Medication persistence implies that the patient must have exhibited at least primary adherence because persistence over time cannot be measured unless the patient has received at least the first dispensing (Fig. 2). We propose that early-stage persistence be defined to include individuals with at least 2 dispensings and later-stage persistence as including individuals with 3 or more dispensings of the medication and with evidence of medication availability. For consistent terminology, we propose the converse of each persistence category to also be “non” as in “early-stage nonpersistence” and “later-stage nonpersistence.”
Medication discontinuation implies that a patient has terminated therapy as evidenced by not refilling a prescription, but no subjective inference regarding appropriateness is made, as discontinuation may be initiated either by the clinician or the patient. Further, in claims database studies, it is usually not possible to determine whether discontinuation was prescriber-initiated or patient-initiated. Medication discontinuation in electronic database studies can only be assessed within the context of a prespecified operational definition for the required number of days without medication available. Thus, very low measured levels of adherence (MPR or PDC<40%; CMG or NPMG>60%) can in some circumstances represent, or be confused with, discontinuation.
Metrics that have been used to calculate medication adherence and/or persistence using electronic databases are summarized in Table 2. In general, these metrics enable calculation of either medication possession (ie, possession measures) or gaps in medications availability (ie, gap measures)18,40,48,49 and most estimate adherence only among individuals with secondary adherence. Most metrics are continuous measures, but they are often categorized (eg, low or inadequate vs. moderate vs. high or adequate adherence). These measures require data including the date of medication dispensing, days’ supply dispensed with each dispensing, and previous (stockpiled) medications (or an indication that it will be set to 0) to estimate medication availability and consumption, usually estimated between the first and terminal dispensings within an observation window. A minority of metrics estimate availability within a single dispensing interval (eg, the continuous, single-interval measure of medication acquisition). The metrics also vary in whether or not the days’ supply dispensed with the terminal dispensing is included in the calculation. The time between any 1 dispensing and the subsequent dispensing is known as the refill interval. Person-time is censored at the last dispensing date, at the time of exhaustion of the last days’ supply, or at a fixed number of days after exhaustion of the last days’ supply. Most gap measures of secondary adherence censor after the last dispensing once stockpiled medications have been exhausted.
The 2 most commonly used secondary adherence medication possession measures are the medication possession ratio (MPR) and the proportion of days covered (PDC).18,40–42,44,47 Both report medication availability by estimating the proportion of prescribed days’ supply obtained during a specified observation period over refill intervals and both are becoming widely applied in health care settings50 in large part because they are easily calculated (a SAS macro has been written for MPR and PDC). For example, as operationalized by the Pharmacy Quality Alliance,50 the PDC has been endorsed by the National Quality Forum as a tool to measure health care quality.51 The main difference between the PDC and the MPR is that with the PDC any oversupply is truncated, whereas adherence values of >100% are allowed with the MPR. There is controversy about whether “over adherence,” often considered as MPR between 100% and 120%, has clinical meaning.41 A shortcoming of these (and other) secondary adherence measures is that, when integrating across several observation periods of multiple refills each, delayed dispensing(s) in 1 observation period can be numerically counterbalanced by early dispensing in a later observation period, thus potentially underascertaining adherence in 1 observation period and overestimating it in another. The converse can also occur. This drawback is of particular importance in longitudinal assessments where changes in adherence behavior are assessed across multiple observation periods by calculating the adherence metric separately within each period. Other strengths and weaknesses of the medication possession and gap measures are summarized in Table 2.
Because most measures of adherence require at least 2 dispensings, the least adherent patients (primary nonadherent) are excluded. Within the last few years, measures have been developed that include patients with either primary or secondary (non)adherence.6 One such metric, the New Prescription Medication Gap (NPMG) measure, is defined as the proportion of days within an interval bounded by the prescriber’s initial EHR prescription medication order date and the end of the observation period (or end of follow-up if censored or the therapy is switched or discontinued).6 As with older gap measures, NPMG is a continuous measure, ranging from 100% for patients who obtain no medication to 0% for those who consistently refill their medication in a timely manner. Unlike secondary adherence measures, NPMG was designed to evaluate medication supply starting at prescribing and ending at a fixed censoring point, thus comprehensively capturing (non) adherence for those who never start the prescribed medication or who discontinue it early as well as for those who have at least 2 dispensings. An additional strength of NPMG is that because it enables evaluation from the point of prescribing in the EHR, person-time can be censored if the prescriber switches or discontinues therapy and documents those orders in the EHR.
In this paper, we offer a standardized set of definitions and terminology for assessing medication adherence and persistence in electronic database studies, whether the databases employed are medications ordered within an EHR, medications dispensed and documented in a pharmacy database, or pharmacy claims processed through an insurance database. These conceptual models and terminology are more comprehensive than current, commonly used definitions. The models we propose include clear and systematic definitions of adherence, and persistence developed to facilitate EHR-based research are specific to medication adherence, and extend to adherence and persistence subcategories and medication discontinuation. We also point out the importance and utility of developing precise operational definitions for adherence research as these definitions enhance the precision of ascertaining whether a patient was likely exposed to a specific medication on a particular date for a specific research purpose (eg, on the date of some clinical measure, event, or outcome).
Research focusing on adherence is voluminous. As obtaining the initial prescription medication and taking the medication are prerequisite health behaviors for medication effectiveness, these are key explanatory variables when observed effectiveness is lower than the efficacy demonstrated in controlled trials. As a consequence, comparative effectiveness studies may be designed to evaluate the effectiveness of various interventions to improve these adherence behaviors in their own right. Meta-analytic studies are also useful in assessing adherence as an outcome, and the level of adherence necessary to achieve treatment goals. To facilitate these types of studies, it is critical that the adherence measure be accurately estimated and consistent across studies.
There are limitations to this work. Our conceptual model is specific to prescription-filling and has not been compared with frameworks for other prescription behaviors such as medication-taking.20,25 Definitions are only part of the decision-making process in adherence research. Many important methodological considerations that should be addressed were beyond the scope of this paper such as identifying clinically meaningful categorizations for adherence (eg,<20% CMG; ≥80% MPR) based on observed relationships between adherence and clinical outcomes for specific disease states, the role of informative censoring (eg, medication stop orders and switches), comprehensive assessment of discrete events (eg, appropriate timeframes to consider for medication discontinuation), and bias associated with assuming dispensing data are complete (eg, prescriptions transferred outside an integrated health care system, paying cash for prescriptions resulting in no prescription insurance claims being filed).
We offer a set of standardized medication adherence terminology and definitions for use with electronic database research. The medication adherence and persistence conceptual models and definitions we present will enable future meta-analytic and comparative effectiveness research, as standardized terminology facilitates rigorous comparisons. As such, this paper is foundational for adherence methods.
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