Current Epidemiology Reports

, Volume 5, Issue 3, pp 272–283 | Cite as

The importance and implications of comparator selection in pharmacoepidemiologic research

  • Monica D’ArcyEmail author
  • Til Stürmer
  • Jennifer L. Lund
Pharmacoepidemiology (S Toh, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Pharmacoepidemiology


Purpose of review

Pharmacoepidemiologic studies employing large databases are critical to evaluating the effectiveness and safety of drug exposures in large and diverse populations. Because treatment is not randomized, researchers must select a relevant comparison group for the treatment of interest. The comparator group can consist of individuals initiating: (1) a similarly indicated treatment (active comparator), (2) a treatment used for a different indication (inactive comparator), or (3) no particular treatment (non-initiators). Herein, we review recent literature and describe considerations and implications of comparator selection in pharmacoepidemiologic studies.

Recent findings

Comparator selection depends on the scientific question and feasibility constraints. Because pharmacoepidemiologic studies rely on the choice to initiate or not initiate a specific treatment, rather than randomization, they are at-risk for confounding related to the comparator choice including by indication, disease severity, and frailty. We describe forms of confounding specific to pharmacoepidemiologic studies and discuss each comparator along with informative examples and a case study. We provide commentary on potential issues relevant to comparator selection in each study, highlighting the importance of understanding the population in whom the treatment is given and how patient characteristics are associated with the outcome.


Advanced statistical techniques may be insufficient for reducing confounding in observational studies. Evaluating the extent to which comparator selection may mitigate or induce systematic bias is a critical component of pharmacoepidemiologic studies.


Pharmacoepidemiology Comparator selection New user Confounding Detection bias 


Compliance with Ethical Standards

Conflict of Interest

Monica D’Arcy declares no conflict of interest; Til Stürmer reports grants from the National Institute on Aging, during the conduct of the study, grants from Astrazeneca and Amgen, outside the submitted work, membership (Center for Pharmacoepidemiology) of GlaxoSmithKline, UCB BioSciences, Merck, and Shire, outside the submitted work, and stock in Novartis, Roche, BASF, AstraZeneca, and NovoNordisk; Jennifer L. Lund reports grants from PhRMA Foundation, outside the submitted work; Dr. Lund’s husband is a full-time, paid employee of GlaxoSmithKline.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major Importance

  1. 1.
    Johnson ES, Bartman BA, Briesacher BA, Fleming NS, Gerhard T, Kornegay CJ, et al. The incident user design in comparative effectiveness research. Pharmacoepidemiol Drug Saf. 2013;22(1):1–6. Scholar
  2. 2.
    Lund JL, Richardson DB, Sturmer T. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application. Curr Epidemiol Rep. 2015;2(4):221–8. Scholar
  3. 3.
    •• Huitfeldt A, Hernan MA, Kalager M, Robins JM. Comparative Effectiveness Research Using Observational Data: Active Comparators to Emulate Target Trials with Inactive Comparators. EGEMS (Wash DC). 2016;4(1):1234. Clearly describes how to think about and design a study using observational data to emulate a target trial. There are very intuitive diagrams in this paper. CrossRefGoogle Scholar
  4. 4.
    Gokhale M, Buse JB, Gray CL, Pate V, Marquis MA, Sturmer T. Dipeptidyl-peptidase-4 inhibitors and pancreatic cancer: a cohort study. Diabetes Obes Metab. 2014;16(12):1247–56. Scholar
  5. 5.
    Graham DJ, Ouellet-Hellstrom R, MaCurdy TE, Ali F, Sholley C, Worrall C, et al. Risk of acute myocardial infarction, stroke, heart failure, and death in elderly Medicare patients treated with rosiglitazone or pioglitazone. JAMA. 2010;304(4):411–8. Scholar
  6. 6.
    Suissa S. Immortal time bias in observational studies of drug effects. Pharmacoepidemiol Drug Saf. 2007;16(3):241–9. Scholar
  7. 7.
    Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol. 2008;167(4):492–9. Scholar
  8. 8.
    Suissa S. Immeasurable time bias in observational studies of drug effects on mortality. Am J Epidemiol. 2008;168(3):329–35. Scholar
  9. 9.
    Suissa S, Dell'aniello S, Vahey S, Renoux C. Time-window bias in case-control studies: statins and lung cancer. Epidemiology. 2011;22(2):228–31. Scholar
  10. 10.
    Suissa S, Azoulay L. Metformin and the risk of cancer: time-related biases in observational studies. Diabetes Care. 2012;35(12):2665–73. Scholar
  11. 11.
    •• Glynn RJ, Knight EL, Levin R, Avorn J. Paradoxical relations of drug treatment with mortality in older persons. Epidemiology. 2001;12(6):682–9. Shows how a series of medications that should not affect mortality are associated with mortality. Factors such as proximity to death and overall health status drive the associations. CrossRefPubMedGoogle Scholar
  12. 12.
    Sturmer T, Rothman KJ, Avorn J, Glynn RJ. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution—a simulation study. Am J Epidemiol. 2010;172(7):843–54. Scholar
  13. 13.
    Garrett JE, Lanes SF, Kolbe J, Rea HH. Risk of severe life threatening asthma and beta agonist type: an example of confounding by severity. Thorax. 1996;51(11):1093–9.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Ernst P, Habbick B, Suissa S, Hemmelgarn B, Cockcroft D, Buist AS, et al. Is the association between inhaled beta-agonist use and life-threatening asthma because of confounding by severity? Am Rev Respir Dis. 1993;148(1):75–9. Scholar
  15. 15.
    Harrold LR, Patterson MK, Andrade SE, Dube T, Go AS, Buist AS, et al. Asthma drug use and the development of Churg-Strauss syndrome (CSS). Pharmacoepidemiol Drug Saf. 2007;16(6):620–6. Scholar
  16. 16.
    Roche MW, Boyle DJ, Cheng CC, Del Pozzo J, Cherneski L, Pascarella J et al. Prevalence and risk of violent ideation and behavior in serious mental illnesses: an analysis of 63,572 patient records. J Interpers Violence. 2018:886260518759976.
  17. 17.
    Thoma P, Daum I. Comorbid substance use disorder in schizophrenia: a selective overview of neurobiological and cognitive underpinnings. Psychiatry Clin Neurosci. 2013;67(6):367–83. Scholar
  18. 18.
    Stokes PRA, Kalk NJ, Young AH. Bipolar disorder and addictions: the elephant in the room. Br J Psychiatry. 2017;211(3):132–4. Scholar
  19. 19.
    Bobo WV, Na PJ, Geske JR, McElroy SL, Frye MA, Biernacka JM. The relative influence of individual risk factors for attempted suicide in patients with bipolar I versus bipolar II disorder. J Affect Disord. 2018;225:489–94. Scholar
  20. 20.
    Regnart J, Truter I, Meyer A. Critical exploration of co-occurring attention-deficit/hyperactivity disorder, mood disorder and substance use disorder. Expert Rev Pharmacoecon Outcomes Res. 2017;17(3):275–82. Scholar
  21. 21.
    Zulauf CA, Sprich SE, Safren SA, Wilens TE. The complicated relationship between attention deficit/hyperactivity disorder and substance use disorders. Curr Psychiatry Rep. 2014;16(3):436. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Gokhale M, Girman C, Chen Y, Pate V, Funk MJ, Sturmer T. Comparison of diagnostic evaluations for cough among initiators of angiotensin converting enzyme inhibitors and angiotensin receptor blockers. Pharmacoepidemiol Drug Saf. 2016;25(5):512–20. Scholar
  23. 23.
    Hong JL, Henderson LM, Jonsson Funk M, Lund JL, Buse JB, Pate V, et al. Differential Use of Screening Mammography in Older Women Initiating Metformin versus Sulfonylurea. Pharmacoepidemiol Drug Saf. 2017;26(6):666–75. Scholar
  24. 24.
    Brookhart MA, Patrick AR, Dormuth C, Avorn J, Shrank W, Cadarette SM, et al. Adherence to lipid-lowering therapy and the use of preventive health services: an investigation of the healthy user effect. Am J Epidemiol. 2007;166(3):348–54. Scholar
  25. 25.
    Sturmer T, Marquis MA, Zhou H, Meigs JB, Lim S, Blonde L, et al. Cancer incidence among those initiating insulin therapy with glargine versus human NPH insulin. Diabetes Care. 2013;36(11):3517–25. Scholar
  26. 26.
    Dollerup J, Vestbo J, Murray-Thomas T, Kaplan A, Martin RJ, Pizzichini E, et al. Cardiovascular risks in smokers treated with nicotine replacement therapy: a historical cohort study. Clin Epidemiol. 2017;9:231–43. Scholar
  27. 27.
    Toh S, Baker MA, Brown JS, Kornegay C, Platt R, Mini-Sentinel I. Rapid assessment of cardiovascular risk among users of smoking cessation drugs within the US Food and Drug Administration’s Mini-Sentinel program. JAMA Intern Med. 2013;173(9):817–9. Scholar
  28. 28.
    Lund JL, Sturmer T, Sanoff HK. Comparative effectiveness of postoperative chemotherapy among older patients with non-metastatic rectal cancer treated with preoperative chemoradiotherapy. J Geriatr Oncol. 2016;7(3):176–86. Scholar
  29. 29.
    •• Jackson LA, Jackson ML, Nelson JC, Neuzil KM, Weiss NS. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. Int J Epidemiol. 2006;35(2):337–44. Elegantly demostrates how much of the observed association between receipt of the influenza vaccine and reduced mortality likely resulted from the differential receipt of the vaccine whereby individuals not expected to survive to the influenza season did not receive the vaccine. CrossRefPubMedGoogle Scholar
  30. 30.
    Jackson ML, Yu O, Nelson JC, Naleway A, Belongia EA, Baxter R, et al. Further evidence for bias in observational studies of influenza vaccine effectiveness: the 2009 influenza A(H1N1) pandemic. Am J Epidemiol. 2013;178(8):1327–36. Scholar
  31. 31.
    Zhang HT, McGrath LJ, Wyss R, Ellis AR, Sturmer T. Controlling confounding by frailty when estimating influenza vaccine effectiveness using predictors of dependency in activities of daily living. Pharmacoepidemiol Drug Saf. 2017;26(12):1500–6. Scholar
  32. 32.
    Patrick AR, Schneeweiss S, Brookhart MA, Glynn RJ, Rothman KJ, Avorn J, et al. The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration. Pharmacoepidemiol Drug Saf. 2011;20(6):551–9. Scholar
  33. 33.
    Schneeweiss S, Patrick AR, Sturmer T, Brookhart MA, Avorn J, Maclure M, et al. Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results. Med Care. 2007;45(10):S131–S42. Scholar
  34. 34.
    Setoguchi S, Glynn RJ, Avorn J, Mogun H, Schneeweiss S. Statins and the risk of lung, breast, and colorectal cancer in the elderly. Circulation. 2007;115(1):27–33. CrossRefPubMedGoogle Scholar
  35. 35.
    Patorno E, Glynn RJ, Levin R, Lee MP, Huybrechts KF. Benzodiazepines and risk of all cause mortality in adults: cohort study. BMJ. 2017;358:j2941. Scholar
  36. 36.
    Dusetzina SB, Brookhart MA, Maciejewski ML. Control outcomes and exposures for improving internal validity of nonrandomized studies. Health Serv Res. 2015;50(5):1432–51. Scholar
  37. 37.
    Knecht SE, Dunn SP, Macaulay TE. Angioedema related to Angiotensin inhibitors. J Pharm Pract. 2014;27(5):461–5. Scholar
  38. 38.
    Mann JJ, Emslie G, Baldessarini RJ, Beardslee W, Fawcett JA, Goodwin FK, et al. ACNP Task Force report on SSRIs and suicidal behavior in youth. Neuropsychopharmacology. 2006;31(3):473–92. Scholar
  39. 39.
    Patetsos E, Horjales-Araujo E. Treating chronic pain with SSRIs: what do we know? Pain Res Manag. 2016;2016:2020915. CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Sansone RA, Sansone LA. Pain, pain, go away: antidepressants and pain management. Psychiatry (Edgmont). 2008;5(12):16–9.Google Scholar
  41. 41.
    Lund JL, Horvath-Puho E, Komjathine Szepligeti S, Sorensen HT, Pedersen L, Ehrenstein V, et al. Conditioning on future exposure to define study cohorts can induce bias: the case of low-dose acetylsalicylic acid and risk of major bleeding. Clin Epidemiol. 2017;9:611–26. Scholar
  42. 42.
    Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512–22. Scholar
  43. 43.
    Jefferson T, Rivetti D, Rivetti A, Rudin M, Di Pietrantonj C, Demicheli V. Efficacy and effectiveness of influenza vaccines in elderly people: a systematic review. Lancet. 2005;366(9492):1165–74. Scholar
  44. 44.
    Nordin J, Mullooly J, Poblete S, Strikas R, Petrucci R, Wei F, et al. Influenza vaccine effectiveness in preventing hospitalizations and deaths in persons 65 years or older in Minnesota, New York, and Oregon: data from 3 health plans. J Infect Dis. 2001;184(6):665–70. Scholar
  45. 45.
    Nichol KL, Nordin J, Mullooly J, Lask R, Fillbrandt K, Iwane M. Influenza vaccination and reduction in hospitalizations for cardiac disease and stroke among the elderly. N Engl J Med. 2003;348(14):1322–32. Scholar
  46. 46.
    Nichol KL, Nordin JD, Nelson DB, Mullooly JP, Hak E. Effectiveness of influenza vaccine in the community-dwelling elderly. N Engl J Med. 2007;357(14):1373–81. Scholar
  47. 47.
    Hak E, Nordin J, Wei F, Mullooly J, Poblete S, Strikas R, et al. Influence of high-risk medical conditions on the effectiveness of influenza vaccination among elderly members of 3 large managed-care organizations. Clin Infect Dis. 2002;35(4):370–7. Scholar
  48. 48.
    Spaude KA, Abrutyn E, Kirchner C, Kim A, Daley J, Fisman DN. Influenza vaccination and risk of mortality among adults hospitalized with community-acquired pneumonia. Arch Intern Med. 2007;167(1):53–9. Scholar
  49. 49.
    • Wirtz HS, Calip GS, Buist DSM, Gralow JR, Barlow WE, Gray S, et al. Evidence for detection bias by medication use in a cohort study of breast cancer survivors. Am J Epidemiol. 2017;185(8):661–72. Demonstrates how individuals at the extremes of health status (very sick/very healthy) are more/less likely to obtain certain medications and/or be adherent. Individuals at both extremes may differentially utilize cancer screening and may therefore be differentially diagnosed with cancer potentially leading to spurious associations. CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Velicer CM, Heckbert SR, Lampe JW, Potter JD, Robertson CA, Taplin SH. Antibiotic use in relation to the risk of breast cancer. JAMA. 2004;291(7):827–35. Scholar
  51. 51.
    Wirtz HS, Buist DS, Gralow JR, Barlow WE, Gray S, Chubak J, et al. Frequent antibiotic use and second breast cancer events. Cancer Epidemiol Biomark Prev. 2013;22(9):1588–99. Scholar
  52. 52.
    Boudreau DM, Yu O, Chubak J, Wirtz HS, Bowles EJ, Fujii M, et al. Comparative safety of cardiovascular medication use and breast cancer outcomes among women with early stage breast cancer. Breast Cancer Res Treat. 2014;144(2):405–16. Scholar
  53. 53.
    Merikangas KR, Akiskal HS, Angst J, Greenberg PE, Hirschfeld RM, Petukhova M, et al. Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. Arch Gen Psychiatry. 2007;64(5):543–52. Scholar
  54. 54.
    Blanco C, Compton WM, Saha TD, Goldstein BI, Ruan WJ, Huang B, et al. Epidemiology of DSM-5 bipolar I disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions—III. J Psychiatr Res. 2017;84:310–7. CrossRefPubMedGoogle Scholar
  55. 55.
    Cohen LS, Friedman JM, Jefferson JW, Johnson EM, Weiner ML. A reevaluation of risk of in utero exposure to lithium. JAMA. 1994;271(2):146–50.CrossRefPubMedGoogle Scholar
  56. 56.
    Yonkers KA, Wisner KL, Stowe Z, Leibenluft E, Cohen L, Miller L, et al. Management of bipolar disorder during pregnancy and the postpartum period. Am J Psychiatry. 2004;161(4):608–20. Scholar
  57. 57.
    Patorno E, Huybrechts KF, Bateman BT, Cohen JM, Desai RJ, Mogun H, et al. Lithium use in pregnancy and the risk of cardiac malformations. N Engl J Med. 2017;376(23):2245–54. Scholar
  58. 58.
    Cloonan SM, Williams DC. The antidepressants maprotiline and fluoxetine induce type II autophagic cell death in drug-resistant Burkitt’s lymphoma. Int J Cancer. 2011;128(7):1712–23.; 10.1002/ijc.25477.
  59. 59.
    Levkovitz Y, Gil-Ad I, Zeldich E, Dayag M, Weizman A. Differential induction of apoptosis by antidepressants in glioma and neuroblastoma cell lines: evidence for p-c-Jun, cytochrome c, and caspase-3 involvement. J Mol Neurosci. 2005;27(1):29–42. CrossRefPubMedGoogle Scholar
  60. 60.
    Reddy KK, Lefkove B, Chen LB, Govindarajan B, Carracedo A, Velasco G, et al. The antidepressant sertraline downregulates Akt and has activity against melanoma cells. Pigment Cell Melanoma Res. 2008;21(4):451–6.; 10.1111/j.1755-148X.2008.00481.x.
  61. 61.
    Xia Z, Bergstrand A, DePierre JW, Nassberger L. The antidepressants imipramine, clomipramine, and citalopram induce apoptosis in human acute myeloid leukemia HL-60 cells via caspase-3 activation. J Biochem Mol Toxicol. 1999;13(6):338–47.CrossRefPubMedGoogle Scholar
  62. 62.
    Barnes TR, Banerjee S, Collins N, Treloar A, McIntyre SM, Paton C. Antipsychotics in dementia: prevalence and quality of antipsychotic drug prescribing in UK mental health services. Br J Psychiatry. 2012;201(3):221–6. Scholar
  63. 63.
    Corbett A, Burns A, Ballard C. Don’t use antipsychotics routinely to treat agitation and aggression in people with dementia. BMJ. 2014;349:g6420. Scholar
  64. 64.
    Kerr SJ, Rowett DS, Sayer GP, Whicker SD, Saltman DC, Mant A. All-cause mortality of elderly Australian veterans using COX-2 selective or non-selective NSAIDs: a longitudinal study. Br J Clin Pharmacol. 2011;71(6):936–42. Scholar
  65. 65.
    Liu Y, Tang W, Wang J, Xie L, Li T, He Y, et al. Association between statin use and colorectal cancer risk: a meta-analysis of 42 studies. Cancer Causes Control. 2014;25(2):237–49. Scholar
  66. 66.
    Lochhead P, Chan AT. Statins and colorectal cancer. Clin Gastroenterol Hepatol. 2013;11(2):109–18; quiz e13-4. Scholar
  67. 67.
    Makar GA, Holmes JH, Yang YX. Angiotensin-converting enzyme inhibitor therapy and colorectal cancer risk. J Natl Cancer Inst. 2014;106(2):djt374. Scholar
  68. 68.
    Fitzgerald PJ. Beta blockers, norepinephrine, and cancer: an epidemiological viewpoint. Clin Epidemiol. 2012;4:151–6. Scholar
  69. 69.
    Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol. 2003;158(9):915–20.CrossRefPubMedGoogle Scholar
  70. 70.
    D'Arcy ME, Sturmer T, Funk MJ, Baron JA, Sandler RS, Pate V, et al. Abstracts 618. Antidepressants (AD) and Colorectal Cancer (CRC). Pharmacoepidemiol Drug Saf. 2015;24(Supplemental S1):1–587. Scholar
  71. 71.
    Fearon ER. Molecular genetics of colorectal cancer. Annu Rev Pathol. 2011;6:479–507.; 10.1146/annurev-pathol-011110-130235.
  72. 72.
    Fearon ER, Vogelstein B. A genetic model for colorectal tumorigenesis. Cell. 1990;61(5):759–67.CrossRefPubMedGoogle Scholar
  73. 73.
    Jass JR. Molecular heterogeneity of colorectal cancer: Implications for cancer control. Surg Oncol. 2007;16(Suppl 1):S7–9. CrossRefPubMedGoogle Scholar
  74. 74.
    Jass JR. Classification of colorectal cancer based on correlation of clinical, morphological and molecular features. Histopathology. 2007;50(1):113–30. Scholar
  75. 75.
    Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet. 2013;
  76. 76.
    Rogers CR, Robinson CD, Arroyo C, Obidike OJ, Sewali B, Okuyemi KS. Colorectal cancer screening uptake’s association with psychosocial and sociodemographic factors among homeless Blacks and Whites. Health Educ Behav. 2017;44(6):928–36. Scholar
  77. 77.
    Owusu D, Quinn M, Wang KS. Alcohol Consumption, Depression, insomnia and colorectal cancer screening: racial differences. Int J High Risk Behav Addict. 2015;4(2):e23424. CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Centers for M, Medicaid Services HHS. Medicare program; revisions to payment policies and five-year review of and adjustments to the relative value units under the physician fee schedule for calendar year 2002. Final rule with comment period. Fed Regist. 2001;66(212):55245–503.Google Scholar
  79. 79.
    Chubak J, Boudreau DM, Rulyak SJ, Mandelson MT. Colorectal cancer risk in relation to antidepressant medication use. Int J Cancer. 2011;128(1):227–32.; 10.1002/ijc.25322.
  80. 80.
    Coogan PF, Strom BL, Rosenberg L. Antidepressant use and colorectal cancer risk. Pharmacoepidemiol Drug Saf. 2009;18(11):1111–4.; 10.1002/pds.1808.
  81. 81.
    Cronin-Fenton DP, Riis AH, Lash TL, Dalton SO, Friis S, Robertson D, et al. Antidepressant use and colorectal cancer risk: a Danish population-based case-control study. Br J Cancer. 2011;104(1):188–92.; 10.1038/sj.bjc.6605911.
  82. 82.
    Haukka J, Sankila R, Klaukka T, Lonnqvist J, Niskanen L, Tanskanen A, et al. Incidence of cancer and antidepressant medication: record linkage study. Int J Cancer. 2010;126(1):285–96.; 10.1002/ijc.24537.
  83. 83.
    Walker AJ, Card T, Bates TE, Muir K. Tricyclic antidepressants and the incidence of certain cancers: a study using the GPRD. Br J Cancer. 2011;104(1):193–7.; 10.1038/sj.bjc.6605996.
  84. 84.
    Xu W, Tamim H, Shapiro S, Stang MR, Collet JP. Use of antidepressants and risk of colorectal cancer: a nested case-control study. Lancet Oncol. 2006;7(4):301–8. Scholar
  85. 85.
    Yanik EL, Pfeiffer RM, Freedman DM, Weinstock MA, Cahoon EK, Arron ST, et al. Spectrum of immune-related conditions associated with risk of keratinocyte cancers among elderly adults in the United States. Cancer Epidemiol Biomark Prev. 2017;26(7):998–1007. Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Monica D’Arcy
    • 1
    Email author
  • Til Stürmer
    • 1
  • Jennifer L. Lund
    • 1
  1. 1.Department of Epidemiology, Gillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel HillUSA

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