It is an honor to write an Editorial to this Special Issue (SI) of Sustainability

Background: Timely treatment of acute ischemic stroke is crucial to optimize outcomes. Mobile stroke units (MSU) have demonstrated ultrafast treatment compared to standard emergency care. Geospatial analysis of the distribution of MSU cases to optimize service delivery has not been reported. Methods: We aggregated all first-year MSU dispatch occurrences and all cases classified by clinical teams as true stroke by zip code and calculated dispatch and true stroke incidence rates. We mapped dispatch and stroke cases and symbolized incidence rates by standard deviation. We confirmed visual impressions of clusters from map inspection by local Moran’s I, boxplot inspection, and t test. We calculated service areas using drive times to meet dispatch and true stroke need. Results: A significant cluster of high dispatch incident rate was confirmed around our MSU base in urban Memphis within a 5-min driving area supporting the initial placement of the MSU based on 911 activation. A significant cluster of high true stroke rate was confirmed to the east of our MSU base in suburban Memphis within a 10-min driving area. Mean incident longitude of cases of true stroke versus disregarded status was significantly eastward (p = 0.001785). Conclusion: Our findings will facilitate determination of socio-spatial antecedents of neighborhood overutilization of 911 and MSU services in our urban neighborhoods and service delivery optimization to reach neighborhoods with true stroke burden.

BACKGROUND: Clinical trialists may be reluctant to enroll socioeconomically disadvantaged participants because of concerns for subject disengagement leading to noncompliance with longitudinal measures and high lost to follow-up (LTFU) rates. OBJECTIVES: We describe the LTFU problem associated with disadvantaged participants and propose strategies to reduce clinical trial disengagement. METHODS: Difficulties encountered in recruiting and retaining socioeconomically disadvantaged participants along with antecedents of disengagement are discussed. Data in the public domain were used to derive, symbolize, and map engagement by census tract. Exemplars of engaged and disengaged clinical trial participants are shared, and geospatial distribution of socio-spatial disengagement risk is presented. RESULTS: Subject disengagement can be visualized by geospatial informatics suggesting areas of low and high socio-spatial disengagement risk. By failing to enroll socioeconomically disadvantaged subjects, researchers may deliberately exclude those who may benefit the most because of significant health disparities. DISCUSSION: We propose a study of realistic LTFU rates for disadvantaged participants. Realistic clinical trial end points and methods may reduce disengagement among disadvantaged participants.