Statistical Analysis
TIA, stroke, Stroke
STROKE, STROKE, ISCHEMIC STROKE, SECONDARY, REGRESSION, TIA
Descriptive statistics were used to describe the reach of the community intervention and the preintervention and postintervention population. The primary analysis was an interrupted time series design. The preintervention period was defined as 87 months before the start of the intervention (October 2017). Logistic regression was used to estimate the overall intervention association (indicator variable) in a model estimating receipt of thrombolysis (binary variable) accounting for stroke type (TIA vs ischemic stroke), time (month since start of intervention), and clustering at the hospital level (as a random effect). In a secondary analysis, the intervention period was separated into the ED and community intervention periods. We did not use this for our primary analysis because we suspected we would be underpowered to identify independent associations. We parameterized the ED intervention as turning on and sustaining over the entire period (binary variable representing periods before vs after the ED intervention) and the community intervention as turning on and changing over time as more people were reached with the sequential quadrant rollout (continuous variable representing months from the start of the community intervention period).Additional secondary analyses explored how likely the association between Stroke Ready and outcomes were to be causal. Because the community intervention was delivered sequentially by Flint geographic quadrants, we explored whether increases in acute thrombolysis treatment rates paralleled the geographic pattern of intervention rollout by including an indicator of months from quadrant intervention. Finally, we conducted a logistic regression to examine thrombolysis trends in Flint vs other large Michigan metropolitan regions in which Black people comprise more than 25% of the population (Detroit, Saginaw, Muskegon, and Benton Harbor) to account for regional trends by using the Michigan State Inpatient Database.For the secondary outcome of EMS utilization among all Flint patients with stroke, we performed a logistic regression model and accounted for known demographics associated with EMS utilization, including age, insurance status (insured vs not insured), and race (Black vs other race and ethnicity).