Objectives: Health-related quality of life (HRQoL) is one of the most important outcome measures for patients. The purpose of this study was to evaluate HRQoL and related factors in Coronavirus disease 2019 (COVID-19) patients. Methods: A total of 420 COVID-19 patients who had been discharged from hospital were selected using a systematic sampling. The EuroQol 5-dimensional-5 levels (EQ-5D-5L) questionnaire along with medical records of the patients were used to gather the data. The t test and analysis of variance were employed to test the difference between mean EQ-5D-5L scores, and the BetaMix model was used to investigate factors associated with EQ-5D-5L scores. Results: The mean score for the patients who completed the EQ-5D-5L questionnaire (n = 409) was 0.6125. The EQ-5D-5L scores were significantly higher in males, patients with younger age , those with a low level of education, the employed, patients who worked in uncrowded workplaces, patients without diabetes, and those who were not admitted to intensive care unit. The BetaMix model showed that gender, age, education, employment status, having diabetes, heart failure, and admission to the intensive care unit were significant independent predictors of the EQ-5D-5L index values. Conclusion: The mean score for EQ-5D-5L in COVID-19 patients was low in this study. Some of the factors, especially aging and having diabetes, should be considered in the aftercare of patients to improve their HRQoL.

Background: Assessing the health-related quality of life in patients with type 2 diabetes mellitus is important for evaluation of treatment outcome. The purpose of this study was to evaluate the health-related quality of life in type 2 diabetes mellitus patients and its related factors in Yazd. Methods: Data were gathered by using the EuroQoL-five-dimension-5 level instrument as well as using medical records of 734 outpatients with type 2 diabetes mellitus who were referred to the largest governmental diabetes center in South of Iran, Yazd province. When appropriate, the Kruskal–Wallis test or the Wilcoxon test was used to test the difference in the health-related quality-of-life scores in each factor. Finally, the adjusted limited dependent variable mixture model was developed to investigate factors associated with health-related quality-of-life scores. Results: The mean and median of the EuroQoL-five-dimension-5 level index values of 717 patients who completed the questionnaires were 0.75 ± 0.006 and 0.72 ± 0.20, respectively, and those of the Visual Analogue Scale scores were 69.25 ± 0.63 and 75 ± 30, respectively. The mean scores for health-related quality of life were significantly higher for employed, educated, single, and male patients, as well as patients without comorbidities, diabetes-related complications, and hemoglobin A1c level >7%. Adjusted limited dependent variable mixture model showed that gender, age, marital status, and diabetes-related complications are significant independent predictors of EuroQoL-five-dimension-5 level index value. Conclusion: The mean scores for health-related quality of life in patients with type 2 diabetes mellitus were moderate in this study, and this finding is consistent with health-related quality-of-life scores reported in other studies conducted in the Middle East region. Therefore, health-related quality of life should be the most important consideration in the management of patients. In parallel, some factors, especially gender, should be considered to improve health-related quality of life.

Purpose: Cardiovascular diseases (CVDs) are the major causes of mortalities worldwide. This study was conducted to evaluate the direct and indirect costs of coronary artery disease (CAD) in Iran. Patients and Methods: This is a prevalence-based cost-of-illness (COI) study that estimates the direct and indirect costs of CAD. The study conducted over a six-month period from April to September in 2017. Patients were recruited from Madani hospital in Tabriz, Iran. A total of 379 patients were investigated from societal perspective. Direct costs were estimated using the bottom-up costing approach and indirect costs were estimated using the Human Capital (HC) approach. A generalized linear model of regression was used to explore the relation between total cost and socio-demographic variables. The total annual mean cost was compared to Gross Domestic Product (GDP) per capita which was reported in the form of Purchasing Power Parity (PPP) index. To deal with uncertainty, one-way sensitivity analysis was performed. Results: Total costs per patient in one year were estimated to be IRR 63452290.17 ($PPP 7736.19) at a 95% confidence interval (58191511.73– 68713068.60), the biggest part of which is related to direct medical costs with IRR 33884019.53 per year ($PPP 4131.18) (54%). Direct non-medical costs were estimated IRR 1655936.68 ($PPP 201.89) per patient (2%) and indirect costs were estimated IRR 27912333.97 per patient ($PPP 3403.11) (44%), which 62% of indirect costs is related to patients’ work absenteeism. Conclusion: This study estimates the direct (56%) and indirect (44%) costs associated with CAD. The study explores the essential drivers of the costs and provides the magnitude of the burden in terms of the share of GDP. The outcomes can be used in priority setting, in particular for cost benefit analysis, and adopting new policies regarding insurance coverage and equity issues.

Introduction: Economic evaluation of healthcare interventions usually needs accurate data on utility and health-related quality-of-life scores. The aim of this study is to map QLQ-C30 scale score onto EQ-5D-5L and SF-6D utility values in colorectal cancer (CRC) patients. Methods: EQ-5D-5L, SF-6D, and QLQ-C30 were completed by 252 patients with CRC who were referred to three cancer centers in Tehran between May and September 2017. Moreover, OLS, Tobit, and CLAD models were used to predict EQ-5D-5L and SF-6D values. The goodness of fit of models was evaluated using Pred R² and Adj R². In addition, their predictive performance was assessed by MAE, RMSE, ICC, MID, and Spearman’s correlation coefficients between observed and predicted EQ-5D-5L and SF-6D values. Models were validated using a 10-fold cross-validation method. Results: Considering the goodness of fit and predictive ability of models, the OLS Model 2 performed best for EQ-5D-5L (Adj R² = 58.09%, Pred R² = 58.93%, MAE = 0.0932, RMSE = 0.129) and the OLS Model 3 performed best for SF-6D (Adj R² = 54.90%, Pred R² = 55.62%, MAE = 0.0485, RMSE = 0.0634). Conclusion: Our results demonstrated that algorithms developed based on OLS Models 1 and 2 are the best for predicted EQ-5D-5L and SF-6D values, respectively.