Effect of rural-to-urban migration on the healthcare utilisation of middle-aged and elderly adults: evidence from the China Health and Retirement Longitudinal Study

Effect of rural-to-urban migration on the healthcare utilisation of middle-aged and elderly adults: evidence from the China Health and Retirement Longitudinal Study | 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 Article Effect of rural-to-urban migration on the healthcare utilisation of middle-aged and elderly adults: evidence from the China Health and Retirement Longitudinal Study Lu Xu, Shengxian Bi, Songhao Yang, Dandan Guo, Huawei Tan, Lei li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4048728/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: An increasing number of middle-aged and elderly adults are migrating from rural to urban areas for employment, to care for their younger generation and due to old age. As these age groups move into urban areas, their healthcare service utilisation are directly related to their health status and basic rights to survival. It also places higher demands on China’s healthcare service provision. This study aims to investigate the effect of middle-aged and elderly adults migrating from rural to urban areas on their healthcare service utilisation. Methods: Panel data from Wave 3 (2015), Wave 4 (2018) and Wave 5 (2020) of the nationally representative China Health and Retirement Longitudinal Study were selected to obtain a sample of 456 participants in the treatment group and 9171 participants in the control group. The difference-in-differences propensity score matching estimator method was used to explore the effect of migrating to a city on the utilisation of healthcare services amongst middle-aged and elderly adults. Results: Calliper nearest-neighbour matching significantly improved overall balance after matching. The DID regression results showed that middle-aged and elderly adults significantly reduced their number of hospitalisations when they moved to a city, with a DID value of −0.093 (p0.05). The DID results were consistent with the robustness test. Further examination of heterogeneity determined that it had a more significant negative effect on the number of hospitalisationsamongst individuals aged 45–59 years, of rural hukou status and with poor self-assessed health status. Conclusion: Middle-aged and elderly migrants moving to cities may reduce their healthcare utilisation. The government should improve its outpatient reimbursement policy and the policy of medical treatment in other places; encourage the society, community and family to give more attention to middle-aged and elderly adults migrating to cities; improve their health literacy and help them set up the correct concept of medical service utilisation to promote the health equity of migrant populations. China Healthcare utilisation Migrant population Difference-in-differences propensity score matching estimator method Middle-aged and older people Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As global industrialisation and urbanisation continue, an increasing number of people are migrating from rural to urban areas or from less economically developed to more developed areas. The continuous and rapid development of China’s economy and urbanisation has resulted in a large number of migrant populations entering cities, and their numbers are growing rapidly, providing impetus for the rapid development of China’s economy and society[1].With the proportion of China’s population over the age of 65 years exceeding 14 percent, the country marks its official entry into an ageing society. The China Migrant Population Development Report 2018 released by the National Health Commission of the People’s Republic of China shows that the number of China’s migrant population has gradually stabilised at around 245 million since 2015; however, the number of elderly migrants has continued to grow[2].The average age of China’s migrant population increased by 2 years between 2000 and 2015, with the proportion of middle-aged migrants aged 45–49 years rising rapidly from 9.7 percent to 15.6 percent, and the number of individuals aged 60 years or over increasing from 5.03 million in 2000 to 13.04 million in 2015, with an average annual growth rate of 6.6 percent. Meanwhile, the proportion of the population aged 60 years or over rose from 10.5 percent to 16.1 percent. The prominence of the family-based migration characteristics of the floating population is growing. The primary reason for the mobility of middle-aged people is work and business; meanwhile, elderly population migration has become a new normal, in accordance with a thematic survey on health services for the mobile elderly released in 2015, the three major reasons for elderly mobility are caring for their offspring, old age and employment[3].Amongst which, caring for their offspring, following their children to migrate or old age accounted for 68 percent. Compared with the citizens of Europe and the United States[4],the Chinese people place more importance to intergenerational relationships in traditional families, and following their children’s mobility to take care of their offspring is still very common. A proportion of the middle-aged and older populations also migrate to cities for work, but they are apparently at a disadvantage in terms of universal health coverage, because they are geographically distant from the place of enrolment, resulting in lower rates of effective health coverage; moreover, access to health services is more difficult for them and they face higher financial burdens for health[5].These people frequently choose not to undergo treatment or self-medicate when they are sick[6].Therefore, as the proportion of the mobile middle-aged and elderly populations in China further expands and urbanisation continues, enjoying healthcare services equivalent to those of the household population is frequently difficult for the mobile middle-aged and elderly populations. In particular, they are influenced by the effect of medical treatment in other places, and they face health inequality and other issues. In the context of population mobility, giving attention to issues, such as the utilisation of healthcare services by migrant middle-aged and elderly adults, is of practical significance. Previous studies have mostly focused on the health status[7],health awareness[8],inequalities[9], and their influencing factors of migrant populations. Evidence shows that the health of migrant people is influenced by individual factors, such as age, gender, education and marriage, and by individual and local economic development[10].Moreover, the health needs of migrant populations cannot be adequately met due to the difference in household registration in China[11]. In addition, factors, such as the time and scope of movement, exert an effect on the health of migrant people[12].A study in Switzerland similarly showed that the regularisation of status helped increase the use of health services by undocumented migrants[13]. A study in Germany also demonstrated the low utilisation of health services by immigrants and the inequality with nonimmigrants[14]. On the one hand, compared with those of the young migrant population, the physical condition and quality of the middle-aged and elderly migrant populations decline gradually with age, and the demand for medical services is more urgent. Meanwhile, the middle-aged and elderly migrant populations lack proper health knowledge, have a high incidence of chronic diseases and a low utilisation rate of medical services, leading to higher risks on their health. On the other hand, when middle-aged and elderly migrants enter cities, they have fewer social activities and experience difficulty in integrating into cities, leading to psychological or physical problems and harm to their health. Therefore, research on the utilisation of healthcare services by the middle-aged and elderly populations migrating from rural to urban areas is essential. The migrant middle-aged and elderly populations contribute to society and their families, whether they are moving to a city for continued employment, intergenerational care or ageing. Migrant middle-aged and elderly adults comprise a major group who require healthcare, and their use of healthcare services is directly related to their health status and basic right to life. The expansion of migrant middle-aged and elderly populations is likely to further exacerbate the shortage of medical resources in China and has put forward higher requirements for the provision of medical services and the rational allocation of health resources. Previous studies have mostly looked at cross-sectional data from the whole country or a particular region, or have directly compared the migrant population with the local population[15]. Research that compares the health service utilisation of middle-aged and elderly migrants before and after transferring to a city remains insufficient. The current study was conducted using 3 years of panel data from the China Health and Retirement Longitudinal Study (CHARLS), i.e. 2015, 2018 and 2020, to explore the dynamics of the healthcare utilisation of middle-aged and elderly migrants. It focused on two indicators, namely, the number of outpatient visits (NOO) and the number of hospitalisations (NOH), to reflect the effects of healthcare utilisation on these population groups. The results of this study will help provide strong support for improving the health of the middle-aged and elderly migrant populations, formulating policies related to their health status and promoting healthy ageing and health equity amongst migrant populations. Data and methods Data sources CHARLS aims to collect a set of high-quality microdata that are representative of Chinese households and individuals aged 45 years and above. The CHARLS national baseline survey was conducted in 2011, covering 150 district units, 450 village units and 17,000 individuals in about 10,000 households[16]. These samples are then followed up every 2–3 years, with CHARLS surveying consumption, work, income and assets, health status and health insurance. Overall, the CHARLS survey offers a good depiction of the elderly population in China. Sample selection In this study, data from the third (2015), fourth (2018) and fifth (2020) waves of CHARLS were selected. In particular, 61,263 observations were obtained from the CHARLS database from 2015 to 2020. After the selection, data from 2015, 2018 and 2020 were matched on the basis of respondent ID to obtain samples who were surveyed in all 3 years. The second step excluded samples living in towns and urban–rural or combined townships in 2015 and those younger than 45 years old or had a missing value for age in 2018. Finally, 28,881 observations were obtained and divided into the rural-to-urban group (treatment group) and the always in the rural group (control group). Variables The dependent variables included in our study were the NOO in the previous month and the NOH in the previous year. The NOO in the last month is the sum of the respondent’s outpatient visits to various medical institutions. The NOH in the last year is based on the following question: ‘How many hospitalisations did the respondent receive in the past year?’ The respondents’ answers to NOO or NOH were coded as continuous variables. Region was set as a processing variable based on the following question in CHARLS’ basic information section: ‘Was it a village or a city/town?’ However, the answer to the question was different in 2015 than it was in 2018 and 2020, and thus, consistency in the meaning of the variables to be achieved. The main urban, town centre and township centre areas in the responses to this question in 2015 were combined into city or town centre areas. Meanwhile, urban–rural and township–rural areas were combined into urban–rural or township–rural areas. The samples who lived in towns in 2015 were deleted, ensuring that all the samples lived in villages in 2015. Regarding the control variables,the Anderson model of healthcare utilisation was used in the current study. This model consists of predisposing, enabling and need factors; it is a theoretical framework that provides a set of behavioural models for measuring healthcare and the most well-known and practical analytical framework in the field of healthcare service research[17, 18].Predisposing factors include age, gender (male = 1, female = 0), education level (lower than primary school = 1, primary school = 2, middle school = 3, high school and above = 4), marital status (divorced, widowed, single = 0; married/have a spouse = 1) and hukou (rural = 0, urban = 1). Enabling factors include economic status (measured by per capita household expenditure, which is logarithmic to reduce the effect of outliers and make the results more stable) and health insurance (with health insurance = 1, without health insurance = 0). Need factors include self-assessed health status (very good and good = 1, fair = 2, poor and very poor = 3), presence of chronic diseases (yes = 1, no = 0), social activities (yes = 1, no = 0), household size (number of people living in the household), whether they smoked cigarettes (smoked = 1, did not smoke = 0) and whether they drank alcohol (drank = 1, did not drink = 0). Statistical analysis This study focused on the effect of middle-aged and elderly adults migrating to cities, i.e. the effects of different times and locations on NOO and NOH. Therefore, the panel data model was used, and the result was the rejection of the original hypothesis after the Hausman test. That is, individual and time effects were present, and thus, the two-way fixed effects model was used. This study used the difference-in-differences (DID) propensity score matching (PSM) estimator method to test the effect of migration to cities on the NOO and NOH of middle-aged and older adults. PSM is effective in overcoming the endogeneity and self-selection problems present in samples[19, 20],and it is currently widely applied to influence health and other policies[21, 22].Firstly, propensity scores were calculated and then matched. Then, a balancing test was conducted to detect the equilibrium of the covariates and the treatment and control groups, and to verify the reduction of sampling bias through matching. Nearest neighbour matching with calliper was adopted for our primary analysis (calliper set to 0.05, neighbour set to 3), as frequently mentioned in the literature[23, 24]. After matching, the deviation should be less than or equal to 5 percent or p > 0.1 as a test of proper matching. Finally, we judged the appropriateness of the matching on the basis of the mean reduction bias of each covariate and the overall mean reduction bias. Samples that were not within the common support were excluded. Secondly, the DID model was constructed based on the sample results after PSM matching and controlling for individual and time effects, with the model set up as follows: Y it =β 0 +β 1 Treated i ×Time t +X it +μ i +λ t +ε it , (1) where Y it represents an individual i’s healthcare utilisation in year t. Treated i is a dummy variable for grouping. It is defined as treatment group = 1 and control group = 0. In the current study, 1 denotes migrating into a city (treatment group) and 0 denotes living in a rural area (control group). Time t is a time dummy variable that defines 2015 = 0, 2018 = 1 and 2020 = 1. β 1 is the coefficient of the core explanatory variable in this study, which is the net effect of a policy on the effect of middle-aged and older adults migrating to the city on the utilisation of healthcare services. X it is the control variable. ε it is the error term, and β 0 is a constant term. μ i is an individual fixed effect, and λ t is a time fixed effect. The current study was statistically analysed using Stata 16.0 with a two-sided statistical significance level set at 0.05. Results Table 1 reports the results of the descriptive statistics for the main variables of the study, with the treatment and control groups differing in age, education, self-assessed health, household registration, social activities and per capita household expenditure. The mean age of the treatment group was less than that of the control group, i.e. about 2.6 years. The average level of education in the treatment group was higher than that in the control group, with a mean of 2.12, which corresponded to a literacy level between primary school and lower secondary school. In the control group, the mean was 1.78, which corresponded to a literacy level between illiterate and primary/private school. The treatment group rated their health as slightly better than the control group. The proportion of urban households in the treatment group was higher than that in the control group. As age increased, a decrease in social activities was noted in both groups. In 2015–2018, the treatment group had slightly more social activities than the control group. In 2020, the treatment group had slightly less social activities than the control group, reflecting to a certain extent the barriers to urban integration and social barriers in the middle-aged and elderly groups who migrated to the city. Per capita household expenditure was higher in the treatment group than in the control group. In particular, the prevalence of chronic diseases increased substantially with age. Table 1 ：Descriptive statistics results Variables 2015 2018 2020 Treatment (N=456) Control (N=9171) Treatment (N=456) Control (N=9171) Treatment (N=456) Control (N=9171) Mean Mean Mean Mean Mean Mean Gender 0.45(0.50) 0.47(0.50) 0.45(0.50) 0.47(0.50) 0.45(0.50) 0.47(0.50) Age 57.07(9.05) 59.60(9.81) 59.94(8.96) 62.61(9.82) 61.89(9.00) 64.62(9.84) Education 2.12(1.01) 1.78(0.91) 2.13(1.07) 1.78(0.95) 2.13(1.07) 1.78(0.95) Marital status 0.91(0.28) 0.89(0.31) 0.87(0.33) 0.86(0.35) 0.86(0.35) 0.84(0.37) Health insurance 0.81(0.39) 0.81(0.39) 0.96(0.18) 0.97(0.17) 0.95(0.21) 0.95(0.22) Self-assessed health 1.98(0.67) 2.01(0.68) 2.00(0.64) 2.06(0.70) 1.96(0.63) 2.06(0.69) Presence of chronic disease 0.16(0.36) 0.16(0.36) 0.42(0.49) 0.42(0.49) 0.82(0.38) 0.82(0.38) Household registration 0.20(0.40) 0.05(0.21) 0.22(0.42) 0.05(0.22) 0.24(0.43) 0.08(0.27) Social activity 0.55(0.50) 0.50(0.50) 0.52(0.50) 0.47(0.50) 0.43(0.49) 0.46(0.50) Per capita household expenditure 17711.37(25292.31) 14410.35(25284.12) 23557.10(28799.91) 16355.94(32448.80) 25852.87(33242.93) 17479.77(25398.44) Family size 2.44(1.01) 2.61(1.19) 2.47(0.82) 2.34(0.83) 2.72(1.21) 2.52(1.27) Drink 0.37(0.48) 0.35(0.48) 0.31(0.46) 0.33(0.47) 0.35(0.48) 0.34(0.47) Smoke 0.39(0.49) 0.41(0.49) 0.40(0.49) 0.43(0.49) 0.37(0.48) 0.40(0.49) Note: Standard deviation in the brackets; PSM results PSM was used to test the balance of the data, and a validity analysis of the balance of the model was a prerequisite for testing whether PSM–DID can be used effectively. To ensure that reliable matching results are obtained, the t -tests for all the control variables in the treatment and control groups should be insignificant. Table 2 presents the results of the test that used calliper k -nearest neighbour matching ( k = 3, calliper = 0.05). As indicated in the table, the standard error of the data for each control variable was less than 10 percent after matching, and no significant difference occurred at the 5 percent level between the means of the variables for the two samples. As shown in Table 3, the results of the likelihood ratio chi-square test that used PSM are considerably smaller than the original results. The results further suggest that PSM improves overall balance after matching. Secondly, kernel density plots were used to visualise whether a difference existed in the propensity scores between the two sets of data before and after matching (e.g. Figure 2). A significant difference was found between the treatment and control groups before matching, and then they became significantly closer after matching. Therefore, the matching effect is good in accordance with the results shown in the figure and table, and using the matched samples is more appropriate for DID regression. Table 2 : Results of the balance test before and after matching Variable Mean %bias %reduct |bias| t-test Treated Control t p>|t| Gender 0.45 0.44 1.90 52.50 0.40 0.69 Age 60.93 60.82 1.20 91.40 0.25 0.80 Education 2.13 2.13 -0.30 99.20 -0.06 0.96 Marriage 0.86 0.86 0.30 11.90 0.07 0.95 Health insurance 0.96 0.97 -3.80 81.60 -1.07 0.29 Self-reported health status 1.98 2.00 -2.90 69.70 -0.23 0.83 Presence of chronic disease 0.42 0.62 -0.70 97.90 -0.14 0.64 Household registration 0.23 0.23 0.00 100.00 0.00 1.00 Social activity 0.47 0.48 -2.2 -91.60 -0.46 0.64 Ln(Per capita household expenditure) 9.72 9.70 1.50 97.0 0.33 0.74 Family size 2.60 2.60 0.10 73.60 0.02 0.99 Smoke 0.39 0.38 1.10 79.10 0.24 0.81 Drink 0.33 0.33 0.10 97.50 0.01 0.99 Table 3 :Logit regression results before and after matching Sample Ps R2 LR chi2 P>chi2 MeanBias MedBias B R %Var Unmatched 0.08 649.85 0.00 17.90 10.30 82.50 1.38 100 Matched 0.00 2.20 1.00 1.20 1.10 6.90 1.19 20 DID results Further estimation of the effects of the migration of middle-aged and elderly adults to urban areas on the utilisation of health services was obtained on the basis of the data after nearest-neighbour matching with callipers. We studied the effects of migrating to a city on the NOO and NOH of migrant middle-aged and elderly adults. The results exhibited a significant negative effect on the NOH of middle-aged and older adults migrating to a city(e.g. Table 4). The DID was −0.093, and the p 0.01. Meanwhile, the DID coefficient for NOO was −0.008 with the p 0.1, indicating no significant effect of migrating to the city on the NOO of the middle-aged and elderly age groups. Table 4 :DID estimate results Variables NOO NOO NOH NOH Did -0.011（0.0671） -0.008（0.0674） -0.089***（0.0326） -0.093***（0.0327） Control variables No Yes No Yes Time fixed effects Yes Yes Yes Yes Individual fixed effects Yes Yes Yes Yes R 2 0.418 0.421 0.497 0.504 N 28824 28824 28824 28824 Note: ***p<0.01; **p<0.05; *p<0.1; Robust SEs in the brackets; Robustness test results Robustness tests were also conducted in this study to further validate the reliability of the above results. (1)Replacement of the matching method To ensure the robustness of the PSM–DID results, we used different matching methods, namely, radius matching and Mahalanobis matching. The above DID regression was then repeated after matching, as shown in Table 5. The results are consistent with the above regression results, indicating that the results of the empirical analyses exhibit good reliability. The finding that middle-aged and older adults have fewer hospitalisations when they migrate to a city is statistically significant, and the results obtained by changing the match continue to support these findings. Simultaneously, the effect of migrating to a city on the NOO of middle-aged and elderly adults is insignificant and robust, as discussed in a later section. Table 5 : DID estimate results after changing the matching method Variables NOO NOO NOH NOH Did -0.008（0.067） -0.009（0.067） -0.095***（0.033） -0.093***（0.033） Control variables Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Individual fixed effects Yes Yes Yes Yes Matching method Radius matching Mahalanobis matching Radius matching Mahalanobis matching R 2 0.422 0.421 0.505 0.504 N 28824 28881 28824 28881 Note: ***p<0.01; **p<0.05; *p<0.1; Robust SEs in the brackets; (2)Placebo testing Referring to previous studies[25],the current work randomly generated new treatment groups with new estimated coefficients and repeated the process 500 times. Figures 3 and 4 present the results of the placebo test. The estimated coefficient values are clustered around the value of 0 and follow a roughly normal distribution. The dotted lines in the graph represent the actual estimated coefficients, which are clearly outliers in Figure 3. The dotted line in Figure 4 is insignificant within the normal value range, proving that the empirical results of this study are robust. Results of heterogeneity analyses Possible heterogeneity in health service utilisation after migrating to a city amongst the middle-aged and elderly populations exhibited different characteristics. Heterogeneity analyses can further explore the effect of different middle-aged and elderly adults’ healthcare service utilisation after migrating to the city, and policy recommendations can be targeted. This study examined the effect of different age groups, household registration and self-assessed health status of the samples on the utilisation of healthcare services. As indicated in the regression results in Table 6, the heterogeneity of age is evident through the following division: the middle-aged group (45–59 years old), the low elderly group (60–69) and the high elderly group (70 years old and above). Table 6 shows a reduction in the NOH of the low elderly group when they migrate to a city, whilst the effect is insignificant for the other age groups. The heterogeneity of self-assessed health is categorised into good, fair and poor in accordance with their assessment of their own health status. Table 6 indicates a significant negative effect (significant at the 5% level) on the NOH of the group with poor self-assessed health after migrating to a city, implying that this group will reduce the utilisation of healthcare services. No significant effect was observed on the NOH of groups with good and fair self-assessed health. The heterogeneity of Household registration is classified into urban and rural. Table 6 shows that migrating to a city reduces the NOH of rural household residents (significant at the 5% level), but exerted no significant effect on urban household residents. Table 6 : Results of the test for heterogeneity in the number of hospitalisations Variables NOH Age Self-assessed health Household registration 45-59 60-69 70- Good Fair Poor City Rural Did -0.081** (0.041) -0.099 (0.083) 0.036 (0.141) 0.039 (0.047) -0.040 (0.048) -0.343** (0.142) -0.128 (0.091) -0.082** (0.036) Control variables Yes Yes Yes Yes Yes Yes Yes Yes Time fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Individual fixed effects Yes Yes Yes Yes Yes Yes Yes Yes R 2 0.483 0.560 0.577 0.521 0.501 0.581 0.552 0.503 N 11108 8573 5997 4330 12525 5484 1439 26786 Note: ***p<0.01; **p<0.05; *p<0.1; Robust SEs in the brackets; Discussion and suggestion The effect of moving to the city on healthcare utilisation among middle-aged and elderly people Similar to previous studies[3, 26], the current work also showed that migrant middle-aged and elderly adults have relatively low levels of healthcare service utilisation, and that the migration to a city of the middle-aged and elderly populations results in fewer hospitalisations, but exerts no significant effect on NOO. The DID coefficient for NOH was −0.093 with p = 0.007. The DID coefficient for NOO was −0.008 with p = 0.891. The result in which migrant middle-aged and older adults will experience fewer hospitalisations may be due to the following reasons. Firstly, the prevalence of chronic diseases gradually increases with age. When middle-aged and elderly migrants move to cities, their status as migrants leads to higher costs for medical treatment and inconvenience in reimbursing medical treatment. Consequently, middle-aged and elderly migrants have limited access to medical services. This conclusion is in agreement with the findings of Zhang and Xi et al.[3, 9, 27]. In 2016, the Chinese government issued the Opinions on Integrating the Basic Medical Insurance System for Urban and Rural Residents, which integrated the basic medical insurance for urban residents and the new type of rural cooperative medical care system, and also called for the unification of the scope of coverage, financing policy, protection treatment, medical insurance catalogue, fixed-point management and fund management. Studies have shown that although the integration of urban and rural residents’ health insurance improves the frequency and equity of use of health services, it exerts no significant effect on the probability of access to health care or the unmet need for hospitalisation; moreover, large disparities remain between urban and rural areas[21, 28]. Despite the continuous improvement of China’s reimbursement policy for medical treatment in other places, the current policy on medical treatment in some places is still based on the principle of ‘catalogue in the place of medical treatment, treatment in the place of insurance, management in the place of medical treatment’. Furthermore, differences exist in the thresholds, payment ratios and payment limits of the medical insurance funds of different coordinating regions, resulting in the reimbursement level of medical treatment in other places being even lower than that in local areas; consequently, middle-aged and elderly adults who migrate from rural areas to urban areas still face considerable difficulties in obtaining medical treatment in other places[29]. Evidence from Europe also suggests similar results, with consistency between lack of formal residence status and limited healthcare utilisation[13, 14]. Secondly, the middle-aged and older adult groups typically migrate to cities for the purpose of working, intergenerational care and old age, and the medical behaviour of these age groups is usually irrational[30]. In accordance with relevant studies, when these people are sick or unwell, they repeatedly choose to disregard their illness or self-medicate, leading to the phenomenon of untimely access to medical care. With the lower literacy level and poor health knowledge of these groups, lower health literacy can lead to poor health behaviour. Middle-aged and elderly adults are less proactive than local residents in accessing health information and utilising healthcare services after migrating to a city; they are also unable to fully utilise prevention, basic public health [31, 32]and healthcare services[8]. Relevant studies have indicated that health literacy enhancement can improve the health level of residents, and thus, local governments should strengthen the promotion of health literacy knowledge amongst migrant populations[33, 34]. Migration to cities did not exert a significant effect on the NOO of middle-aged and elderly adults, probably due to China’s outpatient health insurance reimbursement policy. China has limited resources for community-based and primary health care, and the cost of hospitalisation is high. China’s health insurance has always focused on inpatient coverage, and most insurance policies mostly cover inpatient services or set high deductibles for outpatient services. The overall average reimbursement rate for outpatient treatment in China is relatively low at about 20 percent[35], and older people from poorer households are even less likely to utilise outpatient services[36]. Consequently, the financial burden of outpatient care for middle-aged and elderly adults remains heavy. In the face of low reimbursement rates and complicated reimbursement procedures, middle-aged and elderly adults who have migrated to urban areas whether in rural or urban areas generally choose to put up with ‘minor’ illnesses, self-medicate or not treat them at all, rather than seeking outpatient services to avoid the cost of medical care[37, 38]. Heterogeneity of Age, self-assessed health, and household registration on the effect of moving to urban areas on the use of healthcare services among the middle-aged and elderly peoples. The results of the heterogeneity analyses show that migrating to a city exerts a stronger effect on the utilisation of healthcare services for middle-aged individuals, rural households and groups with poor self-assessed health status. This study found that from the point of view of age group, the middle-aged group (45–59 years old) is in the transition between urban labour and intergenerational care. The middle-aged group of urban labourers experience greater burden on family and pressure of work, with higher intensity and long hours of work. When they are not feeling well, most of these people choose not to treat their illness. Some middle-aged adults live with their children in a state of intergenerational care. To avoid causing trouble to their children, they usually choose to self-medicate minor illnesses, resulting in the under-utilisation of medical services. In terms of household registration, rural residents generally have a low level of education, averaging around the primary school, and a low level of health awareness, limited knowledge of diseases and insufficient use of medical services. The lower level of social integration of rural middle-aged and elderly groups after moving to urban areas may also possibly lead to negative attitudes towards healthcare[39], resulting in untimely access to healthcare. And due to the lack of coordination between locations, it is difficult for middle-aged and elderly migrants to obtain reimbursement for outpatient treatment off-site. They must pay all medical expenses at the time of their visit and then return home for reimbursement. This complex process places a significant financial burden on chronically ill older migrants who need to take medication for a long period of time[9].In terms of self-assessed health, no significant change was observed in the NOH of groups with good and fair self-assessed health. The significantly fewer hospitalisations amongst middle-aged and elderly adults with self-assessed poor health, who mostly have labour-intensive jobs and may have lower socioeconomic status, may be due to the effect of the reimbursement policy for medical care in other places and incorrect perceptions of the use of healthcare services[12]. With a high proportion of the elderly population migrating in China, research on the utilisation of their healthcare services is important for maintaining the stability of the health system and achieving healthcare and health equity. Most previous studies were conducted using cross-sectional data, which could not reflect the dynamic process of healthcare service utilisation before and after migrating to a city. The current study found that the healthcare service utilisation of middle-aged and elderly adults was low after migrating to a city by using three-period tracking data. Therefore, in conjunction with the current study, the following recommendations are made. Firstly, the medical insurance policy should continuously improve the outpatient reimbursement policy and lower the starting line for outpatient reimbursement. And it is recommended to introduce corresponding policies and provide support for insurance participation in other places, and accelerate the implementation of the policy of direct settlement for medical treatment in other places. Secondly, an atmosphere of caring for migrant middle-aged and elderly adults should be created at the social and family levels, and health literacy education and popularisation should be strengthened [40, 41].Thirdly, the community should help middle-aged and elderly adults who have migrated to cities learn and understand the benefits of the rational use of medical services for their health and increase their knowledge of prevention and medical treatment to fully mobilise their motivation in using medical services[42, 43]. Exercise facilities can also be constructed to help middle-aged and elderly adults maintain good health, and interest clubs can be set up to increase their social activities. These measures help middle-aged and elderly individuals resocialise in new environments and actively integrate into town life. Limitation This study also has two limitations. First, an important assumption in the implementation of PSM-DID is that the model should contain all covariates that may influence the policy effect before and after matching. Unobservable covariates will cause different trends between the treatment group and the control group, and such an instance may have led to biased results in this study. Secondly, given the limitations of the database and sample size, further validation is needed and in-depth analyses based on place of inflow are not possible because the data do not show the place of inflow of middle-aged and older people after they have moved to the city. Conclusion This study has two major contributions. Firstly, by exploring the effects of migrating to a city on the utilisation of healthcare services amongst middle-aged and elderly adults, the NOH of these populations was determined to decrease significantly when they move from rural to urban areas, but exerted no significant effect on their NOO. In addition, the government should improve its health insurance policy, increase the percentage of outpatient reimbursement claims and improve its policy on travelling to other places for medical treatment to reduce process and costs. Secondly, the heterogeneity analysis found that the effect of moving to a city was more significant on health service utilisation amongst individuals aged 45–59 years and those with rural household registration and poor self-assessed health status. Policymakers should launch corresponding policies to encourage attention to migrant middle-aged and elderly adults at the social, community and family levels to increase their health knowledge and enhance their health literacy, helping them establish a correct concept of healthcare service utilisation, better integrate into urban life and improve their health standard. Our findings are informative for the literature on migrant populations, ageing societies and health equity. In addition, useful lessons can be learned for other developing countries with large migrant middle-aged and elderly populations. Abbreviations PSM-DID Difference-in-Differences Propensity Score Matching estimator CHARLS China health and retirement longitudinal study PSM Propensity Score Matching DID Difference-in-Differences estimator Declarations Acknowledgements We thank Peking University, the CHARLS research and field team and every respondent in the study for their contributions. Authors’ contributions Lu Xu and Yingchun Chen designed this study and performed the statistical analysis. Lu Xu wrote the main manuscript text. Dandan Guo, Shengxian Bi, Huawei Tan, Songhao Yang, Lei li, Qijiao Yang, Xinyi Peng and Xueyu Zhang participated in data analysis. All authors reviewed the manuscript. Funding This study was supported by grants from the National Natural Science Foundation of China (72374076). Availability of data and materials The datasets generated and analyzed during the current study are available in the CHARLS repository, http://charls.pku.edu.cn. Ethics approval and consent to participate The data used in this study were retrieved from the CHARLS. This survey was endorsed by the Biomedical Ethics Committee of Peking University (NO.IRB00001052–11015). All participants in the survey signed or marked (if illiterate) the informed consent forms. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. 2 Key Research Institute of Humanities & Social Sciences of Hubei Provincial Department of Education, Research Centre for Rural Health Service, Wuhan 430030, China. 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Jerez-Roig J, Medeiros LFB, Silva VAB, Bezerra CLPAM, Cavalcante LAR, Piuvezam G, Souza DLB: Prevalence of Self-Medication and Associated Factors in an Elderly Population: A Systematic Review . Drugs & Aging 2014, 31 : 883-896. Fu Y, Lin W, Yang Y, Du R, Gao D: Analysis of diverse factors influencing the health status as well as medical and health service utilization in the floating elderly of China . BMC Health Serv Res 2021, 21 : 438. Hai Y, Wu WL, Yu LW, Wu L: Health literacy and health outcomes in China's floating population: mediating effects of health service . BMC Public Health 2021, 21 : 691. Xi S, Song Y, Li X, Li M, Lu Z, Yang Y, Wang Y: Local-Migrant Gaps in Healthcare Utilization Between Older Migrants and Local Residents in China . Journal of the American Geriatrics Society 2020, 68 : 1560-1567. Wang Q: Health of the Elderly Migration Population in China: Benefit from Individual and Local Socioeconomic Status? 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Front Public Health 2022, 10 : 840145. He Y, Ouyang W, Li Z, Wei B: Impact of the Industrialization of Older Adult Care Services on Older Individuals' Physical and Mental Health: Evidence from China's Quasi-Natural Experiment . J Multidiscip Healthc 2023, 16 : 3017-3033. Shaaban AN, Morais S, Peleteiro B: Healthcare Services Utilization Among Migrants in Portugal: Results From the National Health Survey 2014 . Journal of Immigrant and Minority Health 2019, 21 : 219-229. Han J, Meng Y: Institutional differences and geographical disparity: the impact of medical insurance on the equity of health services utilization by the floating elderly population - evidence from China . International Journal for Equity in Health 2019, 18 : 91. Wang Z, Chen Y, Pan T, Liu X, Hu H: The comparison of healthcare utilization inequity between URRBMI and NCMS in rural China . Int J Equity Health 2019, 18 : 90. Chen S, Chen Y, Feng Z, Chen X, Wang Z, Zhu J, Jin J, Yao Q, Xiang L, Yao L, et al: Barriers of effective health insurance coverage for rural-to-urban migrant workers in China: a systematic review and policy gap analysis . BMC Public Health 2020, 20 : 408. Zeng Y, Wan Y, Yuan Z, Fang Y: Healthcare-Seeking Behavior among Chinese Older Adults: Patterns and Predictive Factors . Int J Environ Res Public Health 2021, 18. Xu X, Zhang Q, You H, Wu Q: Awareness, Utilization and Health Outcomes of National Essential Public Health Service Among Migrants in China . Front Public Health 2022, 10 : 936275. Tang S, Long C, Wang R, Liu Q, Feng D, Feng Z: Improving the utilization of essential public health services by Chinese elderly migrants: strategies and policy implication . J Glob Health 2020, 10 : 010807. Kim M, Gu H: Relationships between Health Education, Health Behaviors, and Health Status among Migrants in China: A Cross-Sectional Study Based on the China Migrant Dynamic Survey . Healthcare (Basel) 2023, 11. Li X, Yang H, Wang H, Liu X: Effect of Health Education on Healthcare-Seeking Behavior of Migrant Workers in China . International Journal of Environmental Research and Public Health 2020, 17 : 2344. Liao H, Li S, Han D, Zhang M, Zhao J, Wu Y, Ma Y, Yan C, Wang J: Associations between social support and poverty among older adults . BMC Geriatr 2023, 23 : 384. Lu P, Yang C, Yao J, Shelley M: Outpatient and Inpatient Service Use by Chinese Adults Living in Rural Low-Income Households . Social Work in Public Health 2020, 35 : 223-233. Peng Y, Chang W, Zhou H, Hu H, Liang W: Factors associated with health-seeking behavior among migrant workers in Beijing, China . BMC Health Services Research 2010, 10 : 69. Lu L, Zeng J, Zeng Z: What limits the utilization of health services among china labor force? analysis of inequalities in demographic, socio-economic and health status . Int J Equity Health 2017, 16 : 30. Li J, Rose N: Urban social exclusion and mental health of China's rural-urban migrants - A review and call for research . Health Place 2017, 48 : 20-30. Eckman MH, Wise R, Leonard AC, Dixon E, Burrows C, Khan F, Warm E: Impact of health literacy on outcomes and effectiveness of an educational intervention in patients with chronic diseases . Patient Educ Couns 2012, 87 : 143-151. Tian Y, Luo T, Chen Y: The Promotional Effect of Health Education on the Medical Service Utilization of Migrants: Evidence From China . Front Public Health 2021, 9 : 818930. Zhou Y, Li D, Cao Y, Lai F, Wang Y, Long Q, Zhang Z, An C, Xu X: Immunization coverage, knowledge, satisfaction, and associated factors of non-National Immunization Program vaccines among migrant and left-behind families in China: evidence from Zhejiang and Henan provinces . Infect Dis Poverty 2023, 12 : 93. Chen Y, Huang F, Zhou Q: Equality of public health service and family doctor contract service utilisation among migrants in China . Soc Sci Med 2023, 333 : 116148. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4048728","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278063762,"identity":"99825429-d53c-4693-b235-63ce840867e5","order_by":0,"name":"Lu Xu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Xu","suffix":""},{"id":278063764,"identity":"3f02b4c9-dec4-44fc-8c26-8c370ca32e71","order_by":1,"name":"Shengxian Bi","email":"","orcid":"","institution":"Huazhong University of Science and 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19:45:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4048728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4048728/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52620650,"identity":"18e9fd4c-f7c8-4700-be27-c222b78464d7","added_by":"auto","created_at":"2024-03-13 16:49:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30029,"visible":true,"origin":"","legend":"\u003cp\u003eSample selection process diagram.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4048728/v1/0540f7acc9e7b202ec4991f2.png"},{"id":52620652,"identity":"81c78075-b598-43d7-a93c-fd32cb7ba1d8","added_by":"auto","created_at":"2024-03-13 16:49:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67106,"visible":true,"origin":"","legend":"\u003cp\u003ePropensity score values before and after matching\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4048728/v1/87c76f720831af36ced73f67.png"},{"id":52621483,"identity":"c57d6fff-543c-463c-a307-06772e111d81","added_by":"auto","created_at":"2024-03-13 16:57:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34034,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo test results for the coefficient on the number of hospitalisations\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4048728/v1/7dfb300749695433a84a9971.png"},{"id":52620653,"identity":"4ad3fc0d-e907-4a10-bf12-50e95e732325","added_by":"auto","created_at":"2024-03-13 16:49:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlacebo test results for the coefficient on the number of outpatient visits\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4048728/v1/a39d7b76e65e8cb49b40f06a.png"},{"id":52622440,"identity":"0e1d38e2-629a-4a07-ad41-a8f0231d1a17","added_by":"auto","created_at":"2024-03-13 17:13:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":675029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4048728/v1/69396435-960d-47ee-8567-e1a7bd371bd1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of rural-to-urban migration on the healthcare utilisation of middle-aged and elderly adults: evidence from the China Health and Retirement Longitudinal Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs global industrialisation and urbanisation continue, an increasing number of people are migrating from rural to urban areas or from less economically developed to more developed areas. The continuous and rapid development of China\u0026rsquo;s economy and urbanisation has resulted in a large number of migrant populations entering cities, and their numbers are growing rapidly, providing impetus for the rapid development of China\u0026rsquo;s economy and society[1].With the proportion of China\u0026rsquo;s population over the age of 65 years exceeding 14 percent, the country marks its official entry into an ageing society. The China Migrant Population Development Report 2018 released by the National Health Commission of the People\u0026rsquo;s Republic of China shows that the number of China\u0026rsquo;s migrant population has gradually stabilised at around 245 million since 2015; however, the number of elderly migrants has continued to grow[2].The average age of China\u0026rsquo;s migrant population increased by 2 years between 2000 and 2015, with the proportion of middle-aged migrants aged 45\u0026ndash;49 years rising rapidly from 9.7 percent to 15.6 percent, and the number of individuals aged 60 years or over increasing from 5.03 million in 2000 to 13.04 million in 2015, with an average annual growth rate of 6.6 percent. Meanwhile, the proportion of the population aged 60 years or over rose from 10.5 percent to 16.1 percent. The prominence of the family-based migration characteristics of the floating population is growing. The primary reason for the mobility of middle-aged people is work and business; meanwhile, elderly population migration has become a new normal, in accordance with a thematic survey on health services for the mobile elderly released in 2015, the three major reasons for elderly mobility are caring for their offspring, old age and employment[3].Amongst which, caring for their offspring, following their children to migrate or old age accounted for 68 percent. Compared with the citizens of Europe and the United States[4],the Chinese people place more importance to intergenerational relationships in traditional families, and following their children\u0026rsquo;s mobility to take care of their offspring is still very common. A proportion of the middle-aged and older populations also migrate to cities for work, but they are apparently at a disadvantage in terms of universal health coverage, because they are geographically distant from the place of enrolment, resulting in lower rates of effective health coverage; moreover, access to health services is more difficult for them and they face higher financial burdens for health[5].These people frequently choose not to undergo treatment or self-medicate when they are sick[6].Therefore, as the proportion of the mobile middle-aged and elderly populations in China further expands and urbanisation continues, enjoying healthcare services equivalent to those of the household population is frequently difficult for the mobile middle-aged and elderly populations. In particular, they are influenced by the effect of medical treatment in other places, and they face health inequality and other issues. In the context of population mobility, giving attention to issues, such as the utilisation of healthcare services by migrant middle-aged and elderly adults, is of practical significance.\u003c/p\u003e\n\u003cp\u003ePrevious studies have mostly focused on the health status[7],health awareness[8],inequalities[9], and their influencing factors of migrant populations. Evidence shows that the health of migrant people is influenced by individual factors, such as age, gender, education and marriage, and by individual and local economic development[10].Moreover, the health needs of migrant populations cannot be adequately met due to the difference in household registration in China[11]. In addition, factors, such as the time and scope of movement, exert an effect on the health of migrant people[12].A study in Switzerland similarly showed that the regularisation of status helped increase the use of health services by undocumented migrants[13]. A study in Germany also demonstrated the low utilisation of health services by immigrants and the inequality with nonimmigrants[14]. On the one hand, compared with those of the young migrant population, the physical condition and quality of the middle-aged and elderly migrant populations decline gradually with age, and the demand for medical services is more urgent. Meanwhile, the middle-aged and elderly migrant populations lack proper health knowledge, have a high incidence of chronic diseases and a low utilisation rate of medical services, leading to higher risks on their health. On the other hand, when middle-aged and elderly migrants enter cities, they have fewer social activities and experience difficulty in integrating into cities, leading to psychological or physical problems and harm to their health. Therefore, research on the utilisation of healthcare services by the middle-aged and elderly populations migrating from rural to urban areas is essential.\u003c/p\u003e\n\u003cp\u003eThe migrant middle-aged and elderly populations contribute to society and their families, whether they are moving to a city for continued employment, intergenerational care or ageing. Migrant middle-aged and elderly adults comprise a major group who require healthcare, and their use of healthcare services is directly related to their health status and basic right to life. The expansion of migrant middle-aged and elderly populations is likely to further exacerbate the shortage of medical resources in China and has put forward higher requirements for the provision of medical services and the rational allocation of health resources. Previous studies have mostly looked at cross-sectional data from the whole country or a particular region, or have directly compared the migrant population with the local population[15]. Research that compares the health service utilisation of middle-aged and elderly migrants before and after transferring to a city remains insufficient. The current study was conducted using 3 years of panel data from the China Health and Retirement Longitudinal Study (CHARLS), i.e. 2015, 2018 and 2020, to explore the dynamics of the healthcare utilisation of middle-aged and elderly migrants. It focused on two indicators, namely, the number of outpatient visits\u0026nbsp;(NOO) and the number of\u0026nbsp;\u0026nbsp;hospitalisations\u0026nbsp;(NOH), to reflect the effects of healthcare utilisation on these population groups. The results of this study will help provide strong support for improving the health of the middle-aged and elderly migrant populations, formulating policies related to their health status and promoting healthy ageing and health equity amongst migrant populations.\u003c/p\u003e\n"},{"header":"Data and methods","content":"\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS\u0026nbsp;aims to collect a set of high-quality microdata that are representative of Chinese households and individuals aged 45 years and above. The CHARLS national baseline survey was conducted in 2011, covering 150 district units, 450 village units and 17,000 individuals in about 10,000 households[16]. These samples are then followed up every 2\u0026ndash;3 years, with CHARLS surveying consumption, work, income and assets, health status and health insurance. Overall, the CHARLS survey offers a good depiction of the elderly population in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, data from the third (2015), fourth (2018) and fifth (2020) waves of CHARLS were selected. In particular, 61,263 observations were obtained from the CHARLS database from 2015 to 2020. After the selection, data from 2015, 2018 and 2020 were matched on the basis of respondent ID to obtain samples who were surveyed in all 3 years. The second step excluded samples living in towns and urban\u0026ndash;rural or combined townships in 2015 and those younger than 45 years old or had a missing value for age in 2018. Finally, 28,881 observations were obtained and divided into the rural-to-urban group (treatment group) and the always in the rural group (control group).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dependent variables included in our study were the NOO in the previous month and the NOH in the previous year. The NOO in the last month is the sum of the respondent\u0026rsquo;s outpatient visits to various medical institutions. The NOH in the last year is based on the following question: \u0026lsquo;How many hospitalisations did the respondent\u0026nbsp;receive\u0026nbsp;in the past year?\u0026rsquo; The respondents\u0026rsquo; answers to NOO or NOH were coded as continuous variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegion\u0026nbsp;was set as a processing variable based on the following question in CHARLS\u0026rsquo; basic information section: \u0026lsquo;Was it a village or a city/town?\u0026rsquo; However, the answer to the question was different in 2015 than it was in 2018 and 2020, and thus, consistency in the meaning of the variables to be achieved. The main urban, town centre and township centre areas in the responses to this question in 2015 were combined into city or town centre areas. Meanwhile, urban\u0026ndash;rural and township\u0026ndash;rural areas were combined into urban\u0026ndash;rural or township\u0026ndash;rural areas. The samples who lived in towns in 2015 were deleted, ensuring that all the samples lived in villages in 2015.\u003c/p\u003e\n\u003cp\u003eRegarding the control variables,the Anderson model of healthcare utilisation was used in the current study. This model consists of\u0026nbsp;predisposing, enabling and\u0026nbsp;need\u0026nbsp;factors; it is a theoretical framework that provides a set of behavioural models for measuring healthcare and the most well-known and practical analytical framework in the field of healthcare service research[17, 18].Predisposing factors include age, gender (male = 1, female = 0), education level (lower than primary school = 1, primary school = 2, middle school = 3, high school and above = 4), marital status (divorced, widowed, single = 0; married/have a spouse = 1) and hukou (rural = 0, urban = 1). Enabling factors include economic status (measured by per capita household expenditure, which is logarithmic to reduce the effect of outliers and make the results more stable) and health insurance (with health insurance = 1, without health insurance = 0). Need factors include self-assessed health status (very good and good = 1, fair = 2, poor and very poor = 3), presence of chronic diseases (yes = 1, no = 0), social activities (yes = 1, no = 0), household size (number of people living in the household), whether they smoked cigarettes (smoked = 1, did not smoke = 0) and whether they drank alcohol (drank = 1, did not drink = 0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study focused on the effect of middle-aged and elderly adults migrating to cities, i.e. the effects of different times and locations on NOO and NOH. Therefore, the panel data model was used, and the result was the rejection of the original hypothesis after the Hausman test. That is, individual and time effects were present, and thus, the two-way fixed effects model was used.\u003c/p\u003e\n\u003cp\u003eThis study used the difference-in-differences (DID) propensity score matching (PSM) estimator method to test the effect of migration to cities on the NOO and NOH of middle-aged and older adults. PSM is effective in overcoming the endogeneity and self-selection problems present in samples[19, 20],and it is currently widely applied to influence health and other policies[21, 22].Firstly, propensity scores were calculated and then matched. Then, a balancing test was conducted to detect the equilibrium of the covariates and the treatment and control groups, and to verify the reduction of sampling bias through matching. Nearest neighbour matching with calliper was adopted for our primary analysis (calliper set to 0.05, neighbour set to 3), as frequently mentioned in the literature[23, 24]. After matching, the deviation should be less than or equal to 5 percent or p \u0026gt; 0.1 as a test of proper matching. Finally, we judged the appropriateness of the matching on the basis of the mean reduction bias of each covariate and the overall mean reduction bias. Samples that were not within the common support were excluded.\u003c/p\u003e\n\u003cp\u003eSecondly, the DID model was constructed based on the sample results after PSM matching and controlling for individual and time effects, with the model set up as follows:\u003c/p\u003e\n\u003cp\u003eY\u003csub\u003eit\u003c/sub\u003e=\u0026beta;\u003csub\u003e0\u003c/sub\u003e+\u0026beta;\u003csub\u003e1\u003c/sub\u003eTreated\u003csub\u003ei\u003c/sub\u003e\u0026times;Time\u003csub\u003et\u003c/sub\u003e+X\u003csub\u003eit\u003c/sub\u003e+\u0026mu;\u003csub\u003ei\u003c/sub\u003e+\u0026lambda;\u003csub\u003et\u003c/sub\u003e+\u0026epsilon;\u003csub\u003eit\u003c/sub\u003e,\u003csub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003e(1)\u003c/p\u003e\n\u003cp\u003ewhere Y\u003csub\u003eit\u003c/sub\u003e represents an individual i\u0026rsquo;s healthcare utilisation in year t. Treated\u003csub\u003ei\u003c/sub\u003e is a dummy variable for grouping. It is defined as treatment group = 1 and control group = 0. In the current study, 1 denotes \u0026nbsp;migrating into a city (treatment group) and 0 denotes living in a rural area (control group). Time\u003csub\u003et\u003c/sub\u003e is a time dummy variable that defines 2015 = 0, 2018 = 1 and 2020 = 1. \u0026beta;\u003csub\u003e1\u003c/sub\u003e is the coefficient of the core explanatory variable in this study, which is the net effect of a policy on the effect of middle-aged and older adults migrating to the city on the utilisation of healthcare services. X\u003csub\u003eit\u003c/sub\u003e is the control variable. \u0026epsilon;\u003csub\u003eit\u0026nbsp;\u003c/sub\u003eis the error term, and \u0026beta;\u003csub\u003e0\u003c/sub\u003e is a constant term. \u0026mu;\u003csub\u003ei\u003c/sub\u003e is an individual fixed effect, and \u0026lambda;\u003csub\u003et\u003c/sub\u003e is a time fixed effect. The current study was statistically analysed using Stata 16.0 with a two-sided statistical significance level set at 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1 reports the results of the descriptive statistics for the main variables of the study, with the treatment and control groups differing in age, education, self-assessed\u0026nbsp;health,\u0026nbsp;household registration, social activities and per capita household expenditure. The mean age of the treatment group was less than that of the control group, i.e. about 2.6 years. The average level of education in the treatment group was higher than that in the control group, with a mean of 2.12, which corresponded to a literacy level between primary school and lower secondary school. In the control group, the mean was 1.78, which corresponded to a literacy level between illiterate and primary/private school. The treatment group rated their health as slightly better than the control group. The proportion of urban households in the treatment group was higher than that in the control group. As age increased, a decrease in social activities was noted in both groups. In 2015\u0026ndash;2018, the treatment group had slightly more social activities than the control group. In 2020, the treatment group had slightly less social activities than the control group, reflecting to a certain extent the barriers to urban integration and social barriers in the middle-aged and elderly groups who migrated to the city. Per capita household expenditure was higher in the treatment group than in the control group. In particular, the prevalence of chronic diseases increased substantially with age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e：Descriptive statistics results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"752\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.47340425531915%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.861702127659573%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.79787234042553%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.867021276595743%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.72852233676976%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=456)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97938144329897%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=9171)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.353951890034363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=456)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97938144329897%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=9171)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97938144329897%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=456)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97938144329897%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=9171)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.72852233676976%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97938144329897%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.353951890034363%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97938144329897%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97938144329897%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97938144329897%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e0.45(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.47(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e0.45(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.47(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.45(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.47(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e57.07(9.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e59.60(9.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e59.94(8.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e62.61(9.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e61.89(9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e64.62(9.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e2.12(1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e1.78(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e2.13(1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e1.78(0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e2.13(1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e1.78(0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e0.91(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.89(0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e0.87(0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.86(0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.86(0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.84(0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e0.81(0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.81(0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e0.96(0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.97(0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.95(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.95(0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eSelf-assessed health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e1.98(0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e2.01(0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e2.00(0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e2.06(0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e1.96(0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e2.06(0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003ePresence of chronic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e0.16(0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.16(0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e0.42(0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.42(0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.82(0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.82(0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eHousehold registration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e0.20(0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.05(0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e0.22(0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.05(0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.24(0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.08(0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eSocial activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e0.55(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.50(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e0.52(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.47(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.43(0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.46(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003ePer capita household expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e17711.37(25292.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e14410.35(25284.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e23557.10(28799.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e16355.94(32448.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e25852.87(33242.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e17479.77(25398.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eFamily size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e2.44(1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e2.61(1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e2.47(0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e2.34(0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e2.72(1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e2.52(1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eDrink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e0.37(0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.35(0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e0.31(0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.33(0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.35(0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.34(0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.503328894806923%\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.513981358189081%\"\u003e\n \u003cp\u003e0.39(0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.41(0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.448735019973368%\"\u003e\n \u003cp\u003e0.40(0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\"\u003e\n \u003cp\u003e0.43(0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.37(0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.383488681757656%\" valign=\"top\"\u003e\n \u003cp\u003e0.40(0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Standard deviation in the brackets;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSM results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePSM was used to test the balance of the data, and a validity analysis of the balance of the model was a prerequisite for testing whether PSM\u0026ndash;DID can be used effectively. To ensure that reliable matching results are obtained, the \u003cem\u003et\u003c/em\u003e-tests for all the control variables in the treatment and control groups should be insignificant. Table 2 presents the results of the test that used calliper \u003cem\u003ek\u003c/em\u003e-nearest neighbour matching (\u003cem\u003ek\u003c/em\u003e = 3, calliper = 0.05). As indicated in the table, the standard error of the data for each control variable was less than 10 percent after matching, and no significant difference occurred at the 5 percent level between the means of the variables for the two samples. As shown in Table 3, the results of the likelihood ratio chi-square test that used PSM are considerably smaller than the original results. The results further suggest that PSM improves overall balance after matching. Secondly, kernel density plots were used to visualise whether a difference existed in the propensity scores between the two sets of data before and after matching (e.g. Figure 2). A significant difference was found between the treatment and control groups before matching, and then they became significantly closer after matching. Therefore, the matching effect is good in accordance with the results shown in the figure and table, and using the matched samples is more appropriate for DID regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e:\u0026nbsp;Results of the balance test before and after matching\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"745\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.537634408602152%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.236559139784948%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96236559139785%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%bias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.650537634408602%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%reduct |bias|\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.612903225806452%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et-test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.594594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.054054054054054%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.036036036036037%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.73873873873874%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.936936936936938%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026gt;|t|\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e52.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e60.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e60.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e91.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e99.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e11.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e-3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e81.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eSelf-reported health status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e-2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e69.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003ePresence of chronic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e-0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e97.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eHousehold registration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eSocial activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e-2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e-91.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eLn(Per capita household expenditure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e9.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e9.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eFamily size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e73.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eSmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e79.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.503355704697988%\" valign=\"top\"\u003e\n \u003cp\u003eDrink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.87248322147651%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.46979865771812%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.946308724832214%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.630872483221477%\" valign=\"top\"\u003e\n \u003cp\u003e97.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.95973154362416%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.61744966442953%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e:Logit regression results before and after matching\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.57940663176265%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.518324607329843%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePs R2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.169284467713787%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLR chi2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.122164048865619%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026gt;chi2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeanBias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.216404886561955%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedBias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.027923211169284%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.027923211169284%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.900523560209423%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%Var\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.57940663176265%\" valign=\"top\"\u003e\n \u003cp\u003eUnmatched\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.518324607329843%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.169284467713787%\" valign=\"top\"\u003e\n \u003cp\u003e649.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.122164048865619%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" valign=\"top\"\u003e\n \u003cp\u003e17.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.216404886561955%\" valign=\"top\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.027923211169284%\" valign=\"top\"\u003e\n \u003cp\u003e82.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.027923211169284%\" valign=\"top\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.900523560209423%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.57940663176265%\" valign=\"top\"\u003e\n \u003cp\u003eMatched\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.518324607329843%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.169284467713787%\" valign=\"top\"\u003e\n \u003cp\u003e2.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.122164048865619%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.43804537521815%\" valign=\"top\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.216404886561955%\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.027923211169284%\" valign=\"top\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.027923211169284%\" valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.900523560209423%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDID results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther estimation of the effects of the migration of middle-aged and elderly adults to urban areas on the utilisation of health services was obtained on the basis of the data after nearest-neighbour matching with callipers. We studied the effects of migrating to a city on the NOO and NOH of migrant middle-aged and elderly adults. The results exhibited a significant negative effect on the NOH of middle-aged and older adults migrating to a city(e.g. Table 4). The DID was \u0026minus;0.093, and the p\u0026nbsp;0.01.\u0026nbsp;Meanwhile, the DID coefficient for NOO was \u0026minus;0.008 with the p\u0026nbsp;0.1, indicating no significant effect of migrating to the city on the NOO of the middle-aged and elderly age groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e:DID estimate results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"641\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.556942277691107%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18876755070203%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.68486739469579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.556942277691107%\" valign=\"top\"\u003e\n \u003cp\u003eDid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.011（0.0671）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e-0.008（0.0674）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18876755070203%\" valign=\"top\"\u003e\n \u003cp\u003e-0.089***（0.0326）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.68486739469579%\" valign=\"top\"\u003e\n \u003cp\u003e-0.093***（0.0327）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.556942277691107%\" valign=\"top\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18876755070203%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.68486739469579%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.556942277691107%\" valign=\"top\"\u003e\n \u003cp\u003eTime fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18876755070203%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.68486739469579%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.556942277691107%\" valign=\"top\"\u003e\n \u003cp\u003eIndividual fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18876755070203%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.68486739469579%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.556942277691107%\" valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18876755070203%\" valign=\"top\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.68486739469579%\" valign=\"top\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.556942277691107%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e28824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.784711388455538%\" valign=\"top\"\u003e\n \u003cp\u003e28824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.18876755070203%\" valign=\"top\"\u003e\n \u003cp\u003e28824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.68486739469579%\" valign=\"top\"\u003e\n \u003cp\u003e28824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: ***p\u0026lt;0.01; **p\u0026lt;0.05; *p\u0026lt;0.1;\u0026nbsp;Robust SEs in the brackets;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRobustness test results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRobustness tests were also conducted in this study to further validate the reliability of the above results.\u0026nbsp;\u003cstrong\u003e(1)Replacement of the matching method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the robustness of the PSM\u0026ndash;DID results, we used different matching methods, namely, radius matching and\u0026nbsp;Mahalanobis\u0026nbsp;matching.\u0026nbsp;The above DID regression was then repeated after matching, as shown in Table 5. The results are consistent with the above regression results, indicating that the results of the empirical analyses exhibit good reliability.\u0026nbsp;The finding that middle-aged and older adults have fewer hospitalisations when they migrate to a city is statistically significant, and the results obtained by changing the match continue to support these findings.\u0026nbsp;Simultaneously, the effect of migrating to a city on the NOO of middle-aged and elderly adults is insignificant and robust, as discussed in a later section.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e:\u0026nbsp;DID estimate results after changing the matching method\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"695\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003eDid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e-0.008（0.067）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e-0.009（0.067）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e-0.095***（0.033）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e-0.093***（0.033）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003eTime fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003eIndividual fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003eMatching method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eRadius matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eMahalanobis matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eRadius matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003eMahalanobis matching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.72661870503597%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e28824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e28881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e28824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.568345323741006%\" valign=\"top\"\u003e\n \u003cp\u003e28881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: ***p\u0026lt;0.01; **p\u0026lt;0.05; *p\u0026lt;0.1; Robust SEs in the brackets;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2)Placebo testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReferring to previous studies[25],the current work randomly generated new treatment groups with new estimated coefficients and repeated the process 500 times. Figures 3 and 4 present the results of the placebo test. The estimated coefficient values are clustered around the value of 0 and follow a roughly normal distribution. The dotted lines in the graph represent the actual estimated coefficients, which are clearly outliers in Figure 3. The dotted line in Figure 4 is insignificant within the normal value range, proving that the empirical results of this study are robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of heterogeneity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePossible heterogeneity in health service utilisation after migrating to a city amongst the middle-aged and elderly populations exhibited different characteristics.\u0026nbsp;Heterogeneity analyses can further explore the effect of different middle-aged and elderly adults\u0026rsquo; healthcare service utilisation after migrating to the city, and policy recommendations can be targeted.\u0026nbsp;This study examined the effect of different age groups, household registration and self-assessed health status of the samples on the utilisation of healthcare services.\u0026nbsp;As indicated in the regression results in Table 6, the heterogeneity of age is evident through the following division: the middle-aged group (45\u0026ndash;59 years old), the low elderly group (60\u0026ndash;69) and the high elderly group (70 years old and above).\u0026nbsp;Table 6 shows a reduction in the NOH of the low elderly group when they migrate to a city, whilst the effect is insignificant for the other age groups.\u0026nbsp;The\u0026nbsp;heterogeneity of self-assessed health is categorised into good, fair and poor in accordance with their assessment of their own health status.\u0026nbsp;Table 6 indicates a significant negative effect (significant at the 5% level) on the NOH of the group with poor self-assessed health after migrating to a city, implying that this group will reduce the utilisation of healthcare services.\u0026nbsp;No significant effect was observed on the NOH of groups with good and fair self-assessed health.\u0026nbsp;The\u0026nbsp;heterogeneity of Household registration is classified into urban and rural.\u0026nbsp;Table 6 shows that migrating to a city reduces the NOH of rural household residents (significant at the 5% level), but exerted no significant effect on urban household residents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e: Results of the test for heterogeneity in the number of hospitalisations\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"756\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.973544973544975%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.02645502645503%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.52066115702479%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-assessed health\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold registration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e45-59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e60-69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e70-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFair\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.89459815546772%\" valign=\"top\"\u003e\n \u003cp\u003eDid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e-0.081** (0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e-0.099\u003cbr\u003e\u0026nbsp;(0.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003cbr\u003e\u0026nbsp;(0.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e-0.343**\u003c/p\u003e\n \u003cp\u003e(0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003cp\u003e(0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e-0.082**\u003c/p\u003e\n \u003cp\u003e(0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.89459815546772%\" valign=\"top\"\u003e\n \u003cp\u003eControl variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.89459815546772%\" valign=\"top\"\u003e\n \u003cp\u003eTime fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.89459815546772%\" valign=\"top\"\u003e\n \u003cp\u003eIndividual fixed effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.89459815546772%\" valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.89459815546772%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e11108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e8573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e5997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e4330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e12525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e5484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e1439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.013175230566535%\" valign=\"top\"\u003e\n \u003cp\u003e26786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: ***p\u0026lt;0.01; **p\u0026lt;0.05; *p\u0026lt;0.1; Robust SEs in the brackets;\u003c/p\u003e"},{"header":"Discussion and suggestion","content":"\u003cp\u003e\u003cstrong\u003eThe effect of moving to the city on healthcare\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eutilisation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;among middle-aged and elderly people\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to previous studies[3, 26], the current work also showed that migrant middle-aged and elderly adults have relatively low levels of healthcare service utilisation, and that the migration to a city of the middle-aged and elderly populations results in fewer hospitalisations, but exerts no significant effect on NOO.\u0026nbsp;The DID coefficient for NOH was \u0026minus;0.093 with p = 0.007.\u0026nbsp;The DID coefficient for NOO was \u0026minus;0.008 with p = 0.891.\u0026nbsp;The result in which migrant middle-aged and older adults will experience fewer hospitalisations may be due to the following reasons.\u003c/p\u003e\n\u003cp\u003eFirstly, the prevalence of chronic diseases gradually increases with age. When middle-aged and elderly migrants move to cities, their status as migrants leads to higher costs for medical treatment and inconvenience in reimbursing medical treatment. Consequently, middle-aged and elderly migrants have limited access to medical services.\u0026nbsp;This conclusion is in agreement with the findings of Zhang and Xi et al.[3, 9, 27].\u0026nbsp;In 2016, the Chinese government issued the Opinions on Integrating the Basic Medical Insurance System for Urban and Rural Residents, which integrated the basic medical insurance for urban residents and the new type of rural cooperative medical care system, and also called for the unification of the scope of coverage, financing policy, protection treatment, medical insurance catalogue, fixed-point management and fund management.\u0026nbsp;Studies have shown that although the integration of urban and rural residents\u0026rsquo; health insurance improves the frequency and equity of use of health services, it exerts no significant effect on the probability of access to health care or the unmet need for hospitalisation; moreover, large disparities remain between urban and rural areas[21, 28].\u0026nbsp;Despite the continuous improvement of China\u0026rsquo;s reimbursement policy for medical treatment in other places, the current policy on medical treatment in some places is still based on the principle of \u0026lsquo;catalogue in the place of medical treatment, treatment in the place of insurance, management in the place of medical treatment\u0026rsquo;. Furthermore, differences exist in the thresholds, payment ratios and payment limits of the medical insurance funds of different coordinating regions, resulting in the reimbursement level of medical treatment in other places being even lower than that in local areas; consequently, middle-aged and elderly adults who migrate from rural areas to urban areas still face considerable difficulties in obtaining medical treatment in other places[29].\u0026nbsp;Evidence from Europe also suggests similar results, with consistency between lack of formal residence status and limited healthcare utilisation[13, 14].\u003c/p\u003e\n\u003cp\u003eSecondly, the middle-aged and older adult groups typically migrate to cities for the purpose of working, intergenerational care and old age, and the medical behaviour of these age groups is usually irrational[30]. In accordance with relevant studies, when these people are sick or unwell, they repeatedly choose to disregard their illness or self-medicate, leading to the phenomenon of untimely access to medical care.\u0026nbsp;With the lower literacy level and poor health knowledge of these groups, lower health literacy can lead to poor health behaviour. Middle-aged and elderly adults are less proactive than local residents in accessing health information and utilising healthcare services after migrating to a city; they are also unable to fully utilise prevention, basic public health\u0026nbsp;[31, 32]and healthcare services[8].\u0026nbsp;Relevant studies have indicated that health literacy enhancement can improve the health level of residents, and thus, local governments should strengthen the promotion of health literacy knowledge amongst migrant populations[33, 34].\u003c/p\u003e\n\u003cp\u003eMigration to cities did not exert a significant effect on the NOO of middle-aged and elderly adults, probably due to China\u0026rsquo;s outpatient health insurance reimbursement policy.\u0026nbsp;China has limited resources for community-based and primary health care, and the cost of hospitalisation is high.\u0026nbsp;China\u0026rsquo;s health insurance has always focused on inpatient coverage, and most insurance policies mostly cover inpatient services or set high deductibles for outpatient services.\u0026nbsp;The overall average reimbursement rate for outpatient treatment in China is relatively low at about 20 percent[35],\u0026nbsp;and older people from poorer households are even less likely to utilise outpatient services[36].\u0026nbsp;Consequently, the financial burden of outpatient care for middle-aged and elderly adults remains heavy. In the face of low reimbursement rates and complicated reimbursement procedures, middle-aged and elderly adults who have migrated to urban areas\u0026nbsp;whether in rural or urban areas generally choose to put up with \u0026lsquo;minor\u0026rsquo; illnesses, self-medicate or not treat them at all, rather than seeking outpatient services to avoid the cost of medical care[37, 38].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity of Age, self-assessed health,\u003c/strong\u003e \u003cstrong\u003eand\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ehousehold registration\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;on the effect of moving to urban areas on the use of healthcare services among the middle-aged and elderly peoples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the heterogeneity analyses show that migrating to a city exerts a stronger effect on the utilisation of healthcare services for middle-aged individuals, rural households and groups with poor\u0026nbsp;self-assessed health\u0026nbsp;status.\u0026nbsp;This study found that from the point of view of age group, the middle-aged group (45\u0026ndash;59 years old) is in the transition between urban labour and intergenerational care. The middle-aged group of urban labourers experience greater burden on family and pressure of work, with higher intensity and long hours of work. When they are not feeling well, most of these people choose not to treat their illness.\u0026nbsp;Some middle-aged adults live with their children in a state of intergenerational care. To avoid causing trouble to their children, they usually choose to self-medicate minor illnesses, resulting in the under-utilisation of medical services.\u0026nbsp;In terms of household registration, rural residents generally have a low level of education, averaging around the primary school, and a low level of health awareness, limited knowledge of diseases and insufficient use of medical services.\u0026nbsp;The lower level of social integration of rural middle-aged and elderly groups after moving to urban areas may also possibly lead to negative attitudes towards healthcare[39], resulting in untimely access to healthcare.\u0026nbsp;And due to the lack of coordination between locations, it is difficult for middle-aged and elderly migrants to obtain reimbursement for outpatient treatment off-site.\u0026nbsp;They must pay all medical expenses at the time of their visit and then return home for reimbursement. This complex process places a significant financial burden on chronically ill older migrants who need to take medication for a long period of time[9].In terms of self-assessed\u0026nbsp;health, no significant change was observed in the NOH of groups with good and fair self-assessed health.\u0026nbsp;The\u0026nbsp;significantly fewer hospitalisations amongst middle-aged and elderly adults with self-assessed poor health, who mostly have labour-intensive jobs and may have lower socioeconomic status, may be due to the effect of the reimbursement policy for medical care in other places and incorrect perceptions of the use of healthcare services[12].\u003c/p\u003e\n\u003cp\u003eWith a high proportion of the elderly population migrating in China, research on the utilisation of their healthcare services is important for maintaining the stability of the health system and achieving healthcare and health equity.\u0026nbsp;Most previous studies were conducted using cross-sectional data, which could not reflect the dynamic process of healthcare service utilisation before and after migrating to a city. The current study found that the healthcare service utilisation of middle-aged and elderly adults was low after migrating to a city by using three-period tracking data.\u0026nbsp;Therefore, in conjunction with the current study, the following recommendations are made.\u0026nbsp;Firstly, the medical insurance policy should continuously improve the outpatient reimbursement policy and lower the starting line for outpatient reimbursement.\u0026nbsp;And it is recommended to introduce corresponding policies and provide support for insurance participation in other places, and accelerate the implementation of the policy of direct settlement for medical treatment in other places.\u0026nbsp;Secondly, an atmosphere of caring for migrant middle-aged and elderly adults should be created at the social and family levels, and health literacy education and popularisation should be strengthened\u0026nbsp;[40, 41].Thirdly, the community should help middle-aged and elderly adults who have migrated to cities learn and understand the benefits of the rational use of medical services for their health and increase their knowledge of prevention and medical treatment to fully mobilise their motivation in using medical services[42, 43].\u0026nbsp;Exercise facilities can also be constructed to help middle-aged and elderly adults maintain good health, and interest clubs can be set up to increase their social activities. These measures help middle-aged and elderly individuals resocialise in new environments and actively integrate into town life.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study also has two limitations. First, an important assumption in the implementation of PSM-DID is that the model should contain all covariates that may influence the policy effect before and after matching. Unobservable covariates will cause different trends between the treatment group and the control group, and such an instance may have led to biased results in this study. Secondly, given the limitations of the database and sample size, further validation is needed and in-depth analyses based on place of inflow are not possible because the data do not show the place of inflow of middle-aged and older people after they have moved to the city.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has two major contributions. Firstly, by exploring the effects of migrating to a city on the utilisation of healthcare services amongst middle-aged and elderly adults, the NOH of these populations was determined to decrease significantly when they move from rural to urban areas, but exerted no significant effect on their NOO. In addition, the government should improve its health insurance policy, increase the percentage of outpatient reimbursement claims and improve its policy on travelling to other places for medical treatment to reduce process and costs. Secondly, the heterogeneity analysis found that the effect of moving to a city was more significant on health service utilisation amongst individuals aged 45\u0026ndash;59 years and those with rural household registration and poor self-assessed health status. Policymakers should launch corresponding policies to encourage attention to migrant middle-aged and elderly adults at the social, community and family levels to increase their health knowledge and enhance their health literacy, helping them establish a correct concept of healthcare service utilisation, better integrate into urban life and improve their health standard. Our findings are informative for the literature on migrant populations, ageing societies and health equity. In addition, useful lessons can be learned for other developing countries with large migrant middle-aged and elderly populations.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003ePSM-DID Difference-in-Differences Propensity Score Matching estimator\u003c/p\u003e\n\u003cp\u003eCHARLS China health and retirement longitudinal study\u003c/p\u003e\n\u003cp\u003ePSM Propensity Score Matching\u003c/p\u003e\n\u003cp\u003eDID Difference-in-Differences estimator\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Peking University, the CHARLS research and field team and every respondent in the study for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLu Xu and Yingchun Chen designed this study and performed the statistical analysis. Lu Xu wrote the main manuscript text. Dandan Guo, Shengxian Bi, Huawei Tan,\u0026nbsp;Songhao Yang, Lei li, Qijiao Yang, Xinyi Peng and Xueyu Zhang participated in data analysis. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of China (72374076).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the CHARLS repository, http://charls.pku.edu.cn.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were retrieved from the CHARLS. This survey was endorsed by the Biomedical Ethics Committee of Peking University (NO.IRB00001052\u0026ndash;11015). All participants in the survey signed or marked (if illiterate) the informed consent forms. All methods were carried out in accordance with relevant guidelines and regulations.\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 no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Key Research Institute of Humanities \u0026amp; Social Sciences of Hubei Provincial Department of Education, Research Centre for Rural Health Service, Wuhan 430030, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXing Y, Zhang L, Zhang Y, He R: Relationship between social interaction and health of the floating elderly population in China: an analysis based on interaction type, mode and frequency\u003cstrong\u003e.\u003c/strong\u003e BMC Geriatr\u003cem\u003e \u003c/em\u003e2023, 23\u003cstrong\u003e:\u003c/strong\u003e662.\u003c/li\u003e\n\u003cli\u003eGao L, Penning MJ, Wu Z, Sterrett SJ, Li S: Internal Migration and the Health of Middle-Aged and Older Persons in China: The Healthy Migrant Effect Reconsidered\u003cstrong\u003e.\u003c/strong\u003e Res Aging\u003cem\u003e \u003c/em\u003e2021, 43\u003cstrong\u003e:\u003c/strong\u003e345-357.\u003c/li\u003e\n\u003cli\u003eZhang X, Yu B, He T, Wang P: Status and determinants of health services utilization among elderly migrants in China\u003cstrong\u003e.\u003c/strong\u003e Glob Health Res Policy\u003cem\u003e \u003c/em\u003e2018, 3\u003cstrong\u003e:\u003c/strong\u003e8.\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;ck D, Widmer ED, Česnuitytė V: \u003cstrong\u003eConclusion: Changes and Continuities in European Family Lives.\u003c/strong\u003e In \u003cem\u003eFamily Continuity and Change: Contemporary European Perspectives.\u003c/em\u003e Edited by Česnuitytė V, L\u0026uuml;ck D, D. 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As these age groups move into urban areas, their healthcare service utilisation are directly related to their health status and basic rights to survival. It also places higher demands on China’s healthcare service provision. This study aims to investigate the effect of middle-aged and elderly adults migrating from rural to urban areas on their healthcare service utilisation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ePanel data from Wave 3 (2015), Wave 4 (2018) and Wave 5 (2020) of the nationally representative China Health and Retirement Longitudinal Study were selected to obtain a sample of 456 participants in the treatment group and 9171 participants in the control group. The difference-in-differences propensity score matching estimator method was used to explore the effect of migrating to a city on the utilisation of healthcare services amongst middle-aged and elderly adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCalliper nearest-neighbour matching significantly improved overall balance after matching. The DID regression results showed that middle-aged and elderly adults significantly reduced their number of hospitalisations when they moved to a city, with a DID value of −0.093 (p\u0026lt;0.05). However, no significant effect was observed on the number of outpatient visits (p\u0026gt;0.05). The DID results were consistent with the robustness test. Further examination of heterogeneity determined that it had a more significant negative effect on the number of hospitalisationsamongst individuals aged 45–59 years, of rural hukou status and with poor self-assessed health status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Middle-aged and elderly migrants moving to cities may reduce their healthcare utilisation. The government should improve its outpatient reimbursement policy and the policy of medical treatment in other places; encourage the society, community and family to give more attention to middle-aged and elderly adults migrating to cities; improve their health literacy and help them set up the correct concept of medical service utilisation to promote the health equity of migrant populations.\u003c/p\u003e","manuscriptTitle":"Effect of rural-to-urban migration on the healthcare utilisation of middle-aged and elderly adults: evidence from the China Health and Retirement Longitudinal Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 16:48:55","doi":"10.21203/rs.3.rs-4048728/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":"054ba373-a414-4ea5-90f2-c8d6a804e08f","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-13T16:48:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 16:48:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4048728","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4048728","identity":"rs-4048728","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}