Health-Related Quality of Life in Osteoporosis Patients with and without Fractures in Tehran, Iran.

BACKGROUND: This study aimed to measure the health-related quality of life (HRQOL) of patients with osteoporosis with and without fractures in Tehran, the capital city of Iran. METHODS: We surveyed a sample of 478 patients with osteoporosis aged over 50 years. Participants with fractures included patients referred to hospitals due to osteoporotic fractures which were alive 6 months after the fracture. Participants without fractures were randomly selected from patients with a definite diagnosis of osteoporosis admitted to 3 outpatient clinics in Tehran. Data were collected using the EuroQol 5-dimensional 5-level questionnaire. Statistical differences between patients with and without fracture were tested with Pearson's χ2 test, Student's t-test, and the Mann-Whitney U-test. The association between HRQOL and other variables was evaluated using a multiple linear regression model. RESULTS: The patients' mean age±standard deviation was 67.3±11.9 years, and 74.1% were women. One hundred and seventeen (23%) patients had hip fractures, 56 (11%) had vertebral fractures, 127 (25%) had forearm fractures, and 178 (40%) had no fractures. The median (interquartile range) values of HRQOL scores of those with hip, vertebral, and forearm fractures and those with no history of fracture were 0.53 (0.22), 0.60 (0.28), 0.64 (0.26), and 0.64 (0.27), respectively. The multiple regression model revealed a significant relationship between the HRQOL scores and sex, marital status, employment status, presence of any chronic illness in addition to osteoporosis, and type of fracture. CONCLUSIONS: Osteoporosis and its related fractures can reduce the HRQOL.

Cost of Illness of COVID-19 and Its Consequences on Health and Economic System.

The outbreak of COVID-19 has had destructive influences on social and economic systems as well as many aspects of human life. In this study, we aimed to estimate the economic effects of COVID-19 at the individual and societal levels during a fiscal year. This cost of illness analysis was used to estimate the economic burden of COVID-19 in Iran. Data of the COVID-19 patients referred to the hospitals affiliated to Bushehr University of Medical Sciences in 2021 were collected through the Hospital Information System (HIS). The study methodology was based upon the human capital approach and bottom-up technique. The COVID-19 pandemic has resulted in 9711 confirmed hospital cases and 717 deaths in Bushehr province during the study period. The direct and indirect costs were estimated to be $1446.06 and $3081.44 per patient. The economic burden for the province and country was estimated to be $43.97 and $2680.88 million. The results showed that the economic burden of this disease particularly premature death costs is remarkably high. Therefore, in order to increase the resiliency of the health system and the stability in service delivery, preventive-oriented strategies have to be more seriously considered by policymakers.

Direct costs of common osteoporotic fractures (Hip, Vertebral and Forearm) in Iran.

BACKGROUND: Osteoporotic fractures impose significant costs on society. The objective of this study was to estimate the direct costs of the hip, vertebral, and forearm fractures in the first year after fracture incidence in Iran. METHODS: We surveyed a sample of 300 patients aged over 50 years with osteoporotic fractures (hip, vertebral, and forearm) admitted to four hospitals affiliated to Tehran University of Medical Sciences, Iran, during 2017 and were alive six months after the fracture. Inpatient cost data were obtained from the hospital patient records. Using a questionnaire, the data regarding outpatient costs were collected through a phone interview with patients at least six months after the fracture incidence. Direct medical and non-medical costs were estimated from a societal perspective. All costs were converted to the US dollar using the average exchange rate in 2017 (1USD = IRR 34,214) RESULTS: The mean ± standard deviation (SD) age of the patient was 69.83 ± 11.25 years, and 68% were female. One hundred and seventeen (39%) patients had hip fractures, 56 (18.67%) patients had vertebral fractures, and 127 (42.33%) ones had forearm fractures. The mean direct cost (medical and non-medical) during the year after hip, vertebral and forearm fractures were estimated at USD5,381, USD2,981, and USD1,209, respectively. CONCLUSION: The direct cost of osteoporotic fracture in Iran is high. Our findings might be useful for the economic evaluation of preventive and treatment interventions for osteoporotic fractures as well as estimating the economic burden of osteoporotic fractures in Iran.

Mapping the cancer-specific FACT-B onto the generic SF-6Dv2.

INTRODUCTION: The health-related quality of life (HRQoL) data extracted from cancer-specific questionnaires are often non-preference based, while patient preference-based utility data are required for health economic evaluation. This study aimed to map Functional Assessment of Cancer Therapy-Breast (FACT-B) subscales onto the Short Form six Dimension as an independent instrument (SF-6Dv2ind-6) using the data gathered from patients with breast cancer. METHODS: Data for 420 inpatient and outpatient patients with breast cancer were gathered from the largest academic center for cancer patients in Iran. The OLS and Tobit models were used to predict the values of the SF-6Dv2ind-6 with regard to the FACT-B subscales. Prediction accuracy of the models was determined by calculating the root mean square error (RMSE) and mean absolute error (MAE). The relationship between the fitted and observed SF-6Dv2ind-6 values was examined using the Intraclass Correlation Coefficients (ICC). Goodness of fit of models was assessed using the predicted R2 (Pred R2) and adjusted R2 (Adj R2). A tenfold cross-validation method was used for validation of models. RESULTS: Data of 416 patients with breast cancer were entered into final analysis. The model included main effects of FACT-B subscales, and statistically significant clinical and demographic variables were the best predictor for SF-6Dv2ind-6 (Model S3 of OLS with Adj R2 = 61.02%, Pred R2 = 59.25%, MAE = 0.0465, RMSE = 0.0621, ICC = 0.678, AIC = -831.324, BIC = -815.871). CONCLUSION: The best algorithm developed for SF-6Dv2ind-6 enables researchers to convert cancer-specific instruments scores into preference-based scores when the data are gathered using cancer-specific instruments.