Background In this study, the association between the social determinants of health (SDH) as well as other health risk factors and outcomes will be evaluated at different socioeconomic layers. Methods/design This is a prospective cohort study that was launched in January 2018 on Tehran University of Medical Sciences’ employees. The initial enrolment phase will continue up to March 2021, or until a sample size of 5500 is reached. In addition to annual phone-calls, the participants will be followed thrice at 5-year intervals. Data are collected through blood and urine samples, complete physical examination, anthropometric evaluation, and the completion of questionnaires related to SDH, such as socioeconomic status and social capital, history of diseases, lifestyle (including, nutrition, physical activity, cigarette and hookah smoking), occupational exposures (including psychosocial factors at work and work-family conflicts), and different aspects of physical, mental and occupational health as health outcomes. The association between independent variables and health (objective or subjective) are examined using multiple models and by controlling the confounding effects. Moreover, the trend in lifestyle changes and its impact on health are evaluated. Discussion Our study will explore the key social determinants as well as other factors including socioeconomic status and social capital, history of diseases, lifestyle and occupational exposures that affect health. This will provide social and occupational health decision-makers and stakeholders with new and valuable evidence in an era in which we are witnessing huge changes in lifestyle.
Aim Metabolic syndrome is one of the outcomes of a sedentary lifestyle in the modern world. In this study, we want to introduce the predictors of metabolic syndrome using anthropometric indices and Bio-Electrical Impedance Analysis (BIA) test values. Method This cross-sectional study was performed on 2284 employees of Tehran University of Medical Sciences in different job categories. Metabolic syndrome was determined according to IDF criteria. Anthropometric dimensions, para-clinical tests, basic information were collected from the participants. Also, the body analysis of the participants was performed using a BIA method. Result The prevalence of metabolic syndrome in this study was 23.2% based on IDF criteria, which was 21% and 26.6% in men and women, respectively. The most important factor among the components of IDF criteria was HDL deficiency. In this study, neck circumference, fat mass, visceral fat, muscle mass percentage and waist to height ratio were observed as predictors of metabolic syndrome. Conclusion This study realized that there is association between fat mass, fat-free mass, visceral fat and muscle mass which all are some elements of body composition analysis and metabolic syndrome as a major health issue.
Background: Self-rated health (SRH) indicator is widely used and recommended in health research as a standard indicator for measuring health in different populations. This paper reports SRH of employees at Tehran University of Medical Sciences (TUMS), Tehran, Iran; and its related factors. Methods: We used the TUMS Employee's Cohort Study (TEC) data, collected from September 2017 to August 2019. SRH of 2158 employees were assessed using a single question. Univariate and multivariate logistic regression were performed to determine factors associated with sub-optimal SRH. Results: Overall, 14.9% (n = 321) of respondents rated their health as sub-optimal. Differences were observed with subgroups. Women, lowersocioeconomic status (SES), experiences of more economic fluctuation conditions and lower reading scores could predict sub-optimal SRHvariations in crude and adjusted regression analyses. The age, social capital, work experience and employment status could predict sub-optimal SRH variations only in crude regression analyses (P<0.05). Conclusion: Differences in SRH report reflected differences in socioeconomic conditions. The biggest gap was observed between people of different SES (five times). Considering that SRH reflected socioeconomic characteristics of individuals, it may be considered as a quick, non-expensive, and simple way of identifying groups in need of addressing their social determinants of health.
Background: Why is bodyweight not a predictor of lung function, while height, sex, race, and age are predictors of lung capacity and function? In this study, we want to investigate the association between body composition and pulmonary function. And, as much as possible, answer the question of why bodyweight is not predictive of lung function. Methods: This cross-sectional study was performed among 2967 employees of Tehran University of Medical Sciences (TUMS) who participated in the TUMS Employees Cohort (TEC) study. The body composition of the participants was measured using the Bioelectrical Impedance Analysis (BIA) method. Anthropometric variables were also measured as a confounder. The pulmonary function of participants was assessed by a forced spirometry test. Results: The correlation of BIA values including fat-free mass and total body water with a pulmonary function such as FEV1, FVC, and FEF25-75 is higher than most anthropometric values such as weight, wrist circumference, and the waist to hip ratio. Also, in regression analysis, age and sex had an association with pulmonary function, but the weight did not show a significant relationship. On the other hand, fat-free mass and visceral fat were significantly associated with pulmonary function. One is direct and the other is inverse. Conclusion: We observed a negative association between visceral fat and pulmonary function tests and a direct association between Fat-free mass pulmonary function tests (FEV1 and FVC) adjusted for age, sex, and anthropometric indices.
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