Overview
I am coming from a population health background, focusing on how social/commercial factors and policies influence distribution of health and disease across social groups in populations. This thread of thought led me to studies on social systems and how they influences health and wellbeing at individual and population levels. I found complex system theories quite interesting in understanding the social systems.
As a result, I did my PhD in Complex Systems Science where I learned the ways we can use systems thinking approaches in design, implementation, and evaluation of complex interventions where the complex fabric of a social dynamic system is addressed.
After my PhD, I worked at Institute of Population Health Sciences in Liverpool University where we used complex systems approaches to evaluate and implement couple of social interventions aimed to improve wellbeing and reduce health inequalities.
I joined Exeter University Business School from June 2019 to work on a fascinating project, called The Inclusivity Project, which aims to use complex systems science to map, understand, and change the system of employment at SME levels to make it more inclusive for old people, disabled people, and people with long term health conditions in Cornwall, South West of UK.
Qualifications
- MSc (Social Sciences)
- MPhil (Public Health)
- PhD (Complex Systems Science)
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Research
Research interests
- Complex Systems
- Systems Thinking
- Public Health
- Social/Commercial Determinants of Health
- Healthy Workplace
- Policy Studies
- System Dynamics
- Agent-Based Modelling
- Data Science
- Evaluation methods
- Qualitative Comparative Analysis (QCA)
- Systematic Reviews
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Publications
Journal articles
Shadi Y, Lotfi MH, Nedjat S, Amini Rarani M, Khedmati Morasae E (2018). Explaining Unequal Levels of Social Capital in Tehran.
Social Indicators Research,
140(1), 243-265.
Abstract:
Explaining Unequal Levels of Social Capital in Tehran
Social capital may act as an asset to serve people in various situations. However, people do not equally enjoy the same level of social capital and there is inequality in distribution of this asset in societies. There is few research within the wider literature exploring the determinants of inequality in social capital. This study measured and decomposed inequality in the distribution of social capital in Tehran using a concentration index approach. Data was gathered through a survey in 2008, the sample included 2484 of over 18-year old residents. Social Capital Integrated Questionnaire was used to measure social capital status, its dimensions (networking, trust, and cooperation) and outcomes (cohesion and political action). Most of social capital dimensions/outcomes were unequally distributed in Tehran, favouring the rich. However, in terms of political action, the poor were more politically active than the rich in Tehran. Decomposition showed that economic status and education had the highest contributions to the observed inequalities. In efforts to move towards a more just society, these findings can inform future policies in Iran to tackle the observed inequalities in social capital.
Abstract.
Omani-Samani R, Amini Rarani M, Sepidarkish M, Khedmati Morasae E, Maroufizadeh S, Almasi-Hashiani A (2018). Socioeconomic inequality of unintended pregnancy in the Iranian population: a decomposition approach.
BMC Public Health,
18(1).
Abstract:
Socioeconomic inequality of unintended pregnancy in the Iranian population: a decomposition approach
Background: There are several studies regarding the predictors or risk factors of unintended pregnancy, but only a small number of studies have been carried out concerning the socio-economic factors influencing the unintended pregnancy rate. This study aimed to determine the socioeconomic inequality of unintended pregnancy in Tehran, Iran, as a developing country. Methods: in this hospital based cross-sectional study, 5152 deliveries from 103 hospitals in Tehran (the capital of Iran) were included in the analysis in July 2015. Socioeconomic status (SES) was measured through an asset-based method and principal component analysis was carried out to calculate the household SES. The concentration index and curve was used to measure SES inequality in unintended pregnancy, and then decomposed into its determinants. The data was analyzed by statistical Stata software. Results: the Wagstaff normalized concentration index of unintended pregnancy (- 0.108 (95% Confidence Interval (CI) = - 0.119 ~ - 0.054)) endorses that unintended pregnancy is more concentrated among poorer mothers. The results showed that SES accounted for 27% of unintended pregnancy inequality, followed by the mother's nationality (19%), father's age (16%), mother's age (10%), father's education level (7%) and Body Mass Index (BMI) groups (5%). Conclusion: Unintended pregnancy is unequally distributed among Iranian women and is more concentrated among poor women. Economic status had the most positive contribution, explaining 27% of inequality in unintended pregnancy.
Abstract.
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Teaching
- Quantitative Research Methodologies
- Complex Systems Methods
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