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Research Letter |

Cumulative Changes in the Use of Long-Term Medications: A Measure of Prescribing Complexity FREE

Khoa D. Lam, MD1,2; Yinghui Miao, MPH2,3; Michael A. Steinman, MD1,2,3
[+] Author Affiliations
1Department of Medicine, University of California, San Francisco
2San Francisco VA Medical Center, San Francisco
3Division of Geriatrics, University of California, San Francisco
JAMA Intern Med. 2013;173(16):1546-1547. doi:10.1001/jamainternmed.2013.7060.
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Published online

Polypharmacy is a major concern in older adults, yet a simple cross-sectional count of medications does not capture the potential complications (and benefits) that occur when medications are started, stopped, and changed over time. The increased complexity that arises from multiple medication changes may lead to problems with adherence and confusion about proper medication use.1,2 In addition, because adverse drug reactions often occur relatively soon after a patient begins taking a medication, recent medication changes may involve increased risk of adverse drug events.3 In this study, we sought to longitudinally measure medication changes in a national sample of older veterans as a measure of prescribing complexity in older adults.

We used US Department of Veterans Affairs (VA) outpatient pharmacy data combined with VA and Medicare outpatient and inpatient claims data from fiscal year October 1, 2007, through September 30, 2008, to identify persons enrolled in the VA who were 65 years or older, received 1 or more long-term medications from the VA as of April 1, 2007, and were alive and continuing to receive medications from the VA more than 1 year later. We restricted the sample to patients who obtained 80% or more of their outpatient primary care and medicine subspecialty visits in the VA health system. We focused on medications intended for long-term, regular use, defined as medications dispensed with a supply of 25 or more days and no indication to use “as needed.”

We defined 6 types of medication changes: Additions were medications that were not present at study baseline and were added over the following year. Restarts were similar to additions but were medications that had previously been used, were not present at baseline, and were subsequently restarted after an extended period of nonuse. Discontinuations were medications that were present during or beyond baseline but were subsequently not dispensed up through 6 months beyond the study period. Disruptions were interruptions of medication refills for 6 months or longer that were subsequently refilled. Dose changes reflected changes in the daily dose of a medication, and intraclass substitutions were replacement of a medication with another drug in the same drug class.

Among 350 415 veterans, the mean (SD) age was 74 (6) years; 98% were men; and 14% were hospitalized over the year of follow-up. At baseline, patients were using a median (interquartile range [IQR]) of 4 (3-6) chronic medications. One year later, patients were using the same median number of medications, with 77% of patients taking no greater than 1 more or 1 fewer drugs than 1 year earlier (Table).

Table Graphic Jump LocationTable.  Cross-Sectional and Cumulative Changes in Medication Use Over 1 Year of Follow-up

Despite little change in the total number of medications, patients had a median (IQR) of 4 (2-7) medication changes over the 1-year study period. Overall, 88% of patients had at least 1 medication change, and 12% of patients had 10 or more medication changes (Figure). Additions were the most common type of change, being observed in 61% of patients, but other types of changes were common as well (Table).

Place holder to copy figure label and caption
Figure.
Number of Medication Changes
Graphic Jump Location

The number of medications used at baseline was associated with the number of medications changed over the following year (incidence rate ratio, 1.13; 95% CI, 1.13-1.13) (eTable in Supplement). However, this association was not sufficiently robust that baseline medication use strongly predicted the subsequent rate of medication changes (Pearson correlation coefficient, 0.37) (eFigure in Supplement).

In this nationwide study, older adults often had multiple drugs started, stopped, or otherwise changed, with a median of 4 medication changes per patient over a 1-year period. In contrast, cross-sectional counts of the number of medications taken at baseline and 1 year were highly similar.

In older adults, a simple count of medications is often used to identify patients at elevated risk of medication-related problems (polypharmacy). However, this approach does not fully capture the true complexity of medication regimens and the potential for adverse events, patient and clinician confusion, and nonadherence that can arise when medications are started, stopped, and changed.4,5 It is important to note that multiple medication changes may often benefit patients as appropriate responses to changing patient circumstances, and the rate of medication changes should not be interpreted as a measure of quality of care. Future validation of our work is also needed. Nonetheless, the number of medication changes may serve as a novel, valuable, and readily measurable marker of patients at high risk of medication-related problems and may help identify patients who should be targeted for close attention and follow-up.

Corresponding Author: Michael A. Steinman, MD, San Francisco VA Medical Center, 4150 Clement St, PO Box 181G, San Francisco, CA 94121 (mike.steinman@ucsf.edu).

Published Online: June 10, 2013. doi:10.1001/jamainternmed.2013.7060.

Author Contributions: All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Lam and Steinman.

Acquisition of data: Steinman.

Analysis and interpretation of data: Lam, Miao, and Steinman.

Drafting of the manuscript: Lam.

Critical revision of the manuscript for important intellectual content: Lam, Miao, and Steinman.

Statistical analysis: Miao.

Obtained funding: Steinman.

Administrative, technical, and material support: Steinman.

Study supervision: Steinman.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research was supported by grant RC1-AG036377 from the National Institute on Aging and grant 1K23-AG030999 from the National Institute on Aging and the American Federation for Aging Research.

Role of the Sponsors: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: The authors thank Kathy Z. Fung, MS, of the San Francisco VA Medical Center for her assistance compiling data for this manuscript and the residents of the Primary Medical Education Program (PRIME) at the University of California, San Francisco and the San Francisco VA Medical Center for their feedback on manuscript drafts. None of these persons received compensation for their assistance with this manuscript beyond that earned in the course of their standard academic duties.

Van Wijk  BL, Klungel  OH, Heerdink  ER, de Boer  A.  The association between compliance with antihypertensive drugs and modification of antihypertensive drug regimen. J Hypertens. 2004;22(9):1831-1837.
PubMed   |  Link to Article
Choudhry  NK, Fischer  MA, Avorn  J,  et al.  The implications of therapeutic complexity on adherence to cardiovascular medications. Arch Intern Med. 2011;171(9):814-822.
PubMed   |  Link to Article
Forster  AJ, Murff  HJ, Peterson  JF, Gandhi  TK, Bates  DW.  Adverse drug events occurring following hospital discharge. J Gen Intern Med. 2005;20(4):317-323.
PubMed   |  Link to Article
George  J, Phun  Y-T, Bailey  MJ, Kong  DCM, Stewart  K.  Development and validation of the medication regimen complexity index. Ann Pharmacother. 2004;38(9):1369-1376.
PubMed   |  Link to Article
Solem  CT, Lee  TA, Joo  MJ, Lambert  BL, Walton  SM, Pickard  AS.  Complexity of medication use in newly diagnosed chronic obstructive pulmonary disease patients. Am J Geriatr Pharmacother. 2012;10(2):110-122, e1.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure.
Number of Medication Changes
Graphic Jump Location

Tables

Table Graphic Jump LocationTable.  Cross-Sectional and Cumulative Changes in Medication Use Over 1 Year of Follow-up

References

Van Wijk  BL, Klungel  OH, Heerdink  ER, de Boer  A.  The association between compliance with antihypertensive drugs and modification of antihypertensive drug regimen. J Hypertens. 2004;22(9):1831-1837.
PubMed   |  Link to Article
Choudhry  NK, Fischer  MA, Avorn  J,  et al.  The implications of therapeutic complexity on adherence to cardiovascular medications. Arch Intern Med. 2011;171(9):814-822.
PubMed   |  Link to Article
Forster  AJ, Murff  HJ, Peterson  JF, Gandhi  TK, Bates  DW.  Adverse drug events occurring following hospital discharge. J Gen Intern Med. 2005;20(4):317-323.
PubMed   |  Link to Article
George  J, Phun  Y-T, Bailey  MJ, Kong  DCM, Stewart  K.  Development and validation of the medication regimen complexity index. Ann Pharmacother. 2004;38(9):1369-1376.
PubMed   |  Link to Article
Solem  CT, Lee  TA, Joo  MJ, Lambert  BL, Walton  SM, Pickard  AS.  Complexity of medication use in newly diagnosed chronic obstructive pulmonary disease patients. Am J Geriatr Pharmacother. 2012;10(2):110-122, e1.
PubMed   |  Link to Article

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Supplement.

eFigure 1. Number of medication changes by number of medications at baseling

eTable 1. Predictors of number of medication changes

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