Pharmacoepidemiology and Drug Safety’s Core Concepts in Pharmacoepidemiology Section at One Year: Where Do We Go from Here? Jennifer L. Lund,1,2 Vincent Lo Re III3,41University of North Carolina at Chapel Hill, Department of Epidemiology, Gillings School of Global Public Health, Chapel Hill, NC, USA2Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA3Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA4Division of Epidemiology, Center for Real-World Effectiveness and Safety of Therapeutics, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USARunning title: Core Concepts in Pharmacoepidemiology – Year 2Conflicts of interest: The authors report no conflicts of interest.Acknowledgements: The authors appreciated the critical input on the Core Concepts in Pharmacoepidemiology section from Brian Strom, Olaf Klungel, BJ Park, and the members of the International Society for Pharmacoepidemiology’s Publications and Communications Committee, particularly Elena Rivero, Patricia Saddier, Joan Largent, and Blànaid Hicks.
Purpose: To explore the differences among erectile aids (i.e., phosphodiesterase type 5 inhibitors [PDE5i] and intracavernousal drugs) of the relative risk of priapism and identify age groups at risk. Methods: We queried the World Health Organization global database of individual case safety reports (VigiBase) for records of the ADR with sildenafil, tadalafil, avanafil, vardenafil, papaverine, and alprostadil. Disproportionality analyses (case/non-case approach) were performed to assess the relative risk of priapism reporting in PDE5i consumers compared to intracavernousal drug recipients. Results: From a total of 133,819 ADR events for erectogenic medications, 632 were priapism (PDE5is: n=550, 0.41%; intracavernousal drugs: n=82, 9.92%). We observed a strong signal for priapism induction for intracavernousal drugs than PDE5is (reporting odds ratio [ROR]=34.7; confidence interval [CI] 95%: 27.12 - 43.94 vs. ROR= 1.38; CI 95%: 1.24 - 1.54). For all PDE5i agents, the 12-17 years age group had the highest highest ROR (ROR=9.49, CI 95%: 3.76 - 19.93) followed by 2-11 years (ROR=4.31, CI 95%: 1.57 - 9.4). Disproportionality signals for consumers under eighteen for both all PDE5is as a whole (ROR=4.57, CI 95%: 2.48 - 7.73) and sildenafil (ROR=4.89, CI 95%: 2.51 - 8.62) were significantly stronger than individuals eighteen or older (ROR=1.06, CI 95%: 0.93 - 1.21 and ROR=1.08, CI 95%: 0.91 - 1.26, respectively). Conclusions: While the overall risk of priapism following the oral administration of PDE5is is extremely low compared with intracavernousal remedies, adolescents are at a higher risk of priapism than older men.
Purpose: With the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype algorithms to identify TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies. Methods: Authors searched the National Library of Medicine’s PubMed, Scopus, and the American Psychological Association Psyc Info’s databases to identify studies published in the United States that applied CPs to identify TG people within electronic health care data. Results: Twelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n=5), hierarchy of code mechanisms (n=4), key text-strings (n=2), or self-surveys (n=1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text-strings. However, if key text-strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone. Conclusion: CPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text-strings. CPs with high validity are essential to identifying TG patients when self-reported gender identity is not available. Still, self-reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns.
Purpose To describe utilization patterns, characteristics of users and prescriber responsibility of the new oral antiviral medication, molnupiravir, indicated for mild-to-moderate COVID-19. Methods Using nationwide registries, we identified all Danish adults who filled a prescription for molnupiravir from December 16 th, 2021, to August 31 st, 2022. We described weekly incidence rates and patient characteristics over time, prescriber responsibility as well as time between molnupiravir initiation and a positive SARs-CoV-2 test. Patient characteristics were compared to an untreated SARS-CoV-2 positive cohort. Results By August 31 st, 2022, 5,847 individuals had filled a prescription for molnupiravir. The incidence rate gradually increased to 2,000 weekly prescriptions per 100,000 RT-PCR SARS-CoV-2 positives. Users of molnupiravir were most often men (55% vs. 45% women). The majority (81%) had a positive RT-PCR SARS-CoV-2 test and few (2.9%) redeemed molnupiravir outside the recommended window of 5 days from the positive test result. Compared to an untreated SARS-CoV-2 positive cohort, users of molnupiravir had a median age of 74 years vs. 44 years, a higher proportion resided in a nursing home (12% vs. 1.1%) and had a higher number of comorbidities (median of 3 vs. 0); most commonly hypertension (38%), chronic lung disease (35%), diabetes (20%) and mood disorders (20%). General practitioners were the primary prescribers of molnupiravir (91%). Conclusions Molnupiravir was mainly prescribed by general practitioners to RT-PCR SARS-CoV-2 positive individuals who had a potentially increased risk of severe COVID-19. Though some off-label prescribing occurred, our study indicates a high level of adherence to contemporary guidelines.
Purpose: Reducing initial exposure of “opioid naïve” patients to opioids is a public health priority. Identifying opioid naïve patients is difficult, as numerous definitions are used. The objective is to summarize current definitions and evaluate their impact on opioid naïve measures in Alberta. Methods: Using dispense data (2017-2021) and definitions guided by a scoping review, we determined the number of “opioid naïve” patients using descriptive analyses. Three definitions were identified: 1) no opioid use within the previous 30 days/6 months/1 year, based on dispensation date; 2) definition 1, based on dispensation date plus days of supply; 3) exclusion of codeine from definitions 1 and 2. Results: Of over a dozen definitions of opioid naïve identified in the scoping review, most used an ‘opioid free’ period (commonly 30 days/6 months/1 year). Other definitions included “availability of drug” based on days of supply and/or excluded certain opioid products. Approximately 36.4% of Albertans (n=1,551,075) had an opioid dispensation in 2017-2021. The average age was 46.6±18.8 and 52.8% were female. Results were most affected by the “opioid free” period, with 97.4%, 83.2% and 65.6% being classified as opioid naïve using time windows from definition 1. Definitions 2 and 3 did not materially change the results. Conclusions: The most convenient definition for “opioid naïve” was definition 1 using a 1-year window, which aligns with the Canadian Institutes for Health Information definition. Irrespective of definition used, a large proportion of opioid users would be considered opioid naïve despite initiatives to curb opioid prescription in Alberta.
Purpose: While much has been written about how distributed networks address internal validity, external validity is rarely discussed. We aimed to define key terms related to external validity, discuss how they relate to distributed networks, and identify how three networks (the US Food and Drug Administration’s Sentinel System, the Canadian Network for Observational Drug Effect Studies [CNODES], and PCORnet, the National Patient Centered Clinical Research Network, initiated and supported by the Patient-Centered Outcomes Research Institute. Methods: We define external validity, target populations, target validity, generalizability, and transportability and describe how each relates to distributed networks. We then describe Sentinel, CNODES, and PCORnet and how each approaches these concepts. Results: Each network approaches external validity differently Sentinel answers regulatory questions in the general US population using data from commercial health plans and Medicare fee-for-service beneficiaries and considers external validity when exploring outliers or performing subgroup analyses to examine potential heterogeneity of treatment effects. CNODES focuses on a Canadian target population but includes UK and US data and thus has to make decisions about which partners can be included in each analysis. PCORnet supports a wider array of studies including randomized trials and often assesses whether a given study will be representative of the wider US population. Conclusions: There is no one-size-fits-all approach to external validity within distributed networks. With these networks and comparisons between their findings becoming a key part of pharmacoepidemiology, there is a need to adapt tools for improving external validity to the distributed network setting.
Authors:Alecia Clary , Ph.D., MSW (AC) (Corresponding author)Affiliation: The Reagan-Udall Foundation for the FDA, Washington, DC, USA Address: 1333 New Hampshire Ave, NW Suite 420, Washington DC 20036, USA Email: [email protected] Phone: 202-849-2075 ORCID ID: 0000-0002-7774-9808Nancy D Lin , (NDL) Affiliation: IQVIA Real World Solutions, Bridgewater, NJ, USA Address: 77 Corporate Drive, Bridgewater, NJ 08807 Email: [email protected] Lasky , (TL) Affiliation: The United States Food and Drug Administration, Office of Data, Analytics, & Research, Office of the Commissioner, Washington DC, USA Address: 10903 New Hampshire Avenue, Silver Spring, MD, USA. Email: [email protected] W Reynolds , (MR) Affiliation: IQVIA Real-World Solutions, Washington, DC, USA Address: 201 Broadway, Cambridge, MA, USA. Email: [email protected] Chokkalingam , (ACH) Affiliation: Gilead Sciences, Foster City, California, USA Address: 333 Lakeside Drive Email: [email protected] Rodriguez-Watson, (CRW)Affiliation: The Reagan-Udall Foundation for the FDA, Washington, DC, USA Address: 1333 New Hampshire Ave, NW Suite 420, Washington DC 20036, USA Email: [email protected] paper has not been previously printed, currently has not been submitted for publication in any other journal and is not pending acceptance in any other journal. We have not had any prior correspondence with the journal about the manuscript.