ABSTRACT
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.
ABSTRACT
As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- Firefly Algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN model is trained with Levenberg-Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages. In respect to ANN-FA, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, which indicates the outperformance of ANN-FA for predicting COVID-19 new cases in Iran. Finally, the proposed ANN-ABC and ANN-FA are applied for simulating the COVID-19 new cases data in different countries. The results revealed that both models can be used as a robust predictor of COVID-19 data and in a majority of cases ANN-FA outperforms the ANN-ABC.