Health Care Management Science

, Volume 6, Issue 3, pp 147–154 | Cite as

Estimating Out-of-Hospital Mortality Due to Myocardial Infarction

  • Liam O'Neill


We developed a model to estimate out-of-hospital deaths due to Myocardial Infarction (MI), which was based on a detailed database of MI admissions to Pennsylvania hospitals during 1998. Our estimation method addresses the problem of geographical selection bias in inpatient databases, which occurs when MI patients with poor geographic access are undersampled. A Geographic Information System (GIS) was used to determine travel times between hospitals and patients, based on patients' zip code of residence. Nearness to a hospital was positively associated with in-hospital mortality (P<0.01) and emergency admissions (P<0.01) and negatively associated with out-of-hospital mortality (P<0.01). Model predictions were made for a range of input values and validated using empirical data.

myocardial infarction out-of-hospital mortality geographic information systems 


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  1. [1]
    D. Dranove and A. Durac, Are multi-hospital systems more efficient? Health Affairs 15 (1996) 100-104.Google Scholar
  2. [2]
    M. Succi, S.Y. Lee and J. Alexander, Effects of market position and competition on rural hospital closures, Health Services Research 31 (1997) 679-699.Google Scholar
  3. [3]
    J. Leighty, Closing time: California leads nation in hospital closures, Nurseweek (1999).Google Scholar
  4. [4]
    American Heart Association, Heart attack and angina statistics,, date accessed: July 4, 2002.Google Scholar
  5. [5]
    J. Pell, J. Sirel, A. Marsden et al., Effect of reducing ambulance time on deaths from out-of-hospital cardiac arrest: Cohort study, British Medical Journal 322 (2001) 1385-1388.Google Scholar
  6. [6]
    J. Bachman, G. McDonald and P. O'Brien, A study of out-of-hospital cardiac arrests in northeastern Minnesota, JAMA 256 (1986) 477-483.Google Scholar
  7. [7]
    GUSTO Investigators, An international randomized trial comparing four thrombolytic strategies for acute myocardial infarction, New England Journal of Medicine 329 (1993) 673-682.Google Scholar
  8. [8]
    D. Earle, Half of heart patients die before reaching help, Reuters Health (February 14, 2002).Google Scholar
  9. [9]
    R. Gillum, Geographic variation in sudden coronary death, American Heart Journal 119 (1990) 380-389.Google Scholar
  10. [10]
    E. Sigurdsson, G. Thorgeirsson et al., Unrecognized myocardial infarction: Epidemiology, clinical characteristics and the prognostic role of angina pectoris, Annals of Internal Medicine 122 (1995) 103-106.Google Scholar
  11. [11]
    J. Baker, E. Clayton and B. Taylor, A non-linear multi-criteria programming approach for determining county emergency medical service ambulance allocations, Journal of Operational Research Society 40 (1989) 423-432.Google Scholar
  12. [12]
    P. Steen, A. Brewster et al., Predicted probabilities of hospital death as a measure of admission severity of illness, Inquiry 30 (1993) 128-141.Google Scholar
  13. [13]
    L. Iezzoni, Risk-Adjustment for Measuring Health Care Outcomes (Health Administration Press, Chicago, 1997).Google Scholar
  14. [14]
    Pennsylvania Department of Health, Pennsylvania Vital Statistics (1998).Google Scholar
  15. [15]
    D. Ingram and R. Gillum, Regional and urbanization differentials in coronary heart disease mortality in the United States, Journal of Clinical Epidemiology 42 (1989) 857-868.Google Scholar
  16. [16]
    L. Pickle, Atlas of United States Mortality (National Center for Health Statistics, MD, 1996).Google Scholar
  17. [17]
    M. Pozen, M. Berezin et al., Ambulance utilization by patients with acute myocardial infarction, American Journal of Public Health 68 (1978) 568-572.Google Scholar
  18. [18]
    D. Kleinbaum, L. Kupper et al., Applied Regression Analysis and Other Multivariate Methods (Duxbury, Boston, 1998).Google Scholar
  19. [19]
    J. Piette and R. Moos, The influence of distance on ambulatory care use, death and readmission following a myocardial infarction, Health Services Research 31 (1996) 573-591.Google Scholar
  20. [20]
    J. Christianson, Potential effects of managed care organizations in rural communities: A framework, The Journal of Rural Health 14 (1998) 169-179.Google Scholar
  21. [21]
    D. Farley and R. Ozminkowski, Volume-outcome relationships and inhospital mortality: The effect of changes in volume over time, Medical Care 30 (1992) 77-94.Google Scholar
  22. [22]
    H. Luft, S. Hunt and S. Maerki, The volume-outcome relationship: Practice-makes-perfect or selective referral patterns? Health Services Research 22 (1987) 157-182.Google Scholar
  23. [23]
    S. Maerki, H. Luft and S. Hunt, Selecting categories of patients for regionalization, Medical Care 24 (1986) 148-158.Google Scholar
  24. [24]
    D. Thieman, J. Coresh et al., The association between hospital volume and survival after acute myocardial infarction in elderly patients, The New England Journal of Medicine 340 (1999) 1640-1648.Google Scholar
  25. [25]
    R. Dudley, K. Johansen et al., Selective referral to high-volume hospitals: Estimating potentially avoidable deaths, JAMA 283 (2000) 1159-1166.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Liam O'Neill
    • 1
  1. 1.Cornell UniversityIthacaUSA

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