Shiba comes to SPH from a postdoctoral research fellowship in the Department of Epidemiology and Department of Social and Behavioral Sciences at Harvard T.H. Chan School of Public Health. He was also affiliated with the Human Flourishing Program at Harvard’s Institute for Quantitative Social Science.
His research focuses on the application of epidemiologic and social science thinking and methods for rigorous causal inference in studying social determinants of health.
In his bio, Shiba said he has worked on three specific research themes: “First, I study the effects of traumatic events (e.g., disasters, child adversity, pandemic, and global financial crisis) on population health, with a particular focus on older adult populations. Second, I study the roles of social relationships and social engagement in promoting health of older adults and building resiliency. I have also investigated how the internet-based social interactions can influence population health.
“Third, my research extends the traditional “risk factor” epidemiology examining a narrow set of health outcomes. I study the impacts of positive psychological factors (e.g., purpose in life, Ikigai) on health and determinants of multidimensional well-being (i.e., human flourishing).”
He spoke more about his work and why his research lies at the intersection of quantitative research methodology and social epidemiology
Why public health? What speaks to you about the field and the work you do? I am fascinated by the public health concept of treating a population as a whole rather than viewing it as a sum of individuals. The impacts of public health policies and interventions are statistical and intangible, while the value of efforts to improve health at the individual level (such as through clinical practice) is more “visible.” Yet, public health can often achieve more significant impacts on population health and address structural determinants of health that individuals cannot control.
As an epidemiologist, can you talk about your primary research interests? My research interests lie at the intersection of quantitative research methodology and social epidemiology. Specifically, I apply causal inference thinking and methods to study social determinants of health and their impacts on population health and health inequalities. In the past few years, I have studied public health impacts of traumatic exposures such as disasters, pandemics, and adverse childhood experiences.
In addition to these studies focusing on traumatic experiences, I also research the health impacts of positive psychological states (such as having a purpose in life) and determinants of human flourishing, which speaks to a multidimensional understanding of well-being.
In past studies, you have used machine learning techniques to further your analysis. What opportunities can machine learning create in the field of social epidemiology? Social factors shape population health in a complex way, so it is challenging to specify a regression model that correctly captures the relationships between the variables. Machine learning (ML) can help investigators minimize the risk of model misspecification and resulting bias. This is helpful when we are interested in estimating the average effects of specific social determinants of health.
Such flexible modeling will also allow data-driven estimations of how the effect of the exposure varies across groups characterized by multiple variables interacting with each other (known as complex and high-dimensional effect heterogeneity). The traditional approach for estimating effect heterogeneity typically cannot do this.
Assessing effect heterogeneity provides some key insights, including 1) which sub-populations are more affected by the exposure of interest and, thus, need to be prioritized in interventions, 2) mechanisms through which social factors affect health, and 3) effects of interventions for specific exposures on health disparity.
My research theme is broad and touches on many strategic directions: I study mental/behavioral health outcomes, disasters that could increase as climate change progresses, health disparities, and the social consequences and population health impacts of the COVID-19 pandemic.
Why did you choose SPH? What made the institution or the role stand out? I like its people: visionary leadership and great colleagues and students. The strategic research directions of BUSPH align really well with my research agenda and I am eager to continue my work in Boston, a city I love.
Looking forward, what are you most excited about for this new role? Starting collaborations with new people and working with students!
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