of 88,985 CNY (12,940.67 USD) and 780,398 billion CNY (113,489.58 USD), respectively [20], with a migrant worker population of 2.44 million, accounting for 57.5% of the province’s total population [21]. A population-based cross-sectional survey was conducted from May to August 2013 in Hangzhou, Zhejiang Province, China. Two-stage stratified sampling method was employed. First, Xihu District and Binjiang District, according to their economic status and the scale of migrant population, were selected as the study site from 13 districts in Hangzhou. Then, one sub-district was randomly selected from these two districts respectively, they were Sandun sub-district in Xihu District and Puyan sub-district in Binjiang District. Finally, two communities, Lilan and Houchengqiao in Sandun and Zhijiang and Lianzhuang in Puyan, were randomly recruited from each sub-district. All older adults met the inclusion criteria were recruited from these four communities.
Participants
Participants were consisting of urban-to-urban and rural-to-urban migrant older adults. Urban-to-urban migrant older adults refers to those aged 60 years and above who flowed from other cities to inflow areas (Hangzhou) above 6 months as temporary residents with non-agricultural household registration rather than permanent residents. Rural-to-urban older adults refers to those aged 60 years and above who flowed from counties to inflow areas (Hangzhou) above 6 months as a temporary resident with agricultural household registration rather than permanent residents.
Recruitment process was shown in Fig. 1. All participants should met the following inclusion criteria: i) being aged 60 years old and above; ii) not being a registered and permanent residence in Hangzhou; iii) having lived in Hangzhou more than 6 months; and iv) being able to read, write, and communicate in Chinese, and not having a cognitive disorder. Exclusion criteria were: i) having not finish a half of a questionnaire; ii) illogical questionnaire (a questionnaire that participants answered inconsistent on particular questions); iii) being lived in Hangzhou more than 20 years. A total of 1521 participants met these inclusion criteria and enrolled, 1316 of them completed a face-to-face interview. A final total of 1000 questionnaires were deemed valid after performing a quality check. Thus, the response rate was 86.5% and rate of valid questionnaires was 76.0%.
Ethical approval
Informed consent was obtained from participants in the form of verbal agreement and ethical approval for the study has been proved by Zhejiang University Ethical Committee (NO. ZGL201608–1).
Measures
Socio-demographic characteristics
Socio-demographic characteristic included gender (male, female), age (60 to 69 years old, 70 years old or above), marital status (in marriage, not-in-marriage), educational attainment (primary school or below, junior high school or above), mainly economic source (oneself or spouse, offspring or others), years living in local city (6 months to 1 year, one to three years, three to six years, and six to twenty years) and weight status (low weight: BMI < 18.5; normal: 24 > BMI ≥ 18.5; overweight: 28 > BMI ≥ 24; and obesity: BMI ≥ 28). Weight status was rated by body mass index (BMI, a participant’s bodyweight in kilograms divided by body height in squared meters).
Cognitive social capital
“Social capital refers to a sense of community embeddedness, which is in part reflected by group membership, civic participation, and perceptions of trust, cohesion, and engagement” [22]. Cognitive social capital as a proxy of social capital has been widely accepted in the literature [23]. Social trust and reciprocity were used as a proxy for cognitive social capital [16]. The Cronbach’s Alpha of social trust was 0.77. Kaiser-Meyer-Olkin (K-M-O) was 0.83 (p < 0.01). The Cronbach’s Alpha of reciprocity was 0.8. Kaiser-Meyer-Olkin (K-M-O) was 0.77 (p < 0.01). The scale for measuring social capital and social trust had a good reliability and validity. All items in the Cognitive Social Capital Scale were valid. A 5-point Likert scale was used to measure the degree of agreement to each item of social trust and reciprocity. The responses were collapsed into 5-point scale: 1 = strongly disagree, 2 = disagree, 3 = neutrality, 4 = agree, and 5 = strongly agree. Higher total scores indicated higher social trust and reciprocity.
Social integration
“Social integration refers to the process in which individuals come together as a whole in a community through assimilation” [24], which can be seen as a dynamic and structured process in which all members communicate well with each other to achieve and maintain peaceful social relations. We adopted 10 items to reflect social integration of migrant older adults in this study. A 5-point Likert Scale was used to measure the degree of agreement in each item. Responses were collapsed into dichotomous outcomes: 1 = strongly disagree, 2 = disagree, 3 = neutrality, 4 = agree, and 5 = strongly agree. Higher total scores indicated higher social integration. The Cronbach’s Alpha of social integration was 0.62. Kaiser-Meyer-Olkin (K-M-O) was 0.80 (p < 0.01). Unless a item v) my family members always quarrel with me for future living arrangements, the remaining items in the Social Integration Scale were valid.
Physical health and mental health
Chinese version 36-item Short Form Health Survey (SF-36) was used as a comprehensive proxy of health. Calculation of SF-36 can be acquired in published literature [25]. This scale consists of eight components, including Physical Function (PF), Role Physical (RP), Bodily Pain (BP), General Health (GH), these four components constitute Physical Health (PH); the remaining are Vitality (VT), Social Function (SF), Role Emotional (RE), and Mental Health (MH), these four components constitute Mental Health (MH). Higher SF-36 score indicates a better health.
Statistical analysis
Physical health and mental health were dependent variables, category of migrant older adults be designed as the independent variable. Gender, age, marital status, educational attainment, mainly economic source, years living in local city, weight status, cognitive social capital, and social integration were analyzed in this study as the covariates. Frequencies were calculated to describe the participants’ socio-demographic characteristics. Exploratory factor analysis was adopted to measure the validity of scales. Multivariate analysis of variance (MANOVA) was used to evaluate differences in physical health and mental health between urban-to-urban and rural-to-urban migrant older adults. Student’s t-tests and Univariate Analysis of Variance (ANOVA) were employed to evaluate difference in physical health and mental health by gender, age category, mainly economic source, marital status, educational attainment, years living in local city, and weight status. Multiple linear regression was adopted to explore relationships between migrant older adults and health (physical health and mental health) after controlling socio-demographic characteristics. Statistical analyses were conducted using SPSS version 18.0 for Windows.