Background
Despite recent declines in death rates from stroke and acute myocardial infarction (MI), the burdens of these conditions remain high [
1]. Both conditions require time sensitive treatments so transport time to appropriate heath facilities is critical. Improved outcomes have been observed for ischemic stroke patients when intravenous thrombolytic treatment is received within 3 h of the onset of symptoms [
2]. To ensure timely receipt of treatment, it is recommended that stroke patients be transported directly to accredited stroke centers [
2]. For MI, current guidelines recommend that the time from first medical contact to percutaneous coronary intervention (PCI) be 90 min or less [
3]. Health outcomes are improved by up to 50 % when PCI is administered within 60 min and by 23 % if given within 180 min of the onset of symptoms [
4,
5].
While there are two general types of delays (prehospital and in-hospital) that affect timely receipt of stroke and MI treatments, some studies have suggested that the prehospital interval, from onset of symptoms to arrival at the hospital, is the source of the longest delay [
6]. Utilization of Emergency Medical Services (EMS), among other factors, are associated with reduced delays for treatments of stroke [
6] and MI [
4]. However, most past studies considered prehospital time (from the onset of symptoms to arrival at the hospital interval) as one time interval. To better identify disparities and target interventions, prehospital delays should be further sub-divided into decision delays and transport delays [
6]. Since EMS play a critical role in providing rapid transport of acute stroke and MI patients, it is important to investigate the specific time intervals involved: response time, on-scene time, and travel time to the hospital. Unfortunately, only a few recent studies have reported the specific time intervals associated with EMS transport for stroke [
7,
8] and MI [
9]. Thus, additional studies of different populations, geographic areas, and EMS agencies are needed to improve our understanding of this component of prehospital delays. We hypothesize that there are geographic disparities in transport times for suspected stroke and MI cases. If these disparities are identified, it would help guide efforts to improve transport times for suspected stroke and MI patients Therefore, the objective of this study was to identify and describe disparities in EMS transport time delays for suspected stroke and MI patients.
Discussion
A number of past studies investigating prehospital delays treated this delay as one time interval. However, to better identify disparities and target interventions, prehospital delays need to be divided into sub-intervals (i.e. response time, on-scene time, and travel time to the hospital) so as to identify the interval where most delay occurs. Moreover, it is important to identify predictors of these delays so as to guide future studies as well as health planning programs geared towards improving emergency transport of stroke and MI patients. Therefore, the focus of this study was to: (i) identify and describe the disparities in EMS transport delays for suspected MI and stroke patients and (ii) identify predictors of different EMS time interval delays so as to guide future studies and improvement programs. This study addresses this by investigating the predictors of time intervals (response time, on-scene time and total time) not meeting EMS guidelines. It is our belief that the identification of these disparities and their predictors is the first step in addressing the problem of transport delays.
Studies have shown that prehospital delays for suspected stroke and MI patients have a significant impact on eligibility for and timeliness to receive emergency treatments [
6]. Although, EMS utilization has been shown to reduce treatment delays for stroke [
6,
15] and MI [
4] patients, only a few studies have investigated the specific time intervals related to EMS transport as a component of prehospital delay [
7‐
9,
16]. In the current study, the median response times were similar to those from other studies: 5 min [
7], 5.5 min [
8], 6 min [
16], 7.5 min [
9] and 8 min [
17]. Median on-scene times for the current study were also consistent with those from other studies that reported on-scene times of 13 min for stroke [
7] and 14.5 min for MI [
9], but lower than others that reported median on-scene times for stroke patients ranging from 18 to 20 min [
8,
17]. Based on the multivariable logistic models, the adjusted odds of having EMS times, for suspected stroke and MI, greater than the EMS guidelines were generally significantly lower for EMS 1 than EMS 2. This was the case for all investigated delay times except for response times involving stroke patients. The reason for the shorter response times in EMS 2 than EMS 1 for stroke patients is unclear and is evidently an area that needs further investigations in future studies. However, EMS 2 tended to use RLS quite frequently during travel to the scene and this might have contributed to the shorter response times. However, future studies of EMS protocols will need to be performed to specifically investigate reasons for these differences. It is worth noting that although short on-scene times are not necessarily always best for the patient, especially if the short times are due to omissions of indicated basic and/or advanced procedures [
18], other studies have reported that differences in EMS on-scene times may reflect varying levels of efficiency, experience, attitude of EMS personnel serving different populations [
7] as well as potential omission of indicated procedures [
18]. Unfortunately, since this was a retrospective study using EMS call records, data on specific differences in protocols of the two EMS agencies were not available and therefore the reasons for the observed differences could not be investigated further. Suffice it to say that these differences will have been explained by the EMS variable in the multivariable logistic models. However, future studies will need to investigate more factors (such as EMS protocol differences, call volumes in relation to available equipment and personnel, etc.), in addition to geography, that may help explain the observed differences to help better understand the observed differences in the odds of times exceeding guidelines at the EMS level.
The reason for the comparison of on-scene time across seasons was to assess if there was additional delay during winter resulting from the need to prepare the patients for the colder conditions during winter transport compared to the warmer seasons. While the on-scene time accounted for the longest EMS time component in one study [
17], travel time to the hospital comprised the longest time delay in this study. Travel times for EMS 2 are generally longer because of the distribution of hospitals in EMS 2 compared to EMS 1. Generally, the area served by EMS 1 has more hospitals and therefore travel times to the nearest hospital will be shorter. This is evidenced by the fact that EMS 1 tended to have lower adjusted odds of having total time exceeding guidelines compared to EMS 2. Moreover, the areas served by EMS 2 do not have any stroke centers necessitating longer travel times to stroke centers. Unfortunately, data on call locations were not made available to the investigators to maintain anonymity and confidentiality of the patients and therefore distance to the nearest hospital could not be calculated. However, it is interesting to note that the adjusted odds of on-scene time exceeding 10 min was influenced by the age of the patient, season and EMS. Moreover, significant effect modification was noted between EMS and season. This is likely due to the increased traffic during the warner tourism months that potentially results in increased traffic congestion and potential delays during the fall and spring months. The median travel time to the hospital was reported by two studies in urban areas as 11 min for stroke patients [
7,
17]. In this study, the travel times for both EMS agencies were longer than 11 min. This is probably related to the distribution of hospitals in urban versus rural areas. In our previous study that included the current study area, we reported longer travel times for some rural areas [
19]. Clustering of specialty centers in urban areas, compounds the problem of disparities in access to emergency care for suspected stroke and MI patients [
20].
Patient demographic factors reported by some studies to be associated with increased prehospital delay for MI include: older age, females, and black ethnicity [
17]. However, the relationships seem to be less clear for suspected stroke patients with some reporting significant associations [
7], while others reporting no associations [
15]. The observed higher proportion (68 %) of females in the suspected stroke group is unclear but is probably due to the fact that females tend to use the health system more than males. The current study found that both age and gender had significant simple associations with longer delays for some time intervals for both suspected stroke and MI. However, based on the multivariable model, gender was not significantly associated with any of the time delays. Older age had a significant association with all the MI time delays investigated as well as on-scene time delay for stroke patients. The reason for the longer delays associated with older patients may be related to longer times required to stabilize the patients and potential challenges of communication in getting patient history on-scene. It is worth noting that other studies have reported that EMS is more likely to be utilized by older or female patients [
21]. Similar to another study [
17], males were more likely to have serious dispatch reasons compared to females. These dispatch reasons were also associated with increased use of RLS to the hospital and use of specialty centers, resulting in shorter response, travel, and total times for more critical patients in this study. Similar results for serious symptoms have been reported by other studies [
15,
17]. The association between RLS to hospital and response time was investigated to assess if the EMS personnel tended to use RLS to the hospital after realizing the response time to the scene was long and hence attempt to reduce the total time by using RLS in an effort to shorten the time to the hospital. However, there was no association between the two implying this probably did not occur. Race could not be investigated due to lack of variability (96 % white, non-Hispanic) in the population.
Over 40 % of EMS 2 suspected stroke and MI patients were non-residents and despite similarity in patient demographics between study area residents and non-residents, significant differences on delay intervals were observed. For EMS 2, residents were more likely to have longer response times because they were spread throughout the study area, including rural areas, than non-residents that were clustered in the urban areas. Study area residents were more likely to have the hospital chosen by the patient/family member as opposed to EMS, resulting in longer delays for residents than non-residents. Season was investigated in the current study for potential association with transport times due to potential difficult/hazardous road conditions during the winter months especially in the more mountainous areas that present particularly challenging driving conditions during the winter months. Interestingly, longer delays were observed during the fall (not winter season). Similar to findings from another study [
7], the longer delays in response time during the fall season probably reflects increased call volumes and traffic congestion due to increased numbers of tourists/visitors to the study area during the fall.
As expected and based on the multivariable logistic model, patients not transported to specialty centers had lower adjusted odds of total time exceeding 60 min compared to those that were transported to specialty centers. This is expected because patients transported to specialty centers (which are few) have to travel longer distances compared to those not taken to such centers. Although patients with high priority dispatch, requiring use of RLS to the hospital, had significant simple association with longer on-scene times, this association disappeared in the multivariable model. However, it should be noted that a previous study reported longer on-scene times for more serious patients and that once at the hospital, these patients were seen by a physician twice as fast [
22]. It has been reported that the implications of longer on-scene times are unclear [
17]. Other studies have indicated that direct transport to a specialty hospital may not significantly decrease the overall prehospital delay, but that in-hospital delays are significantly reduced and therefore total time to treatment is shorter resulting in better health outcomes [
9,
23]. Thus, recommendations for prehospital protocols to incorporate EMS bypassing non-specialty centers are advocated [
2,
4].
Based on the simple associations, when the hospital choice was made by the patient/family, the travel time was significantly higher than when EMS personnel made the choice. However, this was not important in the multivariable model. However, it should be pointed out here that the policy observed by both EMS in this study was ‘informed decision’, where the hospital choice of the patient/family must be observed after medical information has been given. When EMS personnel make the decision, the policy is generally to take the patient to the nearest facility with exceptions of level 1 trauma. For the majority of patients in both EMS, the closest hospital was a cardiac center, which would explain the high percentages (96-98 %) of transport to cardiac centers observed. It has been reported that the closest hospital for 40 % of the US population is a cardiac center [
24]. However, there were only two specialty stroke centers in the study area. When patients were taken to stroke centers, the decision was more often made by EMS. Given the ‘informed decision’ policies, targeted education encouraging patients/family to choose specialty centers when suspected stroke or MI is suspected would be beneficial. The observed association between age and longer delays has been reported by other studies [
7] and may be due to longer time for older patients to get ready for transport. Similar to this study, other studies [
25] have also reported significant association between transport delays and seriousness of the condition.
The decision delay component (including recognition of symptoms, decision to seek care, and use of EMS) and the factors affecting it that have been investigated by other studies [
16,
21] was beyond the scope of this study which sought to characterize only EMS associated delays. The current study contributes to the body of evidence for only the EMS transport portion of the prehospital delay. Others have suggested, however, that if public education interventions targeted to reduce decision delays are successful over time, delays associated with EMS transport, identified in this study, will become increasingly important [
17].
Limitations
This study was limited by the unavailability of data on the delay before the 911 call as well as data on patient characteristics that may be associated with prehospital delays, including: history of past stroke/MI [
17]; co-morbidities [
17]; type or severity of the attack, being/living alone, awakening with symptoms, and transfer from another hospital [
4,
15]. Furthermore, since all confidential patient data were removed from the database, identified delays for patients could not be linked to their medical records, hospital discharge, or personal outcomes. Future studies should link EMS data to these other databases to investigate impacts of delays on health outcomes.
Although, misclassification of stroke and MI could have occurred since cases were selected based on symptoms, this was unlikely to have had a significant impact on results since the goal was to assess timeliness of EMS transport when MI and stroke are suspected, regardless of final diagnoses. Moreover, a study reported that prehospital times for suspected stroke patients were not different between final diagnoses [
8] while another suggested that prehospital times are not likely to be affected by final diagnoses since they are not rendered by EMS but by physicians at hospitals [
26].
Competing interests
The authors declare that they do not have competing interests.
Authors’ contributions
APG: was involved in the acquisition, analysis and interpretation of data as well as in the writing of the manuscript. She also approved the submitted version of the manuscript. AO: was involved in the conception and design of the research. He was also involved in the collection and interpretation of data as well as revising the manuscript for intellectual content. He has also approved the submitted version of the manuscript.