Introduction

Medication use is an essential element of studies of drug safety or effectiveness[1]. There have been several successful efforts to define medication use, however, these efforts have focused on specific applications of using medications within particular analyses[2–5]. Although real-world data (RWD) is being increasingly used to generate real-world evidence (RWE) to guide clinical, policy, and regulatory decision making, there are limitations associated with conducting studies using these data. Medication data are not captured for research purposes and often need to be transformed from unstructured to structured data when incorporated into studies. The lack of formal conceptual and operational medication use definitions may result in failing to capture the varied dimensions of patients’ medication use. Therefore, it is important to establish conceptual and operational definitions of medication use to aid investigators and the scientific community in designing, conducting, reporting on, and comparing across studies.
To our knowledge, there is no uniform structure to help researchers conceptualize or operationalize medication use in their studies. To address this gap, we developed a structure that includes considerations to address when developing medication use definitions. The structure can be used by researchers and policy makers to understand and contextualize findings from pharmacoepidemiology studies. We categorized our considerations in two major categories: considerations for a conceptual definition, or how medication use would ideally be defined; and considerations for an operational definition, or how the medication usewas defined.

Considerations for the Conceptual Definition of Medication Use

As illustrated in Figure 1, we suggest five key considerations for developing the conceptual definition of the medication use.
Consideration 1: The context under which the medication is being studied (or what about medication use the researcher trying to capture). Consider whether the medication use is the central exposure variable or a covariate in a study assessing effectiveness or safety of a treatment, or an outcome in a study assessing prescribing practices.
Consideration 2: The research concept of interest. That is, whether the research focuses on a specific ingredient that may be found in more than one medication, on a specific medication, or in a class of medications.
Consideration 3: The routes of administration that may be of interest. Many medications have different indications and uses in their different formulations and have correspondingly different routes of administration. For example, corticosteroids may be administered via many routes and for myriad indications. The conceptual definition should also note whether any routes of administration are excluded.
Consideration 4: Medication dose if of interest. If of interest, the conceptual definition should include a description of the dose for each administration or daily dose, and an estimated cumulative dose. It may also be important to describe how different dosage forms (e.g., parenteral versus oral) will figure into the dose calculation if multiple forms are available. Researchers should also consider that specific dosages of some drugs are highly difficult to identify. They might want to incorporate definitions that consider dose changes over time.
Consideration 5: The ideal timing and duration of medication use. Considerations about whether the date the patient initiated the medication, the timing of subsequent uses of that medication, relative timing of medication use and health events, whether continuous or cumulative exposure (i.e., duration), and whether discontinuation or changes to a different medication (as a proxy for standard of clinical care, lack of drug tolerance, or success/failure of a treatment) are of interest will depend on the study question. It is also useful to consider that the drug effect may persist after treatment discontinuation or that there may be gaps in treatment, which may have implications for the timing of administration in the analysis. This will usually require an understanding of the pharmacological characteristics of the medication.

Considerations for the Operational Definition of Medication Use

As illustrated in Figure 2, we suggest five key considerations for developing the operational definition of the medication use.
Consideration 1: The underlying pattern of health seeking behavior and its documentation within the health system that give rise to the observable data. There are various types of encounters patients may have as they move within the health care ecosystem that may influence the operational definition. A strategy to screen for the records or occurrences of the medication use, should be informed by the format in which the medications data are captured and stored. For example, during hospitalization, a patient’s medication use may be collected in different ways; hospital clinical staff use patients’ charts information to enter patient medication use into electronic health record (EHR) (e.g., clinician-generated prescription orders, medication administrations, pharmacy dispensing); hospital clinical staff ask patients about past and current medication use; or prescription orders recorded as part of hospital discharge instructions. This information may be transformed to a common data model, used for billing and payment purposes, and potentially included in medical claims (though detail related to medication use in claims may be limited).
Consideration 2: The characteristics of data source that were used (e.g., location,) and approach used to identify the medication used. As can be seen in Figure 3, identification of a medication requires finding of the likely location (e.g., specific data tables/views and fields) where relevant medications-related information is stored, which is heterogenous. Therefore, we suggest that decisions regarding appropriate sources of medication information first be guided by knowledge of clinical practice and how the data are produced and sorted (e.g., clinician decision to order a medication versus administration of the medication to the patient). The format of the sorted data should also be considered. For example, whereas administrative and claims data are highly structured and standardized in format by virtue of the specific requirements to which providers must adhere to receive payment, other data sources such as EHRs are often more heterogeneous. Medication information may be stored as coded data within structured fields, text within semi-structured or discrete fields, or within free-text clinical notes. Coded medications data may take the form of organization-specific or local codes, or may include standard terminology or classifiers (e.g., RxNorm, NDC, Multum, FirstDataBank). Whether medication information is stored as codes or as text, it is useful to submit supplementary materials include the structured code lists or list of text strings used to identify occurrences of the medication use of interest. Similarly, it is essential to report operational definitions and methods when using information from semi-structured or unstructured data. Regardless, researchers might consider engaging individuals with expertise of the specific data source being used.
Consideration 3: Actual timing elements related to medication use. The ability to define various aspects of timing of medication use is an integral component of pharmacoepidemiologic research questions. The researcher should describe how they identified the date of patient initiation of a medication and the timing of subsequent uses of that medication, date of medication discontinuation, and relative timing of medication use and health events of interest, as they were defined in the conceptual definition. They should also specify how they calculated the duration of medication use if it is of interest, including any assumptions of medication use tied to the definition (e.g., identifying a prescription within an EHR and assuming the medication was filled and used for the full days supplied).
Consideration 4: Other features of medication use. The features of medication use that are of interest should be defined. These features might include whether prevalent or incident use is of interest and how it was defined; whether patients can have multiple qualifying episodes of medication use or only one; and how dose was calculated, if of interest. Researchers should also describe how they addressed missing data and discrepancies if they are using more than one data source.
Consideration 5: Algorithm validation. If an algorithm was used to identify the medication, researchers should specify whether the algorithm for the medication measure was validated.