Skip to main content
Download PDF
Download ePub

Inequities in ambulance allocation associated with transfer delay and mortality in acute coronary syndrome patients: evidence from 89 emergency medical stations in China

International Journal for Equity in Health volume 21, Article number: 178 (2022) Cite this article

Abstract

Background

Allocation of healthcare resources has a great influence on treatment and outcome of patients. This study aimed to access the inequality of ambulance allocation across regions, and estimate the associations between ambulance density and pre-hospital transfer time and mortality of acute coronary syndromes (ACS) patients.

Methods

This cross-sectional study was based on an integrated database of electronic medical system for 3588 ACS patients from 31 hospitals, ambulance information of 89 emergency medical stations, and public geographical information of 8 districts in Shenzhen, China. The primary outcomes were the associations between ambulance allocation and transfer delay and in-hospital mortality of ACS patients. The Theil index and Gini coefficient were used to assess the fairness and inequality degree of ambulance allocation. Logistic regression was used to model the associations.

Results

There was a significant inequality in ambulance allocation in Shenzhen (Theil index: 0.59), and the inequality of inter-districts (Theil index: 0.38) was greater than that of intra-districts (Theil index: 0.21). The gap degree of transfer delay, ambulance allocation, and mortality across districts resulted in a Gini coefficient of 0.35, 0.53, 0.65, respectively. Ambulance density was negatively associated with pre-hospital transfer time (OR = 0.79, 95%CI: 0.64,0.97, P = 0.026), with in-hospital mortality (OR = 0.31, 95%CI:0.14,0.70, P = 0.005). The ORs of Theil index in transfer time and in-hospital mortality were 1.09 (95%CI:1.01,1.10, P < 0.001) and 1.80 (95%CI:1.15,3.15, P = 0.009), respectively.

Conclusions

Regional inequities existed in ambulance allocation and has a significant impact on pre-hospital transfer delay and in-hospital mortality of ACS patients. It was suggested to increase the ambulance accessibility and conduct health education for public.

Introduction

As one of the most dangerous and fatal coronary cardiac diseases, acute coronary syndrome (ACS) is associated with high morbidity and mortality worldwide [1], and has been emerged as one major cause of death in China over the last two decades [2]. Survival and clinical outcomes of ACS patients, especially ST-segment-elevation myocardial infarction (STEMI) patients rely mainly on the early diagnosis and timely medical contact [3, 4]. However, the potential benefits of medical interventions can be largely hampered by pre-hospital delay [5].

Pre-hospital delay is defined as the time interval from patients’ symptom onset to arrival at hospital, which could be divided into “patient delay” (time interval from symptom onset to the decision of seeking medical care) and “transfer delay” (time interval from calling for medical care to arrival at hospital) [6]. It is recommended by guidelines that for ACS patients, the duration from symptom onset to percutaneous coronary intervention (PCI) treatment should be less than 120 minutes [7]. Yet most patients failed to meet this 120-min-criteria: 40% of this “timeout” was due to transfer delay [8].

Emergency medical service (EMS) serves as a key component of pre-hospital care system. By providing rapid diagnosis, early identification, fast treatment, timely alerts to the hospitals, EMS could reduce the transfer delay and improve patient outcomes to a great extent. Over the last decades, the procedure of EMS to treat ACS patients has been well defined, yet it hasn’t resulted in a significant reduction in transfer delay [3, 9]. It is imperative to explore the influential factors in EMS to reduce transfer delay and improve patients’ outcomes.

Allocation of healthcare resources, including personnel, equipment, ambulance, beds in emergency department, have been proven to be one of the major predictors of transfer delay and patient outcome [10]. Many studies indicated that ACS patients living in communities with limited health resources tend to have longer pre-hospital transfer time and worse outcome [11,12,13]. Although the importance of rational distribution of healthcare resources has been widely recognized, and the system-related factors have been identified [14, 15], the role of ambulance allocation in ACS treatment has remained unclear for a long time until recent studies showing that regional disparities in ambulance density could lead to considerable differences in transfer time and mortality of acute cardiac patients [16]. Despite the raising focus on this field, studies on ambulance allocation in ACS treatment are still small in numbers, and limited in developed countries. As large disparities in health care resources persist between developing countries and developed countries [17], evidence from developing countries with low - and middle-income is needed.

Using an integrated database covering 89 emergency medical stations in Shenzhen, China, this study aimed to access the inequality of ambulance allocation across regions, and estimate the associations between ambulance density and pre-hospital transfer time and mortality of ACS patients.

Methods

Data source and study population

This was a cross-sectional study in Shenzhen in 2019. Shenzhen is a special economic city of China with a residential population of approximately 22 million covering 1997.47 km [2]. The EMS in Shenzhen (Shenzhen Emergency Medical Center) was established in 1994, and is now taking the leading position in China with its complete chest pain care system and a 90% automated external defibrillator (AED) coverage.

We used data from an integrated database of Shenzhen hospital electronic medical system for ACS patients, Shenzhen ambulance information, and Shenzhen public population and geographical information. The Shenzhen hospital electronic medical system collected information on numbers, demo characteristics, pre-hospital and in-hospital time records and clinical outcomes of ACS patients from 31 hospitals in Shenzhen. The ambulance information, including numbers of ambulances in each district of Shenzhen was provided by Shenzhen Emergency Medical Center. The public population and geographical information for each district in Shenzhen was collected from Shenzhen Statistics Year Book 2019. We linked these three databases by the code of district, and then derived a two-level integrated database with both the individual-level data on ACS patients and the district-level data on ambulance allocation.

Patients aged over 18 years old with using EMS to arrive at hospital were selected, patients with time records linked to transfer were eligible to be included. From Jan. 1st, 2019 to Dec. 31th, 2019, 3943 out of 14,403 ACS patients aged over 18 years were recorded in the hospital electronic medical system, 355 cases were excluded with missing time records. A total of 3588 ACS cases were included in the final analyses.

Ethics approval and consent to participate this project was approved by the Peking University Health Science Center Institutional Review Board (IRB00001052–21020). Informed consent was obtained from all participants prior to questionnaire administration.

Measures

Outcome measures

The primary outcome variables were analyzed in this study that were: (1) transfer delay time was meaning that the time interval between patients calling EMS and arrival at the emergency department of hospitals and (2) in-hospital mortality.

Inequality assessment

The key independent variable was regional ambulance allocation, which was measured by: (1) ambulance density, applied to analyze the amounts of ambulance per kilometer square; (2) Theil index, used to measure the balance of ambulance distribution in eight districts of Shenzhen. Theil Index was usually used as an essential indicator to measure the balance of social resources distribution in one region. It ranges from 0 to 1, where 0 means complete fair in different regions, and 1 means complete unfair. It divided regional resources inequality into two parts: intra-region and inter-region inequality, which can be used to evaluate the contributes of either part to overall inequalities [18]. Theil index also be widely applied to measure healthcare resources allocation. It checks the fairness and inequality of the distribution of resources by checking whether the weight of the area corresponds to the weight of its resource’s allocation [19]; (3) Gini coefficient, used to assess the degree of the inequality in ambulance allocation. The Gini coefficient calculation was according to Lorenz curve and type of Gini coefficient. The degree of disparities in the allocation of ambulance was analyzed by using quantitative indicators, which obtained from data processing and figure area estimation [20]. The value range of Gini coefficient was from 0 to 1. The smaller the value, the smaller the disparities degree, and vice versa [21,22,23].

Covariables

Covariates and potential confounders in this study included patient-level variables and district-level variables. The patient-level variables were age, gender (male, female), heart rate (normal, atrial fibrillation), heart rate (normal, atrial fibrillation), hypertension or not (normal blood pressure, high blood pressure), emergency risk level diagnosed by emergency physicians (low risk, moderate risk, high risk), Killip grade (class grades from I to IV). District-level variables included population density (resident number per square), geographical area.

Statistical analysis

Basic characteristics of patients were analyzed by descriptive statistics. Continues variables and category variables were presented by mean (SD) and n (%), respectively. The two-sample-T-test and chi-square test were applied to compare differences between patients living in areas with high ambulance density and who living in areas with low ambulance density cases. The Theil index calculation was based on Anand S described in 2010 [24], the formulate was as follows:

$$T=\sum \limits_{i=1}^n{A}_i\log \left(\frac{A_i}{B_i}\right)$$

The Gini index was measured based on the bow area method to analyze the situation of ambulance allocation in districts [25], and Gini coefficient was as follows:

$$G=\sum \limits_{i=1}^n{A}_i{B}_i+2\sum \limits_{i=1}^{n-1}{A}_i\left(1-{C}_i\right)-1$$

The Lorenz curve was plotted with the cumulative percentage geographic area against the cumulative percentage of ambulance resources, transfer time and in-hospital mortality. The Theil index was utilized as the inequality indicator in the regression analysis. Transfer delay was defined as the binary variables by using a 120-minute as cutting point. The Logistic regression models were applied to explore the associations between ambulance allocation and transfer delay and mortality of ACS cases, respectively. All analyses were 2-sided and a P value < 0.05 was considered as statistically significant. All analysis were completed by STATA 16.0 (StataCorp, College Station, TX, USA).

Results

Characteristics of patients

Data from 3588 ACS patients aged over 18 years old with using EMS (1758 patients live in area with high ambulance density versus 1830 patients live in area with low ambulance density) were included for analysis. Table 1 provided the characteristics of these patients. Among 3588 ACS patients, 326 (9.09%) were death cases. Compared with patients who lived in area with low ambulance density, patient lived in area with high ambulance density were older, more females (20.53% vs 16.89%), less STEMI (1064/1758 vs 1248/1830), with higher emergency risk level (255/1758 vs 121/1830), more arrhythmia (24.69% vs 23.01%), more hypertension (50.74% vs 44.3%), and with lower Killip grade (8.99% vs 11.37%).

Table 1 Characteristics of ACS patients (N = 3588)
Full size table

Inequality and gap degree in ambulance allocation, transfer delay and in-hospital mortality

Figure 1 showed the inequalities and gap degree in distribution of ambulance allocation, transfer time and in-hospital mortality across eight districts of Shenzhen in 2019. There was a great inequality in ambulance allocation across districts (Theil index: 0.59, Gini coefficient: 0.53), among which the inequality of inter-districts (Theil index: 0.38) was greater than that of intra-districts (Theil index: 0.21). The greatest inequality in ambulance allocation was found in Futian (Theil index: 0.38, Gini coefficient: 0.42), while Nanshan (Theil index: 0.01, Gini coefficient: 0.06) and Luohu (Theil index: 0.01, Gini coefficient: 0.07) had the lowest inequality in ambulance allocation.

Fig. 1
figure 1

The inequalities and gap degree in distribution of ambulance allocation, transfer time, and mortality across eight districts of Shenzhen. A The inequality in distribution across geography measured by Theil index. (1): The inequality in distribution of ambulance allocation. (2): The inequality in distribution of transfer time. (3): The inequality in distribution of ACS in-hospital mortality. B The gap degree of the distribution across geography measured by Gini coefficient. (1): The gap degree of the distribution of ambulance allocation. (2): The gap degree of the distribution of transfer time. (3): The gap degree of the distribution of ACS in-hospital mortality

Full size image

Figure 1A (2) and B (2) revealed the disparities in pre-hospital transfer time (Theil index: 0.23, Gini coefficient: 0.35). The intra-districts transfer time disparity was higher (Theil index: 0.21) than that of inter-districts disparity (Theil index: 0.02). Fig. 1A (3) and B (3) presented the disparities of ACS in-hospital mortality. The results expressed a significant inequality with highly gap degree of overall mortality (Theil index: 0.85, Gini coefficient: 0.65). The inter-district mortality disparity (Theil index: 0.58) was significantly higher than that of intra-district (Theil index: 0.27).

Figure 2 presented Lorenz curves with cumulative proportion of geographic area on the x-axis and cumulative proportion of indicators on the y-axis. The inequality degree in distribution of transfer time, ambulance allocation, and mortality resulted in a Gini coefficient of 0.35, 0.53, 0.65, respectively.

Fig. 2
figure 2

The Lorenz curve of the distribution of ambulance allocation, transfer time, and in-hospital mortality across geography

Full size image

Association between ambulance allocation inequality with transfer delay and in-hospital mortality for ACS patients

Table 2 showed the odds ratios of multivariate logistic regressions. In the Model a, ambulance density was negatively associated with transfer delay (OR = 0.79, 95%CI:0.64,0.97, P = 0.026). The OR of ambulance density in in-hospital mortality was 0.31 (95%CI:0.14,0.70, P = 0.005) (Model b). In addition, we found that the positive association between Theil index and transfer delay in the Model c (OR = 1.09, 95%CI:1.01,1.10, P < 0.001). Similar pattern was also observed for in-hospital mortality in the Model d (OR = 1.80, 95%CI:1.15,3.15, P = 0.009).

Table 2 Association between ambulance allocation with transfer delay and mortality of ACS patients
Full size table

Discussion

In this cross-sectional study based on an integrated database of 8 districts, 89 EMS centers, and 31 hospitals in Shenzhen, we found significant inequalities with obvious gap degree in ambulance allocation, pre-hospital transfer time and clinical outcomes of ACS patients across districts of Shenzhen. Multilevel regression analysis revealed that regional ambulance density and balance of ambulance distribution were important factors associated with pre-hospital transfer delay and in-hospital mortality of ACS patients. Our findings were consistent with previous studies in which regional disparities in ambulance density could lead to considerable differences in transfer time and mortality of acute cardiac patients [16, 26], and provided evidence from developing countries to show the importance of optimizing the allocation of emergency medical resources.

Ambulance density is a modifiable factor to reduce the pre-hospital transfer delay. Our findings showed that the association between ambulance density and transfer delay was negatively significant. Increasing ambulance density could improve the treatment efficiency of EMS system by solving the crowding in local ambulance demand and influencing the behavior of patients. With increasing demand of EMS, ambulance crowding is a common barrier to timely transfer of ACS patients [27], which could also cause a series of delay in ambulance response and returning [28], and moreover, may hamper the willingness of patients to call for ambulance, especially for patients with time-sensitive illnesses [29]. Ambulance use can reduce the pre-hospital delay to at least half an hour in regardless of the location of the patients [30], however, limitation in health resources could influence the utilization of ambulance among patients with ACS [31]. Studies showed that instead of calling and waiting for ambulance, patients living in regions with inadequate resources preferred to go to hospitals by self-transportations [32]. Consistent with Nogueira et al., our findings indicated that modifying ambulance density was a feasible way to prompt EMS efficiency [33]. It is warranted to standardize the transport protocols, increase the availability and accessibility of ambulance in order to shorten the pre-hospital delay.

Our results showed that higher ambulance density was associated with lower ACS in-hospital mortality, which may be mainly attributable to the increased number of physician and nurses onsite. The presences of physician in the ambulance can improve the clinical outcomes of cardiac disease patients [34, 35]. On the one hand, ambulance crews are the first medical contactors of patients calling EMS, they can provide timely electrocardiogram (ECG) and cardiopulmonary resuscitation (CPR), communicate with the hospitals, and transmit the electronic ECG report to the emergency departments at the same time [36]. On the other hand, the coordination care of pre-hospital and emergency department can fasten the triage of ACS patients, and reduce the time for interventions to patients with high clinical risks, and therefore improve the short-term and long-term outcomes of ACS patients [37]. However, the benefits of pre-hospital care services may be not obvious in some settings of China. Patients’ lack of recognition of ACS, or uncontrolled self- medication before calling for help could all lead to a delay of disease assessment in the ambulance [38]. In parallel to increasing ambulance accessibility, community health education is also warranted.

Our findings revealed an unbalance distribution with obvious gap in ambulance allocation across eight districts in Shenzhen by calculating Theil index and Gini coefficient. The government of Shenzhen city provides sufficient health expenditure to each district. However, there are large disparities in healthcare decentralization across districts [39, 40], giving rise to the large inequalities and disparities in inter-regional ambulance allocation, which accounted for 64% of the overall ambulance allocation inequality. Regional ambulance allocation unfairness with highly degree could also be caused by the different roles of the district governments with respect to spending on emergency medical care [41], and the differences in development priorities to districts polities [42].

Finally, we found that the unfairness distribution and high degree of inequalities in the allocation of ambulance were associated with prolonged transfer time and higher ACS mortality, which is compatible with other studies [26]. Clinical outcome of ACS patients could be affected by both patient-related and system-related factors [43,44,45]. Our study adjusted for patient-related factors to focus on system variables, indicating that equal distribution of ambulance allocation can contribute to an improvement in health system performance and clinical outcomes of ACS patients.

Limitation

There are some limitations in the current study. First, the data of our study were obtained in 2019, whereas during recent years, the number of ambulances might have increased in Shenzhen and the disparities in spatial distribution of ambulance now may be different. Secondly, though we included covariates and potential confounders of both patient-level and district-level, unmeasured confounding factors such as regional traffic environment and its medical care ability may have influenced the outcomes. Lastly, we enrolled ACS cases from 89 EMS centers and 31 hospitals in Shenzhen city with complete registry, which might limit the generalizability of our findings.

Conclusion

Inequities in regional ambulance allocation has a significant impact on pre-hospital transfer delay and in-hospital mortality of ACS patients. Policy interventions and suggestions in increasing ambulance accessibility and health education for public should be considered for future development.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request。.

Abbreviations

ACS:

Acute Coronary Syndrome

STEMI:

ST-segment-elevation Myocardial

PCI:

Percutaneous Coronary Intervention

EMS:

Emergency Medical Service; Shenzhen Emergency Medical Center: The EMS in Shenzhen

AED:

Automated External Defibrillator

NSTEMI:

Non-ST Elevation Myocardial Infarction

UA:

Unstable Angina

EMS:

Emergency Medical Service

SBP:

Systolic Blood Pressure

DBP:

Diastolic Blood Pressure

References

  1. Pocock SJ, Huo Y, Van de Werf F, Newsome S, Chin CT, Vega AM, et al. Predicting two-year mortality from discharge after acute coronary syndrome: an internationally-based risk score. Eur Heart J Acute Cardiovasc Care. 2019. https://doi.org/10.1177/2048872617719638.

  2. Shen C, Ge J. Epidemic of cardiovascular disease in China: current perspective and prospects for the future. Circulation. 2018. https://doi.org/10.1161/CIRCULATIONAHA.118.033484.

  3. Kushner FG, Hand M, Smith SC Jr, King SB 3rd, Anderson JL, Antman EM, et al. Focused updates: ACC/AHA guidelines for the management of patients with ST-elevation myocardial infarction (updating the 2004 guideline and 2007 focused update) and ACC/AHA/SCAI guidelines on percutaneous coronary intervention (updating the 2005 guideline and 2007 focused update) a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines. J Am Coll Cardiol. 2009, 2009. https://doi.org/10.1016/j.jacc.2009.10.015.

  4. Krumholz HM, Bradley EH, Nallamothu BK, Ting HH, Batchelor WB, Kline-Rogers E, et al. A campaign to improve the timeliness of primary percutaneous coronary intervention: door-to-balloon: an Alliance for quality. JACC Cardiovasc Interv. 2008. https://doi.org/10.1016/j.jcin.2007.10.006.

  5. De Luca G, Suryapranata H, Ottervanger JP, Antman EM. Time delay to treatment and mortality in primary angioplasty for acute myocardial infarction: every minute of delay counts. Circulation. 2004. https://doi.org/10.1161/01.CIR.0000121424.76486.20.

  6. Bruins Slot MH, Rutten FH, van der Heijden GJ, Doevendans PA, Mast EG, Bredero AC, et al. Gender differences in pre-hospital time delay and symptom presentation in patients suspected of acute coronary syndrome in primary care. Fam Pract. 2012. https://doi.org/10.1093/fampra/cmr089.

  7. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. ACC/AHA guideline on the primary prevention of cardiovascular disease: executive summary: a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. Circulation. 2019, 2019. https://doi.org/10.1161/CIR.0000000000000677.

  8. Goldberg RJ, Steg PG, Sadiq I, Granger CB, Jackson EA, Budaj A, et al. Extent of, and factors associated with, delay to hospital presentation in patients with acute coronary disease (the GRACE registry). Am J Cardiol. 2002. https://doi.org/10.1016/s0002-9149(02)02186-0.

  9. Jacobs AK. Regional systems of care for patients with ST-elevation myocardial infarction: being at the right place at the right time. Circulation. 2007. https://doi.org/10.1161/CIRCULATIONAHA.107.720946.

  10. McKee G, Mooney M, O'Donnell S, O'Brien F, Biddle MJ, Moser DK. Multivariate analysis of predictors of pre-hospital delay in acute coronary syndrome. Int J Cardiol. 2013. https://doi.org/10.1016/j.ijcard.2013.03.022.

  11. Timmis A, Townsend N, Gale C, Grobbee R, Maniadakis N, Flather M, et al. European Society of Cardiology: cardiovascular disease statistics 2017. Eur Heart J. 2018. https://doi.org/10.1093/eurheartj/ehx628.

  12. Dharma S, Andriantoro H, Dakota I, Purnawan I, Pratama V, Isnanijah H, et al. Organisation of reperfusion therapy for STEMI in a developing country. Open Heart. 2015;2(1):e000240. https://doi.org/10.1136/openhrt-2015-000240 PMID: 26019883; PMCID: PMC4442233.

    Article  Google Scholar 

  13. Galappatthy P, Bataduwaarachchi VR, Ranasinghe P, Galappatthy GKS, Wijayabandara M, Warapitiya DS, et al. Management, characteristics and outcomes of patients with acute coronary syndrome in Sri Lanka. Heart. 2018. https://doi.org/10.1136/heartjnl-2017-312404.

  14. Kurz MC, Schmicker RH, Leroux B, Nichol G, Aufderheide TP, Cheskes S, et al. Advanced vs. Basic Life Support in the Treatment of Out-of-Hospital Cardiopulmonary Arrest in the Resuscitation Outcomes Consortium. Resuscitation. 2018. https://doi.org/10.1016/j.resuscitation.2018.04.031.

  15. Cournoyer A, Notebaert É, Iseppon M, Cossette S, Londei-Leduc L, Lamarche Y, et al. Prehospital advanced cardiac life support for out-of-hospital cardiac arrest: a cohort study. Acad Emerg Med. 2017. https://doi.org/10.1111/acem.13246.

  16. Chocron R, Loeb T, Lamhaut L, Jost D, Adnet F, Lecarpentier E, et al. Ambulance density and outcomes after out-of-hospital cardiac arrest. Circulation. 2019. https://doi.org/10.1161/CIRCULATIONAHA.118.035113.

  17. Dharma S. Comparison of real-life Systems of Care for ST-segment elevation myocardial infarction. Glob Heart. 2020. https://doi.org/10.5334/gh.343.

  18. Harper S, Lynch J, Meersman SC, Breen N, Davis WW, Reichman ME. An overview of methods for monitoring social disparities in cancer with an example using trends in lung cancer incidence by area-socioeconomic position and race-ethnicity, 1992-2004. Am J Epidemiol. 2008. https://doi.org/10.1093/aje/kwn016.

  19. Manz KM, Mansmann U. Inequality indices to monitor geographic differences in incidence, mortality and fatality rates over time during the COVID-19 pandemic. PLoS One. 2021. https://doi.org/10.1371/journal.pone.0251366.

  20. Lorenz MO. Methods of measuring the concentration of wealth. Publ Am Stat Assoc. 1905;9:209–19.

    Google Scholar 

  21. O'Donnell O, O'Neill S, Van Ourti T, Walsh B. Conindex: estimation of concentration indices. Stata J. 2016;16:112–38.

    Article  Google Scholar 

  22. Horev T, Pesis-Katz I, Mukamel DB. Trends in geographic disparities in allocation of health care resources in the US. Health Policy. 2004. https://doi.org/10.1016/j.healthpol.2003.09.011.

  23. Rezaei S, Karyani AK, Fallah R, Matin BK. Relative inequalities in geographic distribution of health care resources in Kermanshah province, Islamic Republic of Iran. East Mediterr Health J. 2016;22:19–26.

    Article  CAS  Google Scholar 

  24. Anand, Sudhir & World Health Organization. Measuring health workforce inequalities: methods and application to China and India. 2010. Available: https://apps.who.int/iris/handle/10665/44417. Accessed 16 Jun 2012.

    Google Scholar 

  25. Erdenee O, Paramita SA, Yamazaki C, Koyama H. Distribution of health care resources in Mongolia using the Gini coefficient. Hum Resour Health. 2017. https://doi.org/10.1186/s12960-017-0232-1.

  26. Chandrashekhar Y, Alexander T, Mullasari A, Kumbhani DJ, Alam S, Alexanderson E, et al. Resource and infrastructure-appropriate management of ST-segment elevation myocardial infarction in low- and middle-income countries. Circulation. 2020. https://doi.org/10.1161/CIRCULATIONAHA.119.041297.

  27. Crilly J, Keijzers G, Tippett V, O'Dwyer J, Lind J, Bost N, et al. Improved outcomes for emergency department patients whose ambulance off-stretcher time is not delayed. Emerg Med Australas. 2015. https://doi.org/10.1111/1742-6723.12399.

  28. Kim DK, Kim TH, Shin SD, Ro YS, Song KJ, Hong KJ, et al. Impact of crowding in local ambulance demand on call-to-ambulance scene arrival in out-of-hospital cardiac arrest. Am J Emerg Med. 2022. https://doi.org/10.1016/j.ajem.2021.12.003.

  29. Miedema MD, Newell MC, Duval S, Garberich RF, Handran CB, Larson DM, et al. Causes of delay and associated mortality in patients transferred with ST-segment-elevation myocardial infarction. Circulation. 2011. https://doi.org/10.1161/CIRCULATIONAHA.111.033118.

  30. Mathews R, Peterson ED, Li S, Roe MT, Glickman SW, Wiviott SD, et al. Use of emergency medical service transport among patients with ST-segment-elevation myocardial infarction: findings from the National Cardiovascular Data Registry Acute Coronary Treatment Intervention Outcomes Network Registry-get with the Guidelines. Circulation. 2011. https://doi.org/10.1161/CIRCULATIONAHA.110.002345.

  31. Sullivan MD, Ciechanowski PS, Russo JE, Soine LA, Jordan-Keith K, Ting HH, et al. Understanding why patients delay seeking care for acute coronary syndromes. Circ Cardiovasc Qual Outcomes. 2009. https://doi.org/10.1161/CIRCOUTCOMES.108.825471.

  32. Hutchings CB, Mann NC, Daya M, Jui J, Goldberg R, Cooper L, et al. Rapid early action for coronary treatment study. Patients with chest pain calling 9-1-1 or self-transporting to reach definitive care: which mode is quicker? Am Heart J. 2004. https://doi.org/10.1016/s0002-8703(03)00510-6.

  33. Nogueira LC Jr, Pinto LR, Silva PM. Reducing emergency medical service response time via the reallocation of ambulance bases. Health Care Manag Sci. 2016. https://doi.org/10.1007/s10729-014-9280-4.

  34. Lossius HM, Søreide E, Hotvedt R, Hapnes SA, Eielsen OV, Førde OH, et al. Prehospital advanced life support provided by specially trained physicians: is there a benefit in terms of life years gained? Acta Anaesthesiol Scand. 2002. https://doi.org/10.1034/j.1399-6576.2002.460703.x.

  35. Hagihara A, Hasegawa M, Abe T, Nagata T, Nabeshima Y. Physician presence in an ambulance Car is associated with increased survival in out-of-hospital cardiac arrest: a prospective cohort analysis. PLoS One. 2014;9:1.

    Article  Google Scholar 

  36. Markel DT, Gold LS, Fahrenbruch CE, Eisenberg MS. Prompt advanced life support improves survival from ventricular fibrillation. Prehosp Emerg Care. 2009. https://doi.org/10.1080/10903120802706245.

  37. Henry TD, Sharkey SW, Burke MN, Chavez IJ, Graham KJ, Henry CR, et al. A regional system to provide timely access to percutaneous coronary intervention for ST-elevation myocardial infarction. Circulation. 2007. https://doi.org/10.1161/CIRCULATIONAHA.107.694141.

  38. Thuresson M, Jarlöv MB, Lindahl B, Svensson L, Zedigh C, Herlitz J. Factors that influence the use of ambulance in acute coronary syndrome. Am Heart J. 2008. https://doi.org/10.1016/j.ahj.2008.01.020.

  39. Romaniuk P, Szromek AR. The evolution of the health system outcomes in central and Eastern Europe and their association with social, economic and political factors: an analysis of 25 years of transition. BMC Health Serv Res. 2016. https://doi.org/10.1186/s12913-016-1344-3.

  40. Sumah AM, Baatiema L, Abimbola S. The impacts of decentralisation on health-related equity: a systematic review of the evidence. Health Policy. 2016. https://doi.org/10.1016/j.healthpol.2016.09.003.

  41. Woldemichael A, Takian A, Akbari Sari A, Olyaeemanesh A. Inequalities in healthcare resources and outcomes threatening sustainable health development in Ethiopia: panel data analysis. BMJ Open. 2019. https://doi.org/10.1136/bmjopen-2018-022923.

  42. Arcaya MC, Arcaya AL, Subramanian SV. Inequalities in health: definitions, concepts, and theories. Glob Health Action. 2015. https://doi.org/10.3402/gha.v8.27106.

  43. Lavery T, Greenslade JH, Parsonage WA, Hawkins T, Dalton E, Hammett C, et al. Factors influencing choice of pre-hospital transportation of patients with potential acute coronary syndrome: an observational study. Emerg Med Australas. 2017. https://doi.org/10.1111/1742-6723.12735.

  44. Rossello X, Bueno H, Pocock SJ, Van de Werf F, Danchin N, Annemans L, et al. Predictors of all-cause mortality and ischemic events within and beyond 1 year after an acute coronary syndrome: results from the EPICOR registry. Clin Cardiol. 2019. https://doi.org/10.1002/clc.23116.

  45. Bates ER, Jacobs AK. Time to treatment in patients with STEMI. N Engl J Med. 2013. https://doi.org/10.1056/NEJMp1308772.

Download references

Acknowledgments

We thank all the colleagues in the Shenzhen Center for Prehospital Care  and Peking University.

Funding

This study is supported by Sanming Project of Medicine in Shenzhen (No. SZSM201911005), the National Natural Science Foundation of China (No. 71904004). The study sponsor has no role in study design, data analysis and interpretation of data, the writing of manuscript, or the decision to submit the paper for publication.

Author information

Author notes

  1. Siwen Li and Xuejie Dong contributed equally to this work and share first authorship.

  2. Zhi-Jie Zheng as senior author.

Authors and Affiliations

  1. Shenzhen Center for Prehospital Care, 3 Meigang South Street, West Nigang Road, Futian District, Shenzhen, 518025, China

    Siwen Li, Dongmei Li, Hongjuan Zhang & Qiang Zhou

  2. Department of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing, 100191, China

    Siwen Li, Xuejie Dong, Shuduo Zhou, Mailikezhati Maimaitiming, Junxiong Ma, Na Li, Yinzi Jin & Zhi-Jie Zheng

  3. Institute for Global Health and Development, Peking University, Beijing, China

    Siwen Li, Xuejie Dong, Shuduo Zhou, Mailikezhati Maimaitiming, Junxiong Ma, Na Li, Yinzi Jin & Zhi-Jie Zheng

Authors
  1. Siwen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuejie Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongmei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongjuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuduo Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mailikezhati Maimaitiming
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Junxiong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Na Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Qiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yinzi Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zhi-Jie Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SL and XD: drafting the manuscript, data analysis, Interpretation, field investigation, data collection, and critical revision of article for important intellectual content. DL, HoZ, SZ, MM, JM, NL, and Z-JZ: critical revision of article for important intellectual content, data analysis and critical revision of article for important intellectual content. QZ and YJ: study concept and design, data analysis, and critical revision of article for important intellectual content. All authors gave final approval of the version to be published.

Corresponding authors

Correspondence to Qiang Zhou or Yinzi Jin.

Ethics declarations

Consent of publication

Not applicable.

Ethics approval and consent to participate

Ethics approval and consent to participate this project was approved by the Peking University Health Science Center Institutional Review Board (IRB00001052–21020). Informed consent was obtained from all participants prior to questionnaire administration.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, S., Dong, X., Li, D. et al. Inequities in ambulance allocation associated with transfer delay and mortality in acute coronary syndrome patients: evidence from 89 emergency medical stations in China. Int J Equity Health 21, 178 (2022). https://doi.org/10.1186/s12939-022-01777-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12939-022-01777-3

Keywords

  • Health resource
  • Ambulance inequality
  • Acute coronary syndrome
  • Prehospital care
  • Emergency medical service

International Journal for Equity in Health

ISSN: 1475-9276

Contact us