Examining the Preferences of Tehran Citizens for Receiving Home-Based Hospitalization Care for COVID-19 Using the Discrete Choice Experiment Method

Examining the Preferences of Tehran Citizens for Receiving Home-Based Hospitalization Care for COVID-19 Using the Discrete Choice Experiment Method | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 August 2025 V1 Latest version Share on Examining the Preferences of Tehran Citizens for Receiving Home-Based Hospitalization Care for COVID-19 Using the Discrete Choice Experiment Method Authors : Mohammad Ehsani Kia [email protected] , Vajihe Ramezani Dorh , and Leili Tapak Authors Info & Affiliations https://doi.org/10.22541/au.175574714.49824985/v1 311 views 98 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background and Objectives: Home care services are an essential component of community-based healthcare. Given the multiple challenges faced by Iran’s healthcare system, utilizing home care services can effectively support the country’s medical needs. This study employs the discrete choice experiment (DCE) method to examine the preferences of Tehran citizens for receiving home-based hospitalization care. Materials and Methods: This descriptive-analytical study utilized the discrete choice experiment method to determine individuals’ preferences among 893 individuals visiting healthcare centers across Tehran. Attributes and their levels were identified through a literature review and expert interviews. Data were collected using a structured questionnaire. The study’s attributes included physician expertise, emergency communication capability, caregiver expertise, and service cost. The significance level was set at p < 0.05 , and conditional logit regression was conducted using STATA software, version 17. Findings: The study involved 893 participants aged 18 to 82. The results showed that the variables of cost and health status had a significant negative association with the likelihood of choosing home-based hospitalization care for COVID-19, reducing the probability of selection. On the other hand, variables such as physician expertise, emergency communication capability, caregiver expertise, higher education levels, and income categories had a significant positive association with the likelihood of selecting home-based hospitalization care, increasing the probability of choice (p < 0.05). Conclusion: Receiving a comprehensive home hospitalization service package that includes emergency contact capability , access to physicians and nurses for care, and affordability increases the likelihood of selecting home-based hospitalization among COVID-19 patients. Examining the Preferences of Tehran Citizens for Receiving Home-Based Hospitalization Care for COVID-19 Using the Discrete Choice Experiment Method Mohammad Ehsani Kia 1 *, Vajihe Ramezani Dorh 1 , Leili Tapak 2 1- Department of Health Management and Economics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. 2- Department of Biostatistics, School of Health Modeling of Noncommunicable Diseases Research Center Institute of Health Sciences and Technology Hamadan University of Medical Sciences, Hamadan, Iran. * Corresponding author: Mohammad Ehsani Kia , Graduate, Department of Health Management and Economics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. (Email Address: [email protected] ) Ethics approval and consent to participate This study was conducted in accordance with established ethical standards and received approval from the Ethics Committee of Hamadan University of Medical Sciences, under ethical code ”IR.UMSHA.REC.1401.295” .The necessary approval was secured through the Biomedical Research Ethics System of the Ministry of Health, Treatment, and Medical Education. Clinical Trial Not applicable Consent for publication Not applicable Availability of data and material The datasets generated and analyzed during the current study are not publicly available due to confidentiality and privacy restrictions but can be made available by the corresponding author upon reasonable request. Conflict of Interest No conflict of interest declared Funding Not applicable Author’s contributions M.E.K : data collection, M.E.K, V.R.D, L.T ; analysis and interpretation of results M.E.K, V.R.D : draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript. Acknowledgement: The authors would like to thank the members of Department of Health Management and Economics, School of Public Health, Hamadan University of Medical Sciences for their unwavering support. Abstract Background and Objectives: Home care services are an essential component of community-based healthcare. Given the multiple challenges faced by Iran’s healthcare system, utilizing home care services can effectively support the country’s medical needs. This study employs the discrete choice experiment (DCE) method to examine the preferences of Tehran citizens for receiving home-based hospitalization care. Materials and Methods: This descriptive-analytical study utilized the discrete choice experiment method to determine individuals’ preferences among 893 individuals visiting healthcare centers across Tehran. Attributes and their levels were identified through a literature review and expert interviews. Data were collected using a structured questionnaire. The study’s attributes included physician expertise, emergency communication capability, caregiver expertise, and service cost. The significance level was set at p < 0.05 , and conditional logit regression was conducted using STATA software, version 17. Findings: The study involved 893 participants aged 18 to 82. The results showed that the variables of cost and health status had a significant negative association with the likelihood of choosing home-based hospitalization care for COVID-19, reducing the probability of selection. On the other hand, variables such as physician expertise, emergency communication capability, caregiver expertise, higher education levels, and income categories had a significant positive association with the likelihood of selecting home-based hospitalization care, increasing the probability of choice (p < 0.05). Conclusion: Receiving a comprehensive home hospitalization service package that includes emergency contact capability , access to physicians and nurses for care, and affordability increases the likelihood of selecting home-based hospitalization among COVID-19 patients. Keywords: Preferences, COVID-19, Home Hospitalization, Discrete Choice Experiment Introduction Home care services are an essential component of community-based healthcare, enabling families to participate in patient care and self-care (1). These services involve providing medical treatment and support at the patient’s home, typically delivered by physicians, nurses, physiotherapists, and other healthcare professionals. Home care allows patients to receive treatment in a familiar and comfortable environment without requiring prolonged hospital stays. It is particularly effective for the elderly, individuals with chronic illnesses, and patients recovering from surgery(2). The primary mission of home care services is to maintain and restore patient independence, ensuring that individuals can meet their healthcare needs at home with the assistance of healthcare providers (3). In Iran, home care services are gradually expanding as an emerging approach, primarily focusing on the elderly, patients with chronic diseases, and individuals requiring rehabilitative care. These services encompass physician visits, nursing care, physiotherapy, and medical treatments such as injections and wound dressing in a home setting. With the increasing elderly population and rising hospitalization costs, the demand for such services in the country is becoming more evident(4). Existing evidence highlights that home healthcare services are a crucial component of the healthcare system (5). This became particularly apparent during the COVID-19 pandemic, when the relative shortage of hospital resources significantly contributed to COVID-19-related mortality (6). The shortage of available hospital beds, particularly in intensive care units (ICUs) for critically ill patients, posed a major challenge during the outbreak (7). Moreover, given the continuous main components s mutation and evolution of the COVID-19 virus, there is a possibility of new variants emerging, some of which may be more transmissible and severe than previous strains (8). In this context, home care has been recognized by the World Health Organization (WHO) as one of the primary care models for addressing the medical needs of patients and vulnerable populations during the pandemic(9). Implementing this approach in Iran could enhance the healthcare system’s performance. Studies indicate that Iran’s healthcare system faces several challenges, including workforce shortages and a lack of hospital beds. With limited resources, a community-based home healthcare system could play a crucial role in managing the country’s healthcare needs (10). Therefore, research that can guide policymakers in making informed decisions regarding the widespread adoption or rejection of home-based hospitalization is of great significance. One such research method is the Discrete Choice Experiment (DCE) , which has been introduced in health economics to identify priorities in health and healthcare services (11). The DCE method describes potential interventions or products based on their characteristics, with each characteristic assigned a range of levels (12). These characteristics and levels are typically combined using experimental designs to generate a set of hypothetical choice options (12, 13). Respondents are then presented with a sequence of two or more competing options and asked to indicate their preference (14). Finally, using the predetermined levels and characteristics, the chosen interventions and respondent preferences are analyzed(15). In health economics, the DCE method is commonly employed to evaluate health outcomes, examine trade-offs between health and non-health outcomes, and develop priority-setting frameworks (16). Several researchers, including Shahsavari et al. (2015)(10, 17) and Rasmussen et al. (2019) (18), have investigated patient preferences for telehealth services during the COVID-19 pandemic. Their findings indicate that patients perceived telehealth as an effective means of improving access to healthcare. Similarly, a study by Sharon Walsh et al. (2020)(19) demonstrated that flexible service delivery, personalized communication, and extended home care hours were highly valued by citizens. Moreover, in the event of a new wave of COVID-19 outbreaks, home care services could play a crucial role in reducing the burden on hospitals, minimizing virus transmission risks, and improving the quality of healthcare services. By shifting treatment to home settings, individuals with mild to moderate symptoms could recover without requiring hospitalization, thereby lowering the potential spread of the virus. This approach is particularly beneficial for patients with underlying chronic conditions, as it protects them from high-risk hospital environments and helps preserve their overall health(20). Given these considerations—and the fact that no prior study in Iran has utilized this method to examine public preferences for receiving home care services — it was essential to employ the discrete choice experiment (DCE) method to assess citizens’ preferences for home-based healthcare. The results of this study could provide valuable insights for healthcare policymakers and decision-makers, facilitating more informed managerial decisions regarding the expansion and optimization of home care services . Research Methodology This study was a descriptive-analytical research conducted cross-sectionally. Since this technique follows an attribute-based survey approach with a stated preference methodology, the study initially identified relevant attributes through a literature review and expert interviews. All potential attributes were assessed based on three criteria: relevance to the research question, applicability to the decision-making context, and interdependence among attributes. As a result, 16 attributes were identified. Given the impracticality of including all identified attributes, a rating method was employed to prioritize them. This method involved assigning weights to the attributes based on their perceived importance. A questionnaire was designed, and a panel of 30 individuals—including 15 healthcare professionals and 15 Tehran residents—rated the attributes on a five-point scale, where 5 represented the most important attribute in receiving home care services, and 1 indicated the least important. Ultimately, the four attributes receiving the highest scores were selected, and their corresponding levels were determined through expert consultation. This decision-making method was developed by Edwards in 1997 and is a multi-criteria decision analysis (MCDA) technique based on the principle that each alternative comprises several criteria, each possessing intrinsic value and associated weight that defines its level of importance(21). In this study, a sample of 1,000 citizens of Tehran who visited comprehensive health centers and health houses across the 22 districts of the city were randomly selected. After excluding individuals who provided incorrect answers to the warm-up question, a total of 893 participants were included in the final analysis. The data collection tool was a questionnaire comprising two sections: demographic and socioeconomic variables, and questions related to the discrete choice experiment (DCE), structured into 4 blocks, 4 attributes, and 2 choice sets. The questionnaire design process involved four stages: 1) identification of attributes and their levels, 2) determination of scenarios to be presented to respondents, 3) evaluation of the questionnaire’s efficiency through a pilot test, and 4) assessment of validity and reliability. To identify the attributes, a literature review was conducted, followed by interviews with professors from the University of Medical Sciences and relevant experts. Consequently, four attributes were selected for in-home care services in this study. The first attribute, service cost, was defined at three levels: 1) free, 2) 600,000 Tomans, and 3) 1,000,000 Tomans. The second attribute, physician expertise, was categorized into three levels: 1) general practitioner, 2) specialist, and 3) both general practitioner and specialist together. The third attribute, availability of emergency contact with the service provider, had two levels: 1) yes, and 2) no. The fourth attribute, expertise of the primary caregiver, was divided into two levels: 1) nursing assistant, and 2) nurse. Prior to the main study, a pretest was conducted to determine whether respondents understood the definitions of the attributes and their levels, whether they could handle the number of scenarios and levels presented, and whether they comprehended the choice tasks. An initial version of the questionnaire was completed by 30 individuals as a pilot test, during which initial issues—such as question phrasing and response options—were identified and addressed. Since the questionnaire was developed based on the input of experts and professors from Hamadan University of Medical Sciences, its validity was confirmed through their expert opinions. Furthermore, the use of orthogonal design and other statistical methods in crafting the scenarios ensured that the tool possessed adequate reliability and validity in practice(22). In order to analyze the data and identify factors associated with the likelihood of choosing an in-home care service package, conditional logit regression was employed. To calculate the utility of receiving in-home care services, the following equation was used: • represents the utility of choosing alternative j in choice set s for individual n • is the vector of service attributes in alternative j • β denotes the vector of preference parameters (utility weights) for individual n • is the random error term capturing unobserved factors (23) A significance level of 0.05 was applied in this study, and all statistical analyses were conducted using STATA software (version 17). Patient and Public Involvement statement Patients were not involved in the development of the research question or the design of data collection methods. However, before completing the questionnaire, participants received detailed information about the study and its requirements to ensure informed participation. Due to the nature of the study, which involved recording participants’ responses anonymously, no contact information was collected, and thus there was no subsequent communication of study results to participants. The questionnaire was developed through consultation with academic experts at Hamadan University of Medical Sciences. Findings The results indicated that the mean (± standard deviation) age and household size of the respondents were 36.76 ± 10.83 years and 3.63 ± 1.27 members, respectively. Further descriptive statistics are presented in Tables 1 and 2. Table 1 - Frequency Distribution of Studied Variables 45.8% 409 Female Gender 54.2% 484 Male 22.17% 198 Single Marital Status 75.59% 675 Married 2.24% 20 Divorced or Widowed 45.46% 406 Yes Head of Household 54.54% 487 No 48.82% 436 Yes Homeownership 51.18% 457 No 3.25% 29 Less than 5 years Education Level 8.06% 72 Between 5 to 8 years 25.87% 231 Between 8 to 12 years 12.21% 109 Associate Degree 33.03% 295 Bachelor’s Degree 12.21% 109 Master’s Degree 4.59% 41 PhD 0.78% 7 Islamic Seminary Studies 52.07% 465 Social Security Insurance Status 23.29% 208 Health Insurance 10.19% 91 Armed Forces Insurance 9.18% 82 No Insurance 5.26% 47 Other 42.11% 376 Yes Supplemental Insurance 57.89% 517 No 12.43% 111 Yes Use of Home Care Services 87.57% 782 No 100% 111 Yes Satisfaction with Home Care Services 0% 0 No 29.12% 260 No History of COVID-19 Infection 4.03% 36 Yes, Hospitalized in Public Hospitals 1.34% 12 Yes, Hospitalized in Private Hospitals 65.51% 585 Yes, Not Hospitalized Table 2 - Continued Frequency Distribution of Studied Variables 78.05% 697 No History of Chronic Diseases History of Chronic Disease 2.80% 25 Cardiovascular Disease 1.12% 10 Respiratory Disease 3.14% 28 Diabetes 0.67% 6 Cancer 1.90% 17 Kidney Disease 2.46% 22 Mental Disorders 1.01% 9 Liver Diseases 5.04% 45 Other 3.81% 34 More than One Chronic Disease 18.03% 161 Less than 8 million Tomans Income Status 39.08% 349 8 to 15 million Tomans 18.92% 169 15 to 25 million Tomans 7.28% 65 More than 25 million Tomans 16.69% 149 No Income 22.17% 198 Less than 5 million Tomans Expenses and Costs 35.95% 321 5 to 10 million Tomans 27.32% 244 10 to 15 million Tomans 14.56% 130 More than 15 million Tomans 20.94% 187 Very Good Health Status 47.59% 425 Good 27.88% 249 Moderate 3.02% 27 Poor 0.56% 5 Very Poor The findings of this study, based on Tables 1 and 2, indicate that the average age of the 893 participants was 36.76 years, with more than half of them being male. The age range of the participants was between 18 and 82 years, and the average household size was reported as 3.63. Approximately half of the participants lived in rental housing. About one-third of the individuals had higher education degrees, whereas less than 10% reported an education level below secondary school. Among the participants, nearly one-fourth were self-employed, making this category the most frequent among occupational groups. Regarding insurance coverage, social security insurance was reported as the primary insurance for more than half of the participants, while around 10% had no basic insurance coverage. An analysis of expenses and income levels revealed that approximately 63% of participants had monthly expenditures ranging between 5 to 15 million Tomans, while about 15% had expenses exceeding 15 million Tomans. In terms of income distribution, around 58% of participants had an income range of 8 to 25 million Tomans. Table 3: Assessment of the Impact of Multiple Socioeconomic Variables on the Willingness to Receive Home Care Services Based on the Conditional Logit Model Price (Reference: Free) 600,000 -0.35 0.74 0.03 0.000 -7.95 0.69–0.80 1,000,000 -0.48 0.62 0.02 0.000 -11.88 0.57–0.67 Physician (Reference: General Practitioner) Specialist 0.27 1.31 0.05 0.000 6.75 1.21–1.42 Mixed (Specialist + General Practitioner) 0.56 1.75 0.07 0.000 14.36 1.62–1.89 Emergency Communication (Reference: No) Yes 0.65 1.91 0.07 0.000 16.71 1.77–2.07 Caregiver Specialty (Reference: Nurse Aide) Nurse 0.19 1.20 0.03 0.000 6.81 1.14–1.27 Education (Reference: Elementary or Lower) 8–12 Years 0.07 1.08 0.14 0.555 0.59 0.84–1.38 Associate’s or Bachelor’s Degree 0.59 1.80 0.20 0.000 5.21 1.44–2.24 Master’s or PhD 0.54 1.71 0.22 0.000 4.25 1.34–2.19 Income (Reference: 25 Million Tomans 0.49 1.63 0.19 0.000 4.16 1.30–2.05 No Income 0.12 1.13 0.12 0.290 1.06 0.91–1.40 Occupation (Reference: Employee) Self-Employed or Freelancer 0.15 1.16 0.11 0.010 1.63 0.97–1.39 Manual Laborer 0.04 1.04 0.12 0.750 0.33 0.83–1.30 Homemaker 0.01 1.01 0.09 0.910 0.12 0.84–1.21 Other 0.001 1.10 0.11 0.990 0.01 0.81–1.24 Health Status (Reference: Very Good) Good -0.20 0.82 0.06 0.000 -2.90 0.71–0.94 Moderate -0.15 0.86 0.07 0.070 -1.83 0.73–1.01 Poor or Very Poor -0.37 0.69 0.10 0.010 -2.53 0.52–0.92 Model Fit Statistics : Wald chi2(26) = 561.28 Log pseudolikelihood = -5002.3795 Prob > chi2 = 0.0000 According to the findings of Table 3, based on the conditional logit model and at a significance level of less than 0.05, the variables price, physician specialization, emergency contact availability, caregiver expertise, higher education, income (15-25 million and more than 25 million tomans), and health status (good and poor/very poor) showed a significant association with the likelihood of choosing home care services (p < 0.05).The results of the conditional logit regression indicate that having a specialist physician or a combination of specialist and general practitioner increases the likelihood of choosing home hospitalization for COVID-19 by 31% and 75%, respectively (odds ratios: 1.31 and 1.75). Additionally, the availability of emergency contact increases the likelihood of selecting home hospitalization services by 91 % (odds ratio: 1.91). Regarding caregiver expertise, having a nurse as the home care provider increases the likelihood of choosing home hospitalization services by only 20% compared to having a nursing assistant (odds ratio: 1.20). Higher education also plays a significant role in service selection . Holding an associate or bachelor’s degree has a considerable impact on opting for home care services (p < 0.001 , odds ratio: 1.80).Conversely , medical expenses of 600,000 and 1,000,000 tomans significantly reduce the willingness to receive home care services by 26% and 38%, respectively (odds ratios: 0.74 and 0.62).The results further reveal that education level of 8-12 years, being unemployed, occupation type, and moderate health status do not have a significant association with home care service selection (p > 0.05). However , health status showed a significant relationship with the likelihood of selection. Individuals with good or poor/very poor health conditions were less likely to choose home care services compared to those with very good health (odds ratios: 0.82 and 0.69). Table 4. Trade-off Values Between Different Features of Inpatient Hospital Care for Tehran Citizens 1.62 0.46 0.54 1.11 -0.63 -1 Cost (600,000) 2.59 0.74 0.86 1.77 -1 1.60 Cost (1,000,000) -1.47 -0.42 -0.49 -1 1.77 0.90 Specialist Physician -3.02 -0.86 -1 -0.49 0.86 1.86 Specialist and General Practitioner -3.51 -1 -0.86 -0.42 0.74 2.16 Emergency Contact Availability -1 -3.51 -3.02 -1.47 2.59 0.62 Caregiver Expertise According to Table 4, if a payment of 600,000 Tomans is feasible, individuals are 11% and 62% more likely to opt for a specialist instead of a general practitioner and for a nurse instead of a healthcare assistant, respectively. Furthermore, when a payment of 1,000,000 Tomans is feasible, these probabilities increase to 77% and 159%, respectively. If the option to receive services from a specialist instead of a general practitioner is available, there is a 9% likelihood that individuals will choose to pay 600,000 Tomans instead of receiving free services. Additionally, under these conditions, the probability of opting for a nurse instead of a healthcare assistant decreases by 47%. If individuals had the option to use both specialist and general practitioner services, they would be 86% more likely to pay 600,000 Tomans. Additionally, under these conditions, the probability of opting for a nurse instead of a healthcare assistant would decrease by 202%.If emergency call access were available, individuals would be 116% more likely to pay 600,000 Tomans instead of receiving free services. Moreover, in this scenario, the probability of choosing a nurse over a healthcare assistant would decrease by 251%.If individuals had the option to choose a nurse instead of a healthcare assistant, they would be 159% more likely to pay 1,000,000 Tomans. However, under these conditions, the likelihood of using both general practitioners and specialists together would decrease by 202%. Discussion In the present study, the odds ratio for the price of home care services was 0.74 for 600,000 Tomans and 0.62 for 1,000,000 Tomans. Consequently, healthcare costs of 600,000 and 1,000,000 Tomans can significantly reduce the willingness to receive home care services by 26% and 38%, respectively. Additionally, individuals with lower incomes are less likely to opt for these services compared to those with higher incomes. This outcome may be attributed to individuals’ economic status, which influences their perceptions and choices.Research indicates that patients highly value information regarding the price and quality of healthcare services, as well as the opportunity to choose their care providers (24-26). Consistent with these findings, Yun Liu et al. (2019) (27) and Sarah Raes et al. (2020) (28) demonstrated that higher costs for home nursing services can lead to reduced accessibility and a lower likelihood of individuals selecting these services. On the other hand, in the present study, treatment by a specialist physician or a combination of a specialist and general practitioner led to an increased likelihood of selecting home-based COVID-19 inpatient care by 31% and 75%, respectively, compared to receiving services from a general practitioner. This finding may stem from individuals’ trust in the knowledge and training of specialists. Additionally, the attitudes of a society toward specialists with higher education levels may influence the selection of available options (29-31). These results are consistent with the findings of Kozikowski et al. (2022)(32) and Yun Liu et al. (2019)(27). The findings of this study indicated that the specialty of the home care provider, when the provider is a nurse, only increases the likelihood of receiving home-based COVID-19 inpatient care by 20% compared to a healthcare assistant (odds ratio of 1.2). Based on previous studies, it can be argued that the reason for selecting a nurse over a healthcare assistant is individuals’ attitudes toward the education level and specialization of caregivers. In this regard, the study by Lucas Goossens et al. (2014) found that having a specialized nurse rather than a general nurse can significantly increase patients’ willingness to pay (33). The results of the study by Kruk et al. (2009) were also consistent with the findings of this study (34). Based on these results, it can be stated that specialization, education level, and job rank are influential factors in the preferences of individuals when choosing the type of caregivers. However, the impact of this factor on receiving home-based inpatient care for COVID-19 was not significant. Another finding of this study is that the availability of communication in emergency situations increases the likelihood of receiving home-based COVID-19 inpatient care by 91% (odds ratio of 1.91). This may be due to patients’ increased sense of security in sensitive situations, such as the COVID-19 pandemic, which makes them more willing to select options that provide easy access to resources. The study by Aqajani Nergesi et al. (2021) was consistent with the findings of this study (35). According to the present study, education levels of ”associate and bachelor’s degree” and ”master’s and doctoral degrees” have a significant impact on increasing the likelihood of receiving home-based COVID-19 inpatient care, with an increase of 80% and 71%, respectively. One of the reasons for this finding could be that individuals with higher education levels typically have greater awareness of different levels of inpatient care and a better understanding of the quality of services provided both at home and in hospitals. For example, it has been shown that individuals with higher education tend to make significantly more research-based decisions (36). In this regard, the results of the study by Marco Terraneo (2015) (37) and Viju Raghupathi et al. (38) were consistent with the findings of this study. Additionally, the present study revealed that individuals’ income at two levels, 15-25 million Tomans and above 25 million Tomans, can increase the likelihood of receiving home-based COVID-19 inpatient care by 31% and 63%, respectively. The affluent class of society is always seeking high-quality services in a calm environment, which may explain these results. Given that the costs of home care may be higher than those of public hospitals, individuals with higher financial status are more likely to prefer receiving healthcare services at home. Consistent with these findings, Yu et al. (2017) (39) and Kim et al. (2012)(40) showed that individuals with higher income levels are more likely to seek the best healthcare services for themselves. The results of the present study showed that health status has a significant relationship with individuals’ likelihood of choosing home care services. Specifically, individuals with ”poor” or ”very poor” health status were less likely to choose home care services compared to those with ”very good” health status (odds ratios of 0.82 and 0.69, respectively). In this regard, the results of the study by Jing Guo et al. (2014) (41) and Fried et al. (2000) (42) were consistent with the findings of this study. Therefore, based on the results of the present study and the aforementioned studies, an individual’s health status is an important factor in the decision to receive home-based COVID-19 inpatient care, and individuals with poorer health are less likely to choose this option. Conclusion Examining patient preferences for selecting the best health and healthcare strategies within a community’s healthcare system is an essential and integral component of any modern society with an efficient health system. This study focuses on one of the most significant strategies for reducing the burden on hospitals, both in crisis situations and under normal conditions, which is the provision of home care services. According to the findings of the present study, offering a home hospitalization package that includes emergency contact options, access to doctors and nurses for care, and affordability increases the likelihood of patients choosing home hospitalization. On the other hand, patients’ health conditions—ranging from good to moderate, poor, and very poor—reduce this likelihood. These factors should be considered in policymaking efforts aimed at reducing hospital visits and promoting the use of home hospitalization services. References 1. Lewin WH, Schaefer KG. Integrating palliative care into routine care of patients with heart failure: models for clinical collaboration. Heart failure reviews. 2017;22:517-24. https://doi.org/10.1007/s10741-017-9599-22. Advances in Patient Safety. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008.3. Cook C, Cole G, Asaria P, Jabbour R, Francis DP. The annual global economic burden of heart failure. International journal of cardiology. 2014;171(3):368-76. https://doi.org/10.1016/j.ijcard.2013.12.0284. Kianian T, Lotfi M, Zamanzadeh V, Rezayan A, Hazrati M, Pakpour V. Exploring barriers to the development of home health Care in Iran: a qualitative study. Home Health Care Management & Practice. 2022;34(2):83-91. https://doi.org/10.1177/108482232110385105. Hashemlu L, Esmaeili R, Bahramnezhad F, Rohani C. The experiences of home care team members regarding the needs of family caregivers of heart failure patients in home health care services in Iran: A qualitative study. ARYA atherosclerosis. 2022;18(4):1. https://doi.org/10.48305/arya.2022.243506. Sen-Crowe B, Sutherland M, McKenney M, Elkbuli A. A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic. Journal of Surgical Research. 2021;260:56-63. https://doi.org/10.1016/j.jss.2020.11.0627. De Nardo P, Gentilotti E, Mazzaferri F, Cremonini E, Hansen P, Goossens H, et al. Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage. International Journal of Infectious Diseases. 2020;98:494-500. https://doi.org/10.1016/j.ijid.2020.06.0828. Chen L, Zheng S. Understand variability of COVID-19 through population and tissue variations in expression of SARS-CoV-2 host genes. Informatics in medicine unlocked. 2020;21:100443. https://doi.org/10.1016/j.imu.2020.1004439. World Health O. Home care for patients with suspected or confirmed COVID-19 and management of their contacts: interim guidance, 12 August 2020. Geneva: World Health Organization; 2020 2020. Contract No.: WHO/2019-nCoV/IPC/HomeCare/2020.4.10. Shahsavari H, Nasrabadi AN, Almasian M, Heydari H, Hazini A. Exploration of the administrative aspects of the delivery of home health care services: a qualitative study. Asia Pacific family medicine. 2018;17:1-7. https://doi.org/10.1186/s12930-018-0038-x11. Ryan M. Discrete choice experiments in health care. British Medical Journal Publishing Group; 2004. p. 360-1. https://doi.org/10.1136/bmj.328.7436.36012. Louviere JJ, Pihlens D, Carson R. Design of discrete choice experiments: a discussion of issues that matter in future applied research. Journal of Choice Modelling. 2011;4(1):1-8. https://doi.org/10.1016/S1755-5345(13)70016-213. Johnson FR, Lancsar E, Marshall D, Kilambi V, Mühlbacher A, Regier DA, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design good research practices task force. Value in health. 2013;16(1):3-13. https://doi.org/10.1016/j.jval.2012.08.222314. de Bekker‐Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health economics. 2012;21(2):145-72. https://doi.org/10.1002/hec.169715. Kjaer T. A review of the Discrete Choice Experiment—with Emphasis on Its Application in Health Care. Proceedings of the University of Southern Denmark. 2005;1.16. Soekhai V, de Bekker-Grob EW, Ellis AR, Vass CM. Discrete choice experiments in health economics: past, present and future. Pharmacoeconomics. 2019;37:201-26. https://doi.org/10.1007/s40273-018-0734-217. Peine A, Paffenholz P, Martin L, Dohmen S, Marx G, Loosen SH. Telemedicine in Germany during the COVID-19 pandemic: multi-professional national survey. Journal of medical Internet research. 2020;22(8):e19745. https://doi.org/10.2196/1974518. Rasmussen B, Perry R, Hickey M, Hua X, Wong ZW, Guy L, et al. Patient preferences using telehealth during the COVID‐19 pandemic in four Victorian tertiary hospital services. Internal Medicine Journal. 2022;52(5):763-9. https://doi.org/10.1111/imj.1572619. Walsh S, O’Shea E, Pierse T, Kennelly B, Keogh F, Doherty E. Public preferences for home care services for people with dementia: A discrete choice experiment on personhood. Social Science & Medicine. 2020;245:112675. https://doi.org/10.1016/j.socscimed.2019.11267520. World Health O. Home care for patients with suspected novel coronavirus (nCoV) infection presenting with mild symptoms and management of contacts: interim guidance, 20 January 2020. Geneva: World Health Organization; 2020 2020.21. Angelis A, Kanavos P. Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in Health Technology Assessment and beyond: The Advance Value Framework. Social Science & Medicine. 2017;188:137-56. https://doi.org/10.1016/j.socscimed.2017.06.02422. World B. How to conduct a discrete choice experiment for health workforce recruitment and retention in remote and rural areas : a user guide with case studies. Washington, DC: International Bank for Reconstruction and Development/World Bank; 2013.23. Abiiro GA, Torbica A, Kwalamasa K, De Allegri M. Eliciting community preferences for complementary micro health insurance: a discrete choice experiment in rural Malawi. Social science & medicine. 2014;120:160-8. https://doi.org/10.1016/j.socscimed.2014.09.02124. Robinson JC, Brown TT. Increases in consumer cost sharing redirect patient volumes and reduce hospital prices for orthopedic surgery. Health Affairs. 2013;32(8):1392-7. https://doi.org/10.1377/hlthaff.2013.018825. Sinaiko AD, Hirth RA. Consumers, health insurance and dominated choices. Journal of health economics. 2011;30(2):450-7. https://doi.org/10.1016/j.jhealeco.2010.12.00826. Brazier JE, Dixon S, Ratcliffe J. The role of patient preferences in cost-effectiveness analysis: a conflict of values? Pharmacoeconomics. 2009;27(9):705-12. https://doi.org/10.2165/11314840-000000000-0000027. Liu Y, Kong Q, de Bekker-Grob EW. Public preferences for health care facilities in rural China: A discrete choice experiment. Social Science & Medicine. 2019;237:112396. https://doi.org/10.1016/j.socscimed.2019.11239628. Raes S, Vandepitte S, De Smedt D, Wynendaele H, DeJonghe Y, Trybou J. The relationship of nursing home price and quality of life. BMC health services research. 2020;20:1-10. https://doi.org/10.1186/s12913-020-05833-y29. Lewis CL, Wickstrom GC, Kolar MM, Keyserling TC, Bognar BA, DuPre CT, et al. Patient preferences for care by general internists and specialists in the ambulatory setting. Journal of general internal medicine. 2000;15:75-83. https://doi.org/10.1046/j.1525-1497.2000.05089.x30. Narayanan S, Chintagunta PK, Miravete EJ. The role of self selection, usage uncertainty and learning in the demand for local telephone service. Quantitative Marketing and economics. 2007;5:1-34. https://doi.org/10.1007/s11129-006-9015-z31. Ryan M, McIntosh E, Dean T, Old P. Trade-offs between location and waiting times in the provision of health care: the case of elective surgery on the Isle of Wight. Journal of Public Health. 2000;22(2):202-10. https://doi.org/10.1093/pubmed/22.2.20232. Kozikowski A, Morton-Rias D, Mauldin S, Jeffery C, Kavanaugh K, Barnhill G. Choosing a provider: what factors matter most to consumers and patients? Journal of Patient Experience. 2022;9:23743735221074175. https://doi.org/10.1177/2374373522107417533. Goossens LM, Utens CM, Smeenk FW, Donkers B, van Schayck OC, Rutten-van Mölken MP. Should I stay or should I go home? A latent class analysis of a discrete choice experiment on hospital-at-home. Value in health. 2014;17(5):588-96. https://doi.org/10.1016/j.jval.2014.05.00434. Kruk ME, Paczkowski MM, Tegegn A, Tessema F, Hadley C, Asefa M, et al. Women’s preferences for obstetric care in rural Ethiopia: a population-based discrete choice experiment in a region with low rates of facility delivery. Journal of Epidemiology & Community Health. 2010;64(11):984-8. https://doi.org/10.1136/jech.2009.08797335. Nargesi DA, Hajizadeh M, Pakdel MJ, Gheysvandi E, Rad EH. Preferences of Iranians to select the emergency department physician at the time of service delivery. BMC Health Services Research. 2021;21:1-7.36. Ellis RJ, Yuce TK, Hewitt DB, Merkow RP, Kinnier CV, Johnson JK, et al. National evaluation of patient preferences in selecting hospitals and health care providers. Medical care. 2020;58(10):867-73.37. Terraneo M. Inequities in health care utilization by people aged 50+: evidence from 12 European countries. Social science & medicine. 2015;126:154-63. https://doi.org/10.1016/j.socscimed.2014.12.02838. Raghupathi V, Raghupathi W. The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015. Archives of public health. 2020;78:1-18. https://doi.org/10.1186/s13690-020-00402-539. Yu W, Li M, Ye F, Xue C, Zhang L. Patient preference and choice of healthcare providers in Shanghai, China: a cross-sectional study. Bmj Open. 2017;7(10):e016418. https://doi.org/10.1136/bmjopen-2017-01641840. Kim D, Shin H, Kim C-y. Equitable access to health care for the elderly in South Korea: Is income-related inequality in health care utilization more pronounced? Research on Aging. 2012;34(4):475-96. https://doi.org/10.1177/016402751142353841. Guo J, Konetzka RT, Magett E, Dale W. Quantifying long-term care preferences. Medical Decision Making. 2015;35(1):106-13. https://doi.org/10.1177/0272989X1455164142. Fried TR, van Doorn C, O’Leary JR, Tinetti ME, Drickamer MA. Older persons’ preferences for home vs hospital care in the treatment of acute illness. Archives of internal medicine. 2000;160(10):1501-6. doi:10.1001/archinte.160.10.1501 Information & Authors Information Version history V1 Version 1 21 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords covid-19 discrete choice experiment home hospitalization preferences Authors Affiliations Mohammad Ehsani Kia [email protected] Hamadan University of Medical Sciences View all articles by this author Vajihe Ramezani Dorh Hamadan University of Medical Sciences View all articles by this author Leili Tapak Hamedan University of Medical Sciences Department of Biostatistics and Epidemiology View all articles by this author Metrics & Citations Metrics Article Usage 311 views 98 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mohammad Ehsani Kia, Vajihe Ramezani Dorh, Leili Tapak. Examining the Preferences of Tehran Citizens for Receiving Home-Based Hospitalization Care for COVID-19 Using the Discrete Choice Experiment Method. Authorea . 21 August 2025. DOI: https://doi.org/10.22541/au.175574714.49824985/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175574714.49824985/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffbd1e779b71640',t:'MTc3OTQ1Mjg5OA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();