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Use of administrative data in healthcare research


Health research based on administrative data and the availability of regional or national administrative databases has been increasing in recent years. We will discuss the general characteristics of administrative data and specific aspects of their use for health research purposes, indicating their advantages and disadvantages. Some fields of application will be discussed and described through examples.

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  1. 1.

    Gavrielov-Yusim N, Friger M (2014) Use of administrative medical databases in population-based research. J Epidemiol Commun Health, to appear. doi:10.1136/jech-2013-202744

    Google Scholar 

  2. 2.

    Nguyen LL, Barshes NR (2010) Analysis of large databases in vascular surgery. J Vasc Surg 52(3):768–774

    PubMed  Article  Google Scholar 

  3. 3.

    Grimes DA (2010) Epidemiologic research using administrative databases—garbage in, garbage out. Obstet Gynecol 116(5):1018–1019

    PubMed  Article  Google Scholar 

  4. 4.

    Hoover KW, Tao G, Kent CK, Aral SO (2011) Epidemiologic research using administrative databases: garbage in, garbage out. Letter to the editor. Obstet Gynecol 117(3):729–730

    PubMed  Article  Google Scholar 

  5. 5.

    Lipscombe LL, Hux JE (2007) Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: a population-based study. Lancet 369:750–756

    PubMed  Article  Google Scholar 

  6. 6.

    Corrada E, Ferrante G, Mazzali C, Barbieri P, Merlino L, Merlini P, Presbiterio P (2014) Eleven-year trends in gender differences of treatments and mortality in ST-elevation acute myocardial infarction in northern Italy, 2000 to 2010 Am J Cardiol 114(3):336–341

  7. 7.

    Ioannidis IP (2013) Are mortality differences detected by administrative data reliable and actionable? JAMA 309(13):1410–1411

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Moise P (2001) Using hospital administrative databases for a disease-based approach to studying health care systems. OECD Ageing related disease study. Accessed 8 Sep 2014

  9. 9.

    Van Walraven C, Austin P (2012) Administrative database research has unique characteristics that can risk biased results. J Clin Epidemiol 65:126–131

    PubMed  Article  Google Scholar 

  10. 10.

    Azimaee M, Smith M, Lix L, Ostapyk T, Burchill C, Pham Hong S. (2014) Manitoba centre for health policy. MCHP data quality framework. Accessed 8 Sep 2014

  11. 11.

    Mazzali C, Maistrello M, Ieva F, Barbieri P (2015) Methodological issues in the use of administrative databases to study heart failure. In: Paganoni AM, Secchi P (eds) Advances in complex data modeling and computational methods in statistics. Contributions to Statistics, Springer, pp 149–160

    Google Scholar 

  12. 12.

    Saczynski JS, Andrade SE, Harrold LR, Tjia J, Cutrona SL, Dodd KS, Goldberg RJ, Gurwitz JH (2012) A systematic review of validated methods for identifying heart failure using administrative data. Pharmacoepidemiol Drug Saf 21(S1):129–140

    PubMed  Article  Google Scholar 

  13. 13.

    Miguel A, Marques B, Freitas A, Lopes F, Azevedo L, Costa Pereira A (2013) Detection of adverse drug reactions using hospital databases—a nationwide study in Portugal. Pharmacoepidemiol Drug Saf 22:907–913

    PubMed  Article  Google Scholar 

  14. 14.

    Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S (2011) A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol 64:749–759

    PubMed Central  PubMed  Article  Google Scholar 

  15. 15.

    Schneeweiss S, Wang PS, Avorn J, Glynn RJ (2003) Improved comorbidity adjustment for predicting mortality in medicare populations. Health Serv Res 38(4):1103–1120

    PubMed Central  PubMed  Article  Google Scholar 

  16. 16.

    Sharabiani MTA, Aylin P, Bottle A (2012) Systematic review of comorbidity indices for administrative data. Med Care 50:1109–1118

    PubMed  Article  Google Scholar 

  17. 17.

    Elixhauser A, Steiner C, Harris DR, Coffey RM (1998) Comorbidity measures for use with administrative data. Med Care 36:8–12

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Romano PS, Roos LL, Jollis JG (1993) Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol 46:1075–1079

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Sterne JAC, Higgins JPT, Reeves BC on behalf of the development group for ACROBAT-NRSI. A cochrane risk of bias assessment tool: for non-randomized studies of interventions (ACROBAT-NRSI), version 1.0.0, 24 September 2014. Accessed 24 Sep 2014

  20. 20.

    Lipsitich M, Tchetgen ET, Cohen T (2010) Negative controls. A tool for detecting confounding and bias in observational studies. Epidemiol 21(3):383–388

  21. 21.

    Zarrinkoub R, Wettermark B, Wandell P, Mejhert M, Szulkin R, Ljunggren G, Kahan T (2013) The epidemiology of heart failure, based on data for 2.1 million inhabitants in Sweden. Eur J Heart Fail 15:995–1002

    PubMed  Article  Google Scholar 

  22. 22.

    Jong P, Vowinckel E, Liu PP, Gong YTuJV (2002) Prognosis and determinants of survival in patients newly hospitalized for heart failure—a population-based study. Arch Intern Med 162:1689–1694

    PubMed  Article  Google Scholar 

  23. 23.

    Koopman C, Bots ML, van Oeffelen AAM, van Dis I, Verschuren WMM, Engelfriet PM, Capewell S, Vaartjes I (2013) Population trends and inequalities in incidence and short-term outcome of acute myocardial infarction between 1998 and 2007. Int J Cardiol 168:993–998

    PubMed  Article  Google Scholar 

  24. 24.

    Sinha S, Peach G, Poloniecki JD, Thompson MM, Holt PJ (2012) Studies using english administrative data (hospital episode statistics) to assess health-care outcomes—systematic review and recommendations for reporting. Eur J Public Health 23(1):86–92

    PubMed  Article  Google Scholar 

  25. 25.

    Joynt KE, Orav EJ, Jha AK (2011) The association between hospital volume and processes, outcomes, and costs of care for congestive heart failure. Ann Intern Med 154(2):94–102

    PubMed Central  PubMed  Article  Google Scholar 

  26. 26.

    Gao J, Moran E, Li Y, Almenoff PL (2014) Predicting potentially avoidable hospitalizations. Med Care 52:164–171

    PubMed  Article  Google Scholar 

  27. 27.

    Dehmer GJ, Drozda JP, Brindis RG, Masoudi FA, Rumsfeld JS, Slattery LE, Oetgen WJ (2014) Public reporting of clinical data—an update for cardiovascular specialists. J Am Coll Cardiol 63(13):1239–1245

    PubMed  Article  Google Scholar 

  28. 28.

    Suissa S, Garbe E (2007) Primer: administrative health databases on observational studies of drug effects—advantages and disadvantages. Nat Clin Pract Rheumatol 3(12):725–732

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Zhang M, Holman CDJ, Price SD, Sanfilippo FM, Preen DB, Bulsara MK (2009) Comorbidity and repeat admission to hospital for adverse drug reactions in older adults: retrospective cohort study. Br Med J. doi:10.1136/bmj.a2752

    Google Scholar 

  30. 30.

    ASSR (2006) Gli Indicatori per la qualità: strumenti, metodi, risultati. Supplemento a Monitor 15. Accessed 24 Sep 2014

  31. 31.

    Mazzali C, Severgnini B, Maistrello M, Barbieri P, Marzegalli M (2013) Heart diseases registries based on healthcare databases. In: Grieco N., Marzegalli M, Paganoni AM (eds) New diagnostic, therapeutic and organizational strategies for acute coronary syndromes patients. Springer-Verlag Italia and Physica-Verlag Heidelberg, Germany, pp 25–46

  32. 32.

    Ballerio S, Cerizza D (2013) Using text mining to validate diagnosis of acute myocardial infarction. In Grieco N, Marzegalli M, Paganoni AM (eds) New diagnostic, therapeutic and organizational strategies for acute coronary syndromes patients. Springer-Verlag Italia and Physica-Verlag Heidelberg, Germany, pp 69–82

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The authors declare that they have no conflict of interest.

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Correspondence to Cristina Mazzali.

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Mazzali, C., Duca, P. Use of administrative data in healthcare research. Intern Emerg Med 10, 517–524 (2015).

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  • Administrative data
  • Utilisation databases
  • Healthcare research
  • Research methods