Inpatient preference among patients with multiple chronic conditions in China: a discrete choice experiment

Inpatient preference among patients with multiple chronic conditions in China: a discrete choice experiment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inpatient preference among patients with multiple chronic conditions in China: a discrete choice experiment Yueqin Wang, Yuehua Chen, Ling Xiao, Qingling Su, Xiaoyin Huang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4011440/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Feb, 2025 Read the published version in BMC Primary Care → Version 1 posted 15 You are reading this latest preprint version Abstract Background Multiple chronic conditions (MCC) have become a leading cause of hypovitalism and death among the population. Although the role of chronic disease management has been strengthened for primary health institutions, patients still have a preference for a higher level of inpatient service, leading to a considerable hindrance in the efficient utilization of healthcare resources. Thus, this study aimed to identify factors that affected MCC patients' inpatient preference and the extent to which these factors impact their decisions, guiding for inpatient service utilization among MCC patients. Methods Five attributes (institutional scale, waiting time for hospital admission, with or without acquaintances, time from residence to hospital, out-of-pocket expenses per time) were identified to estimate inpatient choice for MCC patients through a discrete choice experiment. Partial factor analysis was performed to generate selection sets. Data were collected from MCC patients between 35 and 75 years old, in Fuqing, Fujian Province. A mixed logit model was performed to analyze MCC patients' preferences for each attribute. Willingness to pay was estimated by regression coefficients, and subgroup analysis was conducted based on the patient’s demographic characteristics and overall perceived disease severity. Results Totally 504 valid questionnaires were included in the analysis. MCC patients preferred to have a shorter time from their residence to the hospital (β = 0.7602, p < 0.001), large provincial and municipal tertiary hospitals (β = 0.2635, p < 0.001), and have beds available on the day (β = 0.1962, p = 0.0028). Out-of-pocket expenses per time (β=-0.0006, p < 0.001) are a negative predictor of patients' inpatient preference. Additionally, Patients were willing to pay ¥1253, ¥434, and ¥323 for higher institutional scale, shorter waiting time, and shorter time from residence-to-hospital, respectively. The findings of subgroup analysis indicated that some demographic characteristics (age, gender, education and total household income) and overall perceived disease severity also influence MCC patients' inpatient preferences. Conclusion This study provides evidence on the inpatient preferences among MCC patients. To better meet patients’ needs, it is recommended to improve the geographical accessibility of medical and health services, strengthen the service capacity of medical personnel in county and community-level medical institutions, streamline the inpatient diagnosis and treatment process, and so on. inpatient preferences multiple chronic conditions (MCC) discrete choice experiment (DCE) 1 Introduction Chronic diseases have become a major public health challenge facing the international community, which accounts for 30% of global deaths( 1 ) and almost 80% of all deaths in Chinese people aged 60 years or over( 2 ). According to the World Health Organization report in “China’s assessment report on aging and health”, chronic diseases have emerged as the leading cause of mortality in China with the changes in the disease spectrum of the population, and its prevalence will increase at least 40% by 2030( 3 ). Under this serious context, it is increasingly prevalent for residents to suffer from multiple chronic conditions (MCC) at the same time. MCC refers to the presence of two or more chronic diseases in a patient that persists for at least 1 year or more( 4 ). It can impair patients’ ability to perform activities of daily living, resulting in decreased quality of life, increased psychological stress, escalated treatment costs, and aggravated adverse effects of treatment or intervention( 5 ). To strengthen the management of chronic diseases, China has issued a series of supportive policies. In 2015, it was explicitly proposed to build a hierarchical diagnosis and treatment system (HDTS) mainly focusing on management of chronic diseases, such as hypertension and diabetes. HDTS refers to different levels of medical institutions undertaking the therapy tasks by the disease severity( 6 ); in 2017, it was required that the member institutions of medical alliance should clarify their functional positioning, and the tertiary hospitals gradually reduce the proportion of chronic disease patients with stable conditions( 7 ); in 2018, family doctor contract service was steadily implemented by primary health institutions giving priority to key groups such as patients with chronic diseases( 8 ). However, even with these above-mentioned policies, from 2015 to 2020, the proportion of admissions in primary health institutions decreased from 19.17–16.11%, while that of general hospitals increased from 58.59–59.04%( 9 , 10 ). It indicated that patients still prefer higher levels of inpatient service, which runs counter to HDTS and is not conducive to the efficient utilization of health resources for MCC patients. To reverse this situation and improve pertinent inpatient service delivery strategies, it is particularly important to comprehend the inclination towards inpatient service utilization among MCC patients. Extensive studies have been conducted on the flow of medical treatment for patients with chronic diseases, and the overall finding is that the factors affecting patients' selection of medical institutions include patient personal factors and external environmental factors, such as socio-demographic characteristics, out-of-pocket expenses, convenience of services, and level of medical institutions. However, most scholars pay attention to specific chronic diseases, such as hypertension ( 11 ), Parkinson( 12 ), diabetes( 13 ), etc. Simultaneously, most research focuses on outpatient service preference( 14 , 15 ), while there is a dearth of evidence on MCC patients and their inpatient preference. Furthermore, in the research field of patients' health preferences, the analysis methods routinely used include multivariate analysis( 16 ), logistic regression analysis( 17 ), cross analysis( 18 ), etc., which do not pay attention to the fact that patients' inpatient preference is the result of combinations of multiple factors and that the importance of different factors varies. In recent years, discrete choice experiment (DCE) has been put into use in the research field of patient preference( 12 , 15 ). DCE is based on random utility theory assuming that respondents always prefer the alternative that offers the greatest utility, and its overall utility is decomposed by its attributes( 19 ). It can quantitatively analyze the impact of the determinants on patient preference for seeking medical services to better understand their health demands. Therefore, to bridge the knowledge gap of inpatient preference among MCC patients, as well as overcome the limitations of previous analytical methods, this study aims to investigate MCC patients’ inpatient preferences and identify the key influencing factors of their choices through DCE. The results of this study will not only guide to improve the inpatient service utilization for MCC patients, but also promote optimal allocation of healthcare resources. 2 Methods 2.1 Determination of attributes and levels Firstly, by reviewing relevant literature, this study preliminarily determined five attributes in DCE, including institutional scale, waiting time for hospital admission, time from residence to hospital( 20 ), out-of-pocket expenses per time and hospitals with or without acquaintances( 21 ). Among them, “institutional scale” and "waiting time for hospital admission" were subdivided from the behavioral model of health services use proposed by Andersen( 20 , 22 ). Additionally, since patients generally value the economic accessibility and spatial convenience of health-seeking behavior, for example, the out-of-pocket cost for a 30-day supply( 12 ), service price( 23 ), and travel time were verified in empirical study; and thus “time from residence to hospital” and “out-of-pocket expenses per time” were included in the attribute list. Moreover, considering the nature of China's nepotistic society in which people want to use “relationships” to gain convenience in normal procedures, the attribute of “hospital with or without acquaintances” was considered. Secondly, by consulting with 6 relevant experts in the field of healthcare management, the attributes and their levels were further refined, summarized, and defined. Details of the attributes and levels are shown in Table 1 . Table 1 List of attributes and levels among MCC patients in discrete choice experiment Attributes Levels Explanation of attributes Institutional scale District/county hospitals; Large provincial and municipal tertiary hospitals The rank of inpatient institutions reflects hospital qualifications such as hospital functions, facilities, and sizes Waiting time for hospital admission Bed available on the day; Wait 3 days for beds The waiting time to obtain inpatient services provided by medical institutions Hospital with or without acquaintances Acquaintance; No acquaintance The patient has acquaintances in the inpatient institution or not Time from residence to hospital Less than 1 hour; More than 3 hours The time it takes patients to travel from home to the inpatient institution Out-of-pocket expenses per time ¥800; ¥2000; ¥5000 The average expense that patients need to pay all by themselves per hospitalization Notes: CNY Chinese Yuan; MCC multiple chronic conditions. 2.2 Experiment design and questionnaire development Due to various inpatient facility choice tasks, the number of attributes and levels (2 4 ×3 1 =48) was considered impractical for a full factorial design. To improve the acceptance of the questionnaire and cooperation of the respondents, partial factorial design was conducted to maximize the D efficiency by SAS. Furthermore, 12 representative pairs of choice sets were obtained, which were set up using the unmarked-choice format with two medical facility options. This study assigned all selection sets into three different versions of the questionnaire, each containing four choice sets. The main purpose was to avoid cognitive fatigue among respondents and improve questionnaire quality. Previously, Friedel JE( 24 ) thought that the number of selection sets should not exceed 10 unless the option content was very simple. Bech M’s research( 25 ) has found that the choice of patients who completed 17 selection sets was guided by one attribute. There were also studies( 26 , 27 ) that involved completing 4 selection sets. Ultimately, opt-out options were not set in DCE because while it avoids patients making difficult choices between options, it doesn’t generate the highest utility and provide the most adequate preference information( 28 ). Thus, opt-out options were not set in this study. Ultimately, the questionnaire mainly consisted of three parts: demographic characteristics, health status, and the DCE tasks (the formal questionnaire is in Supplementary material 1, Additional file). 2.3 Sample size This study followed the DCE sample size estimation principle proposed by Orme( 29 ). The following formula was used to calculate the minimum sample size: N ≥(500* C ) / ( T * A ) Among them, N represents the number of respondents, T represents the number of choice sets that the respondents need to complete, A is the number of options that the respondents need to complete in a single choice set, and C is the maximum level of any attribute. According to the above formula, the sample size of this study needed to be greater than 188 respondents. 2.4 DCE implementation and data collection To improve the reliability and validity of the questionnaire, a small-scale pilot survey was conducted before the formal investigation. According to the feedback of the pilot survey, the expression text of the questionnaire was adjusted, especially for the DCE items. For quality assurance, a survey training manual was compiled to train the interviewers before the formal investigation. The formal survey was conducted in Fuqing, Fujian Province from November 2021 to January 2022. The inclusion criteria were as follows: ( 1 ) participants were between 35 and 75 years old; ( 2 ) patients who self-reported being diagnosed with two or more chronic diseases by doctors in community hospitals and above. Interviewers were trained to ensure the formal questionnaire survey was conducted smoothly. 2.5 Statistical analysis Descriptive analysis was used to present demographic characteristics of respondents. In this study, the DCE data analysis used a mixed logit model and a conditional logit model, and the most suitable model was determined based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The smaller the values of AIC and BIC, the better the model fit( 30 ). The “out-of-pocket expenses per time” attribute was used to calculate the willingness to pay (WTP). WTP was the negative ratio of the non-economic attribute coefficient to the economic attribute coefficient, which reflected the monetary value of the non-economic attribute that affected the preference of inpatient institutions. Additionally, a subgroup analysis was performed to estimate the heterogeneity of inpatient preference among MCC patients. All statistical analyses were performed using the SAS.9.3 and Stata 16. The p value ≤ 0.05 indicated statistical significance. 3 Results 3.1 Respondents’ demographic characteristics A total of 642 MCC patients participated in the survey, with 138 excluded due to non-compliance with inclusion criteria and a lack of understanding of DCE choices. Ultimately, a sample of 504 patients is enrolled. Table 2 shows that there are 341 females, 230 patients aged less than 60 years old, 455(90.3%) subjects are married, more than half of the patients have formal education, and 83(16.5%) subjects were farmers. About 60% of patients have 1 to 3 family members. Nearly 70% of the subjects have a total household income of less than ¥60,000 last year, and more than 60% of the patients have an overall perceived disease severity score ranging from 1 to 5 points. Table 2 Demographic characteristics of the study participants ( n = 504) Variable Number Percentage (%) Gender Male 163 32.3 Female 341 67.7 Age < 60 years old 230 45.6 ≥ 60 years old 274 54.4 Marital status Married 455 90.3 Other 49 9.7 Education No formal education 235 46.6 Formal education 269 53.4 Profession Farmer 83 16.5 Worker 38 7.5 Housework 276 54.8 Unemployed/laid off 29 5.8 Other 78 15.5 Number of family member 1-3people 305 60.5 4-13people 199 39.5 Total household income last year(CNY) < 10000 82 16.3 10000–29999 142 28.2 30000–59999 122 24.2 60000–99999 52 10.3 100000–149999 65 12.9 150000–299999 27 5.4 ≥ 300000 14 2.8 Overall perceived severity of illness 1–5 318 63.1 6–10 137 27.2 ≥ 10 49 9.7 Other marital status includes single, widowed, separated/divorced; CNY Chinese yuan 3.2 Model estimation of preferences According to the results in Table 3 and Table 4 , the AIC of the mixed logit model was 2058.564 and the BIC is 2115.282. The AIC of the conditional logit model is 2149.713 and the BIC is 2181.223. By comparing the AIC and BIC of the two models, it can be seen that the mixed logit model was more effective in analyzing the inpatient preference for MCC patients. Thus, the analysis of DCE below is mainly based on the results of the mixed logit model. Table 3 Estimates of the mixed logit model ( n = 504) Attributes Coefficient S.E. p value 95 % CI Institutional scale (Ref: District/county hospital) Large provincial and municipal tertiary hospitals 0.2635 0.0754 < 0.001 (0.1156 to 0.4113) Waiting time for hospital admission (Ref: Wait 3 days for beds) Bed available on the day 0.1962 0.0892 0.028 (0.0214 to 0.3709) Hospitals with or without acquaintances (Ref: No acquaintances) Acquaintances 0.1429 0.0780 0.067 (-0.0099 to 0.2958) Time from residence to hospital (Ref: More than 3 hours) Less than 1 hour 0.7602 0.1060 < 0.001 (0.5526 to 0.9679) Out-of-pocket expenses per time -0.0006 0.00005 < 0.001 (-0.0007 to -0.0005) Log likelihood -1020.282 AIC 2058.564 BIC 2115.282 Observations 4032 Sample size 504 CNY Chinese yuan Table 4 Estimates of the conditional logit model ( n = 504) Attributes Coefficient S.E. p value 95% CI Institutional scale (Ref: District/county hospital) Large provincial and municipal tertiary hospitals 0.1974 0.04810 < 0.001 (0.1032 to 0.2916) Waiting time for hospital admission (Ref: Wait 3 days for beds) Bed available on the day 0.1933 0.06190 0.002 (0.0720 to 0.3145) Hospitals with or without acquaintances (Ref: No acquaintances) Acquaintances 0.0968 0.05180 0.061 (-0.0047 to 0.1983) Time from residence to hospital (Ref: More than 3 hours) Less than 1 hour 0.5220 0.06720 < 0.001 (0.3902 to 0.6537) Out-of-pocket expenses per time -0.0004 0.00002 < 0.001 (-0.0004 to -0.0003) Log likelihood -1020.282 AIC 2149.713 BIC 2181.223 Observations 4032 Sample size 504 CNY Chinese yuan Table 3 reveals that all attributes are statistically significant except for “hospital with or without acquaintances”. Taking “district/county hospital” as the reference, the value of the influence degree β(95%CI) of “large provincial and municipal tertiary hospitals” on the patients’ inpatient preferences is 0.2635(0.1156 to 0.4113). Taking “waiting 3 days for beds” as a reference, the value of the influence degree β(95%CI) of “having beds available on the day of hospitalization” on the patients’ inpatient services utilization is 0.1962(0.0214 to 0.3709). Compared with the time from the residence to the hospital for “more than 3 hours”, the value of the influence degree β(95%CI) of “the time from the residence to the hospital within 1 hour” on the patients’ inpatient preferences is 0.7602(0.5526 to 0.9679). The value of the influence degree β(95%CI) of “the out-of-pocket expenses per time” on patients' inpatient preference is -0.0006(-0.0007 to -0.0005). The results show that the most important attribute for patients when accessing inpatient services is the short time from their residence to the hospital, followed by “large provincial and municipal tertiary hospitals”, “beds available on the day of hospitalization”, and finally the “low out-of-pocket expenses per time”. Among them, “out-of-pocket expenses per time” is a negative predictor of patients' inpatient services utilization. 3.3 Marginal WTP Table 5 shows that the WTP for other attributes is statistically significant except for “hospital with or without acquaintances”. Compared with the district/county hospital, the patient’s WTP for large provincial and municipal tertiary hospitals is ¥434(95%CI:188 to 680). Compared with waiting for beds for 3 days, the patient’s WTP for beds available on the day is ¥323(95%CI:32 to 614). Compared with the time from the residence to the hospital for more than 3 hours, the patient's WTP within 1 hour was ¥1253(95%CI: 927 to 1580). The WTP for attributes from high to low is as follows: “short time from residence to hospital”, “large hospitals”, and “beds available on the day of hospitalization”. Table 5 Willingness to pay for non-economic work attributes Attributes WTP (95%CI) Institutional scale (Ref: District/county hospital) Large provincial and municipal tertiary hospitals 434(188 to 680) Waiting time for hospital admission (Ref: Wait 3 days for beds) Bed available on the day 323(32 to 614) Hospitals with or without acquaintances (Ref: No acquaintances) Acquaintances 236(-17 to 488) Time from residence to hospital (Ref: More than 3 hours) Less than 1 hour 1253(927 to 1580) 3.4 Model estimates for each subgroup Table 6 shows a subgroup analysis of inpatient preference among MCC patients. In the subgroup analysis, it was found that the preference of “time from residence to hospital” and “out-of-pocket expenses per time” were significantly affected by gender, age, education, total household income, and perceived disease severity in all subgroups. Table 6 Results of subgroup analysis ( n = 504) Attributes Gender Age Education Total household income Overall perceived severity of illness Male Female < 60 years old ≥ 60 years old No formal education Formal education <¥30,000 ≥¥30,000 < 10 ≥ 10 Institutional scale (Ref: District/county hospital) Large provincial and municipal tertiary hospitals 0.1632 0.3820*** 0.3964** 0.1762 0.2817* 0.2473* 0.3227** 0.2395* 0.2122** 0.7085** Waiting time for hospital admission (Ref: Wait 3 days for beds) Bed available on the day 0.4726*** 0.0258 0.5410*** -0.0011 0.0772 0.3239** 0.1054 0.3388** 0.2448** 0.1149 Hospitals with or without acquaintances (Ref: No acquaintances) Acquaintances 0.2858** 0.0734 0.3471* 0.0187 -0.0267 0.2736** 0.0316 0.2329* 0.1021 0.4356 Time from residence to hospital (Ref: More than 3 hours) Less than 1 hour 0.5411*** 0.9381*** 0.9230*** 0.7608*** 1.0442*** 0.6197*** 0.8029*** 0.7299*** 0.7193*** 0.9312** Out-of-pocket expenses per time -0.0005*** -0.0007*** -0.0007*** -0.0006*** -0.0008*** -0.0005*** -0.0007*** -0.0005*** -0.0006*** -0.0004*** * p value < 0.05, ** p value < 0.01, *** p value < 0.001; CNY Chinese yuan In the gender group, females tend to take advantage of large tertiary hospitals, while males prefer “bed available on the day” and “have acquaintances in the hospital”. In the age group, “institutional scale”, “waiting time for hospital admission”, and “hospitals with or without acquaintances” are statistically significant for patients younger than 60 years, but not found in the group aged 60 years or greater. In the subgroup analysis of education and total household income, the preference of patients with no formal education or income<¥30000 is affected by the attribute of “institutional scale”, while those with formal education or income≥¥30000 are affected by all attributes. In the perceived disease severity group, patients with perceived disease severity < 10 tend to be hospitalized on the same day and large facility, while patients with a higher perceived disease severity tend to be admitted to large tertiary hospitals. 4 Discussion To the best of our knowledge, there is a dearth of research on the inpatient preference among MCC patients. This study identified the influencing factors and accessed their relative importance on the inpatient preference among MCC patients. It revealed that the ideal inpatient institutions preferred by MCC patients may include following four conditions: “short time from residence to hospital”, “large provincial and municipal tertiary hospitals”, “short waiting time for beds”, and “low out-of-pocket expenses per time”. Gaining insight into the inpatient preferences among MCC patients is crucial for efficient medical services utilization and optimal allocation of healthcare resources. The results showed that MCC patients valued the “short time from residence to hospital” in selecting inpatient institutions, and were willing to pay much more for it than other attributes. There were no differences to this point between different subgroups. The findings were similar to previous studies( 31 ), patients generally preferred medical institutions located in short distance, or can be reached by their private car or public transport( 32 ). The plausible reason for this phenomenon may be that chronic diseases have a long course and require long-term monitoring and management( 33 ), so the patients' demand on the convenience and accessibility of inpatient services would be much greater. Additionally, short distance to medical institutions benefit patients’ health consequences, it was also understandable that MCC patients were more willing to pay an extra fee to obtain medical care in short distance( 34 ). Institutional scale was the second most important attribute of patients' preference for inpatient institutions. Since many tertiary hospitals in China's provinces and cities were medical institutions with better technical capability and efficiency( 35 ), it is not hard to understand that MCC patients were more willing to go to large provincial and municipal tertiary hospitals to obtain high-quality medical services. The qualifications or expertise of medical service providers was an important determinant of patients' medical institution choice( 32 , 36 ). Additionally, the subgroup analysis further revealed that patients in the female group and the non-elderly group showed a preference for seeking inpatient treatment in large provincial and municipal tertiary hospitals( 35 ). Regarding the attribute of waiting time for hospital admission, medical institutions with beds available on the day of hospitalization were preferred by MCC patients. This was consistent with previous studies that demonstrated time spent on waiting lists and in the waiting room( 37 , 38 ) had a negative impact on patient preference for treatment. Reducing waiting time was important for most patients( 39 ), which can reduce waiting time and allow for timely hospitalization. In further subgroup analysis, patients in the male group, non-elderly group, educated group, higher household income group, and overall perceived mild or moderate severity of illness group were shown more likely to choose medical institutions with beds available on the day of hospitalization. These results were consistent with previous studies that some male patients showed reluctance to wait when seeking medical help( 40 ), and those young, middle-to-high-income, mild-symptom, educated patient groups placed more value on the personal experience of the entire medical service process, especially the waiting time( 15 ). Out-of-pocket expense per time was a negative predictor of patients choosing inpatient institutions, and this result persisted in all subgroups, indicating that patient preferences decreased as out-of-pocket expenses per time increased. Patients preferred to visit medical institutions with less out-of-pocket expenses, regardless of whether they perceived a mild or severe disease. Since the common health inequalities caused by income disparities( 41 ), low-income groups faced greater barriers to obtaining adequate healthcare services( 42 ), Especially for the MCC patients confronting more complications and incurring medical expenses, even if they have health insurance as a backup, which does not alleviate the financial burden on those who are already poor. Thus, their preference for inpatient institutions with low out-of-pocket expenses per time made perfect sense. Noteworthy, the attribute “hospitals with or without acquaintances” was less essential for patients’ inpatient service utilization, which was inconsistent with the research hypothesis. The reason for this may be that patients’ trust in doctors had been greatly enhanced with more transparent diagnosis and treatment process. Having got sufficient information resources, patients did not pay much attention to their acquaintances when selecting inpatient service providers( 43 ). Conversely, the results of subgroup analysis showed that patients in the male group, non-elderly group, educated group, and higher household income group tended to be hospitalized with an acquaintance. Since these patient groups were more concerned about the experience of seeking medical care and were reluctant to spend much time in queues( 15 , 44 ), they may be more inclined to obtain more prompt and effective medical services through acquaintances. Based on the patients’ preference for shorter travel time, shorter waiting time, and lower out-of-pocket expenses, the following measures are proposed. Firstly, it is recommended to continuously optimize the allocation of healthcare resources and improve the geographical accessibility of health services. Special attention should be paid to narrowing the gap in the healthcare resource allocation between regions to meet the health needs among vulnerable groups. Secondly, to further shorten the waiting time for inpatients, some efforts can be made to streamline the inpatient diagnosis and treatment process to increase bed turnover rate. Thirdly, on the basis of expanding the coverage of basic medical insurance, it is also recommended to steadily increase the reimbursement rate of designated medical institutions of basic medical insurance. The support of other insurance, such as commercial insurance, can be also integrated to reduce economic burden for patients. This study was strengthened by some distinguished features. Firstly, different from previous studies that have mostly focused on influencing factors of outpatient preference for patients with a chronic disease, this study enriched empirical research in related fields by providing valuable information regarding inpatient preference among MCC patients. Secondly, DCE was applied to better understand the relative importance of the attributes in the process of medical care, with a mixed logit model adopted for further analyzing unobservable utility and taking individual differences scrupulously into account, making it a more appropriate approach for examining behavior selection problems. Thirdly, the WTP of non-economic attributes for different attributes was assessed and subgroup analysis were conducted to understand the heterogeneity of patients' preferences, offering insight into the patterns of inpatient service utilization among MCC patients with different demographic characteristics. Also, there are a few limitations in this study. Firstly, the participants of this study were only from Fuqing, Fujian Province, China, which may lead to insufficient generalization of the research results. Future research could include more participants from different regions to enhance the representativeness. Secondly, considering that the factors influencing MCC patients to choose inpatient institutions are complex and diverse, it is also recommended to include more representative and targeted attributes and levels in future research to meet certain research object and realistic situation. Thirdly, to ensure the compliance of questionnaire filling, this study determined that each patient answered 4 selection sets by drawing lessons from previous research design, which may raise concern that data collected would be limited by only 4 selection sets in each questionnaire. 5 Conclusion The DCE of this study demonstrated that their preferred ideal inpatient conditions include “short time from residence to hospital”, “large provincial and municipal tertiary hospitals”, “short waiting time for beds”, and “low out-of-pocket expenses per time”. Among them, “short time from residence to hospital” may be the most important attribute in the selection of inpatient institutions for MCC patients. The findings of subgroup analysis indicated that some demographic characteristics and overall perceived disease severity also influence inpatient preferences. To better meet the inpatient service needs among MCC patients, it is recommended to improve the geographical accessibility of medical and health services, strengthen the service capacity of medical personnel in county and community-level medical institutions, streamline the inpatient diagnosis and treatment process, and so on. Abbreviations MCC Multiple chronic conditions DCE Discrete choice experiment WTP Willingness to pay CI Confidence interval AIC Akaike information criterion BIC Bayesian information criterion HDTS Hierarchical diagnosis and treatment system Declarations Acknowledgements The authors would like to thank all the interviewers and participants who supported the study. Authors’ contributions Liu WB: Conceptualization, Funding acquisition, Resources, Writing -review &editing. Ye WM: Conceptualization, Funding acquisition, Resources. Wang YQ: Formal analysis, Investigation, Roles/Writing -original draft, Writing -review &editing. Chen YH and Li WX: Writing-review &editing. Du SS, Huang XY and Xiao L: Investigation. Su QL and Wang WK: Formal analysis. Funding This research was funded by the General Program of the Natural Science Foundation of Fujian Province (grant No. 2021J01245), High-level Talents Research Start-up Project of Fujian Medical University (grant No. XRCZX2021026, No. XRCZX2017035, No. XRCZX2020034 and No. XRCZX2020037), and Government of Fuqing city (grant No. 2019B003). Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate This research was approved by the Biomedical Research Ethics Review Committee of Fujian Medical University (No. 2021-154) and (No. 2021-109). Informed consent was obtained from all individual participants included in the study. 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Model Selection and Psychological Theory: A Discussion of the Differences between the Akaike Information Criterion (Aic) and the Bayesian Information Criterion (Bic). Psychological methods (2012) 17(2):228-43. Epub 2012/02/09. doi: 10.1037/a0027127. Wang X, Song K, Zhu P, Valentijn P, Huang Y, Birch S. How Do Type 2 Diabetes Patients Value Urban Integrated Primary Care in China? Results of a Discrete Choice Experiment. Int J Environ Res Public Health (2019) 17(1). Epub 2019/12/28. doi: 10.3390/ijerph17010117. Shah J, Dickinson CL. Establishing Which Factors Patients Value When Selecting Urology Outpatient Care. British Journal of Medical & Surgical Urology (2010) 3(1):25-9. doi: 10.1016/j.bjmsu.2009.10.003. Nieboer AP, Koolman X, Stolk EA. Preferences for Long-Term Care Services: Willingness to Pay Estimates Derived from a Discrete Choice Experiment. Soc Sci Med (2010) 70(9):1317-25. Epub 2010/02/20. doi: 10.1016/j.socscimed.2009.12.027. Kelly C, Hulme C, Farragher T, Clarke G. Are Differences in Travel Time or Distance to Healthcare for Adults in Global North Countries Associated with an Impact on Health Outcomes? A Systematic Review. BMJ Open (2016) 6(11):e013059. Epub 2016/11/26. doi: 10.1136/bmjopen-2016-013059. Wang XY, Wang WJ, Zhao YQ, Liu Y, Wang XH, Du LB, et al. The Choice of Medical Facility and Associated Factors among Chinese Advanced Colorectal Cancer Patients: A Cross-Sectional Multi-Center Study. Annals of translational medicine (2022) 10(6):326. Epub 2022/04/19. doi: 10.21037/atm-22-1020. Guile MW, Schnatz PF, O'Sullivan DM. Relative Importance of Gender in Patients' Selection of an Obstetrics and Gynecology Provider. Connecticut medicine (2007) 71(6):325-32. Epub 2007/07/11. Marang-van de Mheen PJ, Dijs-Elsinga J, Otten W, Versluijs M, Smeets HJ, van der Made WJ, et al. The Importance of Experienced Adverse Outcomes on Patients' Future Choice of a Hospital for Surgery. Quality & safety in health care (2010) 19(6):e16. Epub 2010/12/04. doi: 10.1136/qshc.2008.031690. Ringard Å, Hagen TP. Are Waiting Times for Hospital Admissions Affected by Patients' Choices and Mobility? BMC Health Serv Res (2011) 11:170. Epub 2011/07/19. doi: 10.1186/1472-6963-11-170. Viotti S, Cortese CG, Garlasco J, Rainero E, Emelurumonye IN, Passi S, et al. The Buffering Effect of Humanity of Care in the Relationship between Patient Satisfaction and Waiting Time: A Cross-Sectional Study in an Emergency Department. Int J Environ Res Public Health (2020) 17(8). Epub 2020/04/30. doi: 10.3390/ijerph17082939. Addis ME, Mahalik JR. Men, Masculinity, and the Contexts of Help Seeking. The American psychologist (2003) 58(1):5-14. Epub 2003/04/05. doi: 10.1037/0003-066x.58.1.5. Zeng Y, Xu W, Chen L, Chen F, Fang Y. The Influencing Factors of Health-Seeking Preference and Community Health Service Utilization among Patients in Primary Care Reform in Xiamen, China. Patient Prefer Adherence (2020) 14:653-62. Epub 2020/04/14. doi: 10.2147/PPA.S242141. Wang Z, Li X, Chen M, Si L. Social Health Insurance, Healthcare Utilization, and Costs in Middle-Aged and Elderly Community-Dwelling Adults in China. Int J Equity Health (2018) 17(1):17. Epub 2018/02/06. doi: 10.1186/s12939-018-0733-0. Eaton S, Roberts S, Turner B. Delivering Person Centred Care in Long Term Conditions. Bmj (2015) 350:h181. Epub 2015/02/12. doi: 10.1136/bmj.h181. Tong SF, Ho C, Tan HM. Managing the Aging Man in Asia: A Review. (2011) 18(1):32-42. doi: https://doi.org/10.1111/j.1442-2042.2010.02652.x. Additional Declarations No competing interests reported. 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16:14:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060988,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4011440/v1/82f44202-5e19-4521-8037-7fee296f27b0.pdf"},{"id":52671899,"identity":"49778884-2620-48db-a6d1-d83add0e9a66","added_by":"auto","created_at":"2024-03-14 10:24:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44883,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4011440/v1/d79d7fefda5e0d6e30313927.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inpatient preference among patients with multiple chronic conditions in China: a discrete choice experiment","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChronic diseases have become a major public health challenge facing the international community, which accounts for 30% of global deaths(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and almost 80% of all deaths in Chinese people aged 60 years or over(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). According to the World Health Organization report in \u0026ldquo;China\u0026rsquo;s assessment report on aging and health\u0026rdquo;, chronic diseases have emerged as the leading cause of mortality in China with the changes in the disease spectrum of the population, and its prevalence will increase at least 40% by 2030(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Under this serious context, it is increasingly prevalent for residents to suffer from multiple chronic conditions (MCC) at the same time. MCC refers to the presence of two or more chronic diseases in a patient that persists for at least 1 year or more(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). It can impair patients\u0026rsquo; ability to perform activities of daily living, resulting in decreased quality of life, increased psychological stress, escalated treatment costs, and aggravated adverse effects of treatment or intervention(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo strengthen the management of chronic diseases, China has issued a series of supportive policies. In 2015, it was explicitly proposed to build a hierarchical diagnosis and treatment system (HDTS) mainly focusing on management of chronic diseases, such as hypertension and diabetes. HDTS refers to different levels of medical institutions undertaking the therapy tasks by the disease severity(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e); in 2017, it was required that the member institutions of medical alliance should clarify their functional positioning, and the tertiary hospitals gradually reduce the proportion of chronic disease patients with stable conditions(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e); in 2018, family doctor contract service was steadily implemented by primary health institutions giving priority to key groups such as patients with chronic diseases(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, even with these above-mentioned policies, from 2015 to 2020, the proportion of admissions in primary health institutions decreased from 19.17\u0026ndash;16.11%, while that of general hospitals increased from 58.59\u0026ndash;59.04%(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). It indicated that patients still prefer higher levels of inpatient service, which runs counter to HDTS and is not conducive to the efficient utilization of health resources for MCC patients.\u003c/p\u003e \u003cp\u003eTo reverse this situation and improve pertinent inpatient service delivery strategies, it is particularly important to comprehend the inclination towards inpatient service utilization among MCC patients. Extensive studies have been conducted on the flow of medical treatment for patients with chronic diseases, and the overall finding is that the factors affecting patients' selection of medical institutions include patient personal factors and external environmental factors, such as socio-demographic characteristics, out-of-pocket expenses, convenience of services, and level of medical institutions. However, most scholars pay attention to specific chronic diseases, such as hypertension (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), Parkinson(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), diabetes(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), etc. Simultaneously, most research focuses on outpatient service preference(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), while there is a dearth of evidence on MCC patients and their inpatient preference. Furthermore, in the research field of patients' health preferences, the analysis methods routinely used include multivariate analysis(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), logistic regression analysis(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), cross analysis(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), etc., which do not pay attention to the fact that patients' inpatient preference is the result of combinations of multiple factors and that the importance of different factors varies. In recent years, discrete choice experiment (DCE) has been put into use in the research field of patient preference(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). DCE is based on random utility theory assuming that respondents always prefer the alternative that offers the greatest utility, and its overall utility is decomposed by its attributes(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It can quantitatively analyze the impact of the determinants on patient preference for seeking medical services to better understand their health demands.\u003c/p\u003e \u003cp\u003eTherefore, to bridge the knowledge gap of inpatient preference among MCC patients, as well as overcome the limitations of previous analytical methods, this study aims to investigate MCC patients\u0026rsquo; inpatient preferences and identify the key influencing factors of their choices through DCE. The results of this study will not only guide to improve the inpatient service utilization for MCC patients, but also promote optimal allocation of healthcare resources.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Determination of attributes and levels\u003c/h2\u003e \u003cp\u003eFirstly, by reviewing relevant literature, this study preliminarily determined five attributes in DCE, including institutional scale, waiting time for hospital admission, time from residence to hospital(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), out-of-pocket expenses per time and hospitals with or without acquaintances(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Among them, \u0026ldquo;institutional scale\u0026rdquo; and \"waiting time for hospital admission\" were subdivided from the behavioral model of health services use proposed by Andersen(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Additionally, since patients generally value the economic accessibility and spatial convenience of health-seeking behavior, for example, the out-of-pocket cost for a 30-day supply(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), service price(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), and travel time were verified in empirical study; and thus \u0026ldquo;time from residence to hospital\u0026rdquo; and \u0026ldquo;out-of-pocket expenses per time\u0026rdquo; were included in the attribute list. Moreover, considering the nature of China's nepotistic society in which people want to use \u0026ldquo;relationships\u0026rdquo; to gain convenience in normal procedures, the attribute of \u0026ldquo;hospital with or without acquaintances\u0026rdquo; was considered. Secondly, by consulting with 6 relevant experts in the field of healthcare management, the attributes and their levels were further refined, summarized, and defined. Details of the attributes and levels are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eList of attributes and levels among MCC patients in discrete choice experiment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExplanation of attributes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistrict/county hospitals; Large provincial and municipal tertiary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe rank of inpatient institutions reflects hospital qualifications such as hospital functions, facilities, and sizes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaiting time for hospital admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBed available on the day; Wait 3 days for beds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe waiting time to obtain inpatient services provided by medical institutions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital with or without acquaintances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcquaintance;\u003c/p\u003e \u003cp\u003eNo acquaintance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe patient has acquaintances in the inpatient institution or not\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from residence to hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 1 hour;\u003c/p\u003e \u003cp\u003eMore than 3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe time it takes patients to travel from home to the inpatient institution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut-of-pocket expenses per time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026yen;800; \u0026yen;2000; \u0026yen;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe average expense that patients need to pay all by themselves per hospitalization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: \u003cem\u003eCNY\u003c/em\u003e Chinese Yuan; MCC multiple chronic conditions.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026lt;Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experiment design and questionnaire development\u003c/h2\u003e \u003cp\u003eDue to various inpatient facility choice tasks, the number of attributes and levels (2\u003csup\u003e4\u003c/sup\u003e\u0026times;3\u003csup\u003e1\u003c/sup\u003e=48) was considered impractical for a full factorial design. To improve the acceptance of the questionnaire and cooperation of the respondents, partial factorial design was conducted to maximize the D efficiency by SAS. Furthermore, 12 representative pairs of choice sets were obtained, which were set up using the unmarked-choice format with two medical facility options. This study assigned all selection sets into three different versions of the questionnaire, each containing four choice sets. The main purpose was to avoid cognitive fatigue among respondents and improve questionnaire quality. Previously, Friedel JE(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) thought that the number of selection sets should not exceed 10 unless the option content was very simple. Bech M\u0026rsquo;s research(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) has found that the choice of patients who completed 17 selection sets was guided by one attribute. There were also studies(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) that involved completing 4 selection sets. Ultimately, opt-out options were not set in DCE because while it avoids patients making difficult choices between options, it doesn\u0026rsquo;t generate the highest utility and provide the most adequate preference information(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Thus, opt-out options were not set in this study. Ultimately, the questionnaire mainly consisted of three parts: demographic characteristics, health status, and the DCE tasks (the formal questionnaire is in Supplementary material 1, Additional file).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample size\u003c/h2\u003e \u003cp\u003eThis study followed the DCE sample size estimation principle proposed by Orme(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The following formula was used to calculate the minimum sample size:\u003c/p\u003e \u003cp\u003e \u003cb\u003eN\u003c/b\u003e \u003cb\u003e\u0026ge;(500*\u003c/b\u003e \u003cb\u003eC\u003c/b\u003e \u003cb\u003e) / (\u003c/b\u003e \u003cb\u003eT\u003c/b\u003e \u003cb\u003e*\u003c/b\u003e\u003cb\u003eA\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong them, \u003cem\u003eN\u003c/em\u003e represents the number of respondents, \u003cem\u003eT\u003c/em\u003e represents the number of choice sets that the respondents need to complete, \u003cem\u003eA\u003c/em\u003e is the number of options that the respondents need to complete in a single choice set, and \u003cem\u003eC\u003c/em\u003e is the maximum level of any attribute. According to the above formula, the sample size of this study needed to be greater than 188 respondents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 DCE implementation and data collection\u003c/h2\u003e \u003cp\u003eTo improve the reliability and validity of the questionnaire, a small-scale pilot survey was conducted before the formal investigation. According to the feedback of the pilot survey, the expression text of the questionnaire was adjusted, especially for the DCE items. For quality assurance, a survey training manual was compiled to train the interviewers before the formal investigation.\u003c/p\u003e \u003cp\u003eThe formal survey was conducted in Fuqing, Fujian Province from November 2021 to January 2022. The inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) participants were between 35 and 75 years old; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) patients who self-reported being diagnosed with two or more chronic diseases by doctors in community hospitals and above. Interviewers were trained to ensure the formal questionnaire survey was conducted smoothly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive analysis was used to present demographic characteristics of respondents. In this study, the DCE data analysis used a mixed logit model and a conditional logit model, and the most suitable model was determined based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The smaller the values of AIC and BIC, the better the model fit(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;out-of-pocket expenses per time\u0026rdquo; attribute was used to calculate the willingness to pay (WTP). WTP was the negative ratio of the non-economic attribute coefficient to the economic attribute coefficient, which reflected the monetary value of the non-economic attribute that affected the preference of inpatient institutions. Additionally, a subgroup analysis was performed to estimate the heterogeneity of inpatient preference among MCC patients. All statistical analyses were performed using the SAS.9.3 and Stata 16. The \u003cem\u003ep value\u0026thinsp;\u0026le;\u003c/em\u003e\u0026thinsp;0.05 indicated statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Respondents\u0026rsquo; demographic characteristics\u003c/h2\u003e \u003cp\u003eA total of 642 MCC patients participated in the survey, with 138 excluded due to non-compliance with inclusion criteria and a lack of understanding of DCE choices. Ultimately, a sample of 504 patients is enrolled. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that there are 341 females, 230 patients aged less than 60 years old, 455(90.3%) subjects are married, more than half of the patients have formal education, and 83(16.5%) subjects were farmers. About 60% of patients have 1 to 3 family members. Nearly 70% of the subjects have a total household income of less than \u0026yen;60,000 last year, and more than 60% of the patients have an overall perceived disease severity score ranging from 1 to 5 points.\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\u003eDemographic characteristics of the study participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;504)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.7\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.3\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfession\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed/laid off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of family member\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-3people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4-13people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal household income last year(CNY)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10000\u0026ndash;29999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30000\u0026ndash;59999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60000\u0026ndash;99999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100000\u0026ndash;149999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150000\u0026ndash;299999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;300000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall perceived severity of illness\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eOther marital status\u003c/em\u003e includes single, widowed, separated/divorced; \u003cem\u003eCNY\u003c/em\u003e Chinese yuan\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026lt;Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Model estimation of preferences\u003c/h2\u003e \u003cp\u003eAccording to the results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the AIC of the mixed logit model was 2058.564 and the BIC is 2115.282. The AIC of the conditional logit model is 2149.713 and the BIC is 2181.223. By comparing the AIC and BIC of the two models, it can be seen that the mixed logit model was more effective in analyzing the inpatient preference for MCC patients. Thus, the analysis of DCE below is mainly based on the results of the mixed logit model.\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\u003eEstimates of the mixed logit model (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;504)\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\u003eAttributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95\u003cem\u003e%\u003c/em\u003e CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional scale\u003c/p\u003e \u003cp\u003e(Ref: District/county hospital)\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\u003eLarge provincial and municipal tertiary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1156 to 0.4113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaiting time for hospital admission\u003c/p\u003e \u003cp\u003e(Ref: Wait 3 days for beds)\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\u003eBed available on the day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0214 to 0.3709)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitals with or without acquaintances\u003c/p\u003e \u003cp\u003e(Ref: No acquaintances)\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\u003eAcquaintances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.0099 to 0.2958)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from residence to hospital\u003c/p\u003e \u003cp\u003e(Ref: More than 3 hours)\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\u003eLess than 1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.5526 to 0.9679)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut-of-pocket expenses per time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.0007 to -0.0005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e-1020.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2058.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2115.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e4032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eCNY\u003c/em\u003e Chinese yuan\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of the conditional logit model (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;504)\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\u003eAttributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional scale\u003c/p\u003e \u003cp\u003e(Ref: District/county hospital)\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\u003eLarge provincial and municipal tertiary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1032 to 0.2916)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaiting time for hospital admission\u003c/p\u003e \u003cp\u003e(Ref: Wait 3 days for beds)\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\u003eBed available on the day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0720 to 0.3145)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitals with or without acquaintances\u003c/p\u003e \u003cp\u003e(Ref: No acquaintances)\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\u003eAcquaintances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.0047 to 0.1983)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from residence to hospital\u003c/p\u003e \u003cp\u003e(Ref: More than 3 hours)\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\u003eLess than 1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.3902 to 0.6537)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut-of-pocket expenses per time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.0004 to -0.0003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e-1020.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2149.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e2181.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e4032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eCNY\u003c/em\u003e Chinese yuan\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026lt;Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ehere\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026lt;Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003ehere\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reveals that all attributes are statistically significant except for \u0026ldquo;hospital with or without acquaintances\u0026rdquo;. Taking \u0026ldquo;district/county hospital\u0026rdquo; as the reference, the value of the influence degree β(95%CI) of \u0026ldquo;large provincial and municipal tertiary hospitals\u0026rdquo; on the patients\u0026rsquo; inpatient preferences is 0.2635(0.1156 to 0.4113). Taking \u0026ldquo;waiting 3 days for beds\u0026rdquo; as a reference, the value of the influence degree β(95%CI) of \u0026ldquo;having beds available on the day of hospitalization\u0026rdquo; on the patients\u0026rsquo; inpatient services utilization is 0.1962(0.0214 to 0.3709). Compared with the time from the residence to the hospital for \u0026ldquo;more than 3 hours\u0026rdquo;, the value of the influence degree β(95%CI) of \u0026ldquo;the time from the residence to the hospital within 1 hour\u0026rdquo; on the patients\u0026rsquo; inpatient preferences is 0.7602(0.5526 to 0.9679). The value of the influence degree β(95%CI) of \u0026ldquo;the out-of-pocket expenses per time\u0026rdquo; on patients' inpatient preference is -0.0006(-0.0007 to -0.0005). The results show that the most important attribute for patients when accessing inpatient services is the short time from their residence to the hospital, followed by \u0026ldquo;large provincial and municipal tertiary hospitals\u0026rdquo;, \u0026ldquo;beds available on the day of hospitalization\u0026rdquo;, and finally the \u0026ldquo;low out-of-pocket expenses per time\u0026rdquo;. Among them, \u0026ldquo;out-of-pocket expenses per time\u0026rdquo; is a negative predictor of patients' inpatient services utilization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Marginal WTP\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the WTP for other attributes is statistically significant except for \u0026ldquo;hospital with or without acquaintances\u0026rdquo;. Compared with the district/county hospital, the patient\u0026rsquo;s WTP for large provincial and municipal tertiary hospitals is \u0026yen;434(95%CI:188 to 680). Compared with waiting for beds for 3 days, the patient\u0026rsquo;s WTP for beds available on the day is \u0026yen;323(95%CI:32 to 614). Compared with the time from the residence to the hospital for more than 3 hours, the patient's WTP within 1 hour was \u0026yen;1253(95%CI: 927 to 1580). The WTP for attributes from high to low is as follows: \u0026ldquo;short time from residence to hospital\u0026rdquo;, \u0026ldquo;large hospitals\u0026rdquo;, and \u0026ldquo;beds available on the day of hospitalization\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWillingness to pay for non-economic work attributes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWTP (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional scale (Ref: District/county hospital)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge provincial and municipal tertiary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e434(188 to 680)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaiting time for hospital admission (Ref: Wait 3 days for beds)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBed available on the day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323(32 to 614)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitals with or without acquaintances (Ref: No acquaintances)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcquaintances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236(-17 to 488)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from residence to hospital (Ref: More than 3 hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1253(927 to 1580)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026lt;Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003ehere\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Model estimates for each subgroup\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows a subgroup analysis of inpatient preference among MCC patients. In the subgroup analysis, it was found that the preference of \u0026ldquo;time from residence to hospital\u0026rdquo; and \u0026ldquo;out-of-pocket expenses per time\u0026rdquo; were significantly affected by gender, age, education, total household income, and perceived disease severity in all subgroups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of subgroup analysis (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;504)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAttributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eTotal household income\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eOverall perceived\u003c/p\u003e \u003cp\u003eseverity of illness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003cp\u003eyears old\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003cp\u003eyears old\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFormal education\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026yen;30,000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;\u0026yen;30,000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional scale\u003c/p\u003e \u003cp\u003e(Ref: District/county hospital)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge provincial and municipal tertiary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3820***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3964**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2817*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2473*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3227**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2395*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2122**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7085**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaiting time for hospital admission\u003c/p\u003e \u003cp\u003e(Ref: Wait 3 days for beds)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBed available on the day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4726***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5410***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3239**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3388**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2448**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitals with or without acquaintances\u003c/p\u003e \u003cp\u003e(Ref: No acquaintances)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcquaintances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2858**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3471*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2736**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2329*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from residence to hospital\u003c/p\u003e \u003cp\u003e(Ref: More than 3 hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5411***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9381***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9230***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7608***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0442***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6197***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7193***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.9312**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut-of-pocket expenses per time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0008***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*\u003cem\u003ep value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** \u003cem\u003ep value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eCNY\u003c/em\u003e Chinese yuan\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026lt;Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003ehere\u0026gt;\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the gender group, females tend to take advantage of large tertiary hospitals, while males prefer \u0026ldquo;bed available on the day\u0026rdquo; and \u0026ldquo;have acquaintances in the hospital\u0026rdquo;. In the age group, \u0026ldquo;institutional scale\u0026rdquo;, \u0026ldquo;waiting time for hospital admission\u0026rdquo;, and \u0026ldquo;hospitals with or without acquaintances\u0026rdquo; are statistically significant for patients younger than 60 years, but not found in the group aged 60 years or greater. In the subgroup analysis of education and total household income, the preference of patients with no formal education or income\u0026lt;\u0026yen;30000 is affected by the attribute of \u0026ldquo;institutional scale\u0026rdquo;, while those with formal education or income\u0026ge;\u0026yen;30000 are affected by all attributes. In the perceived disease severity group, patients with perceived disease severity\u0026thinsp;\u0026lt;\u0026thinsp;10 tend to be hospitalized on the same day and large facility, while patients with a higher perceived disease severity tend to be admitted to large tertiary hospitals.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eTo the best of our knowledge, there is a dearth of research on the inpatient preference among MCC patients. This study identified the influencing factors and accessed their relative importance on the inpatient preference among MCC patients. It revealed that the ideal inpatient institutions preferred by MCC patients may include following four conditions: \u0026ldquo;short time from residence to hospital\u0026rdquo;, \u0026ldquo;large provincial and municipal tertiary hospitals\u0026rdquo;, \u0026ldquo;short waiting time for beds\u0026rdquo;, and \u0026ldquo;low out-of-pocket expenses per time\u0026rdquo;. Gaining insight into the inpatient preferences among MCC patients is crucial for efficient medical services utilization and optimal allocation of healthcare resources.\u003c/p\u003e \u003cp\u003eThe results showed that MCC patients valued the \u0026ldquo;short time from residence to hospital\u0026rdquo; in selecting inpatient institutions, and were willing to pay much more for it than other attributes. There were no differences to this point between different subgroups. The findings were similar to previous studies(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), patients generally preferred medical institutions located in short distance, or can be reached by their private car or public transport(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The plausible reason for this phenomenon may be that chronic diseases have a long course and require long-term monitoring and management(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), so the patients' demand on the convenience and accessibility of inpatient services would be much greater. Additionally, short distance to medical institutions benefit patients\u0026rsquo; health consequences, it was also understandable that MCC patients were more willing to pay an extra fee to obtain medical care in short distance(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInstitutional scale was the second most important attribute of patients' preference for inpatient institutions. Since many tertiary hospitals in China's provinces and cities were medical institutions with better technical capability and efficiency(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), it is not hard to understand that MCC patients were more willing to go to large provincial and municipal tertiary hospitals to obtain high-quality medical services. The qualifications or expertise of medical service providers was an important determinant of patients' medical institution choice(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Additionally, the subgroup analysis further revealed that patients in the female group and the non-elderly group showed a preference for seeking inpatient treatment in large provincial and municipal tertiary hospitals(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the attribute of waiting time for hospital admission, medical institutions with beds available on the day of hospitalization were preferred by MCC patients. This was consistent with previous studies that demonstrated time spent on waiting lists and in the waiting room(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) had a negative impact on patient preference for treatment. Reducing waiting time was important for most patients(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), which can reduce waiting time and allow for timely hospitalization. In further subgroup analysis, patients in the male group, non-elderly group, educated group, higher household income group, and overall perceived mild or moderate severity of illness group were shown more likely to choose medical institutions with beds available on the day of hospitalization. These results were consistent with previous studies that some male patients showed reluctance to wait when seeking medical help(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), and those young, middle-to-high-income, mild-symptom, educated patient groups placed more value on the personal experience of the entire medical service process, especially the waiting time(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOut-of-pocket expense per time was a negative predictor of patients choosing inpatient institutions, and this result persisted in all subgroups, indicating that patient preferences decreased as out-of-pocket expenses per time increased. Patients preferred to visit medical institutions with less out-of-pocket expenses, regardless of whether they perceived a mild or severe disease. Since the common health inequalities caused by income disparities(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), low-income groups faced greater barriers to obtaining adequate healthcare services(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), Especially for the MCC patients confronting more complications and incurring medical expenses, even if they have health insurance as a backup, which does not alleviate the financial burden on those who are already poor. Thus, their preference for inpatient institutions with low out-of-pocket expenses per time made perfect sense.\u003c/p\u003e \u003cp\u003eNoteworthy, the attribute \u0026ldquo;hospitals with or without acquaintances\u0026rdquo; was less essential for patients\u0026rsquo; inpatient service utilization, which was inconsistent with the research hypothesis. The reason for this may be that patients\u0026rsquo; trust in doctors had been greatly enhanced with more transparent diagnosis and treatment process. Having got sufficient information resources, patients did not pay much attention to their acquaintances when selecting inpatient service providers(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Conversely, the results of subgroup analysis showed that patients in the male group, non-elderly group, educated group, and higher household income group tended to be hospitalized with an acquaintance. Since these patient groups were more concerned about the experience of seeking medical care and were reluctant to spend much time in queues(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), they may be more inclined to obtain more prompt and effective medical services through acquaintances.\u003c/p\u003e \u003cp\u003eBased on the patients\u0026rsquo; preference for shorter travel time, shorter waiting time, and lower out-of-pocket expenses, the following measures are proposed. Firstly, it is recommended to continuously optimize the allocation of healthcare resources and improve the geographical accessibility of health services. Special attention should be paid to narrowing the gap in the healthcare resource allocation between regions to meet the health needs among vulnerable groups. Secondly, to further shorten the waiting time for inpatients, some efforts can be made to streamline the inpatient diagnosis and treatment process to increase bed turnover rate. Thirdly, on the basis of expanding the coverage of basic medical insurance, it is also recommended to steadily increase the reimbursement rate of designated medical institutions of basic medical insurance. The support of other insurance, such as commercial insurance, can be also integrated to reduce economic burden for patients.\u003c/p\u003e \u003cp\u003eThis study was strengthened by some distinguished features. Firstly, different from previous studies that have mostly focused on influencing factors of outpatient preference for patients with a chronic disease, this study enriched empirical research in related fields by providing valuable information regarding inpatient preference among MCC patients. Secondly, DCE was applied to better understand the relative importance of the attributes in the process of medical care, with a mixed logit model adopted for further analyzing unobservable utility and taking individual differences scrupulously into account, making it a more appropriate approach for examining behavior selection problems. Thirdly, the WTP of non-economic attributes for different attributes was assessed and subgroup analysis were conducted to understand the heterogeneity of patients' preferences, offering insight into the patterns of inpatient service utilization among MCC patients with different demographic characteristics.\u003c/p\u003e \u003cp\u003eAlso, there are a few limitations in this study. Firstly, the participants of this study were only from Fuqing, Fujian Province, China, which may lead to insufficient generalization of the research results. Future research could include more participants from different regions to enhance the representativeness. Secondly, considering that the factors influencing MCC patients to choose inpatient institutions are complex and diverse, it is also recommended to include more representative and targeted attributes and levels in future research to meet certain research object and realistic situation. Thirdly, to ensure the compliance of questionnaire filling, this study determined that each patient answered 4 selection sets by drawing lessons from previous research design, which may raise concern that data collected would be limited by only 4 selection sets in each questionnaire.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe DCE of this study demonstrated that their preferred ideal inpatient conditions include \u0026ldquo;short time from residence to hospital\u0026rdquo;, \u0026ldquo;large provincial and municipal tertiary hospitals\u0026rdquo;, \u0026ldquo;short waiting time for beds\u0026rdquo;, and \u0026ldquo;low out-of-pocket expenses per time\u0026rdquo;. Among them, \u0026ldquo;short time from residence to hospital\u0026rdquo; may be the most important attribute in the selection of inpatient institutions for MCC patients. The findings of subgroup analysis indicated that some demographic characteristics and overall perceived disease severity also influence inpatient preferences. To better meet the inpatient service needs among MCC patients, it is recommended to improve the geographical accessibility of medical and health services, strengthen the service capacity of medical personnel in county and community-level medical institutions, streamline the inpatient diagnosis and treatment process, and so on.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Multiple chronic conditions\u003c/p\u003e\n\u003cp\u003eDCE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Discrete choice experiment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWTP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Willingness to pay\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003eAIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Akaike information criterion\u003c/p\u003e\n\u003cp\u003eBIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Bayesian information criterion\u003c/p\u003e\n\u003cp\u003eHDTS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hierarchical diagnosis and treatment system\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eThe authors would like to thank all the interviewers and participants who supported the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003eLiu WB: Conceptualization, Funding acquisition, Resources, Writing -review \u0026amp;editing. Ye WM: Conceptualization, Funding acquisition, Resources. Wang YQ: Formal analysis, Investigation, Roles/Writing -original draft, Writing -review \u0026amp;editing. Chen YH and Li WX: Writing-review \u0026amp;editing. Du SS, Huang XY and Xiao L: Investigation. Su QL and Wang WK: Formal analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research was funded by the General Program of the Natural Science Foundation of Fujian Province (grant No. 2021J01245), High-level Talents Research Start-up Project of Fujian Medical University (grant No. XRCZX2021026, No. XRCZX2017035, No. XRCZX2020034 and No. XRCZX2020037), and Government of Fuqing city (grant No. 2019B003).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eThis research was approved by the Biomedical Research Ethics Review Committee of Fujian Medical University (No. 2021-154) and (No. 2021-109). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThe L. Gbd 2017: A Fragile World. \u003cem\u003eLancet\u003c/em\u003e (2018) 392(10159):1683. Epub 2018/11/13. doi: 10.1016/s0140-6736(18)32858-7.\u003c/li\u003e\n\u003cli\u003eWHO. China Country Assessment Report on Ageing and Health (2015). 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(2011) 18(1):32-42. doi: https://doi.org/10.1111/j.1442-2042.2010.02652.x.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"inpatient preferences, multiple chronic conditions (MCC), discrete choice experiment (DCE)","lastPublishedDoi":"10.21203/rs.3.rs-4011440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4011440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMultiple chronic conditions (MCC) have become a leading cause of hypovitalism and death among the population. Although the role of chronic disease management has been strengthened for primary health institutions, patients still have a preference for a higher level of inpatient service, leading to a considerable hindrance in the efficient utilization of healthcare resources. Thus, this study aimed to identify factors that affected MCC patients' inpatient preference and the extent to which these factors impact their decisions, guiding for inpatient service utilization among MCC patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFive attributes (institutional scale, waiting time for hospital admission, with or without acquaintances, time from residence to hospital, out-of-pocket expenses per time) were identified to estimate inpatient choice for MCC patients through a discrete choice experiment. Partial factor analysis was performed to generate selection sets. Data were collected from MCC patients between 35 and 75 years old, in Fuqing, Fujian Province. A mixed logit model was performed to analyze MCC patients' preferences for each attribute. Willingness to pay was estimated by regression coefficients, and subgroup analysis was conducted based on the patient\u0026rsquo;s demographic characteristics and overall perceived disease severity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTotally 504 valid questionnaires were included in the analysis. MCC patients preferred to have a shorter time from their residence to the hospital (β\u0026thinsp;=\u0026thinsp;0.7602, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), large provincial and municipal tertiary hospitals (β\u0026thinsp;=\u0026thinsp;0.2635, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and have beds available on the day (β\u0026thinsp;=\u0026thinsp;0.1962, p\u0026thinsp;=\u0026thinsp;0.0028). Out-of-pocket expenses per time (β=-0.0006, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are a negative predictor of patients' inpatient preference. Additionally, Patients were willing to pay \u0026yen;1253, \u0026yen;434, and \u0026yen;323 for higher institutional scale, shorter waiting time, and shorter time from residence-to-hospital, respectively. The findings of subgroup analysis indicated that some demographic characteristics (age, gender, education and total household income) and overall perceived disease severity also influence MCC patients' inpatient preferences.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides evidence on the inpatient preferences among MCC patients. To better meet patients\u0026rsquo; needs, it is recommended to improve the geographical accessibility of medical and health services, strengthen the service capacity of medical personnel in county and community-level medical institutions, streamline the inpatient diagnosis and treatment process, and so on.\u003c/p\u003e","manuscriptTitle":"Inpatient preference among patients with multiple chronic conditions in China: a discrete choice experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-14 10:24:43","doi":"10.21203/rs.3.rs-4011440/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-12T22:04:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-11T18:18:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-29T04:02:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280099275732308676838176227542662584965","date":"2024-10-22T15:43:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281389745520102293277419402335751390841","date":"2024-10-19T18:12:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191874753892934441189251104387937292170","date":"2024-09-02T01:37:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175449890373588820253764409752830967493","date":"2024-08-13T22:32:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-31T16:09:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316572893221260122798841989843626134478","date":"2024-07-19T02:42:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302048195522233163642396030444250213140","date":"2024-05-20T03:42:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-29T10:08:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-13T06:02:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-12T06:52:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-12T06:52:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2024-03-04T09:55:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a024ab4-769e-4b78-8575-301dea969fac","owner":[],"postedDate":"March 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T16:09:28+00:00","versionOfRecord":{"articleIdentity":"rs-4011440","link":"https://doi.org/10.1186/s12875-025-02731-2","journal":{"identity":"bmc-primary-care","isVorOnly":false,"title":"BMC Primary Care"},"publishedOn":"2025-02-07 15:58:10","publishedOnDateReadable":"February 7th, 2025"},"versionCreatedAt":"2024-03-14 10:24:43","video":"","vorDoi":"10.1186/s12875-025-02731-2","vorDoiUrl":"https://doi.org/10.1186/s12875-025-02731-2","workflowStages":[]},"version":"v1","identity":"rs-4011440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4011440","identity":"rs-4011440","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}