QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Résumé Extracto PDF Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Articles Ahead of Print [Order Reprint] Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Hess, L. M Articles by Malone, D. C Search for Related Content PubMed PubMed Citation Articles by Hess, L. M Articles by Malone, D. C

ADHERENCE

Measurement of Adherence in Pharmacy Administrative Databases: A Proposal for Standard Definitions and Preferred Measures

Science Officer, Arizona Cancer Center, University of Arizona, Tucson, AZ

Marsha A Raebel, PharmD

Pharmacotherapy Research Manager, Kaiser Permanente of Colorado, Denver, CO; Clinical Associate Professor, School of Pharmacy, University of Colorado at Denver and Health Sciences Center, Denver

Douglas A Conner, PhD

Senior Research Analyst, Kaiser Permanente of Colorado

Daniel C Malone, PhD

Associate Professor, College of Pharmacy, University of Arizona

Reprints: Ms. Hess, Arizona Cancer Center, 1515 N. Campbell Ave., PO Box 245024, Tucson, AZ 85724-5024, fax 520/626-5350, hess{at}u.arizona.edu

	Abstract

Top Abstract Methods Results Discussion Limitations Conclusions References

 
BACKGROUND: A variety of measures have been developed to calculate refill adherence from administrative data such as pharmacy claims databases. These measures have focused on improving the accuracy of adherence measures or clarifying the evaluation time frame. As a result, there are many measures used to assess adherence that may or may not be comparable or accurate.

OBJECTIVE: To compare available refill adherence measures.

METHODS: A systematic literature review was conducted to identify current or recently used measures of calculating adherence from administrative data. A MEDLINE search (January 1990-March 2006) was undertaken using the search terms adherence or compliance in the title combined with administrative, pharmacy, or records in any field, including subheadings medical, nursing, and hospital records. Non-English articles were excluded. Seven hundred fifteen articles were available for review. Review articles and letters were excluded from measure selection, but were included in the search terms and used to identify additional research articles. Adherence measures were excluded if they were incompletely described, produced non-numeric values, or were duplicates. Eleven refill adherence measures were identified and compared using data from the LOSE Weight (Long-term Outcomes of Sibutramine Effectiveness on Weight) study. Measures compared include Continuous Measure of Medication Acquisition (CMA); Continuous Multiple Interval Measure of Oversupply (CMOS); Medication Possession Ratio (MPR); Medication Refill Adherence (MRA); Continuous Measure of Medication Gaps (CMG); Continuous, Single Interval Measure of Medication Aquisition (CSA); Proportion of Days Covered (PDC); Refill Compliance Rate (RCR); Medication Possession Ratio, modified (MPRm); Dates Between Fills Adherence Rate (DBR); and Compliance Rate (CR).

RESULTS: The results suggest that the CMA, CMOS, MPR, and MRA are identical in terms of measuring adherence to prescription refills throughout the study period, each with a value of 63.5%; CMG and PDC are slightly lower (63.0%) and are equivalent to MRA when oversupply is truncated. CR, MPRm, RCR, and CSA result in higher adherence values of 84.4%, 86.6%, 104.8%, and 109.7%, respectively.

CONCLUSIONS: Five measures produce equivalent results for measuring prescription refill adherence over the evaluation period. Of these, MRA has the fewest calculations, is easily truncated if one desires to exclude surplus medication issues, and requires the least amount of data. MRA is therefore recommended as the preferred measure of adherence using administrative data.

Key Words: adherence, administrative data, compliance, measurement

Published Online, July 25, 2006. www.theannals.com, DOI 10.1345/aph.1H018

Both direct and indirect measures can be used to assess patient medication adherence. Examples include drug assays or markers, self-report, pill counts, electronic monitoring systems, and review of pharmacy records or administrative data. Although comparisons have been made among methods of collecting data to assess adherence,^7-11 no gold standard measure has been applied.^10,12-17 Therefore, when presenting or interpreting adherence data, one should specify the measure used, with particular attention to the time frame evaluated and the medications included. The choice of adherence assessment measure also depends on data availability and cost. These factors additionally contribute to the accuracy of the final value.

Administrative data sets, "data files generally compiled in billing for healthcare services,"¹⁸ are often assessed in pharmacoeconomic and pharmacoepidemiologic research. The adherence value obtained from administrative data does not provide medication consumption information, but rather provides assessment of possession. Medication in-take calculations usually assume that patients consume the drug starting the day of dispensing, use the drug as prescribed, and consume all medications obtained. Administrative data can, therefore, provide the investigator only an estimate of the highest possible level of medication consumption. As described by Christensen et al.,¹⁹ in cases in which the dosage prescribed (eg, determining days' supply of medication obtained) is unavailable or unable to be determined, it is difficult to assess adherence using administrative data. The length of the assessment period may be problematic in the evaluation of adherence using administrative data, as both shorter (eg, <60 days) and longer (eg, >90 days) time periods introduce potential bias when estimating medication adherence.¹⁹ Adherence measures based on administrative data have not correlated with patient reported adherence.^11,20

Despite these limitations, administrative data are convenient, noninvasive, objective, and inexpensive to obtain. In addition, adherence estimates based on administrative data appear to be associated with clinical outcomes.^11,21 Therefore, administrative data are frequently used to obtain medication adherence information. Ideally, an adherence assessment from administrative data should provide an accurate reflection of the number of days the patient had the correct dose of a drug available compared with the number of days the treatment was prescribed. To our knowledge, no study has compared measures of assessing adherence with administrative data using actual patient records.

The purpose of this study was to compare measures used to assess medication adherence from administrative pharmacy data. This comparison assists in identifying a preferred measure and in moving toward standardized terminology when reporting adherence.

	Methods

Top Abstract Methods Results Discussion Limitations Conclusions References

 
A systematic literature review was conducted to identify current or recently used measures of calculating adherence from administrative data. A MEDLINE search (January 1990-March 2006) was undertaken using the search terms adherence or compliance in the title combined with administrative, pharmacy, or records in any field, including subheadings medical, nursing, and hospital records. Non-English articles were excluded. Seven hundred fifteen articles were available for review. Review articles and letters were excluded from measure selection, but were included in the search terms and used to identify additional research articles. Adherence measures were excluded if they were incompletely described, produced non-numeric values, or were duplicates. Eleven evaluable measures were identified.^{13,16,17,22-27} These measures were then evaluated using existing data.

Refill data collected as part of a prospective, randomized study designed to assess the impact of sibutramine in combination with a weight management program—the LOSE Weight (Long-term Outcomes of Sibutramine Effectiveness on Weight) study—were used to test measures of adherence.¹⁵ The LOSE Weight study and the current study were both approved by the Kaiser Foundation Institutional Review Board. In the LOSE Weight study, patients were randomized to a weight management program plus sibutramine (n = 296), or to the weight management program alone (n = 292). Mean weight loss at 6 months was 6.8 kg in the sibutramine group compared with 3.1 kg in the weight management alone group (p < 0.001). Weight loss was maintained at 12 months.

LOSE Weight study patients receiving sibutramine were given prescriptions for sibutramine during study visits. Patients presented these prescriptions to Kaiser Permanente Colorado pharmacies where the prescriptions were dispensed and sold using standard pharmacy procedures. Study patients paid retail price for sibutramine and were reimbursed for 75% of the price paid.¹⁵

Refill data were available from administrative pharmacy records for 283 study participants (96%) randomized to sibutramine. Refill data included participant identification number and date, quantity dispensed, and days' supply dispensed. Each record represented one sibutramine dispensation. Dates of study enrollment and study end were determined from the study database. The time frame evaluated was from the date the participant first obtained sibutramine through the one year anniversary date of study enrollment. The end date was selected based on the intent-to-treat design of the LOSE Weight study.¹⁵

SPSS Version 12.0.1 was used to calculate means and standard deviations (described below) for the adherence measures assessed. In addition to analyses of cumulative data, individual study participant data were sampled to compare each measure in 6 scenarios: (1) excessive medication, (2) insufficient medication, (3) adequate medication, (4) same-day refill, (5) refill on study completion date, and (6) single fill.¹⁵

CONTINUOUS, SINGLE-INTERVAL MEASURE OF MEDICATION AVAILABILITY
Single-interval medication availability was calculated by obtaining an adherence value for each sibutramine dispensation with use of Continuous, Single-Interval Measure of Medication Availability (CSA).¹⁷ The days' supply of a drug was divided by the number of days in the interval from the dispensation date up to, but not including, the next dispensation date (or through study completion date). This provides an adherence value for each participant between dispensations—not for the overall study evaluation period. The mean of all dispensation adherence values provides an overall study adherence value.

CONTINUOUS MEASURE OF MEDICATION ACQUISITION
Continuous Measure of Medication Acquisition (CMA)¹⁷ was calculated for each participant. The days' supply of medication obtained throughout the study period was divided by the number of days from the first dispensation until the participant's study completion date (number of days of study participation). The mean of each participant's CMA value provides an overall study adherence value.

COMPLIANCE RATE
Compliance rate (CR)²⁴ was computed by taking the sum of the days' supplies for each participant, minus the days' supply obtained at the last dispensation and dividing this by the number of days from first up to, but not including, the last dispensation. This provides an overall adherence rate based on day of last refill and does not require study completion date.

DAYS BETWEEN FILLS ADHERENCE RATE
The total days' supply was subtracted from the number of days between dispensations using the Days Between Fills Adherence Rate (DBR)²²; this difference was divided by the number of days between dispensations. Since this value is a nonadherence value, the dividend was subtracted from 1 to obtain an adherence value. The result was multiplied by 100 to provide an adherence percentage for each participant for the overall study period.

CONTINUOUS MEASURE OF MEDICATION GAPS
The Continuous Measure of Medication Gaps (CMG),¹⁷ which is used to determine the total days of treatment gaps (days for which a drug was unavailable), was calculated for each participant by subtracting the total days' supply obtained throughout the study period from total number of days of study participation. Negative values were set to zero. The total days of treatment gaps was then divided by number of days of study participation. The mean of each participant's CMG value provides an overall study nonadherence value based on lack of available medication, with 0.0 reflecting complete adherence, and 1.0 reflecting complete nonadherence.

CONTINUOUS MULTIPLE INTERVAL MEASURE OF OVERSUPPLY
The total days of treatment gaps was calculated using Continuous Multiple Interval Measure of Oversupply (CMOS).¹³ Negative values were included to represent cases in which the participant obtained days' supply of medication exceeding days of study participation. The total number of days' supply or surplus was divided by days of study participation for each participant. The mean of each participant's CMOS value provides an overall study nonadherence value.

MEDICATION POSSESSION RATIO
The Medication Possession Ratio (MPR)¹⁶ is a ratio of total days' supply to number of days of study participation per participant. The ratios alone could not be combined across participants due to different denominators; therefore, the ratios were divided and averaged to provide an overall study adherence value.

REFILL COMPLIANCE RATE
The total days' supply was multiplied by 100 and divided by the number of days from first to last medication dispensation with use of the Refill Compliance Rate (RCR).²⁵ Cases with only one dispensation of a drug were excluded from this calculation because of the invalid denominator. The mean of each participant's RCR provides an overall study adherence value.

MEDICATION POSSESSION RATIO, MODIFIED
Using the Medication Possession Ratio, modified (MPRm),²⁶ the total days' supply of a drug was divided by the sum of the number of days from first dispensation up to, but not including, the date of last dispensation and the number of days' supply obtained at the last dispensation. This value was multiplied by 100 to provide an adherence percent value that can be averaged to find an overall study adherence value.

MEDICATION REFILL ADHERENCE
The total days' supply was divided by number of days of study participation and multiplied by 100 to provide a percent adherence value. The mean of each participant's Medication Refill Adherence (MRA)²³ value provides an overall study adherence value.

PROPORTION OF DAYS COVERED
The total days' supply was divided by number of days of study participation using the Proportion of Days Covered (PDC).²⁷ This value was capped at 1.0 and multiplied by 100 to obtain a percent adherence value. The mean of each participant's PDC value provides an overall study adherence value. This measure is the same as MRA, but adherence was capped at 100%.

	Results

Top Abstract Methods Results Discussion Limitations Conclusions References

 
The 283 patients involved in the study had 2057 sibutramine dispensations (7.3 ± 3.4; mean ± SD). The mean number of days' supply was 222.9 ± 103.6, and the mean number of days of study participation was 349.9 ± 16.1. Adherence values for each measure are presented in Table 1. Adherence was 63.5% using 4 measures: CMA, CMOS, MPR, and MRA. CMG and PDC were slightly lower (63.0%), because they do not include excess medication in the calculation. CR, MPRm, RCR, and CSA resulted in higher adherence values of 84.4%, 86.6%, 104.8%, and 109.7%, respectively.

Scenarios of excessive (eg, drug stockpiling),^17,28 insufficient, and adequate days' supply (obtaining medication as directed) and a variety of other refill scenarios that occurred in the LOSE Weight study are presented in Tables 2, 3, 4. Exceptions to expected refill patterns occurred in approximately 10% of study participants (eg, refills the day of study completion, filling study drug only once, same-day refills).

	Discussion

Top Abstract Methods Results Discussion Limitations Conclusions References

 
Six medication adherence measures—CMA, CMOS, MPR, MRA, CMG, and PDC—provided essentially the same adherence values for participants in the LOSE Weight study (Table 2). Results for CMG and PDC were slightly lower because excess medication on hand is not included in that calculation. All 8 values were equal when excess medication beyond the assessment period was not included.

When participant attrition is an issue (Table 2) and in cases of single refills (Table 4), problems are encountered when using the MPRm, RCR, CR, and DBR adherence measures. These 4 measures evaluate the time period between dispensations and either cannot assess or overestimate adherence because of the smaller denominator (between fills instead of study evaluation period).

The denominator time period can contribute to overestimates of adherence. The measures with consistent results all use the total study evaluation period in the denominator. Regardless of how medication use is calculated by these measures, the final result is the same, suggesting that the choice among these 6 measures can be based on preference or data availability. Measures that require multiple analytic steps and additional data fields (eg, gap days) may result in unnecessary work.

CSA can be biased if participants obtain more than one refill per day (Table 3) and if refills occur close to the study completion date (Table 4). Use of CSA can be beneficial, however, for studies with high participant attrition. With CSA, each refill episode is calculated independently; participants who fill only one prescription do not have the same weight in a cumulative analysis as participants who have multiple refills. Although CSA takes each interval into account, the measure truncates the assessment period and does not permit carryover of medication from one refill interval to another, a practice likely to occur in most settings.

CR calculates adherence between dispensations and disregards the assessment period from last dispensation until study completion. This simplifies calculations but assumes adherence is consistent through study completion, and does not consider participants who discontinue medication prior to study completion. It assumes that the last fill is timely for all participants, which is not the case, as we see when applied to these study participant data.

The MPRm attempted to overcome the limitation of the RCR denominator (number of days in interval between first and last dispensation) by adding a number of days to the evaluation period equal to the days' supply obtained at the last dispensation. This reduced the amount of overestimation, but due to assumption of each participant being 100% adherent during the last dispensation period, consistently produced an adherence value higher than that achieved with other measures.

The gap measures (CMG and CMOS) produce similar adherence values; however, because more calculations and data fields are required, they are less attractive. To avoid misinterpretation, the gap and ratio values must be further converted to an adherence percentage.⁵

The greatest disadvantage with calculating an MPR is that there are at least 4 different published measures that have been termed "MPR."^{14,16,26,29-31} The use of different measures that carry the same terminology leads to confusion in comparing adherence across studies. When evaluating or comparing what is described as MPR, one should be clear as to what the value represents.

PDC truncates total supply to not exceed the study evaluation period (eg, overall adherence is capped at 100% by subtracting surplus medication from the total days' supply available). When PDC is used or when adherence above 100% is not permitted for MRA, CMA, CMOS, or MPR, these measures, as well as CMG, are all equivalent. PDC is also occasionally calculated for smaller intervals within the study period and averaged similar to CSA.^27,32 This may underestimate adherence, as capping adherence at each refill interval does not permit carryover of excess medication from one interval to another.

Accurate calculation of the number of evaluation days is important. One should not select a measure that mandates an end date; the end date should be based on the study design and nature of data collection. In the LOSE Weight study, each participant had specified enrollment and end dates. When using claims databases in which patient-specific end dates are not available, a time frame should be established a priori so that values can be calculated consistently per the study design. One should be cautious in comparing adherence values between studies without a clear understanding of how adherence was calculated, the time frame considered, and how missing values and/or time periods were managed.

The days' supply must be carefully determined in any adherence assessment. When available, it can be calculated by dividing the prescribed dose by the number of pills obtained (eg, 30 tablets twice per day dosage equals 15 days' supply). If the prescribed dosage changes during the study, this must be noted and calculated accordingly. Alternative measures to estimate days' supply are available, such as using standard practice estimates for prescribed dosage assumptions, but these methods are less reliable than using information contained within the administrative data set.

Important features when assessing the usefulness of adherence measures go beyond those associated with the numerator or denominator to include the presentation of the final value, the complexity of the calculations, variables required to perform the calculations, and terminology used when publishing results. The footnotes in Tables 2, 3, 4 indicate the variables required to calculate each adherence measure. It is important to understand data available to researcher when selecting the appropriate adherence measure to assess adherence using administrative data sets.

	Limitations

Top Abstract Methods Results Discussion Limitations Conclusions References

 
Days' supply can be problematic, particularly in a non-experimental setting, with administrative data sets that do not track the dosage for individual prescriptions. Fortunately, in the LOSE Weight study, individual dosage prescribed was maintained in the study records. A limitation of this study—and of all adherence calculations of administrative data—is the inability to determine whether the medication was ingested by the patient. In obtaining adherence values, administrative data analyses all assume that all medication obtained is taken by the patient. The result is an overestimation of actual adherence and only provides a value of the medication obtained by the participant. Therefore, reliance on administrative data may not enable the investigator to determine periods of under- or overuse of drug between refill episodes.³³ This study required a one year medication intervention, a time period that may "smooth out" shorter periods of over- or underadherence. Administrative data have limitations in cases in which patients obtain refills from a variety of pharmacies, and they are not submitted as insurance claims or when patients pay out-of-pocket and no insurance claim is entered. In the LOSE Weight study, study medication could be obtained only at Kaiser Permanente pharmacies, reducing the risk of refill underestimation.

	Conclusions

Top Abstract Methods Results Discussion Limitations Conclusions References

 
Calculation of refill adherence from administrative data can be useful to assist in evaluating patient medication adherence, provided that the context and limitations of the measure and source data are recognized. Further, the work presented here suggests that, because of the comparability of measures, there is no need for the variety of measures and strategies currently used to assess adherence with administrative data. In cases where the days' supply and time period assessed are available, we recommend the MRA technique because of its simplicity, the few data required to obtain the value, and the fact that it provides results identical to those achieved with other refill adherence measures.

	Footnotes

 
This work was supported in part by Grant CA-23074 from the National Institutes of Health, Bethesda, MD.

	References

Top Abstract Methods Results Discussion Limitations Conclusions References

 

1. Haynes RB, Taylor DW, Sackett DL, eds. Compliance in health care. Baltimore: Johns Hopkins University Press, 1979.
2. Enstrom I, Pennert K, Lindholm LH. Durability of improvement achieved in a clinical trial. Is compliance an issue? J Fam Pract 2000;49:634-7.[Medline]
3. Kastrissios H, Suarez JR, Hammer S, Katzenstein D, Blaschke TF. The extent of non-adherence in a large AIDS clinical trial using plasma dideoxynucleoside concentrations as a marker. AIDS 1998;12:2305-11.[CrossRef][Medline]
4. Gaston R, Hudson S, War M, Jones R, Macon R. Late renal allograft loss: noncompliance masquerading as chronic rejection. Transplant Proc 1999;31(4A):21S -23S.[Medline]
5. Kitahata MM, Reed SD, Dillingham PW, et al. Pharmacy-based assessment of adherence to HAART predicts virologic and immunologic treatment response and clinical progression to AIDS and death. Int J STD AIDS 2004;15:803-10.[Abstract/Free Full Text]
6. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. Med Care 2005;43:521-30.[CrossRef][Medline]
7. Curtis JR, Westfall AO, Allison J, Freeman A, Kovac SH, Saag KG. Agreement and validity of pharmacy data versus self-report for use of osteoporosis medications among chronic glucocorticoid users.Pharmacoepidemiol Drug Saf . Epub Feb 23 2006.
8. Hess LM, Saboda K, Malone DC, Salasche S, Warneke J, Alberts DS. Adherence assessment using medication weight in a phase IIb clinical trial of difluoromethylornithine for the chemoprevention of skin cancer. Cancer Epidemiol Biomarkers Prev 2005;14(11 pt 1):2579 -83.[Abstract/Free Full Text]
9. Garber MC, Nau DP, Erickson SR, Aikens JE, Lawrence JB. The concordance of self-report with other measures of medication adherence: a summary of the literature. Med Care 2004;42:649-52.[CrossRef][Medline]
10. Dunbar J. Adherence measures and their utility. Control Clin Trials 1984;5(4 suppl):515 -21.[CrossRef][Medline]
11. Grossberg R, Zhang Y, Gross R. A time-to-prescription-refill measure of antiretroviral adherence predicted changes in viral load in HIV.J Clin Epidemiol 2004;57:1107-10.[CrossRef][Medline]
12. Farmer KC. Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice. Clin Ther 1999;21: 1074-90; discussion 3.[CrossRef][Medline]
13. Morningstar BA, Sketris IS, Kephart GC, Sclar DA. Variation in pharmacy prescription refill adherence measures by type of oral antihyperglycaemic drug therapy in seniors in Nova Scotia, Canada. J Clin Pharm Ther 2002;27:213-20.[CrossRef][Medline]
14. Peterson AM, Sanoski, C, McGhan WF. Total direct medical and drug costs of non-adherence to statin therapy within the first year of treatment.Value Health 2002;5:167.
15. Porter JA, Raebel MA, Conner DA, et al. The Long-term Outcomes of Sibutramine Effectiveness on Weight (LOSE Weight) study; evaluating the role of drug therapy within a weight management program in a group-model health maintenance organization. Am J Manag Care 2004;10:369-76.[Medline]
16. Skaer TL, Sclar DA, Robison LM, Markowski DJ, Won JK. Effect of pharmaceutical formulation for diltiazem on health care expenditures for hypertension. Clin Ther 1993;15:905-11.[Medline]
17. Steiner JF, Prochazka AV. The assessment of refill compliance using pharmacy records: methods, validity, and applications. J Clin Epidemiol 1997;50:105-16.[CrossRef][Medline]
18. Iezzoni LI. Risk adjustment for measuring health care outcomes. Chicago: Health Administrative Press, 1997.
19. Christensen DB, Williams B, Goldberg HI, Martin DP, Engelberg R, LoGerfo JP. Assessing compliance to antihypertensive medications using computer-based pharmacy records. Med Care 1997;35:1164-70.[CrossRef][Medline]
20. Guenette L, Moisan J, Preville M, Boyer R. Measures of adherence based on self-report exhibited poor agreement with those based on pharmacy records. J Clin Epidemiol 2005;58:924-33.[CrossRef][Medline]
21. Weiden PJ, Kozma C, Grogg A, Locklear J. Partial compliance and risk of rehospitalization among California Medicaid patients with schizophrenia. Psychiatr Serv 2004;55:886-91.[Abstract/Free Full Text]
22. Chisholm MA, Mulloy LL, DiPiro JT. Comparing renal transplant patients' adherence to free cyclosporine and free tacrolimus immunosuppressant therapy. Clin Transplant 2005;19:77-82.[CrossRef][Medline]
23. Hamilton RA, Briceland LL. Use of prescription-refill records to assess patient compliance. Am J Hosp Pharm 1992;49:1691-6.[Abstract]
24. Ren XS, Kazis LE, Lee A, Zhang H, Miller DR. Identifying patient and physician characteristics that affect compliance with antihypertensive medications. J Clin Pharm Ther 2002;27:47-56.[CrossRef][Medline]
25. Sturgess IK, Hughes CM, McElnay JC. Refill compliance rates: evidence for use as an outcome measure in practice-based research.Pharmaceut J 2000;265:R35.
26. Vanderpoel DR, Hussein MA, Watson-Heidari T, Perry A. Adherence to a fixed-dose combination of rosiglitazone maleate/metformin hydrochloride in subjects with type 2 diabetes mellitus: a retrospective database analysis.Clin Ther 2004;26:2066-75.[CrossRef][Medline]
27. Benner JS, Glynn RJ, Mogun H, Neumann PJ, Weinstein MC, Avorn J. Long-term persistence in use of statin therapy in elderly patients.JAMA 2002;288:455-61.[Abstract/Free Full Text]
28. Choo PW, Rand CS, Inui TS, et al. Validation of patient reports, automated pharmacy records, and pill counts with electronic monitoring of adherence to antihypertensive therapy. Med Care 1999;37:846-57.[CrossRef][Medline]
29. Day D. Use of pharmacy claims databases to determine rates of medication adherence. Adv Ther 2003;20:164-76.[Medline]
30. Wogen J, Kreilick CA, Livornese RC, Yokoyama K, Frech F. Patient adherence with amlodipine, lisinopril, or valsartan therapy in a usual-care setting. J Manag Care Pharm 2003;9:424-9.[Medline]
31. Thiebaud P, Patel BV, Nichol MB, Berenbeim DM. The effect of switching on compliance and persistence: the case of statin treatment.Am J Manag Care 2005;11:670-4.[Medline]
32. Benner JS, Tierce JC, Ballantyne CM, et al. Follow-up lipid tests and physician visits are associated with improved adherence to statin therapy.Pharmacoeconomics 2004;22(suppl 3):13 -23.[Medline]
33. Vik SA, Maxwell CJ, Hogan DB. Measurement, correlates, and health outcomes of medication adherence among seniors. Ann Pharmacother 2004;38:303-12. Epub 30 Dec 2003. DOI10.1345/aph.1D252[Abstract/Free Full Text]

This article has been cited by other articles:

				M. A. Chisholm-Burns and C. A. Spivey Pharmacoadherence: A new term for a significant problem Am. J. Health Syst. Pharm., April 1, 2008; 65(7): 661 - 667. [Full Text] [PDF]

				E. Mehuys, L. Van Bortel, L. De Bolle, I. Van Tongelen, L. Annemans, J. P. Remon, and G. Brusselle Effectiveness of pharmacist intervention for asthma control improvement Eur. Respir. J., April 1, 2008; 31(4): 790 - 799. [Abstract] [Full Text] [PDF]

				D. S. Friedman, H. A. Quigley, L. Gelb, J. Tan, J. Margolis, S. N. Shah, E. E. Kim, T. Zimmerman, and S. R. Hahn Using Pharmacy Claims Data to Study Adherence to Glaucoma Medications: Methodology and Findings of the Glaucoma Adherence and Persistency Study (GAPS) Invest. Ophthalmol. Vis. Sci., November 1, 2007; 48(11): 5052 - 5057. [Abstract] [Full Text] [PDF]

				D. P Nau, D. T Steinke, L K. Williams, R. Austin, J. E. Lafata, G. Divine, and M. Pladevall Adherence Analysis Using Visual Analog Scale Versus Claims-Based Estimation Ann. Pharmacother., November 1, 2007; 41(11): 1792 - 1797. [Abstract] [Full Text] [PDF]

This Article Abstract Résumé Extracto PDF Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Articles Ahead of Print [Order Reprint] Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Hess, L. M Articles by Malone, D. C Search for Related Content PubMed PubMed Citation Articles by Hess, L. M Articles by Malone, D. C