AIP: A proposed mechanism for evaluating adherence improvement initiatives

AIP: A Proposed Mechanismfor Evaluating AdherenceImprovement Initiatives

David Day, PhDClinical ApplicationsPfizer Pharmaceuticals GroupNew York, New York

ABSTRACT

Pharmacy prescription databases are useful for determining rates of adherence tolong-term medication therapy. Thus far, however, analyses based on such data-bases have provided only snapshots of adherence rates over discrete time inter-vals and have been of limited usefulness for the timely measurement of adherencetrends when adherence improvement strategies change over time. The AdherenceIndex of Performance is new mechanism that can be used to monitor pharmacyprescription databases over time to detect changes that occur when adherenceimprovement strategies are changed during a therapeutic period.

Keywords: adherence; AIP; drugs; medication; pharmacy; prescription

INTRODUCTION

Medication nonadherence—the inability of patients to take medications asprescribed by their physicians for an intended period of time—is a vexingproblem in the United States. It is estimated that of the 1.8 billion prescrip-tions written every year, as few as half are taken correctly.1-3 This may help toexplain why outcomes in clinical practice often fall below the expectationgenerated by the results of clinical trials for many drugs.4

Unfortunately, medication nonadherence is a multifaceted problem.5-7

Consequently, an intervention that improves adherence in one populationmay have little or no effect in another.

Pharmacy prescription databases contain information that can be usedto measure medication adherence rates, including length of therapy, drugpossession ratios, persistence, and (median gap) days.8-12 These databases

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Advancesin Therapy®

Volume 22 No. 2March/April 2005

Address reprint requests toDavid Day, PhDClinical ApplicationsPfizer Pharmaceuticals Group235 East 42nd StreetNew York, NY 10017

©2005 Health Communications IncTransmission and reproduction of this material in wholeor part without prior written approval are prohibited.

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tend to be quite complex in design and the data difficult to manipulate. For this rea-son, they have historically been limited in their usefulness in monitoring changesthat occur over time following the implementation of adherence intervention strate-gies in any patient population. They have been used to provide good “snapshots” ofthe effect of adherence programs, but they are cumbersome when data need ongo-ing adherence surveillance.13-17

ADHERENCE INDEX OF PERFORMANCE (AIP)

AIP is a unique way to utilize pharmacy prescription databases to measure ongo-ing changes in adherence while intervention programs are being conducted. AIP isthe ratio of actual versus expected prescription activity within overlapping timeintervals. This method offers a new means of monitoring the effect of adherenceimprovement intervention strategies as they are introduced over time in a specificpopulation.

The concept behind AIP is simple. AIP is roughly equivalent to the drug possessionratio, but calculated sequentially across overlapping intervals of time. The analysisgenerates ratios of actual-expected prescription activity for any drug or therapeuticclass of drugs. It takes into account any changes in the number of new patients enter-ing therapy within any time interval so that changes in patient volume do not skewthe results. All prescription time periods are converted to the base unit of the durationof the study. For example, if a period of several months is involved, all prescriptionsare converted to “30-day fill equivalents” to compensate for any changes in prescrib-ing habits that may occur during any surveillance period and minimize the risk ofover- or undercalculating adherence rates.

Using the AIP

The following example shows how AIP can be used to track adherence over timeand assess the effectiveness of an intervention designed to improve adherence. The first step is to choose a time period over which the analysis will occur. In thisexample, we will use a period of 6 months. One hundred new 30-day (or equivalent)prescriptions are filled during the first month, then it could be expected that a totalof 600 will have been filled by the end of the sixth month (Fig 1). In the real world,however, patients often do not continue their medications for a number of reasons:lack of efficacy, side effects, and other conscious decisions not to take the prescribeddrug (Fig 2). A comparison of the actual versus ideal number of prescriptions filledover the chosen time interval generates the ratio that is used for the AIP (Fig 3).

The sequential generation of AIPs from month to month over a specific time inter-val enables the user to monitor the success of an adherence intervention activity. Anincrease in AIP values indicates that more new prescriptions are being continuallyrefilled. Thus, an observed increase in AIP values after an adherence intervention isintroduced may indicate the effectiveness of the intervention (Figs 4, 5).

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Adherence Index of Performance


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Volume 22 No. 2, March/April 2005

1 2 3 4 5 6

100 new Rx for a CV agent

600 total Rxfor that agent at the end

of month 6

Should ideally yield

Month

Fig 1. Total prescriptions predicted over time based on new prescription activity.

100 new Rx fora CV agent

Manyfewer refillRxs than

anticipated

In realityyields

1 2 3 4 5 6

Month

Fig 2. Actual number of prescriptions refilled will fall short of anticipated values.

CV=cardiovascular


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600total Rx

anticipated

300total Rx

generated

AIP=300/600=.05

Fig 3. The AIP is the ratio of total prescriptions generated during a given time interval compared with the total prescription potential based on new prescription activity at the beginning of that time interval.

1 2 3 4 5 6 7 8 9

Month

10 11 12 13 14 15 16

AIP1=.45

AIP2=.47

AIP3=.49

AIP4=.62

AIP5=.69

AIP6=.74

Interventionstarts inmonth 4

Fig 4. The calculation of AIPs over sequential time intervals makes it possible to monitor the effectiveness of an adherence intervention strategy in a patient population.


AIP Software

A software application has been devised to allow rapid AIP analysis of prescrip-tion databases (Fig 6). It can be configured to generate analyses of individual agentsas well as therapeutic classes of drugs. As a result, it can be used to perform “intent-to-treat” studies by continually calculating AIPs, even when a switch is madebetween drugs in the same therapeutic class during a therapeutic interval.

The software is inherently flexible enough to permit the user to perform subset AIPanalyses. For instance, AIPs can be configured for comparison by specific providers,pharmacies, type of payment, sex, zip code, and any other variable in the prescriptiondatabase with which the software has been associated.

These calculations are generated using a stepwise structured query approach,based on the user’s specification input, and can be presented from a number ofpoints of view. For example, AIPs can be generated for the entire prescription data-base, for patients whose therapy is ongoing, or for patients who initiated therapywithin each AIP time interval. This allows the user to demonstrate the difference inadherence among patients who are new to therapy compared with those who haveexperience with it.



0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0AIP1 AIP2 AIP3 AIP4 AIP5 AIP6

Adherence-relatedtotal Rx increase

Adherence interventionprogram initiated

AIP

Fig 5. A sequential AIP plot allows the user to observe improvements in adherenceover time.


Preparing a Database for AIP Analysis

The first step in conducting an AIP analysis is to obtain a prescription recordsdatabase in a Microsoft Access® 2000 (Microsoft Corp, Redmond, Wash) format. The database must contain patient identification data, date each prescription isfilled, the drug name, dose unit strength, number of units dispensed, daily dose, andtotal number of days supplied. If the daily dose and days supplied information isnot available, it can be derived from the prescription label (ie, the SIG code), whichis often included in the prescription database. Additional information (eg, provider,pharmacy, age of patient) can be used to generate additional subset analyses.

Data Transformation Wizard

After the information in a database has been determined, it can be converted bythe AIP software into a standardized format through the use of the AIP data trans-formation wizard. This wizard provides the user with the opportunity to organizethe data further (eg, to group drugs into therapeutic classes, determine the length ofan AIP analysis in terms of months, clean the database of negative entries that maybe associated with the failure of patients to initiate therapy after their prescriptionswere filled by the pharmacy, define the timeframe over which AIPs will be calculat-ed, and identify additional fields of information for subset analyses).

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Fig 6. AIP software.


The software will then generate AIPs and store them in a file associated with theAIP analysis parameters. Thus, the input parameters can be recalled and applied tothe database as time progresses without the need for re-entry. Consequently, once anAIP is generated, future analyses can be made using the same criteria as those usedto generate the original study, simply by clicking on the Re-Wizard button andselecting the AIP analysis to be updated.

Analysis Options With the AIP Software

After all analytical parameters have been selected in the data transformation wiz-ard, predetermined rules and procedures are applied to generate an AIP analysis.The program then provides the user with multiple analysis options.

Views

The Views option is provided by a dropdown menu offering the user a chance toview a summary of information on a variety of topics: the AIP, the number of patientsin each analysis, cost for prescriptions generated at the current AIP level, “dropout”patients (ie, patients who received only 1 prescription during any time interval anddid not have their prescriptions refilled in the selected therapeutic category), and refillexpectations relative to actual refill rates during each AIP cycle (Fig 7).



Fig 7. Graphic representation of initial prescription activity, expected refill activity,and actual refill activity during sequential AIP cycles.


Patient Types

The Patient Types option allows the user to review each of the Views options forall patients, for patients who have switched from one agent to another within AIPcycle, and for patients who used a single drug continuously throughout any cycle.Thus, this option allows the user to observe the effect of medication switching onadherence.

Therapeutic Class

AIPs can be calculated for each therapeutic class or by individual agents within a class based on intent to treat. The intent-to-treat analysis can be designed toinclude only those patients who switched or did not switch therapy during any AIPcycle by an appropriate setting of the Patient Types filter.

Filters

The data transformation wizard can use any field in the prescription records data-base to filter data derived from any AIP subset analysis.

Prescription Type

AIP analyses may be generated for an entire prescription records database or lim-ited to patients who initiate therapy during any AIP cycle. This feature allows theuser to examine therapeutic dropout rates for new patients, which tend to be high-er than for patients continuing therapy.

Patient Reports

Patient reports can be generated for patients at or below any specified AIP level.This program also automatically calculates the AIP level that corresponds to patientdropouts after initial therapy. This feature is ideal for developing and implementingre-entry interventions for patients who have discontinued therapy, as well as thosewho are consuming less than a minimally acceptable amount of medication duringany AIP cycle.

CONCLUSIONS

Medication nonadherence is a significant problem in the United States.3,8,18,19

Although numerous interventions have been employed to improve medication-tak-ing behaviors,20,21 timely monitoring of improvements in adherence in large patientpopulations continues be an issue.22 Analyses of pharmacy prescription databases,while useful in the analysis of adherence patterns, are difficult and time-consumingto conduct10 and provide only snapshots of medication adherence over specifiedtime intervals.

The AIP, a new measure in adherence analysis, was designed to allow ongoingmonitoring of medication adherence by comparing expected with actual refill ratesfor individual medications as well as for therapeutic drug classes. The AIP provides a way to perform longitudinal medication adherence analyses quickly so that the ongo-ing benefits of specific interventions can be quantified.

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AIP: A proposed mechanism for evaluating adherence improvement initiatives

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