The data was obtained from the China Migrant Dynamic Survey (CMDS) in 2017 provided by the Migrant Population Service Center. CMDS is an annual national sample survey of the internal migrants 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. Because Beijing, Tianjin and Shanghai do not have an interprovincial migrant population, it is impossible to compare the differences between the intra-provincial and interprovincial groups, so the samples from these three cities were excluded. After the quality audit, 122665 people were finally included in this study. 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.
Utilization of NEPHS
Awareness of NEPHS (ANEPHS) is a prerequisite for NEPHSU . ANEPHS was set as a dependent variable. The question was "Have you heard of the NEPHS" and the answer was "yes or no". Another outcome variable is establishment of health records (EHR). The question was "Have you established health records at the destination" and the answer was "yes or no". EHR is one of the service priorities and reflects the actual use of services by the migrant population.
Demographic variables included in this study included gender, community type, MR, and REDL. The community types were divided into urban and rural areas. The MR was divided into group inter-province and intra-province. According to the provincial GDP per capita division published by the National Bureau of Statistics in 2017, the top 10 were classified as group affluent (Beijing, Tianjin and Shanghai were excluded), the middle 10 as group medium, and the last 11 as group poor.
Education and household income are often used as indicators of SES. In this study, education was divided into four categories according to years of education: ≤6 years, 7-9 years, 10-12 years and >12 years. Considering that relative income can better reflect the impact of income gap when regional economic development levels are unbalanced , the household income in this study is in the form of relative income, that is, the annual household income divided by the per capita GDP of the locality.
SC refers to the resources and benefits received through connections with others, either as individuals or groups, it can be distinguished into two dimensions: SSC and CSC . SSC refers to the presence of formal opportunity structures or activities in which individuals build or strengthen their social connections; CSC generally refers to individuals' perceptions, beliefs, and attitudes toward their social surroundings, with corresponding measures focused mainly on the concepts of generalized and particularized trust . In this study, SC was limited to the destination, and it was a localized SC that reflects the social resources available to the migrant population there.
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", "since 2016, have you participated in property donation, blood donation, volunteer activities, etc.", "since 2016, have you reported the situation/put forward policy suggestions to relevant government departments in various ways ", "since 2016, have you posted online comments on national affairs and social events or participated in related discussions", "since 2016, have you participated in party/youth league organization activities and party branch meetings". The answer to each question was "1= no, 2 = occasionally, 3 = sometimes, and 4 = often", the score ranged from 5 to 20. 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", the score ranged from 0 to 7. According to the distribution characteristics of scores, civic participation was integrated into 5 levels: 1 (5 points), 2 (6 points), 3 (7 points), 4 (8 points), 5 (9-20 points). Social participation was also integrated into 5 levels: 1 (0 points), 2 (1 points), 3 (2 points), 4 (3 points), 5 (4-6 points). The spearman correlation coefficient of civic participation and social participation was 0.366 (p<0.001), and the SSC grade can be obtained by adding the two grades, and 7 grades were: 1 (2 points), 2 (3 points), 3 (4 points), 4 (5 points), 5 (6 points), 6 (7 points), 7 (8-10 points).
The CSC generally refers to IMs' perceptions, beliefs, and attitudes toward their destination, with corresponding measures focused mainly on the concepts particularized trust. There were 5 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". The answer to each question was "1=totally disagree, 2=disagree, 3=basically agree, and 4=totally agree". The α of CSC was 0.779. According to the distribution of scores, CSC was divided into 7 levels: 1 (5-14 points), 2 (15 points), 3 (16 points), 4 (17 points), 5 (18 points), 6 (19 points), 7 (20 points).
SPSS 22.0 was used for data analysis. First of all, we calculated the mean value and standard deviation of the included continuous variables, and counted the frequency distribution of different subgroups of classified variables (Table 1), which comprehensively described the basic situation of the sample. Secondly, we took household income, education and SC as dependent variables, MR and REDL as factors to conduct interaction analysis (Table 2) to verify hypothesis 1 and 2. Thirdly, we used univariate analysis to verify whether gender, education, community type, MR, REDL and SC had significant impacts on ANEPHS and EHR one by one. The verification methods included cross-table, chi-square test and independent sample t test. Fourthly, we used multiple line charts to visually present the cross relationship among MR, REDL, SC and NEPHSU (Figure 1-4). Finally, we used gender, education, community type and household income as the control variables, MR and REDL as the moderating variables, and ANEPHS and EHR as the dependent variables for a hierarchical logistic regression analysis (Table 3-4) to discuss the degree and direction of the interaction among MR, REDL, SC and NEPHSU.