b'To evaluate variations in the impact of bystander interventions on survival rates across urban and rural areas, an interaction term between bystander interventions and Rural Urban Commuting Area (RUCA) codes was incorporated. RUCA codes categorize geographic areas based on their rural or urban attributes and commuting patterns. Assigned to census tracts, these codes reflect the size of the urban area residents commute to for work, ranging from 1 (most urban) to 10 (most rural). 8Pre- and post-hospital survival outcomes varied significantly across the urban-rural spectrum. Rural areas exhibited the lowest proportions of patients achieving ROSC at 23.2% in the field, along with the lowest rates of survival to hospital admission (17.8%), survival to hospital discharge (6.8%), and survival to hospital discharge with good neurological outcome (6.1%). Urban areas demonstrated the highest rate of survival to hospital admission at 28.3%. The results of the regression analysis, shown in Table 1, indicate that compared to no bystander intervention, the likelihoods (AORs with 95% CIs) of bystander AED use and survival to hospital discharge with good neurological outcomes were higher in urban (2.57, 2.372.79), suburban (2.58, 1.813.67), large rural (1.99, 1.442.76), small town (1.90, 1.272.86), and rural areas (3.05, 1.994.68). Additionally, bCPR alone was associated with increased survival in all areas (urban: 1.36, 1.311.41; suburban: 1.29, 1.121.49; large rural: 1.40, 1.201.63; small town: 1.32, 1.111.58; rural: 1.45, 1.171.81). However, the impact of bCPR alone was consistent across the urban-rural spectrum. These findings underscore the importance of not only providing bCPR training but also ensuring the accessibility and utilization of AEDs in all areas to improve outcomes for OHCA patients.OHCAs near healthcare centers:Hofacker et al. (2020) 9evaluated survival rates in outpatient dialysis clinics, focusing on the provision of CPR and AED application by dialysis staff. The study used ArcGIS Pro, a GIS software, to facilitate the identification and selection of study cohorts. By geocoding outpatient hemodialysis clinic addresses from the Dialysis Facility Compare dataset and matching them with geocoded OHCA events, the researchers were able to pinpoint the locations of cardiac arrest incidents relative to dialysis clinics. To protect personally identifiable information (PHI), an honest broker approach was employed, ensuring that PHI was not shared externally, with all data linkage managed by the national CARES team. This spatial analysis allowed for the exclusion of events occurring outside the vicinity of outpatient dialysis clinics, ensuring a focused investigation into resuscitation efforts prior to the arrival of emergency response teams. The study included 1,568 cardiac arrests in 809 hemodialysis clinics, with a racial/ethnic composition of 31.3% White, 32.9% Black, 10.7% Hispanic/Latinx, 2.7% Asian, and 22.5% other/unknown. CPR was initiated by dialysis staff for 88.0% of patients, but rates differed by race: 91% for White, 85% for Black, and 77% for Asian patients (p=0.005). After adjustment, Black patients were 59% less likely (OR=0.41, 95% CI 0.25-0.68) and Asian patients were 72% less likely (OR=0.28, 95% CI 0.12-0.65) than White patients to receive staff-initiated CPR. Staff applied an AED prior to 911 responders in 62% of cases, with no significant difference in AED application by patient race/ethnicity. These findings underscore significant racial disparities in CPR provision by dialysis staff, emphasizing the need to address these disparities in resuscitation practices to ensure equitable care. The study highlights the utility of using spatial analysis to pinpoint healthcare centers for identifying these disparities in resuscitation practices to ensure equitable care for all patients. 50 51'