Michael A. Steinman, MD

 To examine the effect of a patient activation intervention with financial incentives to promote switching to a thiazide in patients with controlled hypertension using calcium channel blockers (CCBs).

The Veterans Affairs Project to Implement Diuretics, a randomized clinical trial, was conducted at 13 Veterans Affairs primary care clinics.

 Patients (n = 236) with hypertension previously controlled using CCBs were randomized to a control group (n = 90) or 1 of 3 intervention groups designed to activate patients to talk with their primary care providers about switching to thiazides: Group A (n = 53) received an activation letter, group B (n = 42) received a letter plus a financial incentive to discuss switching from a CCB to a thiazide, and group C (n = 51) received a letter, a financial incentive, and a telephone call encouraging patients to speak with their primary care providers. The primary outcome was thiazide prescribing at the index visit.

At the index visit, the rate of switching to a thiazide was 1.1% in the control group and 9.4% (group A), 26.2% (group B), and 31.4% (group C) for the intervention groups (

 < .0001). In adjusted analysis, patients randomized to group C were significantly more likely to switch from a CCB to thiazide at the index visit (odds ratio, 4.14; 95% CI, 1.45-11.84; 

This low-cost, low-intensity patient activation intervention resulted in increased rates of switching to a thiazide in those whose hypertension was controlled using another medication, suggesting that such interventions may be used to overcome medication optimization challenges, including clinical inertia.

Medication optimization remains difficult to achieve in practice. Patient-centered interventions in this context, which emphasize increased communication and shared decision-making between the patient and provider, have shown promise.

In this patient activation intervention for hypertension, intervention patients with their hypertension already controlled using another medication were more likely to switch to a thiazide diuretic compared with usual care control patients. Specifically, adjusted odds of switching were greatest for patients randomized to receive a custom letter, modest financial incentive, and brief nurse-led reminder call.

These findings suggest that low-cost, low-intensity patient activation interventions may be useful in addressing medication optimization challenges and clinical inertia.

Deprescribing refers to the process of removing, reducing, or switching medications to reduce risks and improve patient outcomes.

 A conceptually overlapping process is that of medication optimization, which seeks to maximize medication benefit through appropriate prescribing; it shares similar strategies with deprescribing and may involve removing, reducing, or switching medications.

 Both concepts are important given the pervasiveness of polypharmacy; half of older adults take 5 or more medications, at least 1 of which may be potentially inappropriate.

 Achieving optimal medication usage thus may improve numerous outcomes in this population, including quality, cost, adherence, and reduction of adverse drug events,

 but it remains difficult to achieve in practice.

 Several factors that may inhibit optimal medication usage have been identified, including clinical inertia, provider assumptions about patient treatment preferences, patient beliefs about the appropriateness of the medication in question, and patient fears related to cessation (eg, withdrawal effects).

Such factors underscore the need to employ a patient-centered process to achieve medication optimization, which emphasizes increased communication and shared decision-making between the patient and provider in clinical encounters.

 Studies in the deprescribing literature employing patient-centered approaches have reported favorable results,

 suggests that interventions designed to actively involve the patient are more effective than those that do not (eg, those focused solely on prescribers).

Direct-to-consumer (DTC) marketing has been used by the pharmaceutical industry for some time to motivate discussions between patients and physicians to affect prescribing.

 Fundamentally, DTC marketing seeks to increase patient activation, a term that refers to the patient-centered process of imparting knowledge, skills, and confidence, which may facilitate patients becoming more active and informed participants in health care delivery.

 Patient activation interventions have shown benefit in terms of better health outcomes and care experiences,

 and interventions that facilitate patient initiation of discussion about medication optimization may be especially beneficial as they provide opportunities for shared communication and decision-making.

The present study examines data from the Veterans Affairs Project to Implement Diuretics, a randomized clinical trial designed to test the efficacy of a patient activation intervention with financial incentives to promote thiazide prescribing (ClinicalTrials.gov identifier: NCT 00265538). When the trial was initiated in 2006,

 the prevailing hypertension (HTN) guidelines from the Joint National Committee (JNC) 7 for the Management of Hypertension in Adults specifically stated that “thiazide-type diuretics should be used in drug treatment for most patients with uncomplicated [HTN].”

 In addition to being recommended for first-line therapy, thiazides were considerably cheaper than many other antihypertensives, with a favorable efficacy and safety profile. The 2014 guidelines (formerly JNC 8) recommend selection among 4 classes, including thiazides.

 The arm of the trial examined in the current study aimed to promote switching to thiazides in a group of patients with controlled blood pressure (BP) using calcium channel blockers (CCBs) due to the recommendations at the time and considerable cost savings, aligning with the concept of medication optimization in which medications that may not pose significant harm—but are suboptimal—are targeted for switching or reduction. Although much of the deprescribing literature focuses on medication discontinuation, switching is also an important principle of deprescribing/medication optimization and deserves further attention.

 Thus, our primary interest in this study was to examine whether a low-cost, low-intensity intervention would affect medication switching for those already at their BP goal taking a nonpreferred agent. We hypothesized that patients assigned to the most “active” arm of the intervention would exhibit the greatest degree of medication switching.

Participants were sampled from all patients who received primary care at 13 Veterans Affairs (VA) outpatient clinics affiliated with 2 large VA medical centers (N = 61,019) (

). To be eligible for participation, patients must have had a prior diagnosis of HTN, not be taking a thiazide diuretic, have 2 or more VA clinic visits in the past 12 months, not be in a long-term care facility, and have no known thiazide or sulfa allergy or prior adverse drug event. From this, 2 subpopulations were derived: (1) patients not at an appropriate BP goal at the 2 most recent clinic visits, referred to as 

, and (2) patients at an appropriate BP goal, but taking a CCB, referred to as 

. Attainment of goal BP at baseline was determined using the mean of all clinic-recorded BPs from the 2 clinic visits immediately preceding the initial study clinic appointment (hereafter referred to as the index visit). Complete inclusion and exclusion criteria, as well as primary trial results focusing on those not at an appropriate BP goal, have been reported elsewhere.

Participants were recruited following a staged protocol that randomized 168 primary care providers (71 affiliated with the Iowa City VA Medical Center and 97 affiliated with the Minneapolis VA Medical Center) to intervention and control groups and then selected eligible patients within these groups. Upon primary care provider randomization, patients with a primary care appointment within 30 days were identified and screened using the electronic health record. Eligible patients (n = 2853) were mailed a letter describing the study objectives, enrollment process, and data collection. One week later, patients were contacted by telephone to obtain informed consent and administered the Pfeiffer Short Portable Mental Status Questionnaire (SPMSQ) to exclude patients with cognitive impairment (defined as > 2 errors on the SPMSQ).

 Of the 2853 patients who were contacted by mail, 2019 were attempted by phone; 1151 declined to participate, including 86 with no telephone. Enrollment ended when the target number of participants were enrolled. Consenting patients (n = 236) were randomized to an intervention (n = 146) or 1 of 2 usual care control groups (n = 90), one group of approximately 50 who were seen by providers with no intervention patients and another group of approximately 50 seen by providers also with intervention patients. We conducted several analyses that demonstrated that these patient groups were no different from one another and thus collapsed them for this study. Sample sizes were selected to provide sufficient (≥ 80%) power to detect an 18% absolute difference in thiazide use between the control group and a single treatment group. All study procedures were approved by The University of Iowa’s institutional review board (#200506705).

Patients randomized to the intervention were assigned to 1 of 3 intervention groups. Group A (n = 53) received a customized letter 1 to 3 weeks before their scheduled index visit. Information was tailored to the patient’s treatment regimen, BP, and cardiovascular risk factors. The letter requested that the patient initiate a discussion with their primary care provider about switching to a thiazide and provided specific options for doing so. It included a postcard for patients to have providers sign and return, documenting whether thiazides were discussed during the visit.

Group B (n = 42) received the same patient education letter in addition to a financial incentive ($20 by check) to initiate a discussion with their primary care provider (see 

] for an example letter representing group B). The incentive was awarded upon receipt of the signed postcard. For patients with a prescription co-pay (55/93; 59.1% across groups B and C), the letter also indicated that thiazide co-pays for 6 months ($48) would be reimbursed upon receipt of the postcard and confirmation that a thiazide was prescribed.

Group C (n = 51) received the same letter and financial incentives as group B in addition to a brief telephone call within 1 week before their clinic visit. Calls were conducted by a trained health educator who discussed the mailed information, the patient’s role in initiating a discussion with the primary care provider about BP medications, and potential strategies for initiating these conversations. All patients received up to 5 call attempts prior to their scheduled index visit; the mode number of calls was 2. Calls lasted, on average, between 5 and 10 minutes.

The primary outcome was thiazide prescription at the index visit; switching to a thiazide from a CCB was determined by reviewing each patient’s progress note and medication list after the index visit, which included discontinued orders to establish that CCBs were stopped. Intervention and control groups were compared at baseline in terms of sociodemographic characteristics and other clinical variables of importance (eg, baseline BP, smoking, mental status, diabetes) using standard univariate tests including 

 tests. Variables showing a (conservative) significant difference between groups at 

 < .10 were included as covariates in adjusted analyses.

Because of the clustered nature of these data, statistical analyses must account for correlation in order for the observed results to be valid given that traditional regression models assume independence of observations. Therefore, analyses utilized hierarchical logistic regression using the generalized linear mixed model procedure in SPSS version 24.0 (IBM). Models incorporated random physician effects to account for within-physician clustering; clinic sites were included as fixed effects. Analyses tested whether there was a significant difference between the log odds of thiazide prescription for the control group vs the 3 intervention groups at the index visit.

During recruitment, 2853 eligible patients were identified. Of these, 236 who were at an appropriate BP goal and taking a CCB for HTN treatment were consented and enrolled. These patients were randomized to either intervention (n = 146: group A = 53; group B = 42; group C = 51) or control (n = 90) groups. Mean age for the intervention groups was 66.7 years and mean systolic/diastolic BP at baseline was 121.5/74.5 mm Hg (see 

, which also includes a breakdown by intervention group assignment). Mean age for the control group was 66.9 years and mean BP at baseline was 122.2/73.2 mm Hg. Only race varied significantly between groups and was added as a covariate in the adjusted model. No differential attrition by group was observed, which includes appointment no-shows and elective study dropouts. Only 2 of 51 (3.9%) group C participants were not reached by phone, making them functionally more like group B participants; these participants were analyzed according to the group they were randomized to.

Rate of thiazide prescribing, representing a switch from a CCB to thiazide, was the primary outcome of interest. At the index visit, the unadjusted thiazide prescription rates were 1.1% (1/90) for the control group and 9.4% (5/53) for group A, 26.2% (11/42) for group B, and 31.4% (16/51) for group C (

 < .0001). Adjusted odds ratios (ORs) for the 3 intervention groups relative to control are presented in 

. At the index visit, the odds of switching to a thiazide were not significantly greater compared with the control group for group A (OR, 2.37; 95% CI, 0.82-6.82; 

 = .11) and only marginally so for group B (OR, 2.77; 95% CI, 0.96-8.01; 

 = .06). Group C (OR, 4.14; 95% CI, 1.45-11.84; 

 < .01) had significantly greater odds of switching to a thiazide than the control group, the magnitude of which has been suggested to be equivalent to a medium effect size.

This study suggests that a relatively low-cost, low-intensity intervention may affect medication optimization behavior in the context of HTN, even for patients currently controlled on another medication. At the index visit, approximately one-third of patients in group C switched from a CCB to thiazide, which was significantly different from the control group in the adjusted analysis. Perhaps most noteworthy is the fact that these patients had HTN that was controlled at the time of intervention and, presumably, most had no pressing reason to switch other than those primarily tied to financial benefit (ie, nominal monetary incentive, prescription co-pay assistance).

Patients were first activated to engage their primary care providers through a customized letter, which only modestly improved switching compared with control (group A; see unadjusted rates above and adjusted odds ratios in Table 2). Adding financial incentives resulted in a marginally significant increase in the odds of switching for group B and, further, a brief reminder phone call resulted in significantly increased odds for group C. The use of financial incentives to drive behavior change has shown success across multiple health-related contexts, including medication adherence, smoking cessation, and weight loss.

 It has, however, notably failed in others.30 In this study, a letter plus modest financial incentives were not enough to achieve statistically significant switching (

 = .06), but adding a brief reminder call previsit was enough to reach significant change (

 < .01). Although a formal budget impact analysis was not part of the study, the financial incentive was considerably less than the ongoing cost of a CCB and the letters were automatically generated from data in the electronic health record and thus low cost. The phone calls took approximately 5 to 10 minutes but improved the potency of the intervention and served to remind patients about their appointments as well as to bring their medications with them, a frequent part of usual care.

Given that group C participants received a tailored letter encouraging discussion with the provider, modest financial incentives, and a brief phone call, it is likely that successful interventions will require a multifaceted approach to facilitate medication optimization, including active engagement of the patient.

 This is further underscored by calls to implement interventions designed to address patient, provider, and/or systems-level factors.

 In the present study, the tailored letter not only provided information specific to the patient’s condition but also served as a means of facilitating discussion about medication options. Although, on its own, it was not enough to drive significant change, it does represent a key first step in shared decision-making and presumably resulted in some dialogue about the benefits vs harms of switching. Because a majority of (although not all) patients prefer to be actively involved in the decision-making process,

 which includes being offered choices and the freedom to express opinions, it would seem that simple tools such as a patient-directed letter may hold value as one instrument toward broader medication optimization when combined with additional elements like financial incentives and other means to further engage the patient. Even for those preferring to leave final decisions to the provider, such letters could serve as a prompt for discussion, which itself may combat clinical inertia and misperceptions about patient preferences on the part of the provider.

 Indeed, a companion qualitative study of participating intervention providers found that, in addition to very favorable overall perceptions of the intervention and belief in its suitability for wider use, providers specifically noted that the intervention helped align patient objectives with their own.

Not all multimodal interventions are equally feasible in practice, however. Many interventions have demonstrated success in improving deprescribing outcomes, but there is often a disconnect between research and practice in that many interventions may be unrealistic for widespread adoption in busy clinic settings.

 Compared with interventions that require the involvement of multiple providers, considerable effort on the part of a lone provider, and/or complex referral or care coordination processes, the present approach may be more logistically practical and demand fewer resources. Future research should be designed with an eye toward what is reasonably attainable in the clinical setting to address this important issue.

This study has several limitations. First, it is reasonable to argue that because some patients did not have a compelling reason to switch other than financial incentives and then-contemporary practice guidelines, there may have been no need to switch at all. However, this study suggests that financial incentives, when coupled with other modest interventional components, can facilitate discussion of the issue, which is an important first step in the deprescribing/medication optimization process. Previous qualitative work from this trial found that patient participants liked being involved because it allowed them the opportunity to initiate discussions with the provider on a topic of importance.

 Such empowerment may carry over to other aspects of the clinical encounter. Second, we do not have information regarding the exact reasons why patients and providers decided to switch from a CCB to a thiazide; it is assumed that the financial incentives and practice guidelines largely influenced this change, but it is possible that at least some patients were candidates for switching irrespective of our intervention, perhaps due to low tolerance of the medication at the time of visit. This concern is alleviated by the presence of a control group, however. Third, we do not have specific race/ethnicity information for a number of patient participants in this study, which is primarily due to issues with reporting and classification in the VA medical record at the time of this trial.

 Fourth, we did not monitor downstream efforts to address the appropriateness of the switch (eg, follow-up laboratory tests, BP monitoring) and thus cannot speak to patients who did not respond well to the new medication. Finally, HTN guidelines evolved over time, making some recommendations obsolete. However, in addition to the clear connections to deprescribing/medication optimization, this study also may have implications specifically for therapeutic substitution (ie, when a lower-cost alternative within the same class is available), particularly when developing methods to inform and engage patients about less expensive options that are otherwise equivalent for the same indication.

These results suggest that a low-intensity intervention activating patients toward increased communication (in this case, discussion of current HTN medications in clinic) may be relevant to deprescribing and broader medication optimization efforts, helping to overcome clinical inertia. Future work should evaluate the practical barriers of implementing even simple interventions into complex and busy clinical practices and develop ways to overcome those barriers. 


 Center for Access Delivery Research & Evaluation (CADRE), VA Iowa City Healthcare System (MBH, MWVW, PJK), Iowa City, IA; Department of Behavioral Sciences & Social Medicine, College of Medicine, Florida State University (MBH), Tallahassee, FL; Division of Geriatrics, University of California San Francisco (MAS), San Francisco, CA; San Francisco VA Health Care System, San Francisco (MAS), CA; Division of General Internal Medicine, The University of Iowa Carver College of Medicine (BC, MWVW, PJK), Iowa City, IA; Department of Pharmacy Practice and Science, The University of Iowa College of Pharmacy (BC), Iowa City, IA; Department of Psychological & Brain Sciences, The University of Iowa (MWVW), Iowa City, IA.

 The research reported here was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service Merit Review Grant (IMV 04-066-1). The views expressed in this article are those of the authors and do not necessarily represent the view of the Department of Veterans Affairs.

 Dr Steinman has received grants from the National Institutes of Health (NIH) related to appropriate medication use and has received honoraria for talks at universities and participation in programs sponsored by other nonprofit entities around issues related to appropriate medication use. Dr Carter is an investigator on 3 NIH grants and receives honoraria as a member of 2 Data and Safety Monitoring Boards for NIH. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (MBH, BC, MWVW, PJK); acquisition of data (BC, PJK); analysis and interpretation of data (MBH, MAS, MWVW, PJK); drafting of the manuscript (MBH, PJK); critical revision of the manuscript for important intellectual content (MBH, MAS, BC, MWVW, PJK); statistical analysis (MBH, PJK); provision of patients or study materials (PJK); obtaining funding (MBH, PJK); administrative, technical, or logistic support (PJK); supervision (BC, PJK); and content expertise (MAS).

 M. Bryant Howren, PhD, MPH, Florida State University College of Medicine, 1115 W Call St, Tallahassee, FL 32306. Email: matthew.howren@med.fsu.edu.

1. Scott IA, Hilmer SN, Reeve E, et al. Reducing inappropriate polypharmacy: the process of deprescribing. 

2015;175(5):827-834. doi:10.1001/jamainternmed.2015.0324

2. Van Boven JFM, Stuurman-Bieze AGG, Hiddink EG, Postma MJ, Vegter S. Medication monitoring and optimization: a targeted pharmacist program for effective and cost-effective improvement of chronic therapy adherence. 

. 2014;20(8):786-792. doi:10.18553/jmcp.2014.20.8.786

3. Mangin D, Garfinkel D. Foreword to the first special collection: addressing the invisible iatrogenic epidemic: the role of deprescribing in polypharmacy and inappropriate medication use. 

 2019;10:2042098619883156. doi:10.1177/2042098619883156

4. Reeve E, Shakib S, Hendrix I, Roberts MS, Wiese MD. The benefits and harms of deprescribing. 

.2014;201(7):386-389. doi:10.5694/mja13.00200

5. Reeve E, Thompson W, Farrell B. Deprescribing: a narrative review of the evidence and practical recommendations for recognizing opportunities and taking action. 

. 2017;38:3-11. doi:10.1016/j.ejim.2016.12.021

6. Ronquillo C, Day J, Warmouth K, Britten N, Stein K, Lang I. An implementation science perspective on deprescribing. 

. 2018;28(4):134-139. doi:10.1093/ppar/pry032

7. Andersen K, Stowasser D, Freeman C, Scott I. Prescriber barriers and enablers to minimizing potentially inappropriate medications in adults: a systematic review and thematic analysis. 

. 2014;4(12):e006544. doi:10.1136/bmjopen-2014-006544

8. Britten N, Stevenson FA, Barry CA, Barber N, Bradley CP. Misunderstandings in prescribing decisions in general practice: a qualitative study. 

. 2000;320(7233):484-488. doi:10.1136/bmj.320.7233.484

9. Reeve E, To J, Hendrix I, Shakib S, Roberts MS, Wiese MD. Patient barriers to and enablers of deprescribing: a systematic review. 

. 2013;30(10):793-807. doi:10.1007/s40266-013-0106-8

10. Steinman MA, Landefeld CS. Overcoming inertia to improve medication use and deprescribing. 

. 2018;320(18):1867-1869. doi:10.1001/jama.2018.16473

11. Holmes HM, Todd A. The role of patient preferences in deprescribing. 

. 2017;33(2):165-175. doi:10.1016/j.cger.2017.01.004

12. Todd A, Jansen J, Colvin J, McLachlan AJ. The deprescribing rainbow: a conceptual framework highlighting the importance of patient context when stopping medication in older people. 

. 2018;18(1):295. doi:10.1186/s12877-018-0978-x

13. Krol N, Wensing M, Haaijer-Ruskamp F, et al. Patient-directed strategy to reduce prescribing for patients with dyspepsia in general practice: a randomized trial. 

. 2004;19(8):917-922. doi:10.1111/j.1365-2036.2004.01928.x

14. Peterson AW, Shah AS, Simmons SF, et al. Shed-MEDS: pilot of a patient-centered deprescribing framework reduces medications in hospitalized older adults being transferred to inpatient postacute care. 

. 2018;9(9):523-533. doi:10.1177/2042098618781524

15. Tannenbaum C, Martin P, Tamblyn R, Benedetti A, Ahmed S. Reduction of inappropriate benzodiazepine prescriptions among older adults through direct patient education: the EMPOWER cluster randomized trial. 

. 2014;174(6):890-898. doi:10.1001/jamainternmed.2014.949

16. Ostini R, Jackson C, Hegney D, Tett SE. How is medication prescribing ceased? a systematic review. 

. 2011;49(1):24-36. doi:10.1097/MLR.0b013e3181ef9a7e

17. Donohue JM, Cevasco M, Rosenthal MB. A decade of direct-to-consumer advertising of prescription drugs. 

. 2007;357(7):673-681. doi:10.1056/NEJMsa070502

18. Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. 

. 2004;39(4 pt 1):1005-1026. doi:10.1111/j.1475-6773.2004.00269.x

19. Hibbard JH. Patient activation and the use of information to support informed health decisions. 

. 2017;100(1):5-7. doi:10.1016/j.pec.2016.07.006

20. Greene J, Hibbard JH. Why does patient activation matter? an examination of the relationships between patient activation and health-related outcomes. 

. 2012;27(5):520-526. doi:10.1007/s11606-011-1931-2

21. Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. 

. 2013;32(2):207-214.doi:10.1377/hlthaff.2012.1061

22. Kaboli PJ, Howren MB, Ishani A, Carter B, Christensen AJ, Vander Weg MW. Efficacy of patient activation interventions with and without financial incentives to promote prescribing of thiazides and hypertension control: a randomized clinical trial. 

. 2018;1(8):e185017. doi:10.1001/jamanetworkopen.2018.5017

23. Chobanian AV, Bakris GL, Black HR, et al; National Heart, Lung, and Blood Institute Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National High Blood Pressure Education Program Coordinating Committee. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. 

. 2003;289(19):2560-2572. doi:10.1001/jama.289.19.2560

24. James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). 

. 2014;311(5):507-520. doi:10.1001/jama.2013.284427

25. Pfeiffer E. A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. 

. 1975;23(10):433-441. doi:10.1111/j.1532-5415.1975.tb00927.x

26. Chen H, Cohen P, Chen S. How big is a big odds ratio? interpreting the magnitudes of odds ratios in epidemiological studies. 

. 2010;39(4):860-864. doi:10.1080/03610911003650383

27. Halpern SD, French B, Small DS, et al. Randomized trial of four financial-incentive programs for smoking cessation. 

.2015;372(22):2108-2117. doi:10.1056/NEJMoa1414293

28. Volpp KG, John LK, Troxel AB, Norton L, Fassbender J, Loewenstein G. Financial incentive–based approaches for weight loss: a randomized trial. 

. 2008;300(22):2631-2637. doi:10.1001/jama.2008.804

29. Volpp KG, Loewenstein G, Troxel AB, et al. A test of financial incentives to improve warfarin adherence. 

. 2008;8:272. doi:10.1186/1472-6963-8-272

30. Thirumurthy H, Asch DA, Volpp KG. The uncertain effect of financial incentives to improve health behaviors. 

. 2019;321(15):1451-1452. doi:10.1001/jama.2019.2560

31. Kaur S, Mitchell G, Vitetta L, Roberts MS. Interventions that can reduce inappropriate prescribing in the elderly: a systematic review. 

. 2009;26(12):1013-1028. doi:10.2165/11318890-000000000-00000

32. Ng BJ, Le Couteur DG, Hilmer SN. Deprescribing benzodiazepines in older patients: impact of interventions targeting physicians, pharmacists, and patients. 

. 2018;35(6):493-521. doi:10.1007/s40266-018-0544-4

33. Levinson W, Kao A, Kuby A, Thisted RA. Not all patients want to participate in decision making. a national study of public preferences. 

. 2005;20(6):531-535. doi:10.1111/j.1525-1497.2005.04101.x

34. Mulley AG, Trimble C, Elwyn G. Stop the silent misdiagnosis: patients’ preferences matter.

35. Buzza CD, Williams MB, Vander Weg MW, Christensen AJ, Kaboli PJ, Reisinger HS. Part II, provider perspectives: should patients be activated to request evidence-based medicine? a qualitative study of the VA Project to Implement Diuretics (VAPID). 

36. Pilling SA, Williams MB, Brackett RH, et al. Part I, patient perspective: activating patients to engage their providers in the use of evidence-based medicine: a qualitative evaluation of the VA Project to Implement Diuretics (VAPID). 

37. Sohn MW, Zhang H, Arnold N, et al. Transition to the new race/ethnicity data collection standards in the Department of Veterans Affairs. 

Articles

In patients with hypertension, a patient activation intervention increased rates of switching to a thiazide, suggesting that such interventions may address medication optimization challenges.

Effect of a Patient Activation Intervention on Hypertension Medication Optimization: Results From a Randomized Clinical Trial

: To evaluate variation in outpatient antibiotic utilization among US commercial health plans and the implications of this variation for cost and quality.

: We measured antibiotic utilization rates among 229 US commercial health plans that participated in the 2005 Healthcare Effectiveness Data and Information Set. Rates were adjusted to account for health plan age and sex distribution. To estimate antibiotic costs, we multiplied utilization data for each drug class by national estimates of intraclass distribution of drugs, duration of therapy, and median average wholesale price.

: Antibiotic utilization rates varied markedly among plans, ranging from 0.64 antibiotic fills per member per year (PMPY) at the 5th percentile of plans to 1.08 fills PMPY at the 95th percentile, with a mean of 0.88 (SD ± 0.15) antibiotic fills PMPY. US census region was the strongest predictor of antibiotic utilization. Antibiotic costs averaged $49 PMPY and ranged from $34 to $63 PMPY among plans at the 5th and 95th percentiles of cost, respectively. If a health plan with 250,000 members at the 90th percentile of antibiotic costs reduced its costs to the 25th percentile, annual drug cost savings would be approximately $4.1 million.

: Antibiotic utilization varies substantially among commercial health plans and is not accounted for by differences in the age and sex distribution of plan members. Because reducing rates of antibiotic utilization is likely to lower costs and improve quality, high-utilizing plans may reap considerable rewards from investing in

programs to reduce the overuse of antibiotics.

Outpatient antibiotic utilization varies substantially among commercial health plans in ways not explained by patient case mix. As a result:

Antibiotic utilization is likely to be a valuable marker of prescribing quality for health plans.

Health plans with high rates of antibiotic utilization may benefit from targeted quality improvement programs to reduce unnecessary antibiotic use.

Cost savings from reducing unnecessary antibiotic use are substantial.

Antibiotics are widely overused in ambulatory practice, particularly in the management of acute respiratory tract infections. 

 Although bacterial infections cause a small minority of these illnesses, it is estimated that 40% to 50% of all patients in the United States who seek medical attention because of these conditions receive antibiotics.

 The consequences of this overuse are striking. Every year, millions of people are directly exposed to the side effects of antibiotics, ranging from common, bothersome symptoms to infrequent but devastating complications such as Clostridium difficile colitis and anaphylaxis.

 Moreover, research suggests that community levels of bacterial resistance occur in proportion to the volume of community antibiotic use.

 Thus,  perpetuation of antibiotic overuse promotes the continued evolution of ominous resistance profiles. 

Since early in this decade, the European Surveillance of Antimicrobial Consumption project has been collecting country-level data on  antibiotic utilization and has documented large differences in per capita antibiotic utilization between countries in Europe.

 Because the European Surveillance of Antimicrobial Consumption measures are not based on a particular clinical condition, an optimal rate of overall antibiotic utilization is difficult to establish. Nonetheless, clinical evidence and expert opinion strongly suggest that prescribing is most appropriate in countries at the lower end of the spectrum and that the difference between low- and high-utilizing countries to a considerable extent represents potentially unnecessary and avoidable prescriptions.

Recently, the United States adopted a similar (albeit more limited) approach to track and report antibiotic utilization. In 2005, the National Committee for Quality Assurance (NCQA) developed and implemented a new measure to compare overall rates of antibiotic utilization between US health plans. By evaluating variation in antibiotic use within a country, these data can improve understanding of factors that contribute to variation in overall antibiotic utilization without the confounding effects of different countries’ healthcare systems. In addition, implementation of this measure in an established program to compare and improve healthcare quality can directly facilitate efforts to improve quality of care by identifying high-prescribing plans and stimulating them to investigate the factors that contribute to potentially excessive antibiotic use.

In this study, we analyze antibiotic utilization rates among nonelderly members of commercial health maintenance organization (HMO) and point-of-service (POS) health plans in the United States participating in the NCQA’s Healthcare Effectiveness Data and Information Set (HEDIS) program. We quantified the degree of variation in antibiotic utilization rates across health plans, controlling for member characteristics, and estimated the cost implications of this variation.

Data for this study were collected by commercial HMO and POS health plans in the United States that participate in NCQA’s HEDIS program.16 HEDIS is a voluntary program that collects data from health plans on various domains of effectiveness and utilization. These data are used to benchmark and compare the quality of care across health plans. Participating health plans account for more than 85% of individuals enrolled in US commercial plans. We report data from 2005, the first year plans reported data on overall antibiotic utilization for a new HEDIS measure.

We received a core set of data from 248 commercial health plans, representing 83% of commercial plans in established parts of the HEDIS program. The antibiotic utilization measure was a first-year measure. All first-year measures in HEDIS are not publicly reported in order to evaluate the measure, and, as is common, some plans choose not to report during this first year of data collection. We were able to obtain information on approximately 80% of the plans not contributing core data. Plans that did not report data had fewer members than those which did report (55% of nonreporting plans had fewer than 10,000 members vs 7% of reporting plans; 

Among 248 plans contributing core data, we excluded 4 health plans that did not provide basic data necessary for our analyses, 14 health plans whose rate of overall antibiotic utilization was extreme enough to suggest discrepancies in the way these data were collected or reported, and 1 plan that did not use an HMO or POS model of care. Our final analytic dataset thus comprised 229 health plans.

Our main outcome variable was each plan’s rate of antibiotic utilization per member per year (PMPY), as assessed by pharmacy claims billed. In calculating this rate, we counted each antibiotic prescription fill equally, regardless of drug dose or duration. Our analyses focused on people age 0 to 64 years, as most people age 65 years and older are enrolled in government-sponsored Medicare plans and those remaining in commercial plans may not be representative of the larger population of elders.

Each plan reported antibiotic utilization data for enrollees stratified into age-sex groups. To calculate standardized rates of antibiotic utilization, for each plan we multiplied the PMPY utilization rates for each of these age-sex strata by the proportion of the overall study cohort within each of these strata. Next, we summed each of these weighted rates to create an overall antibiotic utilization rate for all plan members age 0 to 64 years. This approach yielded a PMPY antibiotic utilization rate that adjusted for the age and sex distribution of each plan’s members. This standardized rate yielded figures similar to those of a crude rate that did not adjust for differences in the age and sex distribution of plans (see the 

Next, we evaluated variation in use of specific types of antibiotics. Health plans provided utilization information for each of 15 classes of antibiotics, following a categorization scheme defined by a  multistakeholder expert panel that worked with NCQA to arrive at a consensus definition that was both clinically relevant and feasible to implement. This panel further grouped each of the antibiotic classes into 2 categories: “antibiotics of concern,” agents generally considered to have broad-spectrum activity; and all other agents (“other antibiotics”), generally considered to have a narrow spectrum of antimicrobial activity (see Table 2). For purposes of clarity, we will use the terms “broad spectrum” and “narrow spectrum” to refer to these categories. We identified potential discrepancies in class-level utilization data for 20 plans. These discrepancies were most likely due to rounding errors, as plan-level data on utilization rates were provided to only 2 decimal places (eg, if the reported utilization rate for an antibiotic class was 0.04 antibiotic fills PMPY, the actual

rate could be anywhere from 0.0351 to 0.0449, an error rate of up to ±12%). We excluded these 20 plans from our analyses of antibiotic classes, plus an additional 3 plans that did not report data necessary to complete these analyses, leaving 206 plans with usable data. Because these discrepancies could affect estimates of drug expenditures, we also excluded these plans from our cost analyses, described below.

Next, we evaluated plan characteristics associated with the overall rate of antibiotic utilization. First, we conducted bivariate analyses using linear regression, where the outcome of interest was the plan’s age-sex adjusted rate of antibiotic utilization and the predictor variables were plan characteristics available from HEDIS that we hypothesized might be associated with antibiotic utilization. Next, we entered all variables into a multivariable linear regression model, on which further diagnostic testing confirmed the adequacy of model fit.

To estimate the cost of antibiotic utilization for each plan, we used several steps (see the eAppendix for details). Health plans provided data on utilization of antibiotic classes but not specific drugs within those classes. To compensate, we used data from the 2004 and 2005 National Ambulatory and National Hospital Ambulatory Medical Care Surveys to estimate the frequency of use of specific antibiotics within each antibiotic class. Next, we estimated the typical course of therapy for each antibiotic, including the most commonly used formulation, dose, dosing frequency, and duration of therapy, repeating this process separately for persons age 0 to 3 years, 4 to 9 years, and 10 to 64 years. For each antibiotic, we used data from the 2005 Red Book to assess the median price per pill (or other formulation), averaging across all manufacturers of a given drug and all bottle sizes.17 For multisource (generically available) drugs, we evaluated only the price for the generic versions. Finally, we calculated the estimated cost of a typical course of therapy for each antibiotic and then integrated this cost at the level of the antibiotic class and ultimately at the level of the health plan.

This research was approved by the Committee on Human Research at the University of California, San Francisco and the Research and Development Committee at the San Francisco VA Medical Center.

Characteristics of the 229 health plans are shown in 

. Plans were distributed across the United States and varied widely in the size of their enrolled population. Together, these plans accounted for 42.9 million enrollees age 0 to 64 years. Children age 0 to 17 years comprised 27% of the overall member population.

Across all plans, the mean rate of antibiotic utilization was 0.88 prescription fills PMPY. Rates of antibiotic utilization varied widely among plans (

), with a standard deviation (SD) of 0.15 fills PMPY, and 1.7-fold variation in rates of antibiotic utilization between the plans at the 5th and 95th percentiles of utilization (0.64 and 1.08 prescription fills PMPY, respectively). For a plan with 250,000 members, 89,000 fewer antibiotics would be dispensed each year if plan doctors prescribed antibiotics at the 10th percentile rate compared with the 90th percentile rate. If a similarly sized plan were to reduce its utilization from the 75th to 25th percentile, 47,000 fewer antibiotic prescriptions would be filled.

Variation in rates of antibiotic utilization persisted among age and sex groups (

). Mean rates of antibiotic utilization differed across age-sex groups, and there was considerable variation in utilization rates within each group (SDs for each group ranged from 0.10 fills PMPY for males age 18-34 years to 0.24 fills PMPY for males age 0-9 years).

 shows variation in the distribution of types of antibiotics used among health plans. Across plans, a mean of 47% of antibiotics dispensed were broad-spectrum agents (“antibiotics of concern”). The proportion of broad-spectrum antibiotics varied among plans, ranging from 34% of antibiotic prescription fills for plans at the 5th percentile of broad-spectrum utilization to 59% for plans at the 95th percentile (SD ± 8%). There was little correlation between plans’ overall rate of antibiotic utilization and the proportion of antibiotics that were broad-spectrum agents (Pearson 

We next evaluated factors associated with a plan’s rate of overall antibiotic utilization. On bivariate analyses, plans with “excellent” accreditation status (the highest achievable rank) used more antibiotics than plans with “commendable” status (the second-highest rank), and plans with a smaller percentage of physicians who were board certified used more antibiotics than plans with a larger percentage of physicians who were board certified (

). In addition, national region was strongly associated with rates of antibiotic utilization. Compared with plans in the West, plans in the South used a mean of 0.16 more antibiotic prescription fills PMPY, plans in the Midwest used 0.12 more fills PMPY, and plans in the Northeast used 0.07 more fills PMPY. As the mean rate of antibiotic utilization in the West was 0.78 fills PMPY, these differences correspond to a 21% higher rate of antibiotic utilization in the South, a 15% higher rate in the Midwest, and a 9% higher rate in the Northeast (see Table 3 footnote f for a further explanation of how these percentages were derived). On multivariable analysis, geographic region remained the most notable predictor of antibiotic utilization rates. Thirteen percent of the total variance in antibiotic utilization rates was explained by national region, while multivariable adjustment for all variables shown in Table 3 explained 26% of the total variance.

Finally, we estimated the cost implications of variation in antibiotic utilization. Across 206 plans with 38 million enrollees available for this analysis, total antibiotic expenditures totaled $1.90 billion, corresponding to a mean antibiotic cost of $49 PMPY. The SD of PMPY antibiotic costs was $9, and costs ranged from $34 PMPY for plans at the 5th percentile of costs to $63 PMPY for plans at the 95th percentile of costs. If all plans above the 25th percentile of antibiotic costs PMPY reduced their costs to this level, the mean (SD) drug cost savings would be $10 (±$6) PMPY. Across the 38 million enrollees of the plans we studied, that corresponds to $305 million in savings per year. If plans reduced their utilization to the 10th percentile, the drug cost savings would be greater, with mean (SD) savings of $14 (±$7) PMPY and total savings of $492 million per year.

In this study of 229 commercial health plans in the United States, we found substantial variation in the rate of antibiotic utilization, with high-utilizing plans dispensing nearly 70% more antibiotics per capita than low-utilizing plans. There was similarly large variation in the proportion of broad-spectrum antibiotics used by health plans, ranging from 34% of all antibiotics in plans at the lower end of the spectrum to 59% in plans at the higher end. Geographic region was strongly associated with health plans’ rate of antibiotic utilization: after controlling for other factors, plans in the southern United States used 0.16 more antibiotics PMPY than plans in the western United States, a difference larger than 1 SD of utilization rates nationally.

This variation in antibiotic utilization has substantial implications for health and healthcare costs in the United States. Overprescribing of antibiotics results in unnecessary drug side effects and promotes population-level resistance to antibiotics, which—consistent with our data&mdash;is greater in southern than in western states.

 Although resistance to any antibiotic is promoted by high-volume use, excessive prescribing of broad-spectrum agents is of particular concern because this overprescribing promotes resistance to agents that are commonly used to treat serious or complicated infections for which the consequences of treatment failure can be severe.

 Finally, unnecessary antibiotic use has important cost implications.

 Of an estimated $1.9 billion spent on antibiotics per year by health plans in our study cohort, direct drug costs could be reduced by 16% if health plans reduced their antibiotic utilization to the current 25th percentile of costs and by 26% if plans reduced their utilization to the current 10th percentile of costs. As with European antibiotic studies and other investigations into variation in the delivery of healthcare, it is difficult to determine the “correct” amount of antibiotic utilization. Nonetheless, understanding variation can provide valuable information to health plans and policymakers seeking to improve care quality and reduce unnecessary spending.

 Wennberg and colleagues were among the first to highlight the importance of reporting variation, pointing to large geographic differences within the United States in the use of healthcare interventions such as surgical procedures.

 For interventions for which an optimal rate of utilization is known, these data can stimulate benchmarking, allowing health plans and institutions to set achievable goals for their practice and to monitor progress toward these goals.

 Where an optimal rate of utilization is not known, reports of variation can help health plans and institutions understand their performance relative to that of their peers. This information can prompt plans with relatively high rates of utilization to examine why their delivery of services varies substantially from the norm, to determine whether this  represents a remediable problem in quality, and if so to investigate how to improve their care quality.

Our results suggest that overall rates of antibiotic utilization are well suited to a variation-centered approach. A number of commercial health plans are achieving far lower rates of antibiotic utilization than others. Of course, not all health plans are comparable; for example, plans whose enrollees have greater illness severity and barriers to accessing care may have legitimate reasons for prescribing more antibiotics than others.

 Thus, a high observed rate of antibiotic utilization should not be an end unto itself, but should prompt in-depth analysis to identify nonclinical factors that promote increased antibiotic use. Such analyses can be further guided by evaluation of prescribing rates within age and sex strata to determine whether certain patient subgroups have disproportionately high antibiotic utilization rates relative to a health plan’s peers. Where appropriate, local initiatives can be crafted to address the factors that promote increased antibiotic use, preferably borrowing from previous research to use active forms of clinician education and other methods proven to reduce unnecessary antibiotic prescribing.

 The NCQA took a variation-centered approach in creating this measure for the HEDIS program, in which overall antibiotic utilization has been included in the “use of services” domain, which tracks utilization and can be useful for comparison and identification of achievable goals without any specific performance targets.

Although we cannot definitively establish a “correct” rate of antibiotic utilization in commercial health plans, a variety of data suggest opportunities for improvement in the United States. Numerous studies have documented substantial overuse of antibiotics in the United States, and cross-national comparisons have found that Americans receive approximately 20% more antibiotics per capita than Europeans, with only 3 of 27 European countries having higher rates of antibiotic dispensing than the United States.

 Patients in the Netherlands, the lowest-prescribing European country, receive 60% fewer antibiotics than patients in the United States.10,29 Reducing antibiotic utilization is a complex endeavor and requires attention not only to clinical efficacy but also to patient satisfaction and downstream utilization of health services. Nonetheless, controlled trials to reduce inappropriate antibiotic use in the outpatient setting found no increase in subsequent health services utilization (eg, office visits, telephone calls) and little to no adverse impacts on patient satisfaction.

Our study has several limitations. The data collected were from the first year in which this measure was implemented by HEDIS. Although plans were given detailed instructions for complying with this measure, it is possible that certain plans had not perfected their data-collection and reporting processes. In addition, data are not publicly reported for the first year of any HEDIS measure. As is common, some plans chose not to report their results during this first year, and it is difficult to know whether these plans did not participate because they expected their performance to be poor or because of other factors. (For example, more than half of nonreporting plans had fewer than 10,000 members, compared with 7% of reporting plans, suggesting the possibility that smaller plans might have had fewer resources available to put toward a first-year, non— publicly reported measure.) Nonetheless, we did receive data from 83% of plans, and all HEDIS data are audited, suggesting that our results are representative of the target population.

Other characteristics of our methods merit consideration in interpreting this study’s findings. First, the manner in which data were reported by health plans may result in slight imprecision in our calculation of overall antibiotic utilization rates. However, this imprecision is likely to be small in relation to the large variation in rates among plans. Second, our estimates of drug cost data were based on extrapolations from another national data source (National Ambulatory Medical Care Survey), combined with utilization data from the study plans, and do not precisely reflect the actual drug costs incurred by plans (which may be influenced by negotiated deals with drug suppliers, etc). Thus, our drug cost analyses should be interpreted as reasonable estimates rather than a precise accounting of real drug costs, and they do not account for downstream cost expenditures or savings associated with reduced antibiotic use. Third, we did not have access to clinical data such as comorbid conditions; thus, we could not control for interplan differences in members’ health beyond those that are correlated with patient age and sex. Finally, we collected data only on HMOs and POS plans participating in the HEDIS program. Although the strong majority of eligible commercial health plans participate in the HEDIS program, we cannot know the  generalizability of our results to plans not participating in HEDIS or to persons with public insurance (eg, Medicaid), other forms of commercial insurance, or no insurance at all.

It is difficult to improve healthcare quality unless it can be measured. The substantial unexplained variation in antibiotic utilization across US health plans suggests opportunities to improve the quality and costs of antibiotic prescribing. We believe the NCQA antibiotic utilization measure should stimulate additional efforts to understand and improve antibiotic utilization at the health plan level—particularly for health plans in the higher range of antibiotic use. If successful, these efforts are likely to improve quality of care and to generate meaningful cost savings from reduced antibiotic costs&mdash;a winwin scenario for patients, payers, and the public health.

We thank Min Gayles, formerly at the National Committee for Quality Assurance, for her assistance obtaining the data and facilitating this research, and Saunak Sen, PhD, and John Boscardin, PhD, for their assistance with data analysis.

: From the Division of Geriatrics (MAS), San Francisco Veterans Affairs Medical Center, San Francisco, CA; Department of Clinical Pharmacy (KYY) and Department of Medicine (MAS, JHM, RG), University of California, San Francisco, CA; and the National Committee for Quality Assurance (SCB), Washington, DC.

: This work was supported by Career Development Transition Award 01-013 from the VA Health Services Research and Development Service (Dr Steinman), by grant K23-AG030999 from the National Institute on Aging and the American Federation for Aging Research (Dr Steinman), by the Agency for Healthcare Research and Quality (NCQA) Translating Research into Practice Program (R01 HS13915; Dr Gonzales and Ms Maselli) and by grant KL2 RR024130 from the National Center for Research Resources (Dr Yang). We submitted an advance copy of this paper to NCQA for review and comments, but that institution had no control over the analyses and interpretation of data or over the decision to publish this work. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official view of the Department of Veterans Affairs, the National Center for Research Resources, or the National Institutes of Health.

Rates of outpatient antibiotic prescribing vary widely between US commercial health plans. High-utilizing health plans may improve quality and lower costs by reducing unnecessary antibiotic use.

Michael A. Steinman, MD

Articles

Effect of a Patient Activation Intervention on Hypertension Medication Optimization: Results From a Randomized Clinical Trial

Variation in Outpatient Antibiotic Prescribing in the United States