Does Residential Segregation Affect Access to Public Health Education Among Rural Migrants in China?

Does Residential Segregation Affect Access to Public Health Education Among Rural Migrants in China? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Does Residential Segregation Affect Access to Public Health Education Among Rural Migrants in China? zicheng wang, Jiachun Liu, Juan Ming This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-51103/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Rural migrants in China often experience serious residential segregation in destination cities, potentially resulting in limited access to public health education. However, the effect of residential segregation on public health education access remains unexplored. The present paper aims to address three issues. Does residential segregation have significant effects on access to public health education? If it does, what are its potential mechanisms accounting for? Additionally, are any heterogeneity effects differentiated by local duration, migration patterns, migration traits, and regional variations? Methods: The data from the 2014 National Migrants Population Dynamic Monitoring Survey and Logit regression are applied to explore the association between residential segregation and access to public health education. We further use multinomial treatment effects regression to address the endogenous issue. Several Logit models are also used to investigate potential mechanisms and heterogeneous effects. Results : The Logit estimations reveal that rural migrants in segregated neighborhoods are negatively related with lower prevalence of attendance (OR: 0.9200, 95% CI: 0.8500, 0.9958) and online participation of public health education (OR: 0.8709, 95% CI: 0.7893, 0.9609). The negative effects of residential segregation on access to public health education are also drawn in the multinomial treatment effects regressions (attendance model: coefficient: −4.3321, 95% CI: −8.6404, −0.0238; method model: coefficient: −1.6482; 95% CI: −2.6790, −0.6173). The mechanism analysis also demonstrates that residential segregation is negatively associated with the two potential transmission channels: social network formation (OR: 0.6630, 95% CI: 0.6098, 0.7209) and community participation (OR: 0.7880, 95% CI: 0.7106, 0.8737). Conclusion: Residential segregation produces a negative effect on public health education access. Social network and community participation may act as the transmission channel that links residential segregation and access to public health education. Additionally, the effects of residential segregation on public health education are differentiated across regional variations, local duration, migration patterns, and family migration. Health Policy Residential segregation Public health education Social network Community participation Background Immigrants in developed markets are often disproportionately settled in enclaves, and they experience more serious residential segregation than do their white counterparts [1-2], eliciting a series of social problems. Previous studies have demonstrated that residential segregation was related with concentrated poverty, poor academic attainment, joblessness, disease infection, and criminality prevalence [3-6]. Researchers also explored the relationship between residential segregation and health services utilization, with some studies showing that segregation may block access to health services in enclave communities [7-11]. However, some evidence also indicated that living in an ethnic enclave can enhance health information through particular cultural advantages, social networks, and social support within these areas. Such enhancement will promote access to healthcare utilization, especially in Latino or Hispanic enclaves in the United States [12-15]. Since 1978, many rural migrants have moved into urban cities. The number of rural–urban migrants in 2018 was approximately 288 million, accounting for more than 20 percent of the total population. Rural migrants have become an important and integral part of the industrial workforce, and they play a vital role in China’s urbanization, modernization, and economic development [16]. However, with the constraint of hukou , rural migrants have less access to settle permanently when they remain regarded as outsiders in destination cities [17-18] and undertake demeaning jobs but with low pay [19-20]. Without local hukou , they are not eligible for public housing welfare and are excluded from affordable housing [21]. An increasing number of rural migrants are forced to live in marginalized communities such as dormitories or urban villages ( chengzhongcun ). These communities can be viewed as enclaves with over-crowdedness, poverty, insanitation, and discrimination [16][22-25]. Rural migrants in those dwellings consistently experience heavily residential segregation [26-27] and health-risk behaviors [16][24][28-30]. To improve the health status of rural migrants, the Chinese government launched a project, named Essential Public Health Services, to provide basic health services for all urban and rural dwellers in 2009 [31]. Public health education was one of the nine types of basic services that are provided to offer health-related information and consultation towards infectious diseases, occupational diseases and injuries and non-communicable diseases prevention, and maternal health promotion throughout the entire course of health service delivery [32-33]. Public health education is believed to be not only greatly favorable to promote disease prevention of rural migrants but also be beneficial to enhance health literacy for rural migrants [34-36]. However, most rural migrants are not entitled to public health education access in destination cities. Some evidence demonstrated that in 2014, only 57.16% of rural migrants could share public health education, and 22.02% of this public health education information was merely about infectious disease prevention [32]. More than 80% of public health education was still performed through traditional channels in 2015, such as radio, television programs, and billboards [35]. A few researchers also explored the causes and determinants of public health education access in the supply side, indicating that geographic accessibility and regional variations would hinder rural migrants’ utilization of public health education [32]. On the demand side, limited access to health education might stem from poor individual socio-economic characteristics (i.e. age, educational attainment, health status, and health insurance) and migration traits [35][37]. However, the effect of residential segregation on public health education utilization among rural migrants has been underexplored. This paper aims to bridge this gap with three issues. First, does residential segregation have significant effects on access to public health education? If it does, what are the potential mechanisms accounting for? Additionally, do any heterogeneity effects vary by migration patterns, migration traits, and region variation? This paper contributes to previous studies in several ways. First, the present study uses a unique national survey in China and emphasizes the direct association of residential segregation with the use of public health education. Second, this study firstly applies a multinomial treatment effects regression to explore the causal effect of residential segregation on health education access. Third, the two potential transmission channels, namely, social network formation and community participation, are discussed to address the linkage. Methods Data and Study Design This paper applies the 2014 National Migrants Population Dynamic Monitoring Survey, a national representative database, conducted by the National Health and Family Planning Commission of China, to explore the causal effect. The survey is a cross-sectional survey covered migrants aged 15 to 69 years old. These migrants do not have the local hukou but have been working or living in urban cities for more than a month. This survey was carried out in seven provincial units that covered 8 cities (i.e., Beijing, Jiaxing, Xiamen, Zhengzhou, Shenzhen, Zhongshan, and Chengdu) and 64 communities or villages. The sample was drawn using the stratified multistage random sampling method with the probability proportional to size approach. A total of 2,000 migrants were randomly sampled in each city to participate in the survey through face-to-face interviews. The survey was conducted after obtaining informed consent for inclusion from all respondents, and 13,759 rural migrants were further included in this wave. Variables and measures The outcome is access to local public health education, which is measured by two proxy variables, namely, the prevalence of and the method of public health education. First, the prevalence of health education and health education approach is a binary variable based on the question “whether you have any kinds of public health education (i.e., occupational disease, HIV, tuberculosis, sexual-transmitted diseases, mental diseases, chronic diseases, and other communicative diseases prevention, reproduction and contraception, and nutrition health knowledge education) in destination cities?” It is treated as 1 if rural migrants participated in public health education and 0 otherwise. The approach of health education is constructed as a dummy variable, which is set to 1 if rural migrants obtained health education information through the Internet. Otherwise, it is set to 0. The focal variable is residential segregation. We use neighborhood composition as the proxy variable following Dinwiddie, Gaskin, Chan, Norrington, and McCleary (2013) [9]. It is consistent with the three dummy outcomes, namely, migrant-dominant (percentage of migrants>50%), citizen-dominant (percentage of urban dwellers>50%), and integrated (the percentage of both two groups reaches 50%). The controlled variables are selected from demand- and supply-side in accordance with Zhang, Lin, Liang, Qian, Zhang, and Hou (2017) [37]. The demand side variable consists of household location, whereas individual socioeconomic characteristics, such as gender, age, marital status, education attainment, self-reported health status, and social health insurance are incorporated as demand-side factors. Estimation strategy The Pearson chi-square tests and ANOVA tests are used for univariate analysis. Logit regressions are conducted to discuss the association mechanism and heterogeneity effects. We also apply the multinomial treatment effects regression to correct the endogenous of residential segregation following Deb and Trivedi (2006a; 2006b)[38-39]. Results Baseline characteristics Descriptive statistics is presented in Table 1. More than half of rural migrants (64.27%) attended local public health education, whereas less than a quarter (19.85%) received public health resources through the Internet. Additionally, a higher proportion of rural migrants experience serious residential segregation. A total of 47.75% of them reside in migrant-dominant communities, 31.02% live in integrated communities, and only 21.22% of respondents settle in citizen-dominant communities. Among 13,759 rural migrants, most are male (54.87%), married (73.05%), and young with an average age of 32.52 years old. A total of 9,063 (65.87%) respondents only finished nine-year compulsory education with lower educational attainment. Rural migrants have high health status (88.81%), and a higher proportion (73.42%) have poor access to Urban Employee Basic Medical Insurance (UEBMI). In addition, more than 70% of rural migrants choose to live in urban communities, whereas the rest reside in villages. Univariate analysis Pearson chi-square tests and ANOVA tests are shown in Table 1. Rural migrants with poor access to public health education may experience high residential segregation. Compared with participants, rural migrants who could not attend any health education have a high probability to reside in migrant-dominant communities (52.27% vs 45.26%) and live in villages (36.49% vs 23.63%). Most participants are female (52.76% vs 47.24%), married (73.88% vs 26.12%) and healthy (89.68% vs 10.32%), and a higher proportion only completed nine-year compulsory education (89.57% vs 10.43%). Online participants are more prone to concentrate in migrant-dominant or integrated communities (74.37% vs 25.64%), and they are more likely to settle in urban communities (82.17% vs 17.83%). Compared with offline participants, most online participants are male (17.83% vs 30.80%), younger (30.24±0.15 vs 33.09±0.09) with educational attainment of junior high school (15.09% vs 8.00%). Multivariate Logit analysis The association between residential segregation and access to public health education is investigated by Logit regressions. Odds ratios and 95% confidence intervals (CI) are calculated in Table 2. The estimations reveal that residential segregation is negatively related with public health education attendance. Compared with those dwellers in integrated communities, rural migrants living in migrant-dominant communities are less likely to access to public health education (OR: 0.9200, 95% CI: 0.8500–0.9958), whereas the citizen-predominant dwellers have a higher probability to access public health education (OR: 1.5555, 95% CI: 1.4034, 1.7242). In addition, female and married rural migrants are associated with a higher prevalence of public health education attendance (male: OR: 0.7528, 95% CI: 0.7002, 0.8094; married: OR: 1.3018, 95% CI: 1.1795, 1.4368). The increasing prevalence of public health education attendance is related with higher educational attainment. Compared with those whose educations were primary school or below, rural migrants with educational attainments of junior school, senior school, and college or above are more likely to access to public health education (junior school: OR: 1.1952, 95% CI: 1.0566, 1.3520; senior school: OR: 1.3590, 95% CI: 1.1808, 1.5640; college or above: OR: 1.3867, 95% CI: 1.1652, 1.6503). Married respondents have a higher probability to participate in public health education than do their unmarried counterparts (OR: 1.3018, 95% CI: 1.1795, 1.4368). Rural migrants with better self-reported health status and enrollment of UEBMI are more prone to attend public health education than their counterparts (better health status: OR: 1.2516, 95% CI: 1.1182, 1.4010; enrollment of UEBMI: OR: 2.0500, 95% CI: 1.8786, 2.2371). Moreover, the dwellers in urban communities are more likely to access public health education (OR: 1.7432, 95% CI: 1.6126, 1.8845). The results from Logit regression also show that residential segregation has a negative association with the attendance of public health education through the Internet. Compared with those reside in integrated communities, the dwellers in citizen-dominant communities are more likely to be online participants (OR: 0.8709, 95% CI: 0.7893, 0.9609), while those settling in migrant-dominant communities have more probability to participate public health education through Internet (OR: 1.2539, 95% CI: 1.1163, 1.4083). In addition, younger respondents are more likely to use the Internet to access public health education than their counterparts (OR: 0.9708, 95% CI: 0.9642, 0.9775). Relative to those completed the education of primary school below, rural migrants with education attainments of junior school, senior school, and college or above are more prone to be online participants ((junior: OR: 1.5753; 95% CI: 1.2901, 1.9234; senior: OR: 2.2254; 95% CI: 1.8023, 2.7480; college or above: OR: 2.5783, 95% CI: 2.0415, 3.2563). Besides, married respondents are associated with attendance of public health education through the Internet (OR: 1.0235; 95% CI: 0.9097, 1.1515). Rural migrants with better health status and enrollment of UEBMI have a higher probability to attain public health education through the Internet. Rural migrants residing in urban communities are more prone to be online participants than those living in villages (OR: 1.8118; 95% CI: 1.6247, 2.0203). Causal effect analysis With the non-random distribution of residential segregation, the estimations based on logit regressions may be biased. To correct the endogeneity, the multinomial treatment effects model developed by Deb and Trivedi (2006a; 2006b) [38-39] is conducted to explore the causal effect of residential segregation on access to public health education. In this case, we choose homeownership as exclusion restrictions because it would be strongly correlated with residential location and residential segregation but might not exert a direct effect on public health education access [26](Liu, Dijst & Geertman, 2014). The logit density functions with 100 simulation draws are applied in estimation models. We also conduct the likelihood-ratio test to address the robustness of these two models, and the likelihood ratios are less than 0.05 in two models, indicating that these estimations are appropriate. The maximum simulated likelihood estimations are presented in Table 3. The results demonstrate that residential segregation has a significant effect on attendance of public health education. Rural migrants living in migrant-dominant neighborhoods have a lower likelihood of 58.19% to attend public health education (coefficient: −4.3321, 95% CI: −8.6404, -0.0238), whereas the probability among the dwellers in citizen-dominant communities is 10.48% higher than that of residents in integrated communities (coefficient: 1.1179, 95% CI: −0.0679, 2.3038). In addition, residential segregation produces a negative effect on the method of attendance of public health education. Compared with those living in integrated communities, rural migrants in migrant-dominant neighborhoods have a lower likelihood of 14.43% to participate in public health education through the Internet (Coefficient: −1.6482, 95% CI: −2.6790, −0.6173), whereas the dwellers in citizen-dominant communities show a higher probability of 10.90% to become online participants (Coefficient: 1.0015, 95% CI: 0.1901, 1.8130). Mechanism analysis The present study based on baseline estimations provides evidence that access to public health education services would be determined by residential segregation among rural migrants. However, what are the transmission channels between residential segregation and access to public health education? The role of social network and community participation, as we claim, acts as the potential mechanisms accounting for these effects. The social network is measured by two binary proxies including social relationship quality and willingness of being neighbors among citizens, whereas community participation represents the experience of community recreational activities or management for rural migrants. Several logit regressions are applied to identify the nexus of residential segregation with social networks and community participation. As shown in Table 4, residential segregation is significantly associated with poor social networks. Compared with the dwellers in integrated communities, rural migrants settling in migrant-dominant neighborhoods are less likely to build up a harmonious relationship with urban citizens (OR: 0.6630, 95% CI: 0.6098, 0.7209). The respondents living in migrant-dominant communities are also related with weaker intention of being neighbors with rural migrants among local citizens (OR: 0.7880, 95% CI: 0.7106, 0.8737). In contrast with segregated neighborhoods, rural migrants residing in citizen-dominant communities are more likely to maintain better social relationships with local residents (OR: 1.5447, 95% CI: 1.3757, 1.7344) and enhance the intention to be neighbors with rural migrants among local residents (OR: 1.2454, 95% CI: 1.0821, 1.4333). Residential segregation has a negative relationship with community participation. Dwellers in migrant-dominant communities are associated with a lower prevalence of community participation (OR: 0.9023, 95% CI: 0.8274, 0.9840). However, but citizen-predominant dwellers are more likely to engage in community activities with comparison to those living in integrated neighborhoods (OR: 1.1837, 95% CI: 1.0665, 1.3138). Heterogeneous effect Previous studies have indicated that the effect of residential segregation on access to public health education may vary by regional variations, local duration, migration patterns, and family migrations [32][37]. Therefore, the whole samples are further divided into several subgroups: eastern and non-eastern groups, long- (duration>=5) and short-term duration (duration<5), inter- and intra-city migration, and family-migration and split-family migration. Logit regressions are conducted to explore these heterogeneous effects. The estimations presented in Table 5 confirm that residential segregation has a significantly negative association with access to public health education. In terms of migrants in eastern areas, rural migrants residing in migrant-dominant communities are less likely to attend public health education than those in integrated and citizen-dominant communities (OR: 0.8194; SD=0.0346). In terms of long-term duration, the probability of attendance public health education among those dwellers in migrant-dominant communities is relatively lower than those living in other communities (OR: 0.7696; SD=0.0503). In terms of inter-prefectural level migration, rural migrants in migrant-dominant communities are associated with a lower prevalence of attendance of public health education (OR: 0.7766; SD=0.0468). In terms of family migration, dwellers in migrant-dominant communities are related with a lower probability to participate in public health education (OR: 0.7661; SD=0.0419). The results also demonstrate that the effect of residential segregation on the method of public health education attendance differentiates across region variations, migration duration, migration pattern, and family migration. For migrants in eastern areas, the dwellers in migrant-dominant communities are more likely to participate in public health education through the Internet than counterparts in other communities (OR: 0.8536; SD=0.0447). As for long-term duration, the respondents in migrant-dominant communities are less likely to become online participants of public health education (OR: 0.8460; SD=0.0780). Additionally, inter-prefectural level rural migrants living in migrant-dominant communities are associated with offline participants (OR: 0.7766; SD=0.0468), whereas those rural migrants with couples are less likely to attend public health education through the Internet (OR: 0.7661; SD=0.0419). Discussion The empirical results reveal that residential segregation would exhibit significant negative effects on public health education utilization. In other words, rural migrants living in migrant-dominant communities have lower probabilities of public health education attendance. These migrant-dominant dwellers are less likely to become online participants of public health education than their counterparts in citizen-dominant or integrated neighborhoods. These effects are robust after correcting the endogeneity. These findings also agree with several studies in the developed market, indicating that residential segregation, as the fundamental cause of health service disparity, would induce limited access to primary health services among ethnic or racial subgroups [4][9][11-12]. The effect of residential segregation on public health education may be transmitted through social networks and community participation. Public health education delivery is mainly carried out in local health agencies, including primary health care centers in urban areas and township hospitals in rural areas [32-33]. Local citizens would be more likely to access public health education than rural migrants [40], and rural migrants can obtain the public health education information from local citizens. In this case, residential segregation may enhance discrimination towards rural migrants, reduce social interaction, and hamper local social network formation for rural migrants, blocking the information channels of health education from local dwellers. Residential segregation may not facilitate social integration to destination cities and weaken their intention of community participation, greatly hindering social interaction with local residents and reduce access to public health education information [37][41-42]. The estimations also reveal that individual socioeconomic characteristics, such as educational attainment and health insurance, could partly account for attendance and online participation in health education activities. This finding agrees with prior research demonstrating that rural migrants with higher education attainments may be more likely to attain health education and attend health education through the Internet [35][37]. The results also show that gender and age are determinants of health education access, which is also consistent with a previous study indicating that female migrants may tend to focus greatly on health education information and that younger generations are prone to use the Internet for health education [37]. Moreover, dwellers in urban communities may increase the geographical accessibility of public health information, and improved delivery might occur with advanced methods as well, which remains consistent with the findings of Hou, Lin, and Zhang (2017)[32]. Substantial differences are found in access to public health education varying across region, migration duration, migration pattern, and family migration. With low incentive for public education resources delivery for non- hukou residents, rural migrants who move to eastern coastal cities would be excluded from access to public health education compared with those in non-eastern cities [32][37]. Rural migrants with a shorter duration and inter-city migration may decrease the prevalence of access to public health due to their un-integration into local cities [37]. Split-household migration is also related with lower attendance of health education, which is consistent with the previous study that rural migrants followed by couples are less likely to access public health education [37]. Conclusion Main findings Rural migrants experience serious disadvantages in access to public health education. Residential segregation has a negative effect on attendance of public health education. Residential segregation also reduces the probability to participate in public health education through the Internet, which may be transmitted through social networks and community participation, blocking information channels and restrict rural migrants from access to public health education. These effects may be differentiated by regional variations, migration duration, migration patterns, and family migration. Limitations This study investigates the effect of residential segregation on access to public health education among rural migrants using a proxy of neighborhood composition. The limitation in this study is that index of dissimilarity or isolation, commonly used to measure segregation in previous studies, could not be applied in this study to address these issues. Declarations Implications Residential segregation has a negative effect on public health education access for rural migrants. Local governments should promote urban renewal projects and build up mixed-community patterns to improve access to public health education for rural migrants. Acknowledgements None Authors' contributions ZW took leadership and responsibility for the research activity planning and made substantial contributions to the conception and design of the Programme. JL worked on the statistical analysis of the data. JM drafted the concept of the paper as well as participated in finalizing the manuscript. All authors read and approved the final manuscript. Funding This article is funded by the National Social Science Fund of China (Granted number 17BJY044&18ZDA081) Availability of data and material The NMPDMS are open and available dataset from the Migrant Population Service Center of National Health Commission, the People’s Republic of China (PRC). NMPDMS are however available from the Migrant Population Service Center of National Health Commissio upon reasonable research request. NMPDMS can be requested from the website of Migrant Population Service Center: http://www.chinaldrk.org.cn/wjw/ Ethics approval and consent to participate All data came from public domain. Ethics approval and consent to participate was not applicable. Consent for Publication Not applicable. Competing interests The authors declare that they have no competing interests. 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Immigrants in the Netherlands: equal access for equal needs?. Journal of Epidemiology & Community Health, 55(10), 701-707. Tables Table 1 residential segregation and utilization of local public health services: baseline characteristics and univariate analysis Entire sample Public Health education attendance χ 2 Public Health education by Internet χ 2 No N(%) Yes N(%) No N(%) Yes N(%) Neighborhood composition Migrants-dominance Citizen-dominance Integrated Household location Urban community Village Gender Female Male Age a Education attainment Primarily or below Junior Senior College or above Marital status Married Unmarried Self-reported health status Better Worse Urban Employees Basic Medical Insurance Enrolled in UEBMI Didn’t enroll in UEBMI 6183(47.75) 2748(21.22) 4017(31.02) 9875(71.77) 3884(28.23) 3210(45.13) 7549(54.87) 32.52±0.07 1434(10.42) 7629(55.45) 3402(24.73) 1294(9.40) 10051(73.05) 3708(26.95) 12220(88.81) 1539(11.19) 3657(26.58) 10102(73.42) 2409(52.27) 754(16.36) 1446(31.37) 3122(63.51) 1794(36.49) 2883(58.65) 2033(41.35) 32.63±0.13 605(12.31) 2831(57.59) 1108(22.54) 372(7.57) 3518(71.56) 1398(28.44) 4290(87.27) 626(12.73) 871(17.72) 4045(82.28) 3774(45.26) 1994(23.91) 2571(30.83) 6753(76.37) 2090(23.63) 4666(52.76) 4177(47.24) 32.46±0.09 829(9.37) 4798(54.26) 2294(25.94) 922(10.43) 6533(73.88) 2310(26.12) 7930(89.68) 913(10.32) 2786(31.51) 6057(68.49) 110.6056*** 257.8502*** 44.1187*** 40.7395*** 74.6412*** 8.6040*** 18.4629*** 307.7772*** 5085(48.95) 2092(20.14) 3212(30.92) 7631(69.20) 3397(30.80) 6032(54.70) 4996(45.30) 33.09±0.09 1307(11.85) 6313(57.25) 2526(22.91) 882(8.00) 8266(74.95) 2762(28.05) 9686(81.83) 1342(12.17) 2671(24.22) 8357(75.78) 1098(42.91) 656(25.64) 805(31.46) 2244(82.17) 487(17.83) 1517(55.55) 1214(44.45) 30.24±0.15 127(4.65) 1316(48.19) 876(32.08) 412(15.09) 1785(65.36) 946(34.64) 2534(92.79) 197(7.21) 986(36.10) 1745(63.90) 45.1197*** 181.7789*** 0.6391 103.5303*** 332.7006*** 102.3422*** 54.1102*** 158.4110*** N 13759 4916(35.73) 8843(64.27) 11028(80.15) 2731(19.85) a P-value were calculated using ANOVA tests and the variable’ P-value were calculated using Pearson chi-square tests; *** significant (p≤0.001) Table 2 the association between residential segregation and utilization of local public health education: Multivariate logit Attendance of local public health education Public Health education by Internet OR 95% CI p-value OR 95% CI p-value Neighborhood composition (ref: Integrated) Migrants-dominance 0.9200 0.8500,0.9958 0.039 0.8709 0.7893,0.9609 0.006 Citizen-dominance 1.5555 1.4034,1.7242 0.000 1.2539 1.1163,1.4083 0.000 Household location (ref: Village) Urban community 1.7432 1.6126,1.8845 0.000 1.8118 1.6247,2.0203 0.000 Gender (ref: Female) Male 0.7528 0.7002,0.8094 0.000 1.0119 0.9274,1.1041 0.790 Age 0.9977 0.9925,1.0029 0.386 0.9708 0.9642,0.9775 0.000 Education attainment (ref: Primarily or below) Junior 1.1952 1.0566,1.3520 0.005 1.5753 1.2901,1.9234 0.000 Senior 1.3590 1.1808,1.5640 0.000 2.2254 1.8023,2.7480 0.000 College or above 1.3867 1.1652,1.6503 0.000 2.5783 2.0415,3.2563 0.000 Marital status (ref: Unmarried) Married 1.3018 1.1795,1.4368 0.000 1.0235 0.9097,1.1515 0.699 Self-reported health status (ref: Worse) Better 1.2516 1.1182,1.4010 0.000 1.5549 1.3256,1.8239 0.000 Urban Employees Basic Medical Insurance (ref: No) Enrolled in UEBMI 2.0500 1.8786,2.2371 0.000 1.5838 1.4433,1.7380 0.000 Constant 0.6340 0.4937,0.8141 0.000 0.1311 0.0927,0.1854 0.000 N 13759 13759 Table 3 Multinomial Treatment Regression Results Explaining the Effects of residential segregation on public health education access among rural migrants Variables Attendance of public health education Public health education by Internet Coefficient 95% CI Marginal effect Coefficient 95% CI Marginal effect Neighborhood composition (ref: Integrated) Migrants-dominance -4.3321** -8.6404,-0.0238 -0.5819 -1.6482*** -2.6790,-0.6173 -0.1443 Citizen-dominance 1.1179* -0.0679,2.3038 0.1048 1.0015** 0.1901,1.8130 0.1090 Household location (ref: Village) Urban community 1.3656** 0.1796,2.5516 0.1968 0.7471*** 0.5199,0.9742 0.0570 Gender (ref: Female) Male -0.8326** -1.5817,-0.0835 -0.0956 0.0433 -0.0903,0.1769 0.0037 Age -0.0207 -0.0465,0.0050 -0.0024 -0.0500*** -0.0680,-0.0321 -0.0043 Education attainment (ref: Primarily or below) Junior 0.4200 -0.1149,0.9550 0.0503 0.5806*** 0.2817,0.8795 0.0486 Senior 0.8177** 0.0119,1.6235 0.0826 1.0706*** 0.6968,1.4444 0.1159 College or above 0.8870* -0.0236,1.7976 0.0799 1.2519*** 0.8152,1.6885 0.1605 Marital status (ref: Unmarried) Married 0.6317** 0.0124,1.2510 0.0827 0.0218 -0.1581,0.2017 0.0019 Self-reported health status (ref: Worse) Better 0.1785 -0.2000,0.5566 0.0220 0.3926*** 0.1507,0.6346 0.0297 Urban Employees Basic Medical Insurance (ref: No) Enrolled in UEBMI 2.2219** 0.2437,4.2000 0.1910 0.7521*** 0.5053,0.9989 0.0747 Constant 2.1180* -0.2653,4.5014 -1.8691*** -2.5130,-1.2252 Lambda Migrants-dominance 4.8466* -0.0070,9.7002 1.7672*** 0.6656,2.9288 Lambda Citizens-dominance -0.2776 -1.1600,0.6047 -0.9288** -1.8002,-0.0573 Likelihood-ratio test 0.0076 0.0004 Log pseudolikelihood -21463.377 -19474.006 Wald χ 2 (31df) 292.09 407.49 Prob > chi2 0.0000 0.0000 Observation 12948 12948 *Significance at the 10% level; **significance at the 5% level; ***significance at the 1% level Table 4 The mechanism between residential segregation and access of local public health education Social network formation Community participation Get along harmoniously with local citizens Willingness of being neighbors among citizens Participation in community recreational activities or management OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value Neighborhood composition (ref: Integrated) Migrants-dominance 0.6630 0.6098,0.7209 0.000 0.7880 0.7106,0.8737 0.000 0.9023 0.8274,0.9840 0.020 Citizens-dominance 1.5447 1.3757,1.7344 0.000 1.2454 1.0821,1.4333 0.002 1.1837 1.0665,1.3138 0.002 Constant 0.6332 0.4857,0.8253 0.001 3.4077 2.4845,4.6740 0.000 0.1284 0.0968,0.1705 0.000 Controls YES YES YES N 13759 13759 13759 The controlled variables include household location, gender, age, education attainments, martial status, self-rated health status and medical insurance. Table 5 residential segregation and utilization of local public health education: Heterogeneous effect Attendance of local public health education Region Migration Duration Migration Patterns Family Migration Eastern Non- eastern Duration>=5 Duration<5 Inter-city Intra-city family migration split-family migration Migrants-dominance 0.8194*** (0.0346) 0.8501** (0.0691) 0.7696*** (0.0503) 0.7985*** (0.0357) 0.7766*** (0.0468) 0.8643*** (0.0409) 0.7661*** (0.0419) 0.8143*** (0.0409) Constant 0.9370 (0.1374) 0.4254*** (0.1139) 1.0879 (0.2560) 0.7098** (0.1145) 0.9759 (0.2093) 0.7368* (0.1182) 0.4920*** (0.1013) 0.7806 (0.1347) Controls Yes Yes Yes Yes Yes N 10220 3539 4576 9183 5908 7851 6593 7166 Public Health education by Internet Region Migration Duration Migration Patterns Family Migration Eastern Non- eastern Duration>=5 Duration<5 Inter-city Intra-city family migration split-family migration Migrants-dominance 0.8536*** (0.0447) 0.6845*** (0.0666) 0.8460** (0.0780) 0.7825*** (0.0421) 0.7826*** (0.0522) 0.8529** (0.0525) 0.8996 (0.0587) 0.7239*** (0.0448) Constant 0.1219*** (0.0255) 0.3052*** (0.1037) 0.1848*** (0.0591) 0.1302*** (0.0288) 0.2605*** (0.0695) 0.1019*** (0.0245) 0.0899*** (0.0246) 0.1477*** (0.0368) Controls Yes Yes Yes Yes N 10220 3539 4576 9183 5908 7851 6593 7166 The reference is integrated and citizens-dominant neighborhood group; The controlled variables include household location, gender, age, education attainments, marital status, self-rated health status and medical insurance; odds ratios are given as robust standard error presented in parentheses; *Significance at the 10% level; **significance at the 5% level; ***significance at the 1% level. 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Previous studies have demonstrated that residential segregation was related with concentrated poverty, poor academic attainment, joblessness, disease infection, and criminality prevalence [3-6]. Researchers also explored the relationship between residential segregation and health services utilization, with some studies showing that segregation may block access to health services in enclave communities [7-11]. However, some evidence also indicated that living in an ethnic enclave can enhance health information through particular cultural advantages, social networks, and social support within these areas. Such enhancement will promote access to healthcare utilization, especially in Latino or Hispanic enclaves in the United States [12-15].\u003c/p\u003e\n\u003cp\u003eSince 1978, many rural migrants have moved into urban cities. The number of rural\u0026ndash;urban migrants in 2018 was approximately 288 million, accounting for more than 20 percent of the total population. Rural migrants have become an important and integral part of the industrial workforce, and they play a vital role in China\u0026rsquo;s urbanization, modernization, and economic development [16]. However, with the constraint of \u003cem\u003ehukou\u003c/em\u003e, rural migrants have less access to settle permanently when they remain regarded as outsiders in destination cities [17-18] and undertake demeaning jobs but with low pay [19-20]. Without local \u003cem\u003ehukou\u003c/em\u003e, they are not eligible for public housing welfare and are excluded from affordable housing [21]. An increasing number of rural migrants are forced to live in marginalized communities such as dormitories or urban villages (\u003cem\u003echengzhongcun\u003c/em\u003e). These communities can be viewed as enclaves with over-crowdedness, poverty, insanitation, and discrimination [16][22-25]. Rural migrants in those dwellings consistently experience heavily residential segregation [26-27] and health-risk behaviors [16][24][28-30].\u003c/p\u003e\n\u003cp\u003eTo improve the health status of rural migrants, the Chinese government launched a project, named Essential Public Health Services, to provide basic health services for all urban and rural dwellers in 2009 [31]. Public health education was one of the nine types of basic services that are provided to offer health-related information and consultation towards infectious diseases, occupational diseases and injuries and non-communicable diseases prevention, and maternal health promotion throughout\u0026nbsp;the entire course of health service delivery [32-33].\u003c/p\u003e\n\u003cp\u003ePublic health education is believed to be not only greatly favorable to promote disease prevention of rural migrants but also be beneficial to enhance health literacy for rural migrants [34-36]. However, most rural migrants are not entitled to public health education access in destination cities. Some evidence demonstrated that in 2014, only 57.16% of rural migrants could share public health education, and 22.02% of this public health education information was merely about infectious disease prevention [32]. More than 80% of public health education was still performed through traditional channels in 2015, such as radio, television programs, and billboards [35]. A few researchers also explored the causes and determinants of public health education access in the supply side, indicating that geographic accessibility and regional variations would hinder rural migrants\u0026rsquo; utilization of public health education [32]. On the demand side, limited access to health education might stem from poor individual socio-economic characteristics (i.e. age, educational attainment, health status, and health insurance) and migration traits [35][37].\u003c/p\u003e\n\u003cp\u003eHowever, the effect of residential segregation on public health education utilization among rural migrants has been underexplored. This paper aims to bridge this gap with three issues. First, does residential segregation have significant effects on access to public health education? If it does, what are the potential mechanisms accounting for? Additionally, do any heterogeneity effects vary by migration patterns, migration traits, and region variation?\u003c/p\u003e\n\u003cp\u003eThis paper contributes to previous studies in several ways. First, the present study uses a unique national survey in China and emphasizes the direct association of residential segregation with the use of public health education. Second, this study firstly applies a multinomial treatment effects regression to explore the causal effect of residential segregation on health education access. Third, the two potential transmission channels, namely, social network formation and community participation, are discussed to address the linkage.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData and Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper applies the 2014 National Migrants Population Dynamic Monitoring Survey, a national representative database, conducted by the National Health and Family Planning Commission of China, to explore the causal effect. The survey is a cross-sectional survey covered migrants aged 15 to 69 years old. These migrants do not have the local \u003cem\u003ehukou\u003c/em\u003e but have been working or living in urban cities for more than a month. This survey was carried out in seven provincial units that covered 8 cities (i.e., Beijing, Jiaxing, Xiamen, Zhengzhou, Shenzhen, Zhongshan, and Chengdu) and 64 communities or villages.\u003c/p\u003e\n\u003cp\u003eThe sample was drawn using the stratified multistage random sampling method with the probability proportional to size approach. A total of 2,000 migrants were randomly sampled in each city to participate in the survey through face-to-face interviews. The survey was conducted after obtaining informed consent for inclusion from all respondents, and 13,759 rural migrants were further included in this wave.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables and measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome is access to local public health education, which is measured by two proxy variables, namely, the prevalence of and the method of public health education. First, the prevalence of health education and health education approach is a binary variable based on the question \u0026ldquo;whether you have any kinds of public health education (i.e., occupational disease, HIV, tuberculosis, sexual-transmitted diseases, mental diseases, chronic diseases, and other communicative diseases prevention, reproduction and contraception, and nutrition health knowledge education) in destination cities?\u0026rdquo; It is treated as 1 if rural migrants participated in public health education and 0 otherwise. The approach of health education is constructed as a dummy variable, which is set to 1 if rural migrants obtained health education information through the Internet. Otherwise, it is set to 0.\u003c/p\u003e\n\u003cp\u003eThe focal variable is residential segregation. We use neighborhood composition as the proxy variable following Dinwiddie, Gaskin, Chan, Norrington, and McCleary (2013) [9]. It is consistent with the three dummy outcomes, namely, migrant-dominant (percentage of migrants\u0026gt;50%), citizen-dominant (percentage of urban dwellers\u0026gt;50%), and integrated (the percentage of both two groups reaches 50%).\u003c/p\u003e\n\u003cp\u003eThe controlled variables are selected from demand- and supply-side in accordance with Zhang, Lin, Liang, Qian, Zhang, and Hou (2017) [37]. The demand side variable consists of household location, whereas individual socioeconomic characteristics, such as gender, age, marital status, education attainment, self-reported health status, and social health insurance are incorporated as demand-side factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimation strategy \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Pearson chi-square tests and ANOVA tests are used for univariate analysis. Logit regressions are conducted to discuss the association mechanism and heterogeneity effects. We also apply the multinomial treatment effects regression to correct the endogenous of residential segregation following Deb and Trivedi (2006a; 2006b)[38-39].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics is presented in Table 1. More than half of rural migrants (64.27%) attended local public health education, whereas less than a quarter (19.85%) received public health resources through the Internet. Additionally, a higher proportion of rural migrants experience serious residential segregation. A total of 47.75% of them reside in migrant-dominant communities, 31.02% live in integrated communities, and only 21.22% of respondents settle in citizen-dominant communities.\u003c/p\u003e\n\u003cp\u003eAmong 13,759 rural migrants, most are male (54.87%), married (73.05%), and young with an average age of 32.52 years old. A total of 9,063 (65.87%) respondents only finished nine-year compulsory education with lower educational attainment. Rural migrants have high health status (88.81%), and a higher proportion (73.42%) have poor access to Urban Employee Basic Medical Insurance (UEBMI). In addition, more than 70% of rural migrants choose to live in urban communities, whereas the rest reside in villages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson chi-square tests and ANOVA tests are shown in Table 1. Rural migrants with poor access to public health education may experience high residential segregation. Compared with participants, rural migrants who could not attend any health education have a high probability to reside in migrant-dominant communities (52.27% vs 45.26%) and live in villages (36.49% vs 23.63%). Most participants are female (52.76% vs 47.24%), married (73.88% vs 26.12%) and healthy (89.68% vs 10.32%), and a higher proportion only completed nine-year compulsory education (89.57% vs 10.43%).\u003c/p\u003e\n\u003cp\u003eOnline participants are more prone to concentrate in migrant-dominant or integrated communities (74.37% vs 25.64%), and they are more likely to settle in urban communities (82.17% vs 17.83%). Compared with offline participants, most online participants are male (17.83% vs 30.80%), younger (30.24\u0026plusmn;0.15 vs 33.09\u0026plusmn;0.09) with educational attainment of junior high school (15.09% vs 8.00%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Logit analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between residential segregation and access to public health education is investigated by Logit regressions. Odds ratios and 95% confidence intervals (CI) are calculated in Table 2.\u003c/p\u003e\n\u003cp\u003eThe estimations reveal that residential segregation is negatively related with public health education attendance. Compared with those dwellers in integrated communities, rural migrants living in migrant-dominant communities are less likely to access to public health education (OR: 0.9200, 95% CI: 0.8500\u0026ndash;0.9958), whereas the citizen-predominant dwellers have a higher probability to access public health education (OR: 1.5555, 95% CI: 1.4034, 1.7242). In addition, female and married rural migrants are associated with a higher prevalence of public health education attendance (male: OR: 0.7528, 95% CI: 0.7002, 0.8094; married: OR: 1.3018, 95% CI: 1.1795, 1.4368). The increasing prevalence of public health education attendance is related with higher educational attainment. Compared with those whose educations were primary school or below, rural migrants with educational attainments of junior school, senior school, and college or above are more likely to access to public health education (junior school: OR: 1.1952, 95% CI: 1.0566, 1.3520; senior school: OR: 1.3590, 95% CI: 1.1808, 1.5640; college or above: OR: 1.3867, 95% CI: 1.1652, 1.6503). Married respondents have a higher probability to participate in public health education than do their unmarried counterparts (OR: 1.3018, 95% CI: 1.1795, 1.4368). Rural migrants with better self-reported health status and enrollment of UEBMI are more prone to attend public health education than their counterparts (better health status: OR: 1.2516, 95% CI: 1.1182, 1.4010; enrollment of UEBMI: OR: 2.0500, 95% CI: 1.8786, 2.2371). Moreover, the dwellers in urban communities are more likely to access public health education (OR: 1.7432, 95% CI: 1.6126, 1.8845).\u003c/p\u003e\n\u003cp\u003eThe results from Logit regression also show that residential segregation has a negative association with the attendance of public health education through the Internet. Compared with those reside in integrated communities, the dwellers in citizen-dominant communities are more likely to be online participants (OR: 0.8709, 95% CI: 0.7893, 0.9609), while those settling in migrant-dominant communities have more probability to participate public health education through Internet (OR: 1.2539, 95% CI: 1.1163, 1.4083). In addition, younger respondents are more likely to use the Internet to access public health education than their counterparts (OR: 0.9708, 95% CI: 0.9642, 0.9775). Relative to those completed the education of primary school below, rural migrants with education attainments of junior school, senior school, and college or above are more prone to be online participants ((junior: OR: 1.5753; 95% CI: 1.2901, 1.9234; senior: OR: 2.2254; 95% CI: 1.8023, 2.7480; college or above: OR: 2.5783, 95% CI: 2.0415, 3.2563). Besides, married respondents are associated with attendance of public health education through the Internet (OR: 1.0235; 95% CI: 0.9097, 1.1515). Rural migrants with better health status and enrollment of UEBMI have a higher probability to attain public health education through the Internet. Rural migrants residing in urban communities are more prone to be online participants than those living in villages (OR: 1.8118; 95% CI: 1.6247, 2.0203).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCausal effect analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the non-random distribution of residential segregation, the estimations based on logit regressions may be biased. To correct the endogeneity, the multinomial treatment effects model developed by Deb and Trivedi (2006a; 2006b) [38-39] is conducted to explore the causal effect of residential segregation on access to public health education. In this case, we choose homeownership as exclusion restrictions because it would be strongly correlated with residential location and residential segregation but might not exert a direct effect on public health education access [26](Liu, Dijst \u0026amp; Geertman, 2014). The logit density functions with 100 simulation draws are applied in estimation models. We also conduct the likelihood-ratio test to address the robustness of these two models, and the likelihood ratios are less than 0.05 in two models, indicating that these estimations are appropriate. The maximum simulated likelihood estimations are presented in Table 3.\u003c/p\u003e\n\u003cp\u003eThe results demonstrate that residential segregation has a significant effect on attendance of public health education. Rural migrants living in migrant-dominant neighborhoods have a lower likelihood of 58.19% to attend public health education (coefficient: \u0026minus;4.3321, 95% CI: \u0026minus;8.6404, -0.0238), whereas the probability among the dwellers in citizen-dominant communities is 10.48% higher than that of residents in integrated communities (coefficient: 1.1179, 95% CI: \u0026minus;0.0679, 2.3038). In addition, residential segregation produces a negative effect on the method of attendance of public health education. Compared with those living in integrated communities, rural migrants in migrant-dominant neighborhoods have a lower likelihood of 14.43% to participate in public health education through the Internet (Coefficient: \u0026minus;1.6482, 95% CI: \u0026minus;2.6790, \u0026minus;0.6173), whereas the dwellers in citizen-dominant communities show a higher probability of 10.90% to become online participants (Coefficient: 1.0015, 95% CI: 0.1901, 1.8130).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMechanism analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study based on baseline estimations provides evidence that access to public health education services would be determined by residential segregation among rural migrants. However, what are the transmission channels between residential segregation and access to public health education? The role of social network and community participation, as we claim, acts as the potential mechanisms accounting for these effects. The social network is measured by two binary proxies including social relationship quality and willingness of being neighbors among citizens, whereas community participation represents the experience of community recreational activities or management for rural migrants. Several logit regressions are applied to identify the nexus of residential segregation with social networks and community participation.\u003c/p\u003e\n\u003cp\u003eAs shown in Table 4, residential segregation is significantly associated with poor social networks. Compared with the dwellers in integrated communities, rural migrants settling in migrant-dominant neighborhoods are less likely to build up a harmonious relationship with urban citizens (OR: 0.6630, 95% CI: 0.6098, 0.7209). The respondents living in migrant-dominant communities are also related with weaker intention of being neighbors with rural migrants among local citizens (OR: 0.7880, 95% CI: 0.7106, 0.8737). In contrast with segregated neighborhoods, rural migrants residing in citizen-dominant communities are more likely to maintain better social relationships with local residents (OR: 1.5447, 95% CI: 1.3757, 1.7344) and enhance the intention to be neighbors with rural migrants among local residents (OR: 1.2454, 95% CI: 1.0821, 1.4333). Residential segregation has a negative relationship with community participation. Dwellers in migrant-dominant communities are associated with a lower prevalence of community participation (OR: 0.9023, 95% CI: 0.8274, 0.9840). However, but citizen-predominant dwellers are more likely to engage in community activities with comparison to those living in integrated neighborhoods (OR: 1.1837, 95% CI: 1.0665, 1.3138).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneous effect\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies have indicated that the effect of residential segregation on access to public health education may vary by regional variations, local duration, migration patterns, and family migrations [32][37]. Therefore, the whole samples are further divided into several subgroups: eastern and non-eastern groups, long- (duration\u0026gt;=5) and short-term duration (duration\u0026lt;5), inter- and intra-city migration, and family-migration and split-family migration. Logit regressions are conducted to explore these heterogeneous effects.\u003c/p\u003e\n\u003cp\u003eThe estimations presented in Table 5 confirm that residential segregation has a significantly negative association with access to public health education. In terms of migrants in eastern areas, rural migrants residing in migrant-dominant communities are less likely to attend public health education than those in integrated and citizen-dominant communities (OR: 0.8194; SD=0.0346). In terms of long-term duration, the probability of attendance public health education among those dwellers in migrant-dominant communities is relatively lower than those living in other communities (OR: 0.7696; SD=0.0503). In terms of inter-prefectural level migration, rural migrants in migrant-dominant communities are associated with a lower prevalence of attendance of public health education (OR: 0.7766; SD=0.0468). In terms of family migration, dwellers in migrant-dominant communities are related with a lower probability to participate in public health education (OR: 0.7661; SD=0.0419).\u003c/p\u003e\n\u003cp\u003eThe results also demonstrate that the effect of residential segregation on the method of public health education attendance differentiates across region variations, migration duration, migration pattern, and family migration. For migrants in eastern areas, the dwellers in migrant-dominant communities are more likely to participate in public health education through the Internet than counterparts in other communities (OR: 0.8536; SD=0.0447). As for long-term duration, the respondents in migrant-dominant communities are less likely to become online participants of public health education (OR: 0.8460; SD=0.0780). Additionally, inter-prefectural level rural migrants living in migrant-dominant communities are associated with offline participants (OR: 0.7766; SD=0.0468), whereas those rural migrants with couples are less likely to attend public health education through the Internet (OR: 0.7661; SD=0.0419).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe empirical results reveal that residential segregation would exhibit significant negative effects on public health education utilization. In other words, rural migrants living in migrant-dominant communities have lower probabilities of public health education attendance. These migrant-dominant dwellers are less likely to become online participants of public health education than their counterparts in citizen-dominant or integrated neighborhoods. These effects are robust after correcting the endogeneity. These findings also agree with several studies in the developed market, indicating that residential segregation, as the fundamental cause of health service disparity, would induce limited access to primary health services among ethnic or racial subgroups [4][9][11-12].\u003c/p\u003e\n\u003cp\u003eThe effect of residential segregation on public health education may be transmitted through social networks and community participation. Public health education delivery is mainly carried out in local health agencies, including primary health care centers in urban areas and township hospitals in rural areas [32-33]. Local citizens would be more likely to access public health education than rural migrants [40], and rural migrants can obtain the public health education information from local citizens. In this case, residential segregation may enhance discrimination towards rural migrants, reduce social interaction, and hamper local social network formation for rural migrants, blocking the information channels of health education from local dwellers. Residential segregation may not facilitate social integration to destination cities and weaken their intention of community participation, greatly hindering social interaction with local residents and reduce access to public health education information [37][41-42].\u003c/p\u003e\n\u003cp\u003eThe estimations also reveal that individual socioeconomic characteristics, such as educational attainment and health insurance, could partly account for attendance and online participation in health education activities. This finding agrees with prior research demonstrating that rural migrants with higher education attainments may be more likely to attain health education and attend health education through the Internet [35][37]. The results also show that gender and age are determinants of health education access, which is also consistent with a previous study indicating that female migrants may tend to focus greatly on health education information and that younger generations are prone to use the Internet for health education [37]. Moreover, dwellers in urban communities may increase the geographical accessibility of public health information, and improved delivery might occur with advanced methods as well, which remains consistent with the findings of Hou, Lin, and Zhang (2017)[32].\u003c/p\u003e\n\u003cp\u003eSubstantial differences are found in access to public health education varying across region, migration duration, migration pattern, and family migration. With low incentive for public education resources delivery for non-\u003cem\u003ehukou\u003c/em\u003e residents, rural migrants who move to eastern coastal cities would be excluded from access to public health education compared with those in non-eastern cities [32][37]. Rural migrants with a shorter duration and inter-city migration may decrease the prevalence of access to public health due to their un-integration into local cities [37]. Split-household migration is also related with lower attendance of health education, which is consistent with the previous study that rural migrants followed by couples are less likely to access public health education [37].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cstrong\u003eMain findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRural migrants experience serious disadvantages in access to public health education. Residential segregation has a negative effect on attendance of public health education. Residential segregation also reduces the probability to participate in public health education through the Internet, which may be transmitted through social networks and community participation, blocking information channels and restrict rural migrants from access to public health education. These effects may be differentiated by regional variations, migration duration, migration patterns, and family migration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigates the effect of residential segregation on access to public health education among rural migrants using a proxy of neighborhood composition. The limitation in this study is that index of dissimilarity or isolation, commonly used to measure segregation in previous studies, could not be applied in this study to address these issues.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eImplications \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResidential segregation has a negative effect on public health education access for rural migrants. Local governments should promote urban renewal projects and build up mixed-community patterns to improve access to public health education for rural migrants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZW took leadership and responsibility for the research activity planning and made substantial contributions to the conception and design of the Programme. JL worked on the statistical analysis of the data. JM drafted the concept of the paper as well as participated in finalizing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is funded by the National Social Science Fund of China (Granted number 17BJY044\u0026amp;18ZDA081)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NMPDMS are open and available dataset from the Migrant Population Service Center of National Health Commission, the People\u0026rsquo;s Republic of China (PRC). NMPDMS are however available from the Migrant Population Service Center of National Health Commissio upon reasonable research request. NMPDMS can be requested from the website of Migrant Population Service Center: \u003ca href=\"http://www.chinaldrk.org.cn/wjw/\"\u003ehttp://www.chinaldrk.org.cn/wjw/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data came from public domain. Ethics approval and consent to participate was not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e[1] Timberlake, J. M., Howell, A. J., \u0026amp; Staight, A. J. (2011). 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Public health services in Shenzhen: a case study.\u0026nbsp;Public Health,\u0026nbsp;125(1), 15-19.\u003c/p\u003e\n\u003cp\u003e[37] Zhang, J., Lin, S., Liang, D., Qian, Y., Zhang, D., \u0026amp; Hou, Z. (2017). Public health services utilization and its determinants among internal migrants in China: evidence from a nationally representative survey.\u0026nbsp;International journal of environmental research and public health,\u0026nbsp;14(9), e140911002-e140911002.\u003c/p\u003e\n\u003cp\u003e[38] Deb, P., \u0026amp; Trivedi, P. K. (2006). Specification and simulated likelihood estimation of a non‐normal treatment‐outcome model with selection: Application to health care utilization.\u0026nbsp;The Econometrics Journal,\u0026nbsp;9(2), 307-331.\u003c/p\u003e\n\u003cp\u003e[39] Deb, P., \u0026amp; Trivedi, P. K. (2006). Maximum simulated likelihood estimation of a negative binomial regression model with multinomial endogenous treatment.\u0026nbsp;The Stata Journal,\u0026nbsp;6(2), 246-255.\u003c/p\u003e\n\u003cp\u003e[40] Guo, J., Yang, H., Liu, L., \u0026amp; Shao, F. (2019). Analysis on the awareness rate and influencing factors of basic public health services among internal migrants. Chin. Chinese Public Health, 35(1), 63-66.\u003c/p\u003e\n\u003cp\u003e[41] Fassaert, T., Hesselink, A. E., \u0026amp; Verhoeff, A. P. (2009). Acculturation and use of health care services by Turkish and Moroccan migrants: a cross-sectional population-based study.\u0026nbsp;BMC Public Health,\u0026nbsp;9, 332-332.\u003c/p\u003e\n\u003cp\u003e[42] Stronks, K., Ravelli, A. C., \u0026amp; Reijneveld, S. A. (2001). Immigrants in the Netherlands: equal access for equal needs?.\u0026nbsp;Journal of Epidemiology \u0026amp; Community Health,\u0026nbsp;55(10), 701-707.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1 residential segregation and utilization of local public health services: baseline characteristics and univariate analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"1085\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"272\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"123\"\u003e\n\u003cp\u003e\u003cem\u003eEntire sample\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"265\"\u003e\n\u003cp\u003e\u003cem\u003ePublic Health education attendance\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"98\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"227\"\u003e\n\u003cp\u003e\u003cem\u003ePublic Health education by Internet\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"100\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eN(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN(%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eN(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN(%)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"272\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeighborhood composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMigrants-dominance\u003c/p\u003e\n\u003cp\u003eCitizen-dominance\u003c/p\u003e\n\u003cp\u003eIntegrated\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUrban community\u003c/p\u003e\n\u003cp\u003eVillage\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEducation attainment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimarily or below\u003c/p\u003e\n\u003cp\u003eJunior\u003c/p\u003e\n\u003cp\u003eSenior\u003c/p\u003e\n\u003cp\u003eCollege or above\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003cp\u003eUnmarried\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-reported health status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetter\u003c/p\u003e\n\u003cp\u003eWorse\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrban Employees Basic Medical Insurance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnrolled in UEBMI\u003c/p\u003e\n\u003cp\u003eDidn\u0026rsquo;t enroll in UEBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6183(47.75)\u003c/p\u003e\n\u003cp\u003e2748(21.22)\u003c/p\u003e\n\u003cp\u003e4017(31.02)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e9875(71.77)\u003c/p\u003e\n\u003cp\u003e3884(28.23)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3210(45.13)\u003c/p\u003e\n\u003cp\u003e7549(54.87)\u003c/p\u003e\n\u003cp\u003e32.52\u0026plusmn;0.07\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1434(10.42)\u003c/p\u003e\n\u003cp\u003e7629(55.45)\u003c/p\u003e\n\u003cp\u003e3402(24.73)\u003c/p\u003e\n\u003cp\u003e1294(9.40)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e10051(73.05)\u003c/p\u003e\n\u003cp\u003e3708(26.95)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e12220(88.81)\u003c/p\u003e\n\u003cp\u003e1539(11.19)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3657(26.58)\u003c/p\u003e\n\u003cp\u003e10102(73.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2409(52.27)\u003c/p\u003e\n\u003cp\u003e754(16.36)\u003c/p\u003e\n\u003cp\u003e1446(31.37)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3122(63.51)\u003c/p\u003e\n\u003cp\u003e1794(36.49)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2883(58.65)\u003c/p\u003e\n\u003cp\u003e2033(41.35)\u003c/p\u003e\n\u003cp\u003e32.63\u0026plusmn;0.13\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e605(12.31)\u003c/p\u003e\n\u003cp\u003e2831(57.59)\u003c/p\u003e\n\u003cp\u003e1108(22.54)\u003c/p\u003e\n\u003cp\u003e372(7.57)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3518(71.56)\u003c/p\u003e\n\u003cp\u003e1398(28.44)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4290(87.27)\u003c/p\u003e\n\u003cp\u003e626(12.73)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e871(17.72)\u003c/p\u003e\n\u003cp\u003e4045(82.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3774(45.26)\u003c/p\u003e\n\u003cp\u003e1994(23.91)\u003c/p\u003e\n\u003cp\u003e2571(30.83)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6753(76.37)\u003c/p\u003e\n\u003cp\u003e2090(23.63)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4666(52.76)\u003c/p\u003e\n\u003cp\u003e4177(47.24)\u003c/p\u003e\n\u003cp\u003e32.46\u0026plusmn;0.09\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e829(9.37)\u003c/p\u003e\n\u003cp\u003e4798(54.26)\u003c/p\u003e\n\u003cp\u003e2294(25.94)\u003c/p\u003e\n\u003cp\u003e922(10.43)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6533(73.88)\u003c/p\u003e\n\u003cp\u003e2310(26.12)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e7930(89.68)\u003c/p\u003e\n\u003cp\u003e913(10.32)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2786(31.51)\u003c/p\u003e\n\u003cp\u003e6057(68.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e110.6056***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e257.8502***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e44.1187***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e40.7395***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e74.6412***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e8.6040***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e18.4629***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e307.7772***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5085(48.95)\u003c/p\u003e\n\u003cp\u003e2092(20.14)\u003c/p\u003e\n\u003cp\u003e3212(30.92)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e7631(69.20)\u003c/p\u003e\n\u003cp\u003e3397(30.80)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e6032(54.70)\u003c/p\u003e\n\u003cp\u003e4996(45.30)\u003c/p\u003e\n\u003cp\u003e33.09\u0026plusmn;0.09\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1307(11.85)\u003c/p\u003e\n\u003cp\u003e6313(57.25)\u003c/p\u003e\n\u003cp\u003e2526(22.91)\u003c/p\u003e\n\u003cp\u003e882(8.00)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e8266(74.95)\u003c/p\u003e\n\u003cp\u003e2762(28.05)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e9686(81.83)\u003c/p\u003e\n\u003cp\u003e1342(12.17)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2671(24.22)\u003c/p\u003e\n\u003cp\u003e8357(75.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1098(42.91)\u003c/p\u003e\n\u003cp\u003e656(25.64)\u003c/p\u003e\n\u003cp\u003e805(31.46)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2244(82.17)\u003c/p\u003e\n\u003cp\u003e487(17.83)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1517(55.55)\u003c/p\u003e\n\u003cp\u003e1214(44.45)\u003c/p\u003e\n\u003cp\u003e30.24\u0026plusmn;0.15\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e127(4.65)\u003c/p\u003e\n\u003cp\u003e1316(48.19)\u003c/p\u003e\n\u003cp\u003e876(32.08)\u003c/p\u003e\n\u003cp\u003e412(15.09)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1785(65.36)\u003c/p\u003e\n\u003cp\u003e946(34.64)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2534(92.79)\u003c/p\u003e\n\u003cp\u003e197(7.21)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e986(36.10)\u003c/p\u003e\n\u003cp\u003e1745(63.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e45.1197***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e181.7789***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.6391\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e103.5303***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e332.7006***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e102.3422***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e54.1102***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e158.4110***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"272\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cem\u003e13759\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cem\u003e4916(35.73)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cem\u003e8843(64.27)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"98\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cem\u003e11028(80.15)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cem\u003e2731(19.85)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea \u003c/sup\u003eP-value were calculated using ANOVA tests and the variable\u0026rsquo; P-value were calculated using Pearson chi-square tests; \u003csup\u003e***\u003c/sup\u003esignificant (p\u0026le;0.001)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2 the association between residential segregation and utilization of local public health education:\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003eMultivariate logit\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"933\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"302\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAttendance of local public health education\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"283\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePublic Health education by Internet\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeighborhood composition (ref: Integrated)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eMigrants-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.9200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.8500,0.9958\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e0.8709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e0.7893,0.9609\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eCitizen-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.5555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.4034,1.7242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e1.2539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e1.1163,1.4083\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold location (ref: Village)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eUrban community\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.7432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.6126,1.8845\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e1.8118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e1.6247,2.0203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender (ref: Female)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.7528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.7002,0.8094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e1.0119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e0.9274,1.1041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.790\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.9977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.9925,1.0029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.386\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e0.9708\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e0.9642,0.9775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u003cstrong\u003eEducation attainment (ref: Primarily or below)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eJunior\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.1952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.0566,1.3520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e1.5753\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e1.2901,1.9234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eSenior\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.3590\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.1808,1.5640\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e2.2254\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e1.8023,2.7480\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eCollege or above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.3867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.1652,1.6503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e2.5783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e2.0415,3.2563\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital status (ref: Unmarried)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.3018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.1795,1.4368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e1.0235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e0.9097,1.1515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.699\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-reported health status (ref: Worse)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eBetter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.2516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.1182,1.4010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e1.5549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e1.3256,1.8239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003e\u003cstrong\u003eUrban Employees Basic Medical Insurance (ref: No)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eEnrolled in UEBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e2.0500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.8786,2.2371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e1.5838\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e1.4433,1.7380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.6340\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.4937,0.8141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e0.1311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"99\"\u003e\n\u003cp\u003e0.0927,0.1854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"93\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"347\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"302\"\u003e\n\u003cp\u003e13759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"283\"\u003e\n\u003cp\u003e13759\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Multinomial Treatment Regression Results Explaining the Effects of residential segregation on public health education access among rural migrants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"965\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"312\"\u003e\n\u003cp\u003e\u003cstrong\u003eAttendance of public health education\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"323\"\u003e\n\u003cp\u003e\u003cstrong\u003ePublic health education by Internet\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarginal effect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarginal effect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeighborhood composition (ref: Integrated)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eMigrants-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-4.3321**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-8.6404,-0.0238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.5819\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-1.6482***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-2.6790,-0.6173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e-0.1443\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eCitizen-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1.1179*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.0679,2.3038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.1048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e1.0015**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.1901,1.8130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.1090\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold location (ref: Village)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eUrban community\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1.3656**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.1796,2.5516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.1968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.7471***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.5199,0.9742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.0570\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender (ref: Female)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-0.8326**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-1.5817,-0.0835\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.0956\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.0433\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.0903,0.1769\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.0037\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-0.0207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.0465,0.0050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.0024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.0500***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.0680,-0.0321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e-0.0043\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eEducation attainment (ref: Primarily or below)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eJunior\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.4200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.1149,0.9550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0503\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.5806***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.2817,0.8795\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.0486\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eSenior\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.8177**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0119,1.6235\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0826\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e1.0706***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.6968,1.4444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.1159\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eCollege or above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.8870*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.0236,1.7976\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e1.2519***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.8152,1.6885\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.1605\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital status (ref: Unmarried)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.6317**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0124,1.2510\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0827\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.0218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.1581,0.2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.0019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-reported health status (ref: Worse)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eBetter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.1785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.2000,0.5566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.3926***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.1507,0.6346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.0297\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eUrban Employees Basic Medical Insurance (ref: No)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eEnrolled in UEBMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2.2219**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.2437,4.2000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.1910\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.7521***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.5053,0.9989\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e0.0747\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2.1180*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.2653,4.5014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-1.8691***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-2.5130,-1.2252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eLambda Migrants-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e4.8466*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-0.0070,9.7002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e1.7672***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.6656,2.9288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eLambda Citizens-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-0.2776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-1.1600,0.6047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-0.9288**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e-1.8002,-0.0573\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eLikelihood-ratio test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.0076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.0004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eLog pseudolikelihood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e-21463.377\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e-19474.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eWald \u0026chi; \u003csup\u003e2 \u003c/sup\u003e(31df)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e292.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e407.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eProb \u0026gt; chi2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"331\"\u003e\n\u003cp\u003eObservation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e12948\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e12948\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Significance at the 10% level; **significance at the 5% level; ***significance at the 1% level\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 The mechanism between residential segregation and access of local public health education\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"1070\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"6\" width=\"529\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocial network formation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"260\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCommunity participation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"255\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGet along \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eharmoniously\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e with local citizens \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"274\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWillingness of being neighbors among citizens\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"260\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipation in community recreational activities or management\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeighborhood composition (ref: Integrated)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eMigrants-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.6630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.6098,0.7209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e0.7880\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e0.7106,0.8737\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.9023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.8274,0.9840\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eCitizens-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e1.5447\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e1.3757,1.7344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e1.2454\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e1.0821,1.4333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e1.1837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.0665,1.3138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.6332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.4857,0.8253\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e3.4077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e2.4845,4.6740\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.1284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0968,0.1705\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eControls\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"255\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"274\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"260\"\u003e\n\u003cp\u003eYES\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"281\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"255\"\u003e\n\u003cp\u003e13759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"274\"\u003e\n\u003cp\u003e13759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" width=\"260\"\u003e\n\u003cp\u003e13759\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe controlled variables include household location, gender, age, education attainments, martial status, self-rated health status and medical insurance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 residential segregation and utilization of local public health education: Heterogeneous effect\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"957\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"8\" width=\"815\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAttendance of local public health education\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"182\"\u003e\n\u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eMigration Duration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eMigration Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"274\"\u003e\n\u003cp\u003e\u003cstrong\u003eFamily Migration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u003cstrong\u003eEastern\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eNon- eastern\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration\u0026gt;=5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration\u0026lt;5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eInter-city\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eIntra-city\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003efamily migration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003esplit-family migration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eMigrants-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e0.8194***\u003c/p\u003e\n\u003cp\u003e(0.0346)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.8501**\u003c/p\u003e\n\u003cp\u003e(0.0691)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.7696***\u003c/p\u003e\n\u003cp\u003e(0.0503)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.7985***\u003c/p\u003e\n\u003cp\u003e(0.0357)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.7766***\u003c/p\u003e\n\u003cp\u003e(0.0468)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.8643***\u003c/p\u003e\n\u003cp\u003e(0.0409)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e0.7661***\u003c/p\u003e\n\u003cp\u003e(0.0419)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e0.8143***\u003c/p\u003e\n\u003cp\u003e(0.0409)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e0.9370\u003c/p\u003e\n\u003cp\u003e(0.1374)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.4254***\u003c/p\u003e\n\u003cp\u003e(0.1139)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e1.0879\u003c/p\u003e\n\u003cp\u003e(0.2560)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.7098**\u003c/p\u003e\n\u003cp\u003e(0.1145)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.9759\u003c/p\u003e\n\u003cp\u003e(0.2093)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.7368*\u003c/p\u003e\n\u003cp\u003e(0.1182)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e0.4920***\u003c/p\u003e\n\u003cp\u003e(0.1013)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e0.7806\u003c/p\u003e\n\u003cp\u003e(0.1347)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eControls\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"182\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"189\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"170\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e10220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e3539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e4576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e9183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e5908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e7851\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e6593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e7166\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"957\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePublic Health education by Internet\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"182\"\u003e\n\u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"189\"\u003e\n\u003cp\u003e\u003cstrong\u003eMigration Duration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eMigration Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"274\"\u003e\n\u003cp\u003e\u003cstrong\u003eFamily Migration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e\u003cstrong\u003eEastern\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eNon- eastern\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration\u0026gt;=5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration\u0026lt;5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eInter-city\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u003cstrong\u003eIntra-city\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u003cstrong\u003efamily migration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003esplit-family migration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eMigrants-dominance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e0.8536***\u003c/p\u003e\n\u003cp\u003e(0.0447)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.6845***\u003c/p\u003e\n\u003cp\u003e(0.0666)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.8460**\u003c/p\u003e\n\u003cp\u003e(0.0780)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.7825***\u003c/p\u003e\n\u003cp\u003e(0.0421)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.7826***\u003c/p\u003e\n\u003cp\u003e(0.0522)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.8529**\u003c/p\u003e\n\u003cp\u003e(0.0525)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e0.8996\u003c/p\u003e\n\u003cp\u003e(0.0587)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e0.7239***\u003c/p\u003e\n\u003cp\u003e(0.0448)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e0.1219***\u003c/p\u003e\n\u003cp\u003e(0.0255)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.3052***\u003c/p\u003e\n\u003cp\u003e(0.1037)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e0.1848***\u003c/p\u003e\n\u003cp\u003e(0.0591)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.1302***\u003c/p\u003e\n\u003cp\u003e(0.0288)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.2605***\u003c/p\u003e\n\u003cp\u003e(0.0695)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.1019***\u003c/p\u003e\n\u003cp\u003e(0.0245)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e0.0899***\u003c/p\u003e\n\u003cp\u003e(0.0246)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e0.1477***\u003c/p\u003e\n\u003cp\u003e(0.0368)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eControls\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"182\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"189\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"170\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"274\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"88\"\u003e\n\u003cp\u003e10220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e3539\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"95\"\u003e\n\u003cp\u003e4576\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e9183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e5908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e7851\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e6593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e7166\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe reference is integrated and citizens-dominant neighborhood group; The controlled variables include household location, gender, age, education attainments, marital status, self-rated health status and medical insurance; odds ratios are given as robust standard error presented in parentheses; *Significance at the 10% level; **significance at the 5% level; ***significance at the 1% level.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Residential, segregation, Public, health, education, Social network, Community participation","lastPublishedDoi":"10.21203/rs.3.rs-51103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-51103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRural migrants in China often experience serious residential segregation in destination cities, potentially resulting in limited access to public health education. However, the effect of residential segregation on public health education access remains unexplored. The present paper aims to address three issues. Does residential segregation have significant effects on access to public health education? If it does, what are its potential mechanisms accounting for? Additionally, are any heterogeneity effects differentiated by local duration, migration patterns, migration traits, and regional variations?\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe data from the 2014 National Migrants Population Dynamic Monitoring Survey and Logit regression are applied to explore the association between residential segregation and access to public health education. We further use multinomial treatment effects regression to address the endogenous issue. Several Logit models are also used to investigate potential mechanisms and heterogeneous effects.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults :\u003c/strong\u003eThe Logit estimations reveal that rural migrants in segregated neighborhoods are negatively related with lower prevalence of attendance (OR: 0.9200, 95% CI: 0.8500, 0.9958) and online participation of public health education (OR: 0.8709, 95% CI: 0.7893, 0.9609). The negative effects of residential segregation on access to public health education are also drawn in the multinomial treatment effects regressions (attendance model: coefficient: −4.3321, 95% CI: −8.6404, −0.0238; method model: coefficient: −1.6482; 95% CI: −2.6790, −0.6173). The mechanism analysis also demonstrates that residential segregation is negatively associated with the two potential transmission channels: social network formation (OR: 0.6630, 95% CI: 0.6098, 0.7209) and community participation (OR: 0.7880, 95% CI: 0.7106, 0.8737).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eResidential segregation produces a negative effect on public health education access. Social network and community participation may act as the transmission channel that links residential segregation and access to public health education. Additionally, the effects of residential segregation on public health education are differentiated across regional variations, local duration, migration patterns, and family migration.\u003c/p\u003e","manuscriptTitle":"Does Residential Segregation Affect Access to Public Health Education Among Rural Migrants in China?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-03 17:26:54","doi":"10.21203/rs.3.rs-51103/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"26a5a6df-576a-4136-85db-b73b70578544","owner":[],"postedDate":"August 3rd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":233327,"name":"Health Policy"}],"tags":[],"updatedAt":"2020-08-03T17:26:54+00:00","versionOfRecord":[],"versionCreatedAt":"2020-08-03 17:26:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-51103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-51103","identity":"rs-51103","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}