Objectives: Socioeconomic inequality in metabolic syndrome (MetS) remains poorly understood in Iran. The present study examined the extent of the socioeconomic inequalities in MetS and quantified the contribution of its determinants to explain the observed inequality, with a focus on middle-aged adults in Iran. Methods: This cross-sectional study used data from the Ravansar Non-Communicable Disease cohort study. A sample of 9975 middle-aged adults aged 35-65 years was analyzed. MetS was assessed based on the International Diabetes Federation definition. Principal component analysis was used to construct socioeconomic status (SES). The Wagstaff normalized concentration index (CIn) was employed to measure the magnitude of socioeconomic inequalities in MetS. Decomposition analysis was performed to identify and calculate the contribution of the MetS inequality determinants. Results: The proportion of MetS in the sample was 41.1%. The CIn of having MetS was 0.043 (95% confidence interval, 0.020 to 0.066), indicating that MetS was more concentrated among individuals with high SES. The main contributors to the observed inequality in MetS were SES (72.0%), residence (rural or urban, 46.9%), and physical activity (31.5%). Conclusions Our findings indicated a pro-poor inequality in MetS among Iranian middle-aged adults. These results highlight the importance of persuading middle-aged adults to be physically active, particularly those in an urban setting. In addition to targeting physically inactive individuals and those with low levels of education, policy interventions aimed at mitigating socioeconomic inequality in MetS should increase the focus on high-SES individuals and the urban population.

Objectives: This study aimed to investigate the role of time preferences in compliance with coronavirus disease 2019 (COVID-19) preventive behaviors in an adult population of Iran. Methods: A web-based questionnaire was utilized to conduct a cross-sectional survey of 672 Iranian adults. The parameters of time preferences were estimated using a quasi-hyperbolic discounting model, and the relationship between COVID-19 preventive behaviors and time preferences was examined using a probit regression model. Results: A significant association was observed between the preventive behaviors of COVID-19 and the levels of patience and present-biased preferences among the study participants. Individuals who exhibited low levels of patience were found to be 12.8 percentage points less inclined to follow preventive behaviors compared to those with high levels of patience. The likelihood of having good preventive behaviors of COVID-19 was found to decrease by 14.3 percentage points among individuals with a present bias as opposed to those with a bias toward future. Conclusions Patience and present-biased preferences are important determinants of adopting preventive behaviors against COVID-19. These behavioral characteristics should be considered in the design of control and prevention programs. Considering people’s discounting behavior and time (in)consistency in their preferences in the design of COVID-19 policy interventions can provide valuable insights for developing tailored public health policy interventions.

Objectives: This study aimed to investigate the role of time preferences in compliance with coronavirus disease 2019 (COVID-19) preventive behaviors in an adult population of Iran. Methods: A web-based questionnaire was utilized to conduct a cross-sectional survey of 672 Iranian adults. The parameters of time preferences were estimated using a quasi-hyperbolic discounting model, and the relationship between COVID-19 preventive behaviors and time preferences was examined using a probit regression model. Results: A significant association was observed between the preventive behaviors of COVID-19 and the levels of patience and present-biased preferences among the study participants. Individuals who exhibited low levels of patience were found to be 12.8 percentage points less inclined to follow preventive behaviors compared to those with high levels of patience. The likelihood of having good preventive behaviors of COVID-19 was found to decrease by 14.3 percentage points among individuals with a present bias as opposed to those with a bias toward future. Conclusions Patience and present-biased preferences are important determinants of adopting preventive behaviors against COVID-19. These behavioral characteristics should be considered in the design of control and prevention programs. Considering people’s discounting behavior and time (in)consistency in their preferences in the design of COVID-19 policy interventions can provide valuable insights for developing tailored public health policy interventions.

Objectives: This study aimed to investigate the role of time preferences in compliance with coronavirus disease 2019 (COVID-19) preventive behaviors in an adult population of Iran. Methods: A web-based questionnaire was utilized to conduct a cross-sectional survey of 672 Iranian adults. The parameters of time preferences were estimated using a quasi-hyperbolic discounting model, and the relationship between COVID-19 preventive behaviors and time preferences was examined using a probit regression model. Results: A significant association was observed between the preventive behaviors of COVID-19 and the levels of patience and present-biased preferences among the study participants. Individuals who exhibited low levels of patience were found to be 12.8 percentage points less inclined to follow preventive behaviors compared to those with high levels of patience. The likelihood of having good preventive behaviors of COVID-19 was found to decrease by 14.3 percentage points among individuals with a present bias as opposed to those with a bias toward future. Conclusions Patience and present-biased preferences are important determinants of adopting preventive behaviors against COVID-19. These behavioral characteristics should be considered in the design of control and prevention programs. Considering people’s discounting behavior and time (in)consistency in their preferences in the design of COVID-19 policy interventions can provide valuable insights for developing tailored public health policy interventions.

Objectives: This study aimed to examine the effect of the coronavirus disease 2019 (COVID-19) outbreak on the hospitalization rate, emergency department (ED) visits, and outpatient clinic visits in western Iran. Methods: We collected data on the monthly hospitalization rate, rate of patients referred to the ED, and rate of patients referred to outpatient clinics for a period of 40 months (23 months before and 17 months after the COVID-19 outbreak in Iran) from all 7 public hospitals in the city of Kermanshah. An interrupted time series analysis was conducted to examine the impact of COVID-19 on the outcome variables in this study. Results: A statistically significant decrease of 38.11 hospitalizations per 10,000 population (95% confidence interval [CI], 24.93–51.29) was observed in the first month of the COVID-19 outbreak. The corresponding reductions in ED visits and outpatient visits per 10,000 population were 191.65 (95% CI, 166.63–216.66) and 168.57 (95% CI, 126.41–210.73), respectively. After the initial reduction, significant monthly increases in the hospitalization rate (an increase of 1.81 per 10,000 population), ED visits (an increase of 2.16 per 10,000 population), and outpatient clinic visits (an increase of 5.77 per 10,000 population) were observed during the COVID-19 pandemic. Conclusion Our study showed that the utilization of outpatient and inpatient services in hospitals and clinics significantly declined after the COVID-19 outbreak, and use of these services did not return to pre-outbreak levels as of June 2021.