Shaping Long-term Care Insurance Intentions among Chinese Older Adults: Role of Information Interventions in Health Risks

Shaping Long-term Care Insurance Intentions among Chinese Older Adults: Role of Information Interventions in Health Risks | 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 Article Shaping Long-term Care Insurance Intentions among Chinese Older Adults: Role of Information Interventions in Health Risks Anli Leng, Jin Liu, Jiaozhi Hao, Elizabeth Maitland, Stephen Nicholas, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3812000/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 Long-term care insurance (LTCI) is an essential system in the context of fast-growing population aging, yet it lacks evidence on how to improve the acceptance and coverage of non-compulsory LTCI in China. Using a survey experiment, we randomly assigned 1025 older adults to control group, disability information group and dementia information group, and explored whether information about the adverse health outcomes would change their willingness to LTCI insure. We found that disability and dementia information significantly changed respondents’ willingness to LTCI insure, and health status had negative moderating effects on the relationship between information interventions and the change of LTCI intentions. Also, we found respondents with lower education and living in non-pilot cities were more sensitive to information interventions. We recommend LTCI information campaigns differentiating information by dementia-related and disability-related risks; by LTCI pilot and non-pilot cities; by education levels and by physical and mental health status. Scientific community and society/Social sciences Health sciences/Diseases long-term care insurance Chinese older adults information intervention disability dementia Figures Figure 1 Main With the world’s fastest aging population, China’s increasing life expectancy and declining birth rates challenges the provision of healthcare (Peng, 2011 ). In 2020, there were 264 million people aged 60 years old and over, accounting for 18.7% of the Chinese population, forecast to rise to more than 500 million by 2050 (The National Bureau of Statistics, 2021 ; United Nations, 2022). Within the aging population, 52.7 million suffered disabilities, estimated to reach 82.7 million by 2030 and 89.6 million by 2050. (Luo et al., 2021 ). The number of over 60-year-old Chinese suffering dementia was 15.07 million in 2018, and is expected to rise to 50 million by 2050 (Jia et al., 2020 ; Li et al., 2021 ). Older adults with disabilities and dementia pose a serious challenge to the sustainability of China’s long-term care (LTC) system (Feng et al., 2012 ; Lei et al., 2022 ). With the reduction in the size of the Chinese family, the effects of the one-child policy, urban migration and the decline in informal family provided LTC, the demand for formal LTC has surged (Feng et al., 2020 ). To address this challenge, the Chinese government launched in 2016 a long-term care insurance (LTCI) scheme in 15 pilot cities, expanding to 49 cities by 2020, to provide the old aged with disabilities and dementia accessible and affordable long-term care services (National Healthcare Security Administration, 2021 ). LTCI aimed to alleviate the shortage of caregivers for severely disabled patients by providing nursing service payments in public care facilities and also some support for home-based care services (Jiang & Yang, 2023 ). China’s National Medical Insurance Administration aims to build a nation-wide and unified LTCI system independent from China’s basic pension and medical social insurance system (Information Office of the State Council, 2023). How to promote the expansion of China’s LTCI is an urgent challenge for China. Non-compulsory LTCI markets are widespread, with people's LTCI purchasing decisions analyzed from the economic, behavioral, and psychosocial perspectives (Boyer et al., 2017 ; Brown et al., 2012 ; Curry et al., 2009 ; He & Chou, 2020 ; Sloan & Norton, 1997 ). Behavioral economics provides a powerful framework for understanding the purchase decision behavior for LTCI by focusing on individual risk perceptions in intertemporal health decision-making choices (White & Dow, 2015 ). An individual’s risk perception, derived from objective knowledge of LTC risks, subjective knowledge of LTC risks and subjective knowledge of one’s own health, has been shown to shape LTCI needs (Costa-Font & Rovira-Forns, 2008 ; Lambregts & Schut, 2020 ; Ugarte Montero & Wagner, 2023 ). Cognitive biases or overconfidence means individuals’ health cognition may deviate from rational expectations, resulting in a misunderstanding of LTCI risks affecting decision-making (Baicker et al., 2012 ; Y.-P. Chen, 2003 ). Individual LTCI perceptions biases reflect physical, mental, financial and other factors associated with future projection of disability or dementia in old age (Finkelstein & McGarry, 2006 ; Tennyson & Yang, 2014 ). Motivated by self-protection, the probability of older adults purchasing LTCI increases with their knowledge of the risks associated with their future likely LTC needs (Zhou-Richter et al., 2010 ). These studies suggest that expectations of LTC risk are positively correlated with changes in LTCI demand, and thus it is possible to promote older adults’ participation in LTCI by increasing their risk perception. In healthcare research, these behavioral hypotheses have been widely applied to insurance enrollment, vaccination decisions and medication use to facilitate the public's health-favorable decision-making (Capuno et al., 2016 ; Handel et al., 2019 ; Stuart et al., 2013 ). Surprisingly, there is currently a lack of research on the role of information interventions in shaping the LTC insurance risk perceptions of older Chinese adults with disabilities and dementia. To address this research gap, we designed a scenario experiment to explore whether older Chinese adults with information about the adverse health outcomes of disability or dementia would change their willingness to buy LTCI. People’ evaluation of their own health status, their knowledge of the consequences of illness and their understanding of their health behaviors will impact their perception of future health risks (Jones et al., 2014 ), so we also investigate the moderating effect of health status on the impact of information interventions on LTCI decisions. Since our study is concerned with the effect of information interventions on the changes of older adults’ LTC risk perceptions and LTCI intentions, the results are expected to vary depending on the older adults’ own risk perceptions. Participants’ education background and whether they reside in a LTCI pilot city proxy respondents’ knowledge about LTCI (Lassen, 2005 ; Pförtner & Hower, 2022 ). Our paper also conducted a heterogeneity analysis to explore the intervention effects of disability and dementia information for subgroups with different education levels and pilot city conditions. Our study makes contributions in the following three aspects. From the perspective of risk perception, we explore whether increasing people's understanding of the risk of disability or dementia could change their willingness to participate in LTCI. This not only enriches the research on information intervention on health promotion, but also provides empirical reference for the Chinese government to improve the LTCI coverage rate of residents. Second, we examine the moderating role of an individual's health status and LTCI knowledge on LTCI decision-making, providing evidence for the implementation of individualized and effective interventions. Finally, our nationally representative survey experiment provides to government evidence on LTCI willingness in China, helping improve China’s “person-centered” LTC system. Results Respondent characteristic Table 1 shows the respondents’ socioeconomic demographic characteristics. Out of the 1,025 respondents, 466 (45.46%) were male, 572 (55.80%) were between the ages of 50 and 59; 456 (44.49%) had a high education level; most had a spouse (85.76%); were urban residents (62.63%), earned less than RMB6000 a month (76.39%) and had 2 or more children (60.49%). In terms of the health status, 689 (67.22%) respondents rated their own health status as good. After 0–1 standardization, the average physical health status was a high 0.94 level and mental health status was a moderate 0.67 level. The last column of Table 1 shows the ANOVA results. There were significant inter-group differences in age, educational level, monthly personal income, number of children, physical health and mental health. To control the influence of these variables on the intervention results, they were added to the statistical model as control variables. Table 1 Characteristic of respondents Variables Total (n = 1025) Control group (n = 354) Disabled group (n = 339) Dementia group (n = 332) P value N % N % N % N % Sex Female 559 54.54 214 60.45 167 49.26 178 53.61 0.012 Male 466 45.46 140 39.55 172 50.74 154 46.39 Age 50–59 572 55.80 161 45.48 221 65.19 190 57.23 < 0.001 60–69 453 44.20 193 54.52 118 34.81 142 42.77 Marital status No spouse 146 14.24 52 14.69 46 13.57 48 14.46 0.907 Have a spouse 879 85.76 302 85.31 293 86.43 284 85.54 Residence Rural 383 37.37 138 38.98 129 38.05 116 34.94 0.523 Urban 642 62.63 216 61.02 210 61.95 216 65.06 Educational level Low(≤ 6 yrs) 300 29.27 167 47.18 51 15.04 82 24.70 9 yrs) 456 44.49 95 26.84 195 57.52 166 50.00 Monthly personal income (RMB) 1 (≤ 3000) 404 39.41 194 54.8 73 21.53 137 41.27 6000) 242 23.61 34 9.6 133 39.23 75 22.59 Number of children 1 405 39.51 92 25.99 131 38.64 182 54.82 =2 620 60.49 262 74.01 208 61.36 150 45.18 Self-rated health status Not good 336 32.78 123 34.75 94 27.73 119 35.84 0.051 Good 689 67.22 231 65.25 245 72.27 213 64.16 Physical health (mean, SD) 0.941 0.131 0.933 0.102 0.918 0.182 0.973 0.081 < 0.001 Mental health(mean, SD) 0.672 0.194 0.603 0.176 0.679 0.182 0.738 0.199 < 0.001 Willingness to LTCI insure Figure 1 presents the percentages of respondents willing to LTCI insure in the control group, disability group and dementia group before and after the information intervention. Before the information intervention, the proportion of respondents who were willing, unwilling and uncertain about willingness to LTCI was roughly a 4:1:5 ratio, which changed to 7:2:1 ratio after the information intervention. For the control group, the rate of respondents who were not willing to insure LTCI increased from 10.45% to 30.23%; those uncertain decreased from 47.18% to 19.21%; and the willing to insure increased from 42.37% to 50.56%. For the disability group (43.07% to 79.35%) and dementia group (35.54% to 85.54%), the percentage of respondents who were willing to insure LTCI increased substantially. We also analyzed the willingness to LTCI insure before and after the information interventions by ANOVA. The results showed that before the information interventions, there was no significant inter-group difference in the willingness to LTCI insure in the control, disability and dementia groups (F=1.59, P=0.204). However, the differences became statistically significant after the interventions (F=39.45, P<0.001), indicating that the information interventions impacted LTCI decisions. Main results of logit regression Model 1 in Table 2 shows the logit regression results of the disability and dementia information interventions on changes in LTCI willingness among older adults. Compared with the control group, both disabled and dementia information intervention group had a significant positive impact on respondents’ LTCI intentions. After the information intervention, respondents in the dementia group (β = 2.409, p < 0.01) were more willing to change their LTCI intentions than the disability group (β = 2.126, p < 0.01). Sex, age, marriage status, residence status, number of children, self-rated health and mental health had no significant impact on the willingness to change the LTCI insure intention, but education, personal income and physical health significantly influenced the change of LTCI willingness. The more educated the respondents, the less likely they were to change their LTCI insurance intentions. Compared to personal income less than RMB3000, respondents with a middle income level were more willing to change their LTCI intentions. Older adults with better physical health status were more likely to increase their willingness to LTCI insure. Table 2 Logit model Variables Model 1 Model 2 Model 3 Model 4 Disability intervention 2.126*** 3.135*** 8.299*** 4.481*** (0.227) (0.425) (2.946) (0.835) Dementia intervention 2.409*** 2.412*** 9.557*** 3.867*** (0.223) (0.412) (3.189) (0.829) Sex 0.139 0.115 0.108 0.096 (0.148) (0.150) (0.149) (0.149) Age -0.009 -0.011 -0.017 -0.025 (0.155) (0.157) (0.156) (0.156) Marriage status -0.250 -0.265 -0.219 -0.276 (0.216) (0.219) (0.217) (0.219) Educational level (Reference: Low) Medium -0.453** -0.399* -0.467** -0.492** (0.218) (0.220) (0.218) (0.219) High -1.195*** -1.185*** -1.187*** -1.210*** (0.236) (0.239) (0.235) (0.237) Urban-Rural Residence -0.187 -0.205 -0.177 -0.214 (0.172) (0.174) (0.172) (0.173) Monthly personal income (Reference: ≤RMB3000) 2(RMB3000-6000) 0.318* 0.300 0.275 0.311 (0.191) (0.193) (0.192) (0.192) 3(≥ RMB6000) 0.222 0.257 0.196 0.246 (0.239) (0.242) (0.239) (0.240) Number of children 0.173 0.193 0.195 0.184 (0.165) (0.167) (0.164) (0.164) Self-rated health status 0.097 0.607 0.106 0.107 (0.169) (0.406) (0.170) (0.170) Physical health 1.264** 1.510** 7.524** 1.338** (0.608) (0.618) (2.983) (0.613) Mental health 0.677 0.778* 0.604 3.107*** (0.427) (0.435) (0.431) (1.032) Self-rated health status×Disability intervention -1.414*** (0.477) Self-rated health status×Dementia intervention 0.032 (0.469) Physical health×Disability intervention -6.459** (3.041) Physical health×Dementia intervention -7.447** (3.284) Mental health×Disability intervention -3.590*** (1.195) Mental health×Dementia intervention -2.281* (1.167) Constant -3.364*** -4.020*** -9.312*** -4.934*** (0.640) (0.718) (2.879) (0.891) Observations 1,025 1,025 1,025 1,025 Notes: Standard errors in parentheses;***significant at 1% level; ** significant at 5% level; * significant at 10% level Moderation effects Model 2–4 in Table 2 presents the moderation effects of self-reported health status, physical health condition and mental health condition on the effect of disabled and dementia information interventions on the LTCI intentions. Model 2 shows the moderation effect of the self-report health status was significant (β=-1.414, P < 0.01) only for the disability group. Model 3 shows the moderation effect of the physical health status, where older adults with poorer physical health were willing to LTCI insure after the disability (β=-6.459, P < 0.05) and dementia (β=-7.447, P < 0.05) scenario information intervention. The moderation effect of mental health status in Model 4 shows that older adults with poor mental health were willing to LTCI insure after the disability and dementia information interventions. The intervention effect of disability information (β=-3.590, P < 0.01) was greater than that of dementia group (β=-2.281, P < 0.1). Overall, respondents with poorer health tended to change their willingness to LTCI insure after receiving disability and dementia information interventions. Heterogeneity analysis The upper section of Table 3 shows the results of heterogeneity of different educational levels and LTCI pilot cities, and the lower section used simulated empirical evidence to test for differences in effects between the subgroups. For respondents with different levels of education and LTCI pilot cities, disability and dementia interventions both played a significant role in the change of their LTCI intentions. We found that the effects between each subgroup were different by testing the empirical p-values. For the difference between different educational levels, respondents with low education levels were the most sensitive to disability information, and respondents with high education levels were the least sensitive to dementia information. Further, respondents in non-pilot cities were more affected by both disability and dementia information than those in pilot cities. Table 3 Heterogeneity analysis results (1) (2) (3) (4) (5) VARIABLES Education_low Education_medium Education_high Non-pilot city Pilot city Regression estimates Disability intervention 2.731*** 1.375*** 1.663*** 3.233*** 1.123*** (0.424) (0.397) (0.387) (0.402) (0.316) Dementia intervention 3.085*** 2.582*** 1.529*** 3.567*** 1.391*** (0.379) (0.430) (0.387) (0.401) (0.300) Control variables Yes Yes Yes Yes Yes Constant -4.678*** -3.589*** -2.782*** -4.329*** -2.537* (1.311) (1.271) (1.039) (0.829) (1.480) Observations 300 269 456 586 439 Empirical p-values a (1) vs. (2) (1) vs. (3) (2) vs. (3) (4) vs. (5) Disability intervention 1.356** 1.069** -0.287 2.110*** Dementia intervention 0.504 1.557*** 1.053* 2.176*** Notes: Standard errors in parentheses; ***significant at 1% level; ** significant at 5% level; * significant at 10% level; a is the difference of coefficient between the subgroups, and the star represents the significance of the difference, which was obtained by Bootstrap sampling for 500 times. Discussion By conducting a randomized intervention experiment, we analyzed the effect of different types of information intervention on the change of the intention to enroll in LTCI among 50 to 70 year old Chinese adults. After the information intervention, more than a third of respondents changed their LTCI intentions from unwilling or uncertain to willing, including 40.50% in the disability group and 48.32% in the dementia group. Only 11.17% in the control group shifted to willing to LTCI insure. After the information intervention, 71.41% of older adults were willing to LTCI insure, much higher than the 40.39% before the experiment. These percentage are consistent with an intervention survey conducted in two pilot LTCI Chinese cities (Q. Chen & Ma, 2023 ) and previous international studies on LTCI, health insurance, and catastrophic insurance that found information interventions increased people’s willingness to enroll in insurance (Baicker et al., 2012 ; Ganderton et al., 2000 ; Zhou-Richter et al., 2010 ). Our results show that with awareness-raising measures, it is possible for government to increase the acceptance and expand the coverage of non-compulsory LTCI. In the context of sample characteristics, the respondents in our survey were between the ages of 50 and 70 with no cognitive impairment. It has been shown that there is an association between midlife behaviors and a range of later life outcomes, such as disability, dementia and frailty (Lafortune et al., 2016 ), which increase the likelihood of requiring LTCI needs in the near future. Compared to those with disability information, we found that the older aged had a greater probability of changing their intention to enroll in LTCI after dementia information. This might be due more stigma being attached to dementia than disability, and that people show anxiety about loss of self-identity and dignity related to dementia (Corner & Bond, 2004 ). We recommend that government LTCI information campaigns differentiate its message to disabled versus dementia audiences. In the analysis of moderation effects, the health status variables, comprising self-assessed health, physical health, and mental health, had significant negative moderating effects on the relationship between information interventions and the change of LTCI intentions, except for self-assessed health under the dementia information intervention. This implies that when providing participants with information about the risks of long-term care, especially for disability risks, participants with poorer self-rated health status were more affected. One reason is that older adults with poor self-reported health self-assess a high risk of future further decline than older adults with good health (Stuck et al., 1999 ), so they chose to LTCI insure. Information on dementia had a more significant effect on respondents with poor physical health, while information on disability had a more significant effect on respondents with poor mental health. Since the existing research has only focused on the direct impact of health conditions on LTCI demand (Brown et al., 2012 ; Kim et al., 2013 ; McGarry et al., 2014 ; Tennyson & Yang, 2014 ), our study extends the literature by differentiating between physical and mental health respondents’ willingness to LTCI insure. For policymakers, the cost-effectiveness of LTCI policy advocacy can be optimized by differentiating information about dementia-related risks and disability-related risks for individuals with self-reported poor physical health. We also examined the effect of information interventions on individuals with different levels of education and living in different LTCI pilot cities. The results show that respondents with less than 6 years of education were most affected by disability information, and respondents with more than 12 years of education were least affected by dementia information. Respondents with higher levels of education tend to have higher initial risk perceptions (Costa-Font & Costa-Font, 2011 ), and were less exposed to informational interventions. Subgroup regressions for pilot cities suggest that respondents from non-pilot cities with lower levels of initial risk perception were more sensitive to informational interventions. The lower sensitivity of higher education and non-pilot city respondents to information intervention compared to the other groups may reflect their higher perception of risk in old age and previous willingness to enroll in LTCI, with 60.22% of respondents before information intervention willing to enroll in LTCI were highly educated. Our dependent variable was the change in willingness to LTCI insure before and after the information intervention, so the extent of change in their willingness to insure may have been less pronounced than in the other groups. This suggests that the government should conduct policy advocacy by segmenting the target population, focusing on the less educated and non-piloted municipalities, to maximize the conversion rate to LTCI. There are several limitations. First, the questionnaire only has options related to the subjective intentions to measure the respondents’ willingness to insure LTCI, but the degree of risk perception was not directly measured. Although socioeconomic-demographic characteristics that may affect risk perception were controlled, future studies should include variables to measure the degree of risk overestimation or underestimation. Second, our informational intervention was completed through an offline face-to-face survey and only provided information about the risks of LTC through the scenarios. Recent studies have noted the effectiveness of information interventions, such as mass media and the Internet, in promoting health risk cognition (Beleigoli et al., 2019 ; Wang et al., 2023 ). Future research should explore the effectiveness of different types of information intervention measures and the content of information interventions in influencing people’s participation in health-related behaviors. Third, our study measured respondents’ stated preferences, which may not perfectly reflect their actual health behavior in a complex real world. Although relevant studies have demonstrated a positive correlation between people’s willingness to participate in insurance and their actual behavior (Giles et al., 2021 ), the results of this study should be interpreted with caution when generalizing. Conclusion Disability and dementia risk information interventions significantly changed respondents’ willingness to LTCI insure. After information interventions, the disability group’s willingness to LTCI insure increased from 43.07–79.35% of respondents and the dementia group from 35.54–85.54% of respondents. The control group only increased their willingness to insure from 42.37% in the first survey to 50.56% in the re-survey. Our moderation estimates showed the health status variables, comprising self-assessed health, physical health and mental health, had significant negative moderating effects on the relationship between information interventions and the change of LTCI intentions, except for self-assessed health under the dementia information intervention. For respondents with different knowledge levels, measured by levels of education and LTCI pilot cities, we found respondents with lower education and living in non-pilot cities were more sensitive to the information intervention. Government LTCI information campaigns can change people’s risk assessments, increasing the acceptance and expanding the coverage of non-compulsory LTCI. We recommend government LTCI information campaigns differentiate information by dementia-related risks and disability-related risks; by LTCI pilot and non-pilot cities; by education level; by individual initial risk perception levels; and by physical and mental health status. Method Data and sample To examine the relationship between disability and dementia information interventions and the change of LTCI intentions among older adults in China, we conducted a nationwide survey experiment in August 2022. Using a stratified random sampling method, we chose 8 provinces based on the high, medium and low level of GDP per capita in eastern, central and western China. Between one and three LTCI pilot and non-pilot cities were randomly selected in each province and 100 individuals were randomly recruited and interviewed face-to-face by trained interviewers in each city. The inclusion criteria were respondents aged from 50 to 70 and without cognitive impairments. A total of 1,172 questionnaires were distributed, of which 1,025 respondents answered all the options, with a response rate of 87.46%. Respondents were informed about the study, allowed to withdraw at any stage and gave informed consent. Ethical clearance for this study was obtained from the Ethics Committee of Center for Health Management and Policy Research in Shandong University (No. ECSHCMSDU20220901). Procedure The same questionnaire was administered twice. The before information intervention questionnaire ask participants to provide their basic information (sex, age, marital status, urban-rural residence, education level and number of children), self-reported mental and physical health and LTCI intentions. Participants were then randomly assigned to one of the three different scenarios: control group, disability scenario group and dementia scenario group. The after information intervention questionnaires were the same as the before information intervention questionnaire, but re-administered after the disability group was given disability information, the dementia group was given dementia information and the control group was not given any new information. The disability scenario group’s information intervention was: An 80-year-old person was able to eat, wash, go to the toilet or get dressed on his/her own. However, his/her bladder and bowel functions were weakening and he/ she gradually became incontinent. After a broken bone, he/ she has difficulty walking and currently requires a wheelchair, and is unable to bathe and go outdoors independently. Family members are unable to provide adequate care support for the older person due to their own health problems or work needs. The dementia scenario group’s information intervention was: An 80-year-old person is able to eat, wash, go to the bathroom, dress or walk on his/her own. However, his/her bladder and bowel functions were weakening and he/ she gradually became incontinent. With Alzheimer’s disease (which is a form of dementia), there are some symptoms of dementia, such as significant memory loss, forgetting whether or not they have eaten or taken their medication, forgetting to turn off the gas, and it is becoming increasingly difficult to control with drugs, and they are unable to bathe independently and go outdoors to be active. Family members are unable to provide adequate care support for the older person due to their own health problems or work needs. Measures The dependent variable was the change of LTCI intention, or the changes in the before information intervention and after information intervention responses to the question: “Would you like to enroll in long-term care insurance?”. The before and after information intervention question on LTCI choice had the same three answer options, “Yes”, “No”, and “Uncertain”. When respondents’ LTCI intentions changed to ‘Yes”, we assigned a value of 1; when the LTCI intentions were unchanged, changed to “No” or changed to “Uncertain”, we assigned a value of 0. Self-reported health status, physical health condition and mental health condition were used as moderating variables to explore the effect of disability and dementia information intervention on the change of LTCI intention under different health status scenarios. Self-rated overall health status was measured by the question, “How do you think your current health is compared to your peers?”. For responses “very good” or “good” the value was 1, the other values (fair, bad, and very bad) were assigned 0. Physical health and mental health were measured by the activity of daily living (ADL/IADL) scale and ICECAP-O scale, as shown in Table 4 and Table 5 . The number 1 to 4 represented the scores for each item, and we added up the scores of each item for each respondent. To eliminate the impact of dimensions, then we standardized them for final physical health and mental health scores of each respondent. As shown in Table 1 , the control variables comprised individual sociodemographic characteristics, measured by sex, age, marital status, urban—rural residence, education level and number of children. Table 4 Measurement of physical health status: Activities of daily living (ADL/IADL) scale Items Completely able to do Somewhat difficult to do Need help to do Completely cannot do Take public vehicles 4 3 2 1 Do housework 4 3 2 1 Feed yourself 4 3 2 1 Wash your clothes 4 3 2 1 Do the shopping 4 3 2 1 Talk on the phone 4 3 2 1 Brush your hair or teeth 4 3 2 1 Prepare a meal 4 3 2 1 Take medicine 4 3 2 1 Dress yourself 4 3 2 1 Wash your body 4 3 2 1 Get on and off the toilet 4 3 2 1 Handle own money 4 3 2 1 Table 5 Measurement of mental health status: ICECAP-O scale Dimension Content Degree 1 (for 4 scores) Degree 2 (for 3 scores) Degree 3 (for 2 scores) Degree 4 (for 1 scores) Attachment Love and friendship I can have all the love and friendship I want. I can have as much love and friendship as I want. I can only have a little of the love and friendship that I want. I can't have the love and friendship I want. Security Thinking about the future without concern I'll be thinking about the future with no worries. I'll think about the future with a few concerns. I'll think about the future with only a few concerns. I'll think about the future with a lot of concerns. Role Doing things that make you feel valued I am able to do all the things that make me feel valuable. I can do many things that make me feel valuable. I can do a little of what makes me feel valuable. I can't do anything that makes me feel valuable. Enjoyment Enjoyment and pleasure I can have all the enjoyment and pleasure I want. I can have as many pleasures as I want. I can have a few of the pleasures I want. I can't have any of the pleasures I want. Control Independence I can be completely independent. I can be independent of many things. I can be independent of some things. I'm not independent at all. Analytic strategy Given that the dichotomous LTCI outcome variable, we used a logit regression model. All data processing and analyses were performed with the use of STATA 16.0. Declarations Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgments The authors are grateful to research students in in Shandong University, Nanjing Medical University, Inner Mongolia Medical University and Guangxi Medical University for their assistance in collecting data. Funding This work was supported by the National Natural Science Foundation of China (No.72004117). Authors’ information These authors contributed equally: Jin Liu, Anli Leng. Authors and affiliations School of Political Science and Public Administration, Shandong University, Qingdao, China Jin Liu, Jiaozhi Hao, Anli Leng Center for Health Preferences Research, Shandong University, Jinan, China Anli Leng Smart State Governance Lab, Shandong University, Qingdao , China Anli Leng School of Management, University of Liverpool, Liverpool, England Elizabeth Maitland Newcastle Business School, University of Newcastle, Newcastle, Australia Stephen Nicholas Dong Fureng Institute of Economic and Social Development, Wuhan University, Beijing, China Jian Wang Center for Health Economics and Management at School of Economics and Management, Wuhan University, Wuhan, China Jian Wang Contributions JL: Conceptualization, Methodology, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. JH: Investigation, Validation, Writing-review & editing. EM: Writing - review & editing. SN: Writing - review & editing. JW : Conceptualization, Funding acquisition. AL: Conceptualization, Methodology, Resources, Supervision, Writing-review & editing. All authors reviewed the paper and approved its submission. Corresponding authors Correspondence to Anli Leng. *Anli Leng: [email protected] , Tel: +8613256690121 Ethics declarations Competing interests All authors declare that they have no conflicts of interest. References Baicker, K., Congdon, W. J., & Mullainathan, S. (2012). Health Insurance Coverage and Take-Up: Lessons from Behavioral Economics. The Milbank Quarterly , 90 (1), 107–134. https://doi.org/10.1111/j.1468-0009.2011.00656.x Beleigoli, A. M., Andrade, A. Q., Cançado, A. G., Paulo, M. N., Diniz, M. D. F. H., & Ribeiro, A. L. (2019). Web-Based Digital Health Interventions for Weight Loss and Lifestyle Habit Changes in Overweight and Obese Adults: Systematic Review and Meta-Analysis. Journal of Medical Internet Research , 21 (1), e298. https://doi.org/10.2196/jmir.9609 Boyer, M., Donder, P. 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Journal of Risk and Uncertainty , 20 (3), 271–289. https://doi.org/10.1023/A:1007871514658 Giles, J., Meng, X., Xue, S., & Zhao, G. (2021). Can information influence the social insurance participation decision of China’s rural migrants? Journal of Development Economics , 150 , 102645. https://doi.org/10.1016/j.jdeveco.2021.102645 Handel, B. R., Kolstad, J. T., & Spinnewijn, J. (2019). Information Frictions and Adverse Selection: Policy Interventions in Health Insurance Markets. The Review of Economics and Statistics , 101 (2), 326–340. https://doi.org/10.1162/rest_a_00773 He, A., & Chou, K. (2020). What Affects the Demand for Long-Term Care Insurance? A Study of Middle-Aged and Older Adults in Hong Kong. Journal of Applied Gerontology . https://doi.org/10.1177/0733464818766598 Information Office of the State Council. Information Office of the State Council briefing on "Implementing the Major Decisions and Deployments of the 20th CPC National Congress and Promoting the High-Quality Development of Health Insurance". Accessed Nov 26. https://www.gov.cn/lianbo/fabu/202305/content_6874791.htm Jia, L., Du, Y., Chu, L., Zhang, Z., Li, F., Lyu, D., Li, Y., Li, Y., Zhu, M., Jiao, H., Song, Y., Shi, Y., Zhang, H., Gong, M., Wei, C., Tang, Y., Fang, B., Guo, D., Wang, F., & Zhou, A. (2020). Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. The Lancet Public Health, 5(12), e661–e671. https://doi.org/10.1016/S2468-2667(20)30185-7 Jiang, W., & Yang, H. (2023). Health spillover studies of long-term care insurance in China: evidence from spousal caregivers from disabled families. International Journal for Equity in Health, 22(1), 191. Jones, C. J., Smith, H., & Llewellyn, C. (2014). Evaluating the effectiveness of health belief model interventions in improving adherence: A systematic review. Health Psychology Review , 8 (3), 253–269. https://doi.org/10.1080/17437199.2013.802623 Kim, H., Kwon, S., Yoon, N.-H., & Hyun, K.-R. (2013). Utilization of long-term care services under the public long-term care insurance program in Korea: Implications of a subsidy policy. Health Policy , 111 (2), 166–174. https://doi.org/10.1016/j.healthpol.2013.04.009 Lafortune, L., Martin, S., Kelly, S., Kuhn, I., Remes, O., Cowan, A., & Brayne, C. (2016). Behavioural Risk Factors in Mid-Life Associated with Successful Ageing, Disability, Dementia and Frailty in Later Life: A Rapid Systematic Review. PLOS ONE , 11 (2), e0144405. https://doi.org/10.1371/journal.pone.0144405 Lambregts, T. R., & Schut, F. T. (2020). Displaced, disliked and misunderstood: A systematic review of the reasons for low uptake of long-term care insurance and life annuities. The Journal of the Economics of Ageing , 17 , 100236. https://doi.org/10.1016/j.jeoa.2020.100236 Lassen, D. D. (2005). The Effect of Information on Voter Turnout: Evidence from a Natural Experiment. American Journal of Political Science , 49 (1), 103–118. https://doi.org/10.1111/j.0092-5853.2005.00113.x Lei, X., Bai, C., Hong, J., & Liu, H. (2022). Long-term care insurance and the well-being of older adults and their families: Evidence from China. Social Science & Medicine , 296 , 114745. https://doi.org/10.1016/j.socscimed.2022.114745 Li, F., Qin, W., Zhu, M., & Jia, J. (2021). Model-Based Projection of Dementia Prevalence in China and Worldwide: 2020–2050. Journal of Alzheimer’s Disease, 82(4), 1823–1831. https://doi.org/10.3233/jad-210493 Luo, Y., Su, B., & Zheng, X. (2021). Trends and Challenges for Population and Health During Population Aging—China, 2015–2050. China CDC Weekly , 3 (28), 593–598. https://doi.org/10.46234/ccdcw2021.158 McGarry, B. E., Temkin-Greener, H., & Li, Y. (2014). Role of Race and Ethnicity in Private Long-Term Care Insurance Ownership. The Gerontologist , 54 (6), 1001–1012. https://doi.org/10.1093/geront/gnt102 National Healthcare Security Administration. (2021). Reply of the National Healthcare Security Administration to Recommendation No. 5096 of the Fourth Session of the 13th National People’s Congress . http://www.nhsa.gov.cn/art/2021/9/14/art_110_7066.html Peng, X. (2011). China’s Demographic History and Future Challenges. Science , 333 (6042), 581–587. https://doi.org/10.1126/science.1209396 Pförtner, T.-K., & Hower, K. I. (2022). Educational inequalities in risk perception, perceived effectiveness, trust and preventive behaviour in the onset of the COVID-19 pandemic in Germany. Public Health , 206 , 83. https://doi.org/10.1016/j.puhe.2022.02.021 Sloan, F. A., & Norton, E. C. (1997). Adverse Selection, Bequests, Crowding Out, and Private Demand for Insurance: Evidence from the Long-term Care Insurance Market. Journal of Risk and Uncertainty , 15 (3), 201–219. https://doi.org/10.1023/A:1007749008635 Stuart, B., Loh, F. E., Roberto, P., & Miller, L. M. (2013). Increasing Medicare Part D Enrollment In Medication Therapy Management Could Improve Health And Lower Costs. Health Affairs , 32 (7), 1212–1220. https://doi.org/10.1377/hlthaff.2012.0848 Stuck, A. E., Walthert, J. M., Nikolaus, T., Büla, C. J., Hohmann, C., & Beck, J. C. (1999). Risk factors for functional status decline in community-living elderly people: A systematic literature review. Social Science & Medicine , 48 (4), 445–469. https://doi.org/10.1016/S0277-9536(98)00370-0 Tennyson, S., & Yang, H. K. (2014). The role of life experience in long-term care insurance decisions. Journal of Economic Psychology , 42 , 175–188. https://doi.org/10.1016/j.joep.2014.04.002 The National Bureau of Statistics. (2021). Bulletin of the seventh National Census . Central Government of the People’s Republic of China. https://www.gov.cn/guoqing/2021-05/13/content_5606149.htm Ugarte Montero, A., & Wagner, J. (2023). On potential information asymmetries in long-term care insurance: A simulation study using data from Switzerland. Insurance: Mathematics and Economics , 111 , 230–241. https://doi.org/10.1016/j.insmatheco.2023.04.003 United Nations. (2023). World population prospects. UN; United Nations. https://population.un.org/wpp/ Wang, X., Zhu, B., Guo, Q., Wang, W., & Zhao, R. (2023). Designing mindfulness information for interaction in social media: The role of information framing, health risk perception and lay theories of health. Frontiers in Psychology , 13 . https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1041016 White, J. S., & Dow, W. H. (2015). Intertemporal Choices for Health. In C. A. Roberto & I. Kawachi (Eds.), Behavioral Economics and Public Health (pp. 27–68). Oxford University Press. https://doi.org/10.1093/med/9780199398331.003.0002 Xinhua News Agency. By 2050, the elderly will account for about one-third of total Chinese population. Accessed Nov 26, 2023. https://www.gov.cn/xinwen/2018-07/19/content_5307839.htm Zhou-Richter, T., Browne, M. J., & Gründl, H. (2010). Don’t They Care? Or, Are They Just Unaware? Risk Perception and the Demand for Long-Term Care Insurance. Journal of Risk and Insurance , 77 (4), 715–747. https://doi.org/10.1111/j.1539-6975.2010.01362.x Additional Declarations There is NO Competing Interest. 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. 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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-3812000","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":270311092,"identity":"9b51a99f-1b45-4ff5-bfff-24290459f8c3","order_by":0,"name":"Anli Leng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACPghlwcPPwGAAZDAT1sIGoSR4JBtI1cJgcIBoLey9h1/83CEhY3z+8DYJhgrrxAb2swfwa+E5l2bZe0aCx+xGWpkEw5n0xAaevAT8WiRyzAx420BaeMwkGNsOJzZI8Bjg1yL/xszwL1CLcf8ZoJZ/xGgBKn4MssWAIQeopYEYLTw5ZsyyQC0SN9KKLRKOpRu38eTg18LPfsb449s2G3v+/sMbb3yosZbtZz+DXwvYbXBmAgM8pvAC5g9EKBoFo2AUjIKRDABzSDmPFGnkIQAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Anli","middleName":"","lastName":"Leng","suffix":""},{"id":270311093,"identity":"f8b6fa94-15ef-4166-b182-fe74cd252c3e","order_by":1,"name":"Jin Liu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Liu","suffix":""},{"id":270311094,"identity":"16fd5b66-1ecd-415b-9fbb-4366c7fc4312","order_by":2,"name":"Jiaozhi Hao","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jiaozhi","middleName":"","lastName":"Hao","suffix":""},{"id":270311095,"identity":"480a5ce2-b22a-478e-b2c9-5ac233fa1a00","order_by":3,"name":"Elizabeth Maitland","email":"","orcid":"","institution":"[email protected]","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Maitland","suffix":""},{"id":270311096,"identity":"448ec6a2-fcc8-4834-99ff-58d03651d8f6","order_by":4,"name":"Stephen Nicholas","email":"","orcid":"","institution":"University of Newcastle","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Nicholas","suffix":""},{"id":270311097,"identity":"6d8cab46-50e0-428e-83a5-c029f7aa7b24","order_by":5,"name":"Jian Wang","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-12-27 11:30:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3812000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3812000/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50783167,"identity":"cfcf7e59-6091-4dfe-a687-bca9a403817d","added_by":"auto","created_at":"2024-02-07 08:44:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23248,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of LTCI willingness rates before and after the information intervention\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3812000/v1/12abc2ee28ae2f1d707cb4f6.png"},{"id":50783850,"identity":"46da694a-21be-440b-9fd5-1516bb1e9621","added_by":"auto","created_at":"2024-02-07 08:52:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":594533,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3812000/v1/187e10c5-db93-4a5a-a286-e15a0a14b08d.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Shaping Long-term Care Insurance Intentions among Chinese Older Adults: Role of Information Interventions in Health Risks","fulltext":[{"header":"Main","content":"\u003cp\u003eWith the world\u0026rsquo;s fastest aging population, China\u0026rsquo;s increasing life expectancy and declining birth rates challenges the provision of healthcare (Peng, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In 2020, there were 264\u0026nbsp;million people aged 60 years old and over, accounting for 18.7% of the Chinese population, forecast to rise to more than 500\u0026nbsp;million by 2050 (The National Bureau of Statistics, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; United Nations, 2022). Within the aging population, 52.7\u0026nbsp;million suffered disabilities, estimated to reach 82.7\u0026nbsp;million by 2030 and 89.6\u0026nbsp;million by 2050. (Luo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The number of over 60-year-old Chinese suffering dementia was 15.07\u0026nbsp;million in 2018, and is expected to rise to 50\u0026nbsp;million by 2050 (Jia et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Older adults with disabilities and dementia pose a serious challenge to the sustainability of China\u0026rsquo;s long-term care (LTC) system (Feng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lei et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With the reduction in the size of the Chinese family, the effects of the one-child policy, urban migration and the decline in informal family provided LTC, the demand for formal LTC has surged (Feng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To address this challenge, the Chinese government launched in 2016 a long-term care insurance (LTCI) scheme in 15 pilot cities, expanding to 49 cities by 2020, to provide the old aged with disabilities and dementia accessible and affordable long-term care services (National Healthcare Security Administration, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). LTCI aimed to alleviate the shortage of caregivers for severely disabled patients by providing nursing service payments in public care facilities and also some support for home-based care services (Jiang \u0026amp; Yang, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). China\u0026rsquo;s National Medical Insurance Administration aims to build a nation-wide and unified LTCI system independent from China\u0026rsquo;s basic pension and medical social insurance system (Information Office of the State Council, 2023). How to promote the expansion of China\u0026rsquo;s LTCI is an urgent challenge for China.\u003c/p\u003e \u003cp\u003eNon-compulsory LTCI markets are widespread, with people's LTCI purchasing decisions analyzed from the economic, behavioral, and psychosocial perspectives (Boyer et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Brown et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Curry et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; He \u0026amp; Chou, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sloan \u0026amp; Norton, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Behavioral economics provides a powerful framework for understanding the purchase decision behavior for LTCI by focusing on individual risk perceptions in intertemporal health decision-making choices (White \u0026amp; Dow, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). An individual\u0026rsquo;s risk perception, derived from objective knowledge of LTC risks, subjective knowledge of LTC risks and subjective knowledge of one\u0026rsquo;s own health, has been shown to shape LTCI needs (Costa-Font \u0026amp; Rovira-Forns, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lambregts \u0026amp; Schut, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ugarte Montero \u0026amp; Wagner, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cognitive biases or overconfidence means individuals\u0026rsquo; health cognition may deviate from rational expectations, resulting in a misunderstanding of LTCI risks affecting decision-making (Baicker et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Y.-P. Chen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Individual LTCI perceptions biases reflect physical, mental, financial and other factors associated with future projection of disability or dementia in old age (Finkelstein \u0026amp; McGarry, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Tennyson \u0026amp; Yang, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Motivated by self-protection, the probability of older adults purchasing LTCI increases with their knowledge of the risks associated with their future likely LTC needs (Zhou-Richter et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These studies suggest that expectations of LTC risk are positively correlated with changes in LTCI demand, and thus it is possible to promote older adults\u0026rsquo; participation in LTCI by increasing their risk perception.\u003c/p\u003e \u003cp\u003eIn healthcare research, these behavioral hypotheses have been widely applied to insurance enrollment, vaccination decisions and medication use to facilitate the public's health-favorable decision-making (Capuno et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Handel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stuart et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Surprisingly, there is currently a lack of research on the role of information interventions in shaping the LTC insurance risk perceptions of older Chinese adults with disabilities and dementia. To address this research gap, we designed a scenario experiment to explore whether older Chinese adults with information about the adverse health outcomes of disability or dementia would change their willingness to buy LTCI. People\u0026rsquo; evaluation of their own health status, their knowledge of the consequences of illness and their understanding of their health behaviors will impact their perception of future health risks (Jones et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), so we also investigate the moderating effect of health status on the impact of information interventions on LTCI decisions. Since our study is concerned with the effect of information interventions on the changes of older adults\u0026rsquo; LTC risk perceptions and LTCI intentions, the results are expected to vary depending on the older adults\u0026rsquo; own risk perceptions. Participants\u0026rsquo; education background and whether they reside in a LTCI pilot city proxy respondents\u0026rsquo; knowledge about LTCI (Lassen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pf\u0026ouml;rtner \u0026amp; Hower, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our paper also conducted a heterogeneity analysis to explore the intervention effects of disability and dementia information for subgroups with different education levels and pilot city conditions.\u003c/p\u003e \u003cp\u003eOur study makes contributions in the following three aspects. From the perspective of risk perception, we explore whether increasing people's understanding of the risk of disability or dementia could change their willingness to participate in LTCI. This not only enriches the research on information intervention on health promotion, but also provides empirical reference for the Chinese government to improve the LTCI coverage rate of residents. Second, we examine the moderating role of an individual's health status and LTCI knowledge on LTCI decision-making, providing evidence for the implementation of individualized and effective interventions. Finally, our nationally representative survey experiment provides to government evidence on LTCI willingness in China, helping improve China\u0026rsquo;s \u0026ldquo;person-centered\u0026rdquo; LTC system.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRespondent characteristic\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the respondents\u0026rsquo; socioeconomic demographic characteristics. Out of the 1,025 respondents, 466 (45.46%) were male, 572 (55.80%) were between the ages of 50 and 59; 456 (44.49%) had a high education level; most had a spouse (85.76%); were urban residents (62.63%), earned less than RMB6000 a month (76.39%) and had 2 or more children (60.49%). In terms of the health status, 689 (67.22%) respondents rated their own health status as good. After 0\u0026ndash;1 standardization, the average physical health status was a high 0.94 level and mental health status was a moderate 0.67 level. The last column of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the ANOVA results. There were significant inter-group differences in age, educational level, monthly personal income, number of children, physical health and mental health. To control the influence of these variables on the intervention results, they were added to the statistical model as control variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristic of respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1025)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;354)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDisabled group (n\u0026thinsp;=\u0026thinsp;339)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eDementia group (n\u0026thinsp;=\u0026thinsp;332)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e53.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e57.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave a spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e86.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e85.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e34.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e65.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow(\u0026le;\u0026thinsp;6 yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium(6 to \u0026le;\u0026thinsp;9 yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;9 yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e50.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly personal income (RMB)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (\u0026le;\u0026thinsp;3000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (3000\u0026ndash;6000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e36.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 (\u0026gt;\u0026thinsp;6000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e54.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-rated health status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e64.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical health (mean, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMental health(mean, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eWillingness to LTCI insure\u003c/h2\u003e \u003cp\u003eFigure 1 presents the percentages of respondents willing to LTCI insure in the control group, disability group and dementia group before and after the information intervention. Before the information intervention, the proportion of respondents who were willing, unwilling and uncertain about willingness to LTCI was roughly a 4:1:5 ratio, which changed to 7:2:1 ratio after the information intervention. For the control group, the rate of respondents who were not willing to insure LTCI increased from 10.45% to 30.23%; those uncertain decreased from 47.18% to 19.21%; and the willing to insure increased from 42.37% to 50.56%. For the disability group (43.07% to 79.35%) and dementia group (35.54% to 85.54%), the percentage of respondents who were willing to insure LTCI increased substantially. We also analyzed the willingness to LTCI insure before and after the information interventions by ANOVA. The results showed that before the information interventions, there was no significant inter-group difference in the willingness to LTCI insure in the control, disability and dementia groups (F=1.59, P=0.204). However, the differences became statistically significant after the interventions (F=39.45, P\u0026lt;0.001), indicating that the information interventions impacted LTCI decisions.\u0026nbsp;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMain results of logit regression\u003c/h2\u003e \u003cp\u003eModel 1 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the logit regression results of the disability and dementia information interventions on changes in LTCI willingness among older adults. Compared with the control group, both disabled and dementia information intervention group had a significant positive impact on respondents\u0026rsquo; LTCI intentions. After the information intervention, respondents in the dementia group (β\u0026thinsp;=\u0026thinsp;2.409, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were more willing to change their LTCI intentions than the disability group (β\u0026thinsp;=\u0026thinsp;2.126, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Sex, age, marriage status, residence status, number of children, self-rated health and mental health had no significant impact on the willingness to change the LTCI insure intention, but education, personal income and physical health significantly influenced the change of LTCI willingness. The more educated the respondents, the less likely they were to change their LTCI insurance intentions. Compared to personal income less than RMB3000, respondents with a middle income level were more willing to change their LTCI intentions. Older adults with better physical health status were more likely to increase their willingness to LTCI insure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogit model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.126***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.481***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.835)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.409***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.412***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.557***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.867***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.189)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.829)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.149)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.219)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level (Reference: Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.453**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.399*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.467**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.492**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.219)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.195***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.185***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.210***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.237)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban-Rural Residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMonthly personal income (Reference: \u0026le;RMB3000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2(RMB3000-6000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.318*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.192)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3(\u0026ge;\u0026thinsp;RMB6000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.240)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.164)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.170)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.264**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.510**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.524**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.338**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.608)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.613)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.778*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.107***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.032)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSelf-rated health status\u0026times;Disability intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.414***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSelf-rated health status\u0026times;Dementia intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.469)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhysical health\u0026times;Disability intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.459**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhysical health\u0026times;Dementia intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.447**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMental health\u0026times;Disability intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.590***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.195)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMental health\u0026times;Dementia intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.281*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.167)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.364***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.020***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.312***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.934***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.718)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.891)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Standard errors in parentheses;***significant at 1% level; ** significant at 5% level; * significant at 10% level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eModeration effects\u003c/h2\u003e \u003cp\u003eModel 2\u0026ndash;4 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the moderation effects of self-reported health status, physical health condition and mental health condition on the effect of disabled and dementia information interventions on the LTCI intentions. Model 2 shows the moderation effect of the self-report health status was significant (β=-1.414, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) only for the disability group. Model 3 shows the moderation effect of the physical health status, where older adults with poorer physical health were willing to LTCI insure after the disability (β=-6.459, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and dementia (β=-7.447, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) scenario information intervention. The moderation effect of mental health status in Model 4 shows that older adults with poor mental health were willing to LTCI insure after the disability and dementia information interventions. The intervention effect of disability information (β=-3.590, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was greater than that of dementia group (β=-2.281, P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Overall, respondents with poorer health tended to change their willingness to LTCI insure after receiving disability and dementia information interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity analysis\u003c/h2\u003e \u003cp\u003eThe upper section of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of heterogeneity of different educational levels and LTCI pilot cities, and the lower section used simulated empirical evidence to test for differences in effects between the subgroups. For respondents with different levels of education and LTCI pilot cities, disability and dementia interventions both played a significant role in the change of their LTCI intentions. We found that the effects between each subgroup were different by testing the empirical p-values. For the difference between different educational levels, respondents with low education levels were the most sensitive to disability information, and respondents with high education levels were the least sensitive to dementia information. Further, respondents in non-pilot cities were more affected by both disability and dementia information than those in pilot cities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation_low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation_medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEducation_high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-pilot city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePilot city\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegression estimates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.731***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.375***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.663***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.123***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.316)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.085***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.582***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.529***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.567***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.391***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.379)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.678***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.589***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.782***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.329***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.537*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.829)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.480)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpirical p-values \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1) vs. (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1) vs. (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2) vs. (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4) vs. (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.356**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.069**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.110***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.557***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.053*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: Standard errors in parentheses; ***significant at 1% level; ** significant at 5% level; * significant at 10% level; a is the difference of coefficient between the subgroups, and the star represents the significance of the difference, which was obtained by Bootstrap sampling for 500 times.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy conducting a randomized intervention experiment, we analyzed the effect of different types of information intervention on the change of the intention to enroll in LTCI among 50 to 70 year old Chinese adults. After the information intervention, more than a third of respondents changed their LTCI intentions from unwilling or uncertain to willing, including 40.50% in the disability group and 48.32% in the dementia group. Only 11.17% in the control group shifted to willing to LTCI insure. After the information intervention, 71.41% of older adults were willing to LTCI insure, much higher than the 40.39% before the experiment. These percentage are consistent with an intervention survey conducted in two pilot LTCI Chinese cities (Q. Chen \u0026amp; Ma, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and previous international studies on LTCI, health insurance, and catastrophic insurance that found information interventions increased people\u0026rsquo;s willingness to enroll in insurance (Baicker et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ganderton et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Zhou-Richter et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Our results show that with awareness-raising measures, it is possible for government to increase the acceptance and expand the coverage of non-compulsory LTCI.\u003c/p\u003e \u003cp\u003eIn the context of sample characteristics, the respondents in our survey were between the ages of 50 and 70 with no cognitive impairment. It has been shown that there is an association between midlife behaviors and a range of later life outcomes, such as disability, dementia and frailty (Lafortune et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which increase the likelihood of requiring LTCI needs in the near future. Compared to those with disability information, we found that the older aged had a greater probability of changing their intention to enroll in LTCI after dementia information. This might be due more stigma being attached to dementia than disability, and that people show anxiety about loss of self-identity and dignity related to dementia (Corner \u0026amp; Bond, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). We recommend that government LTCI information campaigns differentiate its message to disabled versus dementia audiences.\u003c/p\u003e \u003cp\u003eIn the analysis of moderation effects, the health status variables, comprising self-assessed health, physical health, and mental health, had significant negative moderating effects on the relationship between information interventions and the change of LTCI intentions, except for self-assessed health under the dementia information intervention. This implies that when providing participants with information about the risks of long-term care, especially for disability risks, participants with poorer self-rated health status were more affected. One reason is that older adults with poor self-reported health self-assess a high risk of future further decline than older adults with good health (Stuck et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), so they chose to LTCI insure. Information on dementia had a more significant effect on respondents with poor physical health, while information on disability had a more significant effect on respondents with poor mental health. Since the existing research has only focused on the direct impact of health conditions on LTCI demand (Brown et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; McGarry et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tennyson \u0026amp; Yang, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), our study extends the literature by differentiating between physical and mental health respondents\u0026rsquo; willingness to LTCI insure. For policymakers, the cost-effectiveness of LTCI policy advocacy can be optimized by differentiating information about dementia-related risks and disability-related risks for individuals with self-reported poor physical health.\u003c/p\u003e \u003cp\u003eWe also examined the effect of information interventions on individuals with different levels of education and living in different LTCI pilot cities. The results show that respondents with less than 6 years of education were most affected by disability information, and respondents with more than 12 years of education were least affected by dementia information. Respondents with higher levels of education tend to have higher initial risk perceptions (Costa-Font \u0026amp; Costa-Font, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and were less exposed to informational interventions. Subgroup regressions for pilot cities suggest that respondents from non-pilot cities with lower levels of initial risk perception were more sensitive to informational interventions. The lower sensitivity of higher education and non-pilot city respondents to information intervention compared to the other groups may reflect their higher perception of risk in old age and previous willingness to enroll in LTCI, with 60.22% of respondents before information intervention willing to enroll in LTCI were highly educated. Our dependent variable was the change in willingness to LTCI insure before and after the information intervention, so the extent of change in their willingness to insure may have been less pronounced than in the other groups. This suggests that the government should conduct policy advocacy by segmenting the target population, focusing on the less educated and non-piloted municipalities, to maximize the conversion rate to LTCI.\u003c/p\u003e \u003cp\u003eThere are several limitations. First, the questionnaire only has options related to the subjective intentions to measure the respondents\u0026rsquo; willingness to insure LTCI, but the degree of risk perception was not directly measured. Although socioeconomic-demographic characteristics that may affect risk perception were controlled, future studies should include variables to measure the degree of risk overestimation or underestimation. Second, our informational intervention was completed through an offline face-to-face survey and only provided information about the risks of LTC through the scenarios. Recent studies have noted the effectiveness of information interventions, such as mass media and the Internet, in promoting health risk cognition (Beleigoli et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future research should explore the effectiveness of different types of information intervention measures and the content of information interventions in influencing people\u0026rsquo;s participation in health-related behaviors. Third, our study measured respondents\u0026rsquo; stated preferences, which may not perfectly reflect their actual health behavior in a complex real world. Although relevant studies have demonstrated a positive correlation between people\u0026rsquo;s willingness to participate in insurance and their actual behavior (Giles et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the results of this study should be interpreted with caution when generalizing.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDisability and dementia risk information interventions significantly changed respondents\u0026rsquo; willingness to LTCI insure. After information interventions, the disability group\u0026rsquo;s willingness to LTCI insure increased from 43.07\u0026ndash;79.35% of respondents and the dementia group from 35.54\u0026ndash;85.54% of respondents. The control group only increased their willingness to insure from 42.37% in the first survey to 50.56% in the re-survey. Our moderation estimates showed the health status variables, comprising self-assessed health, physical health and mental health, had significant negative moderating effects on the relationship between information interventions and the change of LTCI intentions, except for self-assessed health under the dementia information intervention. For respondents with different knowledge levels, measured by levels of education and LTCI pilot cities, we found respondents with lower education and living in non-pilot cities were more sensitive to the information intervention. Government LTCI information campaigns can change people\u0026rsquo;s risk assessments, increasing the acceptance and expanding the coverage of non-compulsory LTCI. We recommend government LTCI information campaigns differentiate information by dementia-related risks and disability-related risks; by LTCI pilot and non-pilot cities; by education level; by individual initial risk perception levels; and by physical and mental health status.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData and sample\u003c/h2\u003e \u003cp\u003eTo examine the relationship between disability and dementia information interventions and the change of LTCI intentions among older adults in China, we conducted a nationwide survey experiment in August 2022. Using a stratified random sampling method, we chose 8 provinces based on the high, medium and low level of GDP per capita in eastern, central and western China. Between one and three LTCI pilot and non-pilot cities were randomly selected in each province and 100 individuals were randomly recruited and interviewed face-to-face by trained interviewers in each city. The inclusion criteria were respondents aged from 50 to 70 and without cognitive impairments. A total of 1,172 questionnaires were distributed, of which 1,025 respondents answered all the options, with a response rate of 87.46%. Respondents were informed about the study, allowed to withdraw at any stage and gave informed consent. Ethical clearance for this study was obtained from the Ethics Committee of Center for Health Management and Policy Research in Shandong University (No. ECSHCMSDU20220901).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eThe same questionnaire was administered twice. The before information intervention questionnaire ask participants to provide their basic information (sex, age, marital status, urban-rural residence, education level and number of children), self-reported mental and physical health and LTCI intentions. Participants were then randomly assigned to one of the three different scenarios: control group, disability scenario group and dementia scenario group. The after information intervention questionnaires were the same as the before information intervention questionnaire, but re-administered after the disability group was given disability information, the dementia group was given dementia information and the control group was not given any new information. The disability scenario group\u0026rsquo;s information intervention was:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAn 80-year-old person was able to eat, wash, go to the toilet or get dressed on his/her own. However, his/her bladder and bowel functions were weakening and he/ she gradually became incontinent. After a broken bone, he/ she has difficulty walking and currently requires a wheelchair, and is unable to bathe and go outdoors independently. Family members are unable to provide adequate care support for the older person due to their own health problems or work needs.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe dementia scenario group\u0026rsquo;s information intervention was:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAn 80-year-old person is able to eat, wash, go to the bathroom, dress or walk on his/her own. However, his/her bladder and bowel functions were weakening and he/ she gradually became incontinent. With Alzheimer\u0026rsquo;s disease (which is a form of dementia), there are some symptoms of dementia, such as significant memory loss, forgetting whether or not they have eaten or taken their medication, forgetting to turn off the gas, and it is becoming increasingly difficult to control with drugs, and they are unable to bathe independently and go outdoors to be active. Family members are unable to provide adequate care support for the older person due to their own health problems or work needs.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eThe dependent variable was the change of LTCI intention, or the changes in the before information intervention and after information intervention responses to the question: \u0026ldquo;Would you like to enroll in long-term care insurance?\u0026rdquo;. The before and after information intervention question on LTCI choice had the same three answer options, \u0026ldquo;Yes\u0026rdquo;, \u0026ldquo;No\u0026rdquo;, and \u0026ldquo;Uncertain\u0026rdquo;. When respondents\u0026rsquo; LTCI intentions changed to \u0026lsquo;Yes\u0026rdquo;, we assigned a value of 1; when the LTCI intentions were unchanged, changed to \u0026ldquo;No\u0026rdquo; or changed to \u0026ldquo;Uncertain\u0026rdquo;, we assigned a value of 0.\u003c/p\u003e \u003cp\u003eSelf-reported health status, physical health condition and mental health condition were used as moderating variables to explore the effect of disability and dementia information intervention on the change of LTCI intention under different health status scenarios. Self-rated overall health status was measured by the question, \u0026ldquo;How do you think your current health is compared to your peers?\u0026rdquo;. For responses \u0026ldquo;very good\u0026rdquo; or \u0026ldquo;good\u0026rdquo; the value was 1, the other values (fair, bad, and very bad) were assigned 0. Physical health and mental health were measured by the activity of daily living (ADL/IADL) scale and ICECAP-O scale, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The number 1 to 4 represented the scores for each item, and we added up the scores of each item for each respondent. To eliminate the impact of dimensions, then we standardized them for final physical health and mental health scores of each respondent.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the control variables comprised individual sociodemographic characteristics, measured by sex, age, marital status, urban\u0026mdash;rural residence, education level and number of children.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement of physical health status: Activities of daily living (ADL/IADL) scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompletely able to do\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSomewhat difficult to do\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeed help to do\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCompletely cannot do\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTake public vehicles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo housework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeed yourself\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWash your clothes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDo the shopping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTalk on the phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrush your hair or teeth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrepare a meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTake medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDress yourself\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWash your body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGet on and off the toilet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandle own money\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement of mental health status: ICECAP-O scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree 1\u003c/p\u003e \u003cp\u003e(for 4 scores)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDegree 2\u003c/p\u003e \u003cp\u003e(for 3 scores)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDegree 3\u003c/p\u003e \u003cp\u003e(for 2 scores)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" colspan=\"2\"\u003e \u003cp\u003eDegree 4\u003c/p\u003e \u003cp\u003e(for 1 scores)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttachment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLove and friendship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI can have all the love and friendship I want.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI can have as much love and friendship as I want.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eI can only have a little of the love and friendship that I want.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eI can't have the love and friendship I want.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThinking about the future without concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI'll be thinking about the future with no worries.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI'll think about the future with a few concerns.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eI'll think about the future with only a few concerns.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eI'll think about the future with a lot of concerns.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoing things that make you feel valued\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI am able to do all the things that make me feel valuable.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI can do many things that make me feel valuable.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eI can do a little of what makes me feel valuable.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eI can't do anything that makes me feel valuable.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnjoyment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnjoyment and pleasure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI can have all the enjoyment and pleasure I want.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI can have as many pleasures as I want.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eI can have a few of the pleasures I want.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eI can't have any of the pleasures I want.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI can be completely independent.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI can be independent of many things.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eI can be independent of some things.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eI'm not independent at all.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalytic strategy\u003c/h2\u003e \u003cp\u003eGiven that the dichotomous LTCI outcome variable, we used a logit regression model. All data processing and analyses were performed with the use of STATA 16.0.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to research students in in Shandong University, Nanjing Medical University, Inner Mongolia Medical University and Guangxi Medical University for their assistance in collecting data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China\u0026nbsp;(No.72004117).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese authors contributed equally: Jin Liu, Anli Leng.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Political Science and Public Administration, Shandong University, Qingdao, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJin Liu, Jiaozhi Hao, Anli Leng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCenter for Health Preferences Research, Shandong University, Jinan, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnli Leng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmart State Governance Lab, Shandong University, Qingdao\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnli Leng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Management, University of Liverpool, Liverpool, England\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElizabeth Maitland\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNewcastle Business School, University of Newcastle, Newcastle, Australia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStephen Nicholas\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDong Fureng Institute of Economic and Social Development, Wuhan University, Beijing, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJian Wang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCenter for Health Economics and Management at School of Economics and Management, Wuhan University, Wuhan, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJian Wang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJL:\u003c/strong\u003e Conceptualization, Methodology, Data curation, Formal analysis, Writing - original draft, Writing - review \u0026amp; editing. \u003cstrong\u003eJH:\u003c/strong\u003e Investigation, Validation, Writing-review \u0026amp; editing. \u003cstrong\u003eEM:\u003c/strong\u003e Writing - review \u0026amp; editing. \u003cstrong\u003eSN:\u003c/strong\u003e Writing - review \u0026amp; editing. \u003cstrong\u003eJW\u003c/strong\u003e: Conceptualization, Funding acquisition. \u003cstrong\u003eAL:\u003c/strong\u003e Conceptualization, Methodology, Resources, Supervision, Writing-review \u0026amp; editing. All authors reviewed the paper and approved its submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Anli Leng.\u003c/p\u003e\n\u003cp\u003e*Anli Leng: [email protected], Tel: +8613256690121\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaicker, K., Congdon, W. J., \u0026amp; Mullainathan, S. (2012). Health Insurance Coverage and Take-Up: Lessons from Behavioral Economics. \u003cem\u003eThe Milbank Quarterly\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(1), 107\u0026ndash;134. https://doi.org/10.1111/j.1468-0009.2011.00656.x\u003c/li\u003e\n\u003cli\u003eBeleigoli, A. M., Andrade, A. 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Risk Perception and the Demand for Long-Term Care Insurance. \u003cem\u003eJournal of Risk and Insurance\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(4), 715\u0026ndash;747. https://doi.org/10.1111/j.1539-6975.2010.01362.x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"long-term care insurance, Chinese older adults, information intervention, disability, dementia","lastPublishedDoi":"10.21203/rs.3.rs-3812000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3812000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLong-term care insurance (LTCI) is an essential system in the context of fast-growing population aging, yet it lacks evidence on how to improve the acceptance and coverage of non-compulsory LTCI in China. Using a survey experiment, we randomly assigned 1025 older adults to control group, disability information group and dementia information group, and explored whether information about the adverse health outcomes would change their willingness to LTCI insure. We found that disability and dementia information significantly changed respondents\u0026rsquo; willingness to LTCI insure, and health status had negative moderating effects on the relationship between information interventions and the change of LTCI intentions. Also, we found respondents with lower education and living in non-pilot cities were more sensitive to information interventions. We recommend LTCI information campaigns differentiating information by dementia-related and disability-related risks; by LTCI pilot and non-pilot cities; by education levels and by physical and mental health status.\u003c/p\u003e","manuscriptTitle":"Shaping Long-term Care Insurance Intentions among Chinese Older Adults: Role of Information Interventions in Health Risks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-07 08:44:49","doi":"10.21203/rs.3.rs-3812000/v1","editorialEvents":[],"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":"fd1f123e-4cd4-4302-8ee5-dba1e16160ee","owner":[],"postedDate":"February 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28492782,"name":"Scientific community and society/Social sciences"},{"id":28492783,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2024-04-09T12:51:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-07 08:44:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3812000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3812000","identity":"rs-3812000","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}