Glen D. Stettin, MD

Nobody intends to get addicted to opioids. Sadly, it happens far too often. More sadly, it is easier to get addicted than to get unaddicted.

Americans are 4.8% of the world’s inhabitants, yet we consume a highly disproportionate share of many commercially available opioids, including hydrocodone (99.7%), oxycodone (69%), morphine (49.6%), hydromorphone (45%), and fentanyl (29%). We consume more than 5 times as many daily doses as Europeans, and our use has quintupled since the 1990s.

Americans overconsume opioids for several reasons. Culturally, and I generalize, we believe pain should be treated. We believe drugs are better than nondrug treatments and opioids are better than nonopioids. Our preferences, fueled by marketing budgets of opioid manufacturers, nudged us to seek opioids for pain and our physicians to oblige. Easy access to prescription opioids made getting addicted easy.

There is good news. America’s consumption of prescription opioids has been declining since 2012. During this time, pharmacy benefit managers have expanded efforts to accelerate this trend. For example, the CDC’s revised opioid prescribing guidelines in 2017 were quickly implemented by Express Scripts to reduce unsafe use of prescription opioids. We focused on making better decisions—fewer and smaller prescriptions; safer dosing, formulations, and combinations&mdash;the easiest decisions for physicians, pharmacists, and patients. We implemented coverage policies to curtail overuse and ensure needed access. We provided patients with education, monitoring, counseling, and safe in-home disposal of unused opioids. As a result, first-time users’ average day supply per fill declined by 55%, from 18.6 to 8.3 days, and 93% of first-time fills are now for 7 days or less. And 78% of new users with initial prescriptions for long-acting opioids are redirected to safer, short-acting opioids.

But let us not fool ourselves. We have more to do to prevent and treat opioid addiction. We must advocate for solutions that eliminate unsafe opioid exposure and improve access to effective addiction treatment. Practices can set defaults and prompts in electronic prescribing systems to increase adherence to prescribing guidelines. Examples include defaulting initial prescriptions to a 3-day supply and prompting physicians to reconsider requests for long-acting opioids in opioid-naïve patients, regimens that exceed dosing guidelines, and unsafe combinations of opioids with benzodiazepines or muscle relaxants. Policy makers can improve prescription drug monitoring programs and require that prescribers use them. For people struggling with addiction, we need safer and more effective ways to help them transition off of opioids completely or onto maintenance therapies. Finally, we need easier access to naloxone for preventing overdose deaths.

Our work in preventing and treating opioid addiction may never be complete, but it will be increasingly successful if we continue to focus on making it harder to get addicted and making it easier to get unaddicted or, at least, easier to get maintenance treatment that restores dignity and function to those who got addicted despite their best intentions.

Articles

From the Editorial Board: Glen D. Stettin, MD

 To determine if therapeutic gap identification, notification of community pharmacists, and intervention results in increased gap closure, reduced gap closure time, and fewer adherence gaps reopening.

Prospective, controlled, clusterrandomized study.

State of Illinois employees and beneficiaries of State health plans filling prescriptions at independently owned community pharmacies were included. For selected chronic conditions and medications, gaps in medication adherence and omitted essential therapies were identified from prescription claims and sent as alerts for resolution with the patient and/or physician. Adherence and omission gap closure at 90 days were analyzed with Kaplan-Meier (KM) survival curve approach and Cox proportional hazards models including covariates.

 A total of 1433 intervention and 1181 control adherence gaps were identified, while 677 intervention and 534 control omission gaps were generated. Pharmacists intervened on 639 (44.6%) adherence and 506 (74.7%) omission gaps. Gaps were closed more often in intervention than control at 30 days (55.5% in intervention vs 50.6% in control), 45 days (61.1% vs 58.4%, respectively), 60 days (66.1% vs 65.2%, respectively), and 90 days (73.0% vs 72.9%, respectively; adjusted hazard ratio [HR] = 1.242;

 = .022; 95% confidence interval [CI] 1.115-1.385). Adherence gaps reopened less frequently in the intervention group (HR = 0.863; 

 = .012; 95% CI 0.769-0.968). A total of 89 (13.1%) intervention and 29 (5.4%) control omission gaps closed within 90 days (adjusted HR = 1.770;

Independent community pharmacists reduced gaps in care and had fewer reopened adherence gaps, suggesting improvement in adherence. A continuation study will examine the impact of the program on long-term adherence.

This targeted patient outreach program involved identifying patients with poor adherence and omitted therapies (referred to as gaps in care) and notifying independent community pharmacists of these gaps in care.

		Prior to the program, community pharmacists underwent case-based training focused on disease management, motivational interviewing, and communication.

		The program resulted in increased adherence and omission gap closure and fewer adherence gaps reopened. An indicator of adherence was also improved by the program.

		This evaluation of our program can be used to develop more effective, targeted, evidence- based strategies to improve patient adherence and reduce errors of omission.

Underutilization of medications, exemplified by poor medication adherence and errors of omission, is a significant barrier to optimizing patient care. Adherence to chronic medications is reported to be as low as 50% after 1 year of therapy, thereby reducing the clinical effectiveness of drug therapy.

Medication non-adherence is commonly associated with cost, number of medications, duration of treatment, regimen complexity, and patient cognition.3 Likewise, omission gaps, where essential drug therapies are underutilized, are also common. In 1 Veterans Affairs study, omission errors accounted for 20% of all identified medication errors.

 Omissions in therapy have been shown to increase morbidity and mortality with greater potential to cause harm than unjustified prescribing.

In patients with diabetes, hypertension, hypercholesterolemia, and heart failure, poor adherence has been associated with increased hospitalizations and emergency department visits.

Poor adherence to medications has been shown to increase mortality, as exemplified by increased post—myocardial infarction fatality in patients with low adherence to statins and beta-blockers.

 In patients with diabetes and dyslipidemia, poor adherence has also been associated with increased medical and total costs of care.

 Overall, non-adherence to medications is believed to cost the United States healthcare system an estimated $100 billion annually.

While there are no available direct estimates for the implications and cost associated with omitted therapies, these costs could be greater because indicated, evidence-based therapies are not being used at all.

Automated physician notifications of needed omitted therapies have also been tried to increase use of these medicines.

 While many notifications were either ignored or perhaps were not true gaps, automated physician notification did result in increased use of omitted therapies. The involvement of a pharmacist to screen out these unnecessary notifications may be more efficient, but has not been evaluated.

Studies in the community setting aimed at improving patient adherence using mailings10 and telephone reminder systems

 have metwith mixed results. Those programs that focus solely on reminders tend to be less effective, since medication adherence is generally considered to be multifaceted and affected by system and health barriers, patient behaviors and beliefs, and patient self-efficacy.

As such, multifaceted interventions are more likely to be successful in changing patient

behavior and improving adherence. 16 More comprehensive interventions addressing adherence have generally shown better results.

Pharmacists are in an optimal position to address therapeutic gaps in care. Community pharmacists are knowledgeable about potential barriers and solutions to adherence issues. They have frequent contact with their patients, know them well, and when needed, reach out to physicians on their behalf. They are also widely considered a trusted source for providing information to patients and practitioners about medication therapies.

 Therefore, with the right supportive infrastructure, the community pharmacist may be very effective at validating and addressing therapeutic gaps in care related to underutilization of medications. Such an infrastructure includes pharmacists having access to evidence-based gaps in care alerts and comprehensive medication and fill history at the point of care.

The objective of this study was to determine if the identification of therapeutic gaps in care from prescription claims data, notification of community pharmacists of gaps through alerts, and intervention on those alerts results in higher gap closure, reduced time to closure, and reduced proportion of gaps reopening in the case of adherence gaps.

This study was a prospective, cluster-randomized, controlled quality assurance study. The study was approved by the University of Illinois at Chicago institutional review board and conducted in compliance with all regulations of the Health Insurance Portability and Accountability Act of 1996.

All pharmacies were required to 1) service members in the State of Illinois network, 2) be located in Illinois, 3) be independently owned, 4) utilize a commercially available web-based clinical documentation platform capable of relaying real-time gap information from Medco to pharmacists and capturing pharmacists’ documentation of gap causes and status, patient disposition, and comments, and 5) have 10 or more patients with identified care gaps over the 13-week period prior to randomization. In all, 96 independently owned community pharmacies met inclusion criteria and were invited to participate. Pharmacies were randomized to either provide the intervention or not (usual care control).

Population and Patient Inclusion Criteria

State of Illinois employees and beneficiaries (spouses, children, and retired workers) with continuous eligibility during the study period, and all of their pharmacy claims, were included. Patients were assigned to a particular pharmacy based on where the patient was filling the majority of their prescriptions in the 6 months prior to the study. Patient assignment was solely for the purpose of providing the intervention. Specifically, gap alerts were sent to the assigned intervention pharmacy. Control pharmacies did not receive gap in care alerts. At the start of the project, a letter was sent to the intervention pharmacy patients to inform them of the services being offered. Patient participation was optional; patients were not informed about the study. Patients were not required to fill medications at the assigned pharmacy.

Adherence and omission gaps in care were identified daily, using Medco’s prescription claim warehouse and defined clinical criteria. Gaps in care were transmitted as alerts to the pharmacy via the web-based platform. Adherence gaps occurred when a patient on selected chronic medications had a medication possession ratio (MPR) of less than 80% over the last 18 months and was late to fill the medication. Omission gaps occurred when a patient with a selected chronic condition was missing a medication on their profile. Omission gaps were selected based on documentation of clinical evidence and general consensus supporting the medication’s use for a particular condition. The alert definitions and situations for which they were generated are shown in 

Intervention Pharmacy Training and Practice

Control pharmacies and pharmacists continued to provide the services normally provided to their patients and did not receive gap in care alerts. Beyond the usual patient services, intervention pharmacies provided the additional service of addressing care gaps identified by Medco and communicated via a web-based clinical documentation and communication platform. All participating pharmacists at intervention pharmacies were required to undergo a certified continuing education program comprising 7.5 hours of online and 3.5 hours of in-person case-based training conducted by residencytrained clinical pharmacists with experience in medication therapy management and ambulatory care specialty clinics. Training focused on disease management (see Table 1 for conditions), motivational interviewing, and communication. Program sponsors held monthly telephone conferences with the intervention pharmacists to gather feedback on process improvement and best practice sharing and provide updates. Additional education and communication materials were provided when requested, such as the American Diabetes Association treatment guidelines.

The approach to addressing therapeutic gaps depended on the type of gap identified, as well as patient-, pharmacist-, and pharmacy-specific factors. In-person communication was encouraged for the first patient visit, with follow-up visits being in-person or by telephone. Communication with prescribers by telephone or facsimile was encouraged. However, pharmacists were allowed to conduct the intervention as appropriate, according to the pharmacy’s policies, procedures, and established relationships.

An adherence gap was considered “closed” once a prescription was filled for the medication in question. An adherence gap was considered “reopened” if an alert was generated for the gap for a second time during the study period. An omission gap was considered closed when a new prescription was written for an appropriate medication. Typically, adherence gaps were addressed directly with the patient, while omission gaps were addressed with the patient’s primary care or other provider as appropriate.

Pharmacists documented their interventions for the identified gap in care using the web-based clinical platform. All pharmacist activities were within the scope of practice, as defined by Illinois State and national pharmacy organizations and by the Illinois State practice laws. Pharmacies were reimbursed $30 for the initial session and $15 for each of up to 2 follow-up sessions to a maximum of $60 per gap addressed.

A power analysis, conducted prior to the study start, established that a sample of 410 adherence and omission gaps per group were required to detect a difference in absolute gap closure rate of 10%, assuming alpha of 0.05 and power of 0.8. We estimated that approximately 1500 adherence gaps and 1000 omission gaps would be available for analysis if 45 pharmacies were enrolled in each group. We targeted 48 intervention and 48 control pharmacies, in case any pharmacies needed to drop out.

Prescriber, pharmacy, and patient characteristics were analyzed with summary statistics of counts and proportions, as were data regarding number of gap openings, closings, and in the case of adherence gaps, reopening. Gaps that existed at the start of the study as well as new gaps that occurred within 90 days after the study start date were analyzed. Gap closure was analyzed at 90 days after an alert was sent to the pharmacy (or wouldhave been sent in the case of control pharmacies). Where a patient had 2 gaps during the intervention period for the same gap category, only the first gap was used in assessing gap closure. Unadjusted analyses of gap closure at 90 days were initially conducted using the χ

 test statistic. In the case of adherence gaps, an unadjusted χ

 analysis was conducted assessing the proportion of gaps that reopened prior to the end of the study.

Adherence and omission gap closure over time were analyzed using the Kaplan-Meier (KM) survival curve approach

 For the adherence gap model, patient age and gender and total number of Medco prescription claims during the study period and Medco patient count for the pharmacy were included as potential covariates in the model. For the omission gap model, the same patient and pharmacy covariates were included, along with prescriber age, gender, specialty, practice type, practice size (estimated by number of Medco patients), and number of Medco prescription claims. Variable selection methods were used to find the final model following the selection criteria; both the entry and stay

A program identifying and resolving care gaps involving community pharmacists resulted in increased adherence and omission gaps closure and fewer adherence gap reopenings.

Identification of and Intervention to Address Therapeutic Gaps in Care

 To evaluate the relationship between adherence to antihypertensive medications (AHMs) and subsequent hospitalizations, emergency department (ED) visits, and costs of care.

Retrospective analysis of a national pharmacy benefits database of deidentified pharmacy and medical claims among patients with a diagnosis of hypertension. Adherence was estimated using the medication possession ratio (MPR).

Multivariate logistic and 2-part general linear models were estimated to study the relationship between adherence level (estimated by the MPR) and subsequent association with healthcare costs and cardiovascular (CV)-related hospitalizations and ED visits.

We identified 625,620 patients with at least 2 claims for AHMs and divided them into 3 cohorts based on year 1 MPR of less than 60% (62,388 patients with low adherence), 60% to 79% (96,226 patients with moderate adherence), and 80% or higher (467,006 patients with high adherence). Patients with high adherence to AHMs were more likely to be older and male, have higher chronic disease scores and lower AHM copayments, and fill a greater percentage of prescriptions by mail order. Year 2 total mean (SD) adjusted healthcare costs were significantly lower for patients with an MPR of 80% or higher ($7182 [$27]) vs 60% to 79% ($7560 [$59]) or less than 60% ($7995 [$73]) (P <.001 for both). In addition, patients with low or moderate adherence had higher age- and sex-adjusted odds of CV-related hospitalizations (odds ratio [OR], 1.33; 95% confidence interval [CI], 1.25-1.41) and ED visits (OR, 1.45; 95% CI, 1.33-1.58) (P <.001 for both).

Adherence to AHMs is associated with significantly lower total healthcare costs and with significantly lower odds of CV-related hospitalizations and ED visits.

Adherence to antihypertensive medications (AHMs) is associated with lower total healthcare costs and with reduced odds of cardiovascular-related hospitalizations and emergency department visits.

High adherence to AHMs (medication possession ratio [MPR], 80%-100%) reduces adjusted total healthcare costs up to $813 per patient annually compared with low or moderate adherence to AHMs (MPR, <80%). 

The relationship between medication adherence and healthcare costs was continuous and inversely proportional such that healthcare costs increased significantly as the MPR decreased. 

 Benefits of improved adherence and evidence of total cost savings provide incentive for patient-, provider-, and health system-level interventions to improve AHM adherence.

	More than 74.5 million Americans have hypertension, with an estimated cost of $76.6 billion per year.

 Previous clinical trials in hypertension have shown that reduction of blood pressure is associated with significant decreases in the incidence of stroke, ischemic heart disease, congestive heart failure, and renal failure across age, sex, race/ethnicity, type of antihypertensive used, and severity of hypertension.

 Despite this benefit, it is estimated that only 35% of patients with hypertension achieve optimal control.

	In observational studies, nonadherence to antihypertensive medications (AHMs) is an important factor in the failure to achieve better blood pressure control

and is associated with a higher risk of hospitalization and mortality among patients with diabetes mellitus.

 found that high adherence to AHMs, identified as a medication possession ratio (MPR) of 80% to 100%, was associated with better blood pressure control compared with low-to-medium levels of adherence. Two recent studies describe an association between better adherence to AHMs and decreased subsequent cardiovascular (CV) events

 among patients with newly diagnosed hypertension, but neither study evaluated costs of care. A 2009 review of the importance of medication adherence relative to CV outcomes stated that there is a clear need for more research to better understand the association between adherence and healthcare costs because surprisingly little is known among populations with CV disease.

 demonstrated that better adherence to AHMs is associated with reduced costs of care. However, results from these studies may have been influenced by analysis of adherence and costs in the same year, and all relied on data from the 1990s. In addition, these prior studies evaluated the effect of adherence on costs among limited populations that included a large employer

	Given the limitations of prior work and the growing recognition about the importance of medication adherence,

 more robust studies are needed to evaluate current cost implications associated with adherence to AHMs. The present national retrospective cohort study of 625,620 insured patients with a diagnosis of hypertension evaluated the relationship between adherence to AHMs in 2007 and hospitalizations, emergency department (ED) visits, and costs of care in 2008.

	Data were extracted from the information warehouse of Medco Health Solutions, Inc. The information ware-house is a data repository that includes deidentified demographic and pharmacy claims history, including retail and mail-order use. The information warehouse has deidentified medical claims data on more than 10 million patients that can be matched to pharmacy claims.

	The study included all patients aged 18 to 63 years with an 

International Classification of Diseases, Ninth Revision, Clinical Modification

), diagnosis of hypertension (codes 401.x-405.x), and with 2 or more claims within 1 primary class of AHMs, at least 1 of which was between July 1, 2007, and December 31, 2007, and another within 12 months before the last claim in 2007. We excluded patients with claims for cancer (codes 140.x-172.x and 174.x-208.x) or human immunodeficiency virus (codes 042.x-044.x), as these diseases can be associated with overwhelming pharmacy and medical costs independent of adherence to AHMs. In addition, we excluded patients who were not continuously eligible for prescription drug coverage; who did not have medical or pharmacy data from July 1, 2006, through December 31, 2008; or who were 64 years or older as of January 1, 2007, because the information warehouse of Medco Health Solutions, Inc, does not reliably receive complete medical claims for patients covered by Medicare. For analysis  purposes, we defined year 1 as January 1, 2007, through December 31, 2007, and year 2 as January 1, 2008, through December 31, 2008 (

	were used to create a deidentified data set that was further analyzed for this study.

	Adherence to AHM class was estimated by calculating the MPR, or the ratio of total days of medication supplied to total days in a period. The MPR is the accepted standard for evaluation of adherence using retrospective data because it is easy to calculate and performs as well as other claims-based estimates.

 The MPR was calculated based on the total number of days between the dates of the last fill in year 1 and the first fill in the 365 days before the last fill. This MPR evaluation included a look back through July 1, 2006, to provide a full year of data on adherence. For patients receiving multiple AHMs, the MPR was calculated for each AHM separately, and the overall MPR was the mean of the individual MPR values.

The MPR was capped at 100%, as it is unlikely that patients use AHMs at greater than the prescribed frequency.

 For the primary evaluation, we used year 1 MPR to classify the patients into the following 3 categories: high adherence (MPR, >80%), moderate adherence (MPR, 60%-79%), and low adherence (MPR, <60%). As a secondary analysis of costs of care, we divided the MPR into 5 levels (0%-19%, 20%-39%, 40%-59%, 60%-79%, and 80%-100%). Previous literature recommends using the MPR as a dichotomous variable, with an 80% MPR considered adherent.

 Although this percentage is somewhat arbitrary, it has been accepted as the cutoff in most investigations on adherence and in observational and randomized controlled clinical trials.

	The number of total medications and different classes of AHMs on hand were determined as of December 31, 2007. In addition, we evaluated the following variables during year 1 that were identified as potential mediators of adherence: generic dispensing rates, the number of prescriptions filled for generically available AHMs divided by the total number of prescriptions for AHMs, the percentage of AHMs filled through mail order, the number of prescriptions filled by mail order divided by the total number of prescriptions filled after adjusting for the increased days supply by mail order, and the monthly patient copayment for AHMs from pharmacy claims, which is the total amount paid by the patient, including copayment, deductible, and coinsurance per month covered by prescriptions.

	To control for possible comorbidities, we used pharmacy claims from 2007 to calculate a chronic disease score (CDS).

 The CDS is a comorbidity severity indicator derived from pharmacy-based claims data during a given period. For each patient, we used medical claims to evaluate the number of hospitalizations and ED visits in year 1 and year 2 and the following specific comorbidities identified by 

 codes: coronary artery disease (codes 410.x-414.x and 996.03), dyslipidemia (code 272.x), stroke (codes 430.x-438.x and 997.02), heart failure (codes 402.x1, 404.x1, 404.x3, and 428.x), diabetes mellitus (code 250.x), end-stage renal disease (ESRD) (codes 403.x1, 404.x1, 585.5, and 585.6)

 and depression (codes 296.2x, 296.3x, 298.0, 300.4, 309.1, 309.28, and 311).

	The primary outcome was total healthcare costs during year 2 (January 1, 2008, through December 31, 2008) for the groups having low-to-moderate adherence compared with the group having high adherence. In a secondary analysis, adjusted healthcare costs were also calculated for 5 quintiles of adherence. Total healthcare costs were determined by summing the costs of all pharmacy and medical claims (including outpatient services, hospitalizations, and ED visits) in 2008. Costs were based on total cost to the plan sponsor and excluded atient copayments and deductibles. Nursing home and home healthcare costs were not included in our cost analysis. Secondary outcomes were CV-related hospitalizations and ED visits, evaluated as the risk of having 1 or more medical claims indicating a hospitalization or an ED visit in 2008.

	We compared the baseline demographic factors, comorbidities, and medication factors between adherent and nonadherent patients using χ2 test for binary variables and Wilcox on signed rank test for continuous variables. Regression analyses were performed to assess the relationship between outcome variables and covariates. When the outcome variable had continuous values, we used general linear model (GLM) least squares regression analysis; when the outcome variable had binary values, we used logistic regression analysis. To reveal the determinants of adherence to AHMs, we created an outcome variable of high adherence versus low-to-moderate adherence. The predictors included demographics alone and together with comorbidities, CDS, class of AHMs, out-of-pocket cost for medication, and use of mail order. To explore possible differential effects of sex in critical subgroups of patients, we performed a secondary analysis of determinants of adherence that included interaction terms for sex-

ESRD. We used logistic regressionanalysis to evaluate the association of level of adherence with clinical outcomes of CV disease—related hospitalizations and ED visits after adjusting for important baseline differences. To determine the mean costs in year 2 for each level of adherence, we used GLMs and adjusted for baseline differences in age, sex, year 1 CDS, and year 1 total costs. Least squares means of costs were generated and compared among groups with the 3 levels of adherence. Version 9.1 of SAS (SAS Institute, Cary, NC) was used for all statistical analyses, and 2-sided 

<.05 was considered statistically significant.

	After applying the inclusion and exclusion criteria, we identified 625,620 patients for the analysis (

). On average, patients were 52.6 years old and 48.9% female and had an MPR of 86.3% and a CDS of 22.0 (

). The most common comorbidities in the population included dyslipidemia (58.5%), diabetes mellitus (24.2%), coronary artery disease (11.0%), and depression (7.6%). At the end of year 1, patients averaged 3.7 medications on hand, representing 1.3 different classes of AHMs. Generic medications were used in 63.2% of AHMs, and 46.7% of patients received 1 or more of their AHMs by mail order. Angiotensin-converting enzyme inhibitors (43.9%) and diuretics (48.3% [84.5% of these were thiazides]) were the most common classes of AHMs used. The mean copayment for the AHMs was $10.10 per month.

	In year 1, using the prespecified categories of adherence, 467,006 patients (74.6%) demonstrated high adherence to AHM therapy (MPR, >80%), 96,226 patients (15.4%)  demonstrated moderate adherence (MPR, 60%-79%), and 62,388 (10.0%) demonstrated low adherence (MPR, <60%). In 

, we used logistic regression analysis to evaluate predictors of high adherence to AHMs versus low-to-moderate adherence. When adjusted for age, sex, and comorbidities, older patients were more likely to be adherent (odds ratio [OR], 1.03; 95% confidence interval [CI], 1.03-1.03), and female patients were less likely to be adherent (OR, 0.91; 95% CI, 0.90-0.93). In addition, dyslipidemia diagnosis (OR, 1.14; 95% CI, 1.13-1.15), higher CDS (OR, 1.26; 95% CI, 1.24-1.28), prescriptions for more than 1 class of AHMs at the end of year 1 (OR, 2.08; 95% CI, 2.02-2.13), and receipt of AHMs by mail order (OR, 2.24; 95% CI, 2.21-2.27) were associated with better adherence. The ORs for the sex-related interaction terms were not statistically significant.

	In the multiple linear regression analysis evaluating the relationship between adherence in year 1 and costs of care in year 2 (

), patients in the high adherence group had significantly lower total healthcare costs ($7182) compared with patients in the moderate ($7560) and low ($7995) adherence groups (

 <.001 for both). This represents lower total healthcare costs of $387 to $813 per patient for patients who are adherent to their AHMs (

<.001for both). In a similar analysis, we adjusted for year 1 hospitalizations rather than year 1 total costs. The results of this additional analysis were similar to the results for the relationship between adherence and year 2 costs.

	To determine if this association between adherence and costs was consistent across narrower ranges of adherence, we further subdivided adherence into 5 quintiles (0%-19%, 20%-39%, 40%-59%, 60%-79%, and 80%-100%) and determined adjusted costs for patients in each quintile. In 

, adherence and adjusted total costs seem to have a linear relationship, with lower adherence associated with higher total costs. In addition, adjusted total costs at all levels of adherence were significantly higher compared with costs in the adherent group (

 <.001 for all). Total costs of care for patients in the lowest adherence category ($8427) were $1246 higher than total costs of care for patients in the highest adherence category ($7181).

	During year 2, we identified 11,895 CV-related hospitalizations and 5150 CV-related ED visits in the study cohort. Hospitalization rates were 2.1%, 2.1%, and 1.8% among patients with low adherence, moderate adherence, and high adherence, respectively (

). Emergency department visit rates were 1.0%, 0.9%, and 0.8% among patients with low adherence, moderate adherence, and high adherence, respectively. When adjusted for age and sex, patients with low adherence and moderate adherence were more likely to be hospitalized for a CV-related event (ORs, 1.33 and 1.28, respectively) and were more likely to have 1 or more CV-related ED visits (ORs, 1.45 and 1.27, respectively). When fully adjusted for baseline characteristics, the trend remained the same for risk of hospitalizations and ED visits; however, the differences only remained significant in the low-adherence group.

	In this large retrospective analysis, patients diagnosed as having hypertension who were adherent to their AHMs in year 1 had $387 to $813 lower total healthcare costs and had decreased risk of CV-related hospitalizations and ED visits. These results suggest that patients who are nonadherent may not receive full benefit from their AHM therapy. In addition to worse clinical outcomes, nonadherence to AHMs resultsin higher total healthcare spending. Nonadherence was associated with greater healthcare utilization as demonstrated by CV-related hospitalizations and ED visits, which contribute to the increase in total healthcare costs seen in this population.

 from Medco Health Solutions, Inc, evaluated AHM adherence and costs in an insured population. Data were from a separate database than that used for this study and included patient claims from June 1997 to May 1999. The study found a trend of lower total healthcare costs associated with higher adherence; however, the findings did not reach statistical significance among 7981 patients included in the hypertension cohort. In our analysis, increased drug therapy costs were offset by a corresponding decrease in medical costs in the adherent group, yielding clinically meaningful and statistically significant findings. The effect of adherence to AHMs on medical costs and risk of CV-related hospitalizations and ED visits is notable inthis population of patients younger than 65 years. Based on age alone, this population is at lower risk for CV morbidity and mortality than adults 65 years and older.

 It has been suggested that the primary benefits of hypertension treatment in young and middle-aged populations are due to reduction of subclinical organ damage rather than reduction in clinical events.

 However, our data suggest that AHM adherence in a younger population is associated with a decrease in clinical events, providing further evidence for the importance of enhancing adherence to AHMs in patients of all ages with hypertension.

	Based on the adjusted costs of care in Figure 3, the relationship between medication adherence and healthcare costs was continuous and inversely proportional such that healthcare costs increased significantly as the MPR decreased. The incremental medication costs in the group with low adherence were $446, offset by a reduction in medical cost of $1692, for a net savings of $1246 per patient. Because these were obnservational data, we were unable to run a formal cost-benefit analysis. Based on the analysis shown in Figure 3, our results suggest that there may be an inverse linear relationship between AHM adherence and costs of care.

	In our analysis, patients were more likely to be adherent if they were older and male, filled their prescriptions via mail order, and had dyslipidemia, higher CDS, more total  medications, and prescriptions for more than 1 class of AHMs at the end of year 1. The finding that women had worse adherence is consistent with some

 prior studies. Furthermore, results of the analysis of interactions between sex and age and between sex and each comorbidity suggest that in this study women were less likely to be adherent in younger and older age groups and among patients with and without each comorbidity. The association of higher CDS and more total medications with better adherence is consistent with findings from a recent study

 on AHM adherence in patients with newly diagnosed hypertension, which found that male sex, use of more than 5 concurrent medications, and diagnosis of dyslipidemia were predictors of better adherence. An explanation for this association is that symptomatic patients or patients with poor understanding and perception of hypertension may be more motivated to adhere to their medication regimens because they perceive a greater risk of nonadherence.

However, the presence of most other CV comorbidities among our patients was associated with an increased risk of nonadherence in demographically adjusted and fully adjusted analyses. Further studies are needed to confirm and extend our understanding of the determinants of adherence in patients with hypertension, with particular attention to the role of comorbid conditions.

Retrospective analysis of antihypertensive medication adherence and subsequent total healthcare costs demonstrated a significant, continuous, and inversely proportional effect of adherence on total healthcare costs.

Antihypertensive Medication Adherence and Subsequent Healthcare Utilization and Costs

To measure the timing and frequency of follow-up care during the initiation phase of antidepressant therapy, and to compare the typical pattern of care with current product guidelines. 

The study included 84 514 adult and pediatric patients who started a new course of antidepressant therapy for any indication between July 2001 and September 2003. Patients were members of a large managed care organization in the northeastern United States. Ambulatory visits during the first 12 weeks of treatment were identified using medical claims data. Outcome measures were time to first follow-up visit, frequency of follow-up visits, and percentage of patients receiving recommended levels of care. 

During the first 4 weeks of treatment with antidepressants, only 55.0% of patients saw a healthcare provider for any purpose, and only 17.7% saw a provider for mental healthcare. Ambulatory visit rates during the first 4, 8, and 12 weeks were significantly lower than the minimum levels recommended in product labeling (

<.0001). Only 14.9% of patients received the Food and Drug Administration-recommended level of follow-up care during the first 4 weeks, 18.1% at 8 weeks, and 22.6% at 12 weeks. 

Adults and children who begin a new course of antidepressant therapy tend to receive far less monitoring than is recommended in current product labeling. Given safety concerns during the initiation phase, earlier and more frequent follow-up care appears desirable. Further research is needed to identify the most cost-effective schedule of care. 

In October 2004, the Food and Drug Administration (FDA) directed manufacturers to include a new "black box" warning and expanded warning sections in the labeling for all antidepressant products.

The new labeling warns healthcare providers about the increased risk of suicidal thoughts and actions ("suicidality") in children and adolescents who take antidepressant medications for any indication, including major depressive disorder and obsessive-compulsive disorder (OCD). The FDA also developed a patient medication guide on the risk of suicidality and the precautions that can be taken by patients, parents, and caregivers.

These regulatory actions were based on a combined analysis of adverse events in 24 clinical trials that studied the use of antidepressant drugs in pediatric populations. When results were pooled across all drugs and all indications, the analysis showed a small but statistically significant increase in suicidality risk for antidepressants (compared with placebo) during the first few months of treatment.

The new warning labels advise healthcare providers to monitor pediatric patients closely for signs of clinical worsening, suicidality, or unusual changes in behavior, especially during the first few months of a new course of antidepressant therapy or after a change in dose.

The warning labels recommend a specific pattern of followup care: at least weekly face-to-face visits during the first 4 weeks of treatment, visits every other week during the next 4 weeks, a visit at 12 weeks of treatment, and visits as indicated clinically beyond that point. Additional contacts by telephone as needed are encouraged between the face-to-face visits. More concentrated monitoring during the early stages of treatment is recommended because there is some evidence that the risk of suicidality may be higher during the first few weeks of treatment.

Although the risk of suicidality in children and adolescents has been the primary focus of public and regulatory attention, the FDA also has begun to address the possibility of an increased risk of suicidality in adult users of antidepressants.

Warnings about this risk were first introduced into the labeling of selected products in March 2004, and they were expanded and applied to all antidepressant products in October 2004.

The new product labels recommend "close monitoring" of adult patients for signs of clinical worsening or suicidality, especially during the first few months of a new course of therapy for depression, and they recommend an intensive pattern of follow-up visits similar to the pattern recommended for pediatric care.

 In June 2005, the FDA announced that it had begun a comprehensive analysis of adult clinical trial data (similar to the combined analysis of pediatric trial data) in an effort to create a stronger evidentiary basis for future regulatory actions in this area.

The new suicidality warnings build on a broader framework of clinical practice guidelines for the use of antidepressants in the treatment of depression, anxiety disorders, and other conditions. These guidelines have been developed most extensively for the treatment of depression in adults,

whereas relatively few have been developed for the pharmacological treatment of anxiety disorders

or for pediatric use of antidepressants for any indication. Guidelines for the treatment of depression call for close monitoring of patients during the acute phase of treatment, which generally lasts a few months and ends with remission of symptoms.

The primary goals of monitoring are to assess the effectiveness of therapy, identify side effects, monitor the patient's condition, adjust therapy as needed, and promote adherence.

Guidelines for the treatment of anxiety disorders also call for close monitoring of patients who receive medication therapy.

Guidelines vary widely in the recommended frequency and timing of follow-up visits for patients who begin a new course of antidepressant therapy. The American Psychiatric Association recommends a vigorous schedule of monitoring during the acute phase of treatment for depression–at least weekly contacts in routine cases and multiple contacts per week in more complex cases.

Monitoring contacts are defined broadly to include face-to-face visits, telephone contacts, or contacts with other knowledgeable clinicians, depending on the circumstances.

In an earlier set of guidelines, the Agency for Health Care Policy and Research recommended face-to-face visits every 10 to 14 days during the first 6 to 8 weeks of treatment for patients with less severe depression, and weekly visits in more severe cases.

Other clinical guidelines recommend follow-up contacts on a weekly, biweekly, or monthly basis, depending on a variety of factors.

 The FDA guidelines fall roughly in the middle of this range–weekly face-to-face visits during the first month, tapering to monthly face-to-face visits by the third month. 

Many studies have examined patterns of treatment response to antidepressant medications, but only a few have reported data on the typical frequency of follow-up visits for adult or pediatric patients who initiate antidepressant therapy in usual practice.

The limited available data suggest that follow-up visit rates tend to be low,

but none of these studies provide a direct measure of the concordance between usual practice and the new FDA guidelines. The most commonly used measure of visit frequency was developed by the National Committee for Quality Assurance to assess the pharmacological management of depression.

As defined in Health Employer Data and Information Set (HEDIS) specifications, "optimal practitioner contact" frequency is a minimum of 3 follow-up visits for mental healthcare during the first 12 weeks of a new treatment episode; at least 2 of the measured visits must be face-to-face, and 1 may be a telephone contact.

This metric is useful for evaluating the overall quality of care, but it is not well suited to measuring the concordance of current practice with the new FDA guidelines for antidepressant products. The HEDIS metric is limited to patients with depression (rather than any condition treated with antidepressants), it provides no information on the follow-up care received by pediatric patients (because the metric is defined only for adults), and it provides no information on the time course of visits during the first 12 weeks of care. Without more comprehensive benchmark data, it is difficult to gauge whether the pattern of care defined in the new product labeling is feasible and realistic in the context of current practice. 

The objectives of this study were to measure the timing and frequency of follow-up visits during the initial phase of antidepressant therapy, and to compare the typical pattern of care with the pattern recommended in product labeling. The design of the study mirrored the scope and content of the new FDA guidelines. The study measured the time course of face-to-face visits for any patient (adult or pediatric) who began using an antidepressant medication for any treated condition, and it compared the typical pattern of visits with the time course recommended in product guidelines. Although the new guidelines apply equally to all pediatric users, we measured visit frequency separately for 3 pediatric age groups (0-6 years, 7-12 years, and 13-18 years), because of the wide variations in treated conditions and incidence of use across these groups. 

Patients were participants in medical and pharmacy benefit plans administered by a large managed care organization in the northeastern region of the United States; the medical plans included coverage for mental healthcare. The study population included all plan participants who had combined eligibility for medical and pharmacy benefits at any time during the study period, which extended from January 1, 2001, through December 31, 2003. A total of 2 411 316 plan participants met the criteria for inclusion. 

Medical utilization data (including mental health claims data) were drawn from an administrative claims database maintained by the managed care organization. Drug utilization data were drawn from a prescription claims database maintained by Medco Health Solutions, Inc, the pharmacy benefits management company that manages the prescription benefit plans for this population. Medical and pharmacy claims data were integrated at the patient level; personal identifying information was not retained. 

Patients were initially identified for the study sample if they filled a prescription for an antidepressant during an identification period that extended from July 1, 2001, through September 30, 2003. Prescriptions for all classes of antidepressants were included in the analysis (see the 

available online at www.ajmc.com). A prescription was considered the start of a 

 if no antidepressant prescriptions had been filled for the same patient during the prior 180 days (a 6-month negative medication history). The 

for the new episode was the fill date of the prescription at the start of the episode. Patients were included in the final study sample if they had continuous benefits eligibility during the 6 months before the index date and the 3 months after the index date. A total of 84 514 patients with new episodes of antidepressant therapy met the criteria for inclusion in the study sample. A small percentage of patients (4.9%) had 2 or more new episodes during the identification period; these recurrent episodes were included in the final study sample. As defined here, a new episode is a new course of therapy for any condition treated with antidepressants; this definition accords with the scope of the new warning labels for these products. 

For each patient in the study sample, medical claims were used to track ambulatory follow-up care during a 12-week analysis period after the index date of the new episode. Ambulatory care included office visits and outpatient visits; telephone visits were not included. Follow-up visits were identified at 2 levels of specificity: all ambulatory visits and mental health visits. 

Ambulatory visits were identified by place-of-service codes for office and outpatient visits (Appendix). All visits were included, no matter what diagnostic coding was used in billing. This measure provides an upper bound for follow-up visits where the patient's response to antidepressant therapy may have been addressed. All face-to-face visits provide an opportunity for follow-up care, even if the visit was scheduled or coded for another purpose. 

Mental health visits were identified by Current Procedural Terminology (CPT) codes for evaluation and management visits in conjunction with mental health diagnostic codes, or by CPT codes for psychiatric visits (Appendix). This measure provides a lower bound for follow-up visits where the patient's response to antidepressant therapy was likely addressed. The measure is likely to underestimate the actual number of follow-up visits, because some mental health visits may not be coded as such. 

For each patient starting a new episode of therapy, the patient's age and sex were derived from an eligibility database maintained by the managed care organization. Patient age was measured at the index date of the new episode. 

Analyses were conducted separately for children (age =18 years) and adults (age =19 years). For each age group, 3 sets of measures were derived from the claims data: 

The percentage of patients who had at least 1 follow-up visit during a specified interval after the start of a new episode. This percentage was measured at 1, 2, 3, 4, 8, and 12 weeks after the index date. 

The percentage of patients who received the minimum level of follow-up care recommended in product labeling–at least 4 visits in the first 4 weeks, at least 6 visits in the first 8 weeks, and at least 7 visits in the first 12 weeks. 

All measures, including exact confidence intervals, were computed using SAS version 8.0 (SAS Institute Inc, Cary, NC). 

The age and sex distribution of patients in the study sample is shown in 

. Incidence rates for new episodes varied widely as a function of both sex and age. The incidence rate among adults was nearly twice as high for women as for men, but among children 12 years of age and younger, the incidence of new episodes was twice as high for boys as for girls. Among adolescents (aged 13-18 years), the incidence rate was higher for girls. 

The majority of patients did not have a face-to-face visit with a healthcare provider during the first few weeks after the start of antidepressant therapy (

). During the first week of medication use, only 1 in 4 patients (25.3%) saw a healthcare provider for any purpose, and fewer than 1 in 10 patients (8.0%) saw a healthcare provider for mental health. During the first 4 weeks of treatment, only 55.0% of patients had a face-to-face visit for any purpose, and only 17.7% had a visit for mental healthcare. After 8 weeks and 12 weeks of treatment, many patients still had not had follow- up contact with a healthcare provider (

The cumulative frequency of follow-up visits after the start of antidepressant therapy is shown in 

. In general, adult and pediatric patients had only 1 or 2 face-to-face visits with healthcare providers during the first 4 weeks of treatment. During the first 12 weeks, they averaged 4 to 5 face-to-face visits. The observed visit rates were significantly below the minimum levels recommended in product labeling (

<.0001 at each interval for all age groups). 

The differences between guidelines and practice are illustrated in 

for adult and pediatric patients combined. The average number of ambulatory visits (for any purpose) fell short of the minimum levels recom- mended in product guidelines, and mental health visits fell far short of the recommended frequency at each interval (

<.0001 all comparisons). The average number of visits per month was roughly constant, diminishing only slightly over time. This finding contrasts with the recommendations in product labeling, which call for more frequent visits during the first month of a new episode. 

The gaps between recommended care and current practice were corroborated by an analysis of the number of patients who received the FDA-recommended levels of care during the first 12 weeks (

). At each interval after the start of treatment, only a small percentage of patients received follow-up care that met the minimum level defined in product labeling. 

Although comparing visit rates across age groups was not a primary objective of this study, some significant differences were observed (Table 3). In general, children had more frequent ambulatory contacts during the first 3 months of antidepressant treatment compared with adults (

<.05 at each interval, all children vs all adults). Children had a much greater frequency of mental health visits than adults (

<.05 at each interval), because a larger proportion of their ambulatory visits were with mental health professionals. Adolescent patients (aged 13-18 years) tended to receive more frequent follow-up care than younger children (aged 0-6 years and aged 7-12 years) during the first 3 months of antidepressant treatment. This pattern was especially strong for mental health visits; adolescent patients had significantly more mental health visits than patients in either of the younger age groups (

Clinical practice guidelines recommend frequent follow-up visits with patients who begin a course of antidepressant therapy, and product labels now call for a vigorous schedule of face-to-face visits to monitor for signs of suicidality. The results of our study suggest that follow-up care in practice is far less frequent than what is recommended by the current product labeling. In this study sample, more than 80% of patients had no mental health visits with healthcare providers during the first 4 weeks after starting antidepressant therapy, and the average patient had only 1 or 2 face-to-face visits for any purpose during that period. 

Although FDA guidelines call for vigorous follow-up care for both adults and children who begin using antidepressants, the primary impetus for the new labeling was the risk of increased suicidality in children. A significant increase in suicidality was demonstrated in pediatric studies (when data were pooled across drugs and indications),

but an association of this type has not yet been demonstrated in combined analyses of adult studies.

In this context, it may be reassuring that children generally receive more frequent follow-up care than adult patients after the initiation of treatment with antidepressants. However, the level of follow-up care for children still falls far short of the close monitoring that is recommended during the first few months of therapy. 

The significance of these gaps between guidelines and practice is difficult to assess, because there has been very little research on what patterns of follow-up care are most likely to facilitate recovery or reduce the risk of adverse outcomes. Clinical guidelines on visit frequency are based primarily on professional consensus, because evidence from clinical studies is generally not available.

The recommendations developed by the FDA also appear to be based on expert opinion, in response to public and professional appeals for a more precise definition of close monitoring.

There is some evidence that increased suicidality in children is more likely to occur (if it occurs at all) during the first 9 days after the start of antidepressant therapy, which lends support to the FDA recommendation to schedule more frequent visits during the first few weeks of treatment.

However, there is no published evidence that frequent visits during this period will reduce the risk of suicidal thoughts or actions, and it may be unrealistic to look for evidence of that kind in the near term. The associations between antidepressant use and increased suicidality are somewhat fragile statistically; for some drug classes, medical conditions, and outcome measures, the associations are not statistically significant in pooled analyses.

Given the measurement challenges, it may be difficult to test whether a particular regimen of follow-up care improves the detection of suicidality or reduces the risk of self-harm. 

Although the risk of suicidality is the primary focus of the new warning labels, the clinical rationale for close monitoring during the initial phase of antidepressant therapy is much broader in scope. Follow-up care is designed to assess the patient's response to treatment and adjust medications as needed to optimize the likelihood of recovery. Management of side effects is an important component of follow-up care, because adverse reactions may lead to poor compliance or therapy discontinuation, increasing the risk of treatment failure.

It is widely assumed that more frequent face-to-face visits will improve compliance, thereby increasing the likelihood of recovery, but there is very little direct evidence for this.

In a comparison of usual care with guidelines-based care for depression, researchers found that patients with weekly or biweekly acute-phase visits had significantly better outcomes than patients with less frequent visits

; however, these results may have been confounded by other differences in treatment. In the absence of strong research support, the linkage between increased visit frequency and improved outcomes remains largely intuitive. 

The new product labeling endorses the use of telephone contacts with patients, parents, or caregivers as a supplemental form of patient monitoring.

Telephone contacts are a cost-effective way to evaluate a patient's status between face-to-face visits, and systematic use of telephone interventions can improve outcomes for patients with depression.

The new product labeling also encourages "daily observation" by family members and caregivers, and it advises close communication with the prescriber if significant changes are observed.

 However, the new labeling does not endorse telephone contacts or caregiver observation as substitutes for face-to-face visits. In this study, our analysis focused on faceto- face monitoring by healthcare providers, because that is the primary clinical and quantitative focus of the new labeling. Measuring telephone contacts or caregiver monitoring would require different methodologies, because these forms of monitoring are not generally captured in claims data. 

It is clear from the results of this study that improvements are needed in the timing and frequency of followup care for patients who start antidepressant therapy. However, it is difficult to define what level of follow-up care is a cost-effective and clinically appropriate target for healthcare providers.

Increasing the average visit frequency for acute-care patients will increase costs for office and outpatient visits. For the patients in this study, visit costs would rise by approximately 56% if the average visit frequency increased from 4.45 to 7 visits, as recommended in product labeling. If increased visit frequency translates into improved recovery rates, the increased costs of follow-up care may be offset by reductions in other direct costs (eg, hospitalization) or by reductions in indirect costs (eg, absenteeism). In the absence of solid research data, these linkages remain speculative. 

Given the costs associated with more frequent office visits, it will be important to determine the conditions for which increased visit frequency is clinically appropriate. As a precautionary measure, the FDA recommends close monitoring for 

new episodes of antidepressant use, but the clinical support for close monitoring varies by indication and age group. The data are strongest for children and adolescents who use antidepressants for major depressive disorder, for whom the risk of suicidality is likely to be highest. However, the risk in this age group may extend to anxiety disorders, which also are frequently treated using antidepressants. The new black box warning was based on the results of a pooled analysis that included pediatric trials for both anxiety disorders and depressive disorders, although the pattern of suicidality risk was not as consistent for OCD as for major depression.

For adult users of antidepressants, most attention has been focused on the possible increase in suicidality in patients with major depressive disorder.

However, the FDA's current review of this risk includes an analysis of all studied indications in adult patients, paralleling its analysis of the pediatric trials.

The results of the FDA review will help determine whether close monitoring for suicidality is clinically appropriate for adult patients who begin using antidepressants for conditions other than major depression. In the current study, our measures of visit frequency were pooled across all treated conditions. These measures are consistent with the new product labels, which set the same monitoring targets for all indications. However, the new targets may be overly aggressive for some clinical applications of antidepressants, such as autism in children or smoking cessation in adults. As a result, our analysis may overestimate the gap between usual care and what may be viewed as more appropriate–and more cost-effective–clinical practice for some patients. 

Glen D. Stettin, MD

Articles

From the Editorial Board: Glen D. Stettin, MD

Identification of and Intervention to Address Therapeutic Gaps in Care

Antihypertensive Medication Adherence and Subsequent Healthcare Utilization and Costs

Frequency of Follow-up Care for Adult and Pediatric Patients During Initiation of Antidepressant Therapy