Data
The data was obtained from the CMDS in 2017 provided by the Migrant Population Service Center. CMDS is an annual national sample survey of the IMs organized by the NHC from 2009, with an annual sample size of approximately 200,000 households. CMDS adopts the layered, multi-stage, and proportional to scale PPS (Probability proportional to size) sampling method. This study adopted the individual questionnaire A of CMDS, which was uniformly printed and distributed by the NHC. The questionnaire A includes basic information about respondent's demography, perception of the destination, the state of social interaction, and utilization status of NEPHS, etc. Full-time investigators collected the questionnaire data through household interviews, and each respondent gave informed consent before commencing the interview. Dates were entered through the migrant population health and household planning dynamic monitoring system, input data was subjected to multiple checks to ensure quality. The respondents consisted of IMs aged 15-59 living in the destination for more than one month.
In this study, the inclusion conditions were set as "18-59 years of age, residence duration more than one year". Beijing, Tianjin and Shanghai, the three cities have only inter-provincial IMs, which do not meet the object of this study, so the samples of these three cities were excluded. After the quality audit, 115412 people were finally included. In addition, we introduce GDP per capita to reflect the REDL of each provincial region, and GDP per capita is based on 2017 data from the National Bureau of Statistics.
Measurement
Utilization of NEPHS
Awareness of NEPHS is a prerequisite for NEPHS utilization [26]. Awareness of NEPHS was set as an outcome variable, and the question was "Have you heard of the NEPHS" and the answer was "yes or no". Another outcome variable was establishment of health records (EHR). EHR is one of the service priorities and reflects the actual utilization of NEPHS by the IMs. The question was "Have you established health records at the destination" and the answer was "yes or no".
Demographic variables
Demographic variables included sex, residence duration, community type, MR, and REDL. The residence duration was divided into three groups: ≤3 years, 3-10 years and ≥10 years. The community types were divided into urban and rural communities. The MR was divided into inter-provincial and intra-provincial migration. The REDL was divided into two groups according to per capita GDP in 2017, five provinces with a per capita GDP of more than RMB 70,000 were called HIPs, and the other 23 provinces were called LMIPs.
Soical Capital
SC refers to the resources and benefits received through connections with others, either as individuals or groups, it can be distinguished into two dimensions: cognitive social capital (CSC) and and structural social capital (SSC) [20]. The CSC generally refers to IMs' perceptions, beliefs, and attitudes toward their destination, with corresponding measures focused mainly on the concepts of generalized and particularized trust [36]. In this study, SC was limited to the destination, and it was a localized SC that reflects the social resources available to the IMs there. There were 6 questions in the survey: "I like the city/place I live now", "I am concerned about the changes in the city/place I live now", "I am very willing to blend with the local people and become a part of them", "I think the local people are willing to accept me as a part of them", "I feel locals look down on outsiders" (reverse scoring) and "I feel like I'm already a local". The answer to each question was "1=totally disagree, 2=disagree, 3=basically agree, and 4=totally agree". The Internal consistency coefficient α =0.786. According to the distribution of scores, CSC was divided into 8 levels: 1 (6-14 points), 2 (15-16 points), 3 (17 points), 4 (18 points), 5 (19-20 points), 6 (21-22 points), 7 (23 points), 8 (24 points).
SSC refers to the presence of formal opportunity structures or activities in which individuals build or strengthen their social connections [36]. The SSC of this survey included civic participation and social participation in the destination. Questions of the former were: since 2016, "have you made suggestions to your unit/community/village or supervised the unit/community/ village affairs management", " have you participated in property donation, blood donation, volunteer activities, etc.", "have you reported the situation/put forward policy suggestions to relevant government departments in various ways ", "have you posted online comments on national affairs and social events or participated in related discussions", "have you participated in party/youth league organization activities and party branch meetings". Respondents were assigned a "yes" if they participated in any of these tasks, and a "No" if they did not. The question of social participation was "Have you participated in any of the following activities in the past year: trade unions, volunteer associations, homecoming associations, fellow-students association, home town chamber of commerce, others". Respondents were assigned a "yes" if they participated in any of these organizations, and a "No" if they did not.
Statistical analysis
First, we describe the distribution characteristics of all the included variables (Table 1). Secondly, cross-table and chi-square tests were used to verify the influence of sex, residence duration, community type, MR and REDL on IMs' awareness of NEPHS and EHR (Table 2). Thirdly, we examined the interaction of MR and REDL on the IMs' SC and NEPHS utilization, statistical methods including two-way ANOVA and logistic regression analysis (Table 3, 4). Fourthly, we used sex, residence duration, community type as the control variables, MR and REDL as the moderating variables, and awareness of NEPHS and EHR as the dependent variables for a hierarchical logistic regression analysis (Table 5) to discuss the degree and direction of the interaction of MR, REDL, SC on IMs' NEPHS utilization. Finally, we conducted grouping logistic regression analysis according to REDL (Table 6), sex, residence duration, community type as the control variables, MR as the moderating variables, and awareness of NEPHS and EHR as the dependent variables. Sampling weights were included in all analyses to adjust for the complex survey design. In logistic regression analysis model, Odds ratios (OR) were presented. All the analyses were performed using SPSS 22.0.