Journal of Clinical Epidemiology
Volume 58, Issue 4 , Pages 323-337 , April 2005

A review of uses of health care utilization databases for epidemiologic research on therapeutics

,Accepted 16 October 2004.

References 

  1. Avorn J. Powerful medicines: the benefits, risks, and costs of prescription drugs. New York: Knopf; 2004;
  2. The Centers for Education and Research on Therapeutics (CERTs) Risk Assessment Workshop Participants . Risk assessment of drugs, biologics and therapeutic devices: present and future issues. Pharmacoepidemiol Drug Safety. 2003;12:653–662
  3. Greenland S, Finkle WD. A retrospective cohort study of implantable medical devices and selected chronic disease in Medicare claims data. Ann Epidemiol. 2000;10:205–213
  4. Guess HA. Pharmacoepidemiology in pre-approval clinical trial safety monitoring. J Clin Epidemiol. 1991;44:851–857
  5. Arana A, Rivero E, Egberts TCG. What do we show and who does so? An analysis of the abstracts presented at the 19th ICPE. Pharmacoepidemiol Drug Safety. 2004;13:S330–S331
  6. Hallas J, Gaist D, Bjerrum L. The waiting time distribution as a graphical approach to epidemiologic measures of drug utilization. Epidemiology. 1997;8:666–670
  7. Benner JS, Glynn RJ, Mogun H, Neumann PJ, Weinstein MC, Avorn J. Long-term persistence in use of statin therapy in elderly patients. JAMA. 2002;288:455–461
  8. Soumerai SB, McLaughlin TJ, Spiegelman D, Hertzmark E, Thibault G, Goldman L. Adverse outcomes of underuse of beta-blockers in elderly survivors of acute myocardial infarction. JAMA. 1997;277:115–121
  9. Knight EL, Avorn J. Quality indicators for appropriate medication use in vulnerable elders. Ann Intern Med. 2001;135:703–710
  10. Brookhart MA, Solomon DH, Wang P, Glynn RJ, Avorn J, Schneeweiss S. Quantifying sources of explained variation in multilevel models of therapeutic decision making. J Clin Epidemiol (in press).
  11. Levy AR, Tamblyn RM, Mcleod PJ, Fitchett D, Abrahamowicz M. The effect of physicians' training on prescribing beta-blockers for secondary prevention of myocardial infarction in the elderly. Ann Epidemiol. 2002;12:86–89
  12. Tamblyn R, McLeod P, Hanley JA, Girard N, Hurley J. Physician and practice characteristics associated with the early utilization of new prescription drugs. Med Care. 2003;41:895–908
  13. Gatsonis CA, Epstein AM, Newhouse JP, Normand SL, McNeil BJ. Variations in the utilization of coronary angiography for elderly patients with an acute myocardial infarction: an analysis using hierarchical logistic regression. Med Care. 1995;33:625–642
  14. Goldfield N. Physician profiling and risk adjustment. 2nd edition. Gaithersburg (MD): Aspen; 1999;
  15. Rodriguez EM, Staffa JA, Graham DJ. The role of databases in drug postmarketing surveillance. Pharmacoepidemiol Drug Saf. 2001;10:407–410
  16. Strom BL. Sample size considerations for pharmacoepidemiology studies. In:  Strom BL editors. Pharmacoepidemiology. 3rd edition. Chichester (UK): Wiley; 2000;p. 31–40
  17. Weatherby LB, Nordstrom BL, Fife D, Walker AM. The impact of wording in “Dear doctor” letters and in black box labels. Clin Pharmacol Ther. 2002;72:735–742
  18. Rockhill B, Newman B, Weinberg C. Use and misuse of population attributable fractions. Am J Public Health. 1998;88:15–19
  19. Miettinen OS. The need for randomization in the study of intended effects. Stat Med. 1983;2:267–271
  20. Strom BL, Melmon KL, Miettinen OS. Post-marketing studies of drug efficacy: how?. Am J Med. 1984;77:703–708
  21. Grodstein F, Clarkson TB, Manson JE. Understanding the divergent data on postmenopausal hormone therapy. N Engl J Med. 2003;348:645–650
  22. Walker AM, Lanza LL, Arellano F, Rothman K. Mortality in current and former users of clozapine. Epidemiology. 1997;8:671–677
  23. The West of Scotland Coronary Preventive Study Group . Computerised record linkage: compared with traditional patient follow-up methods in clinical trials and illustrated in a prospective epidemiological study. J Clin Epidemiol. 1995;48:1441–1452
  24. Soumerai SB, Ross-Degnan D, Fortess EE, Abelson J. A critical analysis of studies of state drug reimbursement policies: research in need of discipline. Milbank Q. 1993;71:217–252
  25. Tamblyn R, Laprise R, Hanley JA, Abrahamowicz M, Scott S, Mayo N, et al. Adverse events associated with prescription drug cost-sharing among poor and elderly persons. JAMA. 2001;285:421–429
  26. Soumerai SB, Ross-Degnan D, Avorn J, McLaughlin T, Choodnovskiy I. Effects of Medicaid drug-payment limits on admission to hospitals and nursing homes. N Engl J Med. 1991;325:1072–1077
  27. Schneeweiss S, Walker AM, Glynn RJ, Maclure M, Dormuth C, Soumerai SB. Outcomes of reference pricing for angiotensin-converting enzyme inhibitors. New Engl J Med. 2002;346:822–829
  28. Hennessy S, Bilker WB, Weber A, Strom BL. Descriptive analyses of the integrity of a US Medicaid claims database. Pharmacoepidemiol Drug Saf. 2003;12:103–111
  29. Sorenson HT, Sabroe S, Olsen J. A framework for evaluation of secondary data sources for epidemiological research. Int J Epidemiol. 1996;25:435–442
  30. Hallas J. Evidence of depression provoked by cardiovascular medication: a prescription sequence symmetry analysis. Epidemiology. 1996;7:478–484
  31. Walker AM. Confounding by indication. Epidemiology. 1996;7:335–336
  32. Bright RA, Avorn J, Everitt DE. Medicaid data as a resource for epidemiologic studies: strengths and limitations. J Clin Epidemiol. 1989;42:937–945
  33. Platt R, Davis R, Finkelstein J, Go AS, Gurwitz JH, Roblin D, et al. Multicenter epidemiologic and health services research on therapeutics in the HMO Research Network Center for Education and Research on Therapeutics. Pharmacoepidemiol Drug Saf. 2001;10:373–377
  34. Lewis JD, Brensinger C. Agreement between GPRD smoking data: a survey of general practitioners and a population-based survey. Pharmacoepidemiol Drug Saf. 2004;13:437–441
  35. Jick H, Zornberg GL, Jick SS, Seshadri S, Drachman DA. Statins and the risk of dementia. Lancet. 2000;356:1627–1631
  36. Hallas J. Conducting pharmacoepidemiologic research in Denmark. Pharmacoepidemiol Drug Saf. 2001;10:619–623
  37. Schneeweiss S, Schöffski O, Selke GW. What is Germany's experience on reference based drug pricing and the etiology of adverse health outcomes or substitution?. Health Policy. 1998;44:253–260
  38. Willison DJ. Health services research and personal health information: privacy concerns, new legislation, and beyond. CMAJ. 1998;159:1378–1380
  39. Lynd LD, Warren L, Maclure M, Paré PD, Anis AH. Using administrative data to recruit study participants while protecting patient privacy: experience with “Camouflaged Sampling”. Eur J Epidemiol. 2004;19:517–525
  40. Short PF, Graefe DR, Schoen C. Churn, churn, churn: how instability of health insurance shapes America's uninsured problem. Issue brief. New York, NY: The Commonwealth Fund; 2003;
  41. Maclure M, Schneeweiss S. Causation of bias: the Episcope. Epidemiology. 2000;12:114–122
  42. Stergachis AS. Record linkage studies for postmarketing drug surveillance: data quality and validity considerations. Drug Intell Clin Pharm. 1988;22:157–161
  43. Levy AR, O'Brien BJ, Sellors C, Grootendorst P, Willison D. Coding accuracy of administrative drug claims in the Ontario Drug Benefit database. Can J Clin Pharmacol. 2003;10:67–71
  44. McKenzie DA, Semradek J, McFarland BH, Mullooly JP, McCamant LE. The validity of medicaid pharmacy claims for estimating drug use among elderly nursing home residents: the Oregon experience. J Clin Epidemiol. 2000;53:1248–1257
  45. West S, Savitz DA, Koch G, Strom BL, Guess HA, Hartzema A. Recall accuracy for prescription medications: self report compared with database information. Am J Epidemiol. 1995;142:1103–1112
  46. West S, Strom BL, Freundlich B, Normand E, Koch G, Savitz DA. Completeness of prescription recording in outpatients medical records from a health maintenance organization. J Clin Epidemiol. 1994;47:165–171
  47. WHO Collaborating Centre for Drug Statistics Methodology. ATC index with DDD. Oslo, Norway: WHO; 2003;
  48. McMahon AD, Evans JM, McGilchrist MM, et al. Drug exposure risk windows and unexposed comparator groups for cohort studies in Pharmacoepidemiology. Pharmacoepidemiol Drug Safety. 1998;7:275–280
  49. Dormuth C, Schneeweiss S. Rapid monitoring of drug discontinuation rates in response to restrictions in drug reimbursement. Pharmacoepidemiol Drug Saf. 2004;13:5310–5311
  50. Jacobus S, Schneeweiss S, Chan KA. Exposure misclassification as a result of free sample drug utilization in automated claims databases and its effect on pharmacoepidemiologic studies of selective COX-2 inhibitors. Pharmacoepidemiol Drug Safety. 2004;13:695–702
  51. Guess HA. Behavior of the exposure odds ratio in a case-control study when the hazard function is not constant over time. J Clin Epidemiol. 1989;42:1179–1184
  52. Kelsey JL, Whittemore AS, Evans AS, Thompson WD. Methods in observational epidemiology. 2nd edition. New York: Oxford University Press; 1996;
  53. Wilchesky M, Tamblyn RM, Huang A. Validation of diagnostic codes within medical services claims. J Clin Epidemiol. 2004;57:131–141
  54. Romano PS, Mark DH. Bias in the coding of hospital discharge data and its implications for quality assessment. Med Care. 1994;32:81–90
  55. Kiyota Y, Schneeweiss S, Glynn RJ, Cannuscio CC, Avorn J, Solomon DH. The accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value based on review of hospital records. Am Heart J. 2004;148:99–104
  56. Meier CR, Jick SS, Derby LE, Vasilakis C, Jick H. Acute respiratory-tract infections and risk of first-time acute myocardial infarction. Lancet. 1998;351:1467–1471
  57. Solomon DH, Schneeweiss S, Glynn RJ, Kiyota Y, Levin R, Mogun H, et al. The relationship between selective COX-2 inhibitors and acute myocardial infarction. Circulation. 2004;109:2068–2073
  58. Barbone F, McMahon AD, Davey PG, Morris AD, Reid IC, McDevitt DG, et al. Association of road-traffic accidents with benzodiazepine use. Lancet. 1998;352:1331–1336
  59. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–619
  60. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40:373–383
  61. Schneeweiss S, Seeger J, Maclure M, Wang P, Avorn J, Glynn RJ. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol. 2001;154:854–864
  62. Quan H, Parsons GA, Ghali WA. Assessing accuracy of diagnosis-type indicators for flagging complications in administrative data. J Clin Epidemiol. 2004;57:366–372
  63. Copeland KT, Checkoway H, Holbrook RH, McMichael AJ. Bias due to misclassification in the estimate of relative risk. Am J Epidemiol. 1977;105:488–495
  64. Greenland S. The effect of misclassification in matched-pair case-control studies. Am J Epidemiol. 1982;116:402–406
  65. Marshall RJ. Validation study methods for estimating exposure proportions and odds ratios with misclassified data. J Clin Epidemiol. 1990;43:941–947
  66. Brenner H, Gefeller O. Use of positive predictive value to correct for disease misclassification in epidemiologic studies. Am J Epidemiol. 1993;138:1007–1015
  67. Cook JR, Stefanski LA. Simulation-extrapolation estimation in parametric measurement error models. J Am Stat Assoc. 1994;89:1314–1328
  68. Stefanski LA, Cook JR. Simulation-extrapolation: the measurement error jackknife. J Am Stat Assoc. 1995;90:1247–1256
  69. Schneeweiss S, Spiegelman DL, Avorn J, Glynn RJ. Sensitivity of multivariate logistic regression results towards random misclassification of exposure status and event dates in a study on antiparkinsonian drug use and sudden onset pathologic somnolence. Pharmacoepidemiol Drug Safety. 2003;12:S146–S147
  70. Schneeweiss S, Glynn RJ, Avorn J, Solomon DH. A Medicare database review found that physician preferences increasingly outweighed patient characteristics as determinants of first-time prescriptions for cox-2 inhibitors. J Clin Epidemiol. 2005;58:98–102
  71. Rothman KJ, Greenland S. Modern epidemiology. 2nd edition. Philadelphia: Lippincott Williams & Wilkins; 1998;
  72. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–1379
  73. Miettinen OS. Stratification by a multivariate confounder score. Am J Epidemiol. 1976;104:609–620
  74. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55
  75. Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V. Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol Drug Safety. 2004;13:841–853
  76. Rubin DB. Estimating causal effects from large data sets using propensity scores. Ann Intern Med. 1997;127:757–763
  77. Braitman LE, Rosenbaum PR. Rare outcomes, common treatments: analytic strategies using propensity scores. Ann Intern Med. 2002;137:693–695
  78. Cepeda MS, Boston R, Farrar JT, Strom BL. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am J Epidemiol. 2003;158:280–287
  79. Ray WA, Stein CM, Hall K, Daugherty JR, Griffin MR. Non-steroidal anti-inflammatory drugs and risk of serious coronary heart disease: an observational cohort study. Lancet. 2002;359:118–123
  80. Cook EF, Goldman L. Performance of tests of significance based on stratification by a multivariate confounder score or by a propensity score. J Clin Epidemiol. 1989;42:317–324
  81. Glynn RJ, Knight EL, Levin R, Avorn J. Paradoxical relations of drug treatment with mortality in older persons. Epidemiology. 2001;12:682–689
  82. Schneeweiss S, Wang P. Association between SSRI use and hip fractures and the effect of residual confounding bias in claims database studies. J Clin Psychopharm. 2004;13:695–702
  83. Vandenbroucke JP. When are observational studies as credible as randomized trials?. Lancet. 2004;363:1728–1731
  84. Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol. 2003;158:915–920
  85. Giovannucci E, Egan KM, Hunter DJ, Stampfer MJ, Colditz GA, Willett WC, et al. Aspirin and the risk of colorectal cancer in women. N Engl J Med. 1995;333:609–614
  86. Schneeweiss S, Glynn RJ, Tsai EH, Avorn J, Solomon DH. Assessment of bias by unmeasured confounders in pharmacoepidemiologic claims data studies using external data. Epidemiology. 2005;16:17–24
  87. Velentgas P, Cali C, Diedrick G, Heinen MJ, Verburg KM, Dreyer NA, et al. A survey of aspirin use, non-prescription NSAID use, and cigarette smoking among users and non-users of prescription NSAIDs: estimates of the effect of unmeasured confounding by these factors on studies of NSAID use and risk of myocardial infarction. Pharmacoepidemiol Drug Safety. 2001;10:S103
  88. Suissa S. Relative excess risk: an alternative measure of comparative risk. Am J Epidemiol. 1999;150:279–282
  89. Psaty BM, Koepsell TD, Lin D, Weiss NS, Siscovick DS, Rosendaal FR, et al. Assessment and control for confounding by indication in observational studies. J Am Geriatr Soc. 1999;47:749–754
  90. Lash TL, Fink AK. Semi-automated sensitivity analysis to assess systematic errors in observational data. Epidemiology. 2003;14:451–458
  91. Saliba D, Orlando M, Wenger NS, Hays RD, Rubenstein LZ. Identifying a short functional disability screen for older persons. J Gerontol. 2000;55:750–756
  92. Eppig FJ, Chulis GS, Matching MCBS. Medicare data: the best of both worlds. Health Care Financing Rev. 1997;18:211–229
  93. Sturmer T, Schneeweiss S, Avorn J, Glynn RJ. Correcting effect estimates for unmeasured confounding in cohort studies with validation studies using propensity score calibration. Am J Epidemiol. 2004;in press
  94. Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. Am J Epidemiol. 1990;132:734–745
  95. Maclure M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133:144–153
  96. Hallas J. Evidence of depression provoked by cardiovascular medication: a prescription sequence symmetry analysis. Epidemiology. 1996;7:478–484
  97. Hubbard R, Farrington P, Smith C, Smeeth L, Tattersfield A. Exposure to tricyclic and selective serotonin reuptake inhibitor antidepressants and the risk of hip fracture. Am J Epidemiol. 2003;158:77–84
  98. Farrington CP. Relative incidence estimation from case series for vaccine safety evaluation. Biometrics. 1995;51:228–235
  99. Vines SK, Farrington CP. Within-subject exposure dependency in case-crossover studies. Stat Med. 2001;20:3039–3049
  100. Wang PS, Schneeweiss S, Glynn RJ, Mogun H, Avorn J. Use of the case-crossover design to study prolonged drug exposures and insidious outcomes. Ann Epidemiol. 2004;14:296–303
  101. Suissa S. The case-time-control design. Epidemiology. 1995;6:248–253
  102. Suissa S. The case-time-control design: further assumptions and conditions. Epidemiology. 1998;9:441–445
  103. McClellan M, McNeil BJ, Newhouse JP. Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. JAMA. 1994;272:859–866
  104. Newhouse JP, McClellan M. Econometrics in outcomes research: the use of instrumental variables. Annu Rev Public Health. 1998;19:17–34
  105. Schneeweiss S, Maclure M, Soumerai SB, Walker AM, Glynn RJ. Quasi-experimental longitudinal designs to evaluate drug benefit policy changes with low policy compliance. J Clin Epidemiol. 2002;55:833–841
  106. Ray WA. Policy and program analysis using administrative databases. Ann Intern Med. 1997;127:712–718
  107. Jick H, Jick SS, Derby LE. Validation of information recorded on general practitioner based computerised data resource in the United Kingdom. Br Med J. 1991;302:766–768
  108. Miller DP, Alfredson T, Cook SF, Sands BE, Walker AM. Incidence of colonic ischemia, hospitalized complications of constipation, and bowel surgery in relation to use of alosetron hydrochloride. Am J Gastroenterol. 2003;98:1117–1122
  109. Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22
  110. Snijders T, Bosker R. Multilevel analysis. London: Sage; 1999;
  111. Twisk JWR. Applied longitudinal data analysis for epidemiology. Cambridge, UK: Cambridge University Press; 2003;
  112. Hulley S, Grady D, Bush T, Furberg C, Herrington D, Riggs B, et al. Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women. Heart and Estrogen/progestin Replacement Study (HERS) Research Group. JAMA. 1998;280:605–613
  113. Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women's Health Initiative randomized controlled trial. JAMA. 2002;288:321–333
  114. Grodstein F, Manson JE, Colditz GA, Willett WC, Speizer FE, Stampfer MJ. A prospective, observational study of postmenopausal hormone therapy and primary prevention of cardiovascular disease. Ann Intern Med. 2000;133:933–941
  115. Mamdani M, Rochon P, Juurlink DN, Anderson GM, Kopp A, Naglie G, et al. Effect of selective cyclooxygenase 2 inhibitors and naproxen on short-term risk of acute myocardial infarction in the elderly. Arch Intern Med. 2003;163:481–486
  116. Fisher ES, Whaley FS, Krushat M, Malenka DJ, Fleming C, Barin JA, et al. The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. Am J Public Health. 1992;82:243–248
  117. Strom BL. Data validation issues in using claims data. Pharmacoepidemiol Drug Safety. 2001;10:389–392

PII: S0895-4356(04)00298-7

doi: 10.1016/j.jclinepi.2004.10.012

Journal of Clinical Epidemiology
Volume 58, Issue 4 , Pages 323-337 , April 2005