Community Preferences for 'Mental Health Insurance Coverage in Tehran Using a Discrete Choice Experiment

Community Preferences for 'Mental Health Insurance Coverage in Tehran Using a Discrete Choice Experiment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Community Preferences for 'Mental Health Insurance Coverage in Tehran Using a Discrete Choice Experiment Hamid Talebianpour, Rajabali Daroudi, Ebrahim Jaafaripooyan, Elham Sharafi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6787596/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Dec, 2025 Read the published version in Cost Effectiveness and Resource Allocation → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Understanding insurance preferences for mental health services can help provide appropriate service demand and insurance coverage for such services. Objectives: The present research aimed to investigate the preferences and willingness to pay of the people of Tehran for mental health insurance services using the discrete experiment method. Methods: Quantitative methods were used in this applied research and it was conducted on 420 individuals (210 patients with mental disorders and 210 healthy people) using a discrete choice experiment. The data analysis was performed using the conditional regression model as well. Results: The final model in this study included 6 attributes. The findings of the present research showed a statistically significant relationship (P <.001) between the odds ratios (OR) of choosing health insurance at all levels of insurance coverage except the service limit and the amount of insurance premium. Along with the increase in the cost coverage levels, the likelihood of choosing health insurance for the benefits of inpatient services at 70% and 90% levels (P <.001, OR=1.96 and P <.001, OR=2.28), outpatient services at 70% and 90% levels (P <.001, OR=1.53 and P <.001, OR=1.64), service delivery location (P <.001, OR=1.54), and the use of online services (P <.001, OR=0.84) increased significantly. Conclusions: The findings showed that the people of Tehran had the highest preference and willingness to pay for the coverage of inpatient services. The results of this study can be provided to health managers and policy makers to predict the welfare effects and people's reactions to changes in mental health insurance policies so that they would be able to plan better to provide higher-quality services with the participation of people. Mental Health Health Insurance Discrete Choice Experiment Preferences Willingness to Pay 1. Background Mental illness refers to a wide range of mental disorders that affect the ability to think, feel and behave, and the affected person cannot perform their daily activities well. mental health problem are among the diseases with a high prevalence and a significant burden. Approximately 428 million years of human life are lost due to disability resulting from mental health diseases. Such diseases account for approximately 16% of the years of life lost in the world and impose an economic burden of approximately 5 trillion dollars on societies ( 1 ). Nearly one-third of the diseases associated with disability are caused by mental health problem ( 2 ). According to surveys conducted in Iran, 22–30% of Iranian society (approximately three out of 10 people) suffers from some degree of mental disorders ( 3 ). In Tehran, the situation is worse, such that 30 to 36 percent suffer from mental disorders. mentally disturbed and women have a share of 36% among them ( 8 ). People with mental disorders are less likely to have health insurance than those without mental health problems ( 4 ). In a study conducted by Garfield et al. using the data from the Medical Expenditure Panel Survey, it was found that 37% of working-age adults with severe mental disorders did not have insurance at least during the year, while only approximately 28% of ordinary people did not have insurance ( 4 ). Even after controlling for demographic differences, Pearson et al. found that the chance of having health insurance for people with severe mental health problems was 40% less than that for those without such problems ( 5 ). It seems that models and solutions need to be provided so that all patients can access the best services at the most appropriate time ( 6 ). Investigating the demand for insurance and the willingness to pay for it helps policymakers to determine insurance premiums accurately, and this kind of study determines whether willingness to pay covers health expenditures ( 7 ). Mental illnesses severely reduce people's quality of life, so it is expected that the willingness to pay (WTP) to eliminate the mental illness and the willingness to accept (WTA) to continue the illness are high in affected people. ( 8 – 10 ). Willingness to pay is based on the theory of social welfare, and due to the monetary measurement of outcomes, it can help policymakers in allocating resources. Willingness to pay can be obtained directly or indirectly ( 11 ). The method of joint analysis (or the more recent term, discrete choice test) is also widely used. This method provides useful information for policy makers. The combination analysis method or discrete choice test is designed to reveal individual preferences for choices that are formed by changing the characteristics of a product or service. Random utility models (RUMs) are used to determine the extent to which different characteristics predict individuals' stated choices, allowing outcome variables (such as health-related quality of life), process ( (such as waiting time) and cost were considered simultaneously ( 12 – 14 ). In fact, joint analysis is a special type of survey studies that was first created in mathematical psychology and has a strong theoretical background and has been successfully used in market research, transportation economics ( 15 ), environmental economics ( 16 , 17 ). It has been used and proposed in England to value the quality of public services ( 18 ). Examining insurance demand and willingness to pay for it helps policy makers to set insurance premiums clearly and accurately, and also these studies determine whether willingness to pay covers health costs or not. In this way, it is possible to determine the amount of government subsidies for insurance premiums for poor people. Also, in many countries, insurance organizations have tried to use methods such as measuring preferences and willingness to pay to adopt special strategies to make it cost-effective and increase consumer acceptance of insurance plans ( 1 ). People's preferences should be taken into account when designing health insurance plans. A discrete choice experiment (DCE) is a suitable method for evaluating people's health insurance preferences. For example, this method has been used to generate evidence on Dutch people's preferences among different health insurance schemes (19) and to derive health insurance preferences of various groups of people in Thailand ( 20 , 21 ). The demand for health insurance largely depends on its ability to meet consumer needs, expectations, and preferences ( 21 , 22 ). Considering that little evidence is available about Iranians' mental health insurance priorities, this study aimed to determine the mental health insurance priorities of Iranians living in Tehran using the DCE method. The findings are important inputs for mental health insurance policy-making and providing health insurance benefit packages to people and include their preferences in Iran and perhaps beyond. 2. Objectives The current study seeks to address this gap in the research. 3. Methods Study Area The present study was conducted with the code of ethics IR.TUMS.SPH.REC.1400.032 received from the Vice-Chancellor for Research at Tehran University of Medical Sciences for a doctoral thesis. It was carried out during 2021–2022 to investigate people's insurance preferences for mental health services in Tehran, the capital of Iran. The DCE method was used in this research to extract the insurance preferences of Iranian insured people living in Tehran for mental health services. Experimental Design & Questionnaire Development In the first phase of the study, the components were designed, and the levels were determined. To this end, a scoping review was performed, and then the components and levels were designed by interviewing healthcare experts. Next, the main attributes and levels of mental health insurance were chosen using the opinions of experts in the field of health insurance. The most important attributes of mental health insurance services obtained were as follows: inpatient services coverage (including 30%, 70%, and 90% levels), outpatient services coverage (including 30%, 70%, and 90% levels), service delivery place (including two levels of public sector and all providers), use of online services (including 2 levels: yes and no), determining an upper limit for services (including 2 levels: yes and no), and monthly insurance premium (including 4 levels: 40000 Tomans, 80000 Tomans, 120000 Tomans, and 200000 Tomans) (Table 1 ). also, The resources used to calculate the costs were based on the 2021 tariffs of in the international dollar (purchasing power parity (PPP)) and at the exchange rate of each PPP $ dollar equal to 150000 rials (RlsIRR) ( 23 ). Considering that the results and methods related to this section were mentioned in detail in the study by Rahmani et al. ( 24 ), they are briefly stated here. For more details, refer to the aforementioned study. Table 1 Attributes and attribute-level of mental health insurance in Iran Attribures Levels Inpatient service coverage Coverage 30/70/90% of costs Outpatient service coverage Coverage 30/70/90% of costs Place of receiving the service Public sector, All providers The use of online services Yes, No Services have a limitation Yes, No Monthly premium 400000/800000/1200000/2000000 Rials In the third phase, the D-Efficiency method was used to design the choice sets by choosing the best attributes and levels ( 25 ). The final design consisted of 24 choice sets that were divided into three blocks, each containing 6 choice sets that included plans A and B. The experimental design of the choice sets was performed using SAS9.1 software. The questionnaire developed for this research included three sections: research description and informed consent, socioeconomic characteristics, and demographic characteristics of the participants, in three 8-choice blocks. Therefore, three versions of the questionnaire were prepared, which differed only in the type of choice set (block). Before performing the tasks, the attributes included in the questionnaire were explained to the participants, and the dominant choice set was used to test the consistency of the questions and prepare the samples. At this stage, those who answered incorrectly were excluded. Each block was tested on 15 participants to ensure the reliability and consistency (reliability and validity) of the instrument. The experimental data were excluded from the analysis. The data were completed either online or in person. Due to the prevalence of COVID-19, some patients did not agree to enter the study directly; therefore, the questionnaire was sent to them online. Of course, they had been provided with necessary explanations in advance. The attributes and levels of mental health insurance in Iran and the choice set included in the analysis are presented in Table 2 . Also, informed consent was obtained from the participants in this study. Table 2 One of the choice sets included in the study Attributes Plan A Plan B Inpatient service coverage 90% 70% Outpatient service coverage 70% 90% Place of receiving the service Public sector All provider The use of online services Yes No Services have a limitation Yes No Monthly premium 400000Rials 1200000Rials Which of the health insurance plans would you like to choose (Please tick one box only)? Plan A Plan B Sample Size Calculation To determine the sample size, the rule of thumb suggested by Orem (1998) was used, through which the minimum required sample size was estimated according to the following formula ( 26 ): where n is the total number of respondents, S is the number of choice sets each person would answer, J is the number of alternatives in the choice sets, and \(\:{L}^{max}\) is the maximum number of levels among the investigated attributes. In the present study, the minimum sample size for each group was estimated to be 187 according to the formula above. To increase the accuracy of the study, the minimum sample size for each group was considered to be 210. Sampling Method In this research, cluster sampling was used to select the subjects for both groups of psychiatric disorder patients and normal people of society. To this end, the 22 districts of Tehran were divided into 5 geographical regions (North, South, East, West, and Center). To collect the data of the patients with mental health problem , the researchers referred to Roozbeh Psychiatric Hospital(n = 48), Iran Psychiatric Hospital(n = 52), Razi Psychiatric Hospital(n = 60), and Psychiatry Clinic of Imam Hossein (AS) Public Hospital(n = 50). In addition, to collect the data of the healthy people, the researchers went to the health centers in Tehran and collected the data from each region according to its population. For people with mental health disorders, one of the inclusion criteria was the ability to answer the questions. In this study, healthy people were those who were not suffering from mental health diseases according to their self-reports. Data Analysis Descriptive and quantitative models were used for DCE analysis. The random utility model provided a theoretical basis for DCE data analysis ( 27 ). Thus, the person chose B from the two alternatives A & B. This showed that alternative B was more beneficial to the individual than alternative A and was expressed mathematically as follows: U (B, C) > U (A, C) The utility U was the sum of utilities A, B, and C, where C was a common attribute. Since C was a common element, the previous equation could be written as follows: V (B- A) = U (B, C) - U (A, C) where V was the indirect utility derived from the alternative A compared to alternative B. The fitted utility function was expressed using a linear equation as follows: V = β 1 Insc + β2 Outsc + β3 plrc + β4 onser + β5 serlim + β6 monper + ɛ ( 4 ) in which β1 to β6 are the coefficients of benefit packages and attributes. Inpatient service coverage (Insc), outpatient service coverage (Outsc), place of receiving services (Plrc), use of online services (Onser), service limitation (Serlim), and monthly premium (Monper) were included in the analysis, and Ɛ was the error. Assuming that the errors had a logistic distribution, the conditional logistic regression model was used to analyze the data. This model assumed that the choices made were independent of irrelevant alternatives (IIA), which might be restrictive. Calculation of Marginal Willingness to Pay To calculate the willingness to pay, if one of the attributes was monetary, it could be determined by calculating the final rate of substituting it with other attributes of the respondents' willingness to pay for that attribute. Therefore, if \(\:\varvec{\beta\:}\) was the coefficient of price and \(\:\varvec{\beta\:}1\) was the coefficient of one of the investigated variables, such as changing the coverage level from basic (e.g., 30%) to 70% of inpatient services, the Marginal willingness to pay would be as follows, given that the price coefficient was negative ( 13 ): ( 1 ) In fact, the Marginal willingness to pay for the total final substitution rate shows how willing people are to pay to get one more unit of an attribute ( 28 ). In the end, after estimating the model and obtaining the weight of each attribute, the weight of the possible scenarios was calculated, and the scenarios were ranked based on this weight. In this study, SAS and Stata software were used for data analysis. Validity and reliability of the questionnaire Considering that the questionnaire used was the result of experts' opinions, it was examined in group discussion sessions, so its validity (face validity) was confirmed by the experts. On the other hand, because the autogonal method and other statistical methods were used in the design of the scenarios, in practice, this tool has reliability and validity( 29 ). 4. Results Descriptive Statistics The findings of Table 3 show that the study participants included 211 males and 209 females, with a mean age of 31.14 years. Most of the participants (46.19%) had graduate degrees. A majority of the subjects (86.9%) in this study were not household heads. In terms of employment, most of the subjects (50.48%) were employed in the public and private sectors. In addition, 29.52% and 29.29% of the participants had monthly incomes of 8 million Tomans, respectively. A large number of the participants (54.05%) were sinlgle(no married), and most of the subjects (56.67%) had social security insurance (Table 3 ). Table 3 Demographic and socioeconomic characteristics of the study participants Variable Subcategory Mental Disorder Healthy Total Frequency Percentage** Frequency Percentage** Frequency Percentage* Mean age 210 (32.53)Mean 209 (29.73)Mean 420 (31.14)Mean Gender Male 127 60.47 84 40 211 50.24 Female 83 39.53 126 60 209 49.76 Education High school diploma and lower 20 9.52 28 13.33 48 11.43 Associate or bachelor degrees 77 36.68 101 48.1 178 42.38 graduate education 113 53.8 81 38.57 194 46.19 Household Head Yes 39 18.57 16 7.62 55 13.1 No 171 81.43 194 92.38 365 86.9 Occupation Employed in public and private sectors 132 62.85 80 38.1 212 50.48 Unemployed and housewife 55 26.2 47 22.38 102 24.28 Student 23 10.95 83 39.52 106 25.24 Marital status Single 117 55.71 110 52.38 227 54.05 Married 93 44.29 100 47.62 193 45.95 Income 8 million Tomans 61 30.38 62 29.05 123 29.29 Type of insurance Social security 122 58.1 116 55.24 238 56.67 Iranians 72 34.28 70 33.3 142 33.8 Armed forces 5 2.38 19 9.05 24 5.71 Others 11 5.24 5 2.41 16 3.82 The findings of Table 4 show that all coefficients were statistically significant except monthly insurance premiums and service limitations. The inpatient service coverage variable shows that people preferred higher values of this variable (P < .001), and the odds ratio (OR) of monthly premium (for all) and not using online services (as opposed to using online services) shows that people preferred lower values for these services. In addition, the general results show a significant relationship between all attributes and people's preferences (Table 4 ). Table 4 Regression model of preferences for attributes of mental health insurance Attributes Cost coverage OR B(SE) P-vale Inpatient service coverage (base 30%) 70% 1.96 0.05 < 0.001 90% 2.28 0.57 < 0.001 Outpatient service coverage (base 30%) 70% 1.53 0.06 < 0.001 90% 1.64 0.05 < 0.001 Place of receiving the service(base public sector) All provider 1.54 0.04 < 0.001 The use of online services(yes) No 0.86 0.04 < 0.001 Services have a limitation(yes) NO 1.08 0.04 0.054 Premium (400000) 800000 0.99 0.07 0.925 1200000 0.94 0.06 0.355 2000000 0.89 0.07 0.128 Observation 6432 Log likelihood -1969.81 LR chi2( 12 ) 518.68 Prob > chi2 < 0.001 As shown in Table 5 , inpatient services, outpatient services, place of receiving services, and nonuse of online services (for the healthy group) were significant in both groups. The findings also show that in both groups, people preferred higher values for the coverage of inpatient services (P < .001). In addition, the findings related to the odds ratio (OR) show that people with mental health disorders preferred higher values for most attributes compared to healthy people. Furthermore, the general results show that there was a significant relationship between all attributes and people's preferences (P < .001) (Table 5 ). Table 5 Mental health insurance attributes preferences of participants by sickness and health group level in Tehran Attributes Gender Cost coverage sick Healthy OR B (SE) p value OR B (SE) p value Inpatient service coverage (base 30%) 70% 2.03 0.22 < 0.001 1.97 0.12 < 0.001 90% 2.43 0.29 < 0.001 2.19 0.13 < 0.001 Outpatient service coverage (base 30%) 70% 1.77 0.24 < 0.001 1.49 0.1 < 0.001 90% 1.62 0.2 < 0.001 1.72 0.11 < 0.001 Place of receiving the service(base public sector) All provider 1.62 0.13 < 0.001 1.57 0.07 < 0.001 The use of online services(yes) No 0.88 0.07 0.17 0.82 0.04 < 0.001 Services have a limitation(yes) No 1 0.08 0.95 1.08 0.05 0.07 Premium (400000) 800000 1.16 0.17 0.33 0.96 0.08 0.646 1200000 1.07 0.14 0.6 0.9 0.06 0.163 2000000 107 0.17 0.65 0.84 0.07 0.046 Observation 1.48 5232 Log likelihood 445.06- 1598.47- LR chi2( 10 ) 141.28 429.59 Prob > chi2 < 0.001 < 0.001 The findings of Table 6 show that the Marginal willingness to pay for inpatient and outpatient services was higher in both subgroups compared to other components. The willingness to pay for not using online services was negative (in fact, this value shows the willingness to accept). In other words, people had to be paid for not using such services (Table 6 ). Table 6 Participants' Marginal willingness to pay for mental health insurance attributes Variable Coverage level Marginal willingness to pay(Rial} Dollar (PPP 1 ) Inpatient service coverage 70% 8498770 56.65 90% 10040640 66.94 Outpatient service coverage 70% 5399730 36 90% 6475840 43.17 Place of receiving the service All service providers 5698780 38 The use of online services No (2157450) -14.38 Service limitation No 837640 5.58 [1] Purchasing power parity 5. Discussion The present study calculated people's preferences for different attributes of mental health insurance plans in Tehran. The findings showed increased utility related to the increase in the cost coverage of mental health services for all analyzed attributes. Thus, the highest utility was obtained from the benefits of inpatient services, followed by outpatient services and the place of receiving services, using online services, and service limitations. The results of this research showed that people highly preferred using inpatient and long-term services. In their study, Nieboer et al. examined the relative values of long-term care services (inpatient) among beneficiaries and found that patients’ well-being might be improved by providing services for physical and social needs ( 30 ). The results of the studies by Sneeuw et al. and Froberg and Kane showed that long-term care did not affect the patients’ preferences. This is contrary to the findings of the present research ( 31 , 32 ). However, it is worth noting that one could not only rely on the data obtained from the preferences of such patients because they are usually cared for by their families and friends. The results of this study provide insight into the benefits of a wide range of long-term care services. These benefits are essential for the optimal allocation of resources, as decision makers must weigh them against the costs of any services. A long-term care benefit package should be defined with an optimal mix of services for specific patients. The relative values of services vary for different people because individuals' resources and constraints differ as well. Knowing how different care services contribute to people's physical and social well-being will facilitate finding optimal ways to provide care services in times of increasing scarcity. The results of this study showed that the people preferred more outpatient services with greater insurance coverage. Likewise, the results of the study by Cotten et al. showed that people mainly preferred to receive health information as well as outpatient and clinical services in preliminary stages from their doctors ( 33 ). Herman et al.'s research results showed that outpatient services could significantly increase patients' use of mental health services and were among the most effective ways to influence patients' preferences for receiving such services ( 34 ). The participants in the studies by Marcus et al. and Raue et al. were not very interested in receiving outpatient services ( 35 , 36 ). This is not in line with the results of the present study, the main reason for which could be the differences in healthcare provision systems and insurance. However, the other reason is that many people believe that these services should be provided free of charge or at the lowest cost by governments because the greatest loss of such diseases is imposed on governments. In line with the results of this study and the studies consistent with it, outpatient services play an important role in preventing disease recurrence in people, and timely referral of the recipients to receive such services will reduce social and economic costs. This attribute is of great importance for the choice of mental health insurance. Appropriate insurance coverage suitable for society makes people go to treatment and prevention centers as soon as possible to receive medical services, which causes their diseases to be treated and controlled sooner. According to the results of this research, determining service limitations affected the choice of insurance. Herman et al. showed in their research that the services provided in clinics could significantly increase patients' use of mental health services. It could be one of the most effective ways to change patients' preferences for receiving mental health services, and people preferred to receive services in clinics and near their place of residence ( 34 ). The results of the studies by Gerritsen et al. and Stevernik et al. showed that the type of providers and the quality of services they provided had a great impact on the patients’ choices. Thus, according to the results, the higher the quality and amount of services provided to the patients, the greater the desire to receive services from the service provider ( 37 , 38 ). A study conducted in Germany showed that people tended to pay less for healthcare services. Considering the characteristics of health insurance in this country, two main reasons could be mentioned for it: 1- There was public access to medical services, and the services were provided regularly. In addition, a high percentage of their reimbursement was on medical insurance. 2 - Out-of-pocket payment for outpatient services was very low ( 39 ). The results of the present study showed that the participants preferred to receive services from many private and public centers. The results of the studies by Arcury et al., Caldwell et al., and Koizumi et al. indicated that changes in the places where services were available had the greatest impact on patient choice. Most of the participants preferred to receive medical services in certain clinics. However, there were people who preferred to receive services in places with better facilities and human resources (more experienced specialists) ( 40 – 42 ). Hence, proximity to the services and expertise of the providers reduced referrals to other cities or medical centers ( 43 ). In a study conducted by Ruffin et al., it was found that the choices of two-thirds of the participants were affected by the place of service delivery and the cultural and linguistic characteristics ( 44 ). The results of the studies by Bearman et al. and Schoenwald et al. indicated that most people preferred to receive their required services from several centers that provided appropriate services to patients ( 45 , 46 ). The provision of services in numerous public and private centers and the diversity of service providers will make the quality of services, access, equity, and attention to the cultural components of any region to be taken into consideration. The results of the present study showed that the preferences of society were in line with the use of online services. Cotten and Gupta, Koch-Weser et al., and Lee et al. showed in their studies that the participants who did not have much time and were not interested in receiving face-to-face services preferred to use virtual services or new media that allowed them to work on the Internet by themselves ( 33 , 47 , 48 ). The simulation of Cunningham's study showed that a mixed media approach that allowed for a choice between new media (e.g., the Internet) and conventional media options maximized the use of mental health information ( 49 ). According to the findings of Phillips et al., telephone communication was preferred over other forms of electronic interaction because this method was more personal compared to e-mail communications and more traditional compared to video chats or video conferencing. It seems that building trust and face-to-face relationships would help trust the effectiveness of treatment ( 39 ). Finally, the results showed that the patients preferred digital health technologies mainly for their convenience and communications (60,51). The results of this research were not consistent with the results of the study by Sneeuw et al., in which the participants did not agree that Internet interventions guided with face-to-face psychotherapy were comparable and influential in terms of effectiveness and the ability to create a good therapeutic relationship ( 31 ). There were also studies in which people preferred to obtain their required information through conventional methods because they did not have much trust in the Internet and the data obtained from it ( 52 , 53 ). Considering human progress and the fact that we are living in the age of technology, the health sector, like other sectors, should make progress appropriate to technology and the needs of humans and societies. Failure to consider new aspects of technology and the Internet as well as service delivery applications will reduce quality and lag behind other sectors. Considering the nature of healthcare services and the fact that these services should be based on the trust and satisfaction of service recipients, the services must be provided in a way that conforms to new technologies. The results of this study showed that the amount of payment had an effect on preferences for receiving mental health services. In their research, Dias et al. showed that the preferences of rich people for medical services were greater than those of low-income people, and people with higher incomes had more freedom to choose medical centers ( 51 ). According to McMichael et al., people's attitudes toward the amount paid for mental health services were important. Furthermore, if there were few medical services provided, people would be less willing to pay. Therefore, the services and costs needed to be such that any person would be willing to participate in insurance program payments ( 53 ). The results of the research by Walsh et al. showed that most people were willing to pay additional taxes to finance healthcare services and considered it important to support this sector ( 55 ). The results of a study on preferences of the patients and beneficiaries for several diseases in some European countries showed that the participants did not value the cost in comparison to other items. The main reason for the people and policy makers to choose this option was that they thought the costs would not be paid by the patients but by the insurance companies ( 56 ). Muhlbacher & Bethge conducted a discrete choice study on insurers policyholders and focused on various attributes and components of integrated service delivery. They found that cost was an important attribute and a main factor that affected people’s decisions ( 57 ). The issue of cost has always been important for humans, and considering the economic limitations that people have in society, they should make choices based on their needs. Most people prefer to pay less for services. In addition, the fact that governments need healthy people and populations for economic growth, increasing military power, and other governance issues causes people to expect governments to pay for these expenses and be responsible for them. Willingness to Pay The highest Marginal willingness to pay was related to the coverage of inpatient services and long-term services, followed by the place of receiving services, using online services, and determining service limitations. As we know, if the utility of the expected benefits of the services covered by health insurance is greater than its costs (insurance premiums), people will purchase insurance ( 56 ). Therefore, people tend to pay more for inpatient services because their benefits are greater. Some evidence from low-income countries showed that people tended to obtain higher quality services by purchasing health insurance plans because free government services were usually limited in terms of quantity and quality ( 57 , 58 ). Another issue determined by considering the relative prices of the studied services was that people were willing to pay more for expensive services, the utility of which was more for them than cheaper services. Other evidence also confirmed this issue ( 58 ). According to the results of the analysis, people preferred higher levels of coverage to lower ones for most of the insurance attributes. Thus, their willingness to pay for these levels was higher as well. However, given that higher price/premium levels cause more disutility for people, there was a sign of disutility of insurance premiums for the people participating in this study as well. Limitations and recommendation We acknowledged that the exclusion of service impact, patient experience, and quality as attributes in the study is a limitation and have included it in the limitations section of the manuscript. We emphasized that future research should consider these factors to gain a more comprehensive understanding of community preferences for mental health services. Conclusions The findings of this study show that people are willing to pay premiums for coverage of psychiatric services, and that insurance coverage of these services can increase the well-being of these patients. Inpatient services have a higher priority for insurance coverage. In addition, the results of this research can be helpful for designing mental health insurance packages and planning to change the country's basic insurance to improve people's participation and increase the utility of insurance packages. Declarations Acknowledgments The authors would like to thank the study participants for providing useful information. From the Social Security organization, universities of medical sciences in Tehran and people participating in the data collection of this research. Authors’ contributions HT,ES & HR contributed to conceiving and designing the study. The data was analyzed and interpreted jointly by HT,RD & EJ. All authors contributed equally in writing the manuscript. All authors reviewed and approved the final manuscript. Funding This study is financially supported byTehran University of Medical Sciences. The funding body was not involved in the design of the study, data collection, analysis, and interpretation, as well as in writing the manuscript. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethics Approval and Consent to Participate This study was conducted as part of a Ph.D. dissertation in Health Economics at the Faculty of Public Health, authored by Hamid Talebianpour. The research was supported and formally approved by Tehran University of Medical Sciences (Project ID: 52634). Ethical approval was granted by the Ethics Committee of Tehran University of Medical Sciences (Ethics Code: IR.TUMS.SPH.REC.1400.032). Written informed consent was obtained from all participants. In cases involving illiterate participants, consent was obtained in writing from their legal guardians. To protect participants’ privacy, all personal data were anonymized using unique identifiers and fully de-identified prior to data analysis and storage Consent for publication Not applicable. Conflict of interest The authors declare no competing interests. References Arias D, Saxena S, Verguet S. Quantifying the global burden of mental disorders and their economic value. EClinicalMedicine. 2022;54. Prinz C. Sickness, disability and work: lessons from reforms and lack of change across the OECD countries. Disability and employment–lessons from reforms. 2010:23. Zeighami R, Oskouie F, Joolaee S. Mental health needs of the children of parents with mental illness. Unpublished doctoral dissertation). Tehran University of Medical Sciences, Tehran, Iran. 2011. Garfield RL, Zuvekas SH, Lave JR, Donohue JM. The impact of national health care reform on adults with severe mental disorders. Am J Psychiatry. 2011;168(5):486–94. Pearson WS, Dhingra SS, Strine TW, Liang YW, Berry JT, Mokdad AH. Relationships between serious psychological distress and the use of health services in the United States: findings from the Behavioral Risk Factor Surveillance System. Int J public health. 2009;54:23–9. Rowan K, McAlpine DD, Blewett LA. Access and cost barriers to mental health care, by insurance status, 1999–2010. Health Aff. 2013;32(10):1723–30. Asgary A, Willis K, Taghvaei AA, Rafeian M. Estimating rural households’ willingness to pay for health insurance. Eur J Health Econ formerly: HEPAC. 2004;5:209–15. Revicki DA, Wood M. Patient-assigned health state utilities for depression-related outcomes: Differences by depression severity and antidepressant medications. J Affect Disord. 1998;48(1):25–36. Lenert LA, Sherbourne CD, Sugar C, Wells KB. Estimation of utilities for the effects of depression from the SF-12. Med Care. 2000;38(7):763–8. Wells KB, Sherbourne CD. Functioning and utility for current health of patients with depression or chronic medical conditions in managed, primary care practices. In: Depression: The Science of Mental Health. 2013. pp. 291–8. Zweifel P. The Grossman model after 40 years. Eur J Health Econ. 2012;13:677–82. Ratcliffe J. The use of conjoint analysis to elicit willingness-to-pay values: Proceed with caution? Int J Technol Assess Health Care. 2000;16(1):270–5. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: A user’s guide. Vol. 26, PharmacoEconomics. Springer; 2008. pp. 661–77. Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. Br Med J. 2000;320(7248):1530–3. Sheldon RJ, Steer JK. Use of Conjoint Analysis in Transport Research. 1982;P230:145–58. Gustafsson A, Herrmann A, Huber F. Conjoint Analysis as an Instrument of Market Research Practice. Conjoint Measurement. Springer Berlin Heidelberg; 2000. pp. 5–45. Green PE, Srinivasan V. Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice. J Mark. 1990;54(4):3–19. Biglaiser G, Ma CTA. Moonlighting: Public service and private practice. RAND J Econ. 2007;38(4):1113–33. Van den Berg B, Van Dommelen P, Stam P, Laske-Aldershof T, Buchmueller T, Schut FT. Preferences and choices for care and health insurance. Soc Sci Med. 2008;66(12):2448–59. Nanna A. Centre for International Health Health Insurance in Developing Countries: Willingness to Pay for Health Insurance in Thailand using Discrete Choice Experiment Methods. 2011. Kananurak P. An economic analysis of voluntary health insurance after retirement. 2013. Brau R, Lippi Bruni M. Eliciting the demand for long-term care coverage: a discrete choice modelling analysis. Health Econ. 2008;17(3):411–33. https://tradingeconomics.com/iran/gdp-per-capita-ppp-us-dollar-wb-data.html Rahmani H, Talebianpour H, Sharafi SE, Daroudi R, Jaafaripooyan E. Development of attributes and levels of mental health insurance services using a discrete choice experiment. J Educ Health Promotion. 2023;12(1):134. Louviere JJ, Hensher DA, Swait JD, Adamowicz W. Stated Choice Methods: Analysis and Applications. Stated Choice Methods. 2010. 227–51 p. Orme BK. Sample Size Issues for Conjoint Analysis. Get Started Conjoint Anal Strateg Prod Des Pricing Res. 2010;57–p66. McAlpine DD, Mechanic D. Utilization of specialty mental health care among persons with severe mental illness: the roles of demographics, need, insurance, and risk. Health Serv Res. 2000;35(1 Pt 2):277. Lee EJ, Chan F, Ditchman N, Feigon M. Factors influencing Korean international students’ preferences for mental health professionals: A conjoint analysis. Commun Ment Health J. 2014;50:104–10. Kerssens JJ, Groenewegen PP. Consumer preferences in social health insurance. Eur J Heal Econ. 2005;6(1):8–15. Nieboer AP, Koolman X, Stolk EA. Preferences for long-term care services: willingness to pay estimates derived from a discrete choice experiment. Soc Sci Med. 2010;70(9):1317–25. Sneeuw KC, Sprangers MA, Aaronson NK. The role of health care providers and significant others in evaluating the quality of life of patients with chronic disease. J Clin Epidemiol. 2002;55(11):1130–43. Froberg DG, Kane RL. Methodology for measuring health-state preferences—III: population and context effects. J Clin Epidemiol. 1989;42(6):585–92. Cotten SR, Gupta SS. Characteristics of online and offline health information seekers and factors that discriminate between them. Soc Sci Med. 2004;59(9):1795–806. Herman PM, Ingram M, Rimas H, Carvajal S, Cunningham CE. Patient preferences of a low-income Hispanic population for mental health services in primary care. Adm Policy Mental Health Mental Health Serv Res. 2016;43:740–9. Marcus M, Westra H, Mobilizing Minds Research Group. Mental health literacy in Canadian young adults: results of a national survey. Can J Community Mental Health. 2012;31(1):1–5. Raue PJ, Schulberg HC, Heo M, Klimstra S, Bruce ML. Patients' depression treatment preferences and initiation, adherence, and outcome: a randomized primary care study. Psychiatric Serv. 2009;60(3):337–43. Gerritsen DL, Steverink N, Ooms ME, Ribbe MW. Finding a useful conceptual basis for enhancing the quality of life of nursing home residents. Qual Life Res. 2004;13:611–24. Steverink N. When and why frail elderly people give up independent living: The Netherlands as an example. Ageing Soc. 2001;21(1):45–69. Phillips EA, Himmler SF, Schreyögg J. Preferences for e-mental health interventions in Germany: a discrete choice experiment. Value Health. 2021;24(3):421–30. Caldwell A, Couture A, Nowotny H. Closing the mental health gap: Eliminating disparities in treatment for Latinos. Kansas City, MO: Mattie Rhodes Center; 2008. Koizumi N, Rothbard AB, Kuno E. Distance matters in choice of mental health program: policy implications for reducing racial disparities in public mental health care. Adm Policy Mental Health Mental Health Serv Res. 2009;36:424–31. Arcury TA, Quandt SA. Delivery of health services to migrant and seasonal farmworkers. Annu Rev Public Health. 2007;28:345–63. Kessler R, Stafford D. Primary care is the de facto mental health system. Collaborative medicine case studies: Evidence in practice. 2008:9–21. Ruffin MT, Plegue MA, Rockwell PG, Young AP, Patel DA, Yeazel MW. Impact of an electronic health record (EHR) reminder on human papillomavirus (HPV) vaccine initiation and timely completion. J Am Board Family Med. 2015;28(3):324–33. Bearman SK, Weisz JR, Chorpita BF, Hoagwood K, Ward A, Ugueto AM, Bernstein A, Research Network on Youth Mental Health. More practice, less preach? The role of supervision processes and therapist characteristics in EBP implementation. Adm Policy Mental Health Mental Health Serv Res. 2013;40:518–29. Schoenwald SK, Sheidow AJ, Chapman JE. Clinical supervision in treatment transport: effects on adherence and outcomes. J Consult Clin Psychol. 2009;77(3):410. Koch-Weser S, Bradshaw YS, Gualtieri L, Gallagher SS. The Internet as a health information source: findings from the 2007 Health Information National Trends Survey and implications for health communication. J health communication. 2010;15(sup3):279–93. Lee CJ, Ramírez AS, Lewis N, Gray SW, Hornik RC. Looking beyond the Internet: examining socioeconomic inequalities in cancer information seeking among cancer patients. Health Commun. 2012;27(8):806–17. Cunningham CE, Walker JR, Eastwood JD, Westra H, Rimas H, Chen Y, Marcus M, Swinson RP, Bracken K, Mobilizing Minds Research Group. Modeling mental health information preferences during the early adult years: a discrete choice conjoint experiment. J health communication. 2014;19(4):413–40. Alexander KE, Ogle T, Hoberg H, Linley L, Bradford N. Patient preferences for using technology in communication about symptoms post hospital discharge. BMC Health Serv Res. 2021;21:1–1. Dias N, Yamamoto R, Faulkner KG. Preferences and motivational factors for the use of digital technology in real world oncology studies. J Clin Oncol. 2020;38(15suppl):e14119–14119. Rains SA. Perceptions of traditional information sources and use of the world wide web to seek health information: findings from the health information national trends survey. J health communication. 2007;12(7):667–80. Kroeze W, Oenema A, Campbell M, Brug J. Comparison of use and appreciation of a print-delivered versus CD-ROM-delivered, computer-tailored intervention targeting saturated fat intake: randomized controlled trial. J Med Internet Res. 2008;10(2):e940. McMichael AJ, Kane JP, Rolison JJ, O'Neill FA, Boeri M, Kee F. Implementation of personalised medicine policies in mental healthcare: results from a stated preference study in the UK. BJPsych Open. 2022;8(2):e40. 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. Soc Sci Med. 2020;245:112675. Rutten-van Mölken M, Karimi M, Leijten F, Hoedemakers M, Looman W, Islam K, Askildsen JE, Kraus M, Ercevic D, Struckmann V, Pitter JG. Comparing patients’ and other stakeholders’ preferences for outcomes of integrated care for multimorbidity: a discrete choice experiment in eight European countries. BMJ open. 2020;10(10):e037547. Mühlbacher AC, Bethge S, Reed SD, Schulman KA. Patient preferences for features of health care delivery systems: a discrete choice experiment. Health Serv Res. 2016;51(2):704–27. Folland S, Goodman AC, Stano M. The economics of health and health care: Pearson new international edition. Routledge; 2016 May. p. 23. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Dec, 2025 Read the published version in Cost Effectiveness and Resource Allocation → Version 1 posted Editorial decision: Revision requested 03 Nov, 2025 Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 03 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 11 Jul, 2025 Submission checks completed at journal 11 Jul, 2025 First submitted to journal 30 May, 2025 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-6787596","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492308540,"identity":"2d8df4b1-46c7-498c-a80f-bd843c9c85e1","order_by":0,"name":"Hamid Talebianpour","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Talebianpour","suffix":""},{"id":492308542,"identity":"7cc2fb12-5938-4bcb-8869-b2af3e00e188","order_by":1,"name":"Rajabali Daroudi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rajabali","middleName":"","lastName":"Daroudi","suffix":""},{"id":492308544,"identity":"6c1e9f5c-aa31-4fc6-abf7-6564ff0967c7","order_by":2,"name":"Ebrahim Jaafaripooyan","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ebrahim","middleName":"","lastName":"Jaafaripooyan","suffix":""},{"id":492308545,"identity":"254c9ff2-cc34-417e-b2c4-e5f65dc9a3c1","order_by":3,"name":"Elham Sharafi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Elham","middleName":"","lastName":"Sharafi","suffix":""},{"id":492308546,"identity":"5c26bc23-850b-4fdb-830b-5850c59dbfff","order_by":4,"name":"Hojjat Rahmani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACAwaGBAMoA8isgDIIa0mAaTlDnBaQUiiDsY0ILebsDQ8Kfv64J28udvjZg4fzDsubszcfYPhRsQ2nFsueAwmGPQnFhjtnp5kbJG47bLiz51gCY8+Z27gddiMhwYAnIYFxw+0EMwmgFsYNN3IMmBnb8Gsx/JOQYL/hdvo3icQ5h+2J0mIMtCVxw+0coC0NhxMJazlzIMFYJi0hGailTCLhWHryhjPHEg7i9cvxnjTDNzYJtkCHbZP8UWNtu+F488EHPypwa2Fg4ElDjolmMHkAj3ogYD/8AIlXh1/xKBgFo2AUjEgAAInbYl5EBCZMAAAAAElFTkSuQmCC","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Hojjat","middleName":"","lastName":"Rahmani","suffix":""}],"badges":[],"createdAt":"2025-05-30 21:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6787596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6787596/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12962-025-00687-9","type":"published","date":"2025-12-30T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99545390,"identity":"8a887547-dd31-4e1b-b1fe-f61c9bf44b70","added_by":"auto","created_at":"2026-01-05 16:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":932409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6787596/v1/710fb646-bb64-4072-95e4-f0e46b96584d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCommunity Preferences for 'Mental Health Insurance Coverage in Tehran Using a Discrete Choice Experiment\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eMental illness refers to a wide range of mental disorders that affect the ability to think, feel and behave, and the affected person cannot perform their daily activities well. mental health problem are among the diseases with a high prevalence and a significant burden. Approximately 428\u0026nbsp;million years of human life are lost due to disability resulting from mental health diseases. Such diseases account for approximately 16% of the years of life lost in the world and impose an economic burden of approximately 5 trillion dollars on societies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Nearly one-third of the diseases associated with disability are caused by mental health problem (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to surveys conducted in Iran, 22\u0026ndash;30% of Iranian society (approximately three out of 10 people) suffers from some degree of mental disorders (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In Tehran, the situation is worse, such that 30 to 36 percent suffer from mental disorders. mentally disturbed and women have a share of 36% among them (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). People with mental disorders are less likely to have health insurance than those without mental health problems (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In a study conducted by Garfield et al. using the data from the Medical Expenditure Panel Survey, it was found that 37% of working-age adults with severe mental disorders did not have insurance at least during the year, while only approximately 28% of ordinary people did not have insurance (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Even after controlling for demographic differences, Pearson et al. found that the chance of having health insurance for people with severe mental health problems was 40% less than that for those without such problems (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt seems that models and solutions need to be provided so that all patients can access the best services at the most appropriate time (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Investigating the demand for insurance and the willingness to pay for it helps policymakers to determine insurance premiums accurately, and this kind of study determines whether willingness to pay covers health expenditures (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMental illnesses severely reduce people's quality of life, so it is expected that the willingness to pay (WTP) to eliminate the mental illness and the willingness to accept (WTA) to continue the illness are high in affected people. (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Willingness to pay is based on the theory of social welfare, and due to the monetary measurement of outcomes, it can help policymakers in allocating resources. Willingness to pay can be obtained directly or indirectly (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe method of joint analysis (or the more recent term, discrete choice test) is also widely used. This method provides useful information for policy makers. The combination analysis method or discrete choice test is designed to reveal individual preferences for choices that are formed by changing the characteristics of a product or service. Random utility models (RUMs) are used to determine the extent to which different characteristics predict individuals' stated choices, allowing outcome variables (such as health-related quality of life), process ( (such as waiting time) and cost were considered simultaneously (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn fact, joint analysis is a special type of survey studies that was first created in mathematical psychology and has a strong theoretical background and has been successfully used in market research, transportation economics (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), environmental economics (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). It has been used and proposed in England to value the quality of public services (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eExamining insurance demand and willingness to pay for it helps policy makers to set insurance premiums clearly and accurately, and also these studies determine whether willingness to pay covers health costs or not. In this way, it is possible to determine the amount of government subsidies for insurance premiums for poor people. Also, in many countries, insurance organizations have tried to use methods such as measuring preferences and willingness to pay to adopt special strategies to make it cost-effective and increase consumer acceptance of insurance plans (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePeople's preferences should be taken into account when designing health insurance plans. A discrete choice experiment (DCE) is a suitable method for evaluating people's health insurance preferences. For example, this method has been used to generate evidence on Dutch people's preferences among different health insurance schemes (19) and to derive health insurance preferences of various groups of people in Thailand (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The demand for health insurance largely depends on its ability to meet consumer needs, expectations, and preferences (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Considering that little evidence is available about Iranians' mental health insurance priorities, this study aimed to determine the mental health insurance priorities of Iranians living in Tehran using the DCE method. The findings are important inputs for mental health insurance policy-making and providing health insurance benefit packages to people and include their preferences in Iran and perhaps beyond.\u003c/p\u003e"},{"header":"2. Objectives","content":"\u003cp\u003eThe current study seeks to address this gap in the research.\u003c/p\u003e"},{"header":"3. Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was conducted with the code of ethics IR.TUMS.SPH.REC.1400.032 received from the Vice-Chancellor for Research at Tehran University of Medical Sciences for a doctoral thesis. It was carried out during 2021\u0026ndash;2022 to investigate people\u0026apos;s insurance preferences for mental health services in Tehran, the capital of Iran. The DCE method was used in this research to extract the insurance preferences of Iranian insured people living in Tehran for mental health services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Design \u0026amp; Questionnaire Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the first phase of the study, the components were designed, and the levels were determined. To this end, a scoping review was performed, and then the components and levels were designed by interviewing healthcare experts. Next, the main attributes and levels of mental health insurance were chosen using the opinions of experts in the field of health insurance. The most important attributes of mental health insurance services obtained were as follows: inpatient services coverage (including 30%, 70%, and 90% levels), outpatient services coverage (including 30%, 70%, and 90% levels), service delivery place (including two levels of public sector and all providers), use of online services (including 2 levels: yes and no), determining an upper limit for services (including 2 levels: yes and no), and monthly insurance premium (including 4 levels: 40000 Tomans, 80000 Tomans, 120000 Tomans, and 200000 Tomans) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). also, The resources used to calculate the costs were based on the 2021 tariffs of in the international dollar (purchasing power parity (PPP)) and at the exchange rate of each PPP\u003cspan\u003e$\u003c/span\u003e dollar equal to 150000 rials (RlsIRR) (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). Considering that the results and methods related to this section were mentioned in detail in the study by Rahmani et al. (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), they are briefly stated here. For more details, refer to the aforementioned study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \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 attribute-level of mental health insurance in Iran\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAttribures\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInpatient service coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoverage 30/70/90% of costs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutpatient service coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoverage 30/70/90% of costs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of receiving the service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic sector, All providers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe use of online services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes, No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eServices have a limitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes, No\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly premium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400000/800000/1200000/2000000 Rials\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the third phase, the D-Efficiency method was used to design the choice sets by choosing the best attributes and levels (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). The final design consisted of 24 choice sets that were divided into three blocks, each containing 6 choice sets that included plans A and B. The experimental design of the choice sets was performed using SAS9.1 software. The questionnaire developed for this research included three sections: research description and informed consent, socioeconomic characteristics, and demographic characteristics of the participants, in three 8-choice blocks. Therefore, three versions of the questionnaire were prepared, which differed only in the type of choice set (block). Before performing the tasks, the attributes included in the questionnaire were explained to the participants, and the dominant choice set was used to test the consistency of the questions and prepare the samples. At this stage, those who answered incorrectly were excluded. Each block was tested on 15 participants to ensure the reliability and consistency (reliability and validity) of the instrument. The experimental data were excluded from the analysis. The data were completed either online or in person. Due to the prevalence of COVID-19, some patients did not agree to enter the study directly; therefore, the questionnaire was sent to them online. Of course, they had been provided with necessary explanations in advance. The attributes and levels of mental health insurance in Iran and the choice set included in the analysis are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Also, informed consent was obtained from the participants in this study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \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\u003eOne of the choice sets included in the study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAttributes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlan A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlan B\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInpatient service coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutpatient service coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of receiving the service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic sector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll provider\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe use of online services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eServices have a limitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly premium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400000Rials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200000Rials\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhich of the health insurance plans would you like to choose (Please tick one box only)?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlan A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlan B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the sample size, the rule of thumb suggested by Orem (1998) was used, through which the minimum required sample size was estimated according to the following formula (\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"95\" height=\"38\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAI8AAAA5CAYAAAAP8Hj7AAAAAXNSR0IArs4c6QAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAABl0RVh0U29mdHdhcmUATWljcm9zb2Z0IE9mZmljZX/tNXEAABrtSURBVHhe7X0HeJRV1v9vekuZJJMy6b03CEkgBBKKFKUIsosFsGBZxIJtWXSdmPdbccV1WT8/dRFhP5WmghQ3CBJCMSBJSIH0OjPpyaTOJNPL/74BVsWggQ+XsP85z/PyTN73vveee97fPfecc8+9sLOzs2EnuwRuRALsG3nJ/o5dArQE7OCx4+CGJWAHzw2Lzv6iHTzjDAPLurJCi5uxaFcdnI7U0cxFM1a/s8Uh/r70mtnuOBiNt6O6/3l+xuubD+C/8RDS5/4Gb1m/wNkP/o4Xml2BievxznNOyGzfhvXrz+MIJAhf8Bae3bzYGhzqsnMe0EoqfQDNe/1fWvkg/nJae1kCc0hT0xmbtzzmcE+6R5Mf8BGeZYTtGZAsW7s3GE4r3sTOzZlIU2zCrpfewNrDA/Zpa5xhBy4x8aF+7poXwzU90gK5DVrXKGiZgk4rA3sIr9/o2lwXO3lGPReRpERIqRZMZRs6Y8Phfdc8zMhVoclYh+Z6X9R7RyH4DhMCS7iQ6gxgW2wIBJhWVWmNur9hfV1dU/hwcCamMgZhHtShW8VHszMX3T2mdhaMp4G6Dv3CdXMl+d2Pp8d0Y5BTjeo6T7j0CcAJnIgFsQo7eMYbeJzvkrFih2p5/Z3/RG2jEW2ZC9Hl5PuFhYUPdUB3b4OHyNvXHZl3s3Cx4SLKiypRlDIXS9cnQTbhS3y++SSqitkQTV+KOa9nQPrmBbS1GSFmc0H00uOa7pqhczu2huXq/KDPeAqPr/FCbMuXOHmmEX/pESPv9PDul+/WnCFNrdDFPp8h7q/HGmzHaf0p5OYxoXSWYsLEJVgTr7SDZ7yBRxgQYxYOYpjHKHH1kAjhPiPVyMjwLBp+l6rOIcz+NmzGIb1zC9fFZlji5id1lldxavlDrt88GeAWExgfMjPXTQ5tQApSp8zGZGEl1H4daBtkQ2gxwQNGqdpoQGGlCJqICYifekd1Zhj7C3/9J7G6yMGlKbog28EKl8K/Uh/sl8nShRwvv3Nid5vbpCT+StV5jXdhnRmhc6PhFRSTM0V8Jsdu84w39JjzYWrsQ/2QE4b8YpAR48GNCoD43GU+P68/kfPyhayubknCDESscVbpkys6Dvi/7vPgH1dc7A6c2Rx1D7wmpitncvv07PpC/05HrkCTGAmxRQ9ozuNClRKFnLmYOe0+PBHGHhCo9p3f8/wuoTZzOqbOCmIkz3CbmnTu7pqTH/aVe8zffZHvnR8tLzIt7W3wRADLDZNjfRAU5qws+Kf4tB084w08iiKrqZ0LpdATA5IYBHpwrGmA9Qp4aHarCsDsim1k+k0XI+POBKZexGJiz3amhv8sjOFTcJbl/xG/7s1G1fnvsi2eC8JsDB/Y2tsBYyXaBnrQZBUjXG+GCd0xGnlFdiViXdvPBSJtpha+jup7rWLT9KFOud5c32XpF3s4KpptforzSojEDLS2hsEmDFnEZpoS7OAZZ+Dp/98PNUqHCYxu0QLUwaHJZGR+6QAU0GzKZDIWcGpZXeifHlR+ku/HT/bGE7NYmdMd2v9Wkz89elAIBCcGo9qbXYctfywddA9jmgYrUTPghiKvcCRNmoi4YTUSvzuFop1yvDM00ykhduoEaVovmnMrcfp0HmbErvJISXPyUO+rQHWZCZrUBfDMcEbo4HbUnzyMox9aEHHvOt8FM0N97eAZJ+AhwGAQVny7PvxuYlPxGQHLjQWNWdT4m/dZm7PdKKI2RoguE8r1CPDt6tCXWRv1WOYKhhjm6CZmpLWp3ljmOslsWhoLDdIdmIwmXlHXuXxNRYsGJyZ6I8MpBP4eBYi1nUHeof0ordFhzrqVzExPm03EyrPty2VAPzWO4RXRy2gvz0FOKQ8863w89lCiNXqpD5pKGlCwV4d2UQojIC2UYQfPOAEPYYMDmFcJ4uc9bDhT6+zCl2BadLglx8Y3oPoSkxRFmWWyjF08Nu+4vt/dAAPH5kJWCVhg8Aw2R/PwsNngIuLYPIFmBHP0jCbma6buNoFRKUYHCefoyfTl7iaCF78bvaQ+c10zmtWu4LuIIOQZ0N2sQJveCLajGWa1Em1yRzgOOMFJwoOngwQCovfazW2wtakwTDc8fmT3/zsnTUxgOJphZYUoWn3BCPZEUryIlRwOEaiRb30ZQKfk5Iec+yj952lspUhIZkQncYFILjqPbgWOEqBhHX231ucBYGTpe/g4jlPkon8HZOPVK+vhhr3oNAjhtCj7UrnqQpylwRq7AXfG0jeOI4e8R3t6mELKTBmpDMMbKTt4xg9k62zQNAw1VCpQOhCCAPcQxHpAQAY4zpJLlpXFKs5KijAvfJvzlWOGpjgczVOIJrqV/Ns1z62U/o/a/toKucnap+dB6RuHSH8pUsTQBxAfiQbPmdlIGOp13Mg2cLxcJSieTPBEbrfdSvbt4LmV0v9B2xTlZpIlHtebRZMxEByKDrbXbgKcj4md0ymT3TGr5LW+R0vP9c4NZwXCSwynIEBUfot5t4PnFn8AunmZbKKL+WJxTEfOUJRyUInhLhd89Ep+50fz31KTx+nBwtbfq9Uec470StFcouqZm+qdR8AzbAfPOPh444CFaAZLlK3scEo6vv84+o0krNPdtZQGDrn4r717MIzrk4Y26V1o6uZuXwxsDQZUt5pvu+a51V9gpH1TH4PJPDNk4BfWt/bo9eihb5LYIAT0j8bm7hwMN+kRIbCou1n7jlNUw4jXdIvpVwHPsudlYdEOGCbz9ZXg1i3u5vhunqLKaedYBp8puJv4wndfk91GGkrjpjM3HTz33rsigtUlz5b3MulUJtojsNN/qARuKnhkshfv0KouPKOuli/Y9vHJ7qztZYzw+x+1fPDG45jpPzJH7yDaaPD/KkuZTLL84v8cj1r89JdQkMq8l72OZb9/eXBhMnaepaju/2v99vfHJoGbCh6SqObLZVoCh2q+LW+trvCZPTf1j2GRPGibq3DwQo/6UHGV+euhtIIXVs3WvxCPegIky9jYvFSKrP9ItNripL6K0ucNvPCUeRlTUKntBZfdgrq8s/3bu3wsCY/JDgRL0VpNUdbrqdte9volcJPBg2NspqWWqek1LHvq0YclEf5rh7uaUXpiI45XKZzyWt03TJrpO8hoaWr9Jz/4j4Td0utjWZ1hGma9WlfhFNMnmIbVby+EoCcPh3YfwvZDHS6+lhee9/EOcIUUb5J6SeKdnX5NCbDJaH6t7pMH8eYTn2C73h/Rz+/GG9lpSFf8GbvWbsBLZOlE7383Hvr0TTwyPVyVDBzcRFF0EvVPiKI20ffJFQvZ/Y8YSQC0W1tQxThw/jO09LK9Vs145nd3THdAS9kX8Vuf3qI9IjdVpr+8FWtfmIeprji9haLyfrazivN+zlyXBJvfRCiGBcV3Jvl8BQyhr+zABLXQsrhDwAi4eLohxnqeyZp2VUVeWTuDojY9ujLupZeYxJcp2kVRI8s11yJx1t/9Jmx8elXchg0cMg+W7qGog2P5EDLZujuHCs+mHHqvBNyVv4Xr7NCKfIraO5Z3b7cybKhqsyz8AHimhiCuAvAcKERvgwSNzEBISP7s7K4KlFoHwFB0oCUhvI4rQvElgPw8kSmJjmGNxLEckl5FkexpL7RfMPc3VU74uHy/wNdLMH95XNwyIasaLUfckOvpflCy7GUdO5pd3UlRA6PVrj6yXa/2iIMhdR16hbzvSBsja3ky2SP3TqxmLtpRzmd8kNs7dEHubpt2GT1kcBDtWuBfNCR/QF5bm91z8hTa3YOP8u7+fa9LPL/m6rYu5cxU+RXr5cubGhqye/NOsFo9Q/Lo8sHx/AoyHf6It0vlVaGqi8WuR766wFJ2DD7Nhds8RycueHw1zL1tZ6PUokHH8EwNe2GShqw61BMgkoF1+xO78pP90EbGI/ERV/D2bkNf+2eoOumA9rgETH3oJfwh6hAOvpWDwYIqlIZM59WFk4jEDfSbot4lYXbZJpehAqfIiKiAjFVz3nISD0SfzjmBwq3nwU1ZdoffSl+RD/BqJ/DDxLl/taYoOowuPzkUkkVQOEUxSfj+MulYPIEnIyYqFOkawD3EBhiuPNM7A7ZnJSnS33y1YS/yLjZBPOvxjLTfLvqLO5BF2ro6ZELiK5q1klTf+3PWf846Vt4Ax4zHpk69d/FbPBKvI7WSNesfEYnF2B4Gl3Unw2ZCSUVvSKDIBY6uZrDMDegubJg00DH4N148x8JkIJ94DXQd/xFGPbviyKnXwqfPyIwJYmUac0xobtehshYtfInLV3fP8vcNZckXKXgk86ylH3oDhrUCWEi+yA1SUwKGuQGtilrPtmGuu4OfBD2sAHglBYLl49ZFUFlAlon7r1W5U3Ik36Zj4pvD23DsXMvCllOf08lRyDuWOeHOh57CrGVirHSBg8YIxtkdV2pp50PXPc3FGdJvG+pxBk3ITHyS7xsgnOoBkO1JV1MHF3rVVDdXlnd+bR3yST7f1MgneH5BjpMJ2PxH4Y2wbKnmCBwnJWTEzxrmaNBQ9g26FYfRsdsLnJB5XMnk6dEmL4ezfBvK+T+A9ZW6srKyoslvkjwBehH9tiF27bQp2cuTuVbVeWVmT6uY5M3OgTDlrpbeIOn/xJiPTGlt7l+kFMTDISoeoaEMkcQBrJIxdI9oGTLi+4NQtR0vPvsi+oNTA48e9P6z1MkjQsP3xcltnylqbIFtUavXY8mKexAZwN2T6oK3yFR0TS8pcM7MlpZ8eaFy315uyxklrQCn06ycKuiz1rmrywKmGbAknFdJMlss9Er0JSKRWV3Pt63dXV5RKfFSRV8kWGxHPdeqK5ZC0PLTrlQaYVCdaWlrD4iYEu/d0BMGLt/JwDLpSkn55qvLE35JZjk+lsle7hB718R3nzvnfu7bT1HQ0ASFLhLRZIA4z5+hNcex3j1HUfTeq9GIBk0qubzGINpxU+Syt1Vl1pJUszZ9PLzT0jBrkRRJbtCj+F1jb1MECtyC4BcSjHgpdOEkAW0s4CFm9gKrVvmSopPLEPmE4URFjeC86kTYrLmr4BP/wJBvv9umQ59+9p07wx+eiVxach0/B5wRiXn7n3JwVDVoOpS0xiGZdyADmZDJbOpot+mHwQIBTh+5869Ji7A7CEv/O55x7j2rnl9IiRoTcayefaqtXf8aJIKan36J/iFY+96TRIt7Vqxb8LqDPJF1tIp3RqEYyrrTU0CswmtRB0mz64S6XImWGgM6GX7gkWnUP5I4f0ImPMmQqL32y1Xk0cvk4o0bZIyBkUvgqcjlNjeyUSmUYlJCLNLd4BRP3y86bFab3dDuFYM2jePRLA4+IgZB2bXqpYOElt6ihfJdH2o2rlo9Rzx5ToKzxAfJ92SDV9uILXlNn31db6t+9J6FPe+tXrzbM1Q6QJschSRTrXAMzFKbOomj5NMz5cFsjCS0/Yha0bpnI8mg+zFRVCGdMNUkk8XscPfj2xqLY1k7S1qLhg+8e27HgZ82SlHVdOxJIZPF7XQP5DObS+K4u0vbSyxHP8h/92prhxQkGpYAuOPeAWXNyuZzje7tthB4pa5A2pACgtkxCAixgFd1Smho9X1m8mqZq5sPduZcFSglx9zQU/VYRDAGKf37ioyAx5h3zKgi6nkgMALNTHaLI0BGZO8EnTlhQruqHzyxEzT9TS2OF/9St9/lxSTWY7IOy1ZK8VM2mb4WG2sRxysyoFWea/yvQ7vKU5Yvsa1f+zDui+c1v/wU3qA2UReGjxWCOvbv6yTdEkXJ6fTNP5E8S/xuZGT8PJHytEf5Bl3+EdoiuTYRGbIiTTqz+8V6VbkodUHI7IQ04eDxg2AlJqFbyDSqyk80eMYtdCAoiyVz3G2lXX6h40BDeQfX4CqFV1QQ8vtcdxOD+ASgXaXxmJIhP10IbrAVcyNU8+Uaz1ib0Eqv9H5Mrs2jVPyNxiRgGYOmUfMfDaz44KF3/lCtEpk9o3kIYkNHpqX6X/pot+FzEoxk/ANGy0GbwIk1aYLnKzxl67x2knXMHnKFKNTnvFZS+hrTbNUQh0BDjO6B27CPo7JMgoQZ7x9ZE5fRVc+B112RULhze8n2n5Nkn1mwVmOKF+is3ubSIgw6Z/hU+0720Rm5OWTsNI1WGwkS0mmRH62WyboWTkJ7tlJOzCM5ckiy9M9G5G5jaV5eYvmXObNSIvpvdb3q3MCAEVw1G+5Ct3J558AxeSeRANnO8r0hfxt3+jLrROXappsEXuaa8oEy4xQdkqdBfdmDeH/pUJWBzWI9NViWj3xGKMMqnNPkY5C+yiyhKkfrOpn/ffQduQEn/zC5Z8U7BdyTeky9qhxt6I641z+mYMx+5k+Y/cRvDMHR7LqrA3G3k5gp6m9fE36/Rij5t3AvvRnhukkmy2KcWQjfr1Vwe50OyRKrzS8+lZ3+7Bdc/0w/hXDHr5fqQr6hG/GS/eo+fAJLnvgCxJKnTRsy1bpbwJ6tD392NsJT/TuTq892kgeMNQwux6juZRscyTkeLo7fB7AGq0u/5HASis3dKnT0WNHQ76/1K6FGTo25Bs2xWCzrOGbiqRFD4FKjo4Hl6rcZYBNvhM1AO5kT6TSOUYOE1/0VbtcXjoDDD8MqFyaWeRIJdhHwMIVSBphmG9OKTaRbu3+9rlmXmwarH6sbFsAlSMiAXEuHRMiAt8LBn20VcvjQm10+IPf+zqaoU99CmIzIdeRPA1GtW7+fYI4nb6C9gP7liTSrdHbbxl/iuYXEdr9lcslOQ9qJpiE0JiJvMWwg/9Ju9jWDhGOq6j+hEPvPTOns+uj0wKbE78pKcN4zAeyQdI2Ryf6SgOdX2TFBNI4v8RoX6y7sWF1SWZOY0+mDyNWbENnTgNM5Ofsi0BkZN18S3xgRj4Zhx5EdXTd1VZ1Md7mkzlzwZyHtOXJd14dsgPaLjRhbDOm6Kr79Cs9eb/NG0RDj+NeIFffAELkUxgUPFg2GiV/l7qFGCWzejC72+0Lf+kxbcUH4tq1nUZj8ApY+uKJlTTT2SzdvfhsyRlq+n2bDx6ZBqIaCR+JdNxU8N6ML9jquSKDTNtRrRo/NH16h4QicLDYnBEJX8qt5HheYUCkE9TVaAo5IJCcmwJ8GDkai/q2y59AXU8yscSYniA31CcmSoB084xSrR4ihU4re5n7U6qUI8ZCAHIvDIjsmREQzj2THj0aXloRqVtS8f9b9bK0ZDs8uQ0iw65ckKHlxDB1lQmDlsMwG9Lf2Qdc5BHISXTCxdlJIve0URXJfSIDYifjN6y77znbNMwap/ruLkKi4QXZHtWlATfae86WIlXrAhwMBWZX9SUIDDRizuSq8eH8u4+ThvPjYtOBXrHyxVOykIgcTDKL7okIcVmb7lP/k43zHdKny7KbRc7GAkEFIhs96h/Jmxvt1i/cd3gdPf9EC3+kBoWG+zizSzjGigQZ+KAs7eP7dyBhDe7KoLJZFnsLq0XvD4hsLR7EIJHBrIktDxKe4mvThLDApgcDBu7yyxhF+UVIRRwgXJ+J7aOrRmltzP1zdMmEjpxoCfyXXP0ZnwY9sy2h8JWhewvoVZttD7VuPIXdjLQYVqyKXzJv7JyQ6OZH3ttnBM4YPeKuKkBEeCv3CJapPX0lp79dAGCS1qMzCrwlwPic8jeKJsgcsVluR2Nv3/lQJI6izsww19fvR0VCJflUPXL3T3cUpro4OAs4B4qld09gmWoU+U7dGJpu7JXm2wLaW4bYy5Ni37NN73sPhdlZ477L0x+eSxLqES5mkdpvnVgHkF9oNtbFtzw3oRdLetiG4pvpY8oeFX71LUZ+O9h5FbaSXfAjmnhRgoPFF9c4dqCzcj7J2C/SdQZj2bBJc0/zPc9zxIkmH/UU3n6KOniOV9d8RrXRMihFNc8762POzf7wH50DvlKBkDz7ZhkifUmYHz/gETwfLauri9bRroVaR01BdPeAQSGLMlw94uibP5m5YFW1QlXVA2SKAWuIFaYI//P3IlEcmO2JsjzkMQrRQLQHQBte0Gb9fsqbvscHXjqG/rhwllZO5VSFgXFkisNs84w1B2jyzpbFluKSe56pkhyApwgWhEeAp6UWPUUgmez7cam5b2Vx8cWl33SCGAuciUueDYA8TJOR0JldzLUynjJMa/cL/mr5O9lH+38aWz0AA1ECS+avCpzQg0fcwTqlaIG+H1ugBa+JlPuzgGW/gqf5fnbbe01Yz5IbegFRERDlzM5zAuYaVS7hnOzNgmzSg0gyVdRrloQuXBEX4+mBokKwiTUxAa2GJCo0D7Q6eIdFWC0gU+ac08e9ZEs3aTN/90bvwxRJpfTZFDRPNQw7iKIqW19bjQhcf1kBfBHlDyHf83uOzg2e8gaepmdPZYcKAQzTYfh5wZcFKNgX8zOZIfp0FVplOp2eEJsXFx4eYqO4SrbSNBPM4Wmc4R0/cNZi/91Pip9ELRsrRuus5G/ertebVNgUJB/2XNIsAh0SQm7PQUzE3//RFHO8PRUJwNCbGwOgvgO1KTpwdPOMEPJe2CGG+qZCzXH7uM3E7wUurldGoVmM/0/vSUbqjG8wj27eL6Gcy2TSSGlLF7daq3fsGAEctxxYcHrO/jLuvHKeI7jr14xpIm4EkyXJV1+62J9Qlld6Cgq/gUPzXrMidSuXChdMWB7i4QNGbhuSHpyMwObTA3xHb6XRhO3jGCWh+wAaJwxgWwdHzgS6lFkazCAMcUeN9nxk31zHeHtNpI5fPAXh/pE6SdYY9W37JziaxG+skcXhqr3L3tz29ue+BowObIDCktra2XOyYaV1w3wbbnYvSEJnE3foVRW37YSauXfOMHxCRpJRWtk7dhy6lDf5xIfCYEmk76is0/IqZdCQ1l5nNc3Qhhz5bjNZLG7Tp6Y0OKOoHNN0WAzsQwQlcTGBDSbbn/ojs4Bkn4CFawyR7kjVQdbIE+c3k/3dYnoKkqUKX30ngWJjz/VG6N5Nd0ibZIjmyAxiY8QCmzCCn5f6kgT04SjKIRsn9t6+q38yPcb11yWRRTBQXh5zJLfZ6blchN6wnMKrVGIPBpKXgBLl3hkpQkkQ0wA0kI14vKzdU3q55bkhsN+ulDg4YlgcYTM6y/h4w/vC5zif0vjTEPTzPFpzk8Q9itmwjcwidIDcuyQ6eW/tZrCReW0viNCdtVqatuYs1xBCF6hdMDzVN88ReciDC+DlDbhQ52cFzC8FDMhxM5AwRko88ZfeqNVesjUJothTiwC3ka6xN/z+LjaOX2HFMOwAAAABJRU5ErkJggg==\" alt=\"image\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ewhere n is the total number of respondents, S is the number of choice sets each person would answer, J is the number of alternatives in the choice sets, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{L}^{max}\\)\u003c/span\u003e\u003c/span\u003e is the maximum number of levels among the investigated attributes.\u003c/p\u003e\n\u003cp\u003eIn the present study, the minimum sample size for each group was estimated to be 187 according to the formula above. To increase the accuracy of the study, the minimum sample size for each group was considered to be 210.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling Method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this research, cluster sampling was used to select the subjects for both groups of psychiatric disorder patients and normal people of society. To this end, the 22 districts of Tehran were divided into 5 geographical regions (North, South, East, West, and Center). To collect the data of the patients with \u003cem\u003emental health problem\u003c/em\u003e, the researchers referred to Roozbeh Psychiatric Hospital(n\u0026thinsp;=\u0026thinsp;48), Iran Psychiatric Hospital(n\u0026thinsp;=\u0026thinsp;52), Razi Psychiatric Hospital(n\u0026thinsp;=\u0026thinsp;60), and Psychiatry Clinic of Imam Hossein (AS) Public Hospital(n\u0026thinsp;=\u0026thinsp;50). In addition, to collect the data of the healthy people, the researchers went to the health centers in Tehran and collected the data from each region according to its population. For people with mental health disorders, one of the inclusion criteria was the ability to answer the questions. In this study, healthy people were those who were not suffering from mental health diseases according to their self-reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive and quantitative models were used for DCE analysis. The random utility model provided a theoretical basis for DCE data analysis (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e). Thus, the person chose B from the two alternatives A \u0026amp; B. This showed that alternative B was more beneficial to the individual than alternative A and was expressed mathematically as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eU (B, C)\u0026thinsp;\u0026gt;\u0026thinsp;U (A, C)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe utility U was the sum of utilities A, B, and C, where C was a common attribute. Since C was a common element, the previous equation could be written as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eV (B- A)\u0026thinsp;=\u0026thinsp;U (B, C) - U (A, C)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ewhere V was the indirect utility derived from the alternative A compared to alternative B. The fitted utility function was expressed using a linear equation as follows:\u003c/p\u003e\n\u003cp\u003eV\u0026thinsp;=\u0026thinsp;\u0026beta; 1 Insc\u0026thinsp;+\u0026thinsp;\u0026beta;2 Outsc\u0026thinsp;+\u0026thinsp;\u0026beta;3 plrc\u0026thinsp;+\u0026thinsp;\u0026beta;4\u003csub\u003eonser\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u0026beta;5 serlim\u0026thinsp;+\u0026thinsp;\u0026beta;6 monper + ɛ (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003ein which \u0026beta;1 to \u0026beta;6 are the coefficients of benefit packages and attributes. Inpatient service coverage (Insc), outpatient service coverage (Outsc), place of receiving services (Plrc), use of online services (Onser), service limitation (Serlim), and monthly premium (Monper) were included in the analysis, and Ɛ was the error. Assuming that the errors had a logistic distribution, the conditional logistic regression model was used to analyze the data. This model assumed that the choices made were independent of irrelevant alternatives (IIA), which might be restrictive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculation of Marginal Willingness to Pay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo calculate the willingness to pay, if one of the attributes was monetary, it could be determined by calculating the final rate of substituting it with other attributes of the respondents\u0026apos; willingness to pay for that attribute. Therefore, if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e was the coefficient of price and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}1\\)\u003c/span\u003e\u003c/span\u003e was the coefficient of one of the investigated variables, such as changing the coverage level from basic (e.g., 30%) to 70% of inpatient services, the Marginal willingness to pay would be as follows, given that the price coefficient was negative (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"89\" height=\"33\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eIn fact, the Marginal willingness to pay for the total final substitution rate shows how willing people are to pay to get one more unit of an attribute (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e). In the end, after estimating the model and obtaining the weight of each attribute, the weight of the possible scenarios was calculated, and the scenarios were ranked based on this weight.\u003c/p\u003e\n\u003cp\u003eIn this study, SAS and Stata software were used for data analysis.\u003c/p\u003e\n\u003cp\u003eValidity and reliability of the questionnaire\u003c/p\u003e\n\u003cp\u003eConsidering that the questionnaire used was the result of experts\u0026apos; opinions, it was examined in group discussion sessions, so its validity (face validity) was confirmed by the experts. On the other hand, because the autogonal method and other statistical methods were used in the design of the scenarios, in practice, this tool has reliability and validity(\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e show that the study participants included 211 males and 209 females, with a mean age of 31.14 years. Most of the participants (46.19%) had graduate degrees. A majority of the subjects (86.9%) in this study were not household heads. In terms of employment, most of the subjects (50.48%) were employed in the public and private sectors. In addition, 29.52% and 29.29% of the participants had monthly incomes of \u0026lt;\u0026thinsp;4 million and \u0026gt;\u0026thinsp;8 million Tomans, respectively. A large number of the participants (54.05%) were sinlgle(no married), and most of the subjects (56.67%) had social security insurance (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and socioeconomic characteristics of the study participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSubcategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMental Disorder\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage**\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage**\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(32.53)Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(29.73)Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(31.14)Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school diploma and lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssociate or bachelor degrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egraduate education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHousehold Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed in public and private sectors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed and housewife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;4\u0026nbsp;million Tomans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;6\u0026nbsp;million Tomans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;8\u0026nbsp;million Tomans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;8\u0026nbsp;million Tomans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eType of insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIranians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArmed forces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e show that all coefficients were statistically significant except monthly insurance premiums and service limitations. The inpatient service coverage variable shows that people preferred higher values of this variable (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), and the odds ratio (OR) of monthly premium (for all) and not using online services (as opposed to using online services) shows that people preferred lower values for these services. In addition, the general results show a significant relationship between all attributes and people\u0026apos;s preferences (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression model of preferences for attributes of mental health insurance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttributes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-vale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eInpatient service coverage (base 30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutpatient service coverage (base 30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of receiving the service(base public sector)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll provider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe use of online services(yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eServices have a limitation(yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePremium (400000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e6432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e-1969.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR chi2(\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e518.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, inpatient services, outpatient services, place of receiving services, and nonuse of online services (for the healthy group) were significant in both groups. The findings also show that in both groups, people preferred higher values for the coverage of inpatient services (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). In addition, the findings related to the odds ratio (OR) show that people with mental health disorders preferred higher values for most attributes compared to healthy people. Furthermore, the general results show that there was a significant relationship between all attributes and people\u0026apos;s preferences (P\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMental health insurance attributes preferences of participants by sickness and health group level in Tehran\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAttributes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCost coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003esick\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003cp\u003e(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003cp\u003e(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eInpatient service coverage (base 30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutpatient service coverage (base 30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of receiving the service(base public sector)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll provider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe use of online services(yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eServices have a limitation(yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePremium (400000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e5232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e445.06-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1598.47-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR chi2(\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e141.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e429.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe findings of Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e show that the Marginal willingness to pay for inpatient and outpatient services was higher in both subgroups compared to other components. The willingness to pay for not using online services was negative (in fact, this value shows the willingness to accept). In other words, people had to be paid for not using such services (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipants\u0026apos; Marginal willingness to pay for mental health insurance attributes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoverage level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarginal willingness to pay(Rial}\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDollar (PPP\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eInpatient service coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8498770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10040640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutpatient service coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5399730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6475840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlace of receiving the service\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll service providers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5698780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe use of online services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2157450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eService limitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e837640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e[1]\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003cem\u003ePurchasing power parity\u003c/em\u003e\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe present study calculated people's preferences for different attributes of mental health insurance plans in Tehran. The findings showed increased utility related to the increase in the cost coverage of mental health services for all analyzed attributes. Thus, the highest utility was obtained from the benefits of inpatient services, followed by outpatient services and the place of receiving services, using online services, and service limitations.\u003c/p\u003e\u003cp\u003eThe results of this research showed that people highly preferred using inpatient and long-term services. In their study, Nieboer et al. examined the relative values of long-term care services (inpatient) among beneficiaries and found that patients\u0026rsquo; well-being might be improved by providing services for physical and social needs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results of the studies by Sneeuw et al. and Froberg and Kane showed that long-term care did not affect the patients\u0026rsquo; preferences. This is contrary to the findings of the present research (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). However, it is worth noting that one could not only rely on the data obtained from the preferences of such patients because they are usually cared for by their families and friends.\u003c/p\u003e\u003cp\u003eThe results of this study provide insight into the benefits of a wide range of long-term care services. These benefits are essential for the optimal allocation of resources, as decision makers must weigh them against the costs of any services. A long-term care benefit package should be defined with an optimal mix of services for specific patients. The relative values of services vary for different people because individuals' resources and constraints differ as well. Knowing how different care services contribute to people's physical and social well-being will facilitate finding optimal ways to provide care services in times of increasing scarcity.\u003c/p\u003e\u003cp\u003eThe results of this study showed that the people preferred more outpatient services with greater insurance coverage. Likewise, the results of the study by Cotten et al. showed that people mainly preferred to receive health information as well as outpatient and clinical services in preliminary stages from their doctors (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Herman et al.'s research results showed that outpatient services could significantly increase patients' use of mental health services and were among the most effective ways to influence patients' preferences for receiving such services (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe participants in the studies by Marcus et al. and Raue et al. were not very interested in receiving outpatient services (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This is not in line with the results of the present study, the main reason for which could be the differences in healthcare provision systems and insurance. However, the other reason is that many people believe that these services should be provided free of charge or at the lowest cost by governments because the greatest loss of such diseases is imposed on governments.\u003c/p\u003e\u003cp\u003eIn line with the results of this study and the studies consistent with it, outpatient services play an important role in preventing disease recurrence in people, and timely referral of the recipients to receive such services will reduce social and economic costs. This attribute is of great importance for the choice of mental health insurance. Appropriate insurance coverage suitable for society makes people go to treatment and prevention centers as soon as possible to receive medical services, which causes their diseases to be treated and controlled sooner.\u003c/p\u003e\u003cp\u003eAccording to the results of this research, determining service limitations affected the choice of insurance. Herman et al. showed in their research that the services provided in clinics could significantly increase patients' use of mental health services. It could be one of the most effective ways to change patients' preferences for receiving mental health services, and people preferred to receive services in clinics and near their place of residence (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The results of the studies by Gerritsen et al. and Stevernik et al. showed that the type of providers and the quality of services they provided had a great impact on the patients\u0026rsquo; choices. Thus, according to the results, the higher the quality and amount of services provided to the patients, the greater the desire to receive services from the service provider (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). A study conducted in Germany showed that people tended to pay less for healthcare services. Considering the characteristics of health insurance in this country, two main reasons could be mentioned for it: 1- There was public access to medical services, and the services were provided regularly. In addition, a high percentage of their reimbursement was on medical insurance. 2 - Out-of-pocket payment for outpatient services was very low (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results of the present study showed that the participants preferred to receive services from many private and public centers. The results of the studies by Arcury et al., Caldwell et al., and Koizumi et al. indicated that changes in the places where services were available had the greatest impact on patient choice. Most of the participants preferred to receive medical services in certain clinics. However, there were people who preferred to receive services in places with better facilities and human resources (more experienced specialists) (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Hence, proximity to the services and expertise of the providers reduced referrals to other cities or medical centers (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn a study conducted by Ruffin et al., it was found that the choices of two-thirds of the participants were affected by the place of service delivery and the cultural and linguistic characteristics (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The results of the studies by Bearman et al. and Schoenwald et al. indicated that most people preferred to receive their required services from several centers that provided appropriate services to patients (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The provision of services in numerous public and private centers and the diversity of service providers will make the quality of services, access, equity, and attention to the cultural components of any region to be taken into consideration.\u003c/p\u003e\u003cp\u003eThe results of the present study showed that the preferences of society were in line with the use of online services. Cotten and Gupta, Koch-Weser et al., and Lee et al. showed in their studies that the participants who did not have much time and were not interested in receiving face-to-face services preferred to use virtual services or new media that allowed them to work on the Internet by themselves (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe simulation of Cunningham's study showed that a mixed media approach that allowed for a choice between new media (e.g., the Internet) and conventional media options maximized the use of mental health information (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e According to the findings of Phillips et al., telephone communication was preferred over other forms of electronic interaction because this method was more personal compared to e-mail communications and more traditional compared to video chats or video conferencing. It seems that building trust and face-to-face relationships would help trust the effectiveness of treatment (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Finally, the results showed that the patients preferred digital health technologies mainly for their convenience and communications (60,51).\u003c/p\u003e\u003cp\u003eThe results of this research were not consistent with the results of the study by Sneeuw et al., in which the participants did not agree that Internet interventions guided with face-to-face psychotherapy were comparable and influential in terms of effectiveness and the ability to create a good therapeutic relationship (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). There were also studies in which people preferred to obtain their required information through conventional methods because they did not have much trust in the Internet and the data obtained from it (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsidering human progress and the fact that we are living in the age of technology, the health sector, like other sectors, should make progress appropriate to technology and the needs of humans and societies. Failure to consider new aspects of technology and the Internet as well as service delivery applications will reduce quality and lag behind other sectors. Considering the nature of healthcare services and the fact that these services should be based on the trust and satisfaction of service recipients, the services must be provided in a way that conforms to new technologies.\u003c/p\u003e\u003cp\u003eThe results of this study showed that the amount of payment had an effect on preferences for receiving mental health services. In their research, Dias et al. showed that the preferences of rich people for medical services were greater than those of low-income people, and people with higher incomes had more freedom to choose medical centers (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to McMichael et al., people's attitudes toward the amount paid for mental health services were important. Furthermore, if there were few medical services provided, people would be less willing to pay. Therefore, the services and costs needed to be such that any person would be willing to participate in insurance program payments (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The results of the research by Walsh et al. showed that most people were willing to pay additional taxes to finance healthcare services and considered it important to support this sector (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results of a study on preferences of the patients and beneficiaries for several diseases in some European countries showed that the participants did not value the cost in comparison to other items. The main reason for the people and policy makers to choose this option was that they thought the costs would not be paid by the patients but by the insurance companies (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMuhlbacher \u0026amp; Bethge conducted a discrete choice study on insurers policyholders and focused on various attributes and components of integrated service delivery. They found that cost was an important attribute and a main factor that affected people\u0026rsquo;s decisions (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The issue of cost has always been important for humans, and considering the economic limitations that people have in society, they should make choices based on their needs. Most people prefer to pay less for services. In addition, the fact that governments need healthy people and populations for economic growth, increasing military power, and other governance issues causes people to expect governments to pay for these expenses and be responsible for them.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWillingness to Pay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe highest Marginal willingness to pay was related to the coverage of inpatient services and long-term services, followed by the place of receiving services, using online services, and determining service limitations. As we know, if the utility of the expected benefits of the services covered by health insurance is greater than its costs (insurance premiums), people will purchase insurance (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Therefore, people tend to pay more for inpatient services because their benefits are greater. Some evidence from low-income countries showed that people tended to obtain higher quality services by purchasing health insurance plans because free government services were usually limited in terms of quantity and quality (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Another issue determined by considering the relative prices of the studied services was that people were willing to pay more for expensive services, the utility of which was more for them than cheaper services. Other evidence also confirmed this issue (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccording to the results of the analysis, people preferred higher levels of coverage to lower ones for most of the insurance attributes. Thus, their willingness to pay for these levels was higher as well. However, given that higher price/premium levels cause more disutility for people, there was a sign of disutility of insurance premiums for the people participating in this study as well.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and recommendation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe acknowledged that the exclusion of service impact, patient experience, and quality as attributes in the study is a limitation and have included it in the limitations section of the manuscript. We emphasized that future research should consider these factors to gain a more comprehensive understanding of community preferences for mental health services.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe findings of this study show that people are willing to pay premiums for coverage of psychiatric services, and that insurance coverage of these services can increase the well-being of these patients. Inpatient services have a higher priority for insurance coverage. In addition, the results of this research can be helpful for designing mental health insurance packages and planning to change the country's basic insurance to improve people's participation and increase the utility of insurance packages.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the study participants for providing useful information. From the Social Security organization, universities of medical sciences in Tehran and people participating in the data collection of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHT,ES \u0026amp; HR\u0026nbsp; \u0026nbsp;contributed to conceiving and designing the study. The data was analyzed and interpreted jointly by HT,RD \u0026amp; EJ. All authors contributed equally in writing the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is financially supported byTehran University of Medical Sciences. The funding body was not involved in the design of the study, data collection, analysis, and interpretation, as well as in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of a Ph.D. dissertation in Health Economics at the Faculty of Public Health, authored by Hamid Talebianpour. The research was supported and formally approved by Tehran University of Medical Sciences (Project ID: 52634). Ethical approval was granted by the Ethics Committee of Tehran University of Medical Sciences (Ethics Code: IR.TUMS.SPH.REC.1400.032).\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants. In cases involving illiterate participants, consent was obtained in writing from their legal guardians. To protect participants\u0026rsquo; privacy, all personal data were anonymized using unique identifiers and fully de-identified prior to data analysis and storage\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArias D, Saxena S, Verguet S. Quantifying the global burden of mental disorders and their economic value. EClinicalMedicine. 2022;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrinz C. Sickness, disability and work: lessons from reforms and lack of change across the OECD countries. Disability and employment\u0026ndash;lessons from reforms. 2010:23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeighami R, Oskouie F, Joolaee S. Mental health needs of the children of parents with mental illness. Unpublished doctoral dissertation). Tehran University of Medical Sciences, Tehran, Iran. 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarfield RL, Zuvekas SH, Lave JR, Donohue JM. The impact of national health care reform on adults with severe mental disorders. Am J Psychiatry. 2011;168(5):486\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePearson WS, Dhingra SS, Strine TW, Liang YW, Berry JT, Mokdad AH. Relationships between serious psychological distress and the use of health services in the United States: findings from the Behavioral Risk Factor Surveillance System. Int J public health. 2009;54:23\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRowan K, McAlpine DD, Blewett LA. Access and cost barriers to mental health care, by insurance status, 1999\u0026ndash;2010. Health Aff. 2013;32(10):1723\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsgary A, Willis K, Taghvaei AA, Rafeian M. Estimating rural households\u0026rsquo; willingness to pay for health insurance. Eur J Health Econ formerly: HEPAC. 2004;5:209\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRevicki DA, Wood M. Patient-assigned health state utilities for depression-related outcomes: Differences by depression severity and antidepressant medications. J Affect Disord. 1998;48(1):25\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenert LA, Sherbourne CD, Sugar C, Wells KB. Estimation of utilities for the effects of depression from the SF-12. Med Care. 2000;38(7):763\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWells KB, Sherbourne CD. Functioning and utility for current health of patients with depression or chronic medical conditions in managed, primary care practices. In: Depression: The Science of Mental Health. 2013. pp. 291\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZweifel P. The Grossman model after 40 years. Eur J Health Econ. 2012;13:677\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRatcliffe J. The use of conjoint analysis to elicit willingness-to-pay values: Proceed with caution? Int J Technol Assess Health Care. 2000;16(1):270\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: A user\u0026rsquo;s guide. Vol. 26, PharmacoEconomics. Springer; 2008. pp. 661\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRyan M, Farrar S. Using conjoint analysis to elicit preferences for health care. Br Med J. 2000;320(7248):1530\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheldon RJ, Steer JK. Use of Conjoint Analysis in Transport Research. 1982;P230:145\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGustafsson A, Herrmann A, Huber F. Conjoint Analysis as an Instrument of Market Research Practice. Conjoint Measurement. Springer Berlin Heidelberg; 2000. pp. 5\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreen PE, Srinivasan V. Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice. J Mark. 1990;54(4):3\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBiglaiser G, Ma CTA. Moonlighting: Public service and private practice. RAND J Econ. 2007;38(4):1113\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan den Berg B, Van Dommelen P, Stam P, Laske-Aldershof T, Buchmueller T, Schut FT. Preferences and choices for care and health insurance. Soc Sci Med. 2008;66(12):2448\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNanna A. Centre for International Health Health Insurance in Developing Countries: Willingness to Pay for Health Insurance in Thailand using Discrete Choice Experiment Methods. 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKananurak P. An economic analysis of voluntary health insurance after retirement. 2013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrau R, Lippi Bruni M. Eliciting the demand for long-term care coverage: a discrete choice modelling analysis. Health Econ. 2008;17(3):411\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tradingeconomics.com/iran/gdp-per-capita-ppp-us-dollar-wb-data.html\u003c/span\u003e\u003cspan address=\"https://tradingeconomics.com/iran/gdp-per-capita-ppp-us-dollar-wb-data.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahmani H, Talebianpour H, Sharafi SE, Daroudi R, Jaafaripooyan E. Development of attributes and levels of mental health insurance services using a discrete choice experiment. J Educ Health Promotion. 2023;12(1):134.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLouviere JJ, Hensher DA, Swait JD, Adamowicz W. Stated Choice Methods: Analysis and Applications. Stated Choice Methods. 2010. 227\u0026ndash;51 p.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrme BK. Sample Size Issues for Conjoint Analysis. Get Started Conjoint Anal Strateg Prod Des Pricing Res. 2010;57\u0026ndash;p66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcAlpine DD, Mechanic D. Utilization of specialty mental health care among persons with severe mental illness: the roles of demographics, need, insurance, and risk. Health Serv Res. 2000;35(1 Pt 2):277.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee EJ, Chan F, Ditchman N, Feigon M. Factors influencing Korean international students\u0026rsquo; preferences for mental health professionals: A conjoint analysis. Commun Ment Health J. 2014;50:104\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKerssens JJ, Groenewegen PP. Consumer preferences in social health insurance. Eur J Heal Econ. 2005;6(1):8\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNieboer AP, Koolman X, Stolk EA. Preferences for long-term care services: willingness to pay estimates derived from a discrete choice experiment. Soc Sci Med. 2010;70(9):1317\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSneeuw KC, Sprangers MA, Aaronson NK. The role of health care providers and significant others in evaluating the quality of life of patients with chronic disease. J Clin Epidemiol. 2002;55(11):1130\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFroberg DG, Kane RL. Methodology for measuring health-state preferences\u0026mdash;III: population and context effects. J Clin Epidemiol. 1989;42(6):585\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCotten SR, Gupta SS. Characteristics of online and offline health information seekers and factors that discriminate between them. Soc Sci Med. 2004;59(9):1795\u0026ndash;806.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerman PM, Ingram M, Rimas H, Carvajal S, Cunningham CE. Patient preferences of a low-income Hispanic population for mental health services in primary care. Adm Policy Mental Health Mental Health Serv Res. 2016;43:740\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarcus M, Westra H, Mobilizing Minds Research Group. Mental health literacy in Canadian young adults: results of a national survey. Can J Community Mental Health. 2012;31(1):1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaue PJ, Schulberg HC, Heo M, Klimstra S, Bruce ML. Patients' depression treatment preferences and initiation, adherence, and outcome: a randomized primary care study. Psychiatric Serv. 2009;60(3):337\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerritsen DL, Steverink N, Ooms ME, Ribbe MW. Finding a useful conceptual basis for enhancing the quality of life of nursing home residents. Qual Life Res. 2004;13:611\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteverink N. When and why frail elderly people give up independent living: The Netherlands as an example. Ageing Soc. 2001;21(1):45\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhillips EA, Himmler SF, Schrey\u0026ouml;gg J. Preferences for e-mental health interventions in Germany: a discrete choice experiment. Value Health. 2021;24(3):421\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaldwell A, Couture A, Nowotny H. Closing the mental health gap: Eliminating disparities in treatment for Latinos. Kansas City, MO: Mattie Rhodes Center; 2008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoizumi N, Rothbard AB, Kuno E. Distance matters in choice of mental health program: policy implications for reducing racial disparities in public mental health care. Adm Policy Mental Health Mental Health Serv Res. 2009;36:424\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArcury TA, Quandt SA. Delivery of health services to migrant and seasonal farmworkers. Annu Rev Public Health. 2007;28:345\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKessler R, Stafford D. Primary care is the de facto mental health system. Collaborative medicine case studies: Evidence in practice. 2008:9\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuffin MT, Plegue MA, Rockwell PG, Young AP, Patel DA, Yeazel MW. Impact of an electronic health record (EHR) reminder on human papillomavirus (HPV) vaccine initiation and timely completion. J Am Board Family Med. 2015;28(3):324\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBearman SK, Weisz JR, Chorpita BF, Hoagwood K, Ward A, Ugueto AM, Bernstein A, Research Network on Youth Mental Health. More practice, less preach? The role of supervision processes and therapist characteristics in EBP implementation. Adm Policy Mental Health Mental Health Serv Res. 2013;40:518\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchoenwald SK, Sheidow AJ, Chapman JE. Clinical supervision in treatment transport: effects on adherence and outcomes. J Consult Clin Psychol. 2009;77(3):410.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoch-Weser S, Bradshaw YS, Gualtieri L, Gallagher SS. The Internet as a health information source: findings from the 2007 Health Information National Trends Survey and implications for health communication. J health communication. 2010;15(sup3):279\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee CJ, Ram\u0026iacute;rez AS, Lewis N, Gray SW, Hornik RC. Looking beyond the Internet: examining socioeconomic inequalities in cancer information seeking among cancer patients. Health Commun. 2012;27(8):806\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCunningham CE, Walker JR, Eastwood JD, Westra H, Rimas H, Chen Y, Marcus M, Swinson RP, Bracken K, Mobilizing Minds Research Group. Modeling mental health information preferences during the early adult years: a discrete choice conjoint experiment. J health communication. 2014;19(4):413\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander KE, Ogle T, Hoberg H, Linley L, Bradford N. Patient preferences for using technology in communication about symptoms post hospital discharge. BMC Health Serv Res. 2021;21:1\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDias N, Yamamoto R, Faulkner KG. Preferences and motivational factors for the use of digital technology in real world oncology studies. J Clin Oncol. 2020;38(15suppl):e14119\u0026ndash;14119.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRains SA. Perceptions of traditional information sources and use of the world wide web to seek health information: findings from the health information national trends survey. J health communication. 2007;12(7):667\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKroeze W, Oenema A, Campbell M, Brug J. Comparison of use and appreciation of a print-delivered versus CD-ROM-delivered, computer-tailored intervention targeting saturated fat intake: randomized controlled trial. J Med Internet Res. 2008;10(2):e940.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcMichael AJ, Kane JP, Rolison JJ, O'Neill FA, Boeri M, Kee F. Implementation of personalised medicine policies in mental healthcare: results from a stated preference study in the UK. BJPsych Open. 2022;8(2):e40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalsh 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. Soc Sci Med. 2020;245:112675.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRutten-van M\u0026ouml;lken M, Karimi M, Leijten F, Hoedemakers M, Looman W, Islam K, Askildsen JE, Kraus M, Ercevic D, Struckmann V, Pitter JG. Comparing patients\u0026rsquo; and other stakeholders\u0026rsquo; preferences for outcomes of integrated care for multimorbidity: a discrete choice experiment in eight European countries. BMJ open. 2020;10(10):e037547.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;hlbacher AC, Bethge S, Reed SD, Schulman KA. Patient preferences for features of health care delivery systems: a discrete choice experiment. Health Serv Res. 2016;51(2):704\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFolland S, Goodman AC, Stano M. The economics of health and health care: Pearson new international edition. Routledge; 2016 May. p. 23.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cost-effectiveness-and-resource-allocation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cera","sideBox":"Learn more about [Cost Effectiveness and Resource Allocation](http://resource-allocation.biomedcentral.com)","snPcode":"12962","submissionUrl":"https://submission.nature.com/new-submission/12962/3","title":"Cost Effectiveness and Resource Allocation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mental Health, Health Insurance, Discrete Choice Experiment, Preferences, Willingness to Pay","lastPublishedDoi":"10.21203/rs.3.rs-6787596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6787596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eUnderstanding insurance preferences for mental health services can help provide appropriate service demand and insurance coverage for such services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e The present research aimed to investigate the preferences and willingness to pay of the people of Tehran for mental health insurance services using the discrete experiment method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Quantitative methods were used in this applied research and it was conducted on 420 individuals (210 patients with mental disorders and 210 healthy people) using a discrete choice experiment. The data analysis was performed using the conditional regression model as well.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The final model in this study included 6 attributes. The findings of the present research showed a statistically significant relationship (P \u0026lt;.001) between the odds ratios (OR) of choosing health insurance at all levels of insurance coverage except the service limit and the amount of insurance premium. Along with the increase in the cost coverage levels, the likelihood of choosing health insurance for the benefits of inpatient services at 70% and 90% levels (P \u0026lt;.001, OR=1.96 and P \u0026lt;.001, OR=2.28), outpatient services at 70% and 90% levels (P \u0026lt;.001, OR=1.53 and P \u0026lt;.001, OR=1.64), service delivery location (P \u0026lt;.001, OR=1.54), and the use of online services (P \u0026lt;.001, OR=0.84) increased significantly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The findings showed that the people of Tehran had the highest preference and willingness to pay for the coverage of inpatient services. The results of this study can be provided to health managers and policy makers to predict the welfare effects and people's reactions to changes in mental health insurance policies so that they would be able to plan better to provide higher-quality services with the participation of people.\u003c/p\u003e","manuscriptTitle":"Community Preferences for 'Mental Health Insurance Coverage in Tehran Using a Discrete Choice Experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 08:06:05","doi":"10.21203/rs.3.rs-6787596/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-03T13:24:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T10:49:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283789139659300527898610733675826253328","date":"2025-10-17T14:20:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162540687761468633421197318143578198175","date":"2025-10-15T14:16:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207927792774345548135427875943480993942","date":"2025-10-15T14:10:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T06:06:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92134443660529957627892866898394036703","date":"2025-08-03T09:25:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74039763709647001367735824968647423595","date":"2025-07-30T09:31:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T09:22:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-11T20:37:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-11T11:32:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cost Effectiveness and Resource Allocation","date":"2025-05-30T21:28:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cost-effectiveness-and-resource-allocation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cera","sideBox":"Learn more about [Cost Effectiveness and Resource Allocation](http://resource-allocation.biomedcentral.com)","snPcode":"12962","submissionUrl":"https://submission.nature.com/new-submission/12962/3","title":"Cost Effectiveness and Resource Allocation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b54b6e7-3d44-4cb9-bb6b-fd0e7dc35a8b","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:02:49+00:00","versionOfRecord":{"articleIdentity":"rs-6787596","link":"https://doi.org/10.1186/s12962-025-00687-9","journal":{"identity":"cost-effectiveness-and-resource-allocation","isVorOnly":false,"title":"Cost Effectiveness and Resource Allocation"},"publishedOn":"2025-12-30 15:57:43","publishedOnDateReadable":"December 30th, 2025"},"versionCreatedAt":"2025-07-30 08:06:05","video":"","vorDoi":"10.1186/s12962-025-00687-9","vorDoiUrl":"https://doi.org/10.1186/s12962-025-00687-9","workflowStages":[]},"version":"v1","identity":"rs-6787596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6787596","identity":"rs-6787596","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}