Citation
Social Capital and Suicidal Behaviors among Rural-To-Urban Migrants in China

Material Information

Title:
Social Capital and Suicidal Behaviors among Rural-To-Urban Migrants in China
Creator:
Yu, Bin
Publisher:
University of Florida
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Epidemiology
Committee Chair:
Chen,Xinguang
Committee Co-Chair:
Cottler,Linda B
Committee Members:
Striley,Catherine L
Cook,Robert L
Bussing,Regina
Graduation Date:
5/1/2020

Subjects

Subjects / Keywords:
attempt
ideation
migrants
suicide
Genre:
Unknown ( sobekcm )

Notes

General Note:
Suicide represents a vital public health challenge among migrant populations all over the world. Social capital, sum of the part of personal network connections that are durable, trustworthy, reciprocal and resource-rich, has been related to suicide at the ecological and individual level with data from different populations. However, such associations have not been tested among rural-to-urban migrants in China, currently totaling over 280 million. In this dissertation research, I proposed three studies to examine (1) whether the prevalence rates of suicidal behaviors were higher among rural migrants than non-migrant rural and urban residents; (2) the association between social capital and suicidal behaviors among rural migrants; and (3) the mechanism mediating the association between social capital and suicidal behaviors through employment and mental health. The dissertation research analyzed data from a probability sample of rural migrants and rural and urban residents recruited using GIS/GPS-assisted sampling methods. Suicidal ideation and attempt during the past 12 months were used as outcomes in all three studies. Social capital was measured using the reliable and valid Personal Social Capital Scale (Study 2-3). Employment uncertainty and anxiety were also measured (Study 3). Survey statistical methods of linear and logistic regression were used to consider the sampling design, unequal probabilities, and sampling weight. Study 1 findings indicate that the prevalence rates of suicidal ideation and attempts were higher among rural migrants than non-migrant rural and urban residents. Study 2 found a significant negative association between social capital and suicidal ideation and attempts with a positive interaction between social capital and years of migration. Study 3 revealed a complex chained mediation mechanism in which employment uncertainty and anxiety consecutively mediated the association between social capital and suicidal ideation and attempts. Findings of these studies demonstrate a higher risk of suicide and suggest more attention should be paid to suicidal behaviors among rural migrants. Social capital played a protective role on suicide among rural migrants, particularly in the early years of migration. Furthermore, social capital may exert its effect on suicidal behaviors through employment and mental health. These study findings deepen the understanding of suicide among migrants and provide evidence for devising and implementing social capital-based interventions targeting suicide among migrants.

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Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Embargo Date:
11/30/2020

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SOCIAL CAPITAL AND SUICIDAL BEHAVIORS AMONG RURAL TO URBAN MIGRANTS IN CHINA By BIN YU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2019

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© 2019 Bin Yu

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To my parents for their love and support

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4 ACKNOWLEDGMENTS It has been a long and not easy journey for me to pursue the PhD degre e , and I give my greatest gratitude to my mentor, Dr. Xinguang Chen, who guided me not only in the academy but also in life. I t hank for his selfless assis tance and mentorship during the past eight years since we first met in Wuhan, China . Without his guidance, I would not have been able to make so many achievements. I would also like to thank my dissertation committee members. I thank Dr. Linda B. Cottler for her expertise in psychiatric epidemiology and support for my research. I thank Dr. Catherine W. Striley for her keen ey e for details and assistance in improving the studies. I thank Dr. Regina Bussing for her rich research experience in psychia try and helpful suggestions for my research. I thank Dr. Robert L. Cook for his thoughtful suggestions during the data analyses and results interpretation. This dissertation would not be possible without their support. I thank my friends and colleagues a t the University of Florida within and outside of the Department of Epidemiology . T heir companionship has kept me going over the past few years. It would be tougher without these caring friends. I thank my parents, family, relatives , and friends in China for their support and encouragement of my Ph .D. life. The frequent communication with them always make s me more confident and at ease.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 11 LIST OF ABBREVIATIONS ................................ ................................ ........................... 13 ABSTRACT ................................ ................................ ................................ ................... 14 CHAPTER 1 BACKGROUND ................................ ................................ ................................ ...... 16 Population Migration and Suicide ................................ ................................ ........... 16 Global Patterns of the Suicide Epidemic ................................ ................................ . 17 High Risk of Suicide among Migrants ................................ ................................ ..... 19 Lack of Social Capital as an Explanation for High Suicide Risk .............................. 20 Social Capital Loss and Reconstruction among Migrants ................................ ....... 22 Potential Mechanisms of Social Capital Affecti ng Suicidal Behaviors ..................... 23 Lack of Suicide Research among Rural to urban Migrants in China ...................... 24 Significance and Innovation of the Dissertation Research ................................ ...... 27 Specific Aims ................................ ................................ ................................ .......... 28 2 DESIGN AND METHODS ................................ ................................ ....................... 36 Study Design, Population and Site ................................ ................................ ......... 36 Sample Size, Power and Sampling Process ................................ ........................... 37 Data Collection ................................ ................................ ................................ ....... 39 Statistical Analysis ................................ ................................ ................................ .. 40 3 ARE SUICIDAL BEHAVIORS MORE PREVALENT AMONG RURAL TO URBAN MIGRANTS THAN NON MIGRANT RURAL AND URBAN RESIDENTS IN CHINA? EVIDENCE FROM A PROBABILITY SAMPLE ................................ . 44 Introduction ................................ ................................ ................................ ............. 44 Suicide and Suicidal Behaviors ................................ ................................ ........ 44 Suicide and Suicidal Behaviors among International Immigrants ..................... 45 Suicide Study among Domestic Migrants in China ................................ ........... 46 Differences in Suicidal Behaviors by Demographic Factors ............................. 48 Purpose of the Study ................................ ................................ ........................ 49 Materials and Methods ................................ ................................ ............................ 49 Population and Participants ................................ ................................ .............. 49 A Probability Sample Selected Using GIS/GPS Assisted Method .................... 49

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6 Selection of rural migrants ................................ ................................ ......... 49 Select ion of non migrant urban and rural residents ................................ ... 50 Data Collection ................................ ................................ ................................ . 51 Variables and Measurements ................................ ................................ ........... 51 Suicidal ideation ................................ ................................ ......................... 51 Suicidal attempt ................................ ................................ ......................... 52 Socio economic v ariables ................................ ................................ .......... 52 Statistical Analysis ................................ ................................ ............................ 52 Results ................................ ................................ ................................ .................... 53 Demographic Characteristic s of the Study Sample ................................ .......... 53 Prevalence of Suicidal Ideation among Rural Migrants ................................ .... 54 Comparison of Suicidal Ideation with Non migrant Rural and Urban Residents ................................ ................................ ................................ ...... 54 Prevalence of Suicidal Attempt among Rural Migrants ................................ .... 55 Comparison of Suicidal Attempt with Non migrant Rural and Urban Residents ................................ ................................ ................................ ...... 55 Discussion and Conclusion ................................ ................................ ..................... 56 Rural Migrants are at Increased Risk of Suicide ................................ ............... 56 Characteristics of the Suicidal Behaviors among Chinese Rural Migrants ....... 57 Differences in Suicidal Behaviors between the Rural Domestic Migrants in China and International Immigrants in Other Countries ................................ 59 Limitations and Future Research ................................ ................................ ...... 60 4 INVESTIGATION OF THE RELATIONSHIP BETWEEN SOCIAL CAPITAL, DURATION OF MIGRATION AND SUICIDAL BEHAVIORS AMONG RURAL TO URBAN MIGRANTS IN CHINA ................................ ................................ ......... 70 Introduction ................................ ................................ ................................ ............. 70 Suicide and Suicidal Behaviors ................................ ................................ ........ 70 Soci al Capital and Suicide ................................ ................................ ................ 71 Need for In depth Research on Social Capital Suicide Relationship ............. 72 Rural to Urban Migrants in China as a Unique Opportunity ............................. 73 Potential Interactions of Migration Duration and Social Capital on Suicide ...... 74 Purpose of the Study ................................ ................................ ........................ 75 Materials and Methods ................................ ................................ ............................ 75 Participants and Sampling ................................ ................................ ................ 75 Data Col lection ................................ ................................ ................................ . 76 Variables and Measurement ................................ ................................ ............. 7 7 Suicidal behaviors ................................ ................................ ...................... 77 Personal social capital ................................ ................................ ............... 77 Migration related variables ................................ ................................ ......... 78 Demogr aphic variables ................................ ................................ .............. 78 Statistical Analysis ................................ ................................ ............................ 79 Results ................................ ................................ ................................ .................... 81 Demographic and Migration Characteristics of the Study Sample .................... 81 Prevalence of Suicidal Behaviors ................................ ................................ ..... 81

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7 Differences in Social Capital between Participants with and without a Suicidal Behavior ................................ ................................ .......................... 82 Social Capital and Its Interaction with Years of Migration on Suicidal Behaviors ................................ ................................ ................................ ...... 82 Discussion ................................ ................................ ................................ .............. 84 Protective Effect of Social Capital on Suicidal Behaviors ................................ . 84 Interaction between Social Capital and Duration of Migration .......................... 86 Limitations, Implications and Future Research ................................ ................. 87 5 RELATIONSHIP BETWEEN SOCIAL CAPITAL, EMPLOYMENT UNCERTAINTY, ANXIETY, AND SUICIDAL BEHAVIORS: A CHAINED MULTI MEDIATOR MEDIATION MODELING ANALYSIS ................................ ............... 102 Introduction ................................ ................................ ................................ ........... 102 Suicide among Rural Migrants in China ................................ ......................... 102 Protective Effect of Social Capital ................................ ................................ .. 103 Influence of Employment and Mental Health ................................ .................. 103 Social Capital, Employment and Mental Health ................................ .............. 104 Potential Mediation Mechanisms ................................ ................................ .... 105 Purpose of the Study ................................ ................................ ...................... 106 Materials an d Methods ................................ ................................ .......................... 106 Participants and Sampling ................................ ................................ .............. 106 Data Collection ................................ ................................ ............................... 107 Variables and Measurements ................................ ................................ ......... 108 Predic tor social capital ................................ ................................ ........... 108 Mediator 1 employment uncertainty ................................ ....................... 109 Mediator 2 anxiety ................................ ................................ ................. 109 Moderator years of migration ................................ ................................ . 110 Outcome suicidal behaviors ................................ ................................ ... 110 Cova riates ................................ ................................ ................................ 110 Statistical Analysis ................................ ................................ .......................... 110 Results ................................ ................................ ................................ .................. 112 Characteristics of the S tudy Sample ................................ .............................. 112 Correlations among Predictor, Mediator, Moderator and Outcome Variables 112 Moderated Mediation Analysis ................................ ................................ ....... 113 Chained Mediation Modeling of Suicidal Ideation ................................ ........... 113 Chained Mediation Modeling of Suicidal Attempt ................................ ........... 114 Discussion ................................ ................................ ................................ ............ 114 Mediation Effects of Employment Uncertainty and Anxiety ............................ 115 Differences of Bonding and Bridging Capital ................................ .................. 116 Chained Mediation Mechanisms of Employment Uncertainty and Anxiety ..... 117 Implications for Suicide Reduction ................................ ................................ . 118 Limitations and Future Research ................................ ................................ .... 119 6 CONCLUSION ................................ ................................ ................................ ...... 128 APPENDIX: SUPPLEMENT AL MATERIALS ................................ .............................. 134

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8 LIST OF REFERENCES ................................ ................................ ............................. 136 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 156

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9 LIST OF TABLES Table page 3 1 Characteristics of the study sample of rural to urban migrants, rural and urban residents ................................ ................................ ................................ ... 62 3 2 The prevalence rates of suicidal ideation among rural to urban migrants and non migrant rural and urban residents, overall, and by demographic and other cov ariates, % [95%CI] ................................ ................................ ............... 63 3 3 The prevalence rates of suicidal attempt among rural to urban migrants and non migrant rural and urban res idents, overall and by demographic and other covariates, % [95%CI] ................................ ................................ ........................ 64 4 1 Demographic and migration characteristics of the study sam ple of rural to urban migrants ................................ ................................ ................................ .... 89 4 2 The 12 month prevalence rate [95%CI] of suicidal ideation and attempt among rural migrants in China, overall and by demographic and migration characteristics ................................ ................................ ................................ ..... 91 4 3 The 12 month prevalence rate [95%CI] of suicidal ideation only , attempt only and both ideation and attempt among rural migrants in China, overall and by demographic and migration characteristics ................................ ........................ 93 4 4 Association between social capital and suicidal ideation and attempt, mean [95%CI], overall ................................ ................................ ................................ .. 95 4 5 Association between social capital and suicidal ideation and attempt, mean [95%CI], for subsample of migrants with migration year less 15 years and 15 years or longer ................................ ................................ ................................ .... 96 4 6 Logistic regression of the interactive effects between different social capital measures and years of migration on suicidal ideation among Chinese rural mig rants, regression coefficients [95%CI] ................................ .......................... 98 4 7 Logistic regression of the interactive effects between different social capital measur es and years of migration on suicidal attempts among Chinese rural migrants, regression coefficients [95%CI] ................................ .......................... 99 5 1 Characteristic o f the study sample of rural to urban migrants .......................... 120 5 2 Correlation between social capital, employment uncertainty, anxiety and suicidal behaviors among rural migrants ................................ .......................... 122 5 3 Moderated mediation modeling of the complex associations between social capital and suicidal behaviors among rural migrants, coefficients [95%CI] ...... 123

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10 A 1 Differences in social capital, bonding capital and bridging ca pital by demographic and migration characteristics, mean [95%CI] .............................. 134

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11 LIST OF FIGURES Figure page 1 1 Global pattern of suicide mortality (per 100,000 population), 2015 ..................... 31 1 2 Global pattern of the ge ographic based rate of suicide (No. of suicide per 100,000 km 2 ), 2015 ................................ ................................ ............................ 31 1 3 Global trend in suicide mortality (per 100,000 populatio n), 2000 2015, overall and by regions ................................ ................................ ................................ .... 32 1 4 Relative risk of suicide of the migrant population compared to the general population in different countries ................................ ................................ .......... 33 1 5 Social capital loss and reconstruction among migrants ................................ ...... 34 1 6 Trend of suicide mortality in China 1978 2017, overall and by gender and region ................................ ................................ ................................ ................. 35 2 1 Conceptual diagram of the GIS/GPS assisted probability sampling method ...... 42 2 2 Flow diagram of the dissertation research ................................ .......................... 43 3 1 Comparison of risk of suicidal ideation between rural to urban migrants and rural residents ................................ ................................ ................................ ..... 65 3 2 Comparison of risk of suicidal ideation between rural to urban migrants and urban residents ................................ ................................ ................................ ... 66 3 3 Comparison of risk of suicidal attempt between rural to urban migrants and rural residents ................................ ................................ ................................ ..... 67 3 4 Comparison of risk of suicidal attempt between rural to urban migr ants and urban residents ................................ ................................ ................................ ... 68 3 5 Risk of suicidal ideation and attempt among rural to urban migrants compared to non migrant rural an d urban residents ................................ ........... 69 4 1 Schematic illustration of the interaction between a social capital measure and years of migration ................................ ................................ ............................. 100 4 2 Illustration of the association between social capital and suicidal behaviors across the years of migration ................................ ................................ ............ 101 5 1 Conceptual (a and c) and statistical (b and d) illustration of the mediation modeling (upper panel) and chained mediation modeling (bottom p anel) ........ 125

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12 5 2 Chained mediation modeling of the associations between social capital, employment uncertainty, anxiety, and suicidal i deation ................................ .... 126 5 3 Chained mediation modeling of the associations between social capital, employment uncertainty, anxiety, and suicidal attempt ................................ .... 127

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13 LIST OF ABBREVIATIONS ACASI Audio Computer As sisted Self Interviewing AOR Adjusted Odds Ratio CDC Center for Disease Control and Prevention CI Confidence Interval GDP Gross Domestic Product GIS Geographic Information System GPS Global Positioning System NIH National Institutes of Health PSCS Personal Social Capital Scale PSF Primary Sampling Frame SC Social Cap i tal SD Standard Deviation SSF Secondary Sampling Frame U . S . United States WHO World Health Organization YC Year of Crossover YM Years of Migration

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14 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy SOCIAL CAP IT AL AND SUICIDAL BEHAVIORS AMONG RURAL TO URBAN MIGRANTS IN CHINA By Bin Yu December 2019 Chair: Xinguang Chen Major: Epidemiology Suicide re presents a vital public health challeng e among migrant populations all over the world . Social capital, sum of the are durable, trustworthy, reciprocal and resource rich , has been related to suicide at the ecological and individual level with data from different populations. However, such association been tested among rural to urban migrants in China, currently totaling over 280 million. In this dissertation research, I proposed three studies to examine (1) whether the prevalence rate s of suicidal behaviors were higher among rural migrants than non migrant rural and urban residents; (2) the association between social capital and suicidal behaviors among rural migrants; and (3) the mechanism mediating the association between social capi tal and suicidal behaviors through employment and mental health. The dissertation research analyzed data from a probability sample of rural migrants and rural and urban residents recruited using GIS/GPS assi s ted sampling method s . Suici d al ideation and atte mpt during the past 12 months were used as outcomes in all three studies . Social capital was measured using the reliable and valid Personal Social Capital Scale (Study 2 3) . Employment uncertainty and anxiety were

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15 also measured (Study 3). Survey statistica l method s of linear and logistic regression were used to consider the sampling design, unequal probabilities , and sampling weight. S tudy 1 findings indicate that the prevalence rates of suicidal ideation and attempt s were higher among rural migrants than non migrant rural and urban re sidents. S tudy 2 found a significant negative association between social capital and suicidal ideation and attempt s with a positive interaction between social capital and years of migrati on. Study 3 revealed a complex chained mediation mechanism in which employment uncertainty and anxiety consecutively mediated the association between social capital and suicidal ideation and attempt s . Findings of the se studies demonstrate higher risk of s uicide and suggest more attention should be paid to suicidal behaviors among rural migrants . S ocial capital played a protective role on suicide among rural migrants, particularly in the early years of migration. Furthermore, s ocial capital may exert its ef fect on suicidal behaviors through employment and mental health. These study f indings deepen the understanding of suicide among migrants and provide evidence for devising and implementing social capital based interventions targeting suicide among migrants.

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16 CHAPTER 1 BACKGROUND Population Migration and Suicide Along with the advancing transportation and information technology, the scale of globalization in the world economy has been expanding (United Nations, 2016) . E conomic development and globalization have greatly promoted human migration (Hatton & Williamson, 2005) , including international migration and within country migration (also known as domestic migration) (Yu, Chen, & Li, 2014) . Migrants are people living in a country/area other than their original country/area (United Nations, 2016) . They move from their place s of origin to the new destinations to pursue more opportunities and better quality of life (Yu, Chen, & Li, 2014; Yu, Chen, Li, et al., 2014) . Worldwide, a pproximately 173 million people (2.8% of the world population) migrated to other countries in 2000, and the number increased to 244 million (3.4% of the world population) in 2 015, while the number of within country migrants has reached 750 million (United Nations, 2016) . The process of migration is full of challenges, exerting significant effects on migran physical, psychological and social well being (Bhugra et al., 2011; Ryan, 2011; Torres & Wallace, 2013; Yu, Chen, & Li, 2014; Zhong et al., 2015) . In the past decades, many studies reported health and behavior related problems among migrants, such as acculturation and migration stress (Berry, 1995, 2006; Chen, Yu, Gong, Zeng, & MacDonell, 2015; Phillimore, 2011; Sam & Berry, 1995) , depression and mental disorders (Arévalo, Tucker, & Falcón, 2015; Beutel et al., 2016; Bhugra et al., 2011; George, Thomson, Ch aze, & Guruge, 2015; Li et al., 2007; Naieni, Faghihi, Barati, Salehiniya, & Khani, 2018) , substance use and abuse (Borges et al., 2011; Borges,

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17 Medina Mora, Bresl au, & Aguilar Gaxiola, 2007; Chen, Stanton, Li, Fang, & Lin, 2008; Lopez Tamayo et al., 2016; Schwartz et al., 2015) , and HIV related sexual risk behaviors (Gras, Weide, Langendam, Coutinho, & van den Hoek, 1999; Organista, Carrillo, & Ayala, 2004; Parrado, Flippen, & McQuiston, 2004; Rhodes et al., 2012; Sanchez et al., 2004 ) . Of the many health and behavioral problems, suicide among migrants has not been well investigated. The impact of suicide is enormous. In addition to threatening an health and life, suicide exerts significant impacts on the stability and sustainability of family, community, and society (Baudelot, 2017) . Global Pattern s of the Suicide Epidemic Suicide is death caused by self injurious behaviors with the intention of taking (CDC, 2017) . It is one of the most important causes of death in the world, re presenting a global health challenge (WHO, 2014, 2018a) . The global prevalence rate of suicide was 12.2 per 100,000 population in 2000, followed by 11.6 in 2005, and 11.2 in 2010 . It further declined to 10.7 in 2015 with higher rates in high income countries than in low and middle income countries (WHO, 2014, 2017a, 2017b) ( Figure 1 1). When comparing the suicide rate s based on geographic area size (Chen & Wang, 2017; Chen, Yu, & Zhao, 2019) (Figure 1 2), countries in Asia and Europe show more suicide deaths in certain geographic area s . Despite the global declining trend (Figure 1 3) , suicide remains the second and fifth leading cause of death among people aged 15 29 years and 30 49 years, respectively (WHO, 2014) . Suicide rates vary by gender with m ales hav ing a higher prevalence of suicide than females . There was an est imated gender ratio of 1.7 in 2015 (13.6 per 100,000 for males and 7.8 per 100,000 for females) (WHO, 2017b) . The gender ratio is also h igher in high income countries than

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18 in low and middle income countries (3.5 vs. 1.6 in 2012) (WHO, 2014) . With regard to age, in general, suicide rates are highest in people aged 70 years or older, and lowest in persons under 15 years of age for both men and women across the world (Bertolote & Fleischmann, 2015) . Suicide result s from a cascade of behaviors starting with suicidal ideation, followed by plan, attempt s and actual death by suicide. Among various suicidal behaviors, suicidal ideation and attempt s are the two commonly reported precursors of suicide in research (Nock et al., 2008) . Suicidal ideatio n is defined as thinking about, considering and planning suicide , while a suicidal attempt is a non fatal, self directed, potentially injurious behavior with an intention to die as the result of the behavior, and the behavior may or may not result in injur y (CDC, 2017) . Individuals who have more frequent suicidal ideation s and /or attempt s are at much greater risk of suicide. For each suicide, on averag e, there are 10 20 people who attempt suicide with one attempt per one to two seconds worldwide (Fond et al., 2016) . Among the lifetime suicidal ideators, one third of them will go on to make a suicidal plan, and three in four people with a plan will make a suicide attempt (Nock et al., 2008) . Among the suicidal attempters, 5 10% will complete suicide in 10 years (Gairin, House, & Owens, 2003; Gibb, Beautrais, & Fergusson, 2005; Suominen et al., 2004) , and 10 15% will eventually die by suicide (Maris, Berman, Silverman, & Bongar, 2000; Maris, 1992) . Thus, a deep understanding of suicidal behaviors will contribute significantly to devising intervention programs, and decreasing the rate of suicide over the world.

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19 High Risk of Suicide among Migrants Evidence from diverse sources ( Figure 1 4 ) reveals a higher suicide risk among migrants than among the general population (Burvill, 1998; Ferrada Noli, 1997; Ide, Kõlves, Cassaniti, & De Leo, 2012; L. Johansson et al., 1997; Kliewer & Ward , 1988; Kposowa, McElvain, & Breault, 2008; Lester, Saito, & Ben Park, 2011; Ott, Winkler, Kyobutungi, Laki, & Becher, 2008; Singh & Siahp ush, 2001; Sundaram, Qin, & Zøllner, 2006) , although reserve findings are reported in some s pecific migrant sub populations, such as Chinese migrants in Japan (Lester et al., 2011) , and Asian and Middle East migrants in Australia (Burvill, 1998) . One recent literature review based on the published studies during 1980 2017 , compared the risk of suicide and suicidal behaviors among immigrants and ethnic minorities with the general populatio n, and concluded that despite a few studies reporting lower risk of suicide in immigrants, overall, migrant populations are at greater risk of suicide , including suicidal behaviors , comparing to the general population (Forte et al., 2018) . Relative to non migrant residents , migrants who move to other places for a better quality of life are facing extra and different challenges, including environmental, cultural, life, work , and other aspects. These challenges are hard to deal with and thus may increase the risk of dying by suicide. Suicide has been recognized as a significant public health problem influencing the wel l being of migrant populations . M ore studies are strongly needed to investigate the underlying mechanisms of suicidal behaviors among migrants.

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20 Lack of Social Capital as an Explanation for High Suicide Risk Studies among the general population have report ed a list of risk factors of suicide, including cultural (Hovey & Magaña, 2000; Hovey, 1998) , social (Caine, 2015; Compton, Thompson, & Kaslow, 2005; S. Johansson & Sundquist, 1997; Phillips, Yang, et al., 2002; Yamamura, 2010) , psychological (Beutel et al., 2016; Caine, 2012, 2018; Carpenter, Hasin, Allison, & Faith, 2000; Conwell, Duberstein, & Caine, 2002; Cottler, Campbell, Krishna, Cunningham Williams, & Abdallah, 2005; Simon et al., 2013; 2011) , and genetic biological f actors (Brent & Mann, 2005; Marusic & Farmer, 2001; Roy, 1993) . But only a few such studies examined these factors among migrants (Fortuna et al., 2016; Hovey & Magaña, 2003; Leavey, 1999; Niederkrotenthaler et al., 2017; Ott et al., 2008; Yan, Peng, & Zhang, 2009) . Among the known risk factors, lacking social capital is particularly relevant for migrants. Social capital as a valuable (Berkman, Kawachi, & Glymour, 2014; Coleman, 1988; Saegert & Carpiano, 2017) . Social capital -sum of durable, tr ustworthy, reciprocal and resource rich network connections -was first associated with suicide in 1897 by Durkheim (Durkheim, 1897) . It links an individual to the s ocial environment, and enables individuals to access scattered social resources. Individuals with adequate social capital can thus receive informational, instrumental, and emotional support as they need to reduce suicide risk (Chen, Stanton, Gong, Fang, & Li, 2009) . Social capital may also reduce the risk of suicidal ity through informal social control, such as collective efficacy, voluntary actions by community members against social ly undesirable behaviors , including suicide (Chen et al., 2009; Whiteford, Cullen, & Baingana, 2005) .

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21 Ecological studies with aggregated data from countries all over the world have reported a negative association between social capital and suicide mortality. The first s tudy about the association was conducted by Emile Durkheim in 1897 and found that suicide rates were higher among people with less social cohesion (Durkheim, 1897) . Another study analyzed the data from 117 country waves in 50 countries over the last two decades of the 20 th century and found that lack of social capital and less trust were associated with a higher level of national suicide rate (Helliwell, 2007) . One study conducted among 11 European countries used social trust as a measure of social capital and found a negative association between social capital (social trust) and national suicide rates (Kelly, Davoren, Mhaoláin, Breen, & Casey, 2009) . The negative association between social capital, characterized as social cohesion and trust in the community, and state level suicide rate by firearm was also found amon g veterans in the U . S . (Desai, Dausey, & Rosenheck, 2008) . People with low trust and low social cohesion were more likely to own guns, which in turn was (Desai et al., 2008; Hemenway, Kennedy, Kawachi, & Putnam, 2001) . Data collected in the U . S . have also shown that states with lower social capital had significantly higher suicide rates (Smith & Kawachi, 2014) . Findings in other countries, such as Netherlands (Kunst, van Hooijdonk, Droomers, & Mackenbach, 2013) and Japan (Okamoto, Kawakami, Kido, & Sakurai, 2013) also reported similar results . With regard to individual level data, only a few studies examined social capital and suicide , using samples selected from the homeless population in the U . S . (Fitzpatrick, Irwin, Lagory, & Ritchey, 2007) and general community population in the

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22 United Kingdom (Congdon, 2012) , and Sweden (Lindström & Ros vall, 2015) . These studies reported that people who lack social capital had a greater risk of suicide. In addition to the protective effect of social capital on suicide, social capital is also know n as a modifiable factor which can be intentionally gen erated (Pronyk et al., 2008) . For example, one cluster randomized trial conducted in rural South Africa applied group based intervention to catalyze changes in solidarity, reciprocity and social group reduce the intimate partner violence and HIV t ransmission (Pronyk et al., 2008) . Thus, as a modifiable factor, social capital may be used as an effective approach to prevent health related risk behaviors, including suicide. Studies on the relationship between social capital and suicide in different popula tions will provide informative evidence for future intervention and prevention programs. However, to our knowledge, no reported study has examined the relationship between social capital and suicide among the particular migrant population. Social Capital L oss and Reconstruction among Migrants Social capital is particularly relevant for studying suicide among migrants. Compared to non migrants , migrant s fac e a big challenge of social capital loss and reconstruction ( Figure 1 5 ) (Chen et al., 2011) . When migrants l eave their place of origin, they break the ties with people and groups/organizations in the original place, and their original network connections will be significantly weakened (Chen et al., 2011) . When they settle down in the new places, they are likely to experience a substantial lo ss in social capital (Chen et al., 2009; R yan, Sales, Tilki, & Siara, 2008) . Meanwhile, migrants have to rebuild their new social connections with local people and

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23 organizations to reconstruct social capital (Bhugra, 2004; Li et al., 2007) . Due to the mobility of this population, migrants may not have enough time and opportunities to establish and maintain the network connections, limiting their capabilities in rebuilding social ca pital (Chen et al., 2011) . Therefore, a reduction in social capital is anticipated among migrants while lacking social capital may increase the risk of suicide. Despite the reported association between social capital and suicidal behaviors in general populations, the relationship re mains un tested among migrant populations. Potential Mechanisms of Social Capital Affecting Suicidal Behaviors In addition to social capital, other factors have been recognized to affect suicidal behaviors (Blackmore et al., 2008) . Some of them are particularly relevant for migrant populations, including employment conditions and mental health status . T he main purpose of migration is to pursue more opportunities and a better quality of life. The most urgent task of migrants when they arrive in a new place is to find a job to support themselves and their families. When migrants fail in their employment , they may face a lot of difficulties, such as economic failure, family crises educational opportunity and poor quality of life (Chen, Yu, Gong, Wang, & Elliott, 2017) . Previous studies have shown that employment conditions (e.g. job dissatisfaction, job insecurity) were significantly a ssociated with suicidal behaviors among migrants (Blakely, Collings, & Atkinson, 2003; Chen et al., 2017) . Additionally, d ue to the difficulties in the process of migration, mental health problems are prevalent among migrants (Bhugra, 2004; Carta, Bernal, Hardoy, & Haro Abad, 2005; Li et al., 2007) . One study conducted among 1 , 595 rural migrants in China indicate that approximately one in four rural migrants experienced poor mental health in

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24 the past 30 days , higher than their urban counterparts in which 20% o f them had mental health problems (T. Yang et al., 2012) . Poor mental health has been consistently reported to be highly associated w ith suicidal behaviors in migrant populations , such as depression and anxiety (Chovan, 2017; Hong et al., 2010; Hovey, 2000a, 2000b; Miret, Ayuso Mateos, Sanchez Moreno, & Vieta, 2013; Oquendo, Lizardi, Greenwald, Weissman, & Mann, 2004) . Previous studies have also found significant associations between social capita l and employment conditions and mental health problem s (Chen et al., 2017; D e Silva, McKenzie, Harpham, & Huttly, 2005; Whiteford et al., 2005) . It is likely that social capital not only exerts a direct effect on suicidal behaviors but also affects suicidal behavior s indirectly through employment conditions and mental healt h . O ne aim of the dissertation is to examine the mediation effect of social capital on suicidal behaviors through employment conditions and mental health problems. Lack of Suicide Research among Rural to urban Migrants in China Suicide has been recognized as an important public health challenge in China (Caine, 2013; Cao et al., 2015; Dai et al., 2011; Phillips, Li, & Zhang, 2002; Phillips, Liu, & Zhang, 1999; Phillips, Yang, et al., 2002; Sha, Chang, Law, Hong, & Yip, 2018; Yip, Callanan, & Yuen, 2000; Yip, Liu, Hu, & Song, 2005; Zhang, Dong, Delprino, & Zhou, 2009; Zhang, Jiang, Jia, & Wieczorek, 2002; Zhang, Sun, Liu, & Zhang, 2014; Zhang, Wieczorek, Conwell, & Tu, 2011; Zhang & Jing, 2011) . Approximately 17 % of global suicides are from China (WHO, 2018a) . China has witnessed a dramatic decline in suicide rates from one of the countries with the highest rate above 20 per 100,000 before the economic reform to 17.4 in 2000 and further to 8.5 in 2015 when China

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25 bec a me an upper middle income countr y (Jiang et al., 2018; Phillips, Li, et al., 2002; Phillips et al., 1999; Yip et al., 2005; Zhang & Jing, 2011; Zhang et al., 2010, 2014) . Before 2005, Chinese women have a higher suicide mortality rate than men, which is opposite to the global trend (WHO, 2017a) , and the suicide mor tality among men exceeded women thereafter . Residents in rural area s of China have an average of two fo ld higher suicide death rate than urban residents (Zhang et al., 2014) ( Figure 1 6 ). A meta analysis of suicide in China show ed that the lifetime prevalence rates of suicide ideation and attempt in the general population were 3.9% and 0.8%, respectively (Cao et al., 2015) . The suicide epidemic shows a different map among migrants. Since the economic reform in China in the 1980s , a large number of rural residents left their home and went to the urban ar ea to seek jobs and earn money. The number of rural to urban migrants in China increased from 30 million in 1989 to 242 million in 2010, and reached 282 million in 2016, accounting for 20% of China population (National Bureau of Statistics of the PRC, 2010, 2012, 2017) . In 2017, nearly 60% of the people in China were living in the urban area, and rural migrants have become one important population in the urban area (Statistics, 2018) . The large number of rural to urban migrants in China p rovides us an opportunity to investigate the suicidal behaviors among migrants. Previous studies have reported that , compared to urban and rural residents, rural to urban migrants have a 2.5 to 4 times higher risk of suicide (Di & Xiao, 2004; Yan et al., 2009; L. Yang & Chen, 2015) . For example, one study conducted in Guiyang, Wester n China indicated that 9.6% (2.5 times higher than general population) of rural migrants reported suicidal ideation during the past year (Yan et al., 2009) , while the rate

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26 of suicidal ideation was 17.4% among construction migrant workers in Changsha, Central China (Di & Xiao, 2004) , and 14.1% among young rural migrants in Wenzhou, Southeast China (L. Yang & Chen, 2015) . Another study conducted in Hangzhou, Southeast China fou nd higher odds ratios of suicidal ideation and attempt s (OR=1.2 for both) among rural migrants compared to the general popu lation (Li et al., 2007) . On the other hand, several studies reported a lower suicide risk among migrants. One study in Sichuan, Western China report ed a lower prevalence of suicidal ideation among rural migrants compared to non migrants (1.3% vs. 3.0%) (Dai et al., 2015) . Another study conducted in Southeast China indicated rural migrants had higher suicidal ideation , but lower suicid al attempts (Li et al., 2007) . However, all these studies focused on sp ecific subgroups of the migrant population selected using convenient sampling method s . The externality of these studies is thus questionable. To clarify the controversy and to fill in the data gap, t h is dissertation research aims to compare the prevalence rates of suicidal ideation and attempt among rural to urban migrants with non migrant rural and urban residents by analyzing the data collected from a GIS/GPS assisted probability sample ( Aim #1 ). A search of PubMed records shows that research on social c apital and health in China has mainly been published after 2000. Rural to urban migrants in China have gained much attention from researchers due to their frequent mobility and social capital loss and reconstruction (Chen et al., 201 1, 2017; Du, Li, & Lin, 2015; Hou, Lin, & Zhang, 2017; Q. Yang et al., 2018) . Social capital is of great significance in suicidal research, however, few studies have ever investigated the association between social capital and suicidal behaviors among r ural to urban migrants in China. Thus, the current

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27 dissertation research aims to examine the effect of social capital on suicidal behaviors among rural migrants ( Aim #2 ). The underlying mechanisms of the effect of social capital on suicidal behaviors among rural migrants are complex. As discussed in the previous sections, employment conditions and mental health problems play important roles in suicidal behaviors among migrants, and may mediate the relationship between social capital and suicidal behaviors. Individuals with less social capital are more likely to experience poor employment conditions and mental health problems, which may lead to a greater risk of suicidal behaviors. One of the aims of this dissertation research is to t est the proposed mediation mechanism of the association between social capital and suicidal behaviors ( Aim #3 ). Significance and Innovation of the Disserta t ion Research This dissertation research is of great significance. Firs t, this dissertation analyze d the data collected from a GIS/GPS assisted multi stage cluster disproportionat e probability sample. It provides unbiased estimates of the prevalence of suicidal behaviors, allowing for valid compar ison of the rates between rural to urban migrants and non migrant rural and urban residents . The estimated rate also allows us to describe suicidal behaviors by demographic and migration related variables. The findings of the study will provide valuable evidence regarding the basic characteristics of suicidal beh aviors in the Central China , deepening the understanding of the epidemic of suicidal behaviors among migrant population . Second, despite the fact that many studies have investigated the relationship between social capital and health, few studies have explored the effects of social capital

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28 on suicidal behaviors in China. Findings of the dissertation will attempt to fill this gap . Knowledge on the association between social capital and suicidal behaviors among rural migrants is essential. In addition to advancing suicide etiology research, such evidence is a prerequisite for suicide prevention intervention. Third, the disser tation applied the mediation and moderated mediation modeling to investigate the associations among social capital, employment conditions , mental health problems, and suicidal behaviors among rural migrants. Findings of the study will provide evidence for devising and implementing social capital based intervention programs to prevent suicidal behaviors. The dissertation research is i nnovati ve. First of all, this is the first study to apply the GIS/GPS assisted probability sample to compare the prevalence rates of suicidal behaviorls among rural to urban migrants with non migrant rural and urban residents in China. Secondly, this is the first study to investigate the effect of social capital on suicidal behaviors by detailed attributes and sources of social capital. Thirdly, advanced moderated mediation modeling method is used to investigate the underlying mechanism linking social capital to suicidal behaviors. Specific Aims This dissertation research is a secondary data analysis using survey data collected from a probability sample of rural migrants in China , which was selected using a GIS/GPS assisted multi stage cluster disproportionate probability sampling method. In the dissertation, I focus ed the investigation on the complex relationship between social capital and suicidal behaviors to address the following specific aims.

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29 Aim #1: To a ssess the increased risk of suicide among rural migrants by comparing the prevalence rates of two suicidal beh aviors (ideation and attempt) among rural to urban migrants with that of non migrant rural and urban residents. The prevalence rates of suicidal behaviors will also be compared by demographic variables and other covariates. Hypothesis #1a : Suicidal ideati on is more prevalent among rural migrants than non migrant rural and urban residents. Hypothesis #1b : Rural migrants will have a higher prevalence rate of suicidal attempt s than non migrant rural and urban residents. Hypothesis #1c : Prevalence rates of suicidal behaviors (e.g. suicidal ideation and attempt s ) are higher among people who are female, older age, unmarried/divorced/widowed, have lower education, have a lower income, and have fewer family members. Aim #2: To i nvestigate the association betwe en social capital (overall, bonding and bridging capital, and different attributes and sources) and suicidal behaviors, including suicidal ideation and attempt s , among rural to urban migrants. The other factors will also be considered, including demographi c variables and other covariates. Hypothesis #2 : Social capital is negatively associated with suicidal ideation and attempt s after controlling for the effects of demographic variables and other covariates.

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30 Aim #3: To i nvestigate the underlying behavioral mechanisms relating social capital with suicidal behaviors (e.g. suicidal ideation and attempt s ) through complex interactions among social capital, employment conditions , and mental health problems . Hypothesis #3a : The associations between social capital and suicidal ideation and attempt s are mediated in part by poor employment conditions. Hypothesis # 3b : The associations between social capital and suicidal ideation and attempt s are mediated in part by mental health p roblems .

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31 Figure 1 1. Global pattern of suicide mortality (per 100,000 population) , 2015 Note: Data were from the World Health Organization (WHO) ( http://apps.who.int/gho/data/node.main.MHSUICIDE ) Figure 1 2. Global pattern of the geographic based rate of suicide ( No. of suicide per 100,000 km 2 ) , 2015 Note: Data were from the World Health Organization (WHO) ( http://apps.who.int/gho/data/node.main.MHSUICIDE )

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32 Figure 1 3 . Global t rend in suicide mortality (per 100,000 population) , 2000 2015 , overall and by regions Note: Data were from the World Health Organization ( http://apps.who.int/gho/data/view.main.MHSUICIDEREGv?lang=en )

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33 Figure 1 4 . Relative risk of suicide of the migrant population compared to the general population in different countries Note: Data were derived from published studies (Burvill, 1998; Ferrada Noli, 1997; Ide et al., 2012; L. Johansson et al., 1997; Kliewer & Ward, 1988 ; Kposowa et al., 2008; Lester et al., 2011; Ott et al., 2008; Singh & Siahpush, 2001; Sundaram et al., 2006) .

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34 Figure 1 5 . Social capital loss and reconstruction among migrants

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35 Figure 1 6 . Trend of suicide mortality in China 1978 2017, overall and by gender and region Note: Data were derived from China Health Statistical Yearbook ( http://www.yearbookchina.com/navibooklist n3018112802 1.html ).

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36 CHAPTER 2 DESIGN AND METHODS Study Design, Population and Site Th is disserta ti on research is a secondary analysis using data from the National Institute s of Health funded project (R01 MH086322, PI: Xinguang Chen, Period: 2010 2014). This was a population b ased comparative cross sectional survey study. The target population was rural to urban migrants with rural and urban residents as comparison populations. In this dissertation , rural to urban migrants were defined as Chinese residents who (a) possess a leg al rural residence ( Hukou ) in the Household Registration System; (b) are aged 18 45 years old; (c) have migrated to the city t o earn money; and (d) have stayed in the current city for at least one month. To achieve the goal of this dissertation research , two comparison groups were included. One was the rural residents who share d the same environment where the rural to urban migrants come from. The data collected from rural residents would provide information about the source population these rural migrants ca me from. The non migrant rural residents were defined as Chinese residents who (a) possess a legal rural residence , (b) are aged 18 45 years old , and (c) have not moved to urban areas to earn money in the past 12 months. Another comparison group was th e urban residents who share d the same environment where the rural to urban migrants were currently living and working. Data col lected from urban residents would provide information about the population the rural migrants were going to be. The urban residen ts were the people who (a) have legal urban residence , (b) are aged 18 45 years old , and (c) have lived in the current city for at least five years.

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37 Data were collected in Wuhan City, China. Wuhan is the capital city of Hubei Province in central China, wi th a total population of 10 million and GDP per capita of $1 8,000 in 2017 (Hubei Statistical Bureau, 2017) . According to the Wuhan Statistica l Yearbook (2017) (Hubei Statistical Bureau, 2017) , nearly 10% of the total population (approximately one million) were rural to urban migran ts. Sample Size, Power and Sampling Process By design, t he sample size was 1,200 with 600 males and 600 females for rural to urban migrants, as well as for rural and urban residents. The national lifetime prevalence rate of suicidal ideation was 3.9% (Cao et al., 2015) . Migrants may have at least 2.5 times higher suicide risk than the general population (Yan et al., 2009) . Using SAS (proc power), the sample size of the study (N=3,600) should provide over 90% power to test the potential difference in Aim #1. For Aim #2 and #3, only data from rural migrants were used for analysis. Power analy sis for multivariate regression indicated that the sample size for migrants (N=1,200) should provide 80% power to test the effect. Participants were selected using the GIS/GPS assisted multi stage clustered disproportionate probability sampling method ( Figure 2 1 ) (Chen & Hu, 2018; Chen, Hu, et al., 2018; Chen, Yu, Zhou, et al., 2015) . The sampling was completed in four steps by the Wuhan Center for Disease Contro l and Preven tion (CDC): (a) Wuhan City was geounits using the GIS techniques and these geounits were thus used to construct the primary sampling frame (PSF) after excluding the non resident i al geounits , such as rivers, lakes, mountains, factories, streets , and other public places ; (b) geounits were thus randomly selected from the PSF, and the information regarding these geounits was uploaded to a

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38 GPS receiver; (c) a small team consisting of a project coordinator, a GIS/GPS expert and a senior research staff with field data collection experience went to the field to physically locate the sampled geounits , to obtain support from the local community h ealth center and to plan for data collection; and (d) on a pre scheduled date, a team of trained data collectors was dispatched to the site to enumerate the households located within the sampled geounit , and to create the secondary sampling frame (SSF). T he random walking method was used to determine the geographic segments (geo segments), and individual households were selected sequentially following the natural order , with the first household being selected randomly from the geo segments. The eligible pa rticipants in each household were listed and randomly recruited. To enhance independence, one participant per gender in each individual household was selected. If only one person was eligible, this person was selected. For households with more than one eli gible participant, the Kish Table was used to select only one (Kish, 1949) . To implement the sampling plan, the urban areas were first divided into mutually exclusive geounits of 100 meters by 100 meters as the PSF. Sixty urban geounits were randomly selected from the PSF stratified by the seven d istricts of the city . Twenty participants (10 male s and 10 female s ) were recruited from each selected geounit . The same number of migrants and non migrant urban residents (20 for migrants and 20 for urban residents) were randomly sampled from different households within the same geounits . The sampling procedure was modified for rural residents to fit conditions in the rural area (Chen, Yu, Zhou, et al., 2015) . The targeted rural area was a band region surrounding the urban core of Wuhan City w ith a bandwidth of 25 km , an inner radius of

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39 50 km , and an outer radius of 75 km. This band covers the area where most of the rural residents lived, and where most of the rural migrants working in Wuhan originated from. The band was divided into mutually e xclusive geounits with a size of one km by one km as the PSF. Given the increased travel cost, only 40 geounits with 15 participants per gender per geounit were selected . The 40 geounits were distributed randomly into 40 strata with one unit per strata. Th e strata were developed by dividing the whole band with four co centric circles 5 km apart , and four evenly distributed straight lines from the origin. To ensure an adequate number of participants, an additional 20% of the sample was added. Among the eligible participants approached, 94% agreed to participate (Chen, Yu, Zhou, et al., 2015) . The software ArcGIS, ver sion 10.0 (ESRI, Inc, Redlands, CA) and GPS receiver (Garmin Oregon 450, Garmin, Ltd) were used for sampling geounits and locating the selected geounit , respectively. In the actual sampling, a total of 1 , 414 rural migrants, 1 , 350 rural residents , and 1 , 44 3 urban residents were accessed, and 119 rural migrants, 60 rural residents , and 82 urban residents refused to participate. The response rate s for the three populations were 92%, 96% , and 94%, respectively. After excluding the participants with missing var iables (age, gender, weight, etc . ), a final sample of 1 , 290 rural migrants, 1 , 290 rural residents , and 1 , 371 urban residents were included in the project. Data Collection Field data collection was conducted by Wuhan CDC from 2011 to 2013. On the pre dete rmined survey day generated by the previous visit of the sampling geounit , a group of researchers consisting of 5 6 senior researchers from Wuhan CDC and 8 10

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40 public health graduate students, was dispatched to the selected geounits for participant recruitment and data collection. The survey was voluntary and confidential, and only people who signed the informed consent were enrolled. Survey data were collected with the Migrant Health and Behavioral Survey , delivered using Audio Comp uter Assisted Self Interviewing (ACASI) on tablets and laptops. A brief training of ACASI was provided. The survey was conducted in a designated private room located in the at a local community health center. Data collectors were avai lable to provide assistance while participants were completing the survey. Each participant had the right to skip the questions he/she did not want to answer, and they could withdraw from the survey at any time they want ed . Participants received material r ewards with a value of $6 after completion. Statistical Analysis The NIH funded project innovatively applied the GIS/GPS assisted probability sampling method to recruit participants. The particular survey analysis procedures were used to consider the co mplex sampling design, including stratification, clustering, unequal probability, and sample weights (Woodruff, 1971) . The survey analysis procedures were used in the dissertation research to o btain point estimates for means and prevalence rate and 95% confidence interval (CI). The estimated 95% CI of the estimated prevalence rates was used for comparison with no overlap of two estimated 95% CI as the evidence of significant differences at the l evel of p <0.05 . To investigate the associations between the predictors and outcome variables, the survey analysis procedures of multivariate linear and logistic regression were used.

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41 Adjusted odds ratio (AOR) with 95% CI was estimate d . Likewise, 95% CI not covering 1.00 was used for statistical inference for a significant association at p < 0.05. All statistical analyses were implemented using SAS version 9.4 (SAS Institute, Cary, NC). PROC SURVEYMEANS was used for descriptive and comparison analysis , and PROC SURVEYREG and SURVEYLOGISTIC was used for multivariate linear and logistic regression analysis. More detailed information about the statistical analysis is presented in each chapter. Figure 2 2 is the flow diagram of the dissertation research.

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42 Figure 2 1. Conceptual diagram of the GIS/GPS assisted probability sampling method

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43 Figure 2 2. Flow diagram of the dissertation research

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44 CHAPTER 3 A RE SUICIDAL BEHAVIORS MORE PREVALENT AMONG RURAL TO URBAN MIGRANTS THAN NON MIGRANT RURAL AND URBAN RESIDENTS IN CHINA? EVIDENCE FROM A PROBABILITY SAMPLE Introduction Suicide and Suicidal Behaviors The Centers for Disease Control and Prevention (CDC) classified suicide as the death caused by self injurious behaviors with the intention of taking . Suicide is one of the leading causes of death, presenting a challenging public health problem in countries all over the world (WHO, 2018a) , including China (Caine, 2013; P hillips et al., 1999; Yip et al., 2005; Zhang & Jing, 2011) . China has witnessed a dramatic decline in suicide during the past several decades from among countries with the highest suicide rate of more than 20 ( per 100,000 population ) before the economic reform, to 17.4 in 2000, and further to 8.5 in 2015 (Chen, Sun, et al., 2018) . Despite the declining trend, the estimated number of annual suicide deaths in China was 134,000, accounting for approximately 17% of the world total (WHO, 2018b) . Suicide result s from a cascade of behaviors starting with suicidal ideation, followed by plan ning , attempt s , and actual suicide death. A mong various suicidal behaviors, suicidal ideation and attempt s are two commonly reported precursors of suicide in research (Nock et al., 2008) . Suicidal ideation is defined as thinking about, considering or planning suicide; while a suicidal attempt is defined as a non fatal, self directed, and potentially injurious behavior with an intention to die (CDC, 2017; Nock et al., 2008) . Individuals with suicidal ideation and attempt s are at increased risk of suicide. Among the persons with a suicidal ideation in their lifetime, one third will go on to make a suicidal plan; among those with a suicide plan, three fourths will attempt to

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45 kill themselves (Kessler, Borges, & Walters, 1999; Nock et al., 2008) ; and among those who attempted suicide, 10 15% will eventually die by suicide (Maris et al., 2000; Maris, 1992) . Suicide and Suicidal Behaviors among Internation al Immigrants Economic globalization has greatly promoted international human migration (Hatton & Williamson, 2 005) . In 2015 alone, approximately 244 million people migrated from one country to another (United Nations, 2016) . People move from their home country of origin to another destinati on country to pursue more opportunities and a better quality of life (Y u, Chen, & Li, 2014) . However, the process of migration and settling down may expose migrants to many challenging factors, increasing their risk of engaging in suicidal behaviors, including suicidal ideation, attempt , and actual suicide death. With evi dence from various countries published from 1980 to 2017, authors of a review study conclude d that suicide and suicidal behaviors are more prevalent among migrants than among the general population (Forte et al., 2018) . Despite important information, most of the se published studies were conducted in high income countries, including Sweden (Hjern & Allebeck, 2002; Westman, Sundquist, Johansson, Johansson, & Sundquist, 2006) , Germany (Ott et al., 2008) , Denmark (Sundaram et al., 2006) , Switzerland (Tarik Yilmaz & Riecher Rössler, 2012) , Netherlands (Burger, van Hemert, Schudel, & Middelkoop, 2009; van Bergen, Smit, van Balkom, & Saharso, 2009) , the United Kingdom (Bhugra, 2002) , Australia (Burvill, 19 98) , the United States (Kposowa et al., 2008; Singh & Miller, 2004) , and Japan (Lester et al., 2011) , and few were conducted in China .

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46 Suicide Study among Domestic Migrants in China Since the beginning of economic reform in the 1980s in China, a large and increasing number of residents in rural areas have migrated to urban areas (Hu, Cook, & Salazar, 2008) . The number of these rural to urban migrants reached 282 million in 2016, ac counting for 20% of the total China population, and greater than 244 million, the total number of international immigrants in the world (National Bureau of Statistics of the PRC, 2017) . Different from the international immigrants who move from one country to another, domestic migrants in China often move between rural areas and urban area s within the country (Chen, Yu, Gong, et al., 2015) . Domestic and international migrants differ in many aspects. In this study , we considered the following three differences. First, there are fewer cultural obstacles for domestic migrants than for international immigrants since domestic migrants move within the same or very similar cultural contexts of a country (King & Skeldon, 2010) . Second, a domestic migrant often moves alone from the place of origin, typically a rural area to an urban area, the place of destination, leaving their families behind at home. Therefore domestic migrants of ten travel back and forth between the origin and the destination to reunite with their families in rural homes (Guo et al., 2016) . Third, relative to international immigrants, domestic migrants are often less educated , with less competent skills for demanding occupations (Hu et al., 2008) . T hese factors were considered in my dissertation research . Data from a meta analysis indicate that the lifetime prevalence rates of suicidal ideation and attempt s in the Chinese population in general are 3.9% and 0.8%, respectively (Cao et al., 2015) . However, there is no consensus regarding suicidal

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47 behav iors among migrants in China. One group of studies suggest a higher risk of suicide among the rural migrants than among the general population (Di & Xiao, 2 004; Li et al., 2007; Yan et al., 2009; L. Yang & Chen, 2015) . For example, the prevalence rate of past year suicidal ideation was 9.6% for rural migrants in Guiyang, a city in Western China (Yan et al., 2009) , 17.4% for construction migrant workers in Changsha, Central China (Di & Xiao, 2004) , and 14.1% for young rural migrants in Wenzhou, Southeast China (L. Yang & Chen, 2015) . By contrast , another group of studies reported a lower risk of suicide among the rural migrants than among the general population in China. For example, one st udy conducted in Sichuan, Western China reported a lower prevalence of suicidal ideation among rural migrants than the non migrant residents (1.3% vs. 3.0%) (Dai et al., 2015) . Another study conducted in Southeast China indicated a lower suicidal attempt rate among rural migrants than the rural residents (1.5% vs. 2.1%) and urban residents (1.5% vs. 1.6%) (Li et al., 2007) . A further review of these publis hed studies revealed several vital limitations. First, none of these published studies used data from representative samples. The external validity of these study findings is questiona ble. Second, a majority of the published studies compared migrant suicid al behavior s with the general population, which also contain ed a large number of rural migrants. Third, there is only one s tudy comparing the risk of suicide among rural migrants with rural and urban residents (Li et al., 2007) ; unfortunately , the sample was not selected using any probability sampl ing method , limiting the validity of its conclusion.

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48 Differences in Suicidal Behaviors by Demographic Factors In addition to the overall level, suicidal behavior s among migrants may differ by demographic and socioeconomic factors. Data from one group of migrant studies reported higher rates of suicidal behaviors and deaths among females than male s (Sun, Zhou, Wang, Yu, & Yang, 2008; Tarik Yilmaz & Riecher Rössler, 2012) . Higher rates were also reported for migrants at younger ages (Burger et al., 2009; Gao, 2004) , not married (D. Lin, Ma, Xu, Lin, & Wang, 2010; Mirsky, Kohn, Dolberg, & Levav, 2011) , lower income (Westman, Hasselstrom, Johansson, & Sundqu ist, 2003) , and unemployed (Mirsky et al., 2011) . Inconsistent results are also reported, such as high er risk of suicidal behaviors for migrants who were middle aged (Sun et al., 2008; Westman et al., 2003) , married (Gao, 2004; Tarik Yilmaz & Riecher Rössler, 2012) with more education (Sun et al., 2008) and higher income (Sun et al., 2008; L. Yang & Chen, 2015) . D ifferences in the risk of suicidal behavior s among rural migrants in China may be primarily driven by the motivation to make adequate amount of money. Thus, risk of suicide could be high for younger migrants because of the lack of experience, and high among older and married migrants because of the increased burden from the family. The risk would also be particularly high for migrants with moderate level s of education (e.g., high school graduates in China) who are not competent to acquire high pay ing job s but also neither accept nor be satisfied with low pay ing jobs. A h igh risk of suicidal behavior is also expected for migrants who do not know what to do if they failed in the current city and do not know where to go.

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49 Purpose of the Study The current study estimated the prevalence rates of suicidal ideation and attempt among rural to urban migrants using data collected from a probability sample . This study also compared the rates with non migrant rural and urban residents. The goal is to provide data supporting future research to promote suicide prevention intervention among the large number of rural to urban migrants in China. Materials and Methods Population and Participants Data used in the study were from a health behavior survey conducted in Wuhan, China (Chen et al., 2017; Chen, Yu, Zhou, et al., 2015) . The target population was rural to urban migrants who were 18 45 years old, had a legal rural residence, stayed in the current city for at least one month, and whose main purpose of migration was to earn money. Non migrant rural and urban residents were selected as comparison groups. Rural residents were people who aged 18 45, who had legal rural r esidence, and had not migrated to any cities in the past 12 months. Non migrant urban residents were people who were also within the same age range, had legal urban residence, and had lived in the current city for five years or more. A Probability Sample Selected Using GIS/GPS A ssisted Method Selection of rural migrants Study participants were selected using the GIS/GPS assisted sampling method (Chen & Hu, 2018; Chen, Hu, et al., 2018) . As shown in Figure 2 1 , t o sample the rural to urban migrants, four stages were used. First, the targeted urban area of Wuhan was divided into mutually exclusive 100×100m geographic units (named as geounits). The primary sampling frame (PSF) was then constructed using these geouni ts after

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50 exclusion of all non residential geounits. Second, a total of 60 geounits were randomly selected from the PSF stratified by the seven d istricts of the city. Third, a research team consisting of a project coordinator, a GPS expert , and a research s taff went to the field to physically locate each selected geounit with the assistance of a GPS receiver. Fourth, on a pre scheduled date and time, a data collection team was dispatched to the located geounit, one at a time, to recruit participants and coll ect data. From each selected geounit, only 20 participants (10 male and 10 female) were randomly selected. This step was completed through a random route technique with a natural marker, and then the selected households were enumerated to form the seconda ry sampling frame (SSF). With the SSF, one participant per gender was selected from one household. For a household with more than one eligible participant, the Kish Table was used to randomly select one (Kish, 1949) . Select ion of non migrant urban and rural residents For effective comparison purposes, non migrant urban residents were selected from the same geounits where the rural migrants were sampled and recruited using the exact same method described above. It was diffic ult to select non migrant rural residents from the location of the rural migrants in this study because included rural migrants came from almost all other parts of China. Since most of the migrants in Wuhan came from the surrounding counties, the following protocol was used to sample non migrant rural residents. First, the target geographic area for rural residents was defined as a band region surrounding Wuhan , with the inner radius of 50 kilometers and outer radius of 75 kilometers, covering the place whe re most rural migrants in Wuhan originated from. Second, the band area was divided into mutual ly exclusive, 1 km×1km geounits . T he PSF was thus formed after

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51 exclusion of non residential geounits. Third, the band range was divided into 40 strata using five co centric bands 5 kilometers apart and four lines (north south, west east, northwest southeast, and northeast southwest). Fourth, one geounit was randomly selected from each stratum with a total of 40 geounits. Fifth, the households within the selected g eounit formed the SSF, and 30 participants (15 male and 15 female) per geounit were randomly selected from SSF. To ensure enough participants enrolled, an additional 20% of geounits were included. Among the approached 4215 eligible participants, 6% refus ed to participate , with a final 3951 participants enrolled. Data Collection Data collection was completed from 2011 to 2013. The Migrant Health and Behavioral Survey , a pilot tested survey , was delivered to the participants through the Audio Computer Assisted Self Interviewing (ACASI). The survey was anonymous and confidential. Participants were asked to complete the survey independently in a or in the local community health center. Upon completion of the survey, each participant received a $6 reward. The survey was approved by the Institutional Review Board (IRB) at Wayne State University and Wuhan CDC, and the data analysis was approved by th e IRB at the University of Florida. Variables and Measurements Suicidal ideation

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52 Suicidal attempt Similar measurements of suicidal ideation and attempt have been widely used in different population surveys in the U.S. , including the National Survey on Drug Use and Health (SAMHDA, 2018) and the National Epidemiologic Survey on Alcohol and Related Conditions (NIAAA, 2015) . So cio economic variables Socio economic variables in the study include d age (in years, categorized into 25 or less, 26 35, >35), gender (male , and female), marital status (married , unmarried , divorced , and widowed, categorized into married and not married), education attainment (primary or less , middle school , high school , and college or more), monthly income (in RMB) (<1000 , 1000 2000 , 2000 4000 , and >4000), number of family members (3 or less , 4 5 , and 6 or more), if unemployed in the past 12 months (yes a nd no), and the intention to move in the next six months (likely , unsure , and unlikely). Statistical Analysis Descriptive analyse s ( e.g. frequency, proportion, mean and standard deviation) were used to describe the study sample and to assess the prevalence rate s of the two suicidal behaviors. S urvey analysis procedures for descriptive statistics were used to obtain point estimates for means and prevalence rate and 95% confidence interval s (CI). The survey analysis procedures are needed to consider the complex GIS/GPS assisted probability sampling design, including stratification, clustering, unequal probability, and sample weights (Wood ruff, 1971) . The estimated 95% CI of the

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53 estimated prevalence rates was used for comparison with no overlap between two estimated 95% CI s as the evidence of significant differences at the level of p <0.05. Second, to compare the difference in the suic ide risk between rural migrants and non migrant rural and urban residents while controlling for demographic factors, the survey analysis procedure of multivariate logistic regression was used. The current study first compared the rural migrants with rural residents; followed by the comparison of rural migrants with urban residents. Adjusted odds ratio (AOR) with a 95% CI was estimated, and the 95% CI not covering one indicates significant differences at the level of p <0.05. All statistical analyses were c onducted using SAS 9.4 (SAS Institute, Cary, NC). PROC SURVEYMEANS was used for descriptive analysis, and PROC SURVEYLOGISTIC was used for multivariate logistic regression analysis. Results Demographic Characteristics of the Study Sample Results in Table 3 1 show that among the total sample, 1 , 290 (32.65%) were rural migrants, 1 , 290 (32.65%) rural residents and 1 , 371 (34.70%) urban residents. Among the rural migrants, nearly half were male , with a mean age of 32.33 years old (SD=7.97). More than two thirds were married, and more than 30% had a high school or more education. Other i nformation can be found in the T able 3 1 . Compared to the non migrant urban residents, the migrants were younger, more likely to be married, and less educated . They were also less likely to be unemployed and more likely to move. Relative to the non migrant rural residents, the migrants were also younger, but were less likely to be married, were more educated, and more likely to

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54 move. Significant differences across the three groups were found in age, marital status, education, income, family size, unemployment and likelihood to move (p<0.05). Prevalence of Suicidal Ideation among Rural Migrants The prevalence rate [95% CI] of suicidal ideation estimated using the survey method in Tab le 3 2 indicate that 5.74% [4.81, 6.68] of the rural migrants reported having had suicidal ideation during the past 12 months . The estimated prevalence rates varied dramatically across different demographic and socioeconomic factors as detailed in Table 3 2 . The difference was particularly large by age, gender, education, income, and intention to move. Comparison of Suicidal Ideation with Non migrant Rural and Urban Residents Results in Table 3 2 further indicate that the estimated prevalence rates of suici dal ideation for the rural migrant sample were higher than the non migrant rural resident sample. The differences present ed for the overall sample and by demographic factors, although not all the differences were statistically significant. For example, am ong those aged 26 35, the estimated prevalence rate was 0.79% [0.02, 1.56] for rural migrants but 7.94% [2.70, 13.18] for rural residents, indicating a 10 fold difference. After adjusting the demographic variables ( Figure 3 1 ) , the difference in suicidal ideation was not statistically significant between rural migrants and rural residents with AOR = 0.8 8 [0.46, 1.67]. Likewise, results in Table 3 2 also showed large differences in the estimated prevalence rates of suicidal ideation between rural migrants and non migrant urban residents. Overall, only 3.65% [2.64, 4.65] of the urban residents reported past year suicidal ideation, significantly lower than the rate for rural migrants. In addition, the estimated prevalence rate for urban residents varied across a smaller range compar ed

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55 to the range of rural migrants. After adjusting the demographic variables ( Figure 3 2 ) , rural migrants showed a signifi cantly higher risk of suicidal ideation than urban residents with AOR=1.93 [1.26, 2.94]. Prevalence of Suicidal Attempt among Rural Migrants Table 3 3 shows that 5.47% [4.57, 6.36] of rural migrants have reported suicidal attempt s in the past 12 months. I n addition, among the rural migrants who had suicidal ideation, 88.11% [78.16, 98.07] reported suicidal attempt s , significantly higher than 18.60% [6.33, 30.88] for rural residents and 50.77% [36.83, 64.72] for urban residents. Similar to the rate of suici dal ideation in Table 3 2 , the estimated rate of suicidal attempt s also varied dramatically across the demographic and socioeconomic factors, particularly those of age, gender, education, income and intention to move to another city. Comparison of Suicida l Attempt with Non migrant Rural and Urban Residents Results in Table 3 3 further indicate that rural migrants had a significantly higher prevalence rate of suicidal attempt s than non migrant rural residents, overall (5.47% [4.57, 6.36] vs. 1.14% [0.43, 1. 85]) and by demographic variables, including age, gender, marital status, education, income and intention to move. The difference remained significant after adjusting for the demographic variables with AOR=2.89 [1.12, 7.43] ( see Figure 3 3 ) . Results in Ta ble 3 3 also show that the prevalence rate of suicidal attempt s among rural migrants was significantly higher than that of urban residents, overall (5.47% [4.57, 6.36] vs. 2.01% [1.21, 2.82]) and by demographic variables. The variation of the prevalence ra te of suicidal attempt s was smaller than that among rural migrants.

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56 After adjusting for the demographic variables , t he difference remained significant with AOR=3.97 [2.15, 7.34] ( see Figure 3 4 ) . Results in Figure 3 5 summarized the results in Figures 3 1 to 3 4 and show the risk of suicidal ideation and attempt among rural migrants compared to rural and urban residents. Overall, rural migrants had a greater risk of suicidal ideation and attempt th an non migrant rural and urban residents except the non significant difference of the prevalence rates of suicidal ideation between rural migrants and rural residents. Discussion and Conclusion T his study documented the prevalence rates of suicidal ideati on and attempt, the two most commonly used suicidal behaviors among rural migrants in China using data collected from a probability sample. The study also compared the prevalence rates of these two behaviors in the rural migrants with those in the non migr ant rural and urban residents , respectively. To the best of our knowledge, this is the first suicidal behavior study of rural migrants in China with a probability sample and a systematic comparison of the rural migrants to both non migrant residents in rur al area s where most of the rural migrants come from and non migrant urban residents where the rural migrants work and live. Findings of the study fill the data gap regarding suicidal behaviors among rural to urban migrants in China and provide basic eviden ce for future research to examine factors associated with suicide for risk reduction. Rural Migrants are at Increased Risk of Suicide First of all, findings of this study have helped clarify the contradictory findings regarding suicide risk among rural mi grants . Reported findings of a lower risk of suicide among rural migrants than the general population are not valuable since these studies did not use probability samples (Dai e t al., 2015; Li et al., 2007) . Findings of my study

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57 clearly indicate that s uicide is more prevalent among rural migrants in Wuhan, a typical city with a large number of rural migrants who come from almost all parts of China. Findings of the current study are more valid than other research in several ways. First, results from this study were derived from a probability sample that can be generalized to the whole migrant population. Second, in this study , the samples of rural migrants and rural reside nts shared a similar environment in the rural areas where the migrants come from . Therefore, the study c an conclude that the increased risk of suicide for rural migrants is related to the process of migration and to the issues related to their work and liv ing in urban settings. Last, the sample of rural migrants and urban residents were selected from exactly the same geographic areas in the city . This further demonstrat es that the increased risk of suicide among migrant s is due to migration, including the p rocess of settling down in the urban areas and challenges to adapt to urban working settings and lifestyles. The estimated rate of suicidal ideation and attempt s from this study indicate that in Wuhan , with one million rural migrants, every year approxim ately 57, 4 00 of them may ha ve considered suicide, 5 4 , 7 00 attempted , and 5, 470 8,2 05 would eventually die by suicide, assuming 10 15% of suicide attempters eventually kill themselves (Maris et al., 2000; Maris, 1992) . If the same rates were applied to the total of 282 million rural migrants in China, every year, an estimate of 16 million of them will consider suicide, 15 million will attempt , and 1.5 0 2.25 million will finally die by suicide. As a result, g reat effor ts are needed for suicide prevention among the rural migrant population in China. Characteristics of the Suicidal Behaviors among Chinese Rural Migrants Relative to the migrants aged 26 35, migrants who were 25 or young er , and 36 and older showed a highe r risk of suicide. The young migrants may be less experienced

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58 and more impulsive and ambitious about their future. These obstacles may have prevented them from making adequate amount s of money, putting them at increased risk of suicide because of the discr epancy between the ir aspiration s and reality (McGirr et al., 2008) . F or the older migrants, many of them were married , with the hope that they would make enough money to support old er parents and young children . If the money they earned did not meet those the needs, this may expos e them to increased risk of suicide (Zhang et al., 2009) . The same principle may also explain the increased risk of suicide among rural migrants who were married, as reported in many other studies (D. Lin et al., 2010; Mirs ky et al., 2011; Sun et al., 2008; Westman et al., 2003) . And again, t he same principle may be used to explain the increased risk of suicidal behavior among migrants with large family sizes as observed in this study. This study found a higher risk of suicide among males than females, which was inconsistent with a few published migrant studies conducted in other countries (Mirsky et al., 2011; Sun et al., 2008; Tarik Yilmaz & Ri echer Rössler, 2012; Westman et al., 2003) . One explanation for the gender difference could be that the male rural migrants are still ascribed to the traditional value that complementary to the ity , many female migrants can find better job s and/or make the same or even more money. The upheaval of the idea of the superiority of male over female may make male rural migrants discredit themselves, leading to increased risk of suicide (Chen, Sun, et al., 2018; Guo et al., 2016) . One unique finding of this study is that rural migrants with a high school education and low middle level income are at the highest risk of suicide compared to those with either lower or higher levels of education and income. In a rural area, a

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59 person with a high school education is very competitive . H e/she will thus be very proud of him/herself and respected by oth ers. However, a person with a high school education would not be competent at all to acquire a decent job and make money in the urban environment . The unrealistic expectation s (Chen, Sun, et al., 2018) , lack of competence, and the low middle level income creates the main strains (Zhang et al., 2009) , rendering these migrants at high risk of suicide . Following the same principle, the lower risk for migrants w ith less education/low income could be due to a lower expectation and higher satisfaction among these migrants, despite the limited competence/income (Chen, Sun, et al., 2018) . Lastly, in addition to confirm ing the widely accepted research findings that unemployment put s migrants at risk for suicide (Bhui et al., 2003; Mirsky et al., 2011) , t his study found that rural migrants were at the h ighest suicide risk when they did not know/ were unsure if they inten ded to move or not. It is a common practice for a migrant to move from one place to another to avoid difficulties in the current settings and to look for better opportunities/environments. Therefore, it is not the intention merely to more or not, but the uncertainty or the dilemma about whether or not to move that renders high suicide risk. Although there is hope for the action of moving , i t is not without risk . I f the migrants do not move, and the current setting is not good; what should they do? Differences in Suicidal Behaviors between the Rural Domestic Migrants in China and International Immigrants in Other Countries Findings from thi s study indicate high er rates of suicidal behaviors among the domestic rural migrants in China than the international immigrants in many developed countries, such as the United States (Borges et al., 2009; Duldula o, Takeuchi, & Hong, 2009) , Canada (Pan & Carpiano, 2013) , the United Kingdom (Bhugra, 2002) and a

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60 number of European countries (Bursztein Lips icas et al., 2012) . Although reversed findings are reported in a few studies (Beutel et al., 2016; Fortuna et al., 2016; Ponizovsky, Ritsner, & Modai, 1999) . The extra suicide risk rendered for the rural to urban migrants in China than the international immigrants requires further investigation since no published studies have ever compared the risk of suicide between the two. I ncreased suicide risk among Chinese rural migrants could be due to their unique characteristics although these migrants move within the same country. For example, relative to immigrants in other countries, the rural migrants are less educated, more likely to migrate alone, and have an expectation to support families. Limitations and Future Research There are several limitations in this study . First, the data used for this study were collected in one city in China. Caution is needed when generating the findings of this study to other places within and outside of China. Second, suicidal behaviors were measured based on self report, underreport is likely leading to misclassi fication because of the obvious sensitivity of the topic area. Last, rural resident sample was from a band region where most, but not all rural migrants come from because of practical limitations. The p otential difference in suicidal behaviors among rural residents in the sampled area and all areas where the sampled migrants come from may confound the comparison of migrants with rural residents, although the impact may be a minimal . Despite these limitations, this is the first study to document the prevale nce of suicidal behaviors among rural migrants with a probability sample and to contrast the results with comparable non migrant rural and urban residents. Findings from this study provide basic data for suicide prevention planning and decision making . The evidence

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61 may support future etiological research to understand factors associated with increased suicide risk among rural migrants for evidence based prevention intervention programing.

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62 Table 3 1. Characteristics of the study sample of rural to urban migrants, rural and urban residents Variables Rural migrants Rural residents Urban residents Total Total, n (%) 1290 (32.65) 1290 (32.65) 1371 (34.70) 3951 (100.00) Age in years, n (%) ** 25 or less 324 (25.12) 185 (14.34) 184 (13.42) 693 (17.54) 26 35 468 (36.28) 297 (23.02) 462 (33.70) 1227 (31.06) >35 498 (38.60) 808 (62.64) 725 (52.88) 2031 (51.40) Mean (SD) 32.33 (7.97) 36.02 (7.71) 35.19 (7.51) 34.53 (7.88) Gender, n (%) Male 658 (51.01) 638 (49.46) 640 (46.68) 1936 (49.00) Female 632 (48.99) 652 (50.54) 731 (53.32) 2015 (51.00) Marital status, n (%) ** Not married 293 (22.77) 137 (10.62) 323 (23.56) 753 (19.07) Married 994 (77.23) 1153 (89.38) 1048 (76.44) 3195 (80.93) Education, n (%) ** Primary or less 178 (13.83) 385 (29.64) 58 (4.23) 621 (15.73) Middle school 694 (53.92) 691 (53.57) 308 (22.47) 1693 (42.88) High school 332 (25.80) 191 (14.81) 480 (35.01) 1003 (25.41) College or more 83 (6.45) 23 (1.78) 525 (38.29) 631 (15.98) Income (RMB) , n (%) ** <1000 268 (20.78) 376 (29.15) 286 (20.86) 930 (23.54) 1000 2000 549 (42.56) 395 (30.62) 654 (47.70) 1598 (40.45) 2000 4000 388 (30.08) 391 (30.31) 318 (23.19) 1097 (27.77) >4000 85 (6.59) 128 (9.92) 113 (8.24) 326 (8.25) Family size, n (%) ** 3 or less 401 (31.09) 303 (23.49) 920 (67.10) 1624 (41.10) 4 5 650 (50.39) 791 (61.32) 386 (28.15) 1827 (46.24) 6 or more 239 (18.53) 196 (15.19) 65 (4.74) 500 (12.66) Unemployed, past 12 months, n (%) ** Yes 726 (56.28) NA 939 (68.49) 1665 (62.57) No 564 (43.72) NA 432 (31.51) 996 (37.43) Intention to move in the next six months , n (%) ** Likely 249 (19.30) 185 (14.34) 131 (9.56) 565 (14.30) Unsure 230 (17.83) 152 (11.78) 152 (11.09) 534 (13.52) Unlikely 811 (62.87) 953 (73.88) 1088 (79.36) 2852 (72.18) Note: 1 NA=not available. 2 *p<0.05, **p<0.001

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63 Table 3 2 . The prevalence rates of suicidal ideation among rural to urban migrants and non migrant rural and urban residents, overall , and by demographic and other covariates, % [95%CI] Variables Rural migrants Rural residents Urban residents Total 5.74 [4.81, 6.68] 4.74 [3.07, 6.42] 3.65 [2.64, 4.65] Age in years 25 or less 3.67 [0.88, 6.46] 2.03 [0.00, 4.53] 1.55 [0,00, 3.15] 26 35 0.79 [0.02, 1.56] 7.94 [2.70, 13.18] 5.42 [3.14, 7.71] >35 11.51 [9.61, 13.41] 4.35 [2.44, 6.25] 3.03 [1.88, 4.17] Gender Male 9.95 [8.43, 11.46] 5.13 [2.88, 7.39] 5.51 [3.69, 7.32] Female 1.48 [0.34, 2.62] 4.38 [1.95, 6.81] 1.87 [0.95, 2.79] Marital status Not married 2.90 [0.40, 5.40] 4.17 [0.81, 7.53] 4.89 [2.45, 7.32] Married 6.24 [5.23, 7.26] 4.80 [3.00, 6.59] 3.38 [2.38, 4.49] Education Primary or less 4.10 [0.00, 8.30] 4.73 [1.84, 7.62] 2.94 [0.00, 6.60] Middle school 1.55 [0.49, 2.61] 5.23 [2.66, 7.81] 3.09 [1.08, 5.11] High school 13.58 [12.27, 14.89] 2.08 [0.00, 4.21] 4.72 [2.96, 6.47] College or more 0.90 [0.00, 2.59] 11.85 [0.00, 25.08] 3.01 [1.34, 4.68] Income (RMB) <1000 2.10 [0.00, 4.32] 5.41 [2.02, 8.80] 3.74 [1.33, 6.14] 1000 2000 10.61 [9.11, 12.11] 4.74 [1.85, 7.64] 3.48 [2.06, 4.91] 2000 4000 2.21 [0.16, 4.26] 5.27 [1.92, 8.63] 3.32 [1.65, 5.00] >4000 0.91 [0.00, 2.53] 1.21 [0.00, 2.79] 5.22 [0.61, 9.82] Family size 3 or less 6.63 [5.48, 7.78] 4.60 [2.22, 6.99] 3.57 [2.31, 4.83] 4 5 2.32 [0.50, 4.14] 4.63 [2.47, 6.79] 3.81 [2.04, 5.58] 6 or more 10.03 [7.96, 12.09] 5.55 [0.27, 10.83] 3.60 [0.00, 8.54] Unemployed, past 12 months Yes 7.43 [6.26, 8.61] NA 2.92 [1.94, 3.90] No 2.10 [0.61, 3.59] NA 5.38 [2.92, 7.85] Intention to move in the next six months Likely 0.95 [0.00, 2.02] 6.74 [1.04, 12.44] 6.54 [2.93, 10.15] Unsure 15.12 [11.79, 18.45] 4.18 [1.21, 7.16] 5.60 [1.15, 10.05] Unlikely 4.96 [3.75, 6.17] 4.49 [2.57, 6.41] 3.03 [2.01, 4.05] Note: The prevalence rate s [95% CI] were estimated using the survey estimate method to consider the complex sampling design and different sample weight (see text for more details about the method).

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64 Table 3 3 . The prevalence rates of suicidal attempt among rural to urban migrants and non migrant rural and urban residents, overall and by demographic and other covariates, % [95%CI] Variables Rural migrants Rural residents Urban residents Total 5.47 [4.57, 6.36] 1.14 [0.43, 1.85] 2.01 [1.21, 2.82] Age in years 25 or less 2.51 [0.77, 4.25] 0.77 [0.00, 1.88] 0.00 [0.00, 0.00] 26 35 0.74 [0.00, 1.51] 3.79 [0.25, 7.33] 3.38 [1.49, 5.27] >35 11.36 [9.41, 13.31] 0.54 [0.13, 0.95] 1.61 [0.71, 2.51] Gender Male 9.08 [8.09, 10.07] 1.90 [0.47, 3.34] 3.37 [1.86, 4.89] Female 1.80 [0.24, 3.36] 0.43 [0.12, 0.73] 0.72 [0.15, 1.28] Marital status Not married 2.39 [0.69, 4.09] 3.57 [0.00, 7.89] 2.93 [0.90, 4.96] Married 6.01 [5.00, 7.02] 0.91 [0.25, 1.58] 1.82 [0.95, 2.69] Education Primary or less 2.53 [0.00, 5.42] 0.24 [0.00, 0.52] 3.00 [0.00, 6.05] Middle school 0.97 [0.29, 1.66] 1.33 [0.22, 2.44] 2.50 [0.55, 4.46] High school 14.35 [12.25, 16.44] 2.29 [0.00, 5.20] 2.17 [0.96, 3.38] College or more 1.10 [0.00, 2.80] 3.87 [0.00, 11.40] 1.36 [0.10, 2.62] Income (RMB) <1000 1.65 [0.00, 3.73] 1.18 [0.00, 2.92] 2.37 [0.25, 4.48] 1000 2000 10.07 [8.85, 11.30] 0.69 [0.09, 1.28] 1.80 [0.65, 2.95] 2000 4000 1.32 [0.03, 2.60] 1.41 [0.04, 2.78] 1.57 [0.47, 2.68] >4000 3.81 [0.00, 9.54] 1.47 [0.00, 3.26] 3.57 [0.12, 7.03] Family size 3 or less 7.27 [5.43, 9.11] 1.30 [0.11, 2.49] 2.10 [1.02, 3.17] 4 5 1.35 [0.24, 2.45] 0.50 [0.10, 0.89] 1.82 [0.62, 3.01] 6 or more 9.38 [7.77, 11.00] 4.07 [0.00, 8.65] 2.22 [0.00, 5.60] U nemployed , past 12 months Yes 6.71 [5.92, 7.51] NA 1.19 [0.60, 1.70] No 2.78 [0.52, 5.04] NA 3.99 [1.70, 6.28] Intention to move in the next six months Likely 1.11 [0.00, 2.25] 3.31 [0.00, 7.88] 4.42 [1.30, 7.53] Unsure 15.25 [11.90, 18.61] 2.43 [0.00, 4.93] 3.23 [0.00, 7.11] Unlikely 4.47 [3.34, 5.60] 0.56 [0.25, 0.87] 1.56 [0.80, 2.32] Suicidal ideation No 0.43 [0.00, 1.06] 0.27 [0.00, 0.64] 0.17 [0.03, 0.31] Yes 88.11 [78.16, 98.07] 18.60 [6.33, 30.88] 50.77 [36.83, 64.72] Note: The prevalence rate s [95% CI] were estimated using the survey estimate method to consider the complex sampling design and different sample weight (see text for more details about the meth od). N/A: not applicable.

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65 Figure 3 1. Comparison of risk of suicidal ideation between rural to urban migrants and rural residents Note: PROC SURVEYLOGISTIC was used for data analysis.

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66 Figure 3 2. Comparison of risk of suicidal ideation between rural to urban migrants and urban residents Note: PROC SURVEYLOGITIC was used for data analysis.

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67 Figure 3 3. Comparison of risk of suicidal attempt between rural to urban migrants and rural residents Note: PROC SURVEYLOGITIC was used for data analysis.

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68 Figure 3 4. Comparison of risk of suicidal attempt between rural to urban migrants and urban residents Note: PROC SURVEYLOGITIC was used for data analysis.

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69 Figure 3 5 . Risk of suicidal ideation and attempt among rural to urban migrants compared to non migrant rural and urban residents Note: 1 Age, gender, marital status, education, income , and family size were controlled in the multivariate logistic model. 2 PROC SURVEYLOGISTIC was used for data analysis.

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70 CHAPTER 4 INVESTIGATION OF THE RELATIONSHIP BETWEEN SOCIAL CAPITAL, DURATION OF MIGRATION AND SUICIDAL BEHAVIORS AMONG RURAL TO URBAN MIGRANTS IN CHINA Introduction Suicide and Suicidal Behaviors S uicide is death caused by self directed injurious behaviors with the intent to die (CDC, 2017) . Data from the World Health Organization and other agencies indicate that suicide represents a vital public health challen ge in many countries across the world (WHO, 2018a) , including China (Jiang et al., 2018; P hillips, Li, et al., 2002; Phillips, Yang, et al., 2002) . Suicide results from a cascade of suicidal behaviors starting with suicidal ideation , to planning, attempting, and ultimately dying by suicide. Among these behaviors, suicidal ideation and attemp t are of particular significance since they are the two most important precursors of death by suicide (Nock et al., 2008) . In the literature, suicidal ideation is defined as thinking about, considering, or planning suicide; suicidal attempt is defined as self directed, non fatal injurious behaviors with the intent to die (Nock et al., 2008) . The 12 month prevalence rate of suicide ideation in different populations varie s from 1.8 to 21.3%, and among those who have ever had suicidal ideation, 2.5 3.8% eventually die by suicide. The 12 month prevalence rate of suicidal attempt varies from 0.1 to 3.8%, and among those who have ever attempted suicide, 10 15% will eventually d ie by suicide (Nock et al., 2008) . Therefore, understanding factors related to these two suicidal behaviors will provide data essential for evidence based interventions for suicide.

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71 Social Capital and Suicide Many biopsychosocial influential factors related to suicide are documented in the literature (Steele, Thrower, Noroian, & Saleh, 20 18) . Among the factors known to be associated with suicide, those that are rooted in society are essential for suicide etiology research and prevention (Douglas, 2015; Stack, 2000) . Among many influential social factors, social capital is of particular significance (Berkman et al., 2014) . connections (Berkman et al., 2014; Coleman, 1988) . It functions as social bonds linking individuals with society and enables those who possess it to access the resources that are scattered in society (Chen et al., 2011) . Social capital can empower those who possess it to receive informational, instrumental, and emotional support as needed to reduce suicide risk (Chen et al., 2009) . Social capital may also reduce suicide risk through informal social control, such as collective efficacy through voluntary actions by community members against social ly undesirable behaviors (Chen et al., 2009; Sampson, Raudenbush, & Earls, 1997) . Evidence from ecological studies suggests a negative association between social capital and suicide. In his pioneering study, Durk h eim reported a negative associatio n between social cohesion and suicide (Durkheim, 1897) . A series of cross cultural studies reported higher suicide rates in countries with lower social capital, including a low level of trust and poor social cohesion (Helliwell, 2007; Kelly et al., 2009) . T his negative association has been replicated in studies in which social capital was measured by aggregating data at the individual leve l (Kunst et al., 2013; Okamoto et al., 2013 ; Smith & Kawachi, 2014) . The association has also been replicated with direct

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72 analyses of individual level data (Congdon, 2012; Fitzpatrick et al., 2007; Lindström & Rosvall, 2015) . Need for In depth Research on Social Capital Suicide Relationship Establishment of an overall negative relationship between social capital and suicide provides a foundation for in depth investigation of the underlying mechanisms. Social capital as a social network based property consists of a range of important attributes (Chen et al., 2009; Putnam, 2000) . For example, social capital can be determined either by the size of the network, or the inclusion of highly trustworthy, or highly resource rich, or strong reciprocal persons (Berkman et al., 2014; Chen et al., 2009) . Associations between some of these social capital attributes and suicide risk have already been reported. For example, a net work of highly trustworthy persons has been shown to be associated with a reduced risk of suicide (Lindström & Rosvall, 2015) . Further research is needed to investigate the role of different attributes in suicide. Another important feature of social capital is that social capital can be acquired from different sources. For example, some individuals may acquire social capital primarily from their family members, relatives, neighbors, or friends; others may do so mainly from co workers and/or country fellows (i.e. migrants from the same hometown ) . Social capital obtained from different sour ces may affect suicide risk differently. For example, s eparation from family members in the hometown may increase suicide risk among migrant populations (Guo et al., 2016) . Studies are needed to examine the effects of social capital on suicide from different sources.

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73 Lastly, there are two approaches for individuals in a society to acquire soci al capital. The first approach consists of building ties through cultural, recreational, political and /or governmental organization/groups (Chen et al., 2009; Coleman, 1988) . Social capital obtained through this approach is termed as bridging capital. The second approach to construct social capital is not through organization or groups but through free contact. This type of social capital is termed as bonding capital (Chen et al., 2009; Putnam, 2000) . Studies are also needed to exa mine whether these two types of social capital differ from each other in affecting suicide risk. Rural to U rban Migrants in China as a Unique Opportunity There is a large and growing number of rural to urban migrants (rural migrants hereafter) in China, cu rrently totaling over 280 million (National Bureau of Statistics of the PRC, 2017) . T his population provides a unique opportunity to conduct in depth research dissecting the relationship between social capital and suicide. Suicidal behaviors are prevalent among rural migrants in China with a docume nted median rate of 9.6% (ranging from 1.3 % to 17.4%) of suicidal ideation in the past 12 months (Dai et al., 2015; Di & Xiao, 2004; Li et al., 2007; Yan et al., 2009; L. Yang & Chen, 2015) . These rates are much higher than 3.9%, the rate for the general Chinese population (Cao et al., 2015) . The high prevalence rates of suicidal behavior suggest s the urgent need to examine the social capital suicide relationship. In addition to high prevalence of suicidal behaviors, migrants may experience substantial changes in social capital, including social capital loss due to the migration and social capital reconstruction after migration (Ryan et al., 2008) . When migrating, people may lose social capital because they have to break their ties with the individuals, and social groups/organizations in the place of origin, such as family members,

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74 relatives, ne ighbors and friends who have not migrate d with them (Ryan et al., 2008) . In their new locations, migrants have to rebuild their social capital to live and prosper (Bhugra, 2004; Li et al., 2007) , but their continued mobility may prevent them from accumulating social capital. The r eduction in their social capital may put them at increased risk of suicidal behaviors. Potential Interaction s of Migration Duration and Social Capital on Suicide The relationship between social capital and suicidal behaviors may differ for rural migrants with different length of migration duration. It takes time for rural migrants to gain career success in urban ar eas , and lack of career success is a significant predictor of suicidal behavior s among migrants (Steele et al., 2018) . During the early stage of migration, migrants seek economic success as what is known as an objective career goal, and social capital in this stage may facili tate rural migrants to achieve the goal (Tharmaseelan, Inkson, & Carr, 2010) . During the later stage , once their economic status has been established, rural migrants begin seeking s ocial status as a subjective career goal since established urban residents in China enjoy a host of social programs and welfare benefits traditionally denied to migrants although the situation is changing now. Ironically, in the later stage of migration, r ural migrants with more social capital may find that they become less satisfied as they have acquired economic success. The social capital acquired for objective career goal in the early stage may thus become less helpful for rural migrants to obtain socia l status in the later stage . Relative to those with less social capital, rural migrants with more social capital in the later stage may be more likely to consider them selves as socially less successful as they begin to compare themselves with the urban re sidents and others within their

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75 enlarged network connections. If this hypothesis is true, social capital will reduce suicide risk for migrants with short migration duration; while generat ing no effect or even increas ing suicide risk for migrants with long migration duration. To our knowledge, n o reported studies have ever examined this issue. Purpose of the Study Supported by social capital theory and building upon the previous research, this study aims to examine the relationship between social capital and suicidal behaviors among rural migrants by focusing on different attributes and sources of social capital, as well as the interaction between social capital measures and migration duration. The ultimate goal is to provide evidence supporting future etiological and intervention research for suicide risk reduction. Materials and Methods Participants and Sampling D ata used in the study were derived from a NIH funded proj ect used to examine health risk behaviors among rural migrants in China. More details regarding the project can be found elsewhere (Chen et al., 2017; Chen, Yu, Zhou, et al., 2015) . The current analysis was conducted among rural migrants aged 18 45 years old, who possess ed a legal rural residence, and who have lived in the ir current city for at least one month with the purpose of earning money from the work . Participants were recru ited from Wuhan. As the capital city of Hubei Province, Wuhan has a total population of 12 million, plus approximately one million rural migrants with a per capita GDP of $18,000 (Hubei Statistical Bureau, 2017) . P articipants were selected using the GIS/GPS assisted probability sampling method with a multi stage sampling strategy (Chen & Hu, 2018; Chen, Hu, et al., 2018) .

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76 As shown in Figure 2 1, i n S tage I, Wuhan was divided into mutually exclusive 100m×100m geographic units (geounits) and these geounits were used to construct the primary sampling unit (PSF) after exclusion of all non residential geounits. In S tage II, a total of 60 geounits were randomly selected from the PSF. In S tage III, researchers went to the selected geounits to physically locate the geounit assisted with a GPS receiver. In Stage IV, a fter a geounit was determined, the random route technique with natural markers was used to sample and enumera te all households, forming the secondary sampling frame (SSF). A pproximately 20 participants (10 male and 10 female) were randomly recruited from the SSF with one participant per gender per household. The Kish Table method was used to select participants f or households with more than one eligible migrant (Kish, 1949) . Among the total 1 , 414 participants approached, 124 (8.4%) refused to participate with a final sample of 1 , 290 migrants enrolled. Data Collection Data collection was conducted from 2011 to 2013. Data were collected using the Migrant Health and Behavioral Survey delivered through Audio Computer Assisted Self Interviewing (ACASI). The survey was confidential and voluntary. P articipants were instructed to complete the survey in a private room either in their own home or at a local community health center. Each participant received a material reward with a value of $6 after completing the survey. The original study was approved by the Institutional Revie w Board (IRB) at the Wayne State University and Wuhan CDC, and the current data analysis was approved by the IRB at the University of Florida.

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77 Variables and Measurement Suicidal behaviors Two suicidal behaviors were used in this study. Suicidal ideation w as measured Yes ) and 0 ( No ). Similarly, a Yes ) and 0 ( No ). Personal social capital Social capital was assessed using the Personal Social Capital Scale (PSCS) (Chen et al., 2009) . The PSCS provided detail ed measures for the four important social capital attributes, including 1 ) personal network size , assessed using a Likert scale varying from 1 ( a few ) to 5 ( a lot ) , 2 ) trust, 3 ) reciprocal, and 4 ) resource rich with individual items assessed using another Likert scale with 1 ( very few ) and 5 ( almost everyone ). T he four attributes were assessed with six sources of social capital, including 1 ) family members, 2 ) relatives, 3 ) neighbors, 4 ) friends, 5 ) work colleagues, and 6 ) country fellows (i.e. rural migran ts from the same hometown) . The remaining 8 items provided measures for bridging capital attributes, including 1 ) size of social groups/organizations , measured using a Likert scale varying from 1 ( a few ) to 5 ( a lot ) ; 2 ) representation of interests ; 3 ) provision of assistance ; and 4 ) possession of resources, all being measured with another Likert scale with a range from 1 ( very few ) to 5 ( almost everyone ). The four attributes were assessed among two

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78 sources: 1 ) political, economic and social groups/organ izations, and 2 ) cultural, recreation al and leisure groups/organizations. The PSCS was developed, pilot tested and validated among adults in China (Chen et al., 2009) and cross culturally validated among college students in the United States (Archuleta & Miller, 2011) . The Cronbach alpha of PSCS was 0.91 in the current study. Mean scores were computed for the PSCS, the bonding and bridging capital, all attributes and sources for both bonding and bridging capital. Migration related variables Migration related variables were assessed, including years of migration (in years, categorized into <5, 5~, 10~, and 15 years or more), number of cities migrated to (1, 2 3, and 4 or more), frequency of home visits in the past 12 months (0, 1 2, and 3 or more), occupation ( business store/self employed, restaurant/hotel, manufacturing, construction site, others, and unemployed), intention to move in the next six months ( l ikely, unsure, and unlikely), and future plan (stay in the city, unsure, and go back to rural area ). Demog raphic variables The demographic variables included in the study were: age (in years, categorized into 32 or less and > 32 years old), gender (male and female), marital status (married, and not married), education (primary or less, middle school, high sch ool, and college or more), income (in RMB, <1000, 1000 2000, 2000 4000, and >4000) , and family size (3 or less, 4 5, and 6 or more) .

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79 Statistical Analysis Descriptive statistics were used to present the sample characteristics. S urvey analysis procedures w ere used to compute the means and prevalence rate with a 95% confidence interval (CI) considering the complicated multi stage GIS/GPS assisted probability sampling design with unequal probabilities and sample weights (Woodruff, 1971) . Mean scores were computed for all social capital measures and compared between participants with and without a suicidal behavior , overall and by different years of migration . A difference was considered statistical ly significant at the p< 0 .05 level if the estimated 95% CI between two comparison groups did not overlap with each other. To further investigate the social capital suicidal behavior relationship, including the interaction between social capital and the d uration of migration, the following logistic regression model was used: ( 4 1) w here p: the probability of a suicidal behavior measure, : the intercept, SC : a social capital measure, : years of migration, : social capital years of migration interaction, and : covariates (i=1, 2,..), and , : regression coefficients. Based on Equation 4 1, the total effect of social capi tal on suicidal behaviors is . The main effect ( ) is the effect of social capital on suicidal behaviors regardless of the year of migration, representing the overall mean effect of social capital on suicidal behaviors across the year of migr ation. The interaction effect ( ) is the effect of social capital on suicidal behaviors influenced by the year of migration, indicating the variations of the effect in different years of migration (Price, Jhangiani, Chiang, Leighton, & Cuttler, 2017) .

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80 The logistic regression model in Equation 4 1 was implemented using the survey anal ysis procedure . Adjusted regression coefficients were estimated with a 95% confidence interval (CI) not covering zero as the evidence for a significant association at p < 0.05. A total of 19 logistic regression models were used to assess 19 social capital measures with one model per social capital measure. To account for errors caused by multiple testing except the model using total social capital, corrections were conducted using the False Discovery Rate method (Noble, 2009) . Adjusted p values were estimated with the values smaller than 0.05 indicating significant differences. For a social capital measure with the estimated at p< 0 .05 level indicating significant interactions, the migration year when the effect of social capital on suicidal behaviors crossed from negative to positive (termed as the year of crossover or ) was estimated. Based on Equation 4 1, when , the =0. Since does not contain , the study let =0 to solve for using the E quation 4 2 : ( 4 2) As shown in Figure 4 1 , when , the association between social capital and suicide risk was negative (dashed line); the association was reversed (solid line) when , and the association was zero when . All statistical analyses were implemented using SAS version 9.4 ( SAS Institute, Cary, NC). PROC SURVEYMEANS was used for descriptive and comparison analysis, and PROC SURVEYLOGISTIC was used for multivariate logistic regression analysis.

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81 Results Demographic and Migration Characteristics of the Study Sample Results in Ta ble 4 1 show that among the total sample of 1 , 290 , 658 (51.01%) were male with a mean age of 32.33 (SD=7.97) years. Over two thirds were married, more than half had a middle school education, more than 40% had a monthly income of 1000 2000 RMB, and about half had a family size of 4 5. The mean years of migration were 10.12 (SD=7.32) years with more than 40% having migrated to 2 3 cities, and a bout 20% having migrated to 4 or more cities. Nearly 11% of the rural migrants did not visit their rural home during the past 12 months , while 46% visited their home 3 or more times. One third of the sample worked in a business store or we re self employed , with 15% working in restaurants or hotels and 10% working in manufacturing. Regarding future plans, nearly two thirds were unlikely to move in the next six months , and 64% of them planned to stay in the city in the future. Significant dif ferences were found in marital status, education, income, family size, number of cities migrated to, occupation, and intention to move. More detailed information is presented in Table 4 1 . Prevalence of Suicidal Behaviors Table 4 2 shows that the 12 mont h prevalence rate [95% CI] of suicidal ideation for the total sample was 5.74% [4.81, 6.68]. The rate varied dramatically by demographic and migration characteristics, including gender, age, education, income, family size, years of migration, number of cit ies migrated to , home visit frequency, intention to move, and future plan. The prevalence rate [95% CI] of suicidal attempt s was 5.47% [4.57, 6.36] for the total sample and the prevalence rate also varied across demographic and migration variables. Results in Table 4 3 also show that the

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82 prevalence rate [95%CI] of suicidal ideation only, suicidal attempt only and both suicidal ideation and attempt were 0.68%, 0.41% and 5.06%, respectively. These three rates also varied dramatically across demographic variab les and migration experiences as shown in Table 4 3 . Differences in Social Capital between Participants with and without a Suicidal Behavior Results in Table 4 4 show that participants with a suicidal behavior scored higher on 17 of the total 19 social capital measures, suggesting a positive association between social c apital and suicidal behavior. The two social capital measures with lower scores for participants with a su icidal behavior were (1) the attribute of trust (3.11 vs. 3.27 for suicidal ideation, p< 0 . 05 and 3.08 vs. 3.27 for suicidal attempt s , p< 0 .05) and (2) the source of country fellows (3.11 vs. 3.27 for suicidal ideation, p< 0 .05 and 1.96 vs. 2.58 for suicidal attempt s , p< 0 .05). Results in Table 4 5 show that when the year of migration was less than 15 years, rural migrants with a suicidal behavior scored lower on social capital measures although the differences were not significant , when the year of migration w as greater than 15 years, individuals with a suicidal behaviors scored higher on social capital measures, indicating a potential interaction between social capital and years of migration. More detailed results can be found in Table 4 5. Social Capital and Its Interaction with Years of Migration on Suicidal Behaviors Results from multivariate logistic regression shown in Table 4 6 indicate that after considering the interaction between social capital and years of migration, 18 out of the 19 social capital me asures were negatively associated with suicidal ideation, and 9 were statistically significant at p< 0 .05 level. For example, the regression coefficient for

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83 total social capital was 1.48 and 95% CI = [ 2.86, 1.10] , suggesting a significant negative effect at p< 0 .05 level. Results in Table 4 6 also show a positive significant interaction between social capital and years of migration for 12 social capital measures, including total capital, bridging capital, and the attribute of group size from bridging cap ital. For example, the coefficient for the interaction between the total social capital and years of migration was 0.11 with 95% CI = [0.03, 0.19] and estimated year of crossover was 13.5 years. This result suggests that social capital suicidal ideation wa s negative for those with a migration duration less than 13.5 years , but positive for those with a migration duration greater than 13.5 years. Likewise, Table 4 7 summarizes the same measure of social capital with a suicid al attempt. This was consistent with the results in Table 4 6 , after controlling for the interaction . A total of 17 of the 19 social capital measures (except network size and country fellows) were negatively associated with a suicid al attempt , with 8 being statistically signi ficant at the p< 0 .05 level. For example, the main effect [95% CI] was 1.07[ 2.02, 0.12] for bridging capital and 1.25 [ 2.37, 0.13] for attribute trust of bonding capital. Similarly, the interaction effect was significant for 10 of the 19 social capital measures at the p< 0 .05 level, including total capital, bridging capital and its attributes and sources. For example, the interaction effect [95% CI] was 0.07 [0.02, 0.13] for family members as the source of social capital . In addition, the estimated year of crossover varied from 7 years for the bridging capital measure of representing personal

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84 interest to 12.9 years for the measure of the attribute of bridge social capital, group support. Figure 4 2 presents the association between social capital and th e risk of suicidal behaviors with the year of migration. With the increas e of the year of migration, the slope which represent ed the association between social capital and suicidal behaviors changed from negative (light blue line at the bottom) to positive (dark blue at the top). Discussion T his study thoroughly investigated the association between social capital and suicidal behaviors among rural migrants in China using data collected from a probability sample. The Personal Social Capital Scale (Archuleta & Miller, 2011; Wang, Chen, Gong, & Jacques Tiura, 2013) provides a powerful tool to examine the association not only by total amount of capital, but by detailed measures of bonding and bridging capital and their sou rces and attributes. With the survey data on years of migration, innovatively , this study also investigated the interaction between social capital and the duration of migration on the risk of suicidal behavior s . To the best of our knowledge, this is the first study that systematically and thoroughly investigated the relationship between social capital and suicidal behaviors , considering its interaction with the duration of migration. Findings of the study add new data advancing the understanding of the ro le of social capital in altering the risk o f suicidal behaviors for Chinese rural migrants with different migration durations. Protective Effect of Social Capital on Suicidal Behaviors First of all, findings of the study confirmed the conclusion from repo rted studies that social capital is a modifiable and protective factor in reducing the risk of suicidal behaviors considering the influence of migration duration (Congdon, 2012; Fitzpatrick et

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85 al., 2007; Helliwell, 2007; Kelly et al., 2009; Kunst et al., 2013; Lindström & Rosvall, 2015; Okamoto et al., 2013; Smith & Kawachi, 2014) . Rural migrants who acquired more social capital are less likely to engage in suici dal behaviors, including suicidal ideation and attempt. The effect was more pronounced for bridging capital than for bonding capital. This finding is consistent with a previous study that showed that, relative to bonding capital, bridging capital can exert greater effects in reducing the risk of suicide (Fitzpatrick et al., 2007) . Rural migrants have many disadvantages compared to their u r ban counterparts . Bonding capital formed with other migrants may not be adequate for them to achieve both objective and subjective career goals. Howe ver, bridging capital , formed by connections with political and leisure groups , may greatly expand the horizon of rural migrants to gain access to resources that are not available within their bonding network (Fitzpatrick et al., 2007; N. Lin, 2002) , reducing the risk of suicidal behavior s . Shedding light on more detailed measures of social capital, findings of this study indicate that within bridging social capital, four components are protective: politica l and leisure groups as sources , group sizes and level of support as attributes . This means that if a migrant has more connections with different organization/groups and receives greater support from them, he/she may be able to access more resources, achieve personal career goal s, and find alternatives to deal ing with challenges, reducing the likelihood of engag ing in suicidal behaviors (Oyama et al., 2005) Although the bonding capital w as not significantly associated with either of the two suicidal beh avior measures, detailed analyse s suggest that specific roles of

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86 family members, relatives and neighbors help r educ e t he likelihood of engag ing in suicidal behaviors. On the contrary, social capital acquired from country fellow s tho se who came from the same rural hometowns was associated with an increased risk of suicidal behavior s . It is likely that connections with country fellows may be financially and emotionally more stressful sin ce they may be more likely to ask for help (Offer, 2012) . Interaction between Social Capital and Durati on of Migration A new finding of this study is that the association between social capital and the risk of suicide is not homogenous but varied qualitatively by migration duration. During the early stage of migration, the effect of social capital on suic ide was protective. As discussed in the Introduction, pursuing an objective career goal (i.e., find ing a job and earn ing money) would be the main goal for rural migrants in the early period. Adequate social capital enables migrants to find a better urban a rea in which to settle down and find a job with good pay to support themselves and their family (Guo et al., 2016) . Therefore, social capital in the early migration stage can prevent rural migrants from engaging in suicidal behaviors. Based on the estimated time of effect crossover, the threshold of the protective effect of social capital was 11 years , with a range of 7 to 14.8 years . After passing the year of crossover, the relationships between social capital and the risk of engaging in suicidal behaviors are reversed. Rural migrants with more social capital at that point are more likel y to engage in suicidal behaviors. During this period, rural migrants might already have achieved their objective career goals and start ed seek ing subjective career goals or higher social status (Chen et al., 2017; Tharmaseelan et al., 2010) . However, rural migrants in China have structural barriers to

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87 improving their social status. The Household Registration System ( Hukou ) in China, although changing now, might have prevented the rural migrants in this study from becoming legal urban resident s. These migrants are then excluded from many social rights and welfare benefit s designated for urban residents, including health care, education, and employment (Cai, 2011) . More years of migration increases their connection with more successful migr ant peers as well as more urban residents, which in turn may increase the feeling of failure in meeting subjective career goal s despite being better off economically. However, this is just one possible interpretation. Another interpretation for the risk effect could be that rural migrants with longer length of migration are usually older in age, married , and with less education (Zhong et al., 2015) . Additional research is needed to further verify the different effect s of social capital on the risk of suicide among rural migrants in China a s well as the migrant population in general. Limitation s , Implications and Future Research The study has limitations. First, the study was cross sectional in nature; causal relationships could not be warranted. Second, the measurement of suicidal behavior s was based on self report which may be underestimated due to social desirability bias. Third, the years of migration were also based on self report, which would not be as accurate as longitudinally recorded data. Fourth, the neighborhood variables, such a s neighborhood social capital, were not included in the study. Lastly , data for this study were collected in one city in China. Caution is needed when generalizing the study findings to other places within or outside of China. Despite the limitations, thi s study is the first to systematically investigate the complex relationships between social capital and suicide, and to explore the effects of

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88 different attributes and sources of social capital on suicide among rural migrants, while considering the influen ce of duration of migration. Findings of the study will fill the data gap, and provide useful data supporting longitudinal studies to further confirm the relationship between social capital and suicidal behaviors, particularly time and the crossover effect of social capital on suicidal behaviors. The confirmation of the protective effect of social capital on the risk of suicide provides additional evidence supporting the significance of social capital building as a measure of suicide risk reduction, particu larly for the first 7 14 years of migration for rural migrants in China. Findings of the study can also support future research in other migrant populations, including rural migrants in other cities , urban to rural migrants in China and immigrants in othe r countries, such as Asian immigrants in the United States.

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89 Table 4 1. Demographic and migration characteristics of the study sample of rural to urban migrants Variables Male Female Total Total, n (%) 658 (51.01) 632 (48.99) 1290 (100.00) Demographic variables Age, n (%) 32 or less 339 (51.52) 300 (47.47) 639 (49.53) >32 319 (48.48) 332 (52.53) 651 (50.47) Mean (SD) 31.88 (8.23) 32.80 (7.66) 32.33 (7.97) Marital status, n (%) ** Not married 196 (29.88) 97 (15.37) 293 (22.77) Married 460 (70.12) 534 (84.60) 994 (77.23) Education, n (%) ** Primary or less 69 (10.52) 109 (17.27) 178 (13.83) Middle school 350 (53.35) 344 (54.52) 694 (53.92) High school 193 (29.42) 139 (22.03) 332 (25.80) College or more 44 (6.71) 39 (6.18) 83 (6.45) Income (RMB) , n (%) ** <1000 78 (11.85) 190 (30.06) 268 (20.78) 1000 2000 257 (39.06) 292 (46.20) 549 (42.56) 2000 4000 258 (39.21) 130 (20.57) 388 (30.08) >4000 65 (9.88) 20 (3.16) 85 (6.59) Family size, n (%) ** 3 or less 174 (26.44) 227 (35.92) 401 (31.09) 4 5 357 (54.26) 293 (46.36) 650 (50.39) 6 or more 127 (19.30) 112 (17.72) 239 (18.53) Migration experiences Years of migration, n (%) < 5 years 219 (33.28) 193 (30.54) 412 (31.94) 5 years 124 (18.84) 141 (22.31) 265 (20.54) 10 years 135 (20.52) 147 (23.26) 282 (21.86) 15+ years 180 (27.36) 151 (23.89) 331 (25.66) Mean (SD) 10.18 (7.47) 10.05 (7.16) 10.12 (7.32) No. of cities migrated to , n (%) ** 1 208 (31.61) 300 (47.47) 508 (39.38) 2 3 265 (40.27) 274 (43.35) 539 (41.78) 4 or more 185 (28.12) 58 (9.18) 243 (18.84)

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90 Table 4 1. Continued Variables Male Female Total No. of home visits in the past 12 months, n (%) 0 76 (11.55) 67 (10.60) 143 (11.09) 1 2 265 (40.27) 289 (45.73) 554 (42.95) 3 or more 317 (48.18) 276 (43.67) 593 (45.97) Occupation, n (%) ** Business store/self employed 199 (30.24) 215 (34.02) 414 (32.09) Restaurant/hotel 106 (16.11) 94 (14.87) 200 (15.50) Manufacturing 80 (12.16) 56 (8.86) 136 (10.54) Construction site 60 (9.12) 12 (1.90) 72 (5.58) Others 197 (29.94) 194 (30.70) 391 (30.31) Unemployed 16 (2.43) 61 (9.65) 77 (5.97) Intention to move in the next six months , n (%) ** Likely 171 (25.99) 78 (12.34) 249 (19.30) Unsure 125 (19.00) 105 (16.61) 230 (17.83) Unlikely 362 (55.02) 449 (71.04) 811 (62.87) Future plan, n (%) Stay in city 421 (63.98) 403 (63.77) 824 (63.88) Unsure 155 (23.56) 151 (23.89) 306 (23.72) Go back to rural area 82 (12.46) 78 (12.34) 160 (12.40) Note: *p<0.05, **p<0.001

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91 Table 4 2. The 12 month prevalence rate [95%CI] of suicidal ideation and attempt among rural migrants in China, overall and by demographic and migration characteristics Variables Suicidal ideation Suicidal attempt Total sample 5.74 [4.81, 6.68] 5.47 [4.57, 6.36] Demographic variables Gender Male 9.95 [8.43, 11.46] 9.08 [8.09, 10.07] Female 1.48 [0.34, 2.62] 1.80 [0.24, 3.36] Age in years 32 or less 1.88 [0.66, 3.10] 1.37 [0.54, 2.19] >32 8.49 [7.09, 9.87] 8.38 [6.93, 9.82] Marital status Not married 2.90 [0.40, 5.40] 2.39 [0.69, 4.09] Married 6.24 [5.23, 7.26] 6.01 [5.00, 7.02] Education Primary or less 4.10 [0.00, 8.30] 2.53 [0.00, 5.42] Middle school 1.55 [0.49, 2.61] 0.97 [0.29, 1.66] High school 13.58 [12.27, 14.89] 14.35 [12.25, 16.44] College or more 0.90 [0.00, 2.59] 1.10 [0.00, 2.80] Monthly income (RMB) <1000 2.10 [0.00, 4.32] 1.65 [0.00, 3.73] 1000 2000 10.61 [9.11, 12.11] 10.07 [8.85, 11.30] 2000 4000 2.21 [0.16, 4.26] 1.32 [0.03, 2.60] >4000 0.91 [0.00, 2.53] 3.81 [0.00, 9.54] Family size 3 or less 6.63 [5.48, 7.78] 7.27 [5.43, 9.11] 4 5 2.32 [0.50, 4.14] 1.35 [0.24, 2.45] 6 or more 10.03 [7.96, 12.09] 9.38 [7.77, 11.00] Migration experience Years of migration < 15 years 1.82 [0.70, 2.95] 1.04 [0.40, 1.67] 15+ years 14.08 [12.10, 16.05] 14.89 [12.32, 17.46] No. of cities migrated to 1 city 7.51 [5.63, 9.39] 6.90 [5.45, 8.36] 2 3 cities 6.01 [4.83, 7.20] 6.08 [4.52, 7.64] 4 or more cities 0.91 [0.04, 1.78] 0.54 [0.00, 1.14]

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92 Table 4 2. Continued Variables Suicidal ideation Suicidal attempt Frequency home visits 0 times last year 18.33 [15.31, 21.35] 17.33 [14.93, 19.73] 1 2 times last year 1.95 [0.44, 3.46] 2.32 [0.36, 4.28] 3 or more times last year 5.77 [4.51, 7.04] 5.13 [4.42, 5.83] Occupation Business store/self employed 0.70 [0.00, 1.90] 0.70 [0.00, 1.90] Restaurant/hotel 1.48 [0.00, 3.00] 1.61 [0.14, 3.08] Manufacturing 0.32 [0.00, 0.81] 0.10 [0.00, 0.30] Construction site 2.85 [0.00, 8.41] 2.85 [0.00, 8.41] Others 7.14 [5.38, 8.91] 6.80 [5.03, 8.56] Unemployed 29.74 [19.96, 39.51] 28.10 [19.09, 37.11] Intention to move in the next six months Likely 0.95 [0.00, 2.02] 1.11 [0.00, 2.25] Unsure 15.12 [11.79, 18.45] 15.25 [11.90, 18.61] Unlikely 4.96 [3.75, 6.17] 4.47 [3.34, 5.60] Future plan Stay in city 7.05 [6.20, 7.89] 7.16 [6.07, 8.25] Unsure 2.29 [0.00, 4.81] 2.21 [0.00, 4.73] Go back to rural area 3.53 [0.00, 7.99] 0.80 [0.00, 1.70] Note: N=1290, 658 were male and 632 were female.

PAGE 93

93 Table 4 3. The 12 month prevalence rate [95%CI] of suicidal ideation only, attempt only and both ideation and attempt among rural migrants in China, overall and by demographic and migration characteristi cs Variables I deation O nly A ttempt O nly B oth Total sample 0.68 [0.04, 1.33] 0.41 [0.00, 1.00] 5.06 [4.39, 5.73] Demographics Gender Male 0.92 [0.00, 2.07] 0.05 [0.01, 0.09] 9.03 [8.05, 10.01] Female 0.44 [0.00, 0.99] 0.76 [0.00, 1.97] 1.04 [0.04, 2.03] Age in years 32 or less 0.74 [0.00, 1.68] 0.23 [0.00, 0.47] 1.14 [0.35, 1.93] >32 0.64 [0.00, 1.52] 0.53 [0.00, 1.54] 7.85 [6.77, 8.93] Marital status Not married 1.10 [0.00, 3.06] 0.58 [0.00, 1.25] 1.81 [0.25, 3.36] Married 0.61 [0.00, 1.28] 0.37 [0.00, 1.07] 5.63 [4.88, 6.39] Education Primary or less 1.57 [0.00, 4.59] 0.00 [0.00, 0.00] 2.53 [0.00, 5.43] Middle school 0.71 [0.00, 1.54] 0.13 [0.00, 0.30] 0.84 [0.18, 1.50] High school 0.29 [0.00, 0.64] 1.06 [0.00, 2.92] 13.29 [12.04, 14.54] College or more 0.00 [0.00, 0.00] 0.19 [0.11, 0.29] 0.90 [0.00, 2.59] Monthly income <1000 0.52 [0.00, 1.25] 0.06 [0.05, 0.08] 1.58 [0.00, 3.67] 1000 2000 0.73 [0.00, 1.62] 0.20 [0.00, 0.43] 9.88 [8.69, 11.06] 2000 4000 0.92 [0.00, 2.53] 0.03 [0.00, 0.08] 1.29 [0.00, 2.58] >4000 0.00 [0.00, 0.00] 2.90 [0.00, 8.45] 0.91 [0.00, 2.53] Family size 3 or less 0.14 [0.00, 0.30] 0.78 [0.00, 2.31] 6.49 [5.35, 7.62] 4 5 1.15 [0.00, 2.62] 0.18 [0.00, 0.39] 1.17 [0.08, 2.25] 6 or more 0.79 [0.00, 2.09] 0.14 [0.00, 0.43] 9.24 [7.66, 10.81] Migration experience Years of migration < 15 years 0.94 [0.00, 1.88] 0.15 [0.01, 0.30] 0.88 [0.27, 1.49] 15+ years 0.13 [0.00, 0.30] 0.94 [0.00, 2.78] 13.5 [11.99, 15.90] No. of cities migrated 1 city 0.73 [0.00, 1.98] 0.13 [0.00, 2.94] 6.78 [5.33, 8.22] 2 3 cities 0.75 [0.00, 1.66] 0.81 [0.00, 2.20] 5.27 [4.52, 6.02] 4 or more cities 0.41 [0.00, 1.03] 0.04 [0.00, 0.13] 0.50 [0.00, 1.09] Frequency home visits 0 times last year 1.13 [0.00, 3.18] 0.13 [0.00, 0.31] 17.20 [14.83, 19.57]

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94 Table 4 3. Continued Variables I deation Only A ttempt Only Both 1 2 times last year 0.46 [0.00, 1.19] 0.83 [0.00, 2.29] 1.49 [0.18, 2.81] 3 or more times last year 0.76 [0.00, 1.84] 0.12 [0.00, 0.27] 5.01 [4.33, 5.69] Occupation Business store /self employed 0.03 [0.00, 0.08] 0.03 [0.00, 0.09] 0.67 [0.00, 1.87] Restaurant/hotel 0.78 [0.00, 1.93] 0.90 [0.00, 1.99] 0.71 [0.00, 1.66] Manufacturing 0.22 [0.00, 0.66] 0.00 [0.00, 0.00] 0.10 [0.00, 0.30] Construction site 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 2.85 [0.00, 8.41] Others 1.05 [0.00, 2.39] 0.70 [0.00, 2.07] 6.10 [4.93, 7.27] Unemployed 1.76 [0.00, 5.12] 0.13 [0.09, 0.17] 27.97 [19.00, 36.94] Intention to move in the next six months Likely 0.08 [0.00, 0.17] 0.25 [0.00, 0.64] 0.86 [0.00, 1.93] Unsure 0.13 [0.00, 0.37] 0.27 [0.00, 0.71] 14.98 [11.68, 18.29] Unlikely 0.98 [0.01, 1.95] 0.48 [0.00, 1.37] 3.99 [3.27, 4.70] Future plan Stay in city 0.39 [0.00, 0.85] 0.50 [0.00, 1.36] 6.65 [5.96, 7.35] Unsure 0.12 [0.00, 0.32] 0.05 [0.00, 0.14] 2.16 [0.00, 4.68] Go back to rural 3.10 [0.00, 7.52] 0.38 [0.00, 0.97] 0.43 [0.00, 1.10]

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95 Table 4 4 . Association between s ocial capital and suicidal ideation and attempt, mean [95%CI] , overall Variables Suicidal ideation Suicidal attempt Yes No Yes No Total capital 2.87 [2.74, 3.00] 2.64 [2.60, 2.69] 2.95 [2.90, 3.00] 2.64 [2.59, 2.68] Bonding capital Subtotal 2.94 [2.84, 3.04] 2.86 [2.82, 2.90] 2.98 [2.92, 3.03] 2.86 [2.82, 2.90] By attributes Network size 3.24 [3.08, 3.39] 2.59 [2.54, 2.64] 3.34 [3.23, 3.45] 2.59 [2.54, 2.64] Trust 3.11 [3.01, 3.21] 3.27 [3.22, 3.32] 3.08 [3.00, 3.17] 3.27 [3.22, 3.32] Reciprocity 3.20 [3.10, 3.30] 3.26 [3.21, 3.31] 3.20 [3.12, 3.27] 3.26 [3.21, 3.31] Resource rich 2.21 [2.08, 2.35] 2.32 [2.26, 2.38] 2.28 [2.18, 2.39] 2.32 [2.26, 2.38] By sources Family 3.73 [3.65, 3.81] 3.37 [3.33, 3.41] 3.72 [3.62, 3.83] 3.37 [3.33, 3.41] Relatives 3.31 [3.10, 3.52] 3.05 [2.98, 3.12] 3.38 [3.28, 3.47] 3.04 [2.97, 3.12] Neighbors 3.20 [2.98, 3.43] 2.70 [2.65, 2.76] 3.35 [3.27, 3.43] 2.70 [2.64, 2.75] Friends 2.92 [2.85, 2.99] 2.78 [2.72, 2.85] 2.88 [2.81, 2.95] 2.78 [2.72, 2.85] Colleagues 2.52 [2.39, 2.65] 2.68 [2.61, 2.75] 2.57 [2.48, 2.66] 2.68 [2.61, 2.75] Country fellows 1.95 [1.78, 2.11] 2.58 [2.52, 2.64] 1.96 [1.79, 2.13] 2.58 [2.51, 2.64] Bridging capital Subtotal 2.81 [2.63, 2.98] 2.43 [2.36, 2.49] 2.92 [2.84, 3.01] 2.42 [2.36, 2.48] By attribute Group size 3.02 [2.86, 3.18] 2.70 [2.62, 2.78] 3.11 [3.03, 3.19] 2.69 [2.62, 2.77] Represent interest 2.66 [2.47, 2.85] 2.16 [2.09, 2.23] 2.81 [2.71, 2.91] 2.15 [2.08, 2.22] Group support 2.74 [2.55, 2.93] 2.41 [2.33, 2.49] 2.85 [2.76, 2.93] 2.41 [2.33, 2.48] Group resources 2.80 [2.64, 2.96] 2.44 [2.36, 2.51] 2.94 [2.84, 3.03] 2.43 [2.36, 2.50] By source Political groups 2.84 [2.66, 3.03] 2.51 [2.45, 2.58] 2.94 [2.84, 3.04] 2.51 [2.45, 2.57] Leisure groups 2.77 [2.62, 2.93] 2.38 [2.32, 2.45] 2.91 [2.81, 3.01] 2.38 [2.31, 2.45] Note: 1 The mean scores [95% CI] of social capital were estimated using PROC SURVEYMEANS. 2 Statistical inference was made based on the evidence of no overlap in the two 95% C I s. 3 The scores of social capital measures ranged from 1 to 5.

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96 Table 4 5 . Association b etween s ocial capital and suicidal ideation and attempt, mean [95%CI] , for subsample of migrants with migration year less 15 years and 15 years or longer Variables Suicidal ideation Suicidal attempt Yes No Yes No < 15 years Total capital 2.37 [1.96, 2.79] 2.62 [2.57, 2.68] 2.57 [2.23, 2.91] 2.62 [2.56, 2.68] Bonding capital Subtotal 2.76 [2.37, 3.15] 2.82 [2.77, 2.88] 2.95 [2.57, 3.33] 2.82 [2.77, 2.87] By attributes Network size 2.44 [2.07, 2.80] 2.55 [2.49, 2.61] 2.91 [2.60, 3.22] 2.54 [2.49, 2.60] Trust 3.08 [2.65, 3.50] 3.25 [3.18, 3.31] 3.08 [2.64, 3.53] 3.24 [3.18, 3.31] Reciprocity 3.19 [2.76, 3.63] 3.26 [3.19, 3.32] 3.16 [2.70, 3.61] 3.26 [3.19, 3.32] Resource rich 2.32 [1.70, 2.95] 2.25 [2.17, 2.32] 2.64 [2.22, 3.07] 2.24 [2.17, 3.32] By sources Family 3.32 [3.08, 3.55] 3.32 [3.26, 3.37] 3.23 [2.85, 3.62] 3.32 [3.26, 3.37] Relatives 2.69 [1.95, 3.44] 3.08 [3.00, 3.16] 2.94 [2.64, 3.24] 3.08 [2.99, 3.16] Neighbors 2.25 [1.58, 2.91] 2.64 [2.57, 2.70] 2.58 [2.04, 3.13] 2.63 [2.56, 2.70] Friends 3.01 [2.72, 3.31] 2.74 [2.65, 2.82] 2.90 [2.53, 3.26] 2.74 [2.66, 2.82] Colleagues 2.59 [2.02, 3.16] 2.66 [2.56, 2.75] 2.97 [2.46, 3.48] 2.65 [2.56, 2.75] Country fellows 2.69 [2.10, 3.28] 2.52 [2.44, 2.60] 3.08 [2.53, 3.62] 2.52 [2.43, 2.60] Bridging capital Subtotal 1.99 [1.54, 2.43] 2.42 [2.35, 2.50] 2.18 [1.84, 2.53] 2.42 [2.34, 2.50] By attribute Group size 2.29 [1.86, 2.71] 2.70 [2.60, 2.80] 2.46 [2.07, 2.84] 2.69 [2.59, 2.79] Represent interest 1.69 [1.22, 2.16] 2.12 [2.03, 2.20] 1.95 [1.52, 2.38] 2.11 [2.02, 2.19] Group support 1.89 [1.33, 2.45] 2.44 [2.35, 2.54] 1.99 [1.59, 2.39] 2.44 [2.34, 2.53] Group resources 2.08 [1.64, 2.51] 2.44 [2.35, 2.53] 2.34 [2.01, 2.67] 2.44 [2.35, 2.53] By source Political groups 2.01 [1.53, 2.49] 2.50 [2.42, 2.57] 2.18 [1.81, 2.55] 2.49 [2.41, 2.57] Leisure groups 1.99 [1.63, 2.36] 2.39 [2.31, 2.48] 2.18 [1.86, 2.51] 2.39 [2.30, 2.47]

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97 Table 4 5 . Continued Variables Suicidal ideation Suicidal attempt Yes No Yes No 15 years or more Total capital 3.01 [3.00, 3.01] 2.69 [2.63, 2.75] 3.01 [2.99, 3.02] 2.69 [2.62, 2.75] Bonding capital Subtotal 2.99 [2.97, 3.01] 2.95 [2.89, 3.01] 2.98 [2.95, 3.00] 2.95 [2.89, 3.01] By attributes Network size 3.45 [3.40, 3.51] 2.69 [2.61, 2.76] 3.40 [3.28, 3.52] 2.69 [2.61, 2.77] Trust 3.11 [3.07, 3.16] 3.33 [3.26, 3.40] 3.08 [3.02, 3.15] 3.34 [3.27, 3.41] Reciprocity 3.20 [3.15, 3.26] 3.26 [3.18, 3.34] 3.20 [3.15, 3.25] 3.26 [3.18, 3.34] Resource rich 2.18 [2.15, 2.21] 2.51 [2.43, 2.59] 2.23 [2.13, 2.33] 2.50 [2.42, 2.58] By sources Family 3.85 [3.81, 3.86] 3.50 [3.43, 3.56] 3.80 [3.69, 3.90] 3.50 [3.44, 3.57] Relatives 3.48 [3.45, 3.50] 2.96 [2.82, 3.10] 3.44 [3.35, 3.53] 2.96 [2.82, 3.11] Neighbors 3.47 [3.44, 3.49] 2.86 [2.79, 2.94] 3.46 [3.43, 3.50] 2.86 [2.79, 2.93] Friends 2.90 [2.87, 2.93] 2.89 [2.82, 2.97] 2.87 [2.82, 2.93] 2.90 [2.82, 2.98] Colleagues 2.50 [2.46, 2.54] 2.74 [2.67, 2.81] 2.51 [2.46, 2.56] 2.74 [2.67, 2.81] Country fellows 1.74 [1.61, 1.87] 2.72 [2.63, 2.82] 1.79 [1.63, 1.96] 2.72 [2.63, 2.82] Bridging capital Subtotal 3.03 [2.99, 3.07] 2.43 [2.33, 2.53] 3.03 [2.99, 3.07] 2.42 [2.32, 2.52] By attribute s Group size 3.22 [3.19, 3.25] 2.70 [2.60, 2.81] 3.21 [3.16, 3.25] 2.70 [2.59, 2.81] Represent interest 2.93 [2.85, 3.01] 2.23 [2.15, 2.37] 2.94 [2.86, 3.01] 2.25 [2.15, 2.36] Group support 2.98 [2.94, 3.02] 2.33 [2.22, 2.44] 2.97 [2.94, 3.01] 2.32 [2.22, 2.43] Group resources 3.00 [2.97, 3.03] 2.42 [2.31, 2.53] 3.02 [2.96, 3.09] 2.41 [2.30, 2.51] By source s Political groups 3.07 [2.99, 3.16] 2.56 [2.45, 2.67] 3.05 [2.96, 3.14] 2.56 [2.45, 2.67] Leisure groups 2.99 [2.97, 3.01] 2.37 [2.26, 2.47] 3.02 [2.96, 3.08] 2.36 [2.26, 2.46] Note: 1 The mean scores [95% CI] of social capital were estimated using PROC SURVEYMEANS. Statistical inference was made based on the evidence of no overlap in the two 95% CIs. 2 The scores of social capital measures ranged from 1 to 5.

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98 Table 4 6 . Logistic regression of the interactive effects between different social capital measures and years of migration on suicidal ideation among Chinese rural migrants, regression coefficients [95%CI] Variables Main effect of Social capital Main effect of migration year Interaction YC Grand measure Total capital 1.48 [ 2.86, 0.10] 0.11 [0.05, 0.17] 0.11 [0.03, 0.19] 13.5 Bonding capital 0.73 [ 1.92, 0.46] 0.14 [0.07, 0.21] 0.04 [ 0.02, 0.10] NA Bridging capital 1.30 [ 2.40, 0.21]* 0.10 [0.04, 0.16] 0.10 [0.04, 0.15]* 13.0 Attributes Bonding capital Network size 0.93 [ 2.34, 0.49] 0.08 [0.02, 0.14] 0.11 [0.02, 0.19]* 8.5 Trust 0.82 [ 1.82, 0.18] 0.14 [0.07, 0.21] 0.02 [ 0.03, 0.07] NA Reciprocity 0.35 [ 1.58, 0.19] 0.14 [0.07, 0.21] 0.01 [ 0.05, 0.07] NA Resource rich 0.44 [ 1.59, 0.71] 0.14 [0.08, 0.21] 0.01 [ 0.05, 0.06] NA Bridging capital Group size 1.38 [ 2.35, 0.40]* 0.11 [0.04, 0.17] 0.10 [0.05, 0.16]* 13.8 Represent interest 1.05 [ 2.27, 0.17] 0.08 [0.02, 0.14] 0.08 [0.02, 0.14]* 13.1 Group sources 0.92 [ 1.93, 0.09] 0.11 [0.05, 0.17] 0.07 [0.02, 0.13] 13.1 Group support 1.30 [ 2.51, 0.10] 0.12 [0.06, 0.18] 0.10 [0.03, 0.16]* 13.0 Sources Bonding capital Family 0.64 [ 1.15, 0.13]* 0.12 [0.05, 0.19] 0.08 [0.04, 0.12]* 8.0 Relative 0.99 [ 1.87, 0.17] 0.12 [0.07, 0.18] 0.09 [0.04, 0.13]* 11.0 Neighbors 1.33 [ 2.34, 0.32]* 0.13 [0.06, 0.20] 0.10 [0.06, 0.15]* 13.3 Friends 0.05 [ 1.09, 1.19] 0.14 [0.07, 0.21] 0.01 [ 0.05, 0.05] NA Colleagues 0.71 [ 2.06, 0.63] 0.14 [0.07, 0.21] 0.02 [ 0.05, 0.08] NA Country fellows 0.81 [ 0.43, 2.04] 0.08 [0.01, 0.15] 0.10 [ 0.16, 0.04]* 8.1 Bridging capital Political groups 1.48 [ 2.64, 0.32]* 0.10 [0.05, 0.16] 0.10 [0.04, 0.16]* 14.8 Leisure groups 0.96 [ 1.89, 0.03] 0.09 [0.03, 0.15] 0.08 [0.03, 0.13]* 12.0 Note : 1 YC: Year when the effect of social capital on suicidal behaviors crossed over from negative to positive. 2 All social capital measures were standardized. 3 Age, gender, marital status, and education were controlled as covariates. 4 *P<0.05 after multiple tes ting correction (False Discovery Rate method) for models except the total social capital. 5 NA: not available since the estimated interaction effect was not statistically significant.

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99 Table 4 7 . Logistic regression of the interactive effects between different social capital measures and years of migration on suicidal attempt s among Chinese rural migrants, regression coefficients [95%CI] Variables Main effect of Social capital Main effect of migration year Interaction YC Grand capital Total capital 1.30 [ 2.68, 0.08] 0.17 [0.11, 0.23] 0.11 [0.02, 0.19] 11.8 Bonding capital 0.57 [ 1.96, 0.81] 0.21 [0.14, 0.29] 0.03 [ 0.04, 0.10] NA Bridging capital 1.07 [ 2.02, 0.12] 0.16 [0.10, 0.22] 0.09 [0.04, 0.14]* 11.9 A ttributes Bonding capital Network size 0.59 [ 0.66, 1.83] 0.16 [0.08, 0.23] 0.03 [ 0.04, 0.10] NA Trust 1.25 [ 2.37, 0.13] 0.22 [0.15, 0.30] 0.03 [ 0.02, 0.09] NA Reciprocity 0.76 [ 2.22, 0.70] 0.22 [0.14, 0.29] 0.03 [ 0.04, 0.10] NA Resource rich 0.51 [ 1.71, 0.69] 0.21 [0.14, 0.29] 0.01 [ 0.06, 0.08] NA Bridging capital Group size 1.27 [ 2.31, 0.24] 0.18 [0.11, 0.24] 0.10 [0.04, 0.15]* 12.7 Represent interest 0.42 [ 1.45, 0.61] 0.15 [0.09, 0.21] 0.06 [0.01, 0.11] 7.0 Group sources 0.61 [ 1.51, 0.30] 0.18 [0.12, 0.24] 0.07 [0.02, 0.12]* 8.7 Group support 1.42 [ 2.43, 0.42]* 0.18 [0.12, 0.25] 0.11 [0.05, 0.17]* 12.9 S ources Bonding capital Family 0.81 [ 1.34, 0.27]* 0.21 [0.13, 0.28] 0.07 [0.02, 0.13]* 11.6 Relative 0.93 [ 1.85, 0.01] 0.19 [0.12, 0.26] 0.08 [0.03, 0.14]* 11.6 Neighborhood 1.26 [ 2.19, 0.34]* 0.20 [0.13, 0.28] 0.11 [0.06, 0.16]* 11.5 Friends 0.43 [ 1.83, 0.97] 0.22 [0.14, 0.29] 0.01 [ 0.06, 0.09] NA Colleagues 0.21 [ 1.44, 1.02] 0.21 [0.14, 0.28] 0.01 [ 0.07, 0.05] NA Country fellows 1.20 [0.25, 2.15]* 0.18 [0.10, 0.25] 0.11 [ 0.16, 0.06]* 10.9 Bridging capital Political groups 1.28 [ 2.28, 0.28]* 0.17 [0.11, 0.23] 0.10 [0.04, 0.15]* 12.8 Leisure groups 0.67 [ 1.59, 0.24] 0.16 [0.10, 0.22] 0.07 [0.03, 0.12]* 9.6 Note : 1 YC: Year when the effect of social capital on suicidal behaviors crossed over from negative to positive. 2 All social capital measures were standardized. 3 Age, gender, marital status, and education were controlled as covariates. 4 *P<0.05 after multiple testing correction (False Discovery Rate method) for models except for the total social capital. 5 NA: not available since the estimated interaction effect was not statistically significant.

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100 Figure 4 1. Schematic illustration of the interaction between a social capital measure and years of migration Note: : year of migration; and : the year of migration when the effect of social ca pital on suicidal behaviors crossed over (e.g. the slope becomes zero).

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101 Figure 4 2. Illust r ation of the a ssociation between social capital and suicidal behaviors across the years of migration Note: 1 The black dashed lines indicate that the year when the association between social capital and suicidal behaviors cross ed over was 13.5 for suicidal ideation and 11.8 for the suicidal attempt. 2 The intercepts were modified purposefully to adjust the starting point of the lines. 3 Figure 4 2 presents the association between social capital and the risk of suicidal behaviors with the year of migration. With the increase of the year of migration, the slope which represent s the association between social capital and suicidal behaviors changed from negative (light blue line at the bottom) to positive (dark blue at the top). 4 To better visualize the results, the time interval between the adjacent lines was three year s.

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102 CHAPTER 5 RELATIONSHIP BETWEEN SOCIAL CAPITAL, EMPLOYMENT UNCERTA I NTY , ANXIETY , AND SUICIDAL BEHAVIORS: A CHAINED MULTI MEDIATOR MEDIATION MODELING ANALYSIS Introduction Suicide among Rural Migrants in China Suicide is a significant cause of death for p opulation in general (Nock, Borges, & Ono, 2012; WHO, 20 14) , and for migrant population in particular (Forte et al., 2018) . Suicide is defined as the death caused by self injurious behaviors with the intent to die (CDC, 2017) . Suicide results from a cascade of behaviors from suicidal ideation to plan, attempt, and ultimately to death by suicide. Suicidal ideation and at tempt are two important precursors to suicide with 2.5 3.8% of people who have considered suicide and 10 15% of people who have attempted suicide eventually dying by suicide (Kessler et al., 1999; Maris et al., 2000; Maris, 1992; Nock et al., 2008; Turecki & Brent, 2016) . Migrants leave their home of origin and move to new a destination in pursuing a better quality of life. Challenges in the migration process pu t them at greater risk of suicide (Forte et al., 2018) . Rural to urban migrants (rural migrants thereafter) , a particular migrant population in China , are farmers mov ing from rural area s to a urban area to find jobs and earn money . The rural to urban migration started since the economic reforms in the 1980s. Previous studies have reported higher risk of suicide among rural migrants than the general population, and the pre valence of suicidal ideation during the past 12 months ranged from 1.3% to 17.4% with a median of 9.3% (Dai et al., 2015; Di & Xiao, 2004; Li et al., 2007; Yan et al., 2009; L. Yang & Chen, 20 15; Yu & Chen, 2019) . The large and increasing number of rural migrants in China ,

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103 currently totaling 280 million , provides a window of opportunities to investigate the risk factors for suicide prevention . Protective Effect of Social Capital Social cap ital is one among m any factors that have showed protective effect on suicide for migrants, including rural migrants in China (Chen et al., 2011; Ryan et al., 2008; Yu et al., 2019) . Social capital is a valuable property which is directly embedded in the personal social connections , and is characterized as durable, trustworthy, reciprocal and resource rich (Chen et al., 20 09) . Individuals with great amount of social capital will receive more informational, instrumental, and emotional support, and higher collective efficacy, which may help prevent suicide (Chen et al., 2009) . A negative association between social capital and suicide was reported in ecological studies (Hell iwell, 2007; Kelly et al., 2009; Smith & Kawachi, 2014) , and studies with individual level data (Congdon, 2012; Fitzpatrick et al., 2007; Lindstr öm & Rosvall, 2015; Yu et al., 2019) . As for migrants, they are facing social capital loss and reconstruction in the process of migration. Upon leaving the ir original places, migrants break their ties with people and groups/organizations at home . Then, when settling down in the new destination s , their continued mobility will impede them from rebuilding social capital (Chen et al., 2011) . Studies are needed to examine the association between social capital and suicide in migrant populations. Influence of Employment and Mental Health The main and most important goal of rural migrants in the urban area is to pursue a job to earn money and to improve the quality of life. The most urgent task for migrants is therefore to find a secured job to support themselves and their families since the time when they arrive at the destinations. Thus, uncertainty in employment may be one of

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104 the factors with great est impact on suicide risk for migrants. One previous study indicated that migrants who feel insecure or dissatisfied with their curren t employment were more likely to experience poor mental health, which in turn may increase the risk of dying by suicide (Chen et al., 2017) . Previous studies have also shown that employment conditions (e.g. job dissatisfaction, job insecurity) were significantly associated with suicide (Howa rd & Krannitz, 2017; Sun et al., 2008) . Additionally, mental health problems are prevalent among migrants, including anxiety and depression , due to many challenges in the life and work (Beutel et al., 2016; Bhugra, 2004; Carta et al., 2005; Li et al., 2007; Lindert, Ehrenstein, Pr iebe, Mielck, & Brähler, 2009; T. Yang et al., 2012) . One study conducted among 1 , 595 rural migrants in China indicated that approximately one in four rural migrants experienced depressive and anxious symptoms (T. Yang et al., 2012) . The relationship between depression and suicide has been well established (Chovan, 2017) . Little is known about the relationship be tween anxiety and suicide among rural migrants in China, although such relationship has been reported in other populations within and outside of China (Almeida et al., 2012; Law, Yip, Zhang, & Caine, 2014; Sareen et al., 2005; Tong, Phillips, & Conner, 2016; Zhang & Jia, 2011) . Social Capital , Employment and Mental Health O ne previous stud y ha s demonstrated a negati ve association between social capital and suicidal behaviors among rural migrants, particularly in the early years of migration (Yu et al., 2019) . A nother study ha s demonstrated a positive relationship between social capital with employment and mental health (Chen et al., 2017) . In theory, rural migrants who possess a great amount of social capital may receive more information to secure a job with better pay and higher job satisfaction (Chen et al., 2017;

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105 Mouw, 2003) . Rural migrants with adequate social capital may also feel more competent, more in control of their job s , and have a strong perception of employment security and satisfaction (Chen et al., 2017; Requena, 2003; Seibert, Kraimer, & Liden, 2001) . S ocial capital may also help rural migrants to make broader network connect ions with social groups and organizations, increasing their employment opportunity and reduce uncertainty. For example, social capital could help develop a vertical network with leaders and supervisors, and a horizontal network with colleagues, making them reduce employment uncertainty (Seibert et al., 2001) . Social capital is consistently and positively associated with better mental health for the general population (De Silva, Huttly, Harpha m, & Kenward, 2007; De Silva et al., 2005; Kawachi & Berkman, 2000, 2001; Lochner, Kawachi, Brennan, & Buka, 2003) and for migrant subpopulations, including rural migrants in China (Chen et al., 2017; Yu et al., 2019) . Individuals with greater social capital will receive more emotional and social support, which may relieve the strains from life and work, and help reduce anxious and depressive symptoms (Chen et al., 2009) . A number of studies have revealed a negative association between social capital and anxiety, supporting the protective role of social c apital on mental health (De Silva et al., 2007; Harpham, Grant, & Rodriguez, 2004) . Potential Me diation Mechanism s Evidence presented above suggests a potential mechanism by which employment uncertainty and anxiety may mediate the relationship between social capital and suicidal behaviors. In addition to the direct effect, s ocial capital may exert a n indirect effect on suicide by enhancing employment and reducing anxious symptoms . To the best of our knowledge, this important mediation mechanism has not been fully

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106 understood. Furthermore, there is a relationship between employment uncertainty and anx ious symptom (Chen et al., 2017; Rayens & Reed, 2014) . Additionally, poor mental health may mediate the relationship between em ployment uncertainty and suici dal behaviors (Howard & Krannitz, 2017; Law et al., 2014) . One goal of this study is to test a chained mediation mechanism with two mediators in which employment and anxiety consecutively mediate the relationship between social capital and suicidal behaviors. Purpose of the Study The study aims to investigate the complex underlying mediation mechanism linking social capital to suicidal behaviors through employment uncertainty and poor mental health . T his study is conducted based on findings from the previous research on the relationship between social capital and suicide by analyzing data collected from a probability sample of rural migrants in China. The ultimate goal is to deepen the understanding of the mechanisms of suicidal behaviors and provide evidence for future effective intervention and prevention programs targeting suicide among migrants. Materials and Methods Participants and Sampling D ata were derived from the Migra nt Health and Behavioral Survey for an NIH supported project to examine HIV risk behaviors among rural migrants in Wuhan, China. More details about the project and the survey were described elsewhere (Chen, Yu, Zhou, et al., 2015; Yu et al., 2017) . Briefly, t he project target ed the rural migrants aged 18 45 years old, possessed a legal rural residence, had moved to the urban area for at least one month, were currently working in the urban area , and did not have the intention to permanently move to the urban area .

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107 P articipants were selected using the four stage GIS/GPS assisted probability sampling method (Figure 2 1) . D etailed information can be found elsewhere (Chen & Hu, 2018; Chen, Hu, et al., 2018) . In S tage I , the GIS was used to divide the Wuhan C ity into mutually exclusive 100m× 100m geographic units (geounits ), constructing the primary sampling framework (PSF) after excluding the non residential geounits. In Stage II , 60 geounits were randomly selected from the PSF for further sampling . In Stage III , a research team was sent to physically locate the selected geounits using the GPS receiver. In Stage IV , h ouseholds within the selected geounits were enumerated using the random route technique with natural markers, generating the secondary sampling frame (SSF). 20 participants (10 males and 10 females) were randomly selected from the SSF with only one partici pant per gender per household. If there were more than one eligible participant, the Kish Table would be used to select one (Kish, 1949) . To ensure adequate sample enrolled, an extra of 20% geounits were included . The final sample was 1 , 2 45 participants after excluding subjects refused to participate . Data Collection Data were collected using the Migrant Health and Behavioral Survey delivered by Audio Computer A ssisted Self Interviewing (ACASI). To ensure privacy, participants completed the survey in a private room either in their own house or at the local community health center. The survey was anonymous and confidential, and no identifiable information was coll ected. After completion of the survey, the participants would get a reward with a value of $6. Study approval was obtained from the Institutional Review Board (IRB) at Wayne State University and Wuhan Center for

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108 Disease Prevention and Control, as well as t he University of Florida for analyzing the data. Variables and Measurements Predictor social capital S ocial capital was assessed using the 32 item Personal Social Capital Scale (PSCS) (Chen et al., 2009) . PSCS is a theory based instrument with adequate reli ability and validity (Chen et al., 2009) . The instrument has been validated by studies conducted in China and the United States (Archuleta & Miller, 2011; Chen et al., 2009) . This scale consists of 32 items with two subscales: Bonding Capital (24 items) and Bridging Capital (8 items). The bonding capital was measured using four attributes: (a) personal network size measured as the perceived number of frequently connected frequently connected p trustful, (c) reciprocal, and (d) resource rich . These four attributes were assessed among six populations: (1) family members, (2) relatives, (3) neighbors, (4) friends, (5) work colleagues, and (6) country fellows. Mean scores of bonding capital were computed. The bridging capital was assessed also using four attributes, including (a) the lowed by three questions asking among the groups/organizations how provide help if needed, and (d) possess lots of resources. Likewise, these four attributes were a ssessed among two types of groups/organizations: (1) governmental,

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109 economic and social groups/organizations, and (2) cultural, recreation al and leisure groups/organizations. Mean scores of bridging capital were also computed. Overall social capital was the mean of bonding and bridging capital. Higher scores indicate d greater bonding, bridging capital , and overall social capital. The Cronbach alpha was 0.91 for the total PSCS , 0.88 for the Bonding Capital Subscale, and 0.93 for the Bridging Capital Subscale. Media tor 1 e mployment uncertainty Employment uncertainty was measured using two variables: (1) Job security was ; and (2) Job These two variables were reverse coded a nd m ean scores were computed with higher scores indicating greater employment uncertainty . Mediator 2 a nxiety Anxiety was measured using t he six item anxiety subscale of the Brief Symptom Inventory (BSI) (Derogatis & Melisaratos, 1983) . These items were measured in the past 7 days, and typical items a five with higher scores indicating more anxious symptom s. The Cronbach alpha for the anxiety subscale was 0.87 in the study.

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110 Moderator y ears of migration Years of migration was used as a moderator based on findings in the previous study (Yu et al., 2019) . It was assessed as the number of years the migrants have migrated to the cities and was further categorized into < 1 5 years and 1 5 years or more as reported in Chapter 4. Outcome s uicidal behaviors T wo suicidal behaviors were measured. Suicidal ideation was assessed based st 12 months, have you thought two questions. Cov ar iates The demographic variables of the study included age (in years), gender (male and female), marital status (married and not married), education level (primary or less, middle school, high school, college or more), income ( in RMB, <1000, 1000 2000, 2000 4000, >4000), family size (3 or less, 4 5, and 6 or more), number of cities ever migrated to (1, 2 3, 4 or more), number of home visits in the past 12 months (0, 1 2, 3 or more), occupation (business store/self employed, restaurant/hotel, manufacturing, construction site, others, and unemployed), intention to move to other cities in the next six months (likely, unsure and unlikely) and future plan (stay in the city, unsure and go back to rural area). Statistical Analysis Descriptive analyses (e.g. mean, standard deviation, frequency, and proportion) were used to describe the study sample. Pearson correlation analysis was used to

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111 investigate the correlations amo ng the key variables when considering the weight . Moderated mediation modeling was used to investigate the mediation mechanism of which employment uncertainty and anxiety individually mediate the association between social capital and suicidal behaviors , c onsidering the moderation role of years of migration (Figure 5 1a and 5 1b). As shown in Figure 5 1b, the product of the estimated coefficients a and b ( a*b ) provided a measure of indirect effect, and a significant c 3 provided a measure of the moderation effect of years of migration. Further, the chained mediation model with two mediators was constructed to investigate the complex relationship among these variables considering the influence of years of migration (Figure 5 1c and 5 1d). As shown in Figure 5 1d, the products of the estimated coefficients a 1 and b 1 ( a 1 * b 1 ), a 2 and b 2 ( a 2 * b 2 ), and a 1 , a 3 and b 2 ( a 1 * a 3 * b 2 ) provided measures of different indirect effects of employment uncertainty and anxiety in mediating the social capital suicidal behaviors relationship. A significant c 3 provided a measure of the moderation effect of years of migration. The constructed mediation and moderated mediation models were tested using the survey analys is procedures (PROC SURVEYREG and PROC SURVEYLOGISTIC) consider ing the complex sampling design with unequal sampling probabilities and sample weights (Woodruff, 1971) . With this approach, a m or e accurate point and 95% confidence intervals (CIs) can be estimated. Statistical inference was made with 95% CIs not covering zero as p<0.05. Sobel test was applied to test the significance of the indirect effect (Preacher & Leonardeli, 2001; Taylor, MacKinnon, & Tein, 2008) . Type I error was set at p<0.05 for all statistical analyses. PROC SURVEYREG was used for multivariate linear regression and PROC SURVEYLOGISTIC was used for multivariate

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112 logistic regression. Statistical analyses were conducted using the commercial software SAS version 9.4 (SAS Institute, Cary, NC). Results Characteristics of the Study Sample Results in Table 5 1 show that among the total sample, 50.92% were males with a mean age of 31.49 (SD=8.10) years. Three out of four migrants were married, one thirds had an education of high school or more, and 37.03% had an income of 2000 RMB or more with more than half of them having 4 or more family members. On average, these migrants have stayed in the urban area for 9.47 (SD=6.58) years, more than 60% had migrated to 2 or more cities, and 11% of them did not visit rural home in the past 12 months . Regarding the occupati on, one third were working in business stores or self employed, 16% in restaurants or hotels, 10% in manufacturing, 5% in a construction site, and 6% were unemployed. Among the rural migrants, nearly 63% did not plan to move to other cities, and 64% did pl an to stay in the urban area in the future. Significant differences were found in age, marital status, education, income, family size, number of cities migrated to, occupation, and intention to move. More detailed information could be found in Table 5 1. C orrelations among Predictor, Mediator, Moderator and Outcome Variables Results in Table 5 2 indicate that when years of migration were less than 15 years, social capital was negatively correlated with suicidal ideation ( 0.05, p>0.05) and attempt ( 0.01, p >0.05) although not significant. When years of migration greater than 15 years, social capital was significantly and positively correlated with both suicidal ideation (0.20, p<0.001) and attempt (0.21, p<0.001) , indicating an interaction between social cap ital and years of migration. Additionally, results in Table 5 2 indicate that

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113 social capital , bonding capital and bridging capital were significantly associated with employment uncertainty . Employment uncertainty w as significantly associated with anxiety, while anxiety was positively associated with suicidal ideation and attempt. These results indicate a potential chained mediation mechanism. The detailed information o n the correlations between the variables is presen ted in Table 5 2. Moderated Mediation Analysis Results in Table 5 3 indicate that employment uncertainty significantly mediated the association between total social capital and suicidal ideation (indirect effect = 0.14 [ 0.24, 0.04]), but not for suicida l attempt s (indirect effect = 0.02 [ 0.20, 0.15]). Anxiety significantly mediated the association between total social capital and both suicidal ideation (indirect effect = 0.19 [ 0.37, 0.01]) and attempt ( 0.20 [ 0.40, 0.01]) with significant interact ion with years of migration (2.28 [0.60, 3.97] for suicidal ideation and 1.72 [0.54, 2.91] for suicidal attempt). Further analyses were conducted by bonding and bridging capital, and results show that employment uncertainty mediated the association between bridging capital and suicidal behaviors while anxiety mediated the association between bonding capital and suicidal behaviors. Detailed information is presented in Table 5 3. Chained Mediation Model ing of Suicidal Ideation Results in Figure 5 2a shows th at total social capital was significantly associated with employment uncertainty (coefficient [95% CI] = 0.33 [ 0.41, 0.25]), which in turn associated with anxiety (0.15 [ 0.06, 0.24]), which further associated with suicidal ideation (0.95 [0.50, 1.40]). The chained two step indirect effect of social capital employment uncertainty anxiety suicidal ideation was 0.05 [ 0.09, 0.01].

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114 Results in Figure 5 2b show that employment uncertainty and anxiety significantly mediated the association between bonding capital and suicidal ideation, and bonding capital was also directly associated with anxiety. The indirect effect for the paths bonding capital employment uncertainty anxiety suicida l ideation and bonding capital anxiety suicidal ideation were 0.02 [ 0.04, 0.01] and 0.14 [ 0.24, 0.04], respectively. Results in Figure 5 2c also support the mediation role of employment uncertainty and anxiety and reveal a significant direct eff ect of bridging capital ( 1.36 [ 2.47, 0.25]) on suicidal ideation. The indirect effect for the path bridging capital employment uncertainty anxiety suicidal ideation was 0.05 [ 0.09, 0.01]. Information for other significant association paths is a lso presented in Figure 5 2. Chained Mediation Modeling of Suicidal Attempt Similar results were also found for the chained mediation mechanism of the association between social capital and suicidal attempt. The indirect effect from the two step mediator s on suicidal attempt was 0.05 [ 0.09, 0.01] for total social capital (Figure 5 3a), 0.03 [ 0.05, 0.01 ] for bonding capital (Figure 5 3b) and 0.06 [ 0.11, 0.02] for bridging capital (Figure 5 3c). M ore detailed information is presented in Figure 5 3. Discussion T his study thoroughly investigated the underlying mechanisms by which social capital was associated with suicidal behaviors through two important mediators: employment and mental health status. Data used for this study were from a probability sample. A n a dvanced moderated mediation modeling method was used to control covariates and to consider the moderation effect of the duration of migration. To the best of our knowledge, this is the first study to investigate the relationship b etween

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115 social capital and suicidal behaviors using a probability sample of rural migrants in China and advanced modeling methods. Findings of the study add new data deepening our understanding of the role of social capital in affecting suicidal behaviors t hrough a chained mediation process . The findings also provide data supporting longitudinal stud ies to confirm the causality of the relationship and informing intervention research for suicide reduction among rural migrants in China. Mediation Effects of Employment Uncertainty and Anxiety One important f inding of this study is the demonstration of the effect of employment uncertainty and anxious symptoms in mediating the relationship between social capital and suicidal behaviors . Employment uncertainty and anxiety are two risk factors related to suicide (Beautrais, Joyce, & Mulder, 1998; Blakely et al., 2003) . Migrants with higher social capital are less likely to feel employment uncertainty and to experience anxiety symptoms probably due to the stable and wide connections within social groups/organizations as indicated by the findings from this study on bridging capital and published studies (Chen et al., 2017) . E mployment uncertainty is a risk factor for suicide and may increase the risk for people to consider suicide, but it may be in adequate for them to actually attempt suicide. It is likely that for migrant s, employment uncertainty may serve as a promoting effect or trigger for suicide (Kposowa, 2001) . Additionally, findings of the study indicate that anxiety mediated the relationship between social capital and both suicidal ideation and attempt. Individuals with greater social capital have fewer anxious symptoms, which in turn may reduce the risk of suicide. Relative to employment uncertainty , anxiety has greater effects on suicide, and this may indicate a stronger and closer position for

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116 anxiety on the causal pathway towards suicide (Be autrais et al., 1998; Blakely et al., 2003) . Differences of Bonding and Bridging Capital Findings from the in depth modeling analysis indicate that bonding capital was more likely to affect suicide through anxiety, while bridging capital was tend ed to exert its effect through employment uncertainty . It is likely that bonding capital, the social connections with similar individuals , may be more likely to provide emotional support to reduce (De Silva et al., 2007; Harpham et al., 2004) . Migrants with a large and durable network of trusted family members, relatives, neighbors, friends, colleagues and country fellows who are willing to provide help and own a lot of resources are more likely to receive a greater lev el of emotional support, such as care, concern, affection. This emotional support may help release the negative emotions individuals perceive when they face challenges in their work and life . B ridging capital, differently than bonding capital, connects per sons through various social groups and organizations , such as labor organization, employment . T hus , it may provide more information and instrumental support. For example, if an i ndividual holds more connections with labor organization s and employment center s , he/she will be more likely to get useful information regarding job opportunities , which may greatly increase his/her feeling of employment security, and reduce the risk of considering or attempting suicide. Meanwhile, if an individual maintain s reliable connections within their current employment organizations , such as the close relationship with the managers or supervisors , he/she may receive more information about job promotion s

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117 and feel more secured and satisfied , and then may be less likely to think about or attempt suicide (Requena, 2003; Seibert et al., 2001) . Future suicide prevention programs targeting migrants may consider increasing bridging capital through developing various social groups/organization, including employment center and labor organization . Chained Mediation Mechanism s of Employment Uncertainty and Anxiety Another important finding is the demonstration of a chained mediation in which social capital was associa ted with employment uncertainty, which in turn associated with anxiety and further to suicidal behaviors. In addition to the independent effect, two mediators showed a chained effect with employment uncertainty being significantly associated with anxiety (Chen et al., 2017; Rayens & Reed, 2014) . This finding is consistent with previous studies that mental health cou ld mediate the relationship between employment conditions and suicide (Howard & Krannitz, 2017; Law et al., 2014) . The chained mediation mechanism also indicates that for rural migrants, secured and satisfied employment may be their first priorit y to guarantee the success of their life in the urban area , as the main purpose of migration is to pursue a better quality of life. The success of their employment could further reduce their anxiety mood , which is an immediate risk factor for suici de. To achieve the employment success, social capital may play an important role to provide informational, instrumental and emotional support, and to maintain a sustainable, trustworthy and reciprocal working environment with rich resources (Seibert et al., 2001) . In addition to the effect on employment , bonding capital also exerted a direct effect on anxiety , which was not observed for bridging capital. As mentioned in the previous sect ion, relative to bridging capital accumulated through groups/organizations,

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118 bonding capital may provide more emotional support from other migrants who shared more similar characteristics. On the other hand, compared with bonding capital, bridging capital n ot only affected suicide indirectly through employment and anxiety but also showed significant direct effect s on suicide. It is likely that interventions through bridging capital by organized activities may be an efficient approach in preventing suicide am ong rural migrants, particularly in the early stage of migration. Implication s for Suicide Reduction Findings of this study provide new data informing intervention program s for suicide prevention among rural migrants in China. If these findings can be co nfirmed with longitudinal data, focusing on social capital investment would be an important strategy for suicide prevention for rural migrants in China. Such intervention should focus on bridging capital by expanding existing and/or creating new social gro ups/organizations , such as labor organization, employment center, and other political or social groups/organizations . Findings of the study also provide evidence for studying social capital and suicide in other populations, such as international students, immigrants and refugees. Furthermore, social capital based suicide prevention interventions should be started as early as possible. With the rapid accumulation of social capita l , the risk of suicide would be reduced after rural migrants will be more likel y to find jobs that are suitable for them to reduce the employment uncertainty, so do the mental health problems. Social capital based community interventions have been proven effective in reducing intimate partner violence in South Africa (Pronyk et al., 2008) a nd promoting physical activities in China (Gong, Chen, & Li, 2015) , providing clues for devising effective interventions targeting suicide through social capital based programs.

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119 Limitations and Future Research The study has limitations. First, the data used for this study were cross sectional in nature, no causal relationship can be warranted. Second, the suicidal behaviors were self reported, and underreport and misr eport cannot be ruled out due to the nature of social desirability bias. Third, the data were collected in one city in China, caution may be needed when generalizing the findings to other places within or outside of China. Despite the limitations, this st udy is the first to propose and test a chained mediation model considering the moderation effect of years of migration and the confounding effects from a group of covariates to gain insight into the complex relationships between social capital, employment, mental health , and suicidal behaviors. Findings of the study will help fill the data gap, and provide evidence supporting intervention research for suicide reduction among the large number of rural migrants in China .

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120 Table 5 1. Characteristic of the study sample of rural to urban migrants Variables Male Female Total Total 634 (50.92) 611 (49.08) 1245 (100.00) Age * Mean (SD) 31.49 (8.10) 32.48 (7.55) 31.98 (7.85) Marital status, n (%) ** Married 438 (69.30) 513 (84.10) 951 (76.57) Not married 194 (30.70) 97 (15.90) 291 (23.43) Education, n (%) ** Primary or less 66 (10.44) 107 (17.54) 173 (13.93) Middle school 334 (52.85) 330 (54.10) 664 (53.46) High school 188 (29.75) 135 (22.13) 323 (26.01) College or more 44 (6.96) 38 (6.23) 82 (6.60) Income (RMB) , n (%) ** 2000 or less 320 (50.47) 464 (75.94) 784 (62.97) >2000 314 (49.53) 147 (24.06) 461 (37.03) Family size, n (%) * 3 or less 165 (26.03) 215 (35.19) 380 (30.52) 4 5 343 (54.10) 284 (46.48) 627 (50.36) 6 or more 126 (19.87) 112 (18.33) 238 (19.12) Years of migration Mean (SD) 9.51 (6.74) 9.43 (6.42) 9.47 (6.58) No. of cities migrated to , n (%) ** 1 197 (31.07) 284 (46.48) 481 (38.63) 2 3 260 (41.01) 269 (44.03) 529 (42.49) 4 or more 177 (27.92) 58 (9.49) 235 (18.88) No. of home visits in the past 12 months , n (%) 0 72 (11.36) 65 (10.64) 137 (11.00) 1 2 256 (40.38) 282 (46.15) 538 (43.21) 3 or more 306 (48.26) 264 (43.21) 570 (45.78)

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121 Table 5 1 Continued Variables Male Female Total Occupation, n (%) ** Business store/self employed 192 (30.28) 208 (34.04) 400 (32.13) Restaurant/hotel 104 (16.40) 90 (14.73) 194 (15.58) Manufacturing 78 (12.30) 53 (8.67) 131 (10.52) Construction site 55 (8.68) 11 (1.80) 66 (5.30) Others 190 (29.97) 189 (30.93) 379 (30.44) Unemployed 15 (2.37) 60 (9.82) 75 (6.02) Intention to move in the next six months , n (%) ** Likely 166 (26.18) 73 (11.95) 239 (19.20) Unsure 122 (19.24) 103 (16.86) 225 (18.07) Unlikely 346 (54.57) 435 (71.19) 781 (62.73) Future plan, n (%) Stay in city 411 (64.83) 388 (63.50) 799 (64.18) Unsure 146 (23.03) 146 (23.90) 292 (23.45) Go back to rural area 77 (12.15) 77 (12.60) 154 (12.37) Note: *p<0.05, **p<0.001

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122 Table 5 2. Correlation between social capital, employment uncertainty , anxiety and suicidal behaviors among rural migrants Variables Mean (SD) 2 3 4 5 6 7 YM < 1 5 years 1. Social capital 2.56 (0.58) 0. 75 ** 0.90** 0. 31 ** 0. 16** 0.0 6 0.0 1 2. Bonding capital 2.88 (0.57) 0. 39 ** 0. 20 ** 0. 24 * * 0.0 2 0.0 2 3. Bridging capital 2.25 (0.78) 0. 31 ** 0.0 7* 0.0 7* 0.0 3 4. Employment uncertainty 2.74 (0.78) 0.1 5 ** 0.0 5 0.0 2 5. Anxiety 1.94 (0.67) 0. 09 * 0. 12 ** 6. Suicidal ideation 0.04 (0.18) 0.64 ** 7. Suicidal attempt 0.02 (0.15) 5 years 1. Social capital 2.64 (0.56) 0. 67 ** 0. 90 ** 0.2 6 ** 0.0 8 0. 20** 0.21** 2. Bonding capital 2.93 (0.54) 0. 28 ** 0.2 3 ** 0.0 4 0.0 1 0.0 1 3. Bridging capital 2.35 (0.79) 0. 20* * 0.0 8 0. 26** 0. 28** 4. Employment uncertainty 2.81 (0.75) 0. 28** 0.0 3 0.0 9 5. Anxiety 1.95 (0.70) 0. 39* * 0. 36* * 6. Suicidal ideation 0.02 (0.15) 0. 96 ** 7. Suicidal attempt 0.02 (0.15) Note: 1 YM=Year of migration. 2 ** p<0.001, * p<0.05. 3 Weight was considered when estimating the correlation coefficients.

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123 Table 5 3. Moderated mediation modeling of the complex associations between social capital and suicidal behaviors among rural migrants, coefficients [95%CI] Variables Suicidal ideation Suicidal attempt Moderator W : Years of migration Predictor X : Total capital Mediator M 1 : Employment uncertainty X M 1 (a) 0.33 [ 0.41 , 0.25 ] 0.33 [ 0.41 , 0.25 ] M 1 Y with X (b) 0.42 [ 0.13, 0.70] 0.07 [ 0.48, 0.62] X Y with M 1 (c 1 1.09 [ 2.40, 0.22] 0.45 [ 1.27, 0.37] W Y (c 2 0.43 [ 0.70, 1.57] 1.23 [0.33, 2.13] X*W Y (c 3 2.34 [0.97, 3.71] 1.53 [0.73, 2.34] Indirect effect (a*b) 0.14 [ 0.24, 0.04] 0.02 [ 0.20, 0.15] Mediator M 2 : Anxiety X M 2 (a) 0.15 [ 0.30, 0.01] 0.15 [ 0.30, 0.01] M 2 Y with X (b) 1.27 [0.86, 1.67] 1.36 [0.81, 1.92] X Y with M 2 (c 1 0.92 [ 2.47, 0.63] 0.31 [ 1.38, 0.76] W Y (c 2 0.46 [ 0.61, 1.54] 1.67 [0.65, 2.69] X*W Y (c 3 2.28 [0.60, 3.97] 1.72 [0.54, 2.91] Indirect effect (a*b) 0.19 [ 0.37, 0.01] 0.20 [ 0.40, 0.01] Predictor X : Bonding capital Mediator M 1 : Employment uncertainty X M 1 (a) 0.24 [ 0.33, 0.15] 0.24 [ 0.33, 0.15] M 1 Y with X (b) 0.06 [ 0.21, 0.32] 0.29 [ 0.75, 0.18] X Y with M 1 (c 1 0.55 [ 1.44, 0.33] 0.01 [ 0.76, 0.74] W Y (c 2 1.24 [0.29, 2.19] 2.47 [1.57, 3.37] X*W Y (c 3 0.26 [ 0.72, 1.23] 0.53 [ 1.30, 0.23] Indirect effect (a*b) 0.01 [ 0.07, 0.04] 0.07 [ 0.05, 0.19] Mediator M 2 : Anxiety X M 2 (a) 0.22 [ 0.39, 0.05] 0.22 [ 0.39, 0.05] M 2 Y with X (b) 1.01 [0.68, 1.35] 1.12 [0.74, 1.51] X Y with M 2 (c 1 0.22 [ 1.52, 1.07] 0.50 [ 0.29, 1.28]

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124 Table 5 3. Continued Variables Suicidal ideation Suicidal attempt W Y (c 2 1.24 [0.24, 2.24] 2.53 [1.70, 3.37] X*W Y (c 3 0.34 [ 1.07, 1.75] 0.46 [ 1.31, 0.39] Indirect effect (a*b) 0.22 [ 0.41, 0.03] 0.25 [ 0.46, 0.03] Predictor X : Bridging capital Mediator M 1 : Employment uncertainty X M 1 (a) 0.27 [ 0.35, 0.20] 0.27 [ 0.35, 0.20] M 1 Y with X (b) 0.50 [ 0.12, 0.89] 0.07 [ 0.61, 0.74] X Y with M 1 (c 1 1.13 [ 2.25, 0.01] 0.69 [ 1.53, 0.16] W Y (c 2 0.62 [ 2.26, 1.03] 0.50 [ 0.86, 1.87] X*W Y (c 3 3.09 [1.76, 4.42] 2.54 [1.53, 3.56] Indirect effect (a*b) 0.14 [ 0.25, 0.02] 0.02 [ 0.20, 0.16] Mediator M 2 : Anxiety X M 2 (a) 0.06 [ 0.19, 0.07] 0.06 [ 0.19, 0.07] M 2 Y with X (b) 1.37 [0.95, 1.79] 1.46 [0.92, 2.01] X Y with M 2 (c 1 1.07 [ 2.23, 0.09] 0.73 [ 1.75, 0.28] W Y (c 2 0.26 [ 0.81, 1.33] 1.55 [0.51, 2.59] X*W Y (c 3 2.38 [1.10, 3.66] 2.11 [1.04, 3.19] Indirect effect (a*b) 0.08 [ 0.28, 0.11] 0.09 [ 0.28, 0.11] Note: 1 Age, gender, marital status, and education were included as covariates. 3 Survey estimation of linear and logistic regression was used to consider the sampling design. 4 Sobel test was used to estimate the indirect effect.

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125 Figure 5 1. Conceptual (a and c) and statistical (b and d) illustration of the mediation modeling ( upper panel) and chained mediation modeling (bottom panel) Note: 1 X=predictor, M, M1, and M2=mediator, Y=outcome, W=moderator. 2 Years of migration was modeled as moderator.

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126 Figure 5 2. Chained mediation modeling of the associations between social capital, employment uncertainty , anxiety , and suicidal ideation Note: 1 Year of migration was modeled as moderator; and a ge , gender, marital status, and education were included as covariates. 2 Survey estimation of linear and logistic regression was used to consider the sampling design. 3 Sobel test results for indirect effect: (a) X M 1 M 2 Y: 0.05 [ 0.09, 0.01], (b) X M 2 Y: 0.14 [ 0.24, 0.04], X M 1 M 2 Y: 0.02 [ 0.04, 0.01], and (c) X M 1 M 2 Y: 0.05 [ 0.09, 0.01].

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127 Figure 5 3. Chained mediation modeling of the associations between social capital, employment uncertainty , anxiety , and suicidal attempt Note: 1 Year of migration was modeled as moderator; and a ge , gender, marital status, and education were included as covariates . 2 Survey estimation of linear and logistic regression was used to consider the sampling design. 3 Sobel test results for indirect effect: (a) X M 1 M 2 Y: 0.05 [ 0.09, 0.01], (b) X M 1 Y: 0.13 [ 0.01, 0.26], X M 2 Y: 0.19 [ 0.31, 0.08], X M 1 M 2 Y: 0.03 [ 0.05, 0.01], and (c) X M 1 M 2 Y: 0.06 [ 0.11, 0.02].

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128 CHAPTER 6 CONCLUSION In this dissertation research , I analyzed data from a sample of rural to urban migrants with two non migrant comparison groups of rural and urban residents recruited using a GIS/GPS assi s ted probability sampling method. The probability sample enabled me to compare the prevalence rates of suicidal behaviors among rural to urban migrants, rural residents and urban residents, and can provide a robust conclusion of whether rural to urban migrants have greater risk of suicide than rural and urban residents . T o the best of our knowledge, this dissertation research marks the first time detailed measures of personal social capital , overall, by bond ing and bridging capital, and by attributes and sources, have been used to investigate the association between social capital and suicidal ideation and attempt . Further, we are aware of no prior attempts to consider the interaction between social capital a nd duration of migration. Here, innovatively , the concept of year of crossover when the effect of social capital on suicidal behaviors crossed from negative to positive was applied . Additionally, the complex chained mediation modeling method to investigate the underlying mechanism of the relationship between social capital , employment, mental health , and suicidal behaviors was applied . Findings of this dissertation research provided informative evidence for future studies and interventions targeting suicide among migrant populations. P revious studies have reported that, compared to urban and rural residents, rural to urban migrants have a greater risk of suicide (Di & Xiao, 2004; Yan et al., 2009; L. Yang & Chen, 2015) . On the other hand, several studies reported a lower suicide risk among migrants than the general population in China (Dai et al., 2015; Li et al., 2007) .

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129 The comparisons of suicide risk among rural migrants with rural and urban residents were less than precise in these published studies due to two reason s : (1) A ll these studies focused on specific subgroups of the migrant population selected using convenient sampling methods , and cannot be used to represent the whole migrant population; and (2) The sampling strategies for rural migrant s and the rural and urban residents in these studies were different , and the comparisons were unable to provide robust and valid conclusions. To address the limitations of previous studies, this dissertation research analyzed the data from a GIS/GPS assisted probability sample which can be used to represent the whole population in Wuhan City . Re sults show that in the past 12 months, 5.74% of the rural migrants have reported considering or thinking about suicide , and 5.47% of them have attempted suicide. Rural migrants who were male, older in age, married, with high school education and low middle income, and wo hav e a large family size had higher prevalence rates of suicidal ideation and attempt s . After controlling for the large variations of the prevalence of suicidal beh aviors across demographic variables, rural to urban migrants showed significant greater risk s of suicidal ideation and attempt s compared to non migrant rural and urban residents , underscoring the urgent need to prevent suicide among rural migrants in China . E vidence from many ecological studies suggests a negative association between social capital and suicide (Durkheim, 1897; Helliwell, 2007; Kelly, Davoren, Mhaoláin, Breen, & Casey, 2009) . This negative association has been replicated in studies in which social capital was measured by aggregating data at the individual level (Kunst et al., 2013; Okamoto et al., 2013; Smith & Kawachi, 2014) , and studies w ith direct analyses of individual level data (Congdon, 2012; Fitzpatrick et al., 2007; Lindström &

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130 Rosvall, 2015) . Despite the fact that many studies have investigated the relationship between social capital and health, few studies have explored the effects of social capital on suicidal behaviors among migrant population s , including rural migrants in China . Further, no study has ever systematic ally investigated the effects of social capital on suicide from detailed measures of social capital from various subtypes, attributes and sources. Thus, this dissertation research examined the effects of different social capital components on suicidal idea tion and attempt s among rural migrants when considering the moderation effect of duration of migration and the effects from other covariates. Findings of this dissertation research indicate that social capital was negatively associated with suicidal ideati on and attempt s , particularly in the early years of migration. Findings from detailed measures of different subtypes, attributes and sources of social capital also support the conclusion that social capital exerts protective effect on suicide in the early years of migration. While in the later stage of migration, social capital was positivel y associated with suicidal ideation and attempt s . Further studies may be needed to investigate th is issue. I n addition to social capital, employment and mental health have been recognized to affect suicidal behaviors (Blackmore et al., 2008) . T he most urgent task of migrants when they arrive in a new place is to find a job to support themselves and their families. Previous studies have shown that employment conditions (e.g. job dissatisfaction, job insecurity) were significantly associated with suicidal behaviors among migrants (Blakely, Collings, & Atkinson, 2003; Chen et al., 2017) . In addition, m ental health problems are more prevalent among migrants than the general population (Bhugra, 2004; Carta, Bernal, Hardoy, & Haro Abad, 2005; Li et al., 2007; Yang et al.,

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131 2012) . Poor mental health has been consistently reported to be highly associated with suicidal behaviors in migrant populations, such as depression and anxiety (Chovan, 2017; Hong et al., 2010; Hovey, 2000a, 2000b; Miret, Ayuso Mateos, Sanchez Moreno, & Vieta, 2013; Oquendo, Lizardi, Greenwald, Weissman , & Mann, 2004) . Previous studies have also found significant associations between social capital and employment conditions and mental health problems (Chen et al., 2017; De Silva, McK enzie, Harpham, & Huttly, 2005; Whiteford et al., 2005) . However, to our knowledge, no study has ever investigated the complex mechanisms underlying the relationship between social capital, employment, mental health and suicidal behaviors. This disserta tion research applied the simple mediation modeling and chained mediation modeling method s to investigate the underlying mechanisms of the association between social capital and suicidal behaviors, and findings of the analysis show that employment uncertainty and anxi e ty independently and consecutively mediated the association between social capital and suicidal behaviors. Findings of the study also indicate that bonding capital w as more likely to affect suicidal behaviors through mental health whil e bridging capital was more likely to affect suicidal behaviors through employment. The mediation and chained mediation mechanisms of the relationship between social capital and suicid al behaviors through employment and mental health deepe ned our understan ding of suicide , and provide new data informing future effective intervention programs targeting suicide among migrant populations . In addition to the indirect effect, bridging capital exerted direct effect on suicidal behaviors, providing informative evid ence for future intervention programs.

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132 Findings of the dissertation research also provide informative data for future studies targeting suicide : (1) The target population may be extended to other migrant populations in future studies , such as urban to rur al, urban to urban and rural to rural migrants. Studies in these migrant populations will provide additional information regarding the relationship between social capital and suicide. (2) Longitudinal studies will be conducted to investigate the time trend of social capital , including social capital loss and reconstruction , and suicidal behaviors among the migrants, and examine the longitudinal relationship between social capital and suicidal behaviors. (3) Cross cultural or cross country studies may be des igned to investigate the relationship between social capital and su i cide among immigrants in different countries across the world , such as Asian immigrants in the United States , and to verify the study findings from this dissertation research. (4) Based on the findings of this dissertation research and the fact that social capital can be intentionally generated , researchers may consider developing particular intervention programs through extending existing social groups/organizations and/or developing new s ocial groups/organizations to improve personal bridging social capital, and eventually , to reduce the risk of suicide. The potential social groups/organization may include but are not limited to community health center s , employment center s , labor organizat ion s , and country fellow organization s (groups of rural migrants from the sa me rural hometown) . In summary, findings of the dissertation indicate that (1) rural to urban migrants had greater risk of suicide compared to non migrant rural and urban residen ts in China ; (2) social capital exerted a protective effect on suicide among rural migrants, particularly in the early years of migration; and (3) social capital was associated with suicidal

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133 behaviors indirectly through employment uncertainty and anxiety. Findings of this dissertation underscore the urgent need for suicide prevention among rural migrants, and highlight the protective role of social capital in the early years of migration and underlying chained mediation mechanism of social capital on suicid e through employment and mental health . Findings of this dissertation also provide evidence for future studies that need to target suicide and suicidal behaviors in different migrant populations across different cultures and countries . L ongitudinal studies are also needed to verify the relationship between social capital and suicide . Finally , social capital based intervention program s need to be devised to prevent suicide among migrants. Th is dissertation research has limitations. First, this dissertation was a cross sectional study , and no causal relationship s can be inferred from this data . Second, the suicidal behaviors measured were self reported, and underreport ing and misreport ing cannot be ruled out due to the nature of social desirability bias. Third, the data were collected in one city in China ; , caution is needed when generalizing the findings to other places within or outside of China. Fourth, neighborhood variables, such as neighbor hood social capital, were not included in the study. Despite these limitations, this dissertation research is the first to investigate the relationship between social capital and suicide among rural to urban migrants in China, and findings of the dissertat ion will help fill the data gap, and provide evidence supporting future studies .

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134 APPENDIX SUPP LE MENTAL MATERIALS Table A 1 . Differences in social capital, bonding capital and bridging capital by demographic and migration characteristics, mean [95%CI] Variables Social capital Bonding capital Bridging capital Total 2.66 [2.61, 2.70] 2.86 [2.83, 2.90] 2.45 [2.39, 2.51] Demographic variables Gender Male 2.71 [2.65, 2.77] 2.91 [2.86, 2.97] 2.51 [2.43, 2.58] Female 2.60 [2.54, 2.66] 2.82 [2.76, 2.87] 2.39 [2.30, 2.48] Age 32 or less 2.61 [2.54, 2.68] 2.79 [2.73, 2.86] 2.43 [2.33, 2.52] >32 2.69 [2.63, 2.75] 2.92 [2.87, 2.97] 2.46 [2.38, 2.54] Marital status Not married 2.51 [2.39, 2.62] 2.76 [2.66, 2.87] 2.25 [2.09, 2.41] Married 2.68 [2.64, 2.73] 2.88 [2.84, 2.93] 2.48 [2.42, 2.55] Education Primary or less 2.45 [2.36, 2.54] 2.68 [2.58, 2.77] 2.23 [2.10, 2.35] Middle school 2.60 [2.53, 2.68] 2.86 [2.79, 2.93] 2.35 [2.24, 2.45] High school 2.84 [2.79, 2.89] 2.96 [2.92, 3.00] 2.72 [2.65, 2.80] College or more 2.66 [2.47, 2.86] 2.95 [2.82, 3.08] 2.37 [2.06, 2.68] Income (in RMB) <1000 2.51 [2.38, 2.63] 2.89 [2.77, 3.01] 2.13 [1.94, 2.31] 1000 2000 2.70 [2.63, 2.77] 2.83 [2.77, 2.89] 2.57 [2.47, 2.67] 2000 4000 2.67 [2.61, 2.72] 2.92 [2.86, 2.98] 2.42 [2.35, 2.49] >4000 2.64 [2.49, 2.80] 2.81 [2.65, 2.97] 2.48 [2.32, 2.64] Family size 3 or less 2.70 [2.64, 2.75] 2.83 [2.78, 2.89] 2.56 [2.48, 2.63] 4 5 2.63 [2.55, 2.71] 2.87 [2.80, 2.94] 2.39 [2.28, 2.51] 6 or more 2.63 [2.53, 2.74] 2.91 [2.82, 3.00] 2.35 [2.22, 2.49] Migration experience Years of migration 5 or less 2.57 [2.48, 2.67] 2.77 [2.68, 2.87] 2.37 [2.25, 2.50] 6 10 2.58 [2.50, 2.66] 2.75 [2.67, 2.83] 2.41 [2.30, 2.52] 11 15 2.78 [2.68, 2.87] 3.03 [2.95, 3.11] 2.53 [2.38, 2.68] >15 2.68 [2.61, 2.74] 2.89 [2.83, 2.95] 2.46 [2.37, 2.56] No. of cities migrated to 1 2.74 [2.68, 2.81] 2.92 [2.86, 2.97] 2.57 [2.47, 2.66] 2 3 2.53 [2.48, 2.59] 2.75 [2.69, 2.81] 2.32 [2.24, 2.40] 4 or more 2.76 [2.62, 2.90] 3.03 [2.91, 3.15] 2.49 [2.31, 2.67] No. of home visits in the past 12 months 0 2.74 [2.68, 2.79] 2.77 [2.71, 2.84] 2.70 [2.61, 2.84] 1 2 2.56 [2.49, 2.62] 2.78 [2.72, 2.83] 2.33 [2.24, 2.43]

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135 Table A 1. Continued Variables Social capital Bonding capital Bridging capital 3 or more 2.72 [2.65, 2.79] 2.96 [2.90, 3.03] 2.48 [2.39, 2.57] Occupation Business store/self employed 2.63 [2.58, 2.68] 2.77 [2.72, 2.81] 2.49 [2.42, 2.56] Restaurant/hotel 2.57 [2.43, 2.71] 2.97 [2.84, 3.09] 2.18 [1.99, 2.37] Manufacturing 2.75 [2.62, 2.87] 3.13 [3.00, 3.26] 2.37 [2.18, 2.55] Construction site 2.44 [2.23, 2.66] 2.75 [2.54, 2.95] 2.14 [1.84, 2.44] Others 2.72 [2.64, 2.79] 2.86 [2.79, 2.93] 2.57 [2.47, 2.68] Unemployed 2.51 [2.36, 2.66] 2.91 [2.81, 3.02] 2.11 [1.86, 2.37] Intention to move in the next six months Likely 2.52 [2.46, 2.58] 2.79 [2.73, 2.85] 2.24 [2.16, 2.32] Unsure 2.64 [2.54, 2.74] 2.80 [2.72, 2.87] 2.48 [2.33, 2.63] Unlikely 2.70 [2.64, 2.76] 2.90 [2.85, 2.95] 2.50 [2.42, 2.58] Future plan Stay in city 2.73 [2.69, 2.77] 2.94 [2.91, 2.98] 2.52 [2.45, 2.58] Unsure 2.54 [2.45, 2.63] 2.72 [2.64, 2.80] 2.36 [2.22, 2.49] Go back to rural area 2.42 [2.23, 2.61] 2.64 [2.47, 2.80] 2.20 [1.95, 2.46] Note: 1 The mean scores [95% CI] of social capital were estimated using PROC SURVEYMEANS. Statistical inference was made based on the evidence of no overlap in the two 95% CIs. 2 The scores of social capital measures ranged from 1 to 5.

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156 BIOGRAPHICAL SKETCH Bin Yu earned his M . D . in preventive medicine in 2012 from the School of Public Health, College of Medicine at the Wuhan University, Wuhan, China. During his undergraduate education, he worked part time as an intern in the Zhong Nan Hospital and Wuhan Center for Disease Control and Prevention. After graduation, Bin jo ined the MPH program at the School of Public Hea l th, Wuhan University. During the second year of the master program , Bin received one year training about data analysis and manuscript writing at the Pediatric Prevention Research Center, College of Medicine, Wayne State University, Detroit, and the Department of Epidemiology, Univer sity of Florida, Gainesville . Bin completed his master training in the summer of 2016. Upon graduation, he worked two years with Dr. Xinguang Chen as a Research Assistant in the De partment of Epidemiology at the University of Florida. Bin started his Ph . D . at the University of Florida in August 2016 under the mentorship of Dr. Xinguang Chen. He worked on several NIH funded projects, including studying the relationship between social capital and HIV risk behaviors among rural migrants in China and modeling quantum c hange in adolescent sexual initiation and condom use. Bin has served as the Ph .D. student representative in the Research Committee at the College of Public Health and Health Professions during 2016 2019 and as campus representative in the Association for P sychological Science Student Caucus during 2017 2019. He received C T ravel A ward from the Department of Epidemiology in 2017 and the University of Florida Publishing Fund in 2019 . Upon his graduation , Bin has published 3 5 peer reviewed articles in re search areas of mental health, substance use and suicide. Bin received his Ph.D. from the University of Florida in Spring 2020 .