A methodological comparison of two European primary care databases and replication in a US claims database: inhaled long-acting beta-2-agonists and the risk of acute myocardial infarction



Results from observational studies on inhaled long-acting beta-2-agonists (LABA) and acute myocardial infarction (AMI) risk are conflicting, presumably due to variation in methodology. We aimed to evaluate the impact of applying a common study protocol on consistency of results in three databases.


In the primary analysis, we included patients from two GP databases (Dutch—Mondriaan, UK—CPRD GOLD) with a diagnosis of asthma and/or COPD and at least one inhaled LABA or a “non-LABA inhaled bronchodilator medication” (short-acting beta-2-agonist or short-/long-acting muscarinic antagonist) prescription between 2002 and 2009. A claims database (USA—Clinformatics) was used for replication. LABA use was divided into current, recent (first 91 days following the end of a treatment episode), and past use (after more than 91 days following the end of a treatment episode). Adjusted hazard ratios (AMI-aHR) and 95 % confidence intervals (95 % CI) were estimated using time-dependent multivariable Cox regression models stratified by recorded diagnoses (asthma, COPD, or both asthma and COPD).


For asthma or COPD patients, no statistically significant AMI-aHRs (age- and sex-adjusted) were found in the primary analysis. For patients with both diagnoses, a decreased AMI-aHR was found for current vs. recent LABA use in the CPRD GOLD (0.78; 95 % CI 0.68–0.90) and in Mondriaan (0.55; 95 % CI 0.28–1.08), too. The replication study yielded similar results. Adjusting for concomitant medication use and comorbidities, in addition to age and sex, had little impact on the results.


By using a common protocol, we observed similar results in the primary analysis performed in two GP databases and in the replication study in a claims database. Regarding differences between databases, a common protocol facilitates interpreting results due to minimized methodological variations. However, results of multinational comparative observational studies might be affected by bias not fully addressed by a common protocol.

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  1. 1.

    Global Initiative for Asthma (GINA) (2014) Global strategy for asthma management and prevention (revised 2014). [cited 2015 Dec 15]. Available from:

  2. 2.

    Global Initiative for Chronic Obstructive Lung disease (GOLD) (2014) Global strategy for the diagnosis, management and prevention of COPD. [cited 2015 Dec 15]. Available from:

  3. 3.

    Salpeter SR, Ormiston TM, Salpeter EE (2004) Cardiovascular effects of beta-agonists in patients with asthma and COPD: a meta-analysis. Chest 125(6):2309–2321

  4. 4.

    de Vries F, Pouwels S, Bracke M, Lammers JW, Klungel O, Leufkens H, van Staa T (2008) Use of beta2 agonists and risk of acute myocardial infarction in patients with hypertension. Br J Clin Pharmacol 65(4):580–586. doi:10.1111/j.1365-2125.2007.03077.x

  5. 5.

    Suissa S, Assimes T, Ernst P (2003) Inhaled short acting beta agonist use in COPD and the risk of acute myocardial infarction. Thorax 58(1):43–46

  6. 6.

    Au DH, Lemaitre RN, Curtis JR, Smith NL, Psaty BM (2000) The risk of myocardial infarction associated with inhaled beta-adrenoceptor agonists. Am J Respir Crit Care Med 161(3 Pt 1):827–830

  7. 7.

    Warnier MJ, Blom MT, Bardai A, Berdowksi J, Souverein PC, Hoes AW, Rutten FH, de Boer A, Koster RW, De Bruin ML, Tan HL (2013) Increased risk of sudden cardiac arrest in obstructive pulmonary disease: a case-control study. PLoS One 8(6):e65638. doi:10.1371/journal.pone.0065638

  8. 8.

    Zhang B, de Vries F, Setakis E, van Staa TP (2009) The pattern of risk of myocardial infarction in patients taking asthma medication: a study with the General Practice Research Database. J Hypertens 27(7):1485–1492. doi:10.1097/HJH.0b013e32832af68d

  9. 9.

    Martin RM, Dunn NR, Freemantle SN, Mann RD (1998) Risk of non-fatal cardiac failure and ischaemic heart disease with long acting beta 2 agonists. Thorax 53(7):558–562

  10. 10.

    Abbing-Karahagopian V, Kurz X, de Vries F, van Staa TP, Alvarez Y, Hesse U, Hasford J, Dijk L, de Abajo FJ, Weil JG, Grimaldi-Bensouda L, Egberts AC, Reynolds RF, Klungel OH (2014) Bridging differences in outcomes of pharmacoepidemiological studies: design and first results of the PROTECT project. Curr Clin Pharmacol 9(2):130–138

  11. 11.

    Au DH, Curtis JR, Every NR, McDonell MB, Fihn SD (2002) Association between inhaled beta-agonists and the risk of unstable angina and myocardial infarction. Chest 121(3):846–851

  12. 12.

    Bianchi M, Clavenna A, Bonati M (2010) Inter-country variations in anti-asthmatic drug prescriptions for children. Systematic review of studies published during the 2000–2009 period. Eur J Clin Pharmacol 66(9):929–936. doi:10.1007/s00228-010-0845-y

  13. 13.

    Rottenkolber M, Voogd E, van Dijk L, Primatesta P, Becker C, Schlienger R, de Groot MC, Alvarez Y, Durand J, Slattery J, Afonso A, Requena G, Gil M, Alvarez A, Hesse U, Gerlach R, Hasford J, Fischer R, Klungel OH, Schmiedl S (2015) Time trends of period prevalence rates of patients with inhaled long-acting beta-2-agonists-containing prescriptions: a European comparative database study. PLoS One 10(2):e0117628. doi:10.1371/journal.pone.0117628

  14. 14.

    Feinstein AR, Horwitz RI (1978) A critique of the statistical evidence associating estrogens with endometrial cancer. Cancer Res 38(11 Pt 2):4001–4005

  15. 15.

    Williams T, van Staa T, Puri S, Eaton S (2012) Recent advances in the utility and use of the General Practice Research Database as an example of a UK Primary Care Data resource. Ther Adv Drug Saf 3(2):89–99

  16. 16.

    Clinical J Primary Care Research Network. In: ed. [cited 2015 Dec 15]. Available from:

  17. 17.

    Stirbu-Wagner I, Dorsman SA, Visscher S, Davids R, J.V. G, Abrahamse H, van Althuis T, Jansen B, Schlief A, W. T, Walk C, Wentink E, Wennekes L, Braspenning J, Korevaar JC (2010) Landelijk Infomatienetwerk Huisartsenzorg. Feiten en cijfers over huisartsenzorg in Nederland. Utrecht/Nijmegen: NIVEL/IQ

  18. 18.

    Top Institute Pharma TN. In: ed. [cited 2015 Dec 15]. Available from:

  19. 19.

    Herrett E, Thomas SL, Schoonen WM, Smeeth L, Hall AJ (2010) Validation and validity of diagnoses in the General Practice Research Database: a systematic review. Br J Clin Pharmacol 69(1):4–14. doi:10.1111/j.1365-2125.2009.03537.x

  20. 20.

    Jick SS, Kaye JA, Vasilakis-Scaramozza C, Garcia Rodriguez LA, Ruigomez A, Meier CR, Schlienger RG, Black C, Jick H (2003) Validity of the general practice research database. Pharmacotherapy 23(5):686–689

  21. 21.

    Lawrenson R, Williams T, Farmer R (1999) Clinical information for research; the use of general practice databases. J Public Health Med 21(3):299–304

  22. 22.

    NHS NHS Connecting for Health Read Codes. [cited 2015 Dec 15]. Available from:

  23. 23.

    MULTILEX. [cited 2015 Dec 15]. Available from:

  24. 24.

    The Classification Committee of WONCA (1998) ICPC-2, international classification of primary care. Oxford University Press, Oxford

  25. 25.

    WHO (1992) Anatomical therapeutic chemical (ATC) classification index. WHO Collaborating Centre for Drug Statistics Methodology, Geneva

  26. 26.

    Clinformatics Data Mart overview. [cited 2015 Dec 15]. Available from:

  27. 27.

    Stergachis A, Saunders KW, Davis RL, Kimmel SE, Schinnar R, Chan KA, Shatin D, Rawson NSB, Hennessy S, Downey W, Stang M, Beck P, Osei W, Leufkens HG, Macdonald TM, Gelfand JM (2006) Examples of automated databases. Wiley, Chichester

  28. 28.

    Statistics NCfH (2002) Classification of diseases and injuries. Ninth revision

  29. 29.

    FDA National Drug Code Directory Data Files. [cited 2015 Dec 15]. Available from:

  30. 30.

    ENCePP (2014) European Network of Centres for Pharmacoepidemiology and Pharmacovigilance. [cited 2015 Dec 15]. Available from:

  31. 31.

    Rothman KJ, Greenland S (2008) Modern epidemiology. Lippincott Williams & Wilkins, Philadelphia

  32. 32.

    Masoli M, Fabian D, Holt S, Beasley R, Global Initiative for Asthma P (2004) The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy 59(5):469–478. doi:10.1111/j.1398-9995.2004.00526.x]

  33. 33.

    Afonso AS, Verhamme KM, Sturkenboom MC, Brusselle GG (2011) COPD in the general population: prevalence, incidence and survival. Respir Med 105(12):1872–1884. doi:10.1016/j.rmed.2011.06.012

  34. 34.

    Soriano JB, Maier WC, Egger P, Visick G, Thakrar B, Sykes J, Pride NB (2000) Recent trends in physician diagnosed COPD in women and men in the UK. Thorax 55(9):789–794

  35. 35.

    Buist AS, McBurnie MA, Vollmer WM, Gillespie S, Burney P, Mannino DM, Menezes AM, Sullivan SD, Lee TA, Weiss KB, Jensen RL, Marks GB, Gulsvik A, Nizankowska-Mogilnicka E (2007) International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study. Lancet 370(9589):741–750. doi:10.1016/S0140-6736(07)61377-4

  36. 36.

    Gibson PG, Simpson JL (2009) The overlap syndrome of asthma and COPD: what are its features and how important is it? Thorax 64(8):728–735. doi:10.1136/thx.2008.108027

  37. 37.

    Groenwold RHH, de Groot MCH, Ramamoorthy D, Souverein P, Klungel OH (2016) Unmeasured confounding in pharmacoepidemiology. Ann Epidemiol 26(1):85–86. doi:10.1016/j.annepidem.2015.10.007

  38. 38.

    Requena G, Huerta C, Gardarsdottir H, Logie J, González-González R, Abbing-Karahagopian V, Miret M, Schneider C, Souverein PC, Webb D, Afonso A, Boudiaf N, Martin E, Oliva B, Alvarez A, de Groot MCH, Bate A, Johansson S, Schlienger R, Reynolds R, Klungel OH, de Abajo FJ (2015) Hip/femur fractures associated with the use of benzodiazepines (anxiolytics, hypnotics and related drugs): a methodological approach to assess consistencies across databases from the PROTECT-EU Project. Pharmacoepidemiol Drug Saf 25(Supplement S1):66–78

  39. 39.

    Tinkelman DG, Price DB, Nordyke RJ, Halbert RJ (2006) Misdiagnosis of COPD and asthma in primary care patients 40 years of age and over. J Asthma 43(1):75–80. doi:10.1080/02770900500448738

  40. 40.

    Lacasse Y, Montori VM, Lanthier C, Maltis F (2005) The validity of diagnosing chronic obstructive pulmonary disease from a large administrative database. Can Respir J 12(5):251–256

  41. 41.

    Macaulay D, Sun SX, Sorg RA, Yan SY, De G, Wu EQ, Simonelli PF (2013) Development and validation of a claims-based prediction model for COPD severity. Respir Med 107(10):1568–1577. doi:10.1016/j.rmed.2013.05.012

  42. 42.

    Twiggs JE, Fifield J, Apter AJ, Jackson EA, Cushman RA (2002) Stratifying medical and pharmaceutical administrative claims as a method to identify pediatric asthma patients in a Medicaid managed care organization. J Clin Epidemiol 55(9):938–944

  43. 43.

    Cazzola M, Bettoncelli G, Sessa E, Cricelli C, Biscione G (2010) Prevalence of comorbidities in patients with chronic obstructive pulmonary disease. Respiration 80(2):112–119. doi:10.1159/000281880

  44. 44.

    Schnell K, Weiss CO, Lee T, Krishnan JA, Leff B, Wolff JL, Boyd C (2012) The prevalence of clinically-relevant comorbid conditions in patients with physician-diagnosed COPD: a cross-sectional study using data from NHANES 1999–2008. BMC Pulm Med 12:26. doi:10.1186/1471-2466-12-26

  45. 45.

    Jagana R, Bartter T, Joshi M (2015) Delay in diagnosis of chronic obstructive pulmonary disease: reasons and solutions. Curr Opin Pulm Med 21(2):121–126. doi:10.1097/MCP.0000000000000133

  46. 46.

    Molis WE, Bagniewski S, Weaver AL, Jacobson RM, Juhn YJ (2008) Timeliness of diagnosis of asthma in children and its predictors. Allergy 63(11):1529–1535. doi:10.1111/j.1398-9995.2008.01749.x

  47. 47.

    Papaiwannou A, Zarogoulidis P, Porpodis K, Spyratos D, Kioumis I, Pitsiou G, Pataka A, Tsakiridis K, Arikas S, Mpakas A, Tsiouda T, Katsikogiannis N, Kougioumtzi I, Machairiotis N, Siminelakis S, Kolettas A, Kessis G, Beleveslis T, Zarogoulidis K (2014) Asthma-chronic obstructive pulmonary disease overlap syndrome (ACOS): current literature review. J Thorac Dis 6(Suppl 1):S146–S151. doi:10.3978/j.issn.2072-1439.2014.03.04

  48. 48.

    Hardin M, Silverman EK, Barr RG, Hansel NN, Schroeder JD, Make BJ, Crapo JD, Hersh CP (2011) The clinical features of the overlap between COPD and asthma. Respir Res 12:127. doi:10.1186/1465-9921-12-127

  49. 49.

    Kontos MC, Fritz LM, Anderson FP, Tatum JL, Ornato JP, Jesse RL (2003) Impact of the troponin standard on the prevalence of acute myocardial infarction. Am Heart J 146(3):446–452. doi:10.1016/S0002-8703(03)00245-X

  50. 50.

    Alpert JS, Thygesen K, Antman E, Bassand JP (2000) Myocardial infarction redefined—a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol 36(3):959–969

  51. 51.

    Hammad TA, McAdams MA, Feight A, Iyasu S, Dal Pan GJ (2008) Determining the predictive value of Read/OXMIS codes to identify incident acute myocardial infarction in the General Practice Research Database. Pharmacoepidemiol Drug Saf 17(12):1197–1201. doi:10.1002/pds.1672

  52. 52.

    van Staa TP, Abenhaim L (1994) The quality of information recorded on a UK database of primary care records: a study of hospitalizations due to hypoglycemia and other conditions. Pharmacoepidemiol Drug Saf 3:15–21

  53. 53.

    McCormick N, Lacaille D, Bhole V, Avina-Zubieta JA (2014) Validity of myocardial infarction diagnoses in administrative databases: a systematic review. PLoS One 9(3):e92286. doi:10.1371/journal.pone.0092286

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The research leading to these results was conducted as part of the PROTECT consortium (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium, which is a public-private partnership coordinated by the European Medicines Agency.

The authors thank the excellent collaboration of physicians in the participating countries, whose contribution in recording their professional practice with high quality standards enables the availability of databases used in this research.

The paper is on behalf of the members of work-package 2 (WP2) and work-package 6 (WP6) of PROTECT.

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Correspondence to M. Rottenkolber.

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Conflict of interest

AA, PS, JK, JH, and MR have no conflicts of interest; CB reports that her department at the University of Basel received payment from Novartis Pharma AG, Basel, Switzerland, during the conduct of the study for statistical analyses; MdG reports grants from Top Institute Pharma (NL); SS reports personal fees from Rottapharm Madaus (Cologne, Germany) and travel costs for an investigator meeting reimbursed by Bayer HealthCare AG (Leverkusen, Germany), the division of Pharmacoepidemiology & Clinical Pharmacology employing O.H. Klungel received Top Institute Pharma Grant T6.101 Mondriaan unrestricted grant for pharmacoepidemiological research. STL, PP, EP, YW, RR, and RS belong to EFPIA (European Federation of Pharmaceutical Industries and Association) member companies in the IMI JU and costs related to their part in the research were carried by the respective company as in-kind contribution under the IMI JU scheme. EP was previously employed at Novartis when this work was carried out. EP is publishing as a Novartis former employee.


The PROTECT project has received support from the Innovative Medicine Initiative Joint Undertaking (IMI JU; under Grant Agreement no. 115004, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA (European Federation of Pharmaceutical Industries and Association) companies’ in-kind contribution. In the context of the IMI JU, the Department of Pharmacoepidemiology, Utrecht University, also received a direct financial contribution from Pfizer. The views expressed in this article are those of the authors only and not of their respective institution or company.

Additional information

A. Afonso and S. Schmiedl contributed equally to this work.

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Afonso, A., Schmiedl, S., Becker, C. et al. A methodological comparison of two European primary care databases and replication in a US claims database: inhaled long-acting beta-2-agonists and the risk of acute myocardial infarction. Eur J Clin Pharmacol 72, 1105–1116 (2016).

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  • Secondary data analysis
  • Methodological comparison
  • Long-acting beta-2-agonists
  • Acute myocardial infarction