Search

Purpose: Acute stroke is a serious, time-sensitive condition requiring immediate medical attention. Emergency medical services (EMS) routing and direct transport of acute stroke patients to stroke centers improves timely access to care. This study aimed to describe EMS stroke routing and transports by rurality in North Carolina (NC)., Methods: We conducted a retrospective study using existing data on EMS transports of suspected stroke patients in NC in 2019. The primary study outcome was EMS bypass of the nearest hospital for transport to a nonnearest hospital, determined by geographic information systems (GIS) analysis. Incident addresses were geocoded to census tracts and classified as urban, suburban, or rural by Rural-Urban Commuting Area codes. We compared the frequency of bypass and estimated additional transport times by urban, suburban, and rural incident locations., Findings: Of 3666 patients, 1884 (51%) EMS transports bypassed the nearest hospital. Bypass occurred less often for rural EMS incidents (39%) compared to those in urban (57%) and suburban (63%) tracts. The estimated additional transport time for rural bypasses of nonendovascular-capable stroke centers for endovascular-capable stroke centers was a median of 25 min (interquartile range 13-33)., Conclusions: Using GIS analysis, we found nearly half of EMS transports of suspected stroke patients in NC bypassed the nearest hospital, including noncertified hospitals and stroke centers. Bypasses occurred less often in rural areas, though incurred significantly longer transport times, compared to urban areas. These findings are important for regional stroke system planning, especially for improving rural access to acute stroke care., (© 2024 National Rural Health Association.)

Objectives: Notification by emergency medical services (EMS) to the destination hospital of an incoming suspected stroke patient is associated with timelier in-hospital evaluation and treatment. Current data on adherence to this evidence-based best practice are limited, however. We examined the frequency of EMS stroke prenotification in North Carolina by community socioeconomic status (SES) and rurality., Methods: Using a statewide database of EMS patient care reports, we selected 9-1-1 responses in 2019 with an EMS provider impression of stroke or documented stroke care protocol use. Eligible patients were 18 years old and older with a completed prehospital stroke screen. Incident street addresses were geocoded to North Carolina census tracts and linked to American Community Survey socioeconomic data and urban-rural commuting area codes. High, medium, and low SES tracts were defined by SES index tertiles. Tracts were classified as urban, suburban, and rural. We used multivariable logistic regression to estimate independent associations between tract-level SES and rurality with EMS prenotification, adjusting for patient age, sex, and race/ethnicity; duration of symptoms; incident day of week and time of day; 9-1-1 dispatch complaint; EMS provider primary impression; and prehospital stroke screen interpretation., Results: The cohort of 9527 eligible incidents was mostly at least 65 years old (65%), female (55%), and non-Hispanic White (71%). EMS prenotification occurred in 2783 (29%) patients. Prenotification in low SES tracts (27%) occurred less often than in medium (30%) and high (32%) SES tracts. Rural tracts had the lowest frequency (21%) compared with suburban (28%) and urban (31%) tracts. In adjusted analyses, EMS prenotification was less likely in low SES (vs high SES; odds ratio 0.76, 95% confidence interval 0.67-0.88) and rural (vs urban; odds ratio 0.64, 95% confidence interval 0.52-0.77) tracts., Conclusions: Across a large, diverse population, EMS prenotification occurred in only one-third of suspected stroke patients. Furthermore, low SES and rural tracts were independently associated with a lower likelihood of prehospital notification. These findings suggest the need for education and quality improvement initiatives to increase EMS stroke prenotification, particularly in underserved communities.

Objective: Our objectives were to describe time intervals of EMS encounters for suspected stroke patients in North Carolina (NC) and evaluate differences in EMS time intervals by community socioeconomic status (SES) and rurality., Methods: This cross-sectional study used statewide data on EMS encounters of suspected stroke in NC in 2019. Eligible patients were adults requiring EMS transport to a hospital following a 9-1-1 call for stroke-like symptoms. Incident street addresses were geocoded to census tracts and linked to American Community Survey SES data and to rural-urban commuting area (RUCA) codes. Community SES was defined as high, medium, or low based on tertiles of an SES index. Urban, suburban, and rural tracts were defined by RUCA codes 1, 2-6, and 7-10, respectively. Multivariable quantile regression was used to estimate how the median and 90th percentile of EMS time intervals varied by community SES and rurality, adjusting for each other; patient age, gender, and race/ethnicity; and incident characteristics., Results: We identified 17,117 eligible EMS encounters of suspected stroke from 2028 census tracts. The population was 65% 65+ years old; 55% female; and 69% Non-Hispanic White. Median response, scene, and transport times were 8 (interquartile range, IQR 6-11) min, 16 (IQR 12-20) min, and 14 (IQR 9-22) minutes, respectively. In quantile regression adjusted for patient demographics, minimal differences were observed for median response and scene times by community SES and rurality. The largest median differences were observed for transport times in rural (6.7 min, 95% CI 5.8, 7.6) and suburban (4.7 min, 95% CI 4.2, 5.1) tracts compared to urban tracts. Adjusted rural-urban differences in 90th percentile transport times were substantially greater (16.0 min, 95% CI 14.5, 17.5). Low SES was modesty associated with shorter median (-3.3 min, 95% CI -3.8, -2.9) and 90th percentile (-3.0 min, 95% CI -4.0, -2.0) transport times compared to high SES tracts., Conclusions: While community-level factors were not strongly associated with EMS response and scene times for stroke, transport times were significantly longer rural tracts and modestly shorter in low SES tracts, accounting for patient demographics. Further research is needed on the role of community socioeconomic deprivation and rurality in contributing to delays in prehospital stroke care., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background Timely emergency medical services (EMS) response, management, and transport of patients with suspected acute coronary syndrome (ACS) significantly reduce delays to emergency treatment and improve outcomes. We evaluated EMS response, scene, and transport times and adherence to proposed time benchmarks for patients with suspected ACS in North Carolina from 2011 to 2017. Methods and Results We conducted a population-based, retrospective study with the North Carolina Prehospital Medical Information System, a statewide electronic database of all EMS patient care reports. We analyzed 2011 to 2017 data on patient demographics, incident characteristics, EMS care, and county population density for EMS-suspected patients with ACS, defined as a complaint of chest pain or suspected cardiac event and documentation of myocardial ischemia on prehospital ECG or prehospital activation of the cardiac care team. Descriptive statistics for each EMS time interval were computed. Multivariable logistic regression was used to quantify relationships between meeting response and scene time benchmarks (11 and 15 minutes, respectively) and prespecified covariates. Among 4667 patients meeting eligibility criteria, median response time (8 minutes) was shorter than median scene (16 minutes) and transport (17 minutes) time. While scene times were comparable by population density, patients in rural (versus urban) counties experienced longer response and transport times. Overall, 62% of EMS encounters met the 11-minute response time benchmark and 49% met the 15-minute scene time benchmark. In adjusted regression analyses, EMS encounters of older and female patients and obtaining a 12-lead ECG and venous access were independently associated with lower adherence to the scene time benchmark. Conclusions Our statewide study identified urban-rural differences in response and transport times for suspected ACS as well as patient demographic and EMS care characteristics related to lower adherence to scene time benchmark. Strategies to reduce EMS scene times among patients with ACS need to be developed and evaluated.