Preferences for long-term care among elderly stroke patients with disabilities : a protocol for a discrete choice experiment

Preferences for long-term care among elderly stroke patients with disabilities : a protocol for 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 Study protocol Preferences for long-term care among elderly stroke patients with disabilities : a protocol for a discrete choice experiment Huixian Zha, Wenjun Mao, Ling Jiang, Hongyun Yan, Hua guo, Xianwen Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5357510/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Stroke is one of the leading causes of disability among older adults worldwide, often resulting in significant physical, cognitive, and emotional impairments that require long-term care (LTC). With aging populations and increasing stroke prevalence, the demand for appropriate and sustainable LTC is expected to grow substantially in the coming years. However, designing such LTC to meet the complex and evolving needs of elderly disabled stroke patients remains a challenge. Understanding patients’ preferences is crucial for the development of patient-centered care plans and optimizing the LTC strategy for stoke patients with disabilities. This study uses a discrete choice experiment (DCE) to measure and quantify patients’ preferences for LTC, and aims at (1) identifying and exploring which elements of LTC are essential for elderly disabled stroke patients; (2) measuring patients’ preferences for LTC and summarising relevant characteristics that may influence preference choices and (3) determining whether these preferences vary by participants characteristics and classifying the population types based on the baseline data and activities of daily living. Methods The research was conducted in accordance with the design programme of the DCE study. Seven attributes were developed through a systematic literature review, in-depth interviews and experts consultation. A partial factorial survey design was generated through an orthogonal experimental design to optimize the choice scenario sets. We plan to conduct a DCE questionnaire survey in Suzhou, Jiangsu province, China and recruit at least 200 participants. The final data will be analysed through a mixed logit model and a latent class model to explore the preference of elderly disabled stroke patients for LTC. Discussion The study will provide insights into the LTC preferences of elderly stroke patients with disabilities. By quantifying the importance of various LTC attributes, the findings will help policymakers and healthcare providers design tailored, sustainable LTC services. The use of mixed logit and latent class models will uncover both general preferences and distinct patterns of preference heterogeneity, enabling the development of personalized LTC models that align with patients’ specific needs. stroke long-term care disabilities discrete choice experiment preferences Figures Figure 1 1. Introduction Stroke represents a significant global health challenge, characterized by its high incidence, prevalence, and substantial burden on healthcare systems[ 1 ]. It is the leading cause of mortality and long-term disability, affecting millions of individuals annually[ 2 ]. According to the Global Burden of Disease study[ 3 ], there are currently 101 million people worldwide experience the stroke, with about 12.2 million new cases each year, and about 6.55 million deaths caused by stroke, accounting for 11.6% of all deaths, which has now became the second leading cause of death. In China, the ageing of population have contributed to the prevalence of unhealthy lifestyles, which have led to a significant increase in the number of individuals exposed to risk factors for cardiovascular and cerebrovascular diseases[ 4 ]. Consequently, the burden of stroke in China has exhibited an alarming growth trend. A report published in JAMA on the burden of stroke in China[ 5 ] indicated that the estimated prevalence, incidence, and mortality rate of stroke were 2.6%, 505.2 per 100,000 person-years, and 343.4 per 100,000 person-years, respectively. These figures illustrate the significant disease burden among this disease group. The burden of stroke extends beyond immediate medical consequences. Survivors frequently confront long-term challenges, studies[ 6 , 7 ] have shown that 60% of stroke patients have varying degrees of cognitive impairment, speech, swallowing or physical mobility disorders, depressive symptoms, or social dysfunction, resulting in the loss of daily activities and self-care ability, and the quality of life is severely affected. Nowadays, the reported disabilities rate among stroke patients in China is 12.5(95%CI 12.4–12.5), as defined by a modified Rankin scale[ 8 ]. The disability adjusted life years(DALYs) caused by stroke are higher than a majority of other diseases[ 9 ]. In this context, long-term care(LTC) emerge as a crucial element of comprehensive healthcare, with the objective of enhancing the quality of life of elderly stroke survivors with disabilities and of alleviating the medical burden on families and society[ 10 ]. With an ageing population and increasing prevalence of stroke, the demand for appropriate and sustainable LTC is expected to increase significantly in the coming years[ 11 ]. The World Health Organization (WHO)[ 12 ] defines LTC as a range of care activities provided by professional caregivers or non-professional caregivers (such as family members) to ensure a satisfactory quality of life for individuals with chronic health conditions or disabilities. According to the different places of care services, LTC in different countries is basically divided into three models: institutional care with different types of institutions as the platform, community care with each community as the platform, and home care with the family as the platform, each offering distinct advantages and challenges[ 13 , 14 ]. Institutional care, which includes hospitals and nursing homes, provides access to professional medical staff and medical infrastructure. However, it is often associated with higher costs, greater financial burden, personnel shortages, and uneven regional availability[ 15 ]. In contrast, home-based care and community-based care offer LTC in familiar environments, which may improve patient comfort and well-being. Under this circumstance, family members often serve as primary caregivers, many of whom lack formal training in healthcare[ 16 ]. This dual responsibility—caring for disabled stroke patients while managing other household and childcare duties—can place significant psychological, emotional, and financial strain on families[ 17 ]. Thus, the optimal configuration of LTC for stroke survivors remains a pressing concern, especially in countries like China where the aging population is rapidly expanding[ 18 ]. Developing effective LTC services requires a thorough understanding of patient preferences, as LTC options vary in structure, content, and payment[ 19 ]. Previous research[ 20 ] has examined the preferences for LTC among older adults, and the results demonstrated that the home-based care remains the predominant option for older people, which is closely related to Chinese cultural traditions (e.g. filial piety). But the study also points to a gradual increase in demand for community and institutional care as China goes through a period of change in terms of family structure and social norms. In addition, demographic, psychological, physical condition and economic factors were all correlated with patients’ preference. Research[ 21 ] suggests that there may be significant differences in patients' care preferences when care needs are high, and that policymakers should be flexible in developing resource allocation strategies according to the needs and health status of different groups. However, little attention has been paid to disabled stroke survivors, and there is a lack of research on the LTC preferences of this population. To address this gap, this study employs a discrete choice experiment (DCE) to explore the preferences of elderly stroke patients with disabilities regarding LTC. DCE is a quantitative method that evaluates individual trade-offs between different service attributes, providing insights into which characteristics are most valued by patients[ 22 ]. By presenting respondents with hypothetical LTC scenarios composed of varying attributes and levels, this study will reveal the relative importance of specific service features and identify the optimal configurations of LTC services. The findings will provide valuable guidance for policy makers and healthcare providers to design LTC models that better address the needs and preferences of elderly stroke patients and their caregivers. 2. Methods and analysis 2.1 study design DCE (stated preference method) is a technique that presents hypothetical scenarios, characterised by attributes and their associated levels, to study participants in order to assess their preferences and marginal rates of substitution in healthcare[ 23 ]. DCEs are primarily founded upon the theoretical framework of random utility. In accordance with this framework, it is assumed that an individual respondent will select the alternative that they perceive to offer the greatest utility[ 23 ]. A DCE survey was conducted in this study, in accordance with the guidance set forth in a report by the ISPOR Conjoint Analysis Good Research Practices Task Force[ 24 ]. Respondents were required to make trade-offs between their preferred and less preferred attribute level for each choice set. A DCE comprises four main stages: (1) identifying and defining attributes and levels; (2) the experimental design; (3) the data collection survey; and (4) the analysis and interpretation of results [4] . The procedure of DCE is shown in Fig. 1 . 2.2 Identify attributes and their levels To design a Discrete Choice Experiment (DCE) questionnaire for understanding LTC preferences of elderly stroke patients with disabilities, identifying specific attributes and their levels is a crucial step. These attributes should reflect the factors that are most likely to influence the patients' preferences for LTC services. In the domain of health, the number of attributes is typically 4–6, and the ideal number of choice sets is 8–16. 2.2.1 Literature review To determine the key attributes of LTC in this research, firstly, we performed a systematic literature search of global databases such as PubMed, EMBASE, Web of science along with Chinese journal literature databases including CNKI and Wanfang. The key words included “elderly”, “stroke”, “preference”, “long-term care”, “LTC”, “disabilities”, “disabled”. Concurrently, to gain a more comprehensive understanding, we undertook a review of the references cited in the retrieved documents. After screening the existing literature, we selected attributes such as location of care, type of care, qualification of staff, duration of care,content of care, technical support and cost for a broader retrieval of disabled stroke patients. A list of potential attributes and levels was established, which will serve as the basis for the forthcoming discussion of qualitative research. 2.2.2 In-depths interviews Secondly, based on the results of literature review, we conducted 9 one-on-one semi-structured in-depth interviews. The purpose of one-on-one interviews is to further explore the conceptual attributes derived from the literature review and obtain new and contextual attributes from the perspective of disabled strokes. The topics of one-on-one interviews mainly include the following: (1) the attitudes of disabled stroke patients towards LTC, (2) which LTC services are important, (3)shortcomings of existing LTC system, (4) ideal type and content of LTC care and (5) complementary session. The participants were recruited from the geriatrics and neurology wards of Nanjing Medical University Affiliated Suzhou Hospital (n = 5) and its affiliated Runda Community Health Centre (n = 4). All respondents participated in the study on a voluntary basis and provided written informed consent prior to being included in the study. The interviewers were two researchers from Nanjing Medical University Affiliated Suzhou Hospital who had previously undergone training and were experienced in conducting interviews. Each participant was assigned a unique number, which was used to identify them during both the completion of the demographic questionnaires and the interviews. Detailed information about the interviewed patients is provided in Supplementary file A.1. Two authors (authors ZHX and MWJ) and another author (author TXY) analysed the qualitative data from the records of the in-depth interviews using content analysis[ 25 ], aided by coding and aggregation using Nvivo V.14.0 software. The results include two parts: one to collate and summarise patients’ ranking of attribute priorities to determine the attributes for inclusion, and the other to refine the levels corresponding to each attribute based on patients’ statement. 2.2.3 Experts consultation Thirdly, we invited 6 experts from the fields of neurology, geriatric care, LTC and disabling care for an expert consultation, which was conductive to clarify the suitable attributes, their corresponding levels, exact meanings and expression. Finally, seven attributes were identified, including location of care, type of care, qualification of staff, personalization of care plan, duration of care, technical support and cost, each encompassed three levels. The details of attributes and corresponding levels are shown in Table 1 . Table 1 Attributes and levels for DCE choice questions Attributes Levels Description Location of care Institution Care provided in a hospital or nursing facility. Community Care provided in a community setting, such as a community health center. Home Care provided in the patient's home. Type of care Basic care Essential care mainly focus on assisting with daily activities (like eating, bathing, and dressing). Specialized care Advanced care that addresses specific stroke-related needs, such as rehabilitation therapy or monitoring of stroke complications. Health management Focus on overall health maintenance, such as chronic disease control, medication management, and health education. Qualification of staff Registered nurses Licensed healthcare professionals with advanced medical training who can manage treatments, administer medication, perform assessments, and modify care plans. Nurse aide Support staff trained to assist with daily activities like feeding, bathing, and mobility, mainly focus on personal care but do not perform clinical tasks. Family caregivers Informal care provided by family members with varying degrees of training and experience. Personalization of care plan Standardized care A standardized care plan applied to all patients, with little customization. Partially standardized care A care plan that allows some customization based on the patient’s personal needs. Individualized care A fully customized care plan developed specifically for the patient’s individual preferences and clinical requirements. Duration of care 24-hour care Continuous care provided throughout the day and night, ensuring comprehensive supervision and support. day care services Care provided during the day in specialized centers, with patients returning home in the evening. home visiting care (regular visits) Periodic care visits to the patient’s home for monitoring and support. Technical support Basic technical support Access to simple medical devices (such as walking aids, blood pressure monitors and blood glucose meter) to help with daily health needs. Advanced technical support Access to high-level equipment (such as ventilators or telemedicine and monitoring tools) to manage complex medical conditions. No technical support No specialized medical equipment is provided; care relies mostly on human assistance and personal support. Cost Full reimbursement by health insurance All care-related expenses are covered by insurance, minimizing out-of-pocket costs for the patient. Partial reimbursement by medical insurance Some costs are covered by insurance, but the patient or family needs to pay a portion of the total care expenses. Self-founded The patient or family pays all the care costs directly, without financial help from insurance or government programs. 2.3 Construction of the DCE questionnaires Once the discrete experimental attributes and corresponding levels have been identified, hypothetical scenario choices comprising different combinations of attributes and levels must be constructed using an experimental design. The pre-determined attributes and levels (3 7 ) will result in 2187 choice sets (i.e., a full factorial design). However, in practice, it is often impractical to provide respondents with all hypothetical scenario choices. Huber and Zwerina[ 26 ] posited that the most effective experimental design is achieved when the four principles of orthogonality, level balancing, minimal overlap, and utility balancing are met. Consequently, this study employed a partial factorial design of experimental design methodology to optimise the design of choice scenario sets utilising SPSS 28.0 software. A partial factorial design of experimental design methodology was also conducted to optimise the design of the choice set of options, thereby reducing the number of options for respondents while ensuring the DCE design met the requisite statistical efficiency standards. The following two points were also taken into consideration during the choice set design process[ 27 ]: (1) in order to avoid any exaggeration of the relative weights of the attributes and to improve the efficiency of the questionnaire, this study did not incorporate the opt-out exit option; (2) despite the partial factorial design, there were still 18 choice sets with a total of 9 sets of options were created. The results of the orthogonal experiment on the choice preference of the disabled stroke patients in provided in Supplementary file A.2. To assess the internal consistency of the participants' choices, a random number method was employed to repeat the inclusion of the fifth choice set. However, the data from this choice set were not included in the final data analysis, and the final questionnaire comprised 10 choice sets. An example of the choice set is provided in Table 2 . Finally, the questionnaire was presented in four sections (Supplementary file B). Section 1 described the purpose of the study and obtained informed consent from participant. Section 2 comprised the respondents’ sociodemographic characteristics (age, gender, marital status, educational level, occupation, family income status, primary caregivers and payment of medical expenses), and disease-realted data( time of first stroke, recurrence and comorbiditities), which may influence patients’ preferences for LTC. Section 3 collected Barthel Index scores to assess the severity of the participant's disability. Section 4 comprised an introductory script designed to familiarise respondents with the hypothetical nature of the DCE. Subsequently, participants were presented with 10 sets of choice tasks. A single-center pilot survey (n = 15) was conducted before formal survey to improve and modify the questionnaire and study (The data from these 12 patients were not included in the data analysis of the formal investigation). Table 2 Example of the choice task Attributes Long-term care A Long-term care B location of care home Institution type of care health management Basic care qualification of staff registered nurses registered nurses personalization of care plan standardized care standardized care duration of care day care services 24-hour care technical support advanced technical support Basic technical support cost Partial reimbursement by medical insurance Full reimbursement by health insurance Which option would you prefer to choose? □ □ 2.4 sample and recruitment The target population of this study is elderly stroke patients with disabilities. Criteria are as follows: (1) age ≥ 60 years, (2) comprised patients who met the criteria set forth in the China Cerebrovascular Disease Stroke Classification 2015[ 28 ] and were diagnosed with stroke by cranial CT or MRI, (3) Barthel Index scores ≤ 100, with varying degrees of disabilities, and (4) informed consent and with clear expression. The exclusion criteria are as follows: (1) in the acute phase of a disease, (2) patients with other complicated serious cardiovascular and neurological diseases, (3) patients with hearing impairment and mental abnormality. The calculation of the sample size for DCEs in healthcare is dependent upon a number of factors, including the desired level of precision of the results, the complexity of the choice tasks, the format of the questions, the availability of the respondents, the heterogeneity of the target population and the necessity for subgroup analysis[ 29 ]. To date, researchers have commonly applied a rule of thumb to estimate sample sizes based on the number of attribute levels[ 30 ]. In our study, the sample size calculation is based on the rule of thumb proposed by Johnson and Orme[ 30 ], the calculation formula of the minimum sample size N is as follows: n > 1000c/(t × a). In this equation, t represents the number of choice sets faced by each individual (with the exception of the selection set that is repeatedly included), a indicates the number of alternatives within each choice set, while c denotes the number of analysis cells. When considering the main effect, c is equivalent to the maximum level number of any attribute. The minimum sample size required for each version of the questionnaire is 167 (t = 9, a = 2, c = 3). In light of the possibility that 20% of the recovered questionnaires may be invalid, it is prudent to recruit at least 200 elderly disabled stroke patients to ensure the inclusion of sufficient data in the analysis and to obtain a representative sample. All participants will be recruited by members of the study team from the Departments of Geriatrics and Neurology in Nanjing Medical University affiliated Suzhou Hospital (a tertiary hospital with five hospital districts and nearly 5000 beds in Jiangsu Province, China) and its four affiliated community health centers. Questionnaires will be distributed to participants, with the distribution method being face-to-face. If participants requested an electronic questionnaire, they will be provided the questionnaire via WeChat or e-mail. Each patient will only receive questionnaire once. All questionnaires will be administered in Mandarin, which is a common language throughout China and widely used in daily speaking and writing. 2.5 Statistical analysis The final dataset will be analyzed using SPSS V.28.0 and Stata V.18.0. Descriptive statistics will be performed to summarize the respondents' socio-demographic and clinical characteristics. Continuous variables will be reported as mean ± standard deviation (SD), while categorical variables will be presented as frequencies and percentages. The choice data from the DCE will be coded using dummy variables to represent attribute levels. Two advanced econometric models—a mixed logit model (MXL) and a latent class model (LCM)—based on random utility theory will be used to analyze the data, addressing the following research questions: (1) What are the preferences of elderly disabled stroke patients for LTC services? (2) Is there heterogeneity in the choice of options among patients due to differences in individual characteristics? (3)How do preferences vary across patient subgroups with different characteristics? (4)What is the relative importance (RI) of each attribute in influencing LTC preferences? The mixed logit model will address research questions (1) and (2), analyzing the overall preferences of elderly stroke patients for LTC services and investigating the extent of heterogeneity in their choices[ 31 ]. This model accounts for random variations in preferences across individuals by assuming that coefficients for certain attributes follow a specified distribution (e.g., normal or log-normal). The model will also explore interactions between patient characteristics and attribute preferences to identify how preferences vary by age, disability level, income, or other individual factors[ 32 ]. The mixed logit model will provide individual-level preference weights, which are estimated using regressions where each attribute interacts with baseline characteristics. This allows us to assess whether individual characteristics affect preferences systematically.The mixed logit model will also address research question (4) by evaluating the RI of each attribute. RI reflects the extent to which an attribute contributes to the overall preference[ 33 ]. It is calculated as: Where ΔU attribute is the difference between the utility values of the highest and lowest levels of the attribute, and the denominator is the sum of these differences across all attributes. Effect coding will be applied to the attribute levels to facilitate this calculation. The resulting RI scores for all attributes will sum to 100%, with higher scores indicating greater importance. The LCM will be used to address research question (3) by identifying subgroups of respondents with distinct preference patterns. This model does not assume any specific distribution for the parameters but categorizes respondents based on their choice behavior, revealing the presence of distinct latent classes. The LCM will estimate the probability of each respondent belonging to a specific class and calculate the choice probabilities for alternatives within each class[ 34 ]. The model will use three criteria to determine the optimal number of classes: minimum Akaike Information Criterion (AIC), minimum Bayesian Information Criterion (BIC), and Consistent Akaike Information Criterion (CAIC)[ 35 ]. The class membership probabilities derived from the LCM reflect the likelihood that an individual belongs to a specific class, based on their observed choices. This feature allows us to interpret differences in LTC preferences among various patient subgroups, providing insights into heterogeneity across respondents. For both the mixed logit and latent class models, model fit will be assessed using goodness-of-fit indicators such as AIC and BIC. Statistical significance of model coefficients will be evaluated using z-tests, with a p -value < 0.05 indicating significance. The analysis will also include marginal effects to quantify the impact of a one-unit change in attribute levels on the probability of choosing a given alternative. This multi-model approach, using mixed logit and latent class models, will provide a comprehensive understanding of patient preferences for LTC services, highlight heterogeneity in preferences, and quantify the importance of each attribute. These insights will inform recommendations for designing LTC that are more aligned with the needs and preferences of elderly stroke patients with disabilities. 2.6 Ethics consideration This study has been approved by the Ethics Committee of Nanjing Medical University affiliated Suzhou Hospital (registration number K-2024-096-K01, registration date 3rd March 2024). Patient recruitment for this study began on 1st October 2024 and is expected to conclude by 31st December 2024. In accordance with the principles of voluntariness, confidentiality, and the ethical standards outlined in the Declaration of Helsinki, the investigator will provide participants with an explanation of the study's background, purpose, and potential risks prior to their signing a written informed consent form. All interview materials and questionnaires will be used solely for the purposes of this study and will be provided to researchers in an anonymous format to ensure confidentiality. Participants may withdraw from the study at any time. 3. Discussion The increasing prevalence of stroke, especially among aging populations, presents significant challenges to healthcare systems. Stroke survivors often experience long-term impairments that affect physical, cognitive, and emotional functioning, leading to a high demand for LTC services. Understanding patient preferences is essential to developing patient-centered, efficient, and sustainable LTC models that improve quality of life while alleviating the burden on families and healthcare systems. This study employs DCE to explore the preferences of elderly stroke patients with disabilities for various LTC attributes, offering data-driven insights for optimizing care models. This study provide a quantitative assessment of patient preferences by evaluating how stroke survivors trade off between care attributes, such as location, type, qualification of staff, duration, content, technical support and cost of care. The DCE framework offers a more realistic approach to understanding preferences compared to traditional surveys by simulating decision-making in hypothetical scenarios.The use of MXL and LCM will capture preference heterogeneity within the patient population. The MXL will identify how individual characteristics, such as age or caregiver availability, influence preferences, while the LCM will uncover distinct preference subgroups, providing insights into the diverse needs of patients. These findings will support tailored care strategies for different patient populations. Findings from this study will inform policymakers and providers about the optimal configurations of LTC services that align with patient needs. If patients prefer home-based care with individualized care plans, it is suggested that investments can focus on expanding home nursing services and caregiver training. Results will further facilitate the development of sustainable care models that optimize care quality and efficiency. Abbreviations LTC Long-term Care DCE Discrete Choice Experiment WHO World Health Organization SD Standard Deviation MXL Mixed Logit Model LCT Latent Class Model RI Relative Importance AIC Akaike Information Criterion BIC Bayesian Information Criterion CAIC Consistent Akaike Information Criterion Declarations Ethics approval and consent to participate The design and implementation for this study was approved by the Ethics Committee of Nanjing Medical University affiliated Suzhou Hospital (registration number K-2024-096-K01, registration date 3 th March 2024). Informed consent was obtained from all individual participants included in the study. Details that might disclose the identity of the subjects under study should be omitted. Consent for publication Not applicable. Availability of data and materials The datasets generated and analysed during the current study, including the orthogonal design schemes and choice tasks, are available in the supplementary material for reference. Funding Funded as a grant proposal entitled “Suzhou Medical Key Support Discipline Construction-Clinical Nursing (SZFCXK202101)” and “Nanjing Medical University Wisdom Recreation Industry College Funding Support”. Acknowledgments We would like to express our sincere thanks to all the participants for their support and involvement in this study. Competing interests The authors declare no competing interests. References Feigin VL, Owolabi MO, Abd-Allah F, Akinyemi RO, Bhattacharjee NV, Brainin M, Cao J, Caso V, Dalton B, Davis A, Dempsey R. Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization–Lancet Neurology Commission. The Lancet Neurology. 2023 Dec 1;22(12):1160-206. Ye J, Hu Y, Chen X, Yin Z, Yuan X, Huang L, Li K. Association between the weight-adjusted waist index and stroke: a cross-sectional study. BMC Public Health. 2023 Sep 1;23(1):1689. 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Stated choice methods: analysis and applications. Cambridge University Press; 2000. Orme B. Getting started with conjoint analysis: strategies for product design and pricing research. Second Edition. Chicago: Bibliovault OAI Repository, the University of Chicago Press, 2010 Manski CF. The structure of random utility models. Theory and decision. 1977 Jul 1;8(3):229. Nugraha J. Performance analysis of mixed logit models for discrete choice models. Pakistan Journal of Statistics and Operation Research. 2019 Sep 7:563-75. Lancsar E, Louviere J, Flynn T. Several methods to investigate relative attribute impact in stated preference experiments. Social science & medicine. 2007 Apr 1;64(8):1738-53. Greene WH, Hensher DA. A latent class model for discrete choice analysis: contrasts with mixed logit. Transportation Research Part B: Methodological. 2003 Sep 1;37(8):681-98. Zhou M, Thayer WM, Bridges JF. Using latent class analysis to model preference heterogeneity in health: a systematic review. Pharmacoeconomics. 2018 Feb;36:175-87. Additional Declarations No competing interests reported. Supplementary Files SupplementaryfileA.docx SupplementaryfileBDCEquestionaire.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5357510","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":375128600,"identity":"26f3e427-79a5-4ec9-b348-c1fafbf912c7","order_by":0,"name":"Huixian Zha","email":"","orcid":"","institution":"Nanjing medical university affiliated Suzhou Hospital, Suzhou Manicipal Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huixian","middleName":"","lastName":"Zha","suffix":""},{"id":375128601,"identity":"f5a77f0c-585d-41ee-bc73-8e6808731af4","order_by":1,"name":"Wenjun Mao","email":"","orcid":"","institution":"Nanjing medical university affiliated Suzhou Hospital, Suzhou Manicipal Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Mao","suffix":""},{"id":375128602,"identity":"82d9c110-adec-491a-8fa0-d9753f25fdd7","order_by":2,"name":"Ling Jiang","email":"","orcid":"","institution":"Nanjing medical university affiliated Suzhou Hospital, Suzhou Manicipal Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Jiang","suffix":""},{"id":375128603,"identity":"ff552386-3ddb-4b1a-80cb-67321be75352","order_by":3,"name":"Hongyun Yan","email":"","orcid":"","institution":"Nanjing medical university affiliated Suzhou Hospital, Suzhou Manicipal 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Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3RsW7CMBCAYUeWLouVrIeE4BU8ZaFi4UXOihQmqk5VBgYQlT3QvgLPwMgIQgrLwcyYvEG7MTZ7EU43Bn+zf53PFiIInhDE7vRN5csYZNrUVM79SaK4EDUXeRJ/Sl1z5U8GSFnU2GO0UQy95kN2uJjaF7WxRwk4q0qzAJG6NXl2WZ40XaYJ4GtxNbu+QD5vfVMI6X3UTqHsahiExpknQdJIICPbJm/Gyk5JhmQnkVWciW6J4lxT+8gQ2xyJK+XdZejcobm1XzlcycPPrZwPUvf1OPlD/e94EARBcNcv2wJJjya4ZdsAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing medical university affiliated Suzhou Hospital, Suzhou Manicipal Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xingyue","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2024-10-30 01:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5357510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5357510/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70507928,"identity":"533df68d-59d2-4814-b023-336f84485b30","added_by":"auto","created_at":"2024-12-03 23:59:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289373,"visible":true,"origin":"","legend":"\u003cp\u003eThe procedure of discrete choice experiment\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5357510/v1/219e6780b6ec53702e2471fc.png"},{"id":70510222,"identity":"86f98fa3-700f-4c97-8267-d004ef4b1179","added_by":"auto","created_at":"2024-12-04 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00:07:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":38910,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileBDCEquestionaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-5357510/v1/594c4cfa87fc97cc1dd01fd2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preferences for long-term care among elderly stroke patients with disabilities : a protocol for a discrete choice experiment ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke represents a significant global health challenge, characterized by its high incidence, prevalence, and substantial burden on healthcare systems[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is the leading cause of mortality and long-term disability, affecting millions of individuals annually[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the Global Burden of Disease study[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e], there are currently 101\u0026nbsp;million people worldwide experience the stroke, with about 12.2\u0026nbsp;million new cases each year, and about 6.55\u0026nbsp;million deaths caused by stroke, accounting for 11.6% of all deaths, which has now became the second leading cause of death. In China, the ageing of population have contributed to the prevalence of unhealthy lifestyles, which have led to a significant increase in the number of individuals exposed to risk factors for cardiovascular and cerebrovascular diseases[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, the burden of stroke in China has exhibited an alarming growth trend. A report published in JAMA on the burden of stroke in China[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e] indicated that the estimated prevalence, incidence, and mortality rate of stroke were 2.6%, 505.2 per 100,000 person-years, and 343.4 per 100,000 person-years, respectively. These figures illustrate the significant disease burden among this disease group.\u003c/p\u003e\n\u003cp\u003eThe burden of stroke extends beyond immediate medical consequences. Survivors frequently confront long-term challenges, studies[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e] have shown that 60% of stroke patients have varying degrees of cognitive impairment, speech, swallowing or physical mobility disorders, depressive symptoms, or social dysfunction, resulting in the loss of daily activities and self-care ability, and the quality of life is severely affected. Nowadays, the reported disabilities rate among stroke patients in China is 12.5(95%CI 12.4\u0026ndash;12.5), as defined by a modified Rankin scale[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. The disability adjusted life years(DALYs) caused by stroke are higher than a majority of other diseases[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. In this context, long-term care(LTC) emerge as a crucial element of comprehensive healthcare, with the objective of enhancing the quality of life of elderly stroke survivors with disabilities and of alleviating the medical burden on families and society[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. With an ageing population and increasing prevalence of stroke, the demand for appropriate and sustainable LTC is expected to increase significantly in the coming years[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe World Health Organization (WHO)[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] defines LTC as a range of care activities provided by professional caregivers or non-professional caregivers (such as family members) to ensure a satisfactory quality of life for individuals with chronic health conditions or disabilities. According to the different places of care services, LTC in different countries is basically divided into three models: institutional care with different types of institutions as the platform, community care with each community as the platform, and home care with the family as the platform, each offering distinct advantages and challenges[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. Institutional care, which includes hospitals and nursing homes, provides access to professional medical staff and medical infrastructure. However, it is often associated with higher costs, greater financial burden, personnel shortages, and uneven regional availability[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. In contrast, home-based care and community-based care offer LTC in familiar environments, which may improve patient comfort and well-being. Under this circumstance, family members often serve as primary caregivers, many of whom lack formal training in healthcare[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. This dual responsibility\u0026mdash;caring for disabled stroke patients while managing other household and childcare duties\u0026mdash;can place significant psychological, emotional, and financial strain on families[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, the optimal configuration of LTC for stroke survivors remains a pressing concern, especially in countries like China where the aging population is rapidly expanding[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eDeveloping effective LTC services requires a thorough understanding of patient preferences, as LTC options vary in structure, content, and payment[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous research[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] has examined the preferences for LTC among older adults, and the results demonstrated that the home-based care remains the predominant option for older people, which is closely related to Chinese cultural traditions (e.g. filial piety). But the study also points to a gradual increase in demand for community and institutional care as China goes through a period of change in terms of family structure and social norms. In addition, demographic, psychological, physical condition and economic factors were all correlated with patients\u0026rsquo; preference. Research[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] suggests that there may be significant differences in patients\u0026apos; care preferences when care needs are high, and that policymakers should be flexible in developing resource allocation strategies according to the needs and health status of different groups. However, little attention has been paid to disabled stroke survivors, and there is a lack of research on the LTC preferences of this population.\u003c/p\u003e\n\u003cp\u003eTo address this gap, this study employs a discrete choice experiment (DCE) to explore the preferences of elderly stroke patients with disabilities regarding LTC. DCE is a quantitative method that evaluates individual trade-offs between different service attributes, providing insights into which characteristics are most valued by patients[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. By presenting respondents with hypothetical LTC scenarios composed of varying attributes and levels, this study will reveal the relative importance of specific service features and identify the optimal configurations of LTC services. The findings will provide valuable guidance for policy makers and healthcare providers to design LTC models that better address the needs and preferences of elderly stroke patients and their caregivers.\u003c/p\u003e\n"},{"header":"2. Methods and analysis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 study design\u003c/h2\u003e\n \u003cp\u003eDCE (stated preference method) is a technique that presents hypothetical scenarios, characterised by attributes and their associated levels, to study participants in order to assess their preferences and marginal rates of substitution in healthcare[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. DCEs are primarily founded upon the theoretical framework of random utility. In accordance with this framework, it is assumed that an individual respondent will select the alternative that they perceive to offer the greatest utility[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. A DCE survey was conducted in this study, in accordance with the guidance set forth in a report by the ISPOR Conjoint Analysis Good Research Practices Task Force[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Respondents were required to make trade-offs between their preferred and less preferred attribute level for each choice set. A DCE comprises four main stages: (1) identifying and defining attributes and levels; (2) the experimental design; (3) the data collection survey; and (4) the analysis and interpretation of results\u003csup\u003e[4]\u003c/sup\u003e. The procedure of DCE is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Identify attributes and their levels\u003c/h2\u003e\n \u003cp\u003eTo design a Discrete Choice Experiment (DCE) questionnaire for understanding LTC preferences of elderly stroke patients with disabilities, identifying specific attributes and their levels is a crucial step. These attributes should reflect the factors that are most likely to influence the patients\u0026apos; preferences for LTC services. In the domain of health, the number of attributes is typically 4\u0026ndash;6, and the ideal number of choice sets is 8\u0026ndash;16.\u003c/p\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Literature review\u003c/h2\u003e\n \u003cp\u003eTo determine the key attributes of LTC in this research, firstly, we performed a systematic literature search of global databases such as PubMed, EMBASE, Web of science along with Chinese journal literature databases including CNKI and Wanfang. The key words included \u0026ldquo;elderly\u0026rdquo;, \u0026ldquo;stroke\u0026rdquo;, \u0026ldquo;preference\u0026rdquo;, \u0026ldquo;long-term care\u0026rdquo;, \u0026ldquo;LTC\u0026rdquo;, \u0026ldquo;disabilities\u0026rdquo;, \u0026ldquo;disabled\u0026rdquo;. Concurrently, to gain a more comprehensive understanding, we undertook a review of the references cited in the retrieved documents. After screening the existing literature, we selected attributes such as location of care, type of care, qualification of staff, duration of care,content of care, technical support and cost for a broader retrieval of disabled stroke patients. A list of potential attributes and levels was established, which will serve as the basis for the forthcoming discussion of qualitative research.\u003c/p\u003e\n \u003cp\u003e2.2.2 In-depths interviews\u003c/p\u003e\n \u003cp\u003eSecondly, based on the results of literature review, we conducted 9 one-on-one semi-structured in-depth interviews. The purpose of one-on-one interviews is to further explore the conceptual attributes derived from the literature review and obtain new and contextual attributes from the perspective of disabled strokes. The topics of one-on-one interviews mainly include the following: (1) the attitudes of disabled stroke patients towards LTC, (2) which LTC services are important, (3)shortcomings of existing LTC system, (4) ideal type and content of LTC care and (5) complementary session. The participants were recruited from the geriatrics and neurology wards of Nanjing Medical University Affiliated Suzhou Hospital (n\u0026thinsp;=\u0026thinsp;5) and its affiliated Runda Community Health Centre (n\u0026thinsp;=\u0026thinsp;4). All respondents participated in the study on a voluntary basis and provided written informed consent prior to being included in the study. The interviewers were two researchers from Nanjing Medical University Affiliated Suzhou Hospital who had previously undergone training and were experienced in conducting interviews. Each participant was assigned a unique number, which was used to identify them during both the completion of the demographic questionnaires and the interviews. Detailed information about the interviewed patients is provided in Supplementary file A.1.\u003c/p\u003e\n \u003cp\u003eTwo authors (authors ZHX and MWJ) and another author (author TXY) analysed the qualitative data from the records of the in-depth interviews using content analysis[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], aided by coding and aggregation using Nvivo V.14.0 software. The results include two parts: one to collate and summarise patients\u0026rsquo; ranking of attribute priorities to determine the attributes for inclusion, and the other to refine the levels corresponding to each attribute based on patients\u0026rsquo; statement.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3 Experts consultation\u003c/h2\u003e\n \u003cp\u003eThirdly, we invited 6 experts from the fields of neurology, geriatric care, LTC and disabling care for an expert consultation, which was conductive to clarify the suitable attributes, their corresponding levels, exact meanings and expression. Finally, seven attributes were identified, including location of care, type of care, qualification of staff, personalization of care plan, duration of care, technical support and cost, each encompassed three levels. The details of attributes and corresponding levels are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAttributes and levels for DCE choice questions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eAttributes\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eLevels\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eDescription\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eLocation of care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eInstitution\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCare provided in a hospital or nursing facility.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eCommunity\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCare provided in a community setting, such as a community health center.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHome\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCare provided in the patient\u0026apos;s home.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eType of care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBasic care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eEssential care mainly focus on assisting with daily activities (like eating, bathing, and dressing).\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSpecialized care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAdvanced care that addresses specific stroke-related needs, such as rehabilitation therapy or monitoring of stroke complications.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHealth management\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eFocus on overall health maintenance, such as chronic disease control, medication management, and health education.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eQualification of staff\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eRegistered nurses\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eLicensed healthcare professionals with advanced medical training who can manage treatments, administer medication, perform assessments, and modify care plans.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNurse aide\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSupport staff trained to assist with daily activities like feeding, bathing, and mobility, mainly focus on personal care but do not perform clinical tasks.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eFamily caregivers\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eInformal care provided by family members with varying degrees of training and experience.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003ePersonalization of care plan\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eStandardized care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA standardized care plan applied to all patients, with little customization.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePartially standardized care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA care plan that allows some customization based on the patient\u0026rsquo;s personal needs.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIndividualized care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eA fully customized care plan developed specifically for the patient\u0026rsquo;s individual preferences and clinical requirements.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eDuration of care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e24-hour care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eContinuous care provided throughout the day and night, ensuring comprehensive supervision and support.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eday care services\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eCare provided during the day in specialized centers, with patients returning home in the evening.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ehome visiting care (regular visits)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ePeriodic care visits to the patient\u0026rsquo;s home for monitoring and support.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eTechnical support\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBasic technical support\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAccess to simple medical devices (such as walking aids, blood pressure monitors and blood glucose meter) to help with daily health needs.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eAdvanced technical support\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAccess to high-level equipment (such as ventilators or telemedicine and monitoring tools) to manage complex medical conditions.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eNo technical support\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eNo specialized medical equipment is provided; care relies mostly on human assistance and personal support.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003eCost\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eFull reimbursement by health insurance\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAll care-related expenses are covered by insurance, minimizing out-of-pocket costs for the patient.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePartial reimbursement by medical insurance\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSome costs are covered by insurance, but the patient or family needs to pay a portion of the total care expenses.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSelf-founded\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eThe patient or family pays all the care costs directly, without financial help from insurance or government programs.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cbr\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Construction of the DCE questionnaires\u003c/h2\u003e\n \u003cp\u003eOnce the discrete experimental attributes and corresponding levels have been identified, hypothetical scenario choices comprising different combinations of attributes and levels must be constructed using an experimental design. The pre-determined attributes and levels (3\u003csup\u003e7\u003c/sup\u003e) will result in 2187 choice sets (i.e., a full factorial design). However, in practice, it is often impractical to provide respondents with all hypothetical scenario choices. Huber and Zwerina[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] posited that the most effective experimental design is achieved when the four principles of orthogonality, level balancing, minimal overlap, and utility balancing are met. Consequently, this study employed a partial factorial design of experimental design methodology to optimise the design of choice scenario sets utilising SPSS 28.0 software. A partial factorial design of experimental design methodology was also conducted to optimise the design of the choice set of options, thereby reducing the number of options for respondents while ensuring the DCE design met the requisite statistical efficiency standards. The following two points were also taken into consideration during the choice set design process[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]: (1) in order to avoid any exaggeration of the relative weights of the attributes and to improve the efficiency of the questionnaire, this study did not incorporate the opt-out exit option; (2) despite the partial factorial design, there were still 18 choice sets with a total of 9 sets of options were created. The results of the orthogonal experiment on the choice preference of the disabled stroke patients in provided in Supplementary file A.2. To assess the internal consistency of the participants\u0026apos; choices, a random number method was employed to repeat the inclusion of the fifth choice set. However, the data from this choice set were not included in the final data analysis, and the final questionnaire comprised 10 choice sets. An example of the choice set is provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFinally, the questionnaire was presented in four sections (Supplementary file B). Section 1 described the purpose of the study and obtained informed consent from participant. Section 2 comprised the respondents\u0026rsquo; sociodemographic characteristics (age, gender, marital status, educational level, occupation, family income status, primary caregivers and payment of medical expenses), and disease-realted data( time of first stroke, recurrence and comorbiditities), which may influence patients\u0026rsquo; preferences for LTC. Section 3 collected Barthel Index scores to assess the severity of the participant\u0026apos;s disability. Section 4 comprised an introductory script designed to familiarise respondents with the hypothetical nature of the DCE. Subsequently, participants were presented with 10 sets of choice tasks.\u003c/p\u003e\n \u003cp\u003eA single-center pilot survey (n\u0026thinsp;=\u0026thinsp;15) was conducted before formal survey to improve and modify the questionnaire and study (The data from these 12 patients were not included in the data analysis of the formal investigation).\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eExample of the choice task\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eAttributes\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eLong-term care A\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eLong-term care B\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003elocation of care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ehome\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eInstitution\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003etype of care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ehealth management\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBasic care\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003equalification of staff\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eregistered nurses\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eregistered nurses\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003epersonalization of care plan\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003estandardized care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003estandardized care\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eduration of care\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eday care services\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e24-hour care\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003etechnical support\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eadvanced technical support\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBasic technical support\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ecost\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ePartial reimbursement by medical insurance\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eFull reimbursement by health insurance\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eWhich option would you prefer to choose?\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e□\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e□\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cbr\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 sample and recruitment\u003c/h2\u003e\n \u003cp\u003eThe target population of this study is elderly stroke patients with disabilities. Criteria are as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;60 years, (2) comprised patients who met the criteria set forth in the China Cerebrovascular Disease Stroke Classification 2015[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] and were diagnosed with stroke by cranial CT or MRI, (3) Barthel Index scores\u0026thinsp;\u0026le;\u0026thinsp;100, with varying degrees of disabilities, and (4) informed consent and with clear expression. The exclusion criteria are as follows: (1) in the acute phase of a disease, (2) patients with other complicated serious cardiovascular and neurological diseases, (3) patients with hearing impairment and mental abnormality.\u003c/p\u003e\n \u003cp\u003eThe calculation of the sample size for DCEs in healthcare is dependent upon a number of factors, including the desired level of precision of the results, the complexity of the choice tasks, the format of the questions, the availability of the respondents, the heterogeneity of the target population and the necessity for subgroup analysis[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. To date, researchers have commonly applied a rule of thumb to estimate sample sizes based on the number of attribute levels[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. In our study, the sample size calculation is based on the rule of thumb proposed by Johnson and Orme[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], the calculation formula of the minimum sample size N is as follows: n\u0026thinsp;\u0026gt;\u0026thinsp;1000c/(t \u0026times; a). In this equation, t represents the number of choice sets faced by each individual (with the exception of the selection set that is repeatedly included), a indicates the number of alternatives within each choice set, while c denotes the number of analysis cells. When considering the main effect, c is equivalent to the maximum level number of any attribute. The minimum sample size required for each version of the questionnaire is 167 (t\u0026thinsp;=\u0026thinsp;9, a\u0026thinsp;=\u0026thinsp;2, c\u0026thinsp;=\u0026thinsp;3). In light of the possibility that 20% of the recovered questionnaires may be invalid, it is prudent to recruit at least 200 elderly disabled stroke patients to ensure the inclusion of sufficient data in the analysis and to obtain a representative sample.\u003c/p\u003e\n \u003cp\u003eAll participants will be recruited by members of the study team from the Departments of Geriatrics and Neurology in Nanjing Medical University affiliated Suzhou Hospital (a tertiary hospital with five hospital districts and nearly 5000 beds in Jiangsu Province, China) and its four affiliated community health centers. Questionnaires will be distributed to participants, with the distribution method being face-to-face. If participants requested an electronic questionnaire, they will be provided the questionnaire via WeChat or e-mail. Each patient will only receive questionnaire once. All questionnaires will be administered in Mandarin, which is a common language throughout China and widely used in daily speaking and writing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eThe final dataset will be analyzed using SPSS V.28.0 and Stata V.18.0. Descriptive statistics will be performed to summarize the respondents\u0026apos; socio-demographic and clinical characteristics. Continuous variables will be reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical variables will be presented as frequencies and percentages. The choice data from the DCE will be coded using dummy variables to represent attribute levels. Two advanced econometric models\u0026mdash;a mixed logit model (MXL) and a latent class model (LCM)\u0026mdash;based on random utility theory will be used to analyze the data, addressing the following research questions: (1) What are the preferences of elderly disabled stroke patients for LTC services? (2) Is there heterogeneity in the choice of options among patients due to differences in individual characteristics? (3)How do preferences vary across patient subgroups with different characteristics? (4)What is the relative importance (RI) of each attribute in influencing LTC preferences?\u003c/p\u003e\n \u003cp\u003eThe mixed logit model will address research questions (1) and (2), analyzing the overall preferences of elderly stroke patients for LTC services and investigating the extent of heterogeneity in their choices[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. This model accounts for random variations in preferences across individuals by assuming that coefficients for certain attributes follow a specified distribution (e.g., normal or log-normal). The model will also explore interactions between patient characteristics and attribute preferences to identify how preferences vary by age, disability level, income, or other individual factors[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. The mixed logit model will provide individual-level preference weights, which are estimated using regressions where each attribute interacts with baseline characteristics. This allows us to assess whether individual characteristics affect preferences systematically.The mixed logit model will also address research question (4) by evaluating the RI of each attribute. RI reflects the extent to which an attribute contributes to the overall preference[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is calculated as:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1731657517.png\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWhere \u0026Delta;U\u003csub\u003eattribute\u003c/sub\u003e is the difference between the utility values of the highest and lowest levels of the attribute, and the denominator is the sum of these differences across all attributes. Effect coding will be applied to the attribute levels to facilitate this calculation. The resulting RI scores for all attributes will sum to 100%, with higher scores indicating greater importance. The LCM will be used to address research question (3) by identifying subgroups of respondents with distinct preference patterns. This model does not assume any specific distribution for the parameters but categorizes respondents based on their choice behavior, revealing the presence of distinct latent classes. The LCM will estimate the probability of each respondent belonging to a specific class and calculate the choice probabilities for alternatives within each class[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. The model will use three criteria to determine the optimal number of classes: minimum Akaike Information Criterion (AIC), minimum Bayesian Information Criterion (BIC), and Consistent Akaike Information Criterion (CAIC)[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. The class membership probabilities derived from the LCM reflect the likelihood that an individual belongs to a specific class, based on their observed choices. This feature allows us to interpret differences in LTC preferences among various patient subgroups, providing insights into heterogeneity across respondents.\u003c/p\u003e\n \u003cp\u003eFor both the mixed logit and latent class models, model fit will be assessed using goodness-of-fit indicators such as AIC and BIC. Statistical significance of model coefficients will be evaluated using z-tests, with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significance. The analysis will also include marginal effects to quantify the impact of a one-unit change in attribute levels on the probability of choosing a given alternative. This multi-model approach, using mixed logit and latent class models, will provide a comprehensive understanding of patient preferences for LTC services, highlight heterogeneity in preferences, and quantify the importance of each attribute. These insights will inform recommendations for designing LTC that are more aligned with the needs and preferences of elderly stroke patients with disabilities.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Ethics consideration\u003c/h2\u003e\n \u003cp\u003eThis study has been approved by the Ethics Committee of Nanjing Medical University affiliated Suzhou Hospital (registration number K-2024-096-K01, registration date 3rd March 2024). Patient recruitment for this study began on 1st October 2024 and is expected to conclude by 31st December 2024. In accordance with the principles of voluntariness, confidentiality, and the ethical standards outlined in the Declaration of Helsinki, the investigator will provide participants with an explanation of the study\u0026apos;s background, purpose, and potential risks prior to their signing a written informed consent form. All interview materials and questionnaires will be used solely for the purposes of this study and will be provided to researchers in an anonymous format to ensure confidentiality. Participants may withdraw from the study at any time.\u003c/p\u003e\u003cbr\u003e\n\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe increasing prevalence of stroke, especially among aging populations, presents significant challenges to healthcare systems. Stroke survivors often experience long-term impairments that affect physical, cognitive, and emotional functioning, leading to a high demand for LTC services. Understanding patient preferences is essential to developing patient-centered, efficient, and sustainable LTC models that improve quality of life while alleviating the burden on families and healthcare systems. This study employs DCE to explore the preferences of elderly stroke patients with disabilities for various LTC attributes, offering data-driven insights for optimizing care models.\u003c/p\u003e\n\u003cp\u003eThis study provide a quantitative assessment of patient preferences by evaluating how stroke survivors trade off between care attributes, such as location, type, qualification of staff, duration, content, technical support and cost of care. The DCE framework offers a more realistic approach to understanding preferences compared to traditional surveys by simulating decision-making in hypothetical scenarios.The use of MXL and LCM will capture preference heterogeneity within the patient population. The MXL will identify how individual characteristics, such as age or caregiver availability, influence preferences, while the LCM will uncover distinct preference subgroups, providing insights into the diverse needs of patients. These findings will support tailored care strategies for different patient populations.\u003c/p\u003e\n\u003cp\u003eFindings from this study will inform policymakers and providers about the optimal configurations of LTC services that align with patient needs. If patients prefer home-based care with individualized care plans, it is suggested that investments can focus on expanding home nursing services and caregiver training. Results will further facilitate the development of sustainable care models that optimize care quality and efficiency.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eLTC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLong-term Care\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDCE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiscrete Choice Experiment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWorld Health Organization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandard Deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMXL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMixed Logit Model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLCT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLatent Class Model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRelative Importance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAkaike Information Criterion\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBayesian Information Criterion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAIC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent Akaike Information Criterion\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe design and\u0026nbsp;implementation\u0026nbsp;for this study was approved by the\u0026nbsp;Ethics Committee of Nanjing Medical University affiliated Suzhou Hospital (registration number K-2024-096-K01, registration date 3\u003csup\u003eth\u003c/sup\u003e March 2024).\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study. Details that might disclose the identity of the subjects under study should be omitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study, including the orthogonal design schemes and choice tasks, are available in the supplementary material for reference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunded as a grant proposal entitled \u0026ldquo;Suzhou Medical Key Support Discipline Construction-Clinical Nursing (SZFCXK202101)\u0026rdquo; and \u0026ldquo;Nanjing Medical University Wisdom Recreation Industry College Funding Support\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere thanks to all the participants for their support and involvement in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFeigin VL, Owolabi MO, Abd-Allah F, Akinyemi RO, Bhattacharjee NV, Brainin M, Cao J, Caso V, Dalton B, Davis A, Dempsey R. Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization\u0026ndash;Lancet Neurology Commission. The Lancet Neurology. 2023 Dec 1;22(12):1160-206.\u003c/li\u003e\n\u003cli\u003eYe J, Hu Y, Chen X, Yin Z, Yuan X, Huang L, Li K. Association between the weight-adjusted waist index and stroke: a cross-sectional study. BMC Public Health. 2023 Sep 1;23(1):1689.\u003c/li\u003e\n\u003cli\u003eFeigin VL, Stark BA, Johnson CO, Roth GA, Bisignano C, Abady GG, Abbasifard M, Abbasi-Kangevari M, Abd-Allah F, Abedi V, Abualhasan A. Global, regional, and national burden of stroke and its risk factors, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology. 2021 Oct 1;20(10):795-820.\u003c/li\u003e\n\u003cli\u003eReport on Stroke Center in China Writing Group. Brief report on stroke center in China, 2022. 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Pharmacoeconomics. 2018 Feb;36:175-87.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"stroke, long-term care, disabilities, discrete choice experiment, preferences","lastPublishedDoi":"10.21203/rs.3.rs-5357510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5357510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStroke is one of the leading causes of disability among older adults worldwide, often resulting in significant physical, cognitive, and emotional impairments that require long-term care (LTC). With aging populations and increasing stroke prevalence, the demand for appropriate and sustainable LTC is expected to grow substantially in the coming years. However, designing such LTC to meet the complex and evolving needs of elderly disabled stroke patients remains a challenge. Understanding patients\u0026rsquo; preferences is crucial for the development of patient-centered care plans and optimizing the LTC strategy for stoke patients with disabilities. This study uses a discrete choice experiment (DCE) to measure and quantify patients\u0026rsquo; preferences for LTC, and aims at (1) identifying and exploring which elements of LTC are essential for elderly disabled stroke patients; (2) measuring patients\u0026rsquo; preferences for LTC and summarising relevant characteristics that may influence preference choices and (3) determining whether these preferences vary by participants characteristics and classifying the population types based on the baseline data and activities of daily living.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe research was conducted in accordance with the design programme of the DCE study. Seven attributes were developed through a systematic literature review, in-depth interviews and experts consultation. A partial factorial survey design was generated through an orthogonal experimental design to optimize the choice scenario sets. We plan to conduct a DCE questionnaire survey in Suzhou, Jiangsu province, China and recruit at least 200 participants. The final data will be analysed through a mixed logit model and a latent class model to explore the preference of elderly disabled stroke patients for LTC.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eThe study will provide insights into the LTC preferences of elderly stroke patients with disabilities. By quantifying the importance of various LTC attributes, the findings will help policymakers and healthcare providers design tailored, sustainable LTC services. The use of mixed logit and latent class models will uncover both general preferences and distinct patterns of preference heterogeneity, enabling the development of personalized LTC models that align with patients\u0026rsquo; specific needs.\u003c/p\u003e","manuscriptTitle":"Preferences for long-term care among elderly stroke patients with disabilities : a protocol for a discrete choice experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-03 23:59:28","doi":"10.21203/rs.3.rs-5357510/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04f818e7-dcc8-4f48-b23e-3cafcee9c264","owner":[],"postedDate":"December 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-03T23:59:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-03 23:59:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5357510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5357510","identity":"rs-5357510","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}