I am a researcher in epidemiology, global health and health data science, with passion in global health 'storytelling'. I further have interest in leveraing 'big' data for evaluation of complex interventions in public health. Few of my projects have been described in the section below. For more see my Google Scholar, GitHub & Twitter (@shivarajmisra).
As part of my doctoral work, I examined the association between several reproductive markers of aging (e.g. age at menarche, age at menopause, reproductive lifespan) in the context of cardiovascular health. In work with my doctoral advisers Prof. Gita D. Mishra, Dr. Michael Waller, Dr. Hsin-Fang Chung, we explored the added predictive value of a composite index of oestrogen exposure against several other indices, describing the influence of length of oestorgen exposure on stroke risk at midlife:
This work is derived from our earlier work where we proposed several algorithmic definitions of oestrogen exposure derived using a comprehensive systematic review of the existing literature:
During my master’s program (2016-2017) and Ph.D. program (2017-2020), I have developed substantial expertise in longitudinal data analysis in the context of time-to-event data (e.g., survival analysis), with application in cardiovascular events and mortality. These tasks required a substantial experience in data management and advanced programming experience in SAS.
Building cutting-edge statistical methods, with colleagues we want to further query into a classical problem in epidemiology, "why poor get sick, and why sick gets poor’? This urged us to go beyond the traditional drivers of ill-health and explore the 'causes of causes' using life-course perspective. This project has three objectives:
With colleagues, we used both simple as well as sophisticated statistical techniques to explore geographical variations in disease and risk factors, accompanied by geographical mapping and visualization. Further we compared both single-level and multi-level modelling technique to measures the differences between individual level and population level factors in public health.
Reducing health disparities has become a major public health agenda in Australia and globally. However, policy options on how best to achieve this goal is not clearly understood. Simulation modelling using varying sources of data (e.g., registries, panel data study) has the potential to bridge this gap; this approach can produce unique evidence on the evidence base around the poverty reducing impact of cross-sectoral interventions affecting cardiovascular health. Therefore, exploring the effectiveness of several ‘what if’ counterfactual scenarios can guide future implementation of these policies.
Community based interventions have huge potential to contribute to public health, in low resource setting. With colleagues, at COBIN study, we measured the effectiveness of community-based strategies for CVD risk factors management in Nepal. The study showed that the intervention effectively reduced the systolic blood pressure by 4.90 mm Hg (2.00 to 7.78) among hypertensive patients from a resource-limited setting in Nepal. The study has provided important insights for development of community-based strategies for CVD risk factors management in South Asia.
For last seven years, I have worked as a freelance writer for several newspapers and journals including Lancet family of journals. Additionally, I have invited editorial roles in Journal of Human Hypertension (2017-) and PloS Global Public Health.
More recently, I worked in two book chapters with colleagues, exploring nexus of poverty and governance in humanitarian context.