A document by Juan Hincapie-Castillo. Click on the document to view its contents.

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.

A document by Liza Gibbs R. Click on the document to view its contents.

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.

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.

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.

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.

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: Sickle cell disease (SCD) affects all organ systems and is characterized by numerous acute and chronic complications and comorbidities. Standardized codes are needed for complications/comorbidities used in real-world evidence (RWE) studies that rely on administrative and medical coding. This systematic literature review was conducted to produce a comprehensive list of complications/comorbidities associated with SCD, along with their diagnosis codes used in RWE studies. Methods: A search in MEDLINE and Embase identified studies published from 2016-2023. Studies were included if they were conducted in US SCD populations and reported complications/comorbidities and respective International Classification of Diseases, Clinical Modification (ICD-CM) codes. All identified complications/comorbidities and codes were reviewed by a certified medical coding expert and hematologist. Results: Of 1,851 identified studies, 40 were included. The most reported complications/comorbidities were stroke, acute chest syndrome, pulmonary embolism, venous thromboembolism, vaso-occlusive crisis, and priapism. Most of the studies used ICD-9-CM codes (n=21), while some studies used ICD-10-CM codes (n=3) or both (n=16), depending on the study period. Most codes reported in literature were heterogeneous across complications/comorbidities. The medical coding expert recommended modifications for several conditions. Conclusion: While many studies we identified did not report their codes and were excluded from this review, the studies with codes exhibited diverse coding definitions. By providing a standardized set of diagnosis codes that were reported by studies and reviewed by a coding expert and hematologist, our review can serve as a foundation for accurately identifying complications/comorbidities in future research, and may reduce heterogeneity, enhance transparency, and improve reproducibility.

Barbro Westerholm, 1933-2023

Comorbidity Influence in Observational Studies: Why Ignore the Real World ?Comorbidity is any coexisting disease or medical condition with another medical condition, at a particular time point or during a particular period, in a patient. As such they can be acute or of chronic nature. Comorbidities may or may not interact with each other. Indeed, comorbid conditions may elevate the risk of the development of other conditions, increase the risk of death, reduce the quality of life, worsen the mental and physical health and impair the general functional ability of the patient. However, the literature on empirical evidence of treatment effect modification by comorbidity from randomized clinical trials (RCTs) is very limited, although this has been attributed to the underrepresentation of people with comorbidities in trials.1 Despite this, the standard assumption that efficacy is constant across subgroups of patients by levels of comorbidity in trials, is often criticised despite the power granted by treatment randomization to such studies.2 In contrast, the influence of comorbidity on health outcomes and treatment effectiveness is well established from observational studies.3-4In most observational studies, the influence of comorbidity on treatment effect is modelled as constant regardless of how long the patient has had the condition up to the start of follow-up, with the inherent assumption that the duration does not influence the outcome of interest. The question we inadvertently fail to address is whether it is reasonable to assume a constant influence for each of the comorbidities- namely, a level of influence that is independent of how long the patient has had each comorbid condition since diagnosis? We readily ignore the reality that there may be no evidence on the assumption of a constant, unchanging effect over time by the comorbidity of interest. For example, in assessing the treatment effect of a particular drug in a diabetic cohort, we generally consider the risk associated with each of the comorbidities of interest, such as hypertension as the same between patients diagnosed five years ago and those diagnosed recently. By so doing, we run the risk of not properly controlling for the influence of some of these comorbidities on the outcome of interest. Indeed, this assumption was challenged in a study that demonstrated the duration-dependent nature of the influence of certain comorbidities on survival among chronic obstructive pulmonary disease (COPD) patients in the UK’s Clinical Practice Research Datalink (CPRD) and since then, using similar approaches that are easy to apply, two other studies have also demonstrated the duration-dependent nature of certain comorbidities within the real-world evidence generation space.5-7Another common practice we find in observational studies is the modelling of and adjusting for comorbidity influence additively, either as the number of comorbidities or as an index (such as the Charlson Comorbidity Index), and we do so often without regard to the specific outcome of interest, which may be different from the outcome used in the derivation of the index.8-10 In so doing, we are inadvertently assuming that (1) the outcome is associated linearly with the comorbidity count or index and (2) the impact of every comorbidity is one directional- namely, to elevate the risk of the outcome and hence, the weights we derive from combining the set of comorbidities must be positive (i.e. the null hypothesis that outcome worsens with increasing comorbidity is inadvertently assumed as already proven). Although these common practices are generally suitable in many observational studies, there is evidence some of these assumptions may not necessary hold true in some disease populations, such as in COPD patients.11-13 One such evidence is that as a result of adequate control of COPD by medications and lifestyle changes, among others, its influence on the risk of an outcome of interest, may not necessarily be as a risk elevator - suggesting, risk reduction by an adequately managed comorbid condition is possible in real-life studies. An additional, possible consequence of these approaches is our likely loss of ability to adequately identify specific interactions between those comorbidities that influence prognosis.Some of the problems associated with our current simplistic approach to handling comorbidity influence were first highlighted in a 2012 paper on the conduct of prospective observational post-authorization safety studies.14 The paper recommends assessing the suitability of the assumptions that underpin the methodologies we intend to apply to the data within the context of the specific outcome(s) of interest as the least we could do with real-life data. In other words, we need to demonstrate that such assumptions are appropriate and reasonable on the basis of the results from exploratory assessments of the data. We need not ignore what the specific study data may reveal about the nature of the true association between the outcome and each specific comorbidity and/or combination of comorbidities. We particularly need not ignore evidence from routine clinical practice. The current common assumption in observation studies, that the influence of every comorbid condition on any health outcome of interest is independent of both its duration and management in routine clinical practice, is unlikely to be tenable in the real world.Acknowledgement :The authors are grateful to Professor Gilbert MacKenzie for his valuable contributions to initial discussions on the subject.Prof Victor A Kiri MPH, PhDEpidemiology DirectorMarket Access Consulting, RWE & AnalyticsFortreaMaidenhead, Berkshire SL6 3QHUnited [email protected] iD: https://orcid.org/0000-0001-7171-2011Dr Maurille Feudjo-Tepie PhDVice PresidentHead of GlobalRWE & Digital sciences Head of Global RWE & Digital sciencesUCBSlough, Berkshire SL1 3WEUnited [email protected] P, Hannigan L, Rodriguez-Perez J, et al. Representation of people with comorbidity and multimorbidity in clinical trials of novel drug therapies: an individual-level participant data analysis. BMC Med. 2019; 17(1):201Hanlon P, Butterly EW, Shah ASV, et.al. Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials. PLoS Med 2023; 20(6): e1004176Iversen LH, Nørgaard M, Jacobsen J, et al. The impact of comorbidity on survival of Danish colorectal cancer patients from 1995 to 2006 – a population-based cohort study. Dis Colon Rectum 2009;52(1):71–78Lund L, Jacobsen J, Nørgaard M, et al. The prognostic impact of comorbidities on renal cancer, 1995 to 2006: a Danish population based study. J Urol. 2009;182(1):35–40; discussion 40.Kiri VA, Oyee J. Assessing the time-dependency nature of comorbidity influence in COPD, Pharmacoepidemiology and Drug Safety (2006); 15 (Suppl 2): S8Plana-Ripoll O, Pedersen CB, Holtz Y, et al. Exploring comorbidity within mental disorders among a Danish national population. JAMA Psychiatry 2019b; 76: 259–270McGrath JJ, Lim CCW, Plana-Ripoll O, et al. Comorbidity within mental disorders: A comprehensive analysis based on 145,990 survey respondents from 27 countries. Epidemiology and Psychiatric Sciences 2020; 29: [e153]Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40 (5):373-383Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.Yngvar Nilssen, Trond-Eirik Strand, Robert Wiik, et al. Utilizing national patient-register data to control for comorbidity in prognostic studies, Clinical Epidemiology 2014;395-404Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27Kiri VA, Visick G, Muellerova H, MacKenzie G. A Novel Methodology for Measuring the Influence of Comorbidity in Disease Outcome Studies, Pharmacoepidemiology and Drug Safety (2005); 14 (Suppl 2): S135Kiri VA, Muellerova H, Visick G, MacKenzie G. Assessing the multivariate structure and influence of comorbidity in COPD, Eur Respir J (2005); 26 (Suppl 49):A470sKiri VA. A Pathway to Improved Prospective Observational Post-Authorization Safety Studies. Drug Saf 2012; 35 (9): 711-724