Medication switching, also known as substitution, describes the replacement of one medication for another. Switching medications can occur for a variety of reasons, including poor medication effectiveness, adverse side effects, cost mitigation (ie, generic drug substitution), policies, and patient preference.1-6 Medication switching is related to the broader concept of adherence, or the degree to which a patient's consumption of a medication aligns with their provider's recommendations.7,8 Poor adherence to medications is associated with substantial morbidity, mortality, and economic costs across a number of chronic and infectious diseases.9-12 Indeed, it has been estimated that only 50% of individuals in high-income countries with a chronic condition adhere to their medication regime, and this costs the US health system $100 billion annually.10 As such, medication adherence and its related concepts have become the focus of many pharmacoepidemiologic studies.
Adherence can be measured in a number of ways, such as the concentration of medication metabolites in the blood or urine, via electronic devices that record the number of times a pill bottle is opened, self-reporting, or through direct observation.8 However, these strategies are resource-intensive, expensive, and often unfeasible in population-based studies on medication use, safety, and effectiveness.8 Pharmacy claims data represent a less invasive and often less costly strategy to measure medication patterns and have shown to have good validity and low error rates.13-15 Pharmacoepidemiologic studies that use pharmacy claims data commonly describe compliance and persistence as specific measures of medication adherence, with medication discontinuation and switching being related and nested concepts.8,16-18 Although several approaches for measuring compliance, persistence, and discontinuation with pharmacy claims data exist (eg, medication possession ratio and permissible gaps between medication dispensations), less is known about the methods for measuring medication switching.16
Medication switching can be incorporated into studies’ operational definitions of adherence, such as determining whether switching medications constitutes continuous therapy or treatment discontinuation.19 However, medication switching can also represent its own independent construct of interest, such as examining whether switching antiepileptic medications is associated with seizure-related events.5 Although medication switching can refer to many changes in a treatment regime (eg, changes in medication dose, class, or manufacturer), the focus of the current review is on generic and therapeutic switching. Generic switching refers to a medication being switched for a different formulation of the same medication, including production by a different manufacturer.20 Therapeutic switching refers to a different medication, within the same or different class, replacing the first medication, and intended to have similar therapeutic effects.20 Although switching is relatively common in clinical practice, potential strategies for identifying switching in pharmacy claims data have not been well described to date.
Prior studies that have used pharmacy claims data have specified a “switching window” or fixed period of time after the first medication has been discontinued to capture possible switches.4,16 Although switching windows can be relatively straightforward to parameterize, it is often challenging to determine how long the window should be and what medications should be classified as switches.4 Additionally, specifying switching windows solely after a medication has been discontinued may be insufficient for medications with unique prescribing patterns, such as psychotropic medications (eg, overlapping or cross-tapering medications).1,2,21,22 Notably, our preliminary literature search revealed that while some studies include specific details about their switching measure (eg, timing of eligible switches and specific types of switches [cross-taper, gap, immediate]),21-23 this is not standard practice.5,24 Therefore, mapping how studies have measured medication switching with pharmacy claims data will not only create a repository of possible strategies for future studies, but also lay the groundwork for studies to evaluate which approaches are optimal. Although Andrade et al.16 conducted a review of medication adherence measures, including medication switching, their review is older (published in 2006) and did not include psychotropic medications. To our knowledge, this will be the first scoping review to explore how pharmacoepidemiologic studies have used pharmacy claims data to measure medication switching at the individual level for both psychotropic and non-psychotropic medications.
The aim of this scoping review is to descriptively compare how pharmacoepidemiologic studies have used pharmacy claims data to measure within- and between-class prescription medication switching in individuals – as the primary independent or dependent variable – in order to create a repository of common methods and highlight areas for future research.
- i) How is therapeutic and generic prescription medication switching defined and measured?
- ii) What rationale (eg, prescribing guidelines), if provided, is used to justify the chosen measurement strategy for prescription medication switching?
- iii) Does the measurement strategy for prescription medication switching vary according to the broad class of medication, specifically psychotropic vs non-psychotropic?
We will use a scoping review to map how studies have measured medication switching with pharmacy claims data.25 The JBI Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) were used to generate this protocol and guide the conduct of our scoping review.25,26 This protocol was registered with Open Science Framework on August 29, 2020 (registration link: osf.io/nj7qz).
Studies of human subjects measured and analyzed at the individual level will be included. As prescription medication switching is not necessarily restricted to any clinical demographic, we will include all clinical groups that meet the inclusion criteria.
All studies of orally administered prescription medications with medication switching specified as their primary independent or dependent variable will be included. For feasibility purposes, only generic and therapeutic switching will be considered; therefore, switching doses of the same medication and/or adding a new medication to an existing treatment regime (ie, augmentation) will not be considered. We will focus on studies with switching as the primary independent or dependent variable because we anticipate these to have the most thorough description of their switching methods. The World Health Organization's Anatomical Therapeutic Chemical (ATC) Classification System will be used to broadly classify medications (first and second levels).
The context of the current review is open and all studies, irrespective of geographic-, social-, cultural-, racial-, ethnic-, sex-, and gender-related factors, will be considered for inclusion.25 No specific age criteria will be used as a requirement for inclusion because medication switching is not necessarily restricted to specific age groups. No language restrictions will be applied during the initial screening of articles. For articles not published in English, Google Translate will be used to translate titles and abstracts.27 Non-English studies that meet our inclusion criteria will not be included in the final review due to feasibility (eg, expense of full translations), but they will be enumerated and included in a supplemental table. Last, although the history of medication safety, effectiveness, and surveillance dates back to the early 20th century, pharmacoepidemiology as a field only gained prominence in the 1980s7,28; therefore, we will restrict our literature search from 1980 to 2020.
Types of sources
Observational study designs (cohort, case-control, cross-sectional, and self-controlled study designs [eg, case-crossover]) that have used pharmacy claims data will be included. Reviews will be excluded; however, we will scan the reference lists of these publications for studies that meet our inclusion criteria.
In addition to studies not meeting the inclusion criteria, studies with the following characteristics will be excluded:
- Ecological studies, because their unit of analysis is a group of individuals, and group-level findings may not reflect what is occurring at the individual level (ie, ecological fallacy). The aim of this scoping review is to map how studies measure whether an individual has switched from one medication to another medication using pharmacy claims data;
- Non-observational or experimental study designs (randomized control trials and clinical trials), because we aim to describe how studies measure medication switching under “real world” conditions with pharmacy claims data;
- Studies investigating the use of anti-infective medications (eg, antibiotics, antiparasitics, antivirals, and antifungals), because their treatment courses tend to be acute and/or short in duration;
- Non-peer-reviewed literature (commentaries, editorials, book reviews, opinion articles, dissertations, and other gray literature), because we are solely interested in mapping the methods used in peer-reviewed studies;
- Studies that have used electronic medical records and/or other medical charts, because we aim to examine how studies have used pharmacy claims data to measure medication switching without access to clinical notes16;
- Qualitative research, because we aim to examine how quantitative studies operationalize medication switching as their independent or dependent variable;
- Methodological and simulation studies, because these studies may be examining medication switching under artificial conditions. Validation studies of different switching methods using pharmacy claims data will be flagged for relevance, but not included in the narrative description of our results;
- Studies investigating switches between different doses of the same medication or adding a new medication to an existing regime (ie, treatment augmentation);
- Studies of non-prescribed medications or drugs (eg, over-the-counter medications); and
- Published conference abstracts, because the depth of methodological information is limited.
A preliminary literature search was conducted using MEDLINE and Google Scholar to identify potentially relevant studies and keywords. The research team identified the following core concepts to begin the preliminary search: “medication switch,” “administrative data,” and “observational study.” Using studies found in the preliminary search and subject-matter expertise, each core concept was expanded to include synonyms and other related concepts, for example, “substitute,” “replace,” “cross-taper,” “overlap,” and “change” were recognized as some terms related to the core concept – “medication switch.” Using these terms, a complete search strategy for MEDLINE was prepared and validated by a health sciences research librarian using the Peer Review of Electronic Search Strategies (PRESS) checklist (Appendix I).29 Any new terms identified in the subsequent search of all databases that are deemed relevant will be incorporated and documented in the final scoping review. Lastly, the same search terms will be translated and used across all databases and platforms, and a flow diagram will be included in the final review.
The reference lists of all full-text articles included in the final review will be searched. The citations of all papers included in the final review will be screened to identify articles meeting inclusion criteria that may have been missed. In the event that an article cannot be accessed or clarification is required, we will contact the corresponding author.
Literature search strategies will include the following electronic databases and platforms: MEDLINE (PubMed), Embase (Ovid), Central (Cochrane Library), CINAHL (EBSCO), and Google Scholar. We will restrict our search to articles published between January 1, 1980 and October 31, 2020.
To ensure consistency in the application of inclusion/exclusion criteria, 25 randomly selected titles and abstracts will be screened by the entire research team. Once there is at least 75% agreement on which titles and abstracts should (and should not) undergo full-text review among the research team, two reviewers (DH and ZB) will independently review all other titles and abstracts. Both reviewers will classify titles and abstracts as “potentially relevant” or “irrelevant.” Once all citations have been classified, any discrepancies will be resolved through discussion between the two reviewers, or with involvement of a third reviewer if consensus cannot be reached.
Articles passing the initial title and abstract screening (ie, articles that are “potentially relevant”) will proceed to full-text review. Full-text articles will be independently screened by the same two reviewers who conducted the title and abstract screening. Articles that include medication switching as a primary independent or dependent variable and meet the other inclusion/exclusion criteria will be included in the scoping review. As a calibration exercise, DH and ZB will pilot the full-text review with 25 articles to ensure similar information is abstracted and decisions for exclusions are consistent. Inter-rater reliability will be calculated once all full texts have been classified using Cohen's κ and once any discrepancies between the two reviewers have been reconciled through discussion or with a third reviewer. The Covidence (Veritas Health Innovation, Melbourne, Australia) software package will be used to organize and share studies for the review among the research team. All reasons for exclusions will be reported in the final manuscript.
Two reviewers (DH and ZB) will independently collect information. A preliminary data extraction tool in Excel (Redmond, Washington, USA) will be created by the research team and piloted with five randomly selected articles to ensure the operational definitions for each field are clear and that all necessary fields are represented. Study-specific information (eg, title, year of publication, and corresponding author) as well as information regarding the measurement of prescription medication switching will be collected.
Appendix II contains the preliminary data items, measurement types, and operational definitions for the reviewers to use when collecting study information. In addition to study-specific information such as title and publication year, the reviewers will also collect information related to the study population, medications under study, and measurement strategy for prescription medication switching. A mix of free-text and drop-down tools will be used to ensure flexibility, reduce errors, and facilitate interpretation of the results.
We will descriptively compare the primary and most common strategies that have been used to measure prescription medication switching (eg, switching window parameterizations) with pharmacy claims data.1,2,21 We will then describe the rationale, tools, and strategies that studies have used to guide their operationalizations of prescription medication switching, if provided. We will report possible differences between the methodological strategies for psychotropic vs. non-psychotropic medication switching. Last, we will characterize the types of studies included in the final review (eg, average number of participants and the representation of different study designs), but we will not assess or report the quality of the evidence or attempt to synthesize the findings as per recommendations in JBI methodology for scoping review.25 A risk of bias assessment will not be completed.25
DH is supported by the Alzheimer Society of Canada's doctoral research award. DH and ZB are partially supported by the Canadian Institutes of Health Research (CIHR) Drug Safety Evaluation Cross-Disciplinary Training (DSECT) Program. This research was conducted with support from the Ontario Neurodegenerative Disease Research Initiative (ONDRI) through the Ontario Brain Institute, an independent non-profit corporation, funded partially by the Ontario government. AT is funded by a Tier 2 Canada Research Chair in Knowledge Synthesis.
Health sciences librarian, Henry Lam of Sunnybrook Health Sciences Center, for coordinating the PRESS checklist.
Appendix I: Search strategy
Appendix II: Data items, measurement types, and operational definitions to be included in the data extraction process
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