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Small Area Variations in Out-of-Hospital Cardiac Arrest: Does the Neighborhood Matter? FREE

Comilla Sasson, MD, MS; Carla C. Keirns, MD, PhD, MS; Dylan Smith, PhD; Michael Sayre, MD; Michelle Macy, MD, MS; William Meurer, MD; Bryan F. McNally, MD, MPH; Arthur L. Kellermann, MD, MPH; Theodore J. Iwashyna, MD, PhD, CARES (Cardiac Arrest Registry to Enhance Survival) Study Group
[+] Article and Author Information

From the University of Michigan, Ann Arbor, Michigan; Stony Brook University Medical Center, Stony Brook, New York; Ohio State University, Columbus, Ohio; and Emory University School of Medicine, Atlanta, Georgia.


Acknowledgment: The authors thank Paula W. Yoon, ScD, MPH; Linda Schieb, MSPH; and Greg Schwartz, MS, from the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention, for assistance with data analysis, geographic information system mapping, and manuscript editing. They also thank Allison Crouch and Amanda Bray-Perez for assistance with the data collection; Drs. Eric Ossmann, Ian Greenwald, and Alex Isakov, from the CARES Atlanta site; and the Robert Wood Johnson Foundation Clinical Scholars Program.

Grant Support: By the Robert Wood Johnson Foundation Clinical Scholars Program, National Institutes of Health (grant K08 HL091249), and Centers for Disease Control and Prevention (grant MM-0917-05/05).

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M09-2284.

Reproducible Research Statement:Study protocol: Not applicable. Statistical code: Available from Dr. Sasson (e-mail, comilla@umich.edu). Data set: Not available.

Requests for Single Reprints: Comilla Sasson, MD, MS, Robert Wood Johnson Foundation Clinical Scholars Program, 1150 West Medical Center Drive, 6312 Medical Science Building 1, Campus Box 5604, Ann Arbor, MI 48109; e-mail, comilla@umich.edu.

Current Author Addresses: Dr. Sasson: Robert Wood Johnson Foundation Clinical Scholars Program, 1150 West Medical Center Drive, 6312 Medical Science Building 1, Campus Box 5604, Ann Arbor, MI 48109.

Drs. Keirns and Smith: Department of Preventive Medicine, Stony Brook University Medical Center, HSC Level 3, Room 080, Stony Brook, NY 11794.

Dr. Sayre: Department of Emergency Medicine, Ohio State University, 4821 Cramblett Hall, 456 West 10th Avenue, Columbus, OH 43210.

Dr. Macy: Department of Emergency Medicine, University of Michigan, A. Alfred Taubman Health Care Center, 1500 East Medical Center Drive, Room B1 380, Ann Arbor, MI 48109-5305.

Dr. Meurer: Department of Emergency Medicine, University of Michigan, 1500 East Medical Center Drive, Floor B1, Room B1C255, Ann Arbor, MI 48109-5014.

Dr. McNally: Department of Emergency Medicine, Emory University School of Medicine, 531 Asbury Circle, Annex N340, Atlanta, GA 30322.

Dr. Kellermann: RAND Corporation, 1200 South Hayes Street, Arlington, VA 22202-5050.

Dr. Iwashyna: Division of Pulmonary and Critical Care, Department of Medicine, University of Michigan, North Ingalls Building, 300 North Ingalls, Room 3A23, Ann Arbor, MI 48109-5419.

Author Contributions: Conception and design: C. Sasson, C.C. Keirns, B.F. McNally, T.J. Iwashyna.

Analysis and interpretation of the data: C. Sasson, C.C. Keirns, D. Smith, M. Sayre, M. Macy, W. Meurer, T.J. Iwashyna.

Drafting of the article: C. Sasson, C.C. Keirns, M. Macy, W. Meurer, T.J. Iwashyna.

Critical revision of the article for important intellectual content: C. Sasson, C.C. Keirns, M. Sayre, M. Macy, W. Meurer, B.F. McNally, A.L. Kellermann, T.J. Iwashyna.

Final approval of the article: C. Sasson, C.C. Keirns, M. Sayre, M. Macy, W. Meurer, B.F. McNally, A.L. Kellermann, T.J. Iwashyna.

Statistical expertise: C. Sasson, T.J. Iwashyna.

Obtaining of funding: B.F. McNally.

Administrative, technical, or logistic support: C. Sasson, M. Sayre, A.L. Kellermann.

Collection and assembly of data: C. Sasson, B.F. McNally.


Ann Intern Med. 2010;153(1):19-22. doi:10.7326/0003-4819-153-1-201007060-00255
Text Size: A A A

Background: The incidence and outcomes of out-of-hospital cardiac arrest vary widely across cities. It is unknown whether similar differences exist at the neighborhood level.

Objective: To determine the extent to which neighborhoods have persistently high rates of cardiac arrest but low rates of bystander cardiopulmonary resuscitation (CPR).

Design: Multilevel Poisson regression of 1108 cardiac arrests from 161 census tracts as captured by the Cardiac Arrest Registry to Enhance Survival (CARES).

Setting: Fulton County, Georgia, between 1 October 2005 to 30 November 2008.

Measurements: Incidence of cardiac arrest, by census tract and year and by rates of bystander CPR.

Results: Adjusted rates of cardiac arrest varied across neighborhoods (interquartile range [IQR], 0.57 to 0.73 per 1000 persons; mean, 0.64 per 1000 persons [SD, 0.11]) but were stable from year to year (intraclass correlation, 0.36 [95% CI, 0.26 to 0.50]; P < 0.001). Adjusted bystander CPR rates also varied by census tract (IQR, 19% to 29%; mean, 25% [SD, 10%]).

Limitation: Analysis was based on data from a single county.

Conclusion: Surveillance data can identify neighborhoods with a persistently high incidence of cardiac arrest and low rates of bystander CPR. These neighborhoods are promising targets for community-based interventions.

Primary Funding Source: Robert Wood Johnson Foundation Clinical Scholars Program, National Institutes of Health, and Centers for Disease Control and Prevention.

Editors' Notes
Context

  • The frequency of out-of-hospital cardiac arrest varies greatly across larger geographic areas, such as cities, but we do not know whether the variation also exists across neighborhoods.

Contribution

  • In 161 census tracts in Fulton County, Georgia (including Atlanta), during the 3-year period that ended in November 2008, the frequency of cardiac arrest varied from 0.04 to 2.11 per 1000 persons, rates were stable from year to year, and the frequency of bystander cardiopulmonary resuscitation varied from 0% to 100%.

Caution

  • These results are from a single county.

Implication

  • Neighborhoods with more cardiac arrests and fewer bystander cardiopulmonary resuscitations are promising targets for community-based interventions.

—The Editors

Survival rates for out-of-hospital cardiac arrest have been stagnant for more than 30 years (1). This has sparked a “back-to-basics” approach that emphasizes early recognition, rapid provision of bystander cardiopulmonary resuscitation (CPR), and early defibrillation. Bystander CPR is clearly effective; 1 life is saved for every 24 to 36 persons who receive bystander CPR (1). Boosting bystander CPR rates in the United States from the current average of 27% (2) to 56% (3) could save an additional 1500 lives per year.

Survival rates vary greatly by city (4), but little is known about how the incidence of cardiac arrest and rates of bystander CPR vary within particular cities. If the incidence of cardiac arrest at the neighborhood level is sufficiently stable, this could justify targeting scarce public health resources, such as educational outreach and placement of public-access defibrillators, in specific neighborhoods. If certain neighborhoods have a high incidence of cardiac arrest but relatively low rates of bystander CPR, they may benefit from targeted CPR training. To assess the feasibility of identifying candidate neighborhoods, we analyzed data from a cardiac arrest registry to determine the stability of incidence rates of cardiac arrest within census tracts and the extent of variability in rates of bystander CPR.

The data were drawn from the Cardiac Arrest Registry to Enhance Survival (CARES) in Fulton County, Georgia, which includes Atlanta. In brief, CARES is an emergency medical services (EMS) Web-based registry for out-of-hospital cardiac arrest, in which review of EMS logs is coupled with selected, anonymized extraction of hospital information. Detailed information is published elsewhere (56). From 1 October 2005 to 30 November 2008, CARES captured all 911-activated arrests in which resuscitation was attempted and the cause of arrest was presumed to be cardiac. During the data review process, analysts from CARES confirmed the capture of all cardiac arrests by the city's 911 center.

All cases submitted to the registry during the study interval (n = 2028) were eligible for inclusion. A case was excluded if prehospital resuscitation was not attempted because of local EMS protocols (for example, obvious signs of death, such as rigor mortis, decomposition, or lividity; 66 case patients); if EMS personnel determined that the arrest was due to a noncardiac cause (for example, trauma, electrocution, drowning, or respiratory complications; 283 case patients); or if the patient was not eligible for bystander CPR by a non–health care professional (that is, the arrest occurred in a medical facility, such as a nursing home or medical clinic) or was witnessed by EMS (468 case patients). We also excluded cases in which data on the patient's clinical outcome were missing (24 case patients); if the address of the cardiac arrest location could not be mapped (60 case patients); or if the event occurred in Atlanta's Hartsfield-Jackson International Airport, a public facility that is heavily monitored and has many trained rescuers and public-access defibrillators (19 case patients).

Because the CARES registry contains only anonymized data, our study was considered exempt from regulations regarding human subjects by the University of Michigan Institutional Review Board.

Data Collection and Processing

Fulton County's 4 EMS agencies prospectively submitted data in accordance with the CARES user agreement. The registry collects and links a limited standard set of data elements from 3 sources: 911 call centers, EMS providers, and receiving hospitals. A data analyst independently reviewed all submitted reports.

The CARES data set was geocoded on the basis of the address of the cardiac arrest by using ArcGIS and Spatial Analyst Extension Software (Environmental Systems Research Institute, Redlands, California). We used census tracts as proxies for neighborhoods because they tend to represent social and economically homogenous groups of approximately 4000 to 7000 persons (7). Census tract variables were linked by using the 2000 U.S. Census Summary files (8). All statistical analyses were done by using STATA, version 10.0 (StataCorp, College Station, Texas).

Statistical Analysis

To determine whether certain census tracts produced more cardiac arrest events year by year, we first did a multilevel Poisson regression to assess the stability of the number of cardiac arrests by census tract and year. The 2008 estimates were adjusted for only including data on 11 of the 12 calendar months. We determined the intraclass correlation to quantify the extent to which census tracts have stable rates of cardiac arrest by year. We then determined the incidence of cardiac arrest and the rates of bystander CPR by census tract by using empirical Bayes methods to adjust the estimated rates for reliability by the number of cases observed (910). On the basis of these reliability-adjusted rates for cardiac arrest and CPR, we then identified census tracts that seemed to have the highest and lowest potential for improvement. Census tracts that seemed to have a higher incidence of cardiac arrest and lower rates of bystander CPR were considered to be possible higher-gain neighborhoods compared with census tracts with a lower incidence of cardiac arrest and a higher rate of bystander CPR, which were considered to be lower-gain neighborhoods.

Role of the Funding Source

This study was funded by the Robert Wood Johnson Foundation Clinical Scholars Program, the National Institutes of Health, and the Centers for Disease Control and Prevention. The funding sources had no role in the design and conduct of the study, the analysis and interpretation of the data, or the preparation and review of the manuscript.

A total of 1108 case patients met study criteria. The Table displays demographic, clinical, and EMS statistics of study patients. A total of 279 patients received bystander CPR. Of the 41 patients (3.7%) who survived to hospital discharge, 20 received bystander CPR.

Table Jump PlaceholderTable.  Patient and Cardiac Arrest Characteristics

Census tracts had an average adjusted incidence of 0.64 events per 1000 persons (SD, 0.11) (interquartile range [IQR], 0.57 to 0.73 per 1000 persons); unadjusted rates varied widely, from 0.04 to 2.11 per 1000 persons. The mean number of cardiac arrests per year was 2.21 (SD, 1.91) (IQR, 1 to 3.23). A total of 25 census tracts had more than twice that number in 1 or more of the 3 study years. Seven census tracts had at least 6 cardiac arrests in 2 of the 3 study years (Appendix Table). The intraclass correlation for variation between census tracts was 0.36 (95% CI, 0.26 to 0.50; P < 0.001), indicating that neighborhoods with a high incidence of cardiac arrest in one year were more likely to have high rates the next year. Similar results were obtained when the absolute number of cardiac arrests, rather than a population-adjusted incidence, was examined (intraclass correlation, 0.29 [CI, 0.20 to 0.42]). Figure 1 shows the census tracts from the areas with the highest and lowest gains, highlighting the stability of the occurrence of cardiac arrest across the 3-year period.

Table Jump PlaceholderAppendix Table.  Demographic and Cardiac Arrest Characteristics, by Census Tract
Grahic Jump Location
Figure 1.
Cardiac arrest events across the 3-year period.
Grahic Jump Location

The mean rate of bystander CPR for the entire sample was 25% (SD, 10%) (IQR, 19% to 29%). However, rates of bystander CPR varied widely among neighborhoods: Unadjusted rates ranged from 0% to 100%. After adjustment for reliability, rates still varied from 10% to 57%. There was little association between the incidence of cardiac arrests and the reliability-adjusted rates of bystander CPR (correlation coefficient, 0.13).

Figure 2 shows 14 census tracts in the lower right area that have the highest reliability-adjusted incidence of cardiac arrests and the lowest reliability-adjusted rates of bystander CPR. These census tracts could represent higher-gain areas that may benefit from neighborhood-based CPR interventions. Of note, the triangles in Figure 2 correspond to the solid lines in Figure 1, which represent the higher-gain census tracts, whereas the circles in the upper left corner in Figure 2 correspond to the dashed lines in Figure 1, which represent the lower-gain census tracts. All 14 higher-gain census tracts have more black residents (43.2% to 98.2%; Fulton County mean, 44.6%), lower median household incomes ($13 880 to $45 525; Fulton County median, $47 321), and fewer high school graduates (46.7% to 86.1%; Fulton County mean, 84.0%) than is typical of Fulton County (Appendix Table).

Grahic Jump Location
Figure 2.
Adjusted incidence of cardiac arrests and rates of CPR, by census tract.

Triangles represent higher-gain census tracts. CPR = cardiopulmonary resuscitation.

Grahic Jump Location

To the best of our knowledge, this is the first study to show relative stability in the incidence of cardiac arrest within census tracts from year to year. It is also the first to identify census tracts that are at higher risk for cardiac arrest and have relatively low rates of bystander CPR. Although the presence of cardiac arrest “hot spots” has been inferred (11), it has not been shown that hot spots persist long enough to provide a useful target for intervention. Our data indicate that some census tracts have an incidence of cardiac arrest 2 to 3 times higher than others. We were also able to identify census tracts that have much lower rates of bystander CPR than those in other parts of the same county. These census tracts may be promising settings for heart health promotion, CPR training, public-access defibrillator placement, and other community-based interventions. If we could improve rates of bystander CPR in the full sample to the level achieved by the highest-performing census tracts in Fulton County, an additional 355 persons would receive CPR. This could save an additional 15 lives each year.

These findings have public health implications. Our analysis suggests that neighborhoods, as defined by census tracts, are an appropriate target for interventions to increase cardiac arrest survival by increasing bystander CPR. Offering CPR training to likely witnesses of cardiac arrest in selected neighborhoods—those most likely to benefit from higher rates of bystander CPR—may represent a more effective approach to CPR training than the current strategy, which tends to enlist young, healthy volunteers who are less likely to witness a cardiac arrest. Such neighborhood-based public health approaches may have the added benefit of building social capital (12) and will show the utility of an EMS Web-based registry, such as CARES, to target increasingly scarce public health dollars.

Our study has limitations. First, the data are derived from a cardiac arrest registry based on EMS and 911 reports that records arrests of presumed cardiac cause in which resuscitation was attempted (5). Intensive epidemiologic case-finding may identify additional cases that were not known to EMS or not “worked” as active resuscitations and were therefore excluded from this database, although persons with such arrests would probably not have benefited from CPR. Second, our study is based on data from a single urban county. Replications will be needed to determine whether other communities also have temporal stability. Finally, we used census tracts to approximate neighborhoods. Although these tracts do not precisely match neighborhoods as perceived and described by residents, they are a data structure routinely available for public health planning.

Despite extensive programs aimed at educating the public about CPR at the national and local level, census tracts in Fulton County, Georgia, have wide variations in incidence of cardiac arrest and rates of bystander CPR. Community-based surveillance data can be used to identify areas with high incidence of cardiac arrest and low rates of bystander CPR for targeted interventions. This may be a fruitful method to focus community-level interventions and to increase survival rates of cardiac arrest (1).

Sasson C, Rogers MA, Dahl J, Kellermann AL.  Predictors of survival from out-of-hospital cardiac arrest: a systematic review and meta-analysis. Circ Cardiovasc Qual Outcomes. 2010; 3:63-81. PubMed
CrossRef
 
Rosamond W, Flegal K, Furie K, Go A, Greenlund K, Haase N, et al., American Heart Association Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics—2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2008; 117:25-146. PubMed
 
Eisenberg MS, Mengert TJ.  Cardiac resuscitation. N Engl J Med. 2001; 344:1304-13. PubMed
 
Nichol G, Thomas E, Callaway CW, Hedges J, Powell JL, Aufderheide TP, et al., Resuscitation Outcomes Consortium Investigators.  Regional variation in out-of-hospital cardiac arrest incidence and outcome. JAMA. 2008; 300:1423-31. PubMed
 
McNally B, Stokes A, Crouch A, Kellermann AL, CARES Surveillance Group.  CARES: Cardiac Arrest Registry to Enhance Survival. Ann Emerg Med. 2009; 54:674-683. PubMed
 
Sasson C, Hegg AJ, Macy M, Park A, Kellermann A, McNally B, CARES Surveillance Group.  Prehospital termination of resuscitation in cases of refractory out-of-hospital cardiac arrest. JAMA. 2008; 300:1432-8. PubMed
 
Krieger N.  Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health. 1992; 82:703-10. PubMed
 
Cardiac Arrest Registry to Enhance Survival.  IRB Approval and Modification. Accessed athttps://mycares.net/cares_irb.jspon 30 April 2010.
 
Rabe-Hesketh S, Skrondal A.  Multilevel and Longitudinal Modeling Using Stata. 2nd ed. College Station, TX: Stata Pr; 2008.
 
Hayward RA, Heisler M, Adams J, Dudley RA, Hofer TP.  Overestimating outcome rates: statistical estimation when reliability is suboptimal. Health Serv Res. 2007; 42:1718-38. PubMed
 
Lerner EB, Fairbanks RJ, Shah MN.  Identification of out-of-hospital cardiac arrest clusters using a geographic information system. Acad Emerg Med. 2005; 12:81-4. PubMed
 
McCarthy M.  Social determinants and inequalities in urban health. Rev Environ Health. 2000; 15:97-108. PubMed
 

Figures

Grahic Jump Location
Figure 1.
Cardiac arrest events across the 3-year period.
Grahic Jump Location
Grahic Jump Location
Figure 2.
Adjusted incidence of cardiac arrests and rates of CPR, by census tract.

Triangles represent higher-gain census tracts. CPR = cardiopulmonary resuscitation.

Grahic Jump Location

Tables

Table Jump PlaceholderTable.  Patient and Cardiac Arrest Characteristics
Table Jump PlaceholderAppendix Table.  Demographic and Cardiac Arrest Characteristics, by Census Tract

References

Sasson C, Rogers MA, Dahl J, Kellermann AL.  Predictors of survival from out-of-hospital cardiac arrest: a systematic review and meta-analysis. Circ Cardiovasc Qual Outcomes. 2010; 3:63-81. PubMed
CrossRef
 
Rosamond W, Flegal K, Furie K, Go A, Greenlund K, Haase N, et al., American Heart Association Statistics Committee and Stroke Statistics Subcommittee.  Heart disease and stroke statistics—2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2008; 117:25-146. PubMed
 
Eisenberg MS, Mengert TJ.  Cardiac resuscitation. N Engl J Med. 2001; 344:1304-13. PubMed
 
Nichol G, Thomas E, Callaway CW, Hedges J, Powell JL, Aufderheide TP, et al., Resuscitation Outcomes Consortium Investigators.  Regional variation in out-of-hospital cardiac arrest incidence and outcome. JAMA. 2008; 300:1423-31. PubMed
 
McNally B, Stokes A, Crouch A, Kellermann AL, CARES Surveillance Group.  CARES: Cardiac Arrest Registry to Enhance Survival. Ann Emerg Med. 2009; 54:674-683. PubMed
 
Sasson C, Hegg AJ, Macy M, Park A, Kellermann A, McNally B, CARES Surveillance Group.  Prehospital termination of resuscitation in cases of refractory out-of-hospital cardiac arrest. JAMA. 2008; 300:1432-8. PubMed
 
Krieger N.  Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. Am J Public Health. 1992; 82:703-10. PubMed
 
Cardiac Arrest Registry to Enhance Survival.  IRB Approval and Modification. Accessed athttps://mycares.net/cares_irb.jspon 30 April 2010.
 
Rabe-Hesketh S, Skrondal A.  Multilevel and Longitudinal Modeling Using Stata. 2nd ed. College Station, TX: Stata Pr; 2008.
 
Hayward RA, Heisler M, Adams J, Dudley RA, Hofer TP.  Overestimating outcome rates: statistical estimation when reliability is suboptimal. Health Serv Res. 2007; 42:1718-38. PubMed
 
Lerner EB, Fairbanks RJ, Shah MN.  Identification of out-of-hospital cardiac arrest clusters using a geographic information system. Acad Emerg Med. 2005; 12:81-4. PubMed
 
McCarthy M.  Social determinants and inequalities in urban health. Rev Environ Health. 2000; 15:97-108. PubMed
 

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