Delayed arrival to the hospital in stroke is a major unsolved problem in public health that leads to stark and
persistent racial and socioeconomic disparities in stroke outcomes. The delay generates health disparities
because racial minority and socioeconomically disadvantaged patients arrive later than White patients leading
to less access to treatment and worse outcomes. The most common reason for delay is the time spent by the
patient and witnesses who decide together to watch-and-wait or go to the hospital. Therefore, we propose that
social connectedness is a major determinant of the delay phenomenon. Our team has demonstrated that social
network structure around a specific patient determines the flow of information that leads to decisions to act
rapidly or slowly. Patients who arrived early had large and loosely connected networks, while those who
arrived late had small and close-knit networks. What remains lacking, however, is knowledge of the extent of
the social network effect in a more diverse population of stroke patients, its mechanism, and translation into
interventions to improve stroke delay and disparities. This understanding is critical to establishing rigor and
premise for future social network interventions aimed at reducing disparities in stroke outcomes. Our long-term
goal is to design network-based interventions that reduce delay during stroke and ensure equitable access to
therapies. Therefore, in this project, we use a dual empirical and social simulation approach to characterize
and model social network effects in a diverse patient population. In Aim 1, we will determine whether social
networks affect delay in hospital arrival after stroke differentially by race and socioeconomic status. We will
capture social network data and time to arrival in 500 racially and socioeconomically diverse patients during
their hospital admission. In Aim 2, we will model the potential of network interventions to improve stroke delay
in at-risk populations. Using data from the same 500 patients and persons in their network, we will
parameterize an agent-based model to represent the dynamic decision-making within the social network during
stroke. Then we will evaluate the potential effects of network interventions to improve delay and disparities
within the model. Our central hypothesis is that social network metrics will be associated with hospital arrival
time, social networks will moderate race and SES differences in arrival time, and that network interventions
such as increasing network size will improve outcomes and disparities in social simulations. We have
assembled a multidisciplinary team with expertise in stroke, social networks, agent-based modeling, and health
disparities to execute this project. The proposed research will provide much needed empirical data on social
network effects and the potential of network interventions to address stroke delay and its disparities. These
results will have a positive impact by directly setting the stage for testing social network interventions in acute
stroke clinical trials to improve arrival time and enhance equitable access to stroke therapies.