Social patterns in knowledge evolution and health policy

Using tools from data-driven computational social science, this thesis analyzes and discusses social patterns in knowledge evolution and health policy. Knowledge can be considered to be a network of interconnected concepts, hence, understanding its latent topological structure can reflect many facts about the evolution of knowledge. Here, we study the evolution of computing and physics fields using keywords extracted from scholarly works. Using tools from applied topology, we find that as the scientific conceptual network evolves, knowledge gaps tend to open then subsequently close by the end of the exposition. Such opening and closing of gaps have shown some positive correlation with citation counts of papers. Also, combining different scholarly fields tend to close gaps quicker than having each field separated. During COVID-19 pandemic, the protective behaviors of vaccination, face masking and social distancing have all been essential in controlling contagion. Such behaviors have not been uniformly adopted by communities in the United States. We hypothesize that the social perspective can explain the differences in adoption of public behaviors among communities. Using the social capital index, we found that different social capital facets associate differently to different public behaviors. Particularly, vaccination is associated with institutional health: positively with fully vaccinated population and negatively with vaccination hesitancy. Also, wearing masks negatively associates with community health, whereases reduced mobility associates with better community health. Our results suggest that differential facets of social capital imply a Swiss cheese model of pandemic control planning where, e.g., institutional health and community health, provide partially overlapping behavioral benefits. Not only we looked at association patterns, but also we looked at the causal effects of COVID-19 public policies on the population mental health. Using large-scale medical claims data for mental health patients and publicly available lockdown dates, we designed a difference-in-differences model to estimate the causal effects. We find that lockdown has significantly and causally increased mental health resource usage and urgent treatment-seeking across counties and states. Mental health patients in regions with lockdown orders have significantly increased by 18% compared to 1% decline in regions without a lockdown. Our results show that mitigation policies led to population-wide increase in mental health patients. Our results suggest the need for greater mental health treatment resources in the face of lockdown policies.

Author dc:creator

• Ferwana, Ibtihal

Contributors dc:contributor

• Varshney, Lav R.

• algebraic topology
• causal inference
• science of science
• health policy

Statement dc:rights

• Copyright 2022 Ibtihal Ferwana

Language dc:language

eng

Handle dc:identifier

https://hdl.handle.net/2142/115226

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/115226