A Pharmacoepidemiologic Approach to Evaluate Real-world Effectiveness of Hormonal Contraceptives in the Presence of Drug-drug Interactions.
Journal
Epidemiology (Cambridge, Mass.)
ISSN: 1531-5487
Titre abrégé: Epidemiology
Pays: United States
ID NLM: 9009644
Informations de publication
Date de publication:
01 03 2021
01 03 2021
Historique:
pubmed:
17
11
2020
medline:
1
6
2021
entrez:
16
11
2020
Statut:
ppublish
Résumé
Accurate estimation of conception is critical in the assessment of the effects of drugs used during pregnancy or to prevent pregnancy. In a novel application, we studied the effectiveness of oral contraceptives (OCs), where misclassification of conception relative to OC exposure may obscure effect estimates. We studied OC failure, in a large claims database, among women who used antiepileptic drugs with metabolizing enzyme-inducing properties (carbamazepine or oxcarbazepine), which reduce OC's effectiveness or enzyme-neutral properties (lamotrigine or levetiracetam), with no expected impact on OC effectiveness. We compared conception rates in women 12-48 years of age concomitantly using OCs and enzyme-inducing drugs with rates in concomitant users of OCs and enzyme-neutral drugs. We measured conception with a validated algorithm that estimates gestational age based on pregnancy endpoints. We estimated relative and attributable risk using generalized estimating equation models after standardized mortality ratio weighting. We identified 89,777 concomitant use episodes with adjusted contraceptive failure rates of 1.6 (95% confidence interval (CI) = 1.4, 1.8) per 100 person-years among users of enzyme-neutral drugs and 18,964 episodes with a rate of 2.3 (1.9, 2.8) among users of enzyme-inducing drugs. The relative risk of conception for enzyme-inducing group was 1.4 (1.1, 1.8), and the rate difference was 0.7 (0.2, 1.2). OCs in combination with antiepileptic drugs that interact with metabolic enzymes were associated with increased contraceptive failure rates. Measurement of conception in claims data had adequate accuracy to uncover a strong drug-drug interaction, offering promise for broader application in comparative effectiveness studies on hormonal contraceptives to inform clinical and regulatory decisionmaking.
Sections du résumé
BACKGROUND
Accurate estimation of conception is critical in the assessment of the effects of drugs used during pregnancy or to prevent pregnancy. In a novel application, we studied the effectiveness of oral contraceptives (OCs), where misclassification of conception relative to OC exposure may obscure effect estimates.
METHODS
We studied OC failure, in a large claims database, among women who used antiepileptic drugs with metabolizing enzyme-inducing properties (carbamazepine or oxcarbazepine), which reduce OC's effectiveness or enzyme-neutral properties (lamotrigine or levetiracetam), with no expected impact on OC effectiveness. We compared conception rates in women 12-48 years of age concomitantly using OCs and enzyme-inducing drugs with rates in concomitant users of OCs and enzyme-neutral drugs. We measured conception with a validated algorithm that estimates gestational age based on pregnancy endpoints. We estimated relative and attributable risk using generalized estimating equation models after standardized mortality ratio weighting.
RESULTS
We identified 89,777 concomitant use episodes with adjusted contraceptive failure rates of 1.6 (95% confidence interval (CI) = 1.4, 1.8) per 100 person-years among users of enzyme-neutral drugs and 18,964 episodes with a rate of 2.3 (1.9, 2.8) among users of enzyme-inducing drugs. The relative risk of conception for enzyme-inducing group was 1.4 (1.1, 1.8), and the rate difference was 0.7 (0.2, 1.2).
CONCLUSIONS
OCs in combination with antiepileptic drugs that interact with metabolic enzymes were associated with increased contraceptive failure rates. Measurement of conception in claims data had adequate accuracy to uncover a strong drug-drug interaction, offering promise for broader application in comparative effectiveness studies on hormonal contraceptives to inform clinical and regulatory decisionmaking.
Identifiants
pubmed: 33196560
pii: 00001648-202103000-00014
doi: 10.1097/EDE.0000000000001302
pmc: PMC7850590
doi:
Substances chimiques
Anticonvulsants
0
Contraceptives, Oral
0
Pharmaceutical Preparations
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
268-276Informations de copyright
Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc.
Références
Hernandez-Diaz S, Huybrechts KF, Desai RJ, et al. Topiramate use early in pregnancy and the risk of oral clefts: a pregnancy cohort study. Neurology. 2018;90:e342–e351.
Huybrechts KF, Hernández-Díaz S, Straub L, et al. Association of maternal first-trimester ondansetron use with cardiac malformations and oral clefts in offspring. JAMA. 2018;320:2429–2437.
MacDonald SC, Cohen JM, Panchaud A, McElrath TF, Huybrechts KF, Hernández-Díaz S. Identifying pregnancies in insurance claims data: methods and application to retinoid teratogenic surveillance. Pharmacoepidemiol Drug Saf. 2019;28:1211–1221.
Margulis AV, Setoguchi S, Mittleman MA, Glynn RJ, Dormuth CR, Hernández-Díaz S. Algorithms to estimate the beginning of pregnancy in administrative databases. Pharmacoepidemiol Drug Saf. 2013;22:16–24.
Toh S, Mitchell AA, Werler MM, Hernández-Díaz S. Sensitivity and specificity of computerized algorithms to classify gestational periods in the absence of information on date of conception. Am J Epidemiol. 2008;167:633–640.
Matcho A, Ryan P, Fife D, Gifkins D, Knoll C, Friedman A. Inferring pregnancy episodes and outcomes within a network of observational databases. PLoS One. 2018;13:e0192033.
Hornbrook MC, Whitlock EP, Berg CJ, et al. Development of an algorithm to identify pregnancy episodes in an integrated health care delivery system. Health Serv Res. 2007;42:908–927.
Hertz-Picciotto I, Pastore LM, Beaumont JJ. Timing and patterns of exposures during pregnancy and their implications for study methods. Am J Epidemiol. 1996;143:597–607.
Sarayani A, Albogami Y, Elkhider M, Hincapie-Castillo JM, Brumback BA, Winterstein AG. Comparative effectiveness of risk mitigation strategies to prevent fetal exposure to mycophenolate. BMJ Qual Saf. 2020;29:636–644.
CarbamazepineClinical Pharmacology [database online]. Gold Standard, Inc.
Crawford P, Chadwick DJ, Martin C, Tjia J, Back DJ, Orme M. The interaction of phenytoin and carbamazepine with combined oral contraceptive steroids. Br J Clin Pharmacol. 1990;30:892–896.
Davis AR, Westhoff CL, Stanczyk FZ. Carbamazepine coadministration with an oral contraceptive: effects on steroid pharmacokinetics, ovulation, and bleeding. Epilepsia. 2011;52:243–247.
Lazorwitz A, Davis A, Swartz M, Guiahi M. The effect of carbamazepine on etonogestrel concentrations in contraceptive implant users. Contraception. 2017;95:571–577.
Fattore C, Cipolla G, Gatti G, et al. Induction of ethinylestradiol and levonorgestrel metabolism by oxcarbazepine in healthy women. Epilepsia. 1999;40:783–787.
American College of Obstetricians and Gynecologists. ACOG Practice Bulletin No. 206: Use of hormonal contraception in women with coexisting medical conditions. Obstet Gynecol. 2019;133:e128–e150.
Curtis KM, Jatlaoui TC, Tepper NK, et al. U.S. selected practice recommendations for contraceptive use, 2016. MMWR Recomm Rep. 2016;65:1–66.
Patsalos PN. Drug interactions with the newer antiepileptic drugs (AEDs)—part 2: pharmacokinetic and pharmacodynamic interactions between AEDs and drugs used to treat non-epilepsy disorders. Clin Pharmacokinet. 2013;52:1045–1061.
Patsalos PN. Drug interactions with the newer antiepileptic drugs (AEDs)—part 1: pharmacokinetic and pharmacodynamic interactions between AEDs. Clin Pharmacokinet. 2013;52:927–966.
Chronic Conditions Data Warehouse; Centers for Medicare and Medicaid Services (CMS)CCW Condition Algorithms. Available at: https://www2.ccwdata.org/web/guest/condition-categories. Accessed 15 June 2019.
Hennessy S, Leonard CE, Gagne JJ, et al. Pharmacoepidemiologic methods for studying the health effects of drug-drug interactions. Clin Pharmacol Ther. 2016;99:92–100.
Sarayani A, Wang X, Thai TN, Albogami Y, Jeon N, Winterstein AG. Impact of the transition from ICD-9-CM to ICD-10-CM on the identification of pregnancy episodes in US health insurance claims data. Clin Epidemiol. 2020;12:1129–1138.
Zhu Y, Hampp C, Wang X, et al. Validation of algorithms to estimate gestational age at birth in the Medicaid Analytic eXtract—Quantifying the misclassification of maternal drug exposure during pregnancy. Pharmacoepidemiol Drug Saf. 2020:1–9. doi: 10.1002/pds.5126.
doi: 10.1002/pds.5126
Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34:3661–3679.
Sauer BC, Brookhart MA, Roy J, VanderWeele T. A review of covariate selection for non-experimental comparative effectiveness research. Pharmacoepidemiol Drug Saf. 2013;22:1139–1145.
Sato T, Matsuyama Y. Marginal structural models as a tool for standardization. Epidemiology. 2003;14:680–686.
Gaffield ME, Culwell KR, Lee CR. The use of hormonal contraception among women taking anticonvulsant therapy. Contraception. 2011;83:16–29.
Coulam CB, Annegers JF. Do anticonvulsants reduce the efficacy of oral contraceptives? Epilepsia. 1979;20:519–525.
U.S. Food and Drug Administration; Center for Drug Evaluation and Research (CDER)Labeling for Combined Hormonal Contraceptives. 2017.Guidance for Industry;
Barnett C, Dinger J, Minh TD, Heinemann K. Unintended pregnancy rates differ according to combined oral contraceptive—results from the INAS-SCORE study. Eur J Contracept Reprod Health Care. 2019;24:247–250.
Reeves MF, Zhao Q, Secura GM, Peipert JF. Risk of unintended pregnancy based on intended compared to actual contraceptive use. Am J Obstet Gynecol. 2016;215:71.e1–71.e6.
Dinger J, Minh TD, Buttmann N, Bardenheuer K. Effectiveness of oral contraceptive pills in a large U.S. cohort comparing progestogen and regimen. Obstet Gynecol. 2011;117:33–40.
Funk MJ, Landi SN. Misclassification in administrative claims data: quantifying the impact on treatment effect estimates. Curr Epidemiol Rep. 2014;1:175–185.
Eworuke E, Hampp C, Saidi A, Winterstein AG. An algorithm to identify preterm infants in administrative claims data. Pharmacoepidemiol Drug Saf. 2012;21:640–650.
MacDorman MF, Reddy UM, Silver RM. Trends in stillbirth by gestational age in the United States, 2006-2012. Obstet Gynecol. 2015;126:1146–1150.
Bird ST, Toh S, Sahin L, et al. Misclassification in assessment of first trimester in-utero exposure to drugs used proximally to conception: the example of letrozole utilization for infertility treatment. Am J Epidemiol. 2019;188:418–425.
Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int J Epidemiol. 2014;43:1969–1985.
Lash TL, Fox MP, Fink AK. Applying Quantitative Bias Analysis to Epidemiologic Data. Statistics for Biology and Health. 2011.New YorkSpringer;