The impact of urbanization on the place of death of older adults in China from an interprovincial perspective

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This study aimed to analyse the distribution and changes in the place of death of elderly individuals in China from an interprovincial perspective and its intrinsic association with rapid urbanization. Methods A hierarchical logistic model was constructed to carry out the analysis, using a combination of micro data from the China Health Influence Tracking Survey on the Elderly (CLHLS) 2011, 2014, and 2018 death samples and macro data at the provincial level from the China Statistical Yearbook. Results From 2011–2018, 95.04% of older Chinese adults died at home in rural areas, while 81.53% in urban areas. The overall hospital dying ratio of older adults first increased and then decreased, with the hospital dying ratio of urban older adults showing a significant downwards trend and rural older adults showing a slow upwards trend. The higher the number of medical beds per 10,000 people, the more likely the urban elderly are to die in hospitals. The higher the number of community general practitioners per 10,000 people, the more likely the urban elderly are to die at home. Older adults who were bedridden before death were more likely to die in a hospital, it was negatively moderated by the number of physician assistants per 10,000 population. Older adults with lower income were more likely to die at home, it was negatively moderated by the number of community health posts per 10,000 people. Conclusions Chinese older adults mainly die at home, especially in rural areas. The place of death of the urban elderly has become "deinstitutionalized", while rural elderly individuals are still in the stage of transition from home to the hospital. In urban China, the positive effect of regional medical care level on hospital dying and the positive effect of community medical resources on home dying occur simultaneously. Improvements at the regional medical level can increase the accessibility of medical services for older adults with certain mobility abilities and increase their probability of dying in the hospital. The abundance of community medical resources can alleviate the inequality of medical care utilization caused by the income disparity of elderly individuals. older adults place of death urbanization hierarchical logistic model Figures Figure 1 Figure 2 Background Death is a taboo topic in China, but it is also an important issue that deserves attention. The “Healthy China 2030” blueprint proposes to cover the whole life cycle, realizing complete medical service and protection from the foetal stage to the end of life[ 1 ]. Improving the quality of life of the elderly in China is one of the key points to promote the health China strategy. The place of death is an important measure of the quality of death and the quality of palliative care[ 2 – 4 ]. Analysing the distribution and changing trends of the place of death of the elderly and their causes is crucial for optimizing social policies at the end of life. The factors affecting the place of death of the elderly are very complex. Microindividual characteristics, such as age, income, education level, and medical condition, all have an impact on the place of death of elderly individuals[ 5 – 7 ], while macro factors, such as urbanization level, also play an important role that cannot be ignored[ 8 , 9 ]. The level of urbanization has multiple effects on the place of death, which is reflected in the direct constraints of regional medical level and community medical resources on the one hand and the interaction of these factors with individual characteristics of the elderly on the other hand, thus affecting the place of death of elderly individuals[ 10 – 14 ]. From the historical experience of developed countries, the place of death of the elderly has evolved in phases with the urbanization process. In the early 19th century, France, the United States, Germany, and other developed countries gradually began the urbanization process. During this period, the level of medical technology was low, medical care resources were limited, and most elderly people died at home[ 15 , 16 ]. In the middle of the 20th century, the urbanization process accelerated, the level of medical technology and the accessibility of medical resources such as doctors and hospital beds increased, and the ratio of elderly people dying in hospitals increased rapidly[ 17 – 21 ]. A total of 49.5% of elderly people died in medical and nursing facilities in the United States in 1949, and this proportion increased rapidly to 60.9% in 1958[ 22 ]. Since the 1980s, developed countries have become more urbanized, and the availability and affordability of home care for the elderly have been greatly enhanced by advanced medical technology, abundant community medical services, and home medical care resources, resulting in the rise of the modern hospice movement and the reversal of hospital death ratios[ 23 – 26 ]. Developed countries, such as the United Kingdom, the United States, Australia, and Japan, have introduced a series of acts and regulations to promote the quality of hospice care for elderly individuals[ 27 – 29 ]. In recent studies, a trend towards a rebound in the proportion of older adults dying at home in these countries can also be observed[ 30 – 34 ]. China's urbanization began in 1949 with the founding of New China and has developed rapidly since 1978, with the urbanization rate exceeding 50% in 2011[ 35 ]. In the process of rapid urbanization in China, the problem of uneven interprovincial development of urbanization has become increasingly prominent, with the level of urbanization gradually decreasing from the southeast to the northeast and northwest, and the level of social urbanization lagging behind the level of economic urbanization in most provinces[ 36 – 38 ]. Particularly striking is the disparity in regional medical resource levels and community medical resources across provinces and regions[ 39 – 43 ]. Established studies have found that the home dying ratio for older adults in China is over 80%, and the home dying ratio is even higher for older adults in rural areas[ 16 , 44 , 45 ]. However, it is worth noting that the generally high home dying ratios of older adults in China from these studies may mask regional differences in the place of death in China, ignoring the complex impact of the uneven regional development of urbanization in China and the resulting disparities in regional medical resource levels and community medical resources on the place of death of older adults. The interprovincial differences that have developed in China during the rapid urbanization process provide a good regional stratified sample to study the relationship between urbanization and the place of death of older adults. Therefore, based on the interprovincial spatial perspective, this paper focuses on the impact of two aspects of urbanization, namely, regional medical care level and community medical resources, on the end-of-life location of the elderly in China. This study further explores the interaction between these macrostructural factors and interindividual factors of the elderly and urban-rural differences to further clarify the relationship between urbanization and the end-of-life location of the elderly in China. The aim was to further clarify the relationship between urbanization and the place of death of the elderly in China and then to propose public policy recommendations to optimize the quality of life of the elderly in China from the perspective of social urbanization and to facilitate the implementation of the Health China strategy. Methods Data source The microdata used in this paper are from the death sample of the China Elderly Health Influencing Factor Tracking Survey (CLHLS), a tracking survey of the elderly organized by the Center for Healthy Ageing and Development Research/National Development Research Institute of Peking University, covering 23 provinces, autonomous regions and municipalities across China, with respondents aged 65 years and older and adult children aged 35–64 years. The questionnaires were divided into two types: the surviving respondents' questionnaire and the deceased elderly's family members' questionnaire. The survey project was followed up in 2000, 2002, 2005, 2008–2009, 2011–2012, 2014, and 2017–2018 after the baseline survey in 1998. In view of the missing data of the death sample before 2011, this paper mainly used the questionnaire data of the family members of older adults who died in 2011–2012, 2014, and 2017–2018, with a sample size of 5585 in 2011–2012, 2759 in 2014 and 2157 in 2017–2018 and combined the three periods into one mixed cross-sectional dataset. The macrolevel data at the provincial level used in this paper were obtained from the China Statistical Yearbook, which provides more detailed information on the specifics of economic development indicators and social service indicators for each province in China each year. Variable Measurement Dependent variable: the place of death The place of death of the elderly was measured by the questionnaire "Where did the elderly eventually die?" This question measured "at home", "in a hospital", and "in a nursing home". Since the proportion of the sample who died in a nursing home was very small (2.29%), we combined the options "died in a hospital" and "died in a nursing home" to define the place of death variable as a dichotomous variable; if the elderly died at home, it was defined as "0 = dying at home", and if the elderly died in a hospital or nursing home, it was defined as "1 = dying in hospital". Core Independent Variable: Level Of Urbanization The core independent variable of interest is the level of urbanization; in this paper, we take the provinces where the elderly are located as the benchmark and mainly examine the urbanization rate (ratio of urban resident population to total population), the number of beds in medical institutions per 10,000 people, the number of practising assistant physicians per 10,000 people, the number of elderly beds per 1,000 elderly people, the number of community health service stations per 10,000 people, and the number of family general practitioners per 10,000 people in the provinces where the elderly are located. The six specific indicators correspond to the level of urbanization of the population, the level of regional medical care, and community medical resources, which are all continuous variables. Control Variables The control variables in this paper mainly include the demographic characteristics, socioeconomic characteristics, and residential characteristics of elderly individuals. The demographic characteristics included three variables: gender of the elderly (male = 1, female = 0), age, and whether they were bedridden before death (1 = yes, 0 = no). The socioeconomic characteristics included two variables: years of education and whether they had basic pension insurance (1 = yes, 0 = no). The residence characteristics included whether the elderly lived alone before death (1 = yes, 0 = no), the primary caregiver before death (1 = spouse, 0 = other), annual per capita household income (10,000 yuan), the presence of a doctor in the community (1 = yes, 0 = no), and the urban-rural distribution of residence (1 = urban, 0 = rural). Model Setting In descriptive statistics, we used the chi-square test and t-test to systematically describe the urban-rural differences in categorical and continuous variables, respectively. We simultaneously combined the data from the three surveys to graph the trends in the place of death of elderly individuals. In the empirical model, we incorporated some core independent variables of provincial-level urbanization to interpret the distribution and changing trends of the dying places of the elderly from the perspective of provincial-level urbanization. The hierarchical structure of the data assumes that the distribution and change in the place of death of the elderly is the result of the influence of both "individual-province" factors. From the statistical point of view, we considered that the traditional binary logistic regression model is based on the two assumptions, that the random error term is homoscedastic and uncorrelated with the explanatory variables, and the hierarchical data did not satisfy these two basic assumptions; thus, the traditional logistic model failed here. For this reason, we introduced a hierarchical logistic model that specifically deals with stratified data to analyse the distribution and changes in the place of death of elderly individuals. The advantage of hierarchical models is that the effects of the corresponding hierarchical variables are estimated separately at different levels of the data, allowing further analysis of the effects of provincial macro variables on the dependent variable while controlling for individual micro variables, which is in line with the purpose of this paper to develop the study from an urbanization perspective. The modelling steps of the hierarchical logistic model are as follows. In the first step, a null model without any variables was built. This was used to check whether the data used are suitable for the hierarchical model. First level, individual level model. $${y}_{ij}={\beta }_{0j}+{\epsilon }_{ij}$$ 1 Second level, provincial hierarchy model. $${\beta }_{0j}={\gamma }_{00}+{\mu }_{0j}$$ 2 Full model. $${y}_{ij}={\gamma }_{00}+{\mu }_{0j}+{\epsilon }_{ij}$$ 3 where i denotes the first level unit, i.e., the individual elderly person, j denotes the second level unit, i.e., the province of the elderly person, \({\text{y}}_{\text{i}\text{j}}\) are the dependent variables location of the older person at the end of life, \({{\beta }}_{0\text{j}}\) and \({{\gamma }}_{00}\) denote intercept terms, \({{\epsilon }}_{\text{i}\text{j}}\) denotes the random effect at the individual level, and \({{\mu }}_{0\text{j}}\) denotes the random effect at the province level. The random error term in the null model was decomposed into two aspects, individual error and province error, which could be obtained by model estimation, and the intragroup correlation coefficient ICC was then found. The ICC = 0.175 in this study implies that 17.5% of the variance in the place of death was due to provincial-level factors. According to Cohen's empirical criteria, when the ICC value exceeds 0.059, stratified models must be considered. In addition, the individual error was a constant 3.29 due to the distribution limitations of the logistic regression. $$\text{I}\text{C}\text{C}=\frac{0.6976}{0.6976+3.29}=0.1750$$ In the second step, the study variables were gradually added after the data were determined to be suitable for use in a hierarchical model. First, an individual-level intercept model was constructed by adding control variables describing the important influence of the individual level of older adults on their end-of-life location. The specific model is shown in the following equation, where \({ W}_{ij}\) is the included individual control variable, and \({\beta }_{1j}\) is its corresponding coefficient. $${y}_{ij}={\beta }_{0j}+{\beta }_{1j}\ast {W}_{ij}+{\epsilon }_{ij}$$ 4 Second, provincial-level urbanization variables were introduced. In this paper, six provincial-level urbanization indicators, namely, the population urbanization rate, number of medical institution beds per 10,000 people, number of practising assistant physicians per 10,000 people, number of community health service stations per 10,000 people, number of family general practitioners per 10,000 people, and number of elderly beds per 1,000 elderly people, were included in the model as the core independent variables. The specific model is shown in the following equation, where \({Z}_{j}\) is the core urbanization variable included at the provincial level, and \({\gamma }_{1j}\) are the corresponding coefficients. $${\beta }_{0j}={\gamma }_{00}+{\gamma }_{1j}\ast {Z}_{j}+{\mu }_{0j}$$ 5 The aggregated model is as follows. $${y}_{ij}={\gamma }_{00}+{\beta }_{1j}\ast {W}_{ij}+{\gamma }_{1j}\ast {Z}_{j} +{\mu }_{0j}+{\epsilon }_{ij}$$ 6 Finally, provincial-level and individual-level interaction variables were introduced on the basis of the pooled model to examine the moderating effect of provincial-level urbanization indicators on the effect of individual-level indicators on the place of death of older adults. The moderation effect model is shown in Eq. ( 7 ). $${y}_{ij}={\gamma }_{00}+{\beta }_{1j}\ast {W}_{ij}+{\gamma }_{1j}\ast {Z}_{j} +{\alpha }_{1j}\ast {W}_{ij}\ast {Z}_{ij}+{\mu }_{0j}+{\epsilon }_{ij}$$ 7 Results Descriptive statistics results Figure 1 shows the proportion of older adults who died in the hospital; overall, 10.25% of older adults died in the hospital, and 89.75% died at home. In the urban sample, 18.47% of the older adults died in hospitals, while only 4.96% in the rural sample died in hospitals, with a significantly higher proportion of older adults dying in hospitals in urban than in rural areas (p = 0.000). In terms of the trend of the place of death of elderly individuals, the proportion of elderly people dying in the hospital in the whole sample showed an overall trend of slowly increasing and then gradually decreasing. In the urban sample, the proportion of older adults dying in hospitals showed a significant decreasing trend, with a significant chi-square test (p = 0.002), with 20.75% of urban older adults dying in hospitals in 2011, decreasing to 16.95% in 2014, and further decreasing to 15.96% in 2018. In the rural sample, the percentage of older adults dying in hospitals slowly increased from 4.77% in 2011 to 5.29% in 2018, but the chi-square test was not significant, and the upwards trend was not significant. Insert Fig. 1 About Here Table 1 shows the results of descriptive statistics for individual-level and provincial-level variables for older adults. At the provincial level, the average population urbanization rates of the 23 provinces (municipalities/autonomous regions) covered by the sample data were 50.42%, 54.08%, and 59.39% in 2011, 2014, and 2018, respectively. The higher population urbanization rates were in the three municipalities of Shanghai, Beijing, and Tianjin, while the relatively lower population urbanization rates included the central and western provinces of Henan Province, Guangxi Province, and Sichuan Province. In terms of regional medical level, the average number of medical beds per 10,000 people in each province was approximately 45, the average number of licenced assistant physicians per 10,000 people was approximately 21, and the average number of elderly beds per 1,000 elderly people was approximately 24. Beijing and Shanghai were significantly better equipped than other provinces in terms of medical hardware and equipment, such as medical beds, and there was no significant difference in the resource ownership of licenced physicians in each province. In terms of community medical resources, the average number of community health service stations per 10,000 people in each province was approximately 0.18, and the average number of community general practitioners per 10,000 people was approximately 1. The overall community medical resources were low, with developed provinces such as Beijing, Shanghai, Zhejiang, and Jiangsu having relatively richer community medical resources. At the individual level, 70% of older adults lived in rural areas, approximately 60% were male, the average age at death was 94.5 years (8.944), the average years of education was 1.2 years (2.51), with a mean value of 1.47 years of education in the urban sample, which was significantly higher than that in rural areas (1.08), the average annual household income per capita was approximately 21,700 yuan (2.465), close to 80% of the elderly had different types of pension insurance, more than 90% of the elderly lived in communities with a doctor, approximately 74% of the elderly were bedridden before their death, 14.5% lived alone before their death, and 7.7% were mainly cared for by their spouse before their death. Table 1 Results of descriptive statistics of the sample Variables Full sample (8162) Rural sample (5696) Urban sample (2466) P-value Individual-level independent variables Gender, % Male 60.08 60.85 58.31 0.032 Women 39.92 39.15 41.69 Years of education Mean value (standard deviation) 1.2011 (2.5151) 1.0815 (2.3665) 1.4773 (2.8098) 0.000 Interval 0–25 0–24 0–25 Age Mean value (standard deviation) 94.5004 (8.944) 94.6601 (8.9877) 94.1314 (8.8333) 0.014 Interval 54–119 54–119 63–115 Annual household income per capita Mean value (standard deviation) 2.1696 (2.4653) 2.0453 (2.4173) 2.4565 (2.5501) 0.000 Interval 0–10 0-9.5 0–10 Availability of pension insurance, % Yes 79.86 82.25 74.33 0.000 No 20.14 17.75 25.67 Availability of doctors in the community, % Yes 91.55 92.89 88.44 0.000 No 8.45 7.11 11.56 Was bedridden before passing away, % Yes 74.26 74.14 74.53 0.708 No 25.74 25.86 25.47 Did you live alone before passing away, % Yes 14.54 14.83 13.87 0.254 No 85.46 85.17 86.13 Predeceased primary caregiver, % Spouse 7.72 7.41 8.43 0.113 Other 92.28 92.59 91.57 Province-level independent variables Unit Average value Standard deviation Interval Population urbanization rate % 0.5320 0.0971 0.4057–0.896 Number of beds in medical institutions per 10,000 people Zhang 44.6391 11.6674 28.32–75.48 Number of licenced physician assistants per 10,000 people People 20.8907 6.0329 12–54 Number of elderly beds per 1,000 elderly people Zhang 24.3057 9.1412 7.535–54.17 Number of community health service stations per 10,000 people individual 0.1829 0.2246 0.0262–1.0804 Number of general practitioners per 10,000 people People 1.2799 1.0725 0.39–5.94 Insert Table. 1 About Here Model test results Table 2 shows the regression results of the hierarchical logistic model of urbanization level on the place of death of elderly individuals, where Model 1 is the null model, Model 2 is the individual level model, Model 3 is the model estimation result of introducing province level explanatory variables based on controlling for individual level variables, and Model 4 is the full model regression result of introducing cross-level interaction terms. The within-group variance of the null model was 3.29, the between-group variance was 0.6976, and the ICC value was 0.175, indicating that provincial-level factors had a significant effect on the place of death of older adults and that the choice of a stratified model was appropriate. After the introduction of individual-level variables, the between-group variance decreased to 0.3721, and the ICC value still reached 0.1016, which proved that the provincial-level effect on the place of death of the elderly still existed. In the provincial-level model after controlling for individual variables (Model 3), the between-group variance decreased again to 0.0979, and the ICC was 0.0289, indicating that provincial urbanization factors explained the distribution of the dying location of older adults to a greater extent. Table 2 Estimation results of the hierarchical logistic model of the effect of urbanization level on the place of death of the elderly Model 1 Model 2 Model 3 Model 4 Individual-level independent variables Gender 0.0798 (0.113) 0.0864 (0.1132) 0.0895 (0.1132) Urban and rural 0.7109 *** (0.0998) 0.6521 *** (0.1006) 0.6500 *** (0.1008) Years of education 0.0921 *** (0.0165) 0.0930 *** (0.0165) 0.0944 *** (0.0166) Age of death -0.0315 *** (0.0057) -0.0324 *** (0.0057) -0.0325 *** (0.0057) Annual per capita household income 0.0474 ** (0.0192) 0.0508 *** (0.0192) 0.0786 *** (0.0245) Availability of doctors in the community -0.4234 *** (0.1510) -0.4613 *** (0.1506) -0.4537 *** (0.1507) Was he bedridden before he died? -0.2087 * (0.1069) -0.2057 * (0.1069) 0.4199 (0.3677) Living alone or not -0.0230 (0.1417) -0.1237 (0.1449) -0.1261 (0.1449) Whether the spouse is the primary caregiver at the end of life 0.1889 (0.1612) 0.1557 (0.1617) 0.1604 (0.1616) Province-level independent variables Population urbanization rate 3.0534 *** (1.0577) 3.0561 *** (1.0657) Number of beds in medical institutions per 10,000 people 0.0153 ** (0.0067) 0.0149 ** (0.0067) Number of licenced physician assistants per 10,000 people 0.0141 * (0.0076) 0.0352 ** (0.0136) Number of elderly beds per 1,000 elderly people 0.0157 (0.0103) 0.0161 (0.0104) Number of community health service stations per 10,000 people -0.5043 (0.4875) -0.1101 (0.5264) Number of general practitioners per 10,000 people -0.2124 ** (0.1047) -0.2053 * (0.1049) Whether bedridden before death* Number of practising physician assistants per 10,000 people -0.0295 * (0.0164) Annual per capita household income* Number of community health service stations per 10,000 people -0.1446 * (0.0821) Constant term -1.9213 *** (0.1974) 0.2280 (0.5779) -2.4030 *** (0.7896) -2.9364 *** (0.8311) Random effect parameters Province level variance 0.6976 *** 0.3721 *** 0.0979 *** 0.1013 *** Individual level variance 3.29 *** 3.29 *** 3.29 *** 3.29 *** ICC 0.1750 0.1016 0.0289 0.0299 Note: *** q < 0.01, ** q < 0.05, * q < 0.1. The place of death among older adults showed significant urban-rural differences, with urban older adults being more likely to die in a hospital (B = 0.7109, 1% significant). Older adults with more years of education (B = 0.0921, 1% significant) and higher annual per capita household income (B = 0.0474, 5% significant) were more financially able to choose to go to a hospital at the end of life, thus increasing their likelihood of dying in a hospital. In contrast, older adults of lower age (B=-0.0315, 1% significant) mostly did not die spontaneously and rather chose to seek medical help first when faced with a life-threatening illness incident, which increased their probability of dying in the hospital. Older adults who were bedridden before death were more likely to die at home than in the hospital due to mobility reasons (B=-0.2087, 10% significant). Older adults with a community physician were able to access relatively more convenient medical services close to home than those without a community physician, thus reducing the probability of dying in the hospital (B=-0.4334, 1% significant). For urbanization at the provincial level, higher rates of urbanization of the population in the province (B = 3.0534, 1% significant) correlated with a higher probability of dying in the hospital. At the regional medical care level, a higher number of beds per 10,000 people in health care facilities (B = 0.0153, 5% significant) was associated with better health care hardware provided for older adults in the location and an increased likelihood of dying in the hospital. More practising physicians per 10,000 people in the locality (B = 0.0141, 10% significant) led to a higher likelihood that older adults would die in a hospital. In terms of community medical resources, the more general practitioners there were per 10,000 population (B=-0.2124, 5% significant), the more likely it was that medical care needs close to home would be met, thus increasing the likelihood that older people will die at home. The number of community service stations per 10,000 people was negatively, but not significantly, associated with the probability of dying in the hospital, partly because China's community care system and hospice services are not yet complete, and most provinces place emphasis on the construction of community service stations while ignoring their service quality. This results in community service stations not being able to provide high-quality community services for elderly individuals; thus, not having a direct impact on the dying place of the elderly poses a direct impact. On balance, provinces with higher levels of population urbanization and regional medical care are more likely to have older adults dying in hospitals, while provinces with more community medical resources, especially general practitioners, are more likely to have older adults dying at home. Insert Table. 2 About Here To further analyse the effect of the interaction between urbanization factors and individual factors at the provincial level on the place of death of elderly individuals, we constructed Model 4. As shown in Model 4 and Fig. 2 , urbanization moderated the effect of individual factors to some extent. The number of physician assistants per 10,000 people moderated the effect of being bedridden before death on the place of death. In provinces with a greater abundance of physician assistants, older adults with some mobility before death were more likely to have access to medical care at the time of illness than those who were already bedridden; thus, their probability of dying in a hospital increased accordingly (B = -0.0295, 10% significant). The number of community health service stations per 10,000 people moderated the effect of annual household income per capita on the place of death, with provinces with more community health service stations having a smaller effect of annual household income per capita on the place of death of older people (B=-0.1446, 10% significant). This suggests that the increase in the level of regional health care at the provincial level improves the accessibility of health care services for older adults, especially those with some mobility, and thus increases the probability of dying in a hospital. Community medical resources, on the other hand, can effectively moderate the income effect of elderly people dying in hospitals and to some extent alleviate the inequality of health care utilization brought about by the income gap among elderly individuals. Insert Fig. 2 About Here We attempted to further explore the urban-rural differences in the effect of provincial urbanization factors on the place of death of the elderly in China using subgroup regressions. Table 3 shows the results of urban-rural subgroup regressions on the effect of urbanization level on the place of death of elderly individuals. As shown in Table 3 , there was a significant urban-rural difference in the effect of urbanization level on the place of death of elderly individuals. Except for the level of population urbanization, both the corresponding indicators of regional medical level and community health resources had an effect on the place of death of the elderly in urban areas only, while there was no significant effect on the place of death of the elderly in rural areas. More beds in medical institutions per 10,000 people led to a higher likelihood that urban elderly people in the province would die in hospitals (B = 0.0348, 1% significant), and more general practitioners per 10,000 people was associated with a higher likelihood of dying at home (B=-0.4262, 5% significant). This difference may be related to the disparate urban-rural disparities in basic public services, such as regional medical care levels and community medical resources, within Chinese provinces. Since the coverage and quality of basic public services are generally lower in rural areas than in urban areas, it is difficult to observe the effect of these factors on the distribution of dying locations of rural older adults. Table 3 Regression results of urban-rural subgroups of urbanization level affecting the place of death of the elderly Rural Sample Sample Cities Individual level variables Gender -0.0393 (0.1527) -0.0383 (0.1527) 0.2693 (0.1724) 0.2764 (0.1727) Years of education 0.0677 *** (0.0245) 0.0691 *** (0.0245) 0.1189 *** (0.0235) 0.1201 *** (0.0236) Age -0.0485 *** (0.0077) -0.0485 *** (0.0078) -0.0170 * (0.0088) -0.0172 * (0.0089) Annual household income per capita 0.0140 (0.0285) 0.0365 (0.0365) 0.0798 *** (0.0275) 0.1100 *** (0.0346) Availability of doctors in the community -0.2569 (0.2400) -0.2639 (0.2399) -0.4800 ** (0.2035) -0.4548 ** (0.2042) Was he bedridden before he died? -0.4897 *** (0.1406) -0.2153 (0.4798) 0.1376 (0.1693) 1.1644 * (0.6245) Did you live alone before you passed away? -0.2833 (0.1985) -0.2861 (0.1988) -0.0388 (0.2207) -0.0396 (0.2201) Whether the spouse was the primary caregiver before death -0.0382 (0.2232) -0.0360 (0.2234) 0.4222 * (0.2456) 0.4310 * (0.2450) Province-level variables Population urbanization rate 3.4765 *** (1.1609) 3.4087 *** (1.1641) 3.5183 ** (1.7072) 3.6190 ** (1.6952) Number of beds in medical institutions per 10,000 people 0.0009 (0.0107) 0.0010 (0.0107) 0.0348 *** (0.0105) 0.0344 *** (0.0104) Number of licenced physician assistants per 10,000 people 0.0134 (0.0103) 0.0218 (0.0171) 0.0147 (0.0118) 0.0529 ** (0.0246) Number of elderly beds per 1,000 elderly people 0.0307 ** (0.0129) 0.0310 ** (0.0129) 0.0062 (0.0157) 0.0061 (0.0157) Number of community health service stations per 10,000 people -0.0683 (0.503) 0.2380 (0.5852) -0.8077 (0.7838) -0.3115 (0.8234) Number of general practitioners per 10,000 people -0.1498 (0.1427) -0.1499 (0.1427) -0.4262 ** (0.1678) -0.4152 ** (0.1674) Whether bedridden before death* Number of practising physician assistants per 10,000 people -0.0130 (0.0214) -0.0487 * (0.0283) Annual per capita household income* Number of community health service stations per 10,000 people -0.1053 (0.1113) -0.1730 (0.1208) Constant term -0.8315 (0.9368) -1.0483 (0.9845) -4.3079 *** (1.2600) -5.2584 *** (1.3507) Random effect parameters Province level variance 0.0323 *** 0.0323 *** 0.3116 *** 0.3052 *** Individual level variance 3.29 *** 3.29 *** 3.29 *** 3.29 *** Note: *** q < 0.01, ** q < 0.05, * q < 0.1. Insert Table. 3 About Here Discussion Using a combination of microdata from the China Follow-up Survey on Health Influences on the Elderly (CLHLS) 2011–2018 mortality sample and macro data on economic development and social service indicators at the interprovincial level from the China Statistical Yearbook, this study constructed a hierarchical logistic model to discuss the effects of the level of urbanization, especially the level of regional medical care and community medical resources, on the place of death of Chinese elderly people at the provincial level. The interaction between these macrostructural factors and micro individual factors was also analysed, and urban-rural differences were examined based on urban-rural subsample regression. First, this study found that the proportion of older adults dying at home was very high in both rural and urban China during 2011–2018, with 95.04% dying at home in rural areas and 81.53% dying at home in urban areas. Overall, nearly 90% of older adults nationwide were dying at home. This is generally consistent with the findings of Gu et al. (2007)[ 15 ], Cai et al. (2017)[ 44 ], and Zhang Lilong and Han Runlin (2020)[ 45 ]. In terms of the trend of dying location, the results based on the full sample suggest that the hospital dying ratio of older Chinese adults shows a trend of increasing and then decreasing with urbanization, which is similar to the historical evolution trajectory of developed countries. At the end of the 20th century, some developed countries became more urbanized, and the trend of a continuous increase in the hospital dying ratio peaked and began to reverse[ 46 – 49 ]. Wilson et al. (2001, 2009) found that the proportion of hospital deaths in Canada continued to rise after 1950, peaking at 80.5% in 1994, and then declined substantially to 60.6% in 2004, with a corresponding increase in the number of people dying at home[ 50 , 51 ]. It is worth noting that the place of death of the elderly in China has evolved rapidly, which may be related to the characteristics of rapid urbanization in China. Based on the results of the urban-rural subsample, the change in the place of death of the elderly in urban China has clearly shown a "deinstitutionalization" characteristic similar to late urbanization in developed countries, while the place of death of the elderly in rural China is still in the stage of changing from home to hospital, and the change has been very slow. This finding corroborates the conjecture of urban-rural differences in patterns of the place of death of older adults in China proposed by Dong et al. (2019)[ 16 ]. This significant urban-rural variation may be related to China's long-standing urban-rural dichotomous structure. It is important to note that the above shift, both in the full sample and in the subsample, is based on the fact that China still has the family as the primary place of death; thus, this trend cannot be overestimated. Second, we found that the place of death of Chinese elderly people is highly correlated with the urbanization level of the province in which they live and whether this association shows urban-rural differences varies according to the specific dimension of urbanization level. There was no significant urban-rural difference in the effect of the population urbanization rate on the place of death. In provinces with higher rates of population urbanization, both rural and urban older adults were relatively more likely to die in a hospital. This finding is consistent with the analysis of Lin et al. (2007) using 1995–2004 data from Taiwan, which concluded that the level of population urbanization in a region is negatively associated with the ratio of older adults dying at home in that region[ 9 ]. The level of regional medical care and community medical resources at the provincial level also had important effects on the place of death of older adults, but this effect varied significantly between urban and rural areas in China, as evidenced by the fact that these corresponding variables only had a significant effect on the place of death of older adults in urban areas but not in rural areas. At the provincial level, urban older adults were more likely to die in a hospital when the number of medical beds per 10,000 people was higher. Yang et al. (2006), using Japanese data from 1951–2002, found that the increase in the number of medical beds was the main reason for the increase in the proportion of deaths in hospitals in Japan during this period[ 19 ]. Additionally, we found that higher numbers of community general practitioners per 10,000 people at the provincial level was associated with an increased likelihood of urban older people to die at home. Empirical evidence based on other countries has also found that the abundance of community medical resources and advances in home hospice care are important factors in reversing the trend of hospital death in developed countries. The findings of Xu et al. (2020) related to changes in the place of death of older adults with dementia in the United States from 2000–2014 and the conclusion of Muramatsu et al. (2008) comparing the effects of state spending on home and community services (HCBS) on the place of death of older adults in the United States both showed that investment in community medical resources at the interstate level was highly positively associated with the ratio of older adults dying at home and that increased investment in community and home health care resources helped older adults choose to die at home[ 34 , 52 ]. It is also noteworthy that, in developed countries, the effects of improved regional health care and advances in community medical resources and home hospice on hospital dying ratios occurred over two long periods, but due to rapid urbanization in China, these forces gained rapid growth and overlapping effects in just 8 years between 2011 and 2018, showing a simultaneous effect in urban areas of China. The partial substitution of hospital dying for traditional home dying is based on improved regional medical care, and the reversal of hospital dying to home dying is based on advances in community medical resources and home hospice care. In contrast to urban China, the end-of-life location of older adults in rural China is primarily driven by personal and family factors. Regional differences in urbanization in China and the interprovincial differences in medical resources and community medical resources are concentrated in urban areas, and the changes in end-of-life location caused by these differences are also mainly found in urban areas. Neither the increase in the number of medical beds, the increase in the number of medical practitioners, the increase in the number of community health service stations, nor the increase in the number of general practitioners at the provincial level could effectively lead to changes in the place of death of the elderly in rural areas within the province, which may also be related to the traditional concept that the elderly in rural China "return to their roots". Compared to rural areas in developed countries, the end-of-life location of the elderly in rural China seems to be just at the stage of changing from home to the hospital. This shift still lacks a strong driving force at this time because of the large urban-rural disparity in the distribution of health care resources in most Chinese provinces. This paper also has some shortcomings. First, because the proportion of elderly people dying in nursing homes in the sample covered in this paper was very low, we combined the sample dying in nursing homes with the sample dying in hospitals, thus dichotomizing the dependent variable, with 0 representing dying at home and 1 representing dying in hospitals. However, this treatment did not allow for a separate examination of older adults dying in nursing homes. In the future, when the sample size is sufficient, this part of the sample can be separated for corresponding analysis. Second, given that community health services in China are still incomplete, the indicator system of community medical resources is not yet sound, which makes this paper lack more direct evidence in analysing the impact of community medical resources. Third, due to the limitations of the study data, this paper did not introduce medical variables, such as the type of lethal disease in the modelling, nor did it breakdown the disease status of the elderly before death, which is also an important factor affecting the place of death of elderly individuals. This could be analysed in more detail and depth in subsequent studies when conditions are adequate. For example, the impact of urbanization in China on the place of death of specific populations of older Chinese adults with Alzheimer's disease or cancer could be explored. Conclusions This study analysed the distribution and changing trajectories of the place of death of older adults in China and the impact of urbanization on the place of death of older adults based on both micro- and macrolevel empirical data. The study showed that older Chinese adults die mainly at home, especially in rural areas. The pattern of change in the place of death differs between urban and rural areas, with urban areas showing "deinstitutionalization" similar to that of developed countries in late urbanization, while rural areas are still in the stage of transition from home to the hospital. There were significant urban-rural differences in the impact of urbanization on the place of death of older adults. In cities, improvements in regional health care at the provincial level increase the probability of dying in a hospital, while the abundance of community medical resources and advances in home hospice care provides a boost to dying at home. In urban China, these two effects occur in tandem. The same cannot be said for rural areas. Improvements in regional health care can increase access to care for older adults with some mobility and increase the probability of dying in the hospital. The abundance of community medical resources can alleviate the inequality in health care utilization caused by income disparities among older adults. This study deepens the analysis of the characteristics and causes of the place of death of older adults in China and has important practical implications for proposing social policies that optimize the quality of life of older adults at the end of life and contribute to the implementation of the Healthy China strategy. Declarations Acknowledgements We greatly appreciate the support and the hard work of the participants during this study. Authors ’ contributions Miaoyu Yuan and Li Tu wrote the main manuscript text . Ankang Hu prepared figures 1-2. Nan Xiang and Lin Cheng prepared tables 1-3. All authors reviewed the manuscript. Funding Not applicable. Availability of data and materials The data used in this study are freely available on the Peking University Open Research Data Platform(https://opendata.pku.edu.cn/) and the website of the National Bureau of Statistics of China(http://www.stats.gov.cn/sj/ndsj/). Ethics approval and consent to participate This study is a secondary analysis of the data from the CLHLS. The CLHLS study was approved by the Ethics Committee of Peking University (IRB00001052–13074). The participants provided their written informed consent to participate in this study. And informed consent was obtained from literate participants and legal guardian(s)/next of kin of illiterate participants. All methods were performed in accordance with the guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References The Communist Party of China (CPC) Central Committee and the State Council. 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Place of death among older Americans - Does state spending on home- and community-based services promote home death? MEDICAL CARE. 2008,46:829-838. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2755464","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":190518464,"identity":"70e4d554-9a56-4b4c-9d91-db85d7bbb188","order_by":0,"name":"Miaoyu Yuan","email":"","orcid":"","institution":"Hubei University of Economics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Miaoyu","middleName":"","lastName":"Yuan","suffix":""},{"id":190518465,"identity":"f06860f7-bc1a-44b6-abdf-49ddbc164a16","order_by":1,"name":"Li Tu","email":"","orcid":"","institution":"Hubei University of Economics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Tu","suffix":""},{"id":190518466,"identity":"1ee7d52c-7ebb-4586-a73c-286362d1f2e0","order_by":2,"name":"Lin Cheng","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Cheng","suffix":""},{"id":190518467,"identity":"a9870c34-6146-4d99-ac81-e248414456d2","order_by":3,"name":"Nan Xiang","email":"","orcid":"","institution":"Hubei University of Economics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Xiang","suffix":""},{"id":190518468,"identity":"4eefc3b4-8b8c-4601-bea3-2558944690a6","order_by":4,"name":"Ankang Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie3PMUvDQBTA8TtOrsujWS8UK/0GLxw0FkI/y4XCTR0Uv8CB0KnomoAfIlBwfhjMlA/g4FIEJ4cUF0HROltJMna4//h4P3iPMZ/vKBNEu++fcXD2QthgMu9BZLrNpNChs+Yyu7CLHgS0BinSgpb4Ds0Dd10gHtF0BCDT3NXRJkESbFA+Fm1kdmNseKdA3/LVlV7i85CBtU9tBGtWqTdUp/m1uP8lr4IpmHYQvlJgkBcViz/PseSumwgRAZlJUZ+gZn3IbC35Nnekw0yaaI12Ibt+iSFoaOdoHChB+PGVzINBWbUfdjCRbev/E5/P5/P9bQ9gdE1VEb3ZrgAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ankang","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2023-03-30 08:44:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2755464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2755464/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":35673213,"identity":"2d79e683-4f52-4b3f-8f7a-8970c5e9ec90","added_by":"auto","created_at":"2023-04-12 20:50:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9999,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of older adults dying in the hospital (%)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2755464/v1/acffcbb25ecb92876080e013.png"},{"id":35673860,"identity":"f55ea429-3006-4075-b7f0-9d1b787bb389","added_by":"auto","created_at":"2023-04-12 20:58:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139275,"visible":true,"origin":"","legend":"\u003cp\u003eCross-level moderating effect of urbanization level on the place of death of older adults\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2755464/v1/3a730b97d70eb88971e40be5.png"},{"id":47284222,"identity":"360e8bb0-74fd-4d50-8411-822993c2f1a4","added_by":"auto","created_at":"2023-11-29 13:14:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":558257,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2755464/v1/27ddb7a9-d4d9-49b2-8c33-4f9576b92e98.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of urbanization on the place of death of older adults in China from an interprovincial perspective","fulltext":[{"header":"Background","content":"\u003cp\u003eDeath is a taboo topic in China, but it is also an important issue that deserves attention. The \u0026ldquo;Healthy China 2030\u0026rdquo; blueprint proposes to cover the whole life cycle, realizing complete medical service and protection from the foetal stage to the end of life[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Improving the quality of life of the elderly in China is one of the key points to promote the health China strategy. The place of death is an important measure of the quality of death and the quality of palliative care[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Analysing the distribution and changing trends of the place of death of the elderly and their causes is crucial for optimizing social policies at the end of life.\u003c/p\u003e \u003cp\u003eThe factors affecting the place of death of the elderly are very complex. Microindividual characteristics, such as age, income, education level, and medical condition, all have an impact on the place of death of elderly individuals[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while macro factors, such as urbanization level, also play an important role that cannot be ignored[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The level of urbanization has multiple effects on the place of death, which is reflected in the direct constraints of regional medical level and community medical resources on the one hand and the interaction of these factors with individual characteristics of the elderly on the other hand, thus affecting the place of death of elderly individuals[\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. From the historical experience of developed countries, the place of death of the elderly has evolved in phases with the urbanization process. In the early 19th century, France, the United States, Germany, and other developed countries gradually began the urbanization process. During this period, the level of medical technology was low, medical care resources were limited, and most elderly people died at home[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the middle of the 20th century, the urbanization process accelerated, the level of medical technology and the accessibility of medical resources such as doctors and hospital beds increased, and the ratio of elderly people dying in hospitals increased rapidly[\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A total of 49.5% of elderly people died in medical and nursing facilities in the United States in 1949, and this proportion increased rapidly to 60.9% in 1958[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Since the 1980s, developed countries have become more urbanized, and the availability and affordability of home care for the elderly have been greatly enhanced by advanced medical technology, abundant community medical services, and home medical care resources, resulting in the rise of the modern hospice movement and the reversal of hospital death ratios[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Developed countries, such as the United Kingdom, the United States, Australia, and Japan, have introduced a series of acts and regulations to promote the quality of hospice care for elderly individuals[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In recent studies, a trend towards a rebound in the proportion of older adults dying at home in these countries can also be observed[\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChina's urbanization began in 1949 with the founding of New China and has developed rapidly since 1978, with the urbanization rate exceeding 50% in 2011[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the process of rapid urbanization in China, the problem of uneven interprovincial development of urbanization has become increasingly prominent, with the level of urbanization gradually decreasing from the southeast to the northeast and northwest, and the level of social urbanization lagging behind the level of economic urbanization in most provinces[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Particularly striking is the disparity in regional medical resource levels and community medical resources across provinces and regions[\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Established studies have found that the home dying ratio for older adults in China is over 80%, and the home dying ratio is even higher for older adults in rural areas[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, it is worth noting that the generally high home dying ratios of older adults in China from these studies may mask regional differences in the place of death in China, ignoring the complex impact of the uneven regional development of urbanization in China and the resulting disparities in regional medical resource levels and community medical resources on the place of death of older adults.\u003c/p\u003e \u003cp\u003eThe interprovincial differences that have developed in China during the rapid urbanization process provide a good regional stratified sample to study the relationship between urbanization and the place of death of older adults. Therefore, based on the interprovincial spatial perspective, this paper focuses on the impact of two aspects of urbanization, namely, regional medical care level and community medical resources, on the end-of-life location of the elderly in China. This study further explores the interaction between these macrostructural factors and interindividual factors of the elderly and urban-rural differences to further clarify the relationship between urbanization and the end-of-life location of the elderly in China. The aim was to further clarify the relationship between urbanization and the place of death of the elderly in China and then to propose public policy recommendations to optimize the quality of life of the elderly in China from the perspective of social urbanization and to facilitate the implementation of the Health China strategy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe microdata used in this paper are from the death sample of the China Elderly Health Influencing Factor Tracking Survey (CLHLS), a tracking survey of the elderly organized by the Center for Healthy Ageing and Development Research/National Development Research Institute of Peking University, covering 23 provinces, autonomous regions and municipalities across China, with respondents aged 65 years and older and adult children aged 35\u0026ndash;64 years. The questionnaires were divided into two types: the surviving respondents' questionnaire and the deceased elderly's family members' questionnaire. The survey project was followed up in 2000, 2002, 2005, 2008\u0026ndash;2009, 2011\u0026ndash;2012, 2014, and 2017\u0026ndash;2018 after the baseline survey in 1998. In view of the missing data of the death sample before 2011, this paper mainly used the questionnaire data of the family members of older adults who died in 2011\u0026ndash;2012, 2014, and 2017\u0026ndash;2018, with a sample size of 5585 in 2011\u0026ndash;2012, 2759 in 2014 and 2157 in 2017\u0026ndash;2018 and combined the three periods into one mixed cross-sectional dataset. The macrolevel data at the provincial level used in this paper were obtained from the China Statistical Yearbook, which provides more detailed information on the specifics of economic development indicators and social service indicators for each province in China each year.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariable Measurement\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDependent variable: the place of death\u003c/h2\u003e \u003cp\u003eThe place of death of the elderly was measured by the questionnaire \"Where did the elderly eventually die?\" This question measured \"at home\", \"in a hospital\", and \"in a nursing home\". Since the proportion of the sample who died in a nursing home was very small (2.29%), we combined the options \"died in a hospital\" and \"died in a nursing home\" to define the place of death variable as a dichotomous variable; if the elderly died at home, it was defined as \"0\u0026thinsp;=\u0026thinsp;dying at home\", and if the elderly died in a hospital or nursing home, it was defined as \"1\u0026thinsp;=\u0026thinsp;dying in hospital\".\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCore Independent Variable: Level Of Urbanization\u003c/h3\u003e\n\u003cp\u003eThe core independent variable of interest is the level of urbanization; in this paper, we take the provinces where the elderly are located as the benchmark and mainly examine the urbanization rate (ratio of urban resident population to total population), the number of beds in medical institutions per 10,000 people, the number of practising assistant physicians per 10,000 people, the number of elderly beds per 1,000 elderly people, the number of community health service stations per 10,000 people, and the number of family general practitioners per 10,000 people in the provinces where the elderly are located. The six specific indicators correspond to the level of urbanization of the population, the level of regional medical care, and community medical resources, which are all continuous variables.\u003c/p\u003e\n\u003ch3\u003eControl Variables\u003c/h3\u003e\n\u003cp\u003eThe control variables in this paper mainly include the demographic characteristics, socioeconomic characteristics, and residential characteristics of elderly individuals. The demographic characteristics included three variables: gender of the elderly (male\u0026thinsp;=\u0026thinsp;1, female\u0026thinsp;=\u0026thinsp;0), age, and whether they were bedridden before death (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no). The socioeconomic characteristics included two variables: years of education and whether they had basic pension insurance (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no). The residence characteristics included whether the elderly lived alone before death (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no), the primary caregiver before death (1\u0026thinsp;=\u0026thinsp;spouse, 0\u0026thinsp;=\u0026thinsp;other), annual per capita household income (10,000 yuan), the presence of a doctor in the community (1\u0026thinsp;=\u0026thinsp;yes, 0\u0026thinsp;=\u0026thinsp;no), and the urban-rural distribution of residence (1\u0026thinsp;=\u0026thinsp;urban, 0\u0026thinsp;=\u0026thinsp;rural).\u003c/p\u003e\n\u003ch3\u003eModel Setting\u003c/h3\u003e\n\u003cp\u003eIn descriptive statistics, we used the chi-square test and t-test to systematically describe the urban-rural differences in categorical and continuous variables, respectively. We simultaneously combined the data from the three surveys to graph the trends in the place of death of elderly individuals. In the empirical model, we incorporated some core independent variables of provincial-level urbanization to interpret the distribution and changing trends of the dying places of the elderly from the perspective of provincial-level urbanization. The hierarchical structure of the data assumes that the distribution and change in the place of death of the elderly is the result of the influence of both \"individual-province\" factors. From the statistical point of view, we considered that the traditional binary logistic regression model is based on the two assumptions, that the random error term is homoscedastic and uncorrelated with the explanatory variables, and the hierarchical data did not satisfy these two basic assumptions; thus, the traditional logistic model failed here. For this reason, we introduced a hierarchical logistic model that specifically deals with stratified data to analyse the distribution and changes in the place of death of elderly individuals. The advantage of hierarchical models is that the effects of the corresponding hierarchical variables are estimated separately at different levels of the data, allowing further analysis of the effects of provincial macro variables on the dependent variable while controlling for individual micro variables, which is in line with the purpose of this paper to develop the study from an urbanization perspective.\u003c/p\u003e \u003cp\u003eThe modelling steps of the hierarchical logistic model are as follows.\u003c/p\u003e \u003cp\u003eIn the first step, a null model without any variables was built. This was used to check whether the data used are suitable for the hierarchical model.\u003c/p\u003e \u003cp\u003eFirst level, individual level model.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${y}_{ij}={\\beta }_{0j}+{\\epsilon }_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSecond level, provincial hierarchy model.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\beta }_{0j}={\\gamma }_{00}+{\\mu }_{0j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFull model.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${y}_{ij}={\\gamma }_{00}+{\\mu }_{0j}+{\\epsilon }_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere i denotes the first level unit, i.e., the individual elderly person, j denotes the second level unit, i.e., the province of the elderly person, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{y}}_{\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e are the dependent variables location of the older person at the end of life, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\beta }}_{0\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{00}\\)\u003c/span\u003e\u003c/span\u003e denote intercept terms, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\epsilon }}_{\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e denotes the random effect at the individual level, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\mu }}_{0\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e denotes the random effect at the province level. The random error term in the null model was decomposed into two aspects, individual error and province error, which could be obtained by model estimation, and the intragroup correlation coefficient ICC was then found.\u003c/p\u003e \u003cp\u003eThe ICC\u0026thinsp;=\u0026thinsp;0.175 in this study implies that 17.5% of the variance in the place of death was due to provincial-level factors. According to Cohen's empirical criteria, when the ICC value exceeds 0.059, stratified models must be considered. In addition, the individual error was a constant 3.29 due to the distribution limitations of the logistic regression.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{I}\\text{C}\\text{C}=\\frac{0.6976}{0.6976+3.29}=0.1750$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the second step, the study variables were gradually added after the data were determined to be suitable for use in a hierarchical model.\u003c/p\u003e \u003cp\u003eFirst, an individual-level intercept model was constructed by adding control variables describing the important influence of the individual level of older adults on their end-of-life location. The specific model is shown in the following equation, where\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ W}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the included individual control variable, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1j}\\)\u003c/span\u003e\u003c/span\u003e is its corresponding coefficient.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${y}_{ij}={\\beta }_{0j}+{\\beta }_{1j}\\ast {W}_{ij}+{\\epsilon }_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSecond, provincial-level urbanization variables were introduced. In this paper, six provincial-level urbanization indicators, namely, the population urbanization rate, number of medical institution beds per 10,000 people, number of practising assistant physicians per 10,000 people, number of community health service stations per 10,000 people, number of family general practitioners per 10,000 people, and number of elderly beds per 1,000 elderly people, were included in the model as the core independent variables. The specific model is shown in the following equation, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{j}\\)\u003c/span\u003e\u003c/span\u003e is the core urbanization variable included at the provincial level, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma }_{1j}\\)\u003c/span\u003e\u003c/span\u003e are the corresponding coefficients.\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${\\beta }_{0j}={\\gamma }_{00}+{\\gamma }_{1j}\\ast {Z}_{j}+{\\mu }_{0j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe aggregated model is as follows.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${y}_{ij}={\\gamma }_{00}+{\\beta }_{1j}\\ast {W}_{ij}+{\\gamma }_{1j}\\ast {Z}_{j} +{\\mu }_{0j}+{\\epsilon }_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, provincial-level and individual-level interaction variables were introduced on the basis of the pooled model to examine the moderating effect of provincial-level urbanization indicators on the effect of individual-level indicators on the place of death of older adults. The moderation effect model is shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${y}_{ij}={\\gamma }_{00}+{\\beta }_{1j}\\ast {W}_{ij}+{\\gamma }_{1j}\\ast {Z}_{j} +{\\alpha }_{1j}\\ast {W}_{ij}\\ast {Z}_{ij}+{\\mu }_{0j}+{\\epsilon }_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics results\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the proportion of older adults who died in the hospital; overall, 10.25% of older adults died in the hospital, and 89.75% died at home. In the urban sample, 18.47% of the older adults died in hospitals, while only 4.96% in the rural sample died in hospitals, with a significantly higher proportion of older adults dying in hospitals in urban than in rural areas (p\u0026thinsp;=\u0026thinsp;0.000). In terms of the trend of the place of death of elderly individuals, the proportion of elderly people dying in the hospital in the whole sample showed an overall trend of slowly increasing and then gradually decreasing. In the urban sample, the proportion of older adults dying in hospitals showed a significant decreasing trend, with a significant chi-square test (p\u0026thinsp;=\u0026thinsp;0.002), with 20.75% of urban older adults dying in hospitals in 2011, decreasing to 16.95% in 2014, and further decreasing to 15.96% in 2018. In the rural sample, the percentage of older adults dying in hospitals slowly increased from 4.77% in 2011 to 5.29% in 2018, but the chi-square test was not significant, and the upwards trend was not significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInsert Fig. 1 About Here\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the results of descriptive statistics for individual-level and provincial-level variables for older adults. At the provincial level, the average population urbanization rates of the 23 provinces (municipalities/autonomous regions) covered by the sample data were 50.42%, 54.08%, and 59.39% in 2011, 2014, and 2018, respectively. The higher population urbanization rates were in the three municipalities of Shanghai, Beijing, and Tianjin, while the relatively lower population urbanization rates included the central and western provinces of Henan Province, Guangxi Province, and Sichuan Province. In terms of regional medical level, the average number of medical beds per 10,000 people in each province was approximately 45, the average number of licenced assistant physicians per 10,000 people was approximately 21, and the average number of elderly beds per 1,000 elderly people was approximately 24. Beijing and Shanghai were significantly better equipped than other provinces in terms of medical hardware and equipment, such as medical beds, and there was no significant difference in the resource ownership of licenced physicians in each province. In terms of community medical resources, the average number of community health service stations per 10,000 people in each province was approximately 0.18, and the average number of community general practitioners per 10,000 people was approximately 1. The overall community medical resources were low, with developed provinces such as Beijing, Shanghai, Zhejiang, and Jiangsu having relatively richer community medical resources. At the individual level, 70% of older adults lived in rural areas, approximately 60% were male, the average age at death was 94.5 years (8.944), the average years of education was 1.2 years (2.51), with a mean value of 1.47 years of education in the urban sample, which was significantly higher than that in rural areas (1.08), the average annual household income per capita was approximately 21,700 yuan (2.465), close to 80% of the elderly had different types of pension insurance, more than 90% of the elderly lived in communities with a doctor, approximately 74% of the elderly were bedridden before their death, 14.5% lived alone before their death, and 7.7% were mainly cared for by their spouse before their death.\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\u003eResults of descriptive statistics of the sample\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\u003eFull sample (8162)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural sample (5696)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban sample (2466)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual-level independent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, %\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of education\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\u003eMean value (standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2011 (2.5151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0815 (2.3665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4773 (2.8098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;25\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\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\u003eMean value (standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.5004 (8.944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.6601 (8.9877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.1314 (8.8333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u0026ndash;119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u0026ndash;119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u0026ndash;115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual household income per capita\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\u003eMean value (standard deviation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1696 (2.4653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0453 (2.4173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4565 (2.5501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailability of pension insurance, %\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailability of doctors in the community, %\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWas bedridden before passing away, %\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDid you live alone before passing away, %\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredeceased primary caregiver, %\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\u003eSpouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince-level independent variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAverage value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eStandard deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eInterval\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation urbanization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4057\u0026ndash;0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of beds in medical institutions per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.6674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.32\u0026ndash;75.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of licenced physician assistants per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeople\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.8907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of elderly beds per 1,000 elderly people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.535\u0026ndash;54.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of community health service stations per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eindividual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0262\u0026ndash;1.0804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of general practitioners per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeople\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u0026ndash;5.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eInsert Table. 1 About Here\u003c/h3\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModel test results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the regression results of the hierarchical logistic model of urbanization level on the place of death of elderly individuals, where Model 1 is the null model, Model 2 is the individual level model, Model 3 is the model estimation result of introducing province level explanatory variables based on controlling for individual level variables, and Model 4 is the full model regression result of introducing cross-level interaction terms. The within-group variance of the null model was 3.29, the between-group variance was 0.6976, and the ICC value was 0.175, indicating that provincial-level factors had a significant effect on the place of death of older adults and that the choice of a stratified model was appropriate. After the introduction of individual-level variables, the between-group variance decreased to 0.3721, and the ICC value still reached 0.1016, which proved that the provincial-level effect on the place of death of the elderly still existed. In the provincial-level model after controlling for individual variables (Model 3), the between-group variance decreased again to 0.0979, and the ICC was 0.0289, indicating that provincial urbanization factors explained the distribution of the dying location of older adults to a greater extent.\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\u003eEstimation results of the hierarchical logistic model of the effect of urbanization level on the place of death of the elderly\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\u0026nbsp;\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 \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual-level independent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0798\u003c/p\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0864\u003c/p\u003e \u003cp\u003e(0.1132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0895\u003c/p\u003e \u003cp\u003e(0.1132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban and rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7109\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6521\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6500\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0921\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0930\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0944\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0166)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0315\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0324\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0325\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0057)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual per capita household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0474\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0508\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0786\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0245)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailability of doctors in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.4234\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1510)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4613\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.4537\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1507)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWas he bedridden before he died?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2087\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2057\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4199\u003c/p\u003e \u003cp\u003e(0.3677)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone or not\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0230\u003c/p\u003e \u003cp\u003e(0.1417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1237\u003c/p\u003e \u003cp\u003e(0.1449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1261\u003c/p\u003e \u003cp\u003e(0.1449)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether the spouse is the primary caregiver at the end of life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1889\u003c/p\u003e \u003cp\u003e(0.1612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1557\u003c/p\u003e \u003cp\u003e(0.1617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1604\u003c/p\u003e \u003cp\u003e(0.1616)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince-level independent variables\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation urbanization rate\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\u003e3.0534\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.0577)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0561\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.0657)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of beds in medical institutions per 10,000 people\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\u003e0.0153\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0149\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0067)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of licenced physician assistants per 10,000 people\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\u003e0.0141\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0352\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0136)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of elderly beds per 1,000 elderly people\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\u003e0.0157\u003c/p\u003e \u003cp\u003e(0.0103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0161\u003c/p\u003e \u003cp\u003e(0.0104)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of community health service stations per 10,000 people\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-0.5043\u003c/p\u003e \u003cp\u003e(0.4875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1101\u003c/p\u003e \u003cp\u003e(0.5264)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of general practitioners per 10,000 people\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-0.2124\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2053\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether bedridden before death* Number of practising physician assistants per 10,000 people\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-0.0295\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0164)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual per capita household income* Number of community health service stations per 10,000 people\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-0.1446\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0821)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.9213\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2280\u003c/p\u003e \u003cp\u003e(0.5779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.4030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.7896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.9364\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.8311)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effect parameters\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\u003eProvince level variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6976\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3721\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0979\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1013\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual level variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: \u003csup\u003e***\u003c/sup\u003eq\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e**\u003c/sup\u003eq\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e*\u003c/sup\u003eq\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe place of death among older adults showed significant urban-rural differences, with urban older adults being more likely to die in a hospital (B\u0026thinsp;=\u0026thinsp;0.7109, 1% significant). Older adults with more years of education (B\u0026thinsp;=\u0026thinsp;0.0921, 1% significant) and higher annual per capita household income (B\u0026thinsp;=\u0026thinsp;0.0474, 5% significant) were more financially able to choose to go to a hospital at the end of life, thus increasing their likelihood of dying in a hospital. In contrast, older adults of lower age (B=-0.0315, 1% significant) mostly did not die spontaneously and rather chose to seek medical help first when faced with a life-threatening illness incident, which increased their probability of dying in the hospital. Older adults who were bedridden before death were more likely to die at home than in the hospital due to mobility reasons (B=-0.2087, 10% significant). Older adults with a community physician were able to access relatively more convenient medical services close to home than those without a community physician, thus reducing the probability of dying in the hospital (B=-0.4334, 1% significant).\u003c/p\u003e \u003cp\u003eFor urbanization at the provincial level, higher rates of urbanization of the population in the province (B\u0026thinsp;=\u0026thinsp;3.0534, 1% significant) correlated with a higher probability of dying in the hospital. At the regional medical care level, a higher number of beds per 10,000 people in health care facilities (B\u0026thinsp;=\u0026thinsp;0.0153, 5% significant) was associated with better health care hardware provided for older adults in the location and an increased likelihood of dying in the hospital. More practising physicians per 10,000 people in the locality (B\u0026thinsp;=\u0026thinsp;0.0141, 10% significant) led to a higher likelihood that older adults would die in a hospital. In terms of community medical resources, the more general practitioners there were per 10,000 population (B=-0.2124, 5% significant), the more likely it was that medical care needs close to home would be met, thus increasing the likelihood that older people will die at home. The number of community service stations per 10,000 people was negatively, but not significantly, associated with the probability of dying in the hospital, partly because China's community care system and hospice services are not yet complete, and most provinces place emphasis on the construction of community service stations while ignoring their service quality. This results in community service stations not being able to provide high-quality community services for elderly individuals; thus, not having a direct impact on the dying place of the elderly poses a direct impact. On balance, provinces with higher levels of population urbanization and regional medical care are more likely to have older adults dying in hospitals, while provinces with more community medical resources, especially general practitioners, are more likely to have older adults dying at home.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInsert Table. 2 About Here\u003c/h3\u003e\n\u003cp\u003eTo further analyse the effect of the interaction between urbanization factors and individual factors at the provincial level on the place of death of elderly individuals, we constructed Model 4. As shown in Model 4 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, urbanization moderated the effect of individual factors to some extent. The number of physician assistants per 10,000 people moderated the effect of being bedridden before death on the place of death. In provinces with a greater abundance of physician assistants, older adults with some mobility before death were more likely to have access to medical care at the time of illness than those who were already bedridden; thus, their probability of dying in a hospital increased accordingly (B = -0.0295, 10% significant). The number of community health service stations per 10,000 people moderated the effect of annual household income per capita on the place of death, with provinces with more community health service stations having a smaller effect of annual household income per capita on the place of death of older people (B=-0.1446, 10% significant). This suggests that the increase in the level of regional health care at the provincial level improves the accessibility of health care services for older adults, especially those with some mobility, and thus increases the probability of dying in a hospital. Community medical resources, on the other hand, can effectively moderate the income effect of elderly people dying in hospitals and to some extent alleviate the inequality of health care utilization brought about by the income gap among elderly individuals.\u003c/p\u003e \n\u003ch3\u003eInsert Fig. 2 About Here\u003c/h3\u003e\n\u003cp\u003eWe attempted to further explore the urban-rural differences in the effect of provincial urbanization factors on the place of death of the elderly in China using subgroup regressions. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of urban-rural subgroup regressions on the effect of urbanization level on the place of death of elderly individuals. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, there was a significant urban-rural difference in the effect of urbanization level on the place of death of elderly individuals. Except for the level of population urbanization, both the corresponding indicators of regional medical level and community health resources had an effect on the place of death of the elderly in urban areas only, while there was no significant effect on the place of death of the elderly in rural areas. More beds in medical institutions per 10,000 people led to a higher likelihood that urban elderly people in the province would die in hospitals (B\u0026thinsp;=\u0026thinsp;0.0348, 1% significant), and more general practitioners per 10,000 people was associated with a higher likelihood of dying at home (B=-0.4262, 5% significant). This difference may be related to the disparate urban-rural disparities in basic public services, such as regional medical care levels and community medical resources, within Chinese provinces. Since the coverage and quality of basic public services are generally lower in rural areas than in urban areas, it is difficult to observe the effect of these factors on the distribution of dying locations of rural older adults.\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\u003eRegression results of urban-rural subgroups of urbanization level affecting the place of death of the elderly\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRural Sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSample Cities\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual level variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0393\u003c/p\u003e \u003cp\u003e(0.1527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0383\u003c/p\u003e \u003cp\u003e(0.1527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2693\u003c/p\u003e \u003cp\u003e(0.1724)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2764\u003c/p\u003e \u003cp\u003e(0.1727)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0677\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0691\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1189\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1201\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0236)\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-0.0485\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0485\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0170\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0172\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0089)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual household income per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0140\u003c/p\u003e \u003cp\u003e(0.0285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0365\u003c/p\u003e \u003cp\u003e(0.0365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0798\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1100\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0346)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailability of doctors in the community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2569\u003c/p\u003e \u003cp\u003e(0.2400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2639\u003c/p\u003e \u003cp\u003e(0.2399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4800\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.2035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.4548\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.2042)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWas he bedridden before he died?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4897\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2153\u003c/p\u003e \u003cp\u003e(0.4798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1376\u003c/p\u003e \u003cp\u003e(0.1693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1644\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.6245)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDid you live alone before you passed away?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2833\u003c/p\u003e \u003cp\u003e(0.1985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2861\u003c/p\u003e \u003cp\u003e(0.1988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0388\u003c/p\u003e \u003cp\u003e(0.2207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0396\u003c/p\u003e \u003cp\u003e(0.2201)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether the spouse was the primary caregiver before death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0382\u003c/p\u003e \u003cp\u003e(0.2232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0360\u003c/p\u003e \u003cp\u003e(0.2234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4222\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.2456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4310\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.2450)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProvince-level variables\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation urbanization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4765\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.1609)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4087\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.1641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5183\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.7072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6190\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.6952)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of beds in medical institutions per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003cp\u003e(0.0107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003cp\u003e(0.0107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0348\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0344\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0104)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of licenced physician assistants per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0134\u003c/p\u003e \u003cp\u003e(0.0103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0218\u003c/p\u003e \u003cp\u003e(0.0171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0147\u003c/p\u003e \u003cp\u003e(0.0118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0529\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0246)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of elderly beds per 1,000 elderly people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0307\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0310\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0062\u003c/p\u003e \u003cp\u003e(0.0157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003cp\u003e(0.0157)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of community health service stations per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0683\u003c/p\u003e \u003cp\u003e(0.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2380\u003c/p\u003e \u003cp\u003e(0.5852)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8077\u003c/p\u003e \u003cp\u003e(0.7838)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3115\u003c/p\u003e \u003cp\u003e(0.8234)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of general practitioners per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1498\u003c/p\u003e \u003cp\u003e(0.1427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1499\u003c/p\u003e \u003cp\u003e(0.1427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4262\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.4152\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.1674)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether bedridden before death* Number of practising physician assistants per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0130\u003c/p\u003e \u003cp\u003e(0.0214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0487\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.0283)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual per capita household income* Number of community health service stations per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1053\u003c/p\u003e \u003cp\u003e(0.1113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1730\u003c/p\u003e \u003cp\u003e(0.1208)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.8315\u003c/p\u003e \u003cp\u003e(0.9368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.0483\u003c/p\u003e \u003cp\u003e(0.9845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.3079\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.2600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.2584\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.3507)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effect parameters\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\u003eProvince level variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0323\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0323\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3116\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3052\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual level variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.29 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote:\u003csup\u003e***\u003c/sup\u003e q\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e**\u003c/sup\u003eq\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e*\u003c/sup\u003eq\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eInsert Table. 3 About Here\u003c/h3\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eUsing a combination of microdata from the China Follow-up Survey on Health Influences on the Elderly (CLHLS) 2011\u0026ndash;2018 mortality sample and macro data on economic development and social service indicators at the interprovincial level from the China Statistical Yearbook, this study constructed a hierarchical logistic model to discuss the effects of the level of urbanization, especially the level of regional medical care and community medical resources, on the place of death of Chinese elderly people at the provincial level. The interaction between these macrostructural factors and micro individual factors was also analysed, and urban-rural differences were examined based on urban-rural subsample regression.\u003c/p\u003e \u003cp\u003eFirst, this study found that the proportion of older adults dying at home was very high in both rural and urban China during 2011\u0026ndash;2018, with 95.04% dying at home in rural areas and 81.53% dying at home in urban areas. Overall, nearly 90% of older adults nationwide were dying at home. This is generally consistent with the findings of Gu et al. (2007)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], Cai et al. (2017)[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and Zhang Lilong and Han Runlin (2020)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In terms of the trend of dying location, the results based on the full sample suggest that the hospital dying ratio of older Chinese adults shows a trend of increasing and then decreasing with urbanization, which is similar to the historical evolution trajectory of developed countries. At the end of the 20th century, some developed countries became more urbanized, and the trend of a continuous increase in the hospital dying ratio peaked and began to reverse[\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Wilson et al. (2001, 2009) found that the proportion of hospital deaths in Canada continued to rise after 1950, peaking at 80.5% in 1994, and then declined substantially to 60.6% in 2004, with a corresponding increase in the number of people dying at home[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. It is worth noting that the place of death of the elderly in China has evolved rapidly, which may be related to the characteristics of rapid urbanization in China. Based on the results of the urban-rural subsample, the change in the place of death of the elderly in urban China has clearly shown a \"deinstitutionalization\" characteristic similar to late urbanization in developed countries, while the place of death of the elderly in rural China is still in the stage of changing from home to hospital, and the change has been very slow. This finding corroborates the conjecture of urban-rural differences in patterns of the place of death of older adults in China proposed by Dong et al. (2019)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This significant urban-rural variation may be related to China's long-standing urban-rural dichotomous structure. It is important to note that the above shift, both in the full sample and in the subsample, is based on the fact that China still has the family as the primary place of death; thus, this trend cannot be overestimated.\u003c/p\u003e \u003cp\u003eSecond, we found that the place of death of Chinese elderly people is highly correlated with the urbanization level of the province in which they live and whether this association shows urban-rural differences varies according to the specific dimension of urbanization level. There was no significant urban-rural difference in the effect of the population urbanization rate on the place of death. In provinces with higher rates of population urbanization, both rural and urban older adults were relatively more likely to die in a hospital. This finding is consistent with the analysis of Lin et al. (2007) using 1995\u0026ndash;2004 data from Taiwan, which concluded that the level of population urbanization in a region is negatively associated with the ratio of older adults dying at home in that region[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The level of regional medical care and community medical resources at the provincial level also had important effects on the place of death of older adults, but this effect varied significantly between urban and rural areas in China, as evidenced by the fact that these corresponding variables only had a significant effect on the place of death of older adults in urban areas but not in rural areas. At the provincial level, urban older adults were more likely to die in a hospital when the number of medical beds per 10,000 people was higher. Yang et al. (2006), using Japanese data from 1951\u0026ndash;2002, found that the increase in the number of medical beds was the main reason for the increase in the proportion of deaths in hospitals in Japan during this period[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, we found that higher numbers of community general practitioners per 10,000 people at the provincial level was associated with an increased likelihood of urban older people to die at home. Empirical evidence based on other countries has also found that the abundance of community medical resources and advances in home hospice care are important factors in reversing the trend of hospital death in developed countries. The findings of Xu et al. (2020) related to changes in the place of death of older adults with dementia in the United States from 2000\u0026ndash;2014 and the conclusion of Muramatsu et al. (2008) comparing the effects of state spending on home and community services (HCBS) on the place of death of older adults in the United States both showed that investment in community medical resources at the interstate level was highly positively associated with the ratio of older adults dying at home and that increased investment in community and home health care resources helped older adults choose to die at home[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. It is also noteworthy that, in developed countries, the effects of improved regional health care and advances in community medical resources and home hospice on hospital dying ratios occurred over two long periods, but due to rapid urbanization in China, these forces gained rapid growth and overlapping effects in just 8 years between 2011 and 2018, showing a simultaneous effect in urban areas of China. The partial substitution of hospital dying for traditional home dying is based on improved regional medical care, and the reversal of hospital dying to home dying is based on advances in community medical resources and home hospice care.\u003c/p\u003e \u003cp\u003eIn contrast to urban China, the end-of-life location of older adults in rural China is primarily driven by personal and family factors. Regional differences in urbanization in China and the interprovincial differences in medical resources and community medical resources are concentrated in urban areas, and the changes in end-of-life location caused by these differences are also mainly found in urban areas. Neither the increase in the number of medical beds, the increase in the number of medical practitioners, the increase in the number of community health service stations, nor the increase in the number of general practitioners at the provincial level could effectively lead to changes in the place of death of the elderly in rural areas within the province, which may also be related to the traditional concept that the elderly in rural China \"return to their roots\". Compared to rural areas in developed countries, the end-of-life location of the elderly in rural China seems to be just at the stage of changing from home to the hospital. This shift still lacks a strong driving force at this time because of the large urban-rural disparity in the distribution of health care resources in most Chinese provinces.\u003c/p\u003e \u003cp\u003eThis paper also has some shortcomings. First, because the proportion of elderly people dying in nursing homes in the sample covered in this paper was very low, we combined the sample dying in nursing homes with the sample dying in hospitals, thus dichotomizing the dependent variable, with 0 representing dying at home and 1 representing dying in hospitals. However, this treatment did not allow for a separate examination of older adults dying in nursing homes. In the future, when the sample size is sufficient, this part of the sample can be separated for corresponding analysis. Second, given that community health services in China are still incomplete, the indicator system of community medical resources is not yet sound, which makes this paper lack more direct evidence in analysing the impact of community medical resources. Third, due to the limitations of the study data, this paper did not introduce medical variables, such as the type of lethal disease in the modelling, nor did it breakdown the disease status of the elderly before death, which is also an important factor affecting the place of death of elderly individuals. This could be analysed in more detail and depth in subsequent studies when conditions are adequate. For example, the impact of urbanization in China on the place of death of specific populations of older Chinese adults with Alzheimer's disease or cancer could be explored.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study analysed the distribution and changing trajectories of the place of death of older adults in China and the impact of urbanization on the place of death of older adults based on both micro- and macrolevel empirical data. The study showed that older Chinese adults die mainly at home, especially in rural areas. The pattern of change in the place of death differs between urban and rural areas, with urban areas showing \"deinstitutionalization\" similar to that of developed countries in late urbanization, while rural areas are still in the stage of transition from home to the hospital. There were significant urban-rural differences in the impact of urbanization on the place of death of older adults. In cities, improvements in regional health care at the provincial level increase the probability of dying in a hospital, while the abundance of community medical resources and advances in home hospice care provides a boost to dying at home. In urban China, these two effects occur in tandem. The same cannot be said for rural areas. Improvements in regional health care can increase access to care for older adults with some mobility and increase the probability of dying in the hospital. The abundance of community medical resources can alleviate the inequality in health care utilization caused by income disparities among older adults. This study deepens the analysis of the characteristics and causes of the place of death of older adults in China and has important practical implications for proposing social policies that optimize the quality of life of older adults at the end of life and contribute to the implementation of the Healthy China strategy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe greatly appreciate the support and the hard work of the participants during this study.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo; \u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMiaoyu Yuan and Li Tu wrote the main manuscript text . Ankang Hu prepared figures 1-2. Nan Xiang and Lin Cheng prepared tables 1-3. All authors reviewed the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are freely available on the Peking University Open Research Data Platform(https://opendata.pku.edu.cn/) and the website of the National Bureau of Statistics of China(http://www.stats.gov.cn/sj/ndsj/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a secondary analysis of the data from the CLHLS. The CLHLS study was approved by the Ethics Committee of Peking University (IRB00001052\u0026ndash;13074). The participants provided their written informed consent to participate in this study. And informed consent was obtained from literate participants and legal guardian(s)/next of kin of illiterate participants. All methods were performed in accordance with the guidelines and regulations.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThe Communist Party of China (CPC) Central Committee and the State Council. The \u0026quot;Healthy China 2030\u0026quot; blueprint. http://www.gov.cn/zhengce/2016-10/25/content_5124174.htm. Accessed 24 Dec 2022.\u003c/li\u003e\n\u003cli\u003eHoare S, Morris ZS, Kelly MP, et al. Do patients want to die at home? A systematic review of the UK literature, focused on missing preferences for place of death. 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International Journal of Environmental Research and Public Health. 2017,14:1210.\u003c/li\u003e\n\u003cli\u003eZhang Lilong, Han Runlin. A Study on the Death Site among the Elderly and Its Influencing Factors in China. Population Journal. 2020,42:102-112.\u003c/li\u003e\n\u003cli\u003eMor V, Hiris J. Determinants of site of death among hospice cancer patients. J Health Soc Behav. 1983,24:375-385.\u003c/li\u003e\n\u003cli\u003eMcMillan A, Mentnech RM, Lubitz J, et al. Trends and patterns in place of death for Medicare enrollees. Health Care Financ Rev. 1990,12:1-7.\u003c/li\u003e\n\u003cli\u003eHigginson IJ, Astin P, Dolan S. Where do cancer patients die? Ten-year trends in the place of death of cancer patients in England. Palliat Med. 1998,12:353-363.\u003c/li\u003e\n\u003cli\u003eSleeman KE, Ho YK, Verne J, et al. Reversal of English trend towards hospital death in dementia: a population-based study of place of death and associated individual and regional factors, 2001-2010. BMC Neurol. 2014,14:59.\u003c/li\u003e\n\u003cli\u003eWilson DM, Northcott HC, Truman CD, et al. Location of death in Canada - A comparison of 20th-century hospital and nonhospital locations of death and corresponding population trends. Eval Health Prof. 2001,24:385-403.\u003c/li\u003e\n\u003cli\u003eWilson DM, Truman CD, Thomas R, et al. The rapidly changing location of death in Canada, 1994\u0026ndash;2004. Soc Sci Med. 2009,68:1752-1758.\u003c/li\u003e\n\u003cli\u003eMuramatsu N, Hoyem RL, Yin H, et al. Place of death among older Americans - Does state spending on home- and community-based services promote home death? MEDICAL CARE. 2008,46:829-838.\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":"older adults, place of death, urbanization, hierarchical logistic model","lastPublishedDoi":"10.21203/rs.3.rs-2755464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2755464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe place of death is an important measure of death quality. This study aimed to analyse the distribution and changes in the place of death of elderly individuals in China from an interprovincial perspective and its intrinsic association with rapid urbanization.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA hierarchical logistic model was constructed to carry out the analysis, using a combination of micro data from the China Health Influence Tracking Survey on the Elderly (CLHLS) 2011, 2014, and 2018 death samples and macro data at the provincial level from the China Statistical Yearbook.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFrom 2011\u0026ndash;2018, 95.04% of older Chinese adults died at home in rural areas, while 81.53% in urban areas. The overall hospital dying ratio of older adults first increased and then decreased, with the hospital dying ratio of urban older adults showing a significant downwards trend and rural older adults showing a slow upwards trend. The higher the number of medical beds per 10,000 people, the more likely the urban elderly are to die in hospitals. The higher the number of community general practitioners per 10,000 people, the more likely the urban elderly are to die at home. Older adults who were bedridden before death were more likely to die in a hospital, it was negatively moderated by the number of physician assistants per 10,000 population. Older adults with lower income were more likely to die at home, it was negatively moderated by the number of community health posts per 10,000 people.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eChinese older adults mainly die at home, especially in rural areas. The place of death of the urban elderly has become \"deinstitutionalized\", while rural elderly individuals are still in the stage of transition from home to the hospital. In urban China, the positive effect of regional medical care level on hospital dying and the positive effect of community medical resources on home dying occur simultaneously. Improvements at the regional medical level can increase the accessibility of medical services for older adults with certain mobility abilities and increase their probability of dying in the hospital. The abundance of community medical resources can alleviate the inequality of medical care utilization caused by the income disparity of elderly individuals.\u003c/p\u003e","manuscriptTitle":"The impact of urbanization on the place of death of older adults in China from an interprovincial perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-12 20:50:39","doi":"10.21203/rs.3.rs-2755464/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a52a8cca-ca0f-4405-9351-4f2b1205262b","owner":[],"postedDate":"April 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-29T13:14:39+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-12 20:50:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2755464","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2755464","identity":"rs-2755464","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}