Valuing a Quality-Adjusted Life Year (QALY): Willingness to Pay among Older Adults in Iran

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Measuring WTP clarifies the economic justification for life extension and the rationale for prioritizing end-of-life care. This study, examined the WTP for one additional QALY among older adults in Tehran and its influencing factors in 2024. Methods : This cross-sectional study included 494 older adults selected through multi-stage cluster sampling from Tehran’s 22 districts and nursing homes. Data were gathered via interviews using a demographic checklist, a researcher-developed WTP questionnaire, and the validated EQ-5D instrument. Given the high likelihood of selection bias, the Heckman two-step method was employed to account for potential sample selection issues. Statistical analyses were performed in STATA 14. Results : Participants were willing to trade 8.84 months of full healthy time for one year of their current life. The mean WTP for one QALY was $3,313 (1,969 million Iranian Rials, IRR), with half offering under $495 (294 million IRR). Based on Heckman method, retirees and homemakers had lower WTP, while higher education, income, and expenses increased it. Living in developed areas and religious commitment also positively influenced WTP likelihood. Conclusions : These findings provide a valuable reference threshold for cost-effectiveness studies on interventions for older adults, enabling policymakers to assess the economic viability of allocating healthcare resources to this population. Furthermore, policymakers can consider the reasons behind older adults' reluctance to invest in longer, healthier lives, which could inform targeted strategies to encourage greater investment in their health. Willingness to pay quality-adjusted life year (QALY) older adults Iran Background Population aging, once a concern for developed nations, is now rapidly affecting developing countries, including Iran, which has one of the fastest-growing elderly populations worldwide [ 1 , 2 ]. By 2031, the number of older adults in Iran is expected to double [ 3 ], Leading to a sharp rise in chronic diseases and increased use of health and aged care services[ 4 ]. This demographic shift has significantly increased healthcare costs, making aging a financial burden for many countries. The rise in chronic conditions among older adults’ results in higher mortality rates, reduced quality of life, greater dependency, and increased hospital admissions, all of which strain healthcare resources [ 5 – 7 ]. Given these challenges, the need for efficient resource allocation in healthcare has become more pressing [ 8 ]. Cost-effectiveness analysis, particularly using Quality-Adjusted Life Years (QALYs), provides a standardized measure for evaluating health interventions [ 9 ]. QALYs account for both life expectancy and quality of life, offering a comprehensive framework for comparing treatments [ 10 ]. In healthcare decision-making, calculating the cost per QALY gained helps prioritize interventions with the best value [ 11 ]. However, determining a cost-effectiveness threshold is essential for identifying which interventions justify their expenses [ 12 ]. Understanding older adults' willingness to pay (WTP) for QALYs can help policymakers set more realistic thresholds, ensuring limited resources are directed toward interventions that maximize health benefits [ 13 , 14 ]. QALY-related values are widely used by health technology assessment (HTA) agencies worldwide to evaluate healthcare efficiency. HTA involves various analyses, particularly economic evaluations, to assess the cost-effectiveness and budget impact of medical interventions, ultimately improving resource allocation in healthcare systems [ 15 ]. These evaluations help policymakers determine which interventions provide the most value for the resources spent [ 10 ]. The QALY criterion offers a standardized approach to quantifying health benefits [ 16 ]. Assigning a monetary value to QALYs through willingness-to-pay (WTP) calculations allows for a clearer understanding of how much individuals value additional years of healthy life [ 15 , 17 , 18 ]. WTP is a key factor in health policy decisions, serving as a benchmark for assessing the cost-effectiveness of healthcare services [ 19 , 20 ]. Studies suggest that an acceptable WTP threshold per QALY often falls between one and three times a country's GDP per capita [ 21 ]. In Iran, Lankarani et al. [ 18 ] estimated the average WTP for one QALY at $ 2,847, or 0.57 times the national GDP per capita, with a higher willingness to pay among individuals in the final stages of life. Similarly, Moradi et al. [ 8 ] reported WTP estimates ranging from $ 1,032 to $ 2,666, representing 0.22 to 0.56 times GDP per capita, with income, education, and marital status significantly influencing WTP values. Zipping et al. [ 22 ] found an average WTP per QALY of 1.75 times GDP per capita, emphasizing the impact of age, education, and mental well-being, particularly in reducing anxiety and depression. However, inconsistencies in findings across studies stem from differences in methodologies, target populations, and socio-economic contexts. Given the rapid aging of Iran’s population and the increasing burden of healthcare costs, understanding the economic value of QALY from the perspective of older adults is crucial. It provides a concrete threshold for evaluating the cost-effectiveness of health interventions targeting the older adults. This information can support HTA and cost-effectiveness studies by offering a clear benchmark for determining whether investing in specific health technologies or interventions for older adults is economically justified. This research seeks to determine the monetary value placed on each healthy life-year by the older population in Iran, informing age-sensitive resource allocation decisions. Methods Study Design and Setting The study’s method follows a chained approach [ 23 ], adapted for a population of older adults in Tehran, including both those residing in the community and in nursing homes. This two-step method utilized to determine the maximum monetary value that older adults in Tehran are willing to pay for one additional QALY. In the first step, health utility was calculated using the EQ-5D-5L questionnaire [ 24 ]. In the second step, participants were asked through a contingent valuation (CV) method how much they would be willing to pay to return to their ideal health condition, which represents a fully healthy year of life. The chained approach, which uses hypothetical health scenarios, was employed to capture individuals’ preferences for health conditions. This approach is widely regarded for its applicability [ 25 , 26 ], high sensitivity to health status differences, and reduced susceptibility to biases compared to other valuation methods, such as the direct approach. The results from the chained approach offer higher social generalizability, making it more suitable for policy-making purposes. Unlike the direct method, which can lead to WTP values that exceed individuals’ ability to pay due to large health losses, the chained approach minimizes health losses, reflecting more realistic and manageable trade-offs for participants. This study conducted in 2024 in Tehran, covering both community-dwelling older adults and those residing in nursing homes. Participants and Sampling Community-Dwelling Older Adults A multi-stage cluster sampling approach with proportional allocation was employed to ensure a representative sample of Tehran’s older adult population. The sample size was determined based on the study by Jahanbin et al. [ 27 ], using the formula, (n = Z α/d) ², with a confidence interval (CI) of 95% (α = 0.05), a standard deviation (SD) of 11.6, and a response rate adjustment of 10%. This calculation resulted in a final sample of 460 community-dwelling older adults. To account for socioeconomic diversity, Tehran’s 22 districts were stratified into five development categories according to Sadeghi et al. [ 28 ]. Two districts and two neighborhoods were randomly selected from each category. The number of participants from each region was determined based on the proportion of older adults residing in that area. Nursing Home Residents Given that a segment of the elderly population resides in nursing homes, additional sampling was conducted in these settings. Nursing homes were classified into three socioeconomic strata: low, middle, and high. However, due to lack of cooperation from low-SES facilities, data collection was limited to middle- and high-SES centers. Two centers were randomly selected from each stratum, resulting in a sample of 34 older adults from nursing homes. Inclusion and Exclusion criteria The inclusion criteria for participation in this study included the following: informed and voluntary consent, age over 60 years, proficiency in Persian, ability to communicate verbally and auditory, and the absence of mild cognitive impairment (MCI). The assessment for MCI was conducted using the Mini-Cog test. The Persian version of Mini-Cog is a user-friendly and acceptable cognitive test for Persian-speaking older adults. The study by Rezaei et al. [ 29 ] showed that the sensitivity and specificity of the Mini-Cog test were 0.88 and 0.62, respectively. This study demonstrated that the words and instructions of this test could be clearly understood and were acceptable to the participants. Furthermore, this study highlights the suitable face validity of the Persian version of the Mini-Cog [ 29 ]. Exclusion criteria encompassed any participants who expressed unwillingness to continue with the interview process, as well as those who were unable to proceed due to unforeseen physical health issues. Data collection Data were collected through structured face-to-face interviews conducted by trained interviewers with expertise in gerontology, psychology, and social work. Interviews took place in public locations frequently visited by older adults, such as parks, mosques, shopping centers, and community centers. Measures Demographic and Socioeconomic Variables A structured checklist was used to collect data on marital status, education level, place and type of residence, occupation, household and personal income, living arrangements, health insurance coverage, chronic diseases, tobacco use, independence status, quality of life, and physical and mental disabilities. WTP Questionnaire for QALY The WTP questionnaire was developed by the research team using the contingent valuation method and included five key questions to assess the maximum amount an individual was willing to pay for improved health status. It addressed the definition of the best possible health state, the duration of life in the best health state, the amount of money one was willing to pay for one additional year in optimal health, the willingness to sell property to receive health services, and the amount of money one was willing to pay for five additional years in optimal health. The questionnaire’s face validity was assessed through expert review and pilot testing with 18 older adults, and its reliability was evaluated using test-retest analysis. EQ-5D questionnaire The EQ-5D is a standardized instrument developed by the European Quality of Life Organization to measure health-related quality of life. It evaluates five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The internal consistency of the EQ-5D has been demonstrated with a Cronbach’s alpha of 0.76 [ 30 ], indicating satisfactory reliability. Statistical Analysis Statistical analysis was conducted using STATA version 14. Descriptive statistics were presented as mean and standard deviation for quantitative variables and as frequency (percentage) for qualitative variables. The normality of quantitative variables was assessed using the Kolmogorov-Smirnov test, along with skewness and kurtosis indices. To explore factors influencing WTP, independent t-tests, analysis of variance (ANOVA), and multivariate binary regression analyses (logit and probit models) were employed. Correlation analyses were also conducted to examine the relationships between key variables. Ethical considerations The Ethics Committee in Research has approved the protocol for this study at the University of Social Welfare and Rehabilitation Sciences under the code IR.USWR.REC.1402.218. Participants provided informed consent before data collection. Results This study included 494 older adults residing in both community dwellings and nursing homes, with a mean age of 68.39 (SD ± 6.48) years (Table 1 ). The majority were male (54.05%) and had less than a high school diploma (42.31%). Nearly 44.13% were retired, 33.00% lived with their spouses, and 18.2% lived alone. Social security insurance coverage was reported by 43.32% of participants. The mean monthly income was 258 (SD ± 180) million IR. Among chronic conditions, hypertension had the highest prevalence (34.2%), while cancer had the lowest (3.4%). Table 1 Characteristics of participants Variable Frequency Percent Sex Female Male 226 267 45.95 54.05 Marital status Married 330 66.80 Single (widowed, divorced, never married) 164 33.20 Living arrangements Living alone 90 18.22 With children and spouse 156 31.58 With spouse 163 33.00 With others 47 9.51 In nursing home 38 7.69 Education Illiterate Under high school diploma High school diploma Associate degree and Bachelor's Postgraduate degree 67 209 130 71 17 13.56 42.31 26.32 14.37 3.44 Employment Status Employed Retired Retired but employed Housewife 52 218 44 180 10.53 44.13 8.91 36.44 Having caregiver(s) yes 52 10.53 No 442 89.47 Medication Use yes 398 80.57 No 96 19.43 Daily Activities Employed and socially active 168 34.08 Independent but not working 262 53.14 Partially dependent 29 5.88 Fully dependent 34 6.90 Insurance Coverage Social Security 214 43.32 Private Supplementary Insurance 17 3.44 No Insurance 56 11.34 Full Insurance 207 41.90 Internet Usage Less than 30 minutes 61 12.35 One hour 90 18.22 More than an hour 115 23.28 not use 228 46.15 Based on a single-item assessment, 69.6% of older adults rated their health status as 6 or higher on a 10-point scale. In contrast, less than 1% of participants rated their health as 1, indicating the poorest status. Another single-item question revealed that less than 1% of seniors reported having no religious beliefs, while approximately 86% expressed moderate religious beliefs. Among the participants, about 29% demonstrated strong religious commitment, reflecting complete adherence to their faith. According to the time trade-off approach, older adults were willing to forgo 8.84 months of their remaining life in exchange for one additional year of healthy life (QALY). The average (median) amount they were willing to pay for one extra QALY was approximately 1,969 (294) million IRR, equivalent to $ 3,313 ( $ 495) based on the official exchange rate. However, the large standard deviation indicated considerable variability, suggesting that at least half of the respondents were willing to pay less than this amount. Determinants of Willingness to Pay The study found that 71.8% of older adults were willing to pay for treatment to restore their full health, but only 37.8% were willing to sell major assets for this purpose. Logistic regression analysis showed no significant gender differences in WTP, though women exhibited slightly higher willingness. Age was negatively associated with WTP, with older individuals demonstrating lower willingness. Residents of highly developed areas in Tehran had significantly higher WTP than those in moderately developed areas, while no significant differences were observed in lower-development areas. Employment status was a key factor, as housewives had lower WTP than employed older adults, likely due to financial dependence. However, retirees and working retirees showed no significant differences in WTP. Living arrangements, including residence in nursing homes, did not significantly impact WTP. Income, whether individual or household, was also not a significant predictor, suggesting that healthcare spending decisions may be influenced more by personal values than financial capacity. Religious beliefs, however, had a significant positive effect on WTP, indicating that stronger religious convictions increased willingness to pay, possibly due to differing perspectives on life and health. Health status had a negative but non-significant effect, suggesting that healthier individuals may feel less urgency for healthcare spending (Table 2). Tables 2: The results of logit regression analysis Variable Coefficient t-statistic p-value Sex (Reference: Male) Female 0.52 1.01 0.31 Age group (Reference: Young Older Adults) Old Adults 0.06 0.23 0.82 Oldest-Old Adults -0.08 -0.15 0.88 Residential Area (Reference: Moderately Developed Area) Highly developed Area 1.31 2.83 0.005 Developed Area 0.98 2.27 0.02 Less developed Area 0.19 0.38 0.70 Underdeveloped Area 0.36 0.72 0.47 Employment Status (Reference: Employed) Retired -0.71 -1.26 0.20 Retired but working -0.44 -0.67 0.50 Homemaker -1.56 -2.17 0.03 Living arrangements (Reference: Living Alone) Living with Spouse and Children -0.32 -0.75 0.45 Living with Spouse -0.21 -0.57 0.56 Living with Children or Others 0.05 0.11 0.91 Nursing Home Resident 0.44 0.38 0.70 Insurance coverage (Reference: No Insurance) Social Insurance (Pension and Basic Health Coverage) -0.29 -0.62 0.53 Private Insurance 0.33 0.34 0.73 Comprehensive Insurance -0.33 -0.67 0.50 Income 0.01 1.49 0.13 Self-Reported Religious Commitment -0.16 -2.74 0.006 Self-Rated Health Status -0.08 -1.2 0.22 Constant Term 2.9 2.81 0.005 In this study, alongside logistic regression, the two-step Heckman model was employed to address potential selection bias, as some participants reported zero willingness to pay, and improve the robustness of the results. While logistic regression helped identify the factors associated with whether individuals are willing to pay, the Heckman model allowed us to further investigate the factors influencing the amount of willingness to pay. By including significant variables from the logistic regression in the first stage of the Heckman model, we ensured that the analysis accounted for both the likelihood of willingness to pay and the magnitude of that willingness, providing a more comprehensive understanding of the factors at play. The findings indicate that employment status significantly affects the amount of WTP. Compared to employed individuals, retirees demonstrate a significantly lower WTP (β = -198.12, p = 0.01), while homemakers and those retired but still working also have lower WTP, though not statistically significant. Education level plays a crucial role, as individuals with a master's degree or PhD exhibit a significantly higher WTP (β = 460.65, p = 0.005) compared to illiterate individuals. However, lower levels of education do not show significant effects. Economic factors are strong predictors of WTP. Monthly household expenses (β = 2.68, p = 0.004) and personal monthly income (β = 1.86, p = 0.01) are both positively and significantly associated with WTP, suggesting that individuals with higher financial resources are willing to pay more. Notably, different types of insurance coverage do not have significant effects on WTP, indicating that having insurance does not necessarily increase the amount individuals are willing to invest in additional years of quality life. Second step in The Heckman results indicate the factors associated with having or not having a willingness to pay for one additional QALY. Residential area influences the likelihood of having WTP. Compared to those living in moderately developed areas, individuals residing in highly developed (β = 0.69, p = 0.01) and developed areas (β = 0.56, p = 0.03) are significantly more likely to express WTP. However, those from less developed or underdeveloped areas do not show statistically significant differences. Self-reported religious commitment (β = 0.005, p = -2.81) has a significant negative effect, suggesting that individuals with stronger religious beliefs may be less inclined to express WTP for life extension. Employment status does not significantly impact the likelihood of WTP, as the coefficients for retirees, homemakers, and those retired but working are not statistically significant. The coefficient for athrho (ρ) is not significant, indicating that the correlation between the selection equation (having WTP) and the outcome equation (amount of WTP) is weak. This suggests that selection bias is not a major concern in the estimation. Additionally, the significant coefficient for lnsigma (β = 5.7, p = 0.01) confirms the appropriateness of the model specification (Table 3 ). Table 3 Heckman's two-stage model Variable Coefficient t-statistic p-value Amount of Willingness to pay Employment Status (Reference: Employed) Retired -198.12 -2.34 1.01 Retired but working -157.04 -1.46 0.14 Homemaker -127 -1.42 0.15 Illiterate ( Reference) Less than high school diploma 73.75 1.13 0.25 High school diploma 75.69 1.02 0.30 Associate degree 38.55 0.42 0.67 Bachelor degree 134.07 1.14 0.25 Master's degree and PhD 460.65 2.83 0.005 Insurance coverage (Reference: No Insurance) Social Insurance (Pension and Basic Health Coverage) 55.65 0.8 0.42 Private Insurance -29.44 -0.23 0.82 Comprehensive Insurance -68.87 -1 0.31 Monthly household expenses 2.68 2.9 0.004 Personal monthly income 1.86 3.54 0.01 Fixed coefficient 231.87 2.24 0.02 Having a willingness to pay Residential Area (Reference: Moderately Developed Area) Highly developed Area 0.69 2.49 0.01 Developed Area 0.56 2.14 0.03 Less developed Area 0.60 0.21 0.83 Underdeveloped Area 0.11 0.38 0.70 Employment Status (Reference: Employed) Retired -0.29 -0.9 0.36 Retired but working -0.29 -0.74 0.45 Homemaker -0.45 -1.4 0.16 Self-Reported Religious Commitment 0.005 -2.81 -0.09 Constant Term 1.11 2.55 0.01 athrho -0.28 -1.35 0.17 lnsigma 5.70 98.71 0.01 Rho -0.27 sigma 299.31 lambda -83.72 Discussion This study aimed to assess the willingness to pay for QALYs and related factors among older adults in Tehran. The average WTP for QALYs was estimated at 1,969 million IRR (3,313 USD), while the median WTP was approximately 495 USD, representing about 0.28 times the GDP per capita in 2023. This figure indicates a significant decrease compared to the findings of Moradi et al. (2017), who reported an average WTP of 300 million IRR (roughly 8,000 USD) in 2007 [ 25 ]. However, it is important to note that Moradi et al.'s study focused on the general population, whereas this study specifically examines older adults, who tend to value QALYs less than the general population. Furthermore, the decline in WTP over time can be attributed to factors such as high inflation, reduced purchasing power, and significant fluctuations in exchange rates, all of which have notably impacted the valuation of life in Iranian society. Studies show higher WTP for health improvements. For instance, Shiroiwa et al. [ 31 ] reported an average WTP of 5 million yen (50,000 USD) in Japan, and Ha et al. [ 32 ] found that 79% of patients in Vietnam were willing to pay for QALYs at or above the GDP per capita level, which was about 2,342 USD at that time. These studies linked the WTP to income levels, comprehensive health insurance coverage, and cultural factors. This study found that 71.8% of older adults willing to pay for a treatment that improve their quality of life, but economic constraints and personal preferences were significant factors influencing their decisions. Only 37.8% were willing to sell major assets for treatment, reflecting financial limitations or conservative attitudes toward asset preservation. Demographic factors such as age, marital status, and education significantly influenced WTP. Younger old individuals exhibited greater WTP, likely due to more optimistic health outlooks and longer life expectancies. While no significant differences were found between middle-aged and older groups, data showed a decrease in WTP as age increased. However, age did not significantly impact WTP for QALYs, potentially because the perceived value of QALYs may be similar across different age groups. This aligns partially with the findings of Fernandez et al. [ 33 ], who observed an 18% decrease in WTP per decade of life, and other studies that confirmed age's negative correlation with WTP [ 13 , 22 ]. Some studies, such as Pinto et al. [ 17 ], suggest that older adults generally exhibit lower WTP for QALYs compared to the general population, which could be due to focusing exclusively on older individuals. Conversely, Ha et al. [ 32 ] reported higher WTP among older patients, and Huang et al. [ 34 ] found that older individuals demonstrated a higher WTP compared to other groups. In our study, which specifically targeted older adults, no significant gender differences in WTP for QALYs were observed. In contrast, Moradi et al. [ 35 ] reported a significant gender effect, with women exhibiting higher WTP than men. This discrepancy may be attributed to the unique characteristics of the older adult population, where gender roles and economic responsibilities might differ from those in the general population. Additionally, variations in societal, economic, and cultural contexts across studies [ 33 , 36 ] could contribute to these inconsistent findings. Some research suggests that, in broader populations, men may be more inclined to invest in health-related expenses due to their traditional provider roles [ 32 ]. However, among older adults, the influence of gender on WTP may be less pronounced, possibly due to converging financial constraints and shared health priorities in later life. This study found that older adults in highly developed areas of Tehran were more willing to pay for QALYs compared to those in moderately developed areas. These results align with economic theories suggesting that more developed regions have greater financial access and awareness of quality of life [ 37 ]. Employment status was found to be a significant predictor of WTP. Housewives, who tend to be financially dependent, had lower WTP, while retirees also exhibited lower WTP, likely due to fixed income and different attitudes toward health expenditures. This finding aligns with previous studies, which generally report that employed individuals are more likely to have higher WTP for health-related expenses [ 32 , 34 , 38 ]. Interestingly, income did not significantly influence WTP in our study. Higher-income individuals may not perceive healthcare costs as urgently as those with lower incomes, which contrasts with some studies that found a positive correlation between income and WTP for healthcare [ 32 , 39 ]. While income is a significant factor in WTP for the general population, the influence of income on WTP among older adults may be diminished by factors such as dependence on household income, the fixed nature of retirement income, and the prioritization of family support systems. Living arrangements also did not significantly affect WTP in this study. This contrasts with studies by Shiroiwa et al. [ 31 ] and Moradi et al. [ 8 ], which indicated that living with a spouse positively influenced WTP. This result suggests that, for older adults, the financial and emotional support from family members, as well as access to public or institutional healthcare services, may outweigh the direct influence of where they live. Therefore, it is important to consider a more holistic approach when analyzing WTP among older adults, taking into account the various sources of support that may influence their healthcare decisions. Educational attainment was significantly associated with higher WTP for QALYs. Individuals with higher education levels exhibited a stronger willingness to pay, consistent with studies suggesting that education enhances awareness, understanding of health importance, and financial capability [ 12 , 25 , 40 ]. Additionally, the study found a negative, but non-significant, relationship between religious beliefs and WTP, suggesting that individuals with stronger religious convictions may prioritize health decisions differently. No similar studies were identified in this context, indicating that this finding might be context-specific. Health status had a negative but non-significant effect on WTP. One possible interpretation is that healthier individuals may perceive less urgency for additional health interventions, as they already feel well and therefore might not see substantial benefit in investing further in their health. This perspective is consistent with the findings of Shiroiwa et al. [ 31 ] but contradicts the findings of Huang et al. [ 34 ] and Fernandez et al. [ 33 ], who found a stronger correlation between health status and WTP for full health restoration. These discrepancies could be due to differences in sample characteristics or cultural contexts; for instance, in populations where individuals experience more acute health challenges, the perceived need for health restoration might be more pronounced, leading to a higher willingness to invest in health improvements. In this study, older people living in the community-dwelling and in nursing homes centers participated, which can be considered a strength of the study compared to other studies. Although this study endeavored to minimize methodological, ethical, and environmental constraints, research limitations are an inherent aspect of the research process. Consequently, the present study was not immune to these limitations and included several constraints. Some nursing homes were excluded from our study because all of their residents suffered from severe cognitive impairment. Some older individuals in the centers lacked accurate information regarding their own and their families' income status. Older women were less inclined to participate in the study compared to men, primarily due to their limited knowledge of household income. Conclusions The findings of this study reveal that the willingness to pay for a QALY among older adults in Tehran is significantly lower than the benchmarks suggested by the World Health Organization (WHO). However, the WTP value for QALYs was much lower, indicating that older adults in Iran are less willing to invest in additional years of healthy life. This low WTP suggests a gap between the perceived value of health and the actual financial commitment older adults are willing or able to make for improved health outcomes. These findings highlight the influence of economic capacity, education, and residential development on both the presence and amount of WTP for a QALY. The significant role of financial variables suggests that affordability is a key constraint in WTP for extended life. Additionally, the lower WTP among retirees and homemakers underscores the need for targeted policies addressing financial security in old age. Policymakers should consider these factors when designing healthcare financing strategies and interventions aimed at improving older adults' access to quality life-extending treatments. Furthermore, the influence of religious beliefs on WTP suggests that cultural and ethical considerations should be integrated into policy discussions on end-of-life care investments. Policies that enhance the economic security of older adults, particularly those who rely on pensions or family income, could help raise their willingness to invest in health interventions. Abbreviations QALY Quality-Adjusted Life Year WTP Willingness to Pay WHO World Health Organization GDP Gross Domestic Product USD United States dollar MCI Mild Cognitive Impairment SES Socio-Economic Status IRR Iranian Rials HTA Health Technology Assessment Statements and Declarations Ethics approval and consent to participate The Ethics Committee in Research has approved the protocol for this study at the University of Social Welfare and Rehabilitation Sciences under the code IR.USWR.REC.1402.218. Participants provided informed consent before data collection. Consent for publication Not applicable' for that section. Availability of data and materials The data that supports the findings of this study are available from the corresponding author, upon reasonable request. Competing interests Not applicable' for that section. Funding Not applicable' for that section. Acknowledgements Not applicable' for that section. Clinical Trial Number not applicable. Authors' contributions MB, ZA, ZKh, ZKh, MS, and AI contributed to the study's design. ZA and FH collected data. MB performed statistical analyses. MB and ZA wrote the draft of the manuscript. All authors reviewed the manuscript. Authors' information (optional) Not applicable' for that section. References Yahyavi Dizaj, J., M. Tajvar, and Y. Mohammadzadeh, The effect of the presence of an elderly member on health care costs of Iranian households. Iranian Journal of Ageing, 2020. 14 (4): p. 462-477. https://doi. org/ 10.32598/sija.13.10.420 Basakha, M., et al., Health care cost disease as a threat to Iranian aging society. Journal of research in health sciences, 2013. 14 (2): p. 152-156. Nikookar, R., et al., Assessing the duration of unnecessary hospitalization and expenses in older individuals suffering from cerebral vascular accident in the chronic care unit. Iran J Age, 2015. 10 (2): p. 180-7. Bulamu, N.B., B. Kaambwa, and J. 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Clin Psychol Eur, 2021. 3 (4): p. e3855. https://doi.org/ 10.32872/cpe.3855 Hashempour, R., et al., QALY league table of Iran: a practical method for better resource allocation. Cost Effectiveness and Resource Allocation, 2021. 19 : p. 1-11. https://doi.org/10.1186/s12962-020-00256-2. Kouakou, C.R. and T.G. Poder, Willingness to pay for a quality-adjusted life year: a systematic review with meta-regression. The European Journal of Health Economics, 2022. 23 (2): p. 277-299. https://doi.org/ 10.1007/s10198-021-01364-3 Weinstein, M.C., G. Torrance, and A. McGuire, "QALYs: The basics": Erratum. Value in Health, 2010. 13 (8): p. 1065-1065. https://doi.org/10.1111/j.1524-4733.2009.00515.x. Pennington, M., et al., Comparing WTP values of different types of QALY gain elicited from the general public. Health economics, 2015. 24 (3): p. 280-293. . https://doi.org/10.1002/hec.3018. Baker, R., et al., Searchers vs surveyors in estimating the monetary value of a QALY: resolving a nasty dilemma for NICE. Health Economics, Policy and Law, 2011. 6 (4): p. 435-447. https://doi.org/10.1017/S1744133111000181. Pinto-Prades, J.L., et al., Valuing QALYs at the end of life. Soc Sci Med, 2014. 113 : p. 5-14. https://doi.org/ 10.1016/j.socscimed.2014.04.039. Epub 2014 May 2. Lankarani, K.B., et al., Willingness-to-pay for one quality-adjusted life-year: a population-based study from Iran. Applied health economics and health policy, 2018. 16 : p. 837-846. https://do i.org/10.1007/s40258-018-0424-4. Pinto-Prades, J.L., G. Loomes, and R. Brey, Trying to estimate a monetary value for the QALY. Journal of health economics, 2009. 28 (3): p. 553-562. https://do i.org/ 10.1016/j.jhealeco.2009.02.003. Walton, S.M., et al., Observed/revealed willingness to pay for QALYs in older adults: evidence from planned commonly used surgical procedures. International Journal of Healthcare Management, 2020 . https://doi.org/10.1080/20479700.2017.1336836. Menzel, P.T., How should willingness-to-pay values of quality-adjusted life-years be updated and according to whom? AMA Journal of Ethics, 2021. 23 (8): p. 601-606. https://doi.org/ 10.1001/amajethics.2021.601 . Ye, Z., et al., Willingness to Pay for One Additional Quality Adjusted Life Year: A Population Based Survey from China. Applied Health Economics and Health Policy, 2022. 20 (6): p. 893-904. Robinson, A., et al., Estimating a WTP-based value of a QALY: the ‘chained’approach. Social Science & Medicine, 2013. 92 : p. 92-104. h ttps://doi.org/10.1016/j.socscimed.2013.05.013. Epub 2013 Jun 4. Afshari, S., et al., A national survey of Iranian general population to estimate a value set for the EQ-5D-5L. Quality of Life Research, 2023. 32 (7): p. 2079-2087. https://doi.org/ h ttps://doi.org/10.1007/s11136-023-03378-1. Epub 2023 Mar 10. Moradi, N., et al., Monetary Value of Quality-Adjusted Life Years (QALY) among Patients with Cardiovascular Disease: a Willingness to Pay Study (WTP). Iran J Pharm Res, 2017. 16 (2): p. 823-833. Asim, O. and S. Petrou, Valuing a QALY: review of current controversies. Expert review of pharmacoeconomics & outcomes research, 2005. 5 (6): p. 667-669. https://doi.org/10.1586/14737167.5.6.667 Jahanbin, S.-F., et al., Value of willingness to pay for a QALY gained in Iran; a modified chained-approach. BMC Health Services Research, 2021. 21 : p. 1-12. https://doi.org/10.1186/s12913-021-07344-w. Sadeghi, R. and N. Zanjari, The inequality of development in the 22 districts of Tehran metropolis. Social Welfare Quarterly, 2017. 17 (66): p. 149-184. Rezaei, M., et al., Psychometric properties of the Persian adaptation of mini-cog test in Iranian older adults. The International Journal of Aging and Human Development, 2018. 86 (3): p. 266-280.https://doi.org/10.1177/0091415017724547. Epub 2017 Aug 31. Zare, F., et al., Validity and reliability of the EQ-5D-3L (a generic preference-based instrument used for calculating quality-adjusted life-years) for patients with type 2 diabetes in Iran. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2021. 15 (1): p. 319-324. https://doi.org/10.1016/j.dsx.2021.01.009. Shiroiwa, T., et al., WTP for a QALY and health states: More money for severer health states? Cost Effectiveness and Resource Allocation, 2013. 11 (1): p. 22. Ha, T.V., et al., Willingness to pay for a quality-adjusted life year among advanced non-small cell lung cancer patients in Viet Nam, 2018. Medicine (Baltimore), 2020. 99 (9): p. e19379. https://doi.org/10.1097/MD.0000000000019379 Martín-Fernández, J., et al., Willingness to pay for a quality-adjusted life year: an evaluation of attitudes towards risk and preferences. BMC Health Services Research, 2014. 14 (1): p. 287. Huang, L., et al., Estimation of the value of curative therapies in oncology: a willingness-to-pay study in China. Cost Eff Resour Alloc, 2023. 21 (1): p. 37. https://doi.org/10.1186/s12962-023-00442-y. Moradi, N., et al., An exploratory study to estimate cost-effectiveness threshold value for life saving treatments in western Iran. Cost Effectiveness and Resource Allocation, 2020. 18 : p. 1-9. https://doi.org/ 10.1186/s12962-020-00241-9. Nimdet, K. and S. Ngorsuraches, Willingness to pay per quality-adjusted life year for life-saving treatments in Thailand. BMJ Open, 2015. 5 (10): p. e008123. https://doi.org/10.1186/s12962-020-00241-9. https://doi.org/ 10.1186/s12962-020-00241-9 . Xu, L., et al., Establishing cost-effectiveness threshold in China: a community survey of willingness to pay for a healthylife year. BMJ Global Health, 2024. 9 (1): p. e013070. https://doi.org/ 10.1136/ bmjgh-2023-013070. Shirin, N., et al., Willingness to Pay for Complementary Health Care Insurance in Iran. Iranian Journal of Public Health, 2017. 46 (9). Reckers-Droog, V., J. van Exel, and W. Brouwer, Willingness to pay for health-related quality of life gains in relation to disease severity and the age of patients. Value in Health, 2021. 24 (8): p. 1182-1192. https://doi.org/10.1016/j.jval.2021.01.012 . Van Ha, T., et al., Willingness to pay for a quality-adjusted life year among advanced non-small cell lung cancer patients in Viet Nam, 2018. Medicine, 2020. 99 (9): p. e19379. https://doi.org/ 10.1097/MD.0000000000019379 . Additional Declarations No competing interests reported. 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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-6550748","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483328847,"identity":"b5edcc96-e666-4e72-910f-9d7ae0631947","order_by":0,"name":"Mehdi Basakha","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Basakha","suffix":""},{"id":483328848,"identity":"fda888d1-2136-4a0e-a785-b370bed616f9","order_by":1,"name":"Zeinab Anbari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYJACZiDm4WdmPnDgA5DFxk6sFsn2tsSHM0BamInUwmBw5oyxMQ+ciwfINzA/e1xQc0+G4UaCmbTNr23yfMwMjB8+5uDWwtjAZm4841gxD+OMhDTp3L7bhm3MDMySM7fhdZSZNA9bAg+zRMIx6dye24xALWzMvHi0sDGwf5Pm+ZfAwyaR2CZt2XPbnqAWHgYeM2netgQeHp7DzMYMP24nEtQiwcxTJj2zL4FHgr2N8WFvw+3kNmbGZrx+kW9v3yZd8C3B3v4w/4cDP/7ctp3f3nzww0c8WlBjgbENTDbgUY8B/pCieBSMglEwCkYKAACcvUYtPfTRNgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":true,"prefix":"","firstName":"Zeinab","middleName":"","lastName":"Anbari","suffix":""},{"id":483328849,"identity":"31639886-65b2-47f1-8518-1a84f4a648d9","order_by":2,"name":"Zahra Khiyali","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Khiyali","suffix":""},{"id":483328854,"identity":"112ad5f5-d1fd-422b-b5a0-aa1e0ffbb980","order_by":3,"name":"Zahra Khalili","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Khalili","suffix":""},{"id":483328855,"identity":"402d94ec-8b02-4bcb-a98b-38f594a6f868","order_by":4,"name":"Athena Izadpanah","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Athena","middleName":"","lastName":"Izadpanah","suffix":""},{"id":483328857,"identity":"d9c655d6-ea7b-4ef1-b7be-7716aaf6dfd4","order_by":5,"name":"Mohadeseh Sadri","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohadeseh","middleName":"","lastName":"Sadri","suffix":""},{"id":483328859,"identity":"d917a228-3866-43d6-9fb1-94dab88d36f2","order_by":6,"name":"Fatemeh Hosseinpour","email":"","orcid":"","institution":"University of Social Welfare and Rehabilitation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Hosseinpour","suffix":""}],"badges":[],"createdAt":"2025-04-28 20:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6550748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6550748/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86647084,"identity":"fc08f29b-bb92-4f57-8428-8985a2390156","added_by":"auto","created_at":"2025-07-14 09:07:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1025325,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6550748/v1/085aa2cf-4c6d-4a62-80ad-7bcf0f1aa249.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Valuing a Quality-Adjusted Life Year (QALY): Willingness to Pay among Older Adults in Iran","fulltext":[{"header":"Background","content":"\u003cp\u003ePopulation aging, once a concern for developed nations, is now rapidly affecting developing countries, including Iran, which has one of the fastest-growing elderly populations worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2031, the number of older adults in Iran is expected to double [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], Leading to a sharp rise in chronic diseases and increased use of health and aged care services[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This demographic shift has significantly increased healthcare costs, making aging a financial burden for many countries. The rise in chronic conditions among older adults\u0026rsquo; results in higher mortality rates, reduced quality of life, greater dependency, and increased hospital admissions, all of which strain healthcare resources [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Given these challenges, the need for efficient resource allocation in healthcare has become more pressing [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCost-effectiveness analysis, particularly using Quality-Adjusted Life Years (QALYs), provides a standardized measure for evaluating health interventions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. QALYs account for both life expectancy and quality of life, offering a comprehensive framework for comparing treatments [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In healthcare decision-making, calculating the cost per QALY gained helps prioritize interventions with the best value [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, determining a cost-effectiveness threshold is essential for identifying which interventions justify their expenses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Understanding older adults' willingness to pay (WTP) for QALYs can help policymakers set more realistic thresholds, ensuring limited resources are directed toward interventions that maximize health benefits [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eQALY-related values are widely used by health technology assessment (HTA) agencies worldwide to evaluate healthcare efficiency. HTA involves various analyses, particularly economic evaluations, to assess the cost-effectiveness and budget impact of medical interventions, ultimately improving resource allocation in healthcare systems [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These evaluations help policymakers determine which interventions provide the most value for the resources spent [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe QALY criterion offers a standardized approach to quantifying health benefits [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Assigning a monetary value to QALYs through willingness-to-pay (WTP) calculations allows for a clearer understanding of how much individuals value additional years of healthy life [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. WTP is a key factor in health policy decisions, serving as a benchmark for assessing the cost-effectiveness of healthcare services [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies suggest that an acceptable WTP threshold per QALY often falls between one and three times a country's GDP per capita [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In Iran, Lankarani et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] estimated the average WTP for one QALY at \u003cspan\u003e$\u003c/span\u003e2,847, or 0.57 times the national GDP per capita, with a higher willingness to pay among individuals in the final stages of life. Similarly, Moradi et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported WTP estimates ranging from \u003cspan\u003e$\u003c/span\u003e1,032 to \u003cspan\u003e$\u003c/span\u003e2,666, representing 0.22 to 0.56 times GDP per capita, with income, education, and marital status significantly influencing WTP values. Zipping et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] found an average WTP per QALY of 1.75 times GDP per capita, emphasizing the impact of age, education, and mental well-being, particularly in reducing anxiety and depression. However, inconsistencies in findings across studies stem from differences in methodologies, target populations, and socio-economic contexts.\u003c/p\u003e\u003cp\u003eGiven the rapid aging of Iran\u0026rsquo;s population and the increasing burden of healthcare costs, understanding the economic value of QALY from the perspective of older adults is crucial. It provides a concrete threshold for evaluating the cost-effectiveness of health interventions targeting the older adults. This information can support HTA and cost-effectiveness studies by offering a clear benchmark for determining whether investing in specific health technologies or interventions for older adults is economically justified. This research seeks to determine the monetary value placed on each healthy life-year by the older population in Iran, informing age-sensitive resource allocation decisions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eThe study\u0026rsquo;s method follows a chained approach [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], adapted for a population of older adults in Tehran, including both those residing in the community and in nursing homes. This two-step method utilized to determine the maximum monetary value that older adults in Tehran are willing to pay for one additional QALY. In the first step, health utility was calculated using the EQ-5D-5L questionnaire [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the second step, participants were asked through a contingent valuation (CV) method how much they would be willing to pay to return to their ideal health condition, which represents a fully healthy year of life.\u003c/p\u003e\u003cp\u003eThe chained approach, which uses hypothetical health scenarios, was employed to capture individuals\u0026rsquo; preferences for health conditions. This approach is widely regarded for its applicability [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], high sensitivity to health status differences, and reduced susceptibility to biases compared to other valuation methods, such as the direct approach. The results from the chained approach offer higher social generalizability, making it more suitable for policy-making purposes. Unlike the direct method, which can lead to WTP values that exceed individuals\u0026rsquo; ability to pay due to large health losses, the chained approach minimizes health losses, reflecting more realistic and manageable trade-offs for participants.\u003c/p\u003e\u003cp\u003eThis study conducted in 2024 in Tehran, covering both community-dwelling older adults and those residing in nursing homes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants and Sampling\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eCommunity-Dwelling Older Adults\u003c/h2\u003e\u003cp\u003eA multi-stage cluster sampling approach with proportional allocation was employed to ensure a representative sample of Tehran\u0026rsquo;s older adult population. The sample size was determined based on the study by Jahanbin et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], using the formula, (n\u0026thinsp;=\u0026thinsp;Z α/d) \u0026sup2;, with a confidence interval (CI) of 95% (α\u0026thinsp;=\u0026thinsp;0.05), a standard deviation (SD) of 11.6, and a response rate adjustment of 10%. This calculation resulted in a final sample of 460 community-dwelling older adults.\u003c/p\u003e\u003cp\u003eTo account for socioeconomic diversity, Tehran\u0026rsquo;s 22 districts were stratified into five development categories according to Sadeghi et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Two districts and two neighborhoods were randomly selected from each category. The number of participants from each region was determined based on the proportion of older adults residing in that area.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNursing Home Residents\u003c/h3\u003e\n\u003cp\u003eGiven that a segment of the elderly population resides in nursing homes, additional sampling was conducted in these settings. Nursing homes were classified into three socioeconomic strata: low, middle, and high. However, due to lack of cooperation from low-SES facilities, data collection was limited to middle- and high-SES centers. Two centers were randomly selected from each stratum, resulting in a sample of 34 older adults from nursing homes.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion criteria\u003c/h3\u003e\n\u003cp\u003eThe inclusion criteria for participation in this study included the following: informed and voluntary consent, age over 60 years, proficiency in Persian, ability to communicate verbally and auditory, and the absence of mild cognitive impairment (MCI). The assessment for MCI was conducted using the Mini-Cog test. The Persian version of Mini-Cog is a user-friendly and acceptable cognitive test for Persian-speaking older adults. The study by Rezaei et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] showed that the sensitivity and specificity of the Mini-Cog test were 0.88 and 0.62, respectively. This study demonstrated that the words and instructions of this test could be clearly understood and were acceptable to the participants. Furthermore, this study highlights the suitable face validity of the Persian version of the Mini-Cog [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Exclusion criteria encompassed any participants who expressed unwillingness to continue with the interview process, as well as those who were unable to proceed due to unforeseen physical health issues.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003eData were collected through structured face-to-face interviews conducted by trained interviewers with expertise in gerontology, psychology, and social work. Interviews took place in public locations frequently visited by older adults, such as parks, mosques, shopping centers, and community centers.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eDemographic and Socioeconomic Variables\u003c/h2\u003e\u003cp\u003eA structured checklist was used to collect data on marital status, education level, place and type of residence, occupation, household and personal income, living arrangements, health insurance coverage, chronic diseases, tobacco use, independence status, quality of life, and physical and mental disabilities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eWTP Questionnaire for QALY\u003c/h2\u003e\u003cp\u003eThe WTP questionnaire was developed by the research team using the contingent valuation method and included five key questions to assess the maximum amount an individual was willing to pay for improved health status. It addressed the definition of the best possible health state, the duration of life in the best health state, the amount of money one was willing to pay for one additional year in optimal health, the willingness to sell property to receive health services, and the amount of money one was willing to pay for five additional years in optimal health. The questionnaire\u0026rsquo;s face validity was assessed through expert review and pilot testing with 18 older adults, and its reliability was evaluated using test-retest analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEQ-5D questionnaire\u003c/h2\u003e\u003cp\u003eThe EQ-5D is a standardized instrument developed by the European Quality of Life Organization to measure health-related quality of life. It evaluates five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The internal consistency of the EQ-5D has been demonstrated with a Cronbach\u0026rsquo;s alpha of 0.76 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], indicating satisfactory reliability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was conducted using STATA version 14. Descriptive statistics were presented as mean and standard deviation for quantitative variables and as frequency (percentage) for qualitative variables. The normality of quantitative variables was assessed using the Kolmogorov-Smirnov test, along with skewness and kurtosis indices. To explore factors influencing WTP, independent t-tests, analysis of variance (ANOVA), and multivariate binary regression analyses (logit and probit models) were employed. Correlation analyses were also conducted to examine the relationships between key variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEthical considerations\u003c/h2\u003e\u003cp\u003e The Ethics Committee in Research has approved the protocol for this study at the University of Social Welfare and Rehabilitation Sciences under the code IR.USWR.REC.1402.218. Participants provided informed consent before data collection.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 494 older adults residing in both community dwellings and nursing homes, with a mean age of 68.39 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;6.48) years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority were male (54.05%) and had less than a high school diploma (42.31%). Nearly 44.13% were retired, 33.00% lived with their spouses, and 18.2% lived alone. Social security insurance coverage was reported by 43.32% of participants. The mean monthly income was 258 (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;180) million IR. Among chronic conditions, hypertension had the highest prevalence (34.2%), while cancer had the lowest (3.4%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e226\u003c/p\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.95\u003c/p\u003e\u003cp\u003e54.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle (widowed, divorced, never married)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eLiving arrangements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith children and spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWith others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIn nursing home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003cp\u003eUnder high school diploma\u003c/p\u003e\u003cp\u003eHigh school diploma\u003c/p\u003e\u003cp\u003eAssociate degree and Bachelor's Postgraduate degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003cp\u003e209\u003c/p\u003e\u003cp\u003e130\u003c/p\u003e\u003cp\u003e71\u003c/p\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.56\u003c/p\u003e\u003cp\u003e42.31\u003c/p\u003e\u003cp\u003e26.32\u003c/p\u003e\u003cp\u003e14.37\u003c/p\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003cp\u003eRetired\u003c/p\u003e\u003cp\u003eRetired but employed\u003c/p\u003e\u003cp\u003eHousewife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003cp\u003e218\u003c/p\u003e\u003cp\u003e44\u003c/p\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.53\u003c/p\u003e\u003cp\u003e44.13\u003c/p\u003e\u003cp\u003e8.91\u003c/p\u003e\u003cp\u003e36.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHaving caregiver(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMedication Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eDaily Activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed and socially active\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndependent but not working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePartially dependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFully dependent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eInsurance Coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Security\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate Supplementary Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFull Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eInternet Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than 30 minutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than an hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enot use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on a single-item assessment, 69.6% of older adults rated their health status as 6 or higher on a 10-point scale. In contrast, less than 1% of participants rated their health as 1, indicating the poorest status. Another single-item question revealed that less than 1% of seniors reported having no religious beliefs, while approximately 86% expressed moderate religious beliefs. Among the participants, about 29% demonstrated strong religious commitment, reflecting complete adherence to their faith.\u003c/p\u003e\u003cp\u003eAccording to the time trade-off approach, older adults were willing to forgo 8.84 months of their remaining life in exchange for one additional year of healthy life (QALY). The average (median) amount they were willing to pay for one extra QALY was approximately 1,969 (294) million IRR, equivalent to \u003cspan\u003e$\u003c/span\u003e3,313 (\u003cspan\u003e$\u003c/span\u003e495) based on the official exchange rate. However, the large standard deviation indicated considerable variability, suggesting that at least half of the respondents were willing to pay less than this amount.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDeterminants of Willingness to Pay\u003c/h2\u003e\u003cp\u003eThe study found that 71.8% of older adults were willing to pay for treatment to restore their full health, but only 37.8% were willing to sell major assets for this purpose. Logistic regression analysis showed no significant gender differences in WTP, though women exhibited slightly higher willingness. Age was negatively associated with WTP, with older individuals demonstrating lower willingness. Residents of highly developed areas in Tehran had significantly higher WTP than those in moderately developed areas, while no significant differences were observed in lower-development areas.\u003c/p\u003e\u003cp\u003eEmployment status was a key factor, as housewives had lower WTP than employed older adults, likely due to financial dependence. However, retirees and working retirees showed no significant differences in WTP. Living arrangements, including residence in nursing homes, did not significantly impact WTP. Income, whether individual or household, was also not a significant predictor, suggesting that healthcare spending decisions may be influenced more by personal values than financial capacity. Religious beliefs, however, had a significant positive effect on WTP, indicating that stronger religious convictions increased willingness to pay, possibly due to differing perspectives on life and health. Health status had a negative but non-significant effect, suggesting that healthier individuals may feel less urgency for healthcare spending (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTables\u0026nbsp;2: The results of logit regression analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (Reference: Male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group (Reference: Young Older Adults)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOld Adults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOldest-Old Adults\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential Area (Reference: Moderately Developed Area)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHighly developed Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeveloped Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess developed Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderdeveloped Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment Status (Reference: Employed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired but working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomemaker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving arrangements (Reference: Living Alone)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with Spouse and Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with Spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with Children or Others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNursing Home Resident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance coverage (Reference: No Insurance)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Insurance (Pension and Basic Health Coverage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComprehensive Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Reported Religious Commitment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-Rated Health Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this study, alongside logistic regression, the two-step Heckman model was employed to address potential selection bias, as some participants reported zero willingness to pay, and improve the robustness of the results. While logistic regression helped identify the factors associated with whether individuals are willing to pay, the Heckman model allowed us to further investigate the factors influencing the amount of willingness to pay. By including significant variables from the logistic regression in the first stage of the Heckman model, we ensured that the analysis accounted for both the likelihood of willingness to pay and the magnitude of that willingness, providing a more comprehensive understanding of the factors at play.\u003c/p\u003e\u003cp\u003eThe findings indicate that employment status significantly affects the amount of WTP. Compared to employed individuals, retirees demonstrate a significantly lower WTP (β = -198.12, p\u0026thinsp;=\u0026thinsp;0.01), while homemakers and those retired but still working also have lower WTP, though not statistically significant. Education level plays a crucial role, as individuals with a master's degree or PhD exhibit a significantly higher WTP (β\u0026thinsp;=\u0026thinsp;460.65, p\u0026thinsp;=\u0026thinsp;0.005) compared to illiterate individuals. However, lower levels of education do not show significant effects. Economic factors are strong predictors of WTP. Monthly household expenses (β\u0026thinsp;=\u0026thinsp;2.68, p\u0026thinsp;=\u0026thinsp;0.004) and personal monthly income (β\u0026thinsp;=\u0026thinsp;1.86, p\u0026thinsp;=\u0026thinsp;0.01) are both positively and significantly associated with WTP, suggesting that individuals with higher financial resources are willing to pay more. Notably, different types of insurance coverage do not have significant effects on WTP, indicating that having insurance does not necessarily increase the amount individuals are willing to invest in additional years of quality life.\u003c/p\u003e\u003cp\u003eSecond step in The Heckman results indicate the factors associated with having or not having a willingness to pay for one additional QALY. Residential area influences the likelihood of having WTP. Compared to those living in moderately developed areas, individuals residing in highly developed (β\u0026thinsp;=\u0026thinsp;0.69, p\u0026thinsp;=\u0026thinsp;0.01) and developed areas (β\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;=\u0026thinsp;0.03) are significantly more likely to express WTP. However, those from less developed or underdeveloped areas do not show statistically significant differences. Self-reported religious commitment (β\u0026thinsp;=\u0026thinsp;0.005, p = -2.81) has a significant negative effect, suggesting that individuals with stronger religious beliefs may be less inclined to express WTP for life extension. Employment status does not significantly impact the likelihood of WTP, as the coefficients for retirees, homemakers, and those retired but working are not statistically significant.\u003c/p\u003e\u003cp\u003eThe coefficient for athrho (ρ) is not significant, indicating that the correlation between the selection equation (having WTP) and the outcome equation (amount of WTP) is weak. This suggests that selection bias is not a major concern in the estimation. Additionally, the significant coefficient for lnsigma (β\u0026thinsp;=\u0026thinsp;5.7, p\u0026thinsp;=\u0026thinsp;0.01) confirms the appropriateness of the model specification (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeckman's two-stage model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"16\" rowspan=\"17\"\u003e\u003cp\u003eAmount of Willingness to pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment Status (Reference: Employed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"16\" rowspan=\"17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-198.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired but working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-157.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHomemaker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate \u003cb\u003e(\u003c/b\u003eReference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than high school diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssociate degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster's degree and PhD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e460.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInsurance coverage (Reference: No Insurance)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Insurance (Pension and Basic Health Coverage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-29.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComprehensive Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-68.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonthly household expenses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePersonal monthly income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e231.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e\u003cp\u003eHaving a\u0026nbsp;willingness to pay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResidential Area (Reference: Moderately Developed Area)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"10\" rowspan=\"11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHighly developed Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeveloped Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess developed Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderdeveloped Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployment Status (Reference: Employed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired but working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHomemaker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-Reported Religious Commitment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eathrho\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003elnsigma\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRho\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003esigma\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e299.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003elambda\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-83.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess the willingness to pay for QALYs and related factors among older adults in Tehran. The average WTP for QALYs was estimated at 1,969\u0026nbsp;million IRR (3,313 USD), while the median WTP was approximately 495 USD, representing about 0.28 times the GDP per capita in 2023. This figure indicates a significant decrease compared to the findings of Moradi et al. (2017), who reported an average WTP of 300\u0026nbsp;million IRR (roughly 8,000 USD) in 2007 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, it is important to note that Moradi et al.'s study focused on the general population, whereas this study specifically examines older adults, who tend to value QALYs less than the general population. Furthermore, the decline in WTP over time can be attributed to factors such as high inflation, reduced purchasing power, and significant fluctuations in exchange rates, all of which have notably impacted the valuation of life in Iranian society.\u003c/p\u003e\u003cp\u003eStudies show higher WTP for health improvements. For instance, Shiroiwa et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] reported an average WTP of 5\u0026nbsp;million yen (50,000 USD) in Japan, and Ha et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] found that 79% of patients in Vietnam were willing to pay for QALYs at or above the GDP per capita level, which was about 2,342 USD at that time. These studies linked the WTP to income levels, comprehensive health insurance coverage, and cultural factors.\u003c/p\u003e\u003cp\u003eThis study found that 71.8% of older adults willing to pay for a treatment that improve their quality of life, but economic constraints and personal preferences were significant factors influencing their decisions. Only 37.8% were willing to sell major assets for treatment, reflecting financial limitations or conservative attitudes toward asset preservation. Demographic factors such as age, marital status, and education significantly influenced WTP. Younger old individuals exhibited greater WTP, likely due to more optimistic health outlooks and longer life expectancies. While no significant differences were found between middle-aged and older groups, data showed a decrease in WTP as age increased. However, age did not significantly impact WTP for QALYs, potentially because the perceived value of QALYs may be similar across different age groups. This aligns partially with the findings of Fernandez et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], who observed an 18% decrease in WTP per decade of life, and other studies that confirmed age's negative correlation with WTP [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Some studies, such as Pinto et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], suggest that older adults generally exhibit lower WTP for QALYs compared to the general population, which could be due to focusing exclusively on older individuals. Conversely, Ha et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] reported higher WTP among older patients, and Huang et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] found that older individuals demonstrated a higher WTP compared to other groups.\u003c/p\u003e\u003cp\u003eIn our study, which specifically targeted older adults, no significant gender differences in WTP for QALYs were observed. In contrast, Moradi et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] reported a significant gender effect, with women exhibiting higher WTP than men. This discrepancy may be attributed to the unique characteristics of the older adult population, where gender roles and economic responsibilities might differ from those in the general population. Additionally, variations in societal, economic, and cultural contexts across studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] could contribute to these inconsistent findings. Some research suggests that, in broader populations, men may be more inclined to invest in health-related expenses due to their traditional provider roles [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, among older adults, the influence of gender on WTP may be less pronounced, possibly due to converging financial constraints and shared health priorities in later life.\u003c/p\u003e\u003cp\u003eThis study found that older adults in highly developed areas of Tehran were more willing to pay for QALYs compared to those in moderately developed areas. These results align with economic theories suggesting that more developed regions have greater financial access and awareness of quality of life [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmployment status was found to be a significant predictor of WTP. Housewives, who tend to be financially dependent, had lower WTP, while retirees also exhibited lower WTP, likely due to fixed income and different attitudes toward health expenditures. This finding aligns with previous studies, which generally report that employed individuals are more likely to have higher WTP for health-related expenses [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInterestingly, income did not significantly influence WTP in our study. Higher-income individuals may not perceive healthcare costs as urgently as those with lower incomes, which contrasts with some studies that found a positive correlation between income and WTP for healthcare [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. While income is a significant factor in WTP for the general population, the influence of income on WTP among older adults may be diminished by factors such as dependence on household income, the fixed nature of retirement income, and the prioritization of family support systems.\u003c/p\u003e\u003cp\u003eLiving arrangements also did not significantly affect WTP in this study. This contrasts with studies by Shiroiwa et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and Moradi et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which indicated that living with a spouse positively influenced WTP. This result suggests that, for older adults, the financial and emotional support from family members, as well as access to public or institutional healthcare services, may outweigh the direct influence of where they live. Therefore, it is important to consider a more holistic approach when analyzing WTP among older adults, taking into account the various sources of support that may influence their healthcare decisions.\u003c/p\u003e\u003cp\u003eEducational attainment was significantly associated with higher WTP for QALYs. Individuals with higher education levels exhibited a stronger willingness to pay, consistent with studies suggesting that education enhances awareness, understanding of health importance, and financial capability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, the study found a negative, but non-significant, relationship between religious beliefs and WTP, suggesting that individuals with stronger religious convictions may prioritize health decisions differently. No similar studies were identified in this context, indicating that this finding might be context-specific.\u003c/p\u003e\u003cp\u003eHealth status had a negative but non-significant effect on WTP. One possible interpretation is that healthier individuals may perceive less urgency for additional health interventions, as they already feel well and therefore might not see substantial benefit in investing further in their health. This perspective is consistent with the findings of Shiroiwa et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] but contradicts the findings of Huang et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and Fernandez et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], who found a stronger correlation between health status and WTP for full health restoration. These discrepancies could be due to differences in sample characteristics or cultural contexts; for instance, in populations where individuals experience more acute health challenges, the perceived need for health restoration might be more pronounced, leading to a higher willingness to invest in health improvements.\u003c/p\u003e\u003cp\u003eIn this study, older people living in the community-dwelling and in nursing homes centers participated, which can be considered a strength of the study compared to other studies. Although this study endeavored to minimize methodological, ethical, and environmental constraints, research limitations are an inherent aspect of the research process. Consequently, the present study was not immune to these limitations and included several constraints. Some nursing homes were excluded from our study because all of their residents suffered from severe cognitive impairment. Some older individuals in the centers lacked accurate information regarding their own and their families' income status. Older women were less inclined to participate in the study compared to men, primarily due to their limited knowledge of household income.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe findings of this study reveal that the willingness to pay for a QALY among older adults in Tehran is significantly lower than the benchmarks suggested by the World Health Organization (WHO). However, the WTP value for QALYs was much lower, indicating that older adults in Iran are less willing to invest in additional years of healthy life. This low WTP suggests a gap between the perceived value of health and the actual financial commitment older adults are willing or able to make for improved health outcomes.\u003c/p\u003e\u003cp\u003eThese findings highlight the influence of economic capacity, education, and residential development on both the presence and amount of WTP for a QALY. The significant role of financial variables suggests that affordability is a key constraint in WTP for extended life. Additionally, the lower WTP among retirees and homemakers underscores the need for targeted policies addressing financial security in old age. Policymakers should consider these factors when designing healthcare financing strategies and interventions aimed at improving older adults' access to quality life-extending treatments. Furthermore, the influence of religious beliefs on WTP suggests that cultural and ethical considerations should be integrated into policy discussions on end-of-life care investments. Policies that enhance the economic security of older adults, particularly those who rely on pensions or family income, could help raise their willingness to invest in health interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eQALY Quality-Adjusted Life Year\u003c/p\u003e\n\u003cp\u003eWTP Willingness to Pay\u003c/p\u003e\n\u003cp\u003eWHO World Health Organization\u003c/p\u003e\n\u003cp\u003eGDP Gross Domestic Product\u003c/p\u003e\n\u003cp\u003eUSD United States dollar\u003c/p\u003e\n\u003cp\u003eMCI Mild Cognitive Impairment\u003c/p\u003e\n\u003cp\u003eSES Socio-Economic Status\u003c/p\u003e\n\u003cp\u003eIRR Iranian Rials\u003c/p\u003e\n\u003cp\u003eHTA Health Technology Assessment\u003c/p\u003e"},{"header":"Statements and Declarations ","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee in Research has approved the protocol for this study at the University of Social Welfare and Rehabilitation Sciences under the code IR.USWR.REC.1402.218. Participants provided informed consent before data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable' for that section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable' for that section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable' for that section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable' for that section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMB, ZA, ZKh, ZKh, MS, and AI contributed to the study's design. ZA and FH collected data. MB performed statistical analyses. MB and ZA wrote the draft of the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable' for that section.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYahyavi Dizaj, J., M. Tajvar, and Y. 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Brouwer, \u003cem\u003eWillingness to pay for health-related quality of life gains in relation to disease severity and the age of patients.\u003c/em\u003e Value in Health, 2021. \u003cstrong\u003e24\u003c/strong\u003e(8): p. 1182-1192. https://doi.org/10.1016/j.jval.2021.01.012\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eVan Ha, T., et al., \u003cem\u003eWillingness to pay for a quality-adjusted life year among advanced non-small cell lung cancer patients in Viet Nam, 2018.\u003c/em\u003e Medicine, 2020. \u003cstrong\u003e99\u003c/strong\u003e(9): p. e19379. \u003cu\u003ehttps://doi.org/ \u003c/u\u003e10.1097/MD.0000000000019379\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Willingness to pay, quality-adjusted life year (QALY), older adults, Iran","lastPublishedDoi":"10.21203/rs.3.rs-6550748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6550748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eUnderstanding this helps policymakers allocate healthcare resources efficiently, especially in aging societies with budget constraints. Measuring WTP clarifies the economic justification for life extension and the rationale for prioritizing end-of-life care. This study, examined the WTP for one additional QALY among older adults in Tehran and its influencing factors in 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This cross-sectional study included 494 older adults selected through multi-stage cluster sampling from Tehran’s 22 districts and nursing homes. Data were gathered via interviews using a demographic checklist, a researcher-developed WTP questionnaire, and the validated EQ-5D instrument. Given the high likelihood of selection bias, the Heckman two-step method was employed to account for potential sample selection issues. Statistical analyses were performed in STATA 14.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Participants were willing to trade 8.84 months of full healthy time for one year of their current life. The mean WTP for one QALY was $3,313 (1,969 million Iranian Rials, IRR), with half offering under $495 (294 million IRR). Based on Heckman method, retirees and homemakers had lower WTP, while higher education, income, and expenses increased it. Living in developed areas and religious commitment also positively influenced WTP likelihood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: These findings provide a valuable reference threshold for cost-effectiveness studies on interventions for older adults, enabling policymakers to assess the economic viability of allocating healthcare resources to this population. Furthermore, policymakers can consider the reasons behind older adults' reluctance to invest in longer, healthier lives, which could inform targeted strategies to encourage greater investment in their health.\u003c/p\u003e","manuscriptTitle":"Valuing a Quality-Adjusted Life Year (QALY): Willingness to Pay among Older Adults in Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 08:51:17","doi":"10.21203/rs.3.rs-6550748/v1","editorialEvents":[{"type":"communityComments","content":1},{"type":"reviewersInvited","content":"","date":"2025-07-09T15:49:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-09T13:19:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T08:46:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-13T08:42:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-04-28T20:41:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5309ca1c-7895-4e90-8a42-b5d0b73eca51","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T08:51:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 08:51:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6550748","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6550748","identity":"rs-6550748","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}