1.Measuring and Decomposing Socioeconomic Inequalities in Adult Obesity in Western Iran.
Farid NAJAFI ; Yahya PASDAR ; Behrooz HAMZEH ; Satar REZAEI ; Mehdi MORADI NAZAR ; Moslem SOOFI
Journal of Preventive Medicine and Public Health 2018;51(6):289-297
OBJECTIVES: Obesity is a considerable and growing public health concern worldwide. The present study aimed to quantify socioeconomic inequalities in adult obesity in western Iran. METHODS: A total of 10 086 participants, aged 35-65 years, from the Ravansar Non-communicable Disease Cohort Study (2014-2016) were included in the study to examine socioeconomic inequalities in obesity. We defined obesity as a body mass index ≥30 kg/m2. The concentration index and concentration curve were used to illustrate and measure wealth-related inequality in obesity. Additionally, we decomposed the concentration index to identify factors that explained wealth-related inequality in obesity. RESULTS: Overall, the prevalence of obesity in the total sample was 26.7%. The concentration index of obesity was 0.04; indicating that obesity was more concentrated among the rich (p < 0.001). Decomposition analysis indicated that wealth, place of residence, and marital status were the main contributors to the observed inequality in obesity. CONCLUSIONS: Socioeconomic-related inequalities in obesity among adults warrant more attention. Policies should be designed to reduce both the prevalence of obesity and inequalities in obesity by focusing on those with higher socioeconomic status, urban residents, and married individuals.
Adult*
;
Body Mass Index
;
Cohort Studies
;
Health Equity
;
Humans
;
Iran*
;
Marital Status
;
Obesity*
;
Prevalence
;
Public Health
;
Social Class
;
Socioeconomic Factors*
2.Measurement and Decomposition of Socioeconomic Inequality in Metabolic Syndrome: A Cross-sectional Analysis of the RaNCD Cohort Study in the West of Iran
Moslem SOOFI ; Farid NAJAFI ; Shahin SOLTANI ; Behzad KARAMIMATIN
Journal of Preventive Medicine and Public Health 2023;56(1):50-58
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.
3.The Role of Time Preferences in Compliance With COVID-19 Preventive Behaviors in Iran: A Quasi-hyperbolic Discounting Approach
Moslem SOOFI ; Ali Kazemi KARYANI ; Shahin SOLTANI ; Zahra ALIPOOR ; Behzad KARAMIMATIN
Journal of Preventive Medicine and Public Health 2025;58(3):326-335
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.
4.The Role of Time Preferences in Compliance With COVID-19 Preventive Behaviors in Iran: A Quasi-hyperbolic Discounting Approach
Moslem SOOFI ; Ali Kazemi KARYANI ; Shahin SOLTANI ; Zahra ALIPOOR ; Behzad KARAMIMATIN
Journal of Preventive Medicine and Public Health 2025;58(3):326-335
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.
5.The Role of Time Preferences in Compliance With COVID-19 Preventive Behaviors in Iran: A Quasi-hyperbolic Discounting Approach
Moslem SOOFI ; Ali Kazemi KARYANI ; Shahin SOLTANI ; Zahra ALIPOOR ; Behzad KARAMIMATIN
Journal of Preventive Medicine and Public Health 2025;58(3):326-335
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.