Introduction - University of Pittsburgh
5715001143000UNDERSTANDING THE DISPROPORTIONALLY HIGH INCIDENCE OF CARDIAC ARRESTS IN THE ALLEGHENY COUNTY POPULATION UNDER THE AGE OF 65byThomas UrayMD, University of Vienna, Austria, 2001Submitted to the Graduate Faculty ofthe Multidisciplinary Master of Public Health ProgramGraduate School of Public Health in partial fulfillment of the requirements for the degree ofMaster of Public Health University of Pittsburgh201300UNDERSTANDING THE DISPROPORTIONALLY HIGH INCIDENCE OF CARDIAC ARRESTS IN THE ALLEGHENY COUNTY POPULATION UNDER THE AGE OF 65byThomas UrayMD, University of Vienna, Austria, 2001Submitted to the Graduate Faculty ofthe Multidisciplinary Master of Public Health ProgramGraduate School of Public Health in partial fulfillment of the requirements for the degree ofMaster of Public Health University of Pittsburgh2013center301625UNIVERSITY OF PITTSBURGHGRADUATE SCHOOL OF PUBLIC HEALTHThis essay is submittedbyThomas UrayonAugust 7th, 2013and approved byEssay Advisor:David Finegold, MD________________________________Director, Multidisciplinary MPH ProgramProfessor of Pediatrics Department of Human GeneticsGraduate School of Public HealthUniversity of PittsburghEssay Reader:Cameron Dezfulian, MD________________________________Assistant ProfessorDepartment of Critical Care MedicineVascular Medicine InstituteSchool of MedicineUniversity of Pittsburgh00UNIVERSITY OF PITTSBURGHGRADUATE SCHOOL OF PUBLIC HEALTHThis essay is submittedbyThomas UrayonAugust 7th, 2013and approved byEssay Advisor:David Finegold, MD________________________________Director, Multidisciplinary MPH ProgramProfessor of Pediatrics Department of Human GeneticsGraduate School of Public HealthUniversity of PittsburghEssay Reader:Cameron Dezfulian, MD________________________________Assistant ProfessorDepartment of Critical Care MedicineVascular Medicine InstituteSchool of MedicineUniversity of Pittsburghcenter4648200Copyright ? by Thomas Uray201300Copyright ? by Thomas Uray2013-228600304800David Finegold, MDUNDERSTANDING THE DISPROPORTIONALLY HIGH INCIDENCE OF CARDIAC ARRESTS IN THE ALLEGHENY COUNTY POPULATION UNDER THE AGE OF 65Thomas Uray, MPHUniversity of Pittsburgh, 2013ABSTRACT00David Finegold, MDUNDERSTANDING THE DISPROPORTIONALLY HIGH INCIDENCE OF CARDIAC ARRESTS IN THE ALLEGHENY COUNTY POPULATION UNDER THE AGE OF 65Thomas Uray, MPHUniversity of Pittsburgh, 2013ABSTRACTIntroduction- Every year approximately 300,000 out of hospital cardiac arrests (OHCA) occur in the US. Across seven large North American urban populations, the City of Pittsburgh had the highest crude rate in cardiac arrests. This discrepancy was most pronounced in young patients 18 to 64 years of age with the lowest quartile median household income. The aim of this thesis was to test the hypothesis that people in Pittsburgh who suffer a cardiac arrest live in poorer neighborhoods compared to the Pittsburgh/Allegheny/National average. In addition, I wanted to test the additional hypothesis that these cardiac arrest victims have more preexisting comorbid conditions and engage in poor health behaviors (e.g. illicit drug use).Methods- A retrospective analysis of all treated in- and out-of hospital cardiac arrest patients at an inner-city hospital during a 16-month period was analyzed. Patients with all causes of cardiac arrest between the age of 18 and 64 years were included. Next to existing data about neurological outcome and basic demographic data, data about residence, insurance status, employment, marital status, history of drug abuse and urine toxicology screening was analyzed. Patient’s residences were geocoded and depicted in a map of Allegheny, which also contained census tracts color coded by poverty status. Results- Between March 2011 and July 2012, 95 patients between the ages of 18 and 64 (median age of 53) were included in this analysis. Among those, 69 were living in Allegheny County at time of arrest. 19 patients (20.2%) had a history of active drug abuse, and forty patients (42.1%) had no insurance or Medicaid or Medicare listed as support. 25 (36.2%) were living in a neighborhood with 20-30% poverty at the time of the arrest.Conclusion- Cardiac arrest patients between the age of 18 and 64 treated at UPMC Mercy have a higher incidence of being unemployed, uninsured, living alone, being obese and using drugs than the rest of the population in Allegheny County. This major public health problem needs to be addressed by local authorities by designing special prevention programs targeted to this subpopulation.TABLE OF CONTENTS TOC \o "2-4" \h \z \t "Heading 1,1,Appendix,1,Heading,1" 1.0Introduction PAGEREF _Toc362612831 \h 12.0MEtHODS PAGEREF _Toc362612832 \h 33.0RESULTS PAGEREF _Toc362612833 \h 53.1Demographics PAGEREF _Toc362612834 \h 53.2Comorbid conditions and health behaviors PAGEREF _Toc362612835 \h 53.3Social background PAGEREF _Toc362612836 \h 63.4Neighborhood poverty PAGEREF _Toc362612837 \h 73.5Medical characteristics PAGEREF _Toc362612838 \h 73.6Outcomes PAGEREF _Toc362612839 \h 84.0DISCUSSSION PAGEREF _Toc362612840 \h 94.1Limitations PAGEREF _Toc362612841 \h 114.2Conclusion PAGEREF _Toc362612842 \h 12APPENDIX: TABLES AND FIGURES.................................................................................. 13BIBLIOGRAPHY PAGEREF _Toc362612843 \h 19List of tables TOC \h \z \c "Table" Table 1. Race and social characteristics; good versus bad outcome PAGEREF _Toc362534470 \h 13List of figures TOC \h \z \c "Figure" Figure 1. Insurance status PAGEREF _Toc362612386 \h 14Figure 2. Employment status PAGEREF _Toc362612387 \h 14Figure 3. Etiology overall PAGEREF _Toc362612388 \h 15Figure 4. In-hospital arrests (n=55) PAGEREF _Toc362612389 \h 15Figure 5. Out-of hospital arrests (n=40) PAGEREF _Toc362612390 \h 16Figure 6. Mapping of residences PAGEREF _Toc362612391 \h 17Figure 7. Percentage of poverty in area of residence (n=69) PAGEREF _Toc362612392 \h 18IntroductionIschemic heart disease is the leading cause of death in the United States.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HbzwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+PFJlY051
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ADDIN EN.CITE.DATA 2,3 Previous cardiac arrest studies have shown that incidence and survival rates vary by race. For instance, black people in urban Chicago were more likely to suffer an OHCA across all age groups and had an overall 3-fold lower survival rate compared to white people.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CZWNrZXI8L0F1dGhvcj48WWVhcj4xOTkzPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA 4 These findings are consistent with data that link the ‘typical’ cardiac arrest victim to an adverse socioeconomic background, i.e. lower average household income, lower educational level, and poor neighborhood residence.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GZWVybzwvQXV0aG9yPjxZZWFyPjE5OTU8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 7,8 Interestingly, Pittsburgh (Pennsylvania, USA) had the highest crude cardiac arrest incidence rate among these seven populations (159.3 per 100,000 in Pittsburgh vs. 71.5 per 100,000 overall incidence, respectively).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SZWluaWVyPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48
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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SZWluaWVyPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48
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ADDIN EN.CITE.DATA 8 This discrepancy was most pronounced in young patients 18 to 64 years of age with the lowest quartile median household income.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SZWluaWVyPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48
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ADDIN EN.CITE.DATA 8 The aim of this study was to understand factors contributing to the disproportionally high cardiac arrest incidence rate among Pittsburghers 18-64 years of age. Previous data from Allegheny County showed a 73% higher coronary heart disease mortality in black men compared to white men. ADDIN EN.CITE <EndNote><Cite><Author>Traven</Author><Year>1996</Year><RecNum>40</RecNum><DisplayText><style face="superscript">9</style></DisplayText><record><rec-number>40</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">40</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Traven, N. D.</author><author>Kuller, L. H.</author><author>Ives, D. G.</author><author>Rutan, G. H.</author><author>Perper, J. A.</author></authors></contributors><auth-address>Department of Epidemiology, University of Pittsburgh, PA, USA.</auth-address><titles><title>Coronary heart disease mortality and sudden death among the 35-44-year age group in Allegheny County, Pennsylvania</title><secondary-title>Ann Epidemiol</secondary-title><alt-title>Annals of epidemiology</alt-title></titles><periodical><full-title>Ann Epidemiol</full-title><abbr-1>Annals of epidemiology</abbr-1></periodical><alt-periodical><full-title>Ann Epidemiol</full-title><abbr-1>Annals of epidemiology</abbr-1></alt-periodical><pages>130-6</pages><volume>6</volume><number>2</number><edition>1996/03/01</edition><keywords><keyword>Adult</keyword><keyword>*African Continental Ancestry Group</keyword><keyword>Coronary Disease/*mortality</keyword><keyword>Death, Sudden, Cardiac/*epidemiology/etiology</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Pennsylvania/epidemiology</keyword><keyword>Prevalence</keyword></keywords><dates><year>1996</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>1047-2797 (Print)
1047-2797 (Linking)</isbn><accession-num>8775593</accession-num><work-type>Research Support, U.S. Gov't, P.H.S.</work-type><urls><related-urls><url> We tested the hypotheses that patients 18-64 years of age who reside in Allegheny county and suffered a cardiac arrest are more likely to be black, are more likely live in a poor neighborhood, are more likely to have preexisting co-morbid conditions (e.g. hypertension) or unhealthy behaviors (e.g. smoking) that predispose them to cardiovascular disease and potentially a cardiac arrest. We also examined whether cardiac arrest victims are less likely to have health insurance compared to the overall Allegheny population. To put our findings into perspective, we compared the results form Allegheny County with the national average. MEtHODSWe conducted a retrospective study and analyzed cardiac arrest patients 18-64 years of age that were transferred to University of Pittsburgh Medical Center (UPMC) Mercy Hospital (Pittsburgh, PA) between March 1, 2011 and July 30, 2012. All in- and out-of hospital cardiac arrests were identified through a preexisting cardiac arrest database. Clinical and demographic data was obtained by cross-linking the cardiac arrest database with individual electronic medical records. We abstracted the following information from patient charts: Full address and zip code, insurance status, race, marital status, religious status, first spoken language, history of drug abuse, known past history of illicit drug abuse, urine drug screen at admission, smoking status, preexisting hypertension, diabetes, hyperlipidemia, cardiovascular arterial disease (CAD), stroke, and cause of arrest. We calculated body mass index (BMI) to classify overweight and obesity. ADDIN EN.CITE <EndNote><Cite><Author>Stavig</Author><Year>1984</Year><RecNum>41</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>41</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">41</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Stavig, G. R.</author><author>Leonard, A. R.</author><author>Igra, A.</author><author>Felten, P.</author></authors></contributors><titles><title>Indices of relative body weight and ideal weight charts</title><secondary-title>J Chronic Dis</secondary-title><alt-title>Journal of chronic diseases</alt-title></titles><periodical><full-title>J Chronic Dis</full-title><abbr-1>Journal of chronic diseases</abbr-1></periodical><alt-periodical><full-title>J Chronic Dis</full-title><abbr-1>Journal of chronic diseases</abbr-1></alt-periodical><pages>255-62</pages><volume>37</volume><number>4</number><edition>1984/01/01</edition><keywords><keyword>Adolescent</keyword><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Body Height</keyword><keyword>*Body Weight</keyword><keyword>Evaluation Studies as Topic</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Reference Standards</keyword><keyword>Regression Analysis</keyword><keyword>Sex Factors</keyword></keywords><dates><year>1984</year></dates><isbn>0021-9681 (Print)
0021-9681 (Linking)</isbn><accession-num>6715491</accession-num><work-type>Research Support, U.S. Gov't, P.H.S.</work-type><urls><related-urls><url> Last, based on abstracted preexisting co-morbid conditions, we calculated the Charlson co-morbidity index (CCI) for every patient. ADDIN EN.CITE <EndNote><Cite><Author>Charlson</Author><Year>1987</Year><RecNum>44</RecNum><DisplayText><style face="superscript">11</style></DisplayText><record><rec-number>44</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">44</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Charlson, M. E.</author><author>Pompei, P.</author><author>Ales, K. L.</author><author>MacKenzie, C. R.</author></authors></contributors><titles><title>A new method of classifying prognostic comorbidity in longitudinal studies: development and validation</title><secondary-title>J Chronic Dis</secondary-title><alt-title>Journal of chronic diseases</alt-title></titles><periodical><full-title>J Chronic Dis</full-title><abbr-1>Journal of chronic diseases</abbr-1></periodical><alt-periodical><full-title>J Chronic Dis</full-title><abbr-1>Journal of chronic diseases</abbr-1></alt-periodical><pages>373-83</pages><volume>40</volume><number>5</number><edition>1987/01/01</edition><keywords><keyword>Actuarial Analysis</keyword><keyword>Age Factors</keyword><keyword>Breast Neoplasms/epidemiology</keyword><keyword>*Epidemiologic Methods</keyword><keyword>Female</keyword><keyword>Follow-Up Studies</keyword><keyword>Humans</keyword><keyword>*Longitudinal Studies</keyword><keyword>*Morbidity</keyword><keyword>New York City</keyword><keyword>Prognosis</keyword><keyword>Prospective Studies</keyword><keyword>Risk</keyword></keywords><dates><year>1987</year></dates><isbn>0021-9681 (Print)
0021-9681 (Linking)</isbn><accession-num>3558716</accession-num><work-type>Comparative Study
Research Support, Non-U.S. Gov't</work-type><urls><related-urls><url> The cardiac arrest database contained information regarding initial cardiac arrest rhythm, specific cardiac arrest treatments (i.e. mild therapeutic hypothermia), neurologic outcome (cerebral performance category [CPC] ADDIN EN.CITE <EndNote><Cite><Author>Safar</Author><Year>1981</Year><RecNum>45</RecNum><DisplayText><style face="superscript">12</style></DisplayText><record><rec-number>45</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">45</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Safar, P.</author></authors><secondary-authors><author>Grenvik, A.</author><author>Safar, P.</author></secondary-authors></contributors><titles><title>Resuscitation after Brain Ischemia</title><secondary-title>Brain Failure and Resuscitation</secondary-title></titles><pages>155-184</pages><dates><year>1981</year></dates><pub-location>New York</pub-location><publisher>Churchill Livingstone</publisher><urls></urls></record></Cite></EndNote>12), date of withdrawal of care and time of death where applicable. A cerebral performance score of 1 (good function) or 2 (moderate disability) on a 5-category scale was considered a good outcome; the remaining categories were 3 (severe disability), 4 (a vegetative state), and 5 (death). Patients with good recovery or moderate disability typically have sufficient cerebral function to live independently and work at least part-time. ADDIN EN.CITE <EndNote><Cite><Author>Safar</Author><Year>1981</Year><RecNum>45</RecNum><DisplayText><style face="superscript">12</style></DisplayText><record><rec-number>45</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">45</key></foreign-keys><ref-type name="Book Section">5</ref-type><contributors><authors><author>Safar, P.</author></authors><secondary-authors><author>Grenvik, A.</author><author>Safar, P.</author></secondary-authors></contributors><titles><title>Resuscitation after Brain Ischemia</title><secondary-title>Brain Failure and Resuscitation</secondary-title></titles><pages>155-184</pages><dates><year>1981</year></dates><pub-location>New York</pub-location><publisher>Churchill Livingstone</publisher><urls></urls></record></Cite></EndNote>12Patient addresses were geocoded and depicted in a map of Allegheny County using commercially available geographic information system software (ArcGIS 10.1, Esri, Redlands, CA, USA). Similarly, poverty status (defined as proportion of people living below the poverty threshold) at the census tract level was obtained from 2011 American Community Survey (ACS) data, color coded into 4 groups, and depicted in the same Allegheny County Map.All data was analyzed with a commercially available statistical software package (Stata SE Version 12.0, StataCorp, College Station, Texas, USA). Continuous data were compared using Wilcoxon rank sum tests and are presented as means with standard deviations or median with inter-quartile ranges (IQR) if not normally distributed. Categorical data between groups as compared using two-sided chi square tests. A p-level smaller than 0.05 was considered statistically significant.RESULTSDemographicsDuring the period of March 1st 2011 and July 30th 2012, 197 patients after in- and out-of hospital cardiac arrests due to different causes were treated at UPMC Mercy hospital. Of these, we excluded 102 (52.8%) because they did not meet the pre-specified age criteria, and 95 (48.2%) patients were included in the final analysis cohort.Among the 95 patients, 40 (42.1%) had an out of hospital cardiac arrest (OHCA). Among those, 19 (47.5%) were male, 32 (80%) were white and 8 (20%) were black. Median age was 48 years (IQR: 39 to 57.5). Of the remaining 55 patients (57.9%) who had an in-hospital cardiac arrest (IHCA), 33 (60%) were male, 43 (78.2%) were white and 12 (21.8%) were black. Median age was 54 years (IQR: 41 to 59).Comorbid conditions and health behaviorsThe median age-adjusted Charlson comorbidity index (CCI), calculated in 85 patients (89.5%), was 2 (IQR: 1 to 3). Arterial Hypertension was documented in 53 cases (56.4%), hyperlipidemia in 29 patients (30.9%), and diabetes in 30 patients (31%). Preexisting coronary artery disease (CAD) was documented in 23 patients (24.5%), six patients (6.4%) had a past medical history of cerebrovascular accident (CVA). The median body mass index (BMI) for all 95 patients was 30kg/m2 (IQR: 25 to 38), and approximately a third (29/95) of the patients were documented smokers. These numbers did not show any statistical significant difference between OHCA and IHCA patients.Social backgroundAll 95 patients (100%) spoke English as their first language. Seventy-one patients (74.7%) were either single or divorced, and 29 (30.5%) had either no religious denomination listed or their denomination was unknown. These numbers did not differ between patients with IHCA or OHCA patients (74.5% vs. 75% single/divorce and 31% vs. 30% without or unknown religious denomination, respectively, both P<0.05)Of all 95 patients, 10 (10.5%) did not have a next of kin (NOK) listed in their patient data file. As expected, the proportion of patients without a NOK was slightly higher among OHCA patients, although this different was not statistically significant (15% vs. 7.3%, P=0.34). Forty patients (42.1%) had no insurance or Medicaid or Medicare listed as support. Of these, 20 (21.1% out of 95) had no insurance (Figure 1). At time of cardiac arrest or prior to hospital admission in case of in hospital cardiac arrests, 69 patients (72.6%) were unemployed or disabled (Figure 2).Neighborhood povertySixty-nine of the 95 patients (72.6%) had their residence in Allegheny County. Among those 69 patients, 18 (26.1%) lived in neighborhoods with a poverty level (i.e. proportion of people living below the poverty level) below 10%, 18 patients (26.1%) in neighborhoods with a poverty level of 10-20%, 25 patients (36.2%) in neighborhoods with 20-30% poverty level, and 8 patients (11.6%) in areas with >30% poverty level (Figure 6 and 7).Medical characteristicsA history of active drug abuse was reported in 19 patients (20.2%), and in 20 cases (23.2%) an illicit drug was identified on urine drug screen (UDS), and ethanol was detected in three instances. Among the 21 patients with a positive UDS, opiates were most frequently used in 9 instances (42.9%). The proportion of patients with a positive UDS was higher in OHCA vs. IHCA patients, but did not reach statistical significance (28.2% vs. 14.6%, P=0.10). Overall, drug overdose occurred in 8 patients (8.4%) and was the third most common cause of arrest in both OHCA and IHCA patients. 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ADDIN EN.CITE.DATA 13 was applied in 45 cases (47.4%). Patients who suffered an out-of-hospital arrest were more twice as likely to be treated with hypothermia compared to patients who suffered an in-hospital arrest (67.5% vs. 32.7%, P<0.001). OutcomesThirty patients (31.6%) survived to hospital discharge, with no difference between OHCA and IHCA patients (27.5 vs. 34.6%, p=0.47). A favorable neurologic outcome (=CPC categories 1 or 2) at discharge was reported in 17 (17.9%) of patients, a good outcome (CPC 1-2) at discharge was reported in 17 patients (17.9%), without any difference between OHCA and IHCA patients (20.0% vs. 16.4%, P=0.30). DISCUSSSIONThis A previous study in cardiac arrest patients reported a disproportionally high incidence of cardiac arrests in adult Pittsburghers ages 18 to 64.3 To understand factors contributing to this high cardiac arrest incidence rate, we analyzed data from cardiac arrest patients who were treated at an inner city hospital (UPMC Mercy, Pittsburgh, PA).Our data show that a high proportion of cardiac arrest patients have pre-existing co-morbidities, engage in unhealthy behaviors (i.e. more likely to be overweight and obese, more likely to use illicit drugs), and have an adverse social background compared to the Allegheny County or the whole US. For instance, they are more likely to live in poor neighborhoods, are very likely to be unemployed or disabled and uninsured at the time of their arrest. Besides that, our cohort was more likely to be single or divorced, which by itself is associated with adverse health outcomes and reduced life expectancy. ADDIN EN.CITE <EndNote><Cite><Author>Macintyre</Author><Year>1992</Year><RecNum>81</RecNum><DisplayText><style face="superscript">14</style></DisplayText><record><rec-number>81</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">81</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Macintyre, S.</author></authors></contributors><auth-address>MRC Medical Sociology Unit, Glasgow, Scotland.</auth-address><titles><title>The effects of family position and status on health</title><secondary-title>Soc Sci Med</secondary-title><alt-title>Social science & medicine</alt-title></titles><periodical><full-title>Soc Sci Med</full-title><abbr-1>Social science & medicine</abbr-1></periodical><alt-periodical><full-title>Soc Sci Med</full-title><abbr-1>Social science & medicine</abbr-1></alt-periodical><pages>453-64</pages><volume>35</volume><number>4</number><edition>1992/08/01</edition><keywords><keyword>*Achievement</keyword><keyword>*Birth Order</keyword><keyword>*Cross-Cultural Comparison</keyword><keyword>*Health Status</keyword><keyword>Humans</keyword><keyword>*Life Change Events</keyword><keyword>Marriage</keyword><keyword>*Morbidity</keyword><keyword>*Mortality</keyword><keyword>Outcome Assessment (Health Care)</keyword><keyword>Risk Factors</keyword><keyword>Social Environment</keyword></keywords><dates><year>1992</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>0277-9536 (Print)
0277-9536 (Linking)</isbn><accession-num>1519098</accession-num><work-type>Comparative Study
Review</work-type><urls><related-urls><url> Per definition of the Centers for Disease Control and prevention (CDC), a body mass index (BMI) between 25 and 29.9 is considered overweight, and a BMI of >30 is considered obese in adults. In our study, 22.1% of patients were overweight and 44.2% were obese. This is significantly higher than average adult obesity prevalence in Allegheny County, Pittsburgh and the US (26.3%, 27.3% and 35.7%, respectively).19,20 Obesity is most prevalent in non-Hispanic blacksPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GbGVnYWw8L0F1dGhvcj48WWVhcj4yMDEyPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA 16-18 For example, obesity was identified as independent risk factor for sudden cardiac arrest in middle-aged people aged 35 to 60 in an epidemiologic surveillance study in Portland, Oregon. ADDIN EN.CITE <EndNote><Cite><Author>Noheria</Author><Year>2013</Year><RecNum>84</RecNum><DisplayText><style face="superscript">19</style></DisplayText><record><rec-number>84</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">84</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Noheria, A.</author><author>Teodorescu, C.</author><author>Uy-Evanado, A.</author><author>Reinier, K.</author><author>Mariani, R.</author><author>Gunson, K.</author><author>Jui, J.</author><author>Chugh, S. S.</author></authors></contributors><auth-address>The Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.</auth-address><titles><title>Distinctive profile of sudden cardiac arrest in middle-aged vs. older adults: A community-based study</title><secondary-title>Int J Cardiol</secondary-title><alt-title>International journal of cardiology</alt-title></titles><periodical><full-title>Int J Cardiol</full-title><abbr-1>International journal of cardiology</abbr-1></periodical><alt-periodical><full-title>Int J Cardiol</full-title><abbr-1>International journal of cardiology</abbr-1></alt-periodical><edition>2013/05/21</edition><dates><year>2013</year><pub-dates><date>May 16</date></pub-dates></dates><isbn>1874-1754 (Electronic)
0167-5273 (Linking)</isbn><accession-num>23684602</accession-num><urls><related-urls><url> In addition, hypertension and diabetes, which are known cardiovascular risk factors, were very prevalent in our cohort.A significant proportion of our patients either had a past history of illicit drug use or a positive urine drug screen on admission. The most common substance group identified was opiates. Currently, it is estimated that approximately 2% of the US adult population (~4.3 million) regularly take opioids. ADDIN EN.CITE <EndNote><Cite><Author>Parsells Kelly</Author><Year>2008</Year><RecNum>105</RecNum><DisplayText><style face="superscript">20</style></DisplayText><record><rec-number>105</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">105</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Parsells Kelly, J. </author><author>Cook, S. F. </author><author>Kaufman, D. W.</author><author>Anderson, T.</author><author>Rosenberg, L.</author><author>Mitchell, A. A.</author></authors></contributors><titles><title>Prevalence and characteristics of opioid use in the US adult population</title><secondary-title>Pain</secondary-title></titles><periodical><full-title>Pain</full-title></periodical><pages>507-513</pages><volume>138</volume><number>3</number><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>20 Regular opioid use is associated with lower education level, female gender and non-Hispanic white race. ADDIN EN.CITE <EndNote><Cite><Author>Parsells Kelly</Author><Year>2008</Year><RecNum>105</RecNum><DisplayText><style face="superscript">20</style></DisplayText><record><rec-number>105</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">105</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Parsells Kelly, J. </author><author>Cook, S. F. </author><author>Kaufman, D. W.</author><author>Anderson, T.</author><author>Rosenberg, L.</author><author>Mitchell, A. A.</author></authors></contributors><titles><title>Prevalence and characteristics of opioid use in the US adult population</title><secondary-title>Pain</secondary-title></titles><periodical><full-title>Pain</full-title></periodical><pages>507-513</pages><volume>138</volume><number>3</number><dates><year>2008</year></dates><urls></urls></record></Cite></EndNote>20 The societal costs for prescription opioid abuse in the US are estimated at 55.7 billion US dollars in 2007, over 90% comprising workplace and health care costs.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CaXJuYmF1bTwvQXV0aG9yPjxZZWFyPjIwMTE8L1llYXI+
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ADDIN EN.CITE.DATA 7,8 In Allegheny County the number of people below poverty level is estimated at 12.4% for the general population and 11.8% for the population between 18 and 64, which is lower than national numbers (15.9% and 14.8%, respectively). In contrast, on average, our patients lived in neighborhoods where 18.7% of the total population and 17.3% of 18 to 64 year olds live below the poverty level. These numbers are more similar to the poverty levels of the City of Pittsburgh, where 22.2% of the general population and 21.7% of 18-64 year olds are estimated to live below the poverty level. Similarly, the vast majority of our patients were unemployed at the time of their cardiac arrest, which is in contrast to county or national estimates. Seventy-three percent of our patients were unemployed or on disability compared to an unemployment civilian labor force rate of 7.2% in Allegheny County, 9.0% in Pittsburgh, and 9.1% in the whole US. These findings are also consistent with a disproportionally high proportion of uninsured patients. In our cohort 21.1% did not have any medical insurance at the time of their arrest vs. a non-insurance rate of 11.7% in the 18-64 year old Allegheny County and 12.2% in the Pittsburgh population.Poverty is strongly associated with race, and some researchers argue that racial health disparities are entirely explained by differences in socioeconomic status.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TY2h3YXJ0ejwvQXV0aG9yPjxZZWFyPjIwMDE8L1llYXI+
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ADDIN EN.CITE.DATA 22-24 In our study 20.1% of patients were black, which is significantly higher than the proportion of black people living in Allegheny County (13.4%) or the US (13.1%). LimitationsMajor limitations of this study include its retrospective study design and the data collection from a single hospital within a fairly small sample size, which limits generalizability and any statistical inference. In order to correct this shortcoming, we are currently conduction a follow-up investigation and will include several hundred cardiac arrest patients that were treated at UPMC Presbyterian University Hospital for sudden cardiac arrest. We will analyze approximately 420 patients in total, which should increase the external validity of our data. In addition, our data do not include any autopsy reports. Overall, the non-forensic autopsy rate has been sharply declining in the US over the past decades. ADDIN EN.CITE <EndNote><Cite><Author>Shojania</Author><Year>2008</Year><RecNum>137</RecNum><DisplayText><style face="superscript">25</style></DisplayText><record><rec-number>137</rec-number><foreign-keys><key app="EN" db-id="z9ttdefv1rv9rje2z23xtevffp2w0sttesws">137</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Shojania, K. G.</author><author>Burton, E. C.</author></authors></contributors><auth-address>University of Ottawa and Ottawa Health Research Institute, Ottawa.</auth-address><titles><title>The vanishing nonforensic autopsy</title><secondary-title>N Engl J Med</secondary-title><alt-title>The New England journal of medicine</alt-title></titles><periodical><full-title>N Engl J Med</full-title><abbr-1>The New England journal of medicine</abbr-1></periodical><alt-periodical><full-title>N Engl J Med</full-title><abbr-1>The New England journal of medicine</abbr-1></alt-periodical><pages>873-5</pages><volume>358</volume><number>9</number><edition>2008/02/29</edition><keywords><keyword>Autopsy/*statistics & numerical data</keyword><keyword>*Diagnostic Errors</keyword><keyword>Humans</keyword><keyword>Pathology</keyword><keyword>United States</keyword></keywords><dates><year>2008</year><pub-dates><date>Feb 28</date></pub-dates></dates><isbn>1533-4406 (Electronic)
0028-4793 (Linking)</isbn><accession-num>18305264</accession-num><urls><related-urls><url> We identified the cause of arrest by reviewing death summaries within the medical record. Last, one must exert caution when interpreting poverty data. The fact that a person lives in a “poor” neighborhood does not necessarily mean that person is poor. ConclusionTogether, our data suggests that patients who were treated at UPMC Mercy hospital after a sudden cardiac arrest have more preexisting comorbid conditions, poor health behaviors and an adverse socioeconomic background when compared to the average person in Allegheny County or the US. The combination of being uninsured, unemployed and single in the setting of preexisting obesity and poor health behaviors (smoking and illicit drug use) seems to be the ideal recipe for disaster (i.e. cardiac arrest). Based on our findings, local public health institutions need to invest in specially targeted prevention programs aimed at this population group. As a desirable result, young patients at risk could be identified in advance and sufficiently treated by local health providers in order to prevent the potentially devastating consequences of a sudden cardiac arrest.APPENDIX: TABLE AND FIGURESTable SEQ Table \* ARABIC 1. Race and social characteristics; good versus bad outcome n=95Good outcome(n=20)Bad outcome(n=75)p valueBlack3 (15%)17 (22.7%)0.55No insurance4 (20%)16 (21.3%)0.74Medicare3 (15%)8 (10.7%)0.38Medicaid3 (15%)8 (10.7%)0.38Insurance10 (50%)43 (57.3%)0.62Employment5 (25%)13 (17.3%)0.52Unemployment14 (70%)51 (68%)1.0Disabled1 (5%)3 (4%)1.0Unknown status0 (0%)8 (10.7%)0.2 USERPROPERTY \* MERGEFORMAT 4572002368550Figure SEQ Figure \* ARABIC 1. Insurance statusFigure SEQ Figure \* ARABIC 1. Insurance status0000 USERPROPERTY \* MERGEFORMAT 0000 USERPROPERTY \* MERGEFORMAT 4572002540635Figure SEQ Figure \* ARABIC 2. Employment statusFigure SEQ Figure \* ARABIC 2. Employment status0000 USERPROPERTY \* MERGEFORMAT 0000 Figure SEQ Figure \* ARABIC 3. Etiology overall Figure SEQ Figure \* ARABIC 4. In-hospital arrests (n=55) Figure SEQ Figure \* ARABIC 5. Out-of hospital arrests (n=40) Figure SEQ Figure \* ARABIC 6. Mapping of residences USERPROPERTY \* MERGEFORMAT Figure SEQ Figure \* ARABIC 7. Percentage of poverty in area of residence (n=69)BIBLIOGRAPHY ADDIN EN.REFLIST 1.Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics--2013 update: a report from the American Heart Association. Circulation. 2013;127:e6-e245.2.McNally B, Robb R, Mehta M, et al. Out-of-hospital cardiac arrest surveillance --- Cardiac Arrest registry to Enhance Survival (CARES), United States, October 1, 2005--December 31, 2010. Morbidity and mortality weekly report. Surveillance summaries. 2011;60:1-19.3.Nichol G, Thomas E, Callaway CW, et al. Regional variation in out-of-hospital cardiac arrest incidence and outcome. 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Archives of internal medicine. 2002;162:1867-1872.19.Noheria A, Teodorescu C, Uy-Evanado A, et al. Distinctive profile of sudden cardiac arrest in middle-aged vs. older adults: A community-based study. International journal of cardiology. 2013.20.Parsells Kelly J, Cook SF, Kaufman DW, Anderson T, Rosenberg L, Mitchell AA. Prevalence aand characteristics of opioid use in the US adult population. Pain. 2008;138:507-513.21.Birnbaum HG, White AG, Schiller M, Waldman T, Cleveland JM, Roland CL. Societal costs of prescription opioid abuse, dependence, and misuse in the United States. Pain medicine. 2011;12:657-667.22.Schwartz RS. Racial profiling in medical research. The New England journal of medicine. 2001;344:1392-1393.23.LaVeist TA. Disentangling race and socioeconomic status: a key to understanding health inequalities. Journal of urban health : bulletin of the New York Academy of Medicine. 2005;82:iii26-34.24.Bleich SN, Thorpe RJ, Jr., Sharif-Harris H, Fesahazion R, Laveist TA. 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