BACKGROUND:This study investigated the impact of changes in health state duration in a discrete choice experiment where duration is an attribute (DCETTO). METHODS:A sample of the general population in Quebec, Canada, completed DCETTO in two phases using SF-6Dv2. In the first phase (DCEoriginal), participants were presented with binary choice sets, each containing two health states and associated durations. In the second phase (DCEchanged), they completed the same choice sets as before, but with modified durations. Adjustments were made based on participants' initial responses. Durations were 1, 4, 7, 10, 14, and 20 years. McNemar's Test was used to evaluate the change in respondents' choices before and after the duration modification. RESULTS:A total of 411 participants were included. Significant increases in duration of the option not chosen (by a factor of two or more) led it to become the preferred option (p < 0.05). A decrease in the duration of the option chosen could still lead to its acceptance. Overall, participants showed a strong anchoring effect of their initial choice, while changes in duration had a moderate positive effect on the likelihood of switching choices. CONCLUSIONS:Findings highlight non-linear effects of time on preferences and have important implications for the design of DCETTO studies.

To empirically assess the impact of immediate death as an alternative or an attribute on health utilities using discrete choice experiment with duration (DCETTO). Three DCETTO approaches were developed using the SF-6Dv2 to assess the impact of immediate death on health preferences in an online sample of the general adult population in Quebec, Canada. The first approach combined DCETTO with a probability of immediate death (POD) as an attribute, referred to as DCEPOD. The second approach combined DCETTO within a best-worst scaling with duration (BWSTTO) including immediate death as an alternative, referred to as DCEBWS. The third approach was a standard DCETTO model (used for a reference). Mixed logit models were used to analyze data. The models’ performance in estimating values was assessed based on parameters’ logical consistency, the significance of levels, and an analysis of the range of estimated values. A total of 369 participants were included for analysis. The number of parameters with inconsistent orders and non-significant coefficients was lower in DCEPOD compared to DCETTO and DCEBWS. The dimensions of mental health and social functioning provided the largest and smallest decrements in all approaches, respectively. DCEPOD produced a wider range of values (-2.393 to 1) compared to DCETTO (-1.959 to 1) and DCEBWS (-1.474 to 1). The findings demonstrated that when the probability of immediate death was designed as an attribute in DCEPOD, it decreased health utility values and produced a broader value set, while when immediate death was presented as an alternative in BWSTTO, it increased health utility values and provided a narrower value set.

Quality-adjusted life expectancy (QALE) provides a comprehensive measure of population health. Despite its value, QALE norms are not available for the Canadian population. This study aimed to develop age- and sex-specific QALE norms using both the 5-level version of EQ-5D (EQ-5D-5L) and version 2 of SF-6D (SF-6Dv2) instruments. EQ-5D-5L and SF-6Dv2 data from 3,844 individuals in a national survey (2024) were used to derive health utility scores. These scores were then combined with official life tables from the Office for National Statistics (2021–2023) using the Sullivan method to calculate age- and sex-specific QALE. Estimates were produced for both undiscounted and discounted values (1.5

OBJECTIVES:Quality-adjusted life expectancy (QALE) is a composite indicator integrating life expectancy and health utility values. Most studies have used the Sullivan method to calculate QALE, whereas Markov modeling offers a more flexible alternative simulating health transitions over time. The primary objective of this study was to estimate age- and sex-specific QALE for Quebec and to compare results across 4 methodological approaches: Sullivan versus Markov modeling, each with and without cubic polynomial fit of age-specific utilities. METHODS:We analyzed 4803 EQ-5D-5L records from 2016 to 2024 health surveys and pooled 2021 to 2023 life tables. Age-specific utilities were smoothed using cubic polynomial regression. Age- and sex-specific QALE norms were estimated using both Sullivan method and a stochastic 2-state Markov microsimulation, with Monte Carlo simulations applied to both methods to quantify uncertainty. Sensitivity analyses assessed the impact of reducing the Markov cycle length from 1 year to 0.5 year for the combined population, and differences between methods were evaluated using a 1-sample t test. RESULTS:Cubic polynomial regressions produced smooth age-utility curves with excellent fit for the combined population (R2 = 0.86), males (R2 = 0.96), and females (R2 = 0.92). Smoothed utilities modestly reduced uncertainty, particularly at older ages. Total QALE estimates from the Sullivan and Markov approaches were highly consistent, with small absolute differences (0.28-0.32 QALE) and strong correlation (R2 = 0.99) over the full remaining lifetime. The 1-sample t test showed that Markov QALE estimates were slightly higher than Sullivan estimates (mean difference 0.315 QALE, 95% CI 0.308-0.322; t = 85.47, P < .001), though the absolute differences were minor relative to overall QALE. Sensitivity analyses demonstrated that reducing the Markov cycle length had minimal impact on QALE estimates (differences ≤0.29 QALE), confirming robustness. CONCLUSIONS:These findings support the use of either method for population health assessment and health technology evaluation because both produce valid and reliable QALE estimates.

To evaluate the psychometric properties of EQ-5D-5L and SF-6Dv2 in a group of patients with breast or colorectal cancer. EQ-5D-5L, SF-6Dv2, and QLQ-C30 were completed at baseline and follow-up by patients with breast or colorectal cancer in Quebec, Canada. Ceiling effect was assessed by calculating the percentage of respondents reporting the best health state. Agreement between EQ-5D-5L and SF-6Dv2 was evaluated using intraclass correlation coefficients (ICC) and the Bland–Altman plot. Convergent validity for both instruments was assessed using the Spearman rank correlation coefficient (r) with QLQ-C30 serving as a calibration standard. Known-group validity was evaluated by comparing the scores of patients with different health conditions, while sensitivity was further assessed within these known groups using relative efficiency (RE). Finally, test–retest reliability and responsiveness were tested using ICC and the area under the receiver operating characteristic curve (AUC), respectively. 204 patients were enrolled at baseline and 103 were followed up. No ceiling effect was found for SF-6Dv2 compared to 16.67

Dans ce rapport, nous comparons empiriquement sept techniques d’élicitation pour évaluer les utilités associées aux états de santé décrits par le Short-Form 6-Dimension version 2 (SF-6Dv2) et où le pari ordinaire (standard gamble – SG) est considérée comme l’approche de référence. Ce faisant, cela nous permet d’estimer un ensemble de valeurs d’utilité pour le SF-6Dv2 à utiliser au Québec et d’introduire une nouvelle approche basée sur le choix dichotomique multiple borné (multiple bounded discrete choice, MBDC). Diverses techniques d’estimation économétrique ont été utilisés et toutes les analyses ont été réalisées à partir de données recueillies auprès de personnes francophones âgées de 18 ans et plus résidant au Québec, Canada, en 2016 et 2018. L’approche du choix expérimental discret (discrete choice experiment – DCE) combinée avec un best-worst scaling (DCEBWS) apparaît comme étant une alternative cohérente et comparable au SG. Il ressort également de nos analyses que l’’approche MBDC est réalisable et permet de générer des données d’utilité représentatives des préférences des répondants. L’ensemble de référence de valeurs d’utilité du SF-6Dv2 retenu pour le Québec est issu du DCEBWS et varie de -0,683 pour le pire état de santé (555655) à 1 pour une santé parfaite (111111). La dimension de la douleur présente ici les plus fortes diminutions de coefficients en termes d’ampleur, ce qui indique sa grande influence dans l’établissement des valeurs d’utilité des états de santé.

To develop algorithms mapping the Functional Assessment of Cancer Therapy—General Scale (FACT-G) onto the EuroQol 5-Dimension 5-level (EQ-5D-5 L) and the Short-Form Six-Dimension version 2 (SF-6Dv2) for patients with breast or colorectal cancers. An online survey was conducted to collect responses to FACT-G, EQ-5D-5L, and SF-6Dv2 from cancer patients in Quebec, Canada (N = 202). Linear models including ordinary least squares (OLS), Censored Least Absolute Deviations (CLAD), the robust MM-estimator model (MM), as well as mixture models including two-part model (TPM), and beta-based mixture (betamix) model were used. Mean absolute error (MAE), root mean squared error (RMSE), R2, Bayesian information criteria (BIC), and limits of agreement (LOA) calculated using the five cross-validation to assess the predictive ability of the models. Furthermore, the distribution of observed versus predicted values was assessed using Bland-Altman plot. Based on RMSE and MAE, mixture models better performed than linear models. The betamix model with truncation that included domains and squared terms was the best-performing algorithm for EQ-5D-5 L (MAE = 0.0518, RMSE = 0.0744, R2 = 46.40

BACKGROUND:A key challenge in ordinal methods is to anchor estimated health utility values onto the full health-dead scale. This study assessed five methods of anchoring. METHODS:Data were collected between 2016 and 2020 through two surveys conducted in the Quebec general population, with 1,176 and 908 respondents. Health utilities for the Short-Form 6-Dimension version 2 (SF-6Dv2) were estimated using ranking, composite time trade-off (cTTO), discrete choice experiment without (DCE) and with duration (DCETTO) methods. Anchoring was performed using five approaches: the dead state for ranking (Rank), linear mapping for DCE (DCEMapping), mean value for the worst health state (DCEWHS), hybrid modeling (DCEHybrid), and duration (DCETTO). Conditional logit was used for rank and DCE approaches, while a hybrid model and generalized least squares (GLS) were applied for the DCEHybrid and cTTO methods, respectively. Approaches were compared based on the sign and ordering of their coefficients, the mean absolute difference (MAD) between the observed mean cTTO values and the estimates of models, and the overall pattern of their estimations. RESULTS:A total of 17,200, 59,960, 8,500, and 16,464 observations were included for the DCETTO, ranking, cTTO, and DCE methods, respectively. The DCEHybrid method achieved the lowest MAD (0.056) from observed cTTO values, while DCETTO method had the highest (MAD = 0.423). Ranking and DCEMapping tended to underestimate utilities for better health states and overestimate for severe ones, while DCEWHS and DCETTO underestimated health utilities for all health states. Visual comparisons confirmed that predictions from models closely aligned with observed cTTO values for better health states. CONCLUSION:Hybrid anchoring method demonstrated better performance in predicting observed cTTO values, whereas DCETTO model showed greater deviations and a tendency to underestimate health state values. Hybrid anchoring method also appeared as the most appropriate and reliable approach for anchoring ordinal utility data to the full health-dead scale.

ObjectiveTo evaluate the comparative performance of SF-6Dv2 and EQ-5D-5L in the general population of Quebec (Canada), Tehran (Iran), and Japan.MethodsData on SF-6Dv2 and EQ-5D-5L were collected in the three countries. Descriptive differences in utility values between SF-6Dv2 and EQ-5D-5L were assessed using t-tests, as well as ceiling effects were evaluated based on the percentage of "no problem" levels reported. The known-group validity of both measures was assessed by comparing utility scores across health and demographic subgroups using t-tests or ANOVA and by calculating effect sizes across known groups. The area under the receiver operating characteristic curve (AUROC) analysis and F-statistic ratios were used to further validate the findings from the known-group validity analyses. Convergent validity for both instruments was assessed using Spearman's rank correlation coefficient. The agreement between instruments was evaluated using intraclass correlation coefficients (ICC) and Bland-Altman plots.ResultsA total of 2,378 respondents for Quebec, 3,061 for Tehran, and 3,933 for Japan were included. Differences in utility values between SF-6Dv2 and EQ-5D-5L were statistically significant, with SF-6Dv2 generally yielding lower utility scores. Both instruments demonstrated strong known-group validity, effectively distinguishing between diseased and healthy groups as well as various demographic characteristics. However, EQ-5D-5L outperformed SF-6Dv2 for most demographic characteristics based on AUROC analysis and F-statistic ratios. In contrast, their performance in distinguishing between healthy and diseased groups did not favor a particular instrument. Convergent validity analyses indicated strong associations between SF-6Dv2 and EQ-5D-5L utility values in Quebec (0.760) and Tehran (0.737). The agreement between SF-6Dv2 and EQ-5D-5L utility values was moderate in Quebec (0.69) and strong in Tehran (0.837). Bland-Altman plots indicated that differences between the two instruments tended to increase as the average score decreased.ConclusionBoth EQ-5D-5L and SF-6Dv2 demonstrated favorable psychometric performance in terms of known-group validity and convergent validity. These findings suggest that both instruments are valid tools for health utility measurement for use in general population.

To derive the first population norms from the EQ-5D-5L in Iran during the COVID-19 pandemic. Data were obtained from a study conducted in Iran during the COVID-19 pandemic in 2021. Face-to-face, computer-assisted interviews were conducted in a single visit, involving respondents from diverse sociodemographic backgrounds and providing a balanced representation of age and gender groups. Normative data for the EQ-5D-5L dimensions, EQ-5D-5L index values, and EuroQol visual analogue scale (EQ-VAS) scores were summarized by dimensions and disaggregated by age groups and gender. Multivariable logistic and Tobit regression models were used to investigate the associations between participants’ sociodemographic characteristics and their self-reported health, as measured by the EQ-5D-5L instrument, including responses on the five dimensions, EQ-5D-5L index values, and EQ-VAS scores. Among 1,004 participants, the mean index value and EQ-VAS score were 0.832 and 78.27, respectively. Full health was reported in 37.65

Context-specific cost-effectiveness thresholds (CETs) are critical for informed health policy decisions. This study provides the first direct estimate of willingness-to-pay per quality-adjusted life-year (WTP-Q) in Quebec, Canada. An online survey was conducted using a single-bounded dichotomous choice (SBDC) method to determine each respondent’s maximum WTP value. Using Wang and Turnbull models, WTP-Q values were estimated based on different scenarios adjusted for various time horizons: a few weeks (representing the final stages of life), 10, and 20 years. Data from 3,490 participants were used for analysis. WTP-Q differed significantly by scenario (P < 0.001), with higher values for shorter time horizons. For Wang model, mean WTP-Q decreased with longer time horizons, ranging from CA65,344 (95

Objective: To develop a value set for the Short-Form 6-Dimension version 2 (SF-6Dv2) by incorporating societal preferences obtained from three distinct approaches: Standard Gamble (SG), composite Time Trade-Off (cTTO), and Discrete Choice Experiment (DCE). Methods: Data were gathered from the general population of Quebec, Canada, using the standardized valuation protocol developed by EuroQol for the cTTO and DCE tasks, as well as the valuation protocol developed by Sheffield University for the SG. The SG and cTTO data were analyzed using OLS, GLS, GLS Tobit, and heteroskedastic Tobit models. Conditional logit model was used for the DCE, while hybrid, hybrid Tobit, and heteroskedastic hybrid were applied to analyze the combined data from SG, cTTO, and DCE. The performance of models was assessed using mean absolute error (MAE), the logical consistency of the parameters, and significance levels. Results: Over 56,000 observations collected from the SG, cTTO, and DCE were analyzed. The utility values generated by DCE were generally lower than those provided by cTTO and SG. Among the models tested, the heteroskedastic hybrid model demonstrated the best fit in terms of logical consistency and statistically significant coefficients. This model generated a value set ranging from-0.216 for the worst health state (555655) to 1 for full health (111111), with 0.52% of the values being negative and a MAE of 0.281. Among dimensions, the largest decrements were consistently found in the pain dimension, highlighting its significant impact on overall health state valuations. Conclusion: A heteroskedastic hybrid model using data from SG, cTTO, and DCE was identified as the most effective approach for generating the SF-6Dv2 value set and is expected to provide key input for healthcare decision-making.

Objective To identify and classify the challenges of implementing strategic purchasing for use in decision-making. Methods We conducted a systematic review of qualitative studies using a meta-synthesis approach based on the methods described by Paterson et al. in "Meta-study of qualitative health research: a practical guide to meta-analysis and meta synthesis, vol 3." Three electronic databases (PubMed, Scopus, and Web of Sciences) were searched up to 30 October 2020. Results Eleven studies were included in this review. Seven overarching concepts that acted as the main challenges of services strategic purchasing emerged as follows: structure and organization, policy-making and management, resources, service provision, contract, performance, and public involvement. Conclusion The results of this study showed that implementation of strategic purchasing in developing countries faces different challenges at different levels. Identifying challenges in each context is vital for policymakers because these challenges can vary from place to place. However, awareness of these challenges in peer countries can give good clues to decision-makers to make appropriate decisions.

Background: An updated version of the Short-Form 6-Dimension (SF-6D) Classification System has been developed. This new version (SF-6Dv2) with improved consistency and dimension descriptors is now requiring the development of new utility value sets. The aim of this study was to estimate an SF-6Dv2 value set from a general population in Quebec, Canada. Methods: A discrete choice experiment with time trade-off (DCETTO) was conducted using two designs: binary choice sets (Design 1) and best-worst choice sets (Design 2). Design 1 consisted of binary choice sets along with an associated duration, and Design 2 included Design 1 and a third scenario describing "immediate death." Various logit model specifications were employed to estimate value sets separately for Design 1 and in combination with Design 2. Heterogeneity in preferences was assessed using a mixed logit model. Results: The survey was completed online by 1208 participants and 1153 were included for analysis. The model combining Design 1 and 2 data was considered as the best fitting model for estimating the final value set. It provided a value set with logical consistent coefficients and showed the lowest standard errors. Values ranged from -0.683 for the worst health state (555655) to 1 for full health (111111), with 13.01% of the values being negative. Preference values were the most affected by pain dimension and the least by vitality dimension. Preference heterogeneity existed for all the most severe levels of dimensions. Conclusion: This study provided the SF-6Dv2 value set for use in Quebec, Canada. The recommended value set is the anchored consistent model combining data from Design 1 and 2 using a conditional logit.

To empirically compare four preference elicitation approaches, the discrete choice experiment with time (DCETTO), the Best-Worst Scaling with time (BWSTTO), DCETTO with BWSTTO (DCEBWS), and the Standard Gamble (SG) method, in valuing health states using the SF-6Dv2. A representative sample of the general population in Quebec, Canada, completed 6 SG tasks or 13 DCEBWS (i.e., 10 DCETTO followed by 3 BWSTTO). Choice tasks were designed with the SF-6Dv2. Several models were used to estimate SG data, and the conditional logit model was used for the DCE or BWS data. The performance of SG models was assessed using prediction accuracy (mean absolute error [MAE]), goodness of fit using Bayesian information criterion (BIC), t-test, Jarque-Bera (JB) test, Ljung-Box (LB) test, the logical consistency of the parameters, and significance levels. Comparison between approaches was conducted using acceptability (self-reported difficulty and quality levels in answering, and completion time), consistency (monotonicity of model coefficients), accuracy (standard errors), dimensions coefficient magnitude, correlation between the value sets estimated, and the range of estimated values. The variance scale factor was computed to assess individuals’ consistency in their choices for DCE and BWS approaches. Out of 828 people who completed SG and 1208 for DCEBWS tasks, a total of 724 participants for SG and 1153 for DCE tasks were included for analysis. Although no significant difference was observed in self-reported difficulties and qualities in answers among approaches, the SG had the longest completion time and excluded participants in SG were more prone to report difficulties in answering. The range of standard errors of the SG was the narrowest (0.012 to 0.015), followed by BWSTTO (0.023 to 0.035), DCEBWS (0.028 to 0.050), and DCETTO (0.028 to 0.052). The highest number of insignificant and illogical parameters was for BWSTTO. Pain dimension was the most important across dimensions in all approaches. The correlation between SG and DCEBWS utility values was the strongest (0.928), followed by the SG and BWSTTO values (0.889), and the SG and DCETTO (0.849). The range of utility values generated by SG tended to be shorter (-0.143 to 1) than those generated by the other three methods, whereas BWSTTO (-0.505 to 1) range values were shorter than DCETTO (-1.063 to 1) and DCEBWS (-0.637 to 1). The variance scale factor suggests that respondents had almost similar level of certainty or confidence in both DCE and BWS responses. The SG had the narrowest value set, the lowest completion rates, the longest completion time, the best prediction accuracy, and produced an unexpected sign for one level. The BWSTTO had a narrower value set, lower completion time, higher parameter inconsistency, and higher insignificant levels compared to DCETTO and DCEBWS. The results of DCEBWS were more similar to SG in number of insignificant and illogical parameters, and correlation.

The second version of the Short-Form 6-Dimension (SF-6Dv2) classification system has recently been developed. The objective of this study was to develop a value set for SF-6Dv2 based on the societal preferences of a general population in the capital of Iran. A representative sample of the capital of Iran (n = 3061) was recruited using a stratified multistage quota sampling technique. Face-to-face interviews were conducted using binary choice sets from the international valuation protocol of the discrete choice experiment with duration. The conditional logit was used to estimate the final value set, and a latent class model was employed to assess heterogeneity of preferences. Coefficients generated from the models were logically consistent and significant. The best model was the one that included an additional interaction term for cases where one or more dimensions reached their most severe levels. It provides a value set with logical consistent coefficients and the lowest percentage of worse than death health states. Predicted values for the SF-6Dv2 were within the range of − 0.796–1. Pain dimension had the largest impact on utility decrement, whereas vitality had the least impact. The presence of preference heterogeneity was evident, and the Bayesian Information Criterion indicated the optimal fit for a latent class model with two classes. This study provided the SF-6Dv2 value set for application in the context of Iran. This value set will facilitate the use of the SF-6Dv2 instrument in health economic evaluations and clinical settings.

Objective: to assess the feasibility of a new stated preference approach, the multiple bounded dichotomous choice (MBDC), designed to generate value sets for preference-based measurement of health-related quality of life. Methods: MBDC and standard gamble (SG) tasks were completed to derive SF-6Dv2 value sets from a sample of the general population in Quebec, Canada. Participants were randomized between the two approaches: 6 health states were evaluated in SG and 11 health states in MBDC. Several models were used to estimate data in each approach, and the preferred models were chosen by using mean absolute error (MAE), logical consistency of parameters, and significance levels. Results of MBDC were compared with SG in terms of acceptability (selfreported difficulty and quality levels in answering, and completion time), consistency (monotonicity of model coefficients), accuracy (standard errors), dimensions coefficient magnitude, correlation between the value sets estimated, and the range of estimated values. The intra-class correlation coefficient (ICC) was computed to assess value sets' consistency. Results: Out of 655 individuals who completed MBDC tasks and 828 who completed SG tasks, a total of 585 participants for MBDC and 714 for SG tasks were included for analysis. The preferred models for both approaches were GLS Tobit. No significant difference was observed in self-reported difficulties and qualities in answers among approaches, but MBDC had less excluded participants and was less prone to report difficulties in answering. Additionally, completion time in the MBDC group was significantly lower (99.80 vs 68.12 s). Most standard errors in the MBDC were lower than those in SG, and the number of non-significant parameters was also lower. The range of utility values generated by MBDC tended to be wider (-0.372 to 1) than those generated by the SG (-0.137 to 1) and the number of worse-than-dead states in MBDC (0.91%) was higher than for SG (0.08%). The Pain dimension was identified as the most significant, while the Vitality dimension showed the lowest significant decrement. Both approaches exhibited a tendency to overestimate severe health state values and underestimate better health state values. The correlation and ICC between the two value sets were 0.937 and 0.983, respectively. Conclusion: Based on empirical evidence, it can be inferred that the MBDC method is not only feasible but also holds the potential to generate meaningful and well-informed preference data from respondents. This approach can be used to derive a value set for preference-based instrument.

Background: Self-medication is one of the main socioeconomic and health problems in different societies and irrational drug consumption can sometimes lead to unfavorable outcomes and even death. The present study has been performed to investigate self-medication and its relevant factors in students of Shahid Sadoughi University of Medical Sciences in Yazd. Methods: This analytical study investigate 300 students of Shahid Sadoughi University of Medical Sciences Using Cochran's formula in Yazd (2020). The samples were selected by stratified sampling proportional to the strata size and the data were collected by a researcher-made questionnaire from Gholipoor etal's article (2012) and analyzed by descriptive statistics such as frequency, percentage, mean ± SD and multiple response analysis (MRA), chi-square and binary logistic regression. All statistical analyses were done by SPSS 20 (α=0.05). Results: The total self-medication rate was estimated 39%. The most common causes of self-medication among the students were the experience of the disease (50.4%) and considering the disease non-risky (47.0%).The most common self-medication case was cold (53.0%) and the most common way of supplying the drug for self-medication was purchasing from drugstores (52.1%).The results of multiple logistic regression showed that the only variable having a significant effect on the probability of self-medication was income (p = 0.02); odds of self-medication in students with an average income of 20-30 million Rials was about twice as high as students with an income of more than 30 million Rials (p = 0.02, OR = 1.8). Conclusion: Self-medication is not a safe habit and it can cause complications such as drug interaction, addiction, drug poisoning, concealment of the disease and in general, health risks. Meanwhile, the students’ attitude towards self-medication can affect the behavior of their future patients. So, it is suggested to inform students in this area.

COVID-19 is a global challenge that negatively affects the health-related quality of life (HRQoL) of the general population. The current study aimed to evaluate HRQoL and its associated factors among the Iranian general population during the COVID-19 pandemic. The data were collected in 2021 using the EuroQol 5-Dimension 3-Level (EQ-5D-3L) and EQ-5D Visual Analog Scale (EQ VAS) questionnaires through an online survey. Participants were recruited via social media from the Fars province. The multiple binary logistic regression model was used to identify factors influencing participants' HRQoL. Kolmogorov-Smirnov, the t-test, ANOVA, and the chi-square test were used. All tests were conducted at a significance level of 5% using Stata 14.2 and SPSS 16. A total of 1,198 participants were involved in this cross-sectional study. The mean age of participants was 33.3 (SD:10.2), and more than half were women (55.6%). The mean EQ-5D-3L index value and EQ-VAS of the respondents were 0.80 and 77.53, respectively. The maximum scores of the EQ-5D-3L and EQ-VAS in the present study were 1 and 100, respectively. The most frequently reported problems were anxiety/depression (A/D) (53.7%), followed by pain/discomfort (P/D) (44.2%). Logistic regression models showed that the odds of reporting problems on the A/D dimension increased significantly with supplementary insurance, including concern about getting COVID-19, hypertension, and asthma, by 35% (OR = 1.35; P = 0.03), 2% (OR = 1.02; P = 0.02), 83% (OR = 1.83; P = 0.02), and 6.52 times (OR = 6.52; P = 0.01), respectively. The odds of having problems on the A/D dimension were significantly lower among male respondents, those in the housewives + students category, and employed individuals by 54% (OR = 0.46; P = 0.04), 38% (OR = 0.62; P = 0.02) and 41% (OR = 0.59; P = 0.03), respectively. Moreover, the odds of reporting a problem on the P/D dimension decreased significantly in those belonging in a lower age group and with people who were not worried about getting COVID-19 by 71% (OR = 0.29; P = 0.03) and 65% (OR = 0.35; P = 0.01), respectively. The findings of this study could be helpful for policy-making and economic evaluations. A significant percentage of participants (53.7%) experienced psychological problems during the pandemic. Therefore, effective interventions to improve the quality of life of these vulnerable groups in society are essential.

The main aim of this study is to estimate a national value set of the EQ-5D-5L questionnaire for Iran. The composite time trade-off (cTTO) and discrete choice experiment (DCE) methods; and the protocol for EuroQol Portable Valuation Technology (EQ-PVT) were used to estimate the Iran national value set. 1179 face-to-face computer-assisted interviews were conducted with adults that were recruited from five Iran major cities in 2021. Generalized least squares, Tobit, heteroskedastic, logit, and hybrid models were used to analyze the data and to identify the best fitting model. According to the logical consistency of the parameters, significance levels and prediction accuracy indices of the MAE; a heteroscedastic censored Tobit hybrid model combining cTTO and DCE responses was considered as the best fitting model to estimate the final value set. The predicted values ranged from − 1.19 for the worst health state (55555) to 1 for full health (11111), with 53.6% of the predicted values being negative. Mobility was the most influential dimension on health state preference values. The present study estimated a national EQ-5D-5L value set for Iranian policy makers and researchers. The value set enables the EQ-5D-5L questionnaire to use to calculate QALYs to assist the priority setting and efficient allocation of limited healthcare resources.