To examine the relationship between medication adherence and healthcare outcomes and expenses and to investigate whether the duration of type 2 diabetes mellitus (T2DM) has a role in the aforementioned relationship.

 Health insurance claims data under a universal coverage system in Taiwan.

Seven years of longitudinal analysis was performed to examine the association between medication adherence of oral antihyperglycemic drugs and outcomes among patients with newly diagnosed T2DM. Generalized estimating equations were conducted to assess the temporal relationship while controlling for unobserved characteristics of patients.

Better medication adherence was associated with decreased hospitalization and emergency department (ED) visits for diabetes or related conditions. The results also revealed that medication adherence was negatively associated with the expenses of hospitalization and ED visits for diabetes or related conditions, but medication adherence was positively associated with patients’ total healthcare expenses. However, the adherence-related differences in total healthcare expenses began to decrease 5 years after the time of diabetes onset.

Adherence to medication can improve healthcare outcomes but is associated with higher total healthcare expenses, especially during the years immediately following the onset of diabetes. Long-term follow-up is needed for further investigation.

The present study uses a longitudinal design to examine the relationship between medication adherence and healthcare outcomes and expenses, and to investigate whetherthe duration of type 2 diabetes mellitus has a role in the aforementioned relationship for patients with newly diagnosed diabetes.

		Better medication adherence was associated with favorable healthcare outcomes.

		Better medication adherence was associated with decreased expenses of hospitalization and emergency department visits for diabetes-related conditions, but was associated with higher total healthcare expenses.

		However, the adherence-related differences in total healthcare expenses began to decrease 5 years after the time of diabetes onset

Diabetes is a prevalent health condition that is associated with significant morbidity and mortality.

 In Taiwan and other countries, the prevalence and incidence of diabetes have been increasing over the past decade.

 This increase imposes a substantial economic burden on the healthcare system.

 Improving glycemic control may delay the onset and hamper the progression of macrovascular and microvascular complications associated with diabetes.

 Long-term glycemic control can be effectively  managed through dietary control and lifestyle changes, but adherence to prescribed medication is the most influential aspect of diabetes management.

An improvement in medication adherence may lead to better glycemic control,

 which, in turn, may reduce complications and healthcare utilization, such as the likelihood of hospitalization and emergency department (ED) visits. Asche et al (2011) summarized the findings of empirical studies and concluded that better medication adherence is associated with improved glycemic control and decreased healthcare service utilization.10 In addition, several researchers have reported that increasing adherence to oral antihyperglycemic medications may actually increase the expenses of these medications

 and decrease  the expenses for other healthcare services, such as expenses for diabetesrelated hospitalizations or ED visits.

 However, whether the higher drug expenses would be offset by the reduction in healthcare expenses of other healthcare services is still controversial.

The present study adds to the growing body of literature in 3 ways. First, most of the studies use observational designs which are subject to the problem of endogeneity, due to the healthy user effect

 If certain observed or unobserved characteristics of patients are related to both the medication adherence and their outcomes and expenses of healthcare services, then the characteristics may bias the result of the study. A typical example is that patients who are adherent to their medications are healthier and are more likely to adopt a healthy lifestyle, which may result in lower healthcare utilization and expenses irrespective of the effects of medication adherence. This study employed a multi-year longitudinal design to account for unobserved time-invariant characteristics of patients and to ensure the temporality of the association.

 Furthermore, in the sensitivity analysis section, we used a propensity score matching (PSM) approach to reduce the potential differences in the observed characteristics of patients between the adherent group and the nonadherent group for minimizing possible selection bias.

Second, the majority of previous studies have only conducted short-term follow-up analyses.

 Therefore, there is limited evidence regarding the long-term effects of adherence to oral antihyperglycemic medications on the outcomes and expenses of healthcare.

 A longitudinal design may clarify whether medication adherence has different effects on healthcare expenses during the different duration of diabetes, which is currently unknown. Finally, this study focused on newly diagnosed patients only; therefore, the results might exclude the cumulative effects of adherence that occurred prior to the study period. A similar sample selection was used by Hong and Kong (2011).

Medication adherence is closely associated with the healthcare system. The majority of studies concerning the relationship between medication adherence and the outcomes and expenses of healthcare have been conducted in the United States. Existing evidence shows that higher prescription cost-sharing reduces medication adherence, due to financial barriers.

 In recent years, a number of employers have implemented the policy of value-based insurance design (VBID). This policy  encourages medication adherence for patients with chronic diseases by reducing their medication copayment.

Taiwan’s National Health Insurance (NHI) program was launched in 1995 and provides a comprehensive benefits package to the public with a low drug cost-sharing requirement. 

The outpatient prescription drug copayment was either zero or up to US$6, depending on the expenses of the prescribed medication for each physician visit (in 2009, NT$33 = US$1). Financial barriers to accessing prescribed medications should only be a minor concern in Taiwan compared with in the United States. Therefore, medication adherence in Taiwan should be less confounded by the ability of patients to afford the medication. Through the use of a longitudinal study design, this study aimed to examine the relationship between medication adherence and healthcare outcomes and expenses for adult patients with newly diagnosed type 2 diabetes mellitus (T2DM). Furthermore, this study also examined whether the duration of diabetes may play a role in the aforementioned relationship.

The NHI claim data set for this study was obtained from the National Health Research Institutes of Taiwan. Using the NHI claims data set from 1999 to 2009, we identified patients with T2DM by 

codes 250.xx while excluding type 1 diabetes mellitus codes 250.x1 and 250.x3.

 New patients with T2DM were identified by lacking T2DM-related claims during 1999 and 2001, before the index date (the date of the first claim with the diagnosis of T2DM for each patient) of diagnosis in 2002. Patients were included in the analysis if they: (1) were at least 18 years old on the index date; (2) had a prescription for oral antihyperglycemic medications at the index date of initial diagnosis to capture the appropriate timing of initial prescriptions for new patients, as done in previous studies

; (3) had at least 1 prescription for oral antihyperglycemic medications after the second year during the study period to ensure that patients who were in the study required ongoing pharmacologic therapy; and (4) had no insulin prescriptions during any of the years in the study period because the claims data did not provide sufficient information about the insulin regimen of each patient, such as the use of a sliding-scale insulin regimen, as employed in a previous study.

We compiled baseline information from the first year following initial diagnosis of T2DM, and we  subsequently collected follow-up information for each patient over the subsequent 6 years in he analysis. As a result, a total of  11,580 patients and 69,480 patient-years were included in the analysis. The unit of analysis was patient-years.

The dependent variables were healthcare outcomes and healthcare expenses. Healthcare outcomes were based on whether the patient was hospitalized or had an ED for diabetes or cardiovascular/cerebrovascular conditions during each year of the study period; the definition has been used by Lau and Nau (2004).

 Three variables were used to measure healthcare expenses: expenses associated with oral antihyperglycemic medications, expenses for hospitalization or ED visits due to diabetes or cardiovascular/cerebrovascular conditions, and total healthcare expenses forall conditions incurred by the patients. Total healthcare expenses included expenses for ambulatory care, ED visit, hospitalization, laboratory tests, pharmaceuticals, and patient’s copayment. The healthcare expenses were adjusted for inflation by using the consumer price index to facilitate the comparison of figures from various years with those from 2009.

 The independent variables that we included in the analysis consisted of adherence to medications,the duration of diabetes, and the interaction of the duration and adherence to medications. Adherence to medications was measured using the medication possession ratio (MPR) which was based on filled prescriptions for 7 oral antihyperglycemic medications. According to the ATC (Anatomical Therapeutic Chemical) code, these medications included biguanides (A10BA), sulfonamides or urea derivatives (A10BB), combinations of oral blood glucose-lowering drugs (A10BD), alpha glucosidase inhibitors (A10BF), thiazolidinediones (A10BG), dipeptidyl peptidase 4 (DPP-4) inhibitors (A10BH), and other blood glucose—lowering drugs, excluding insulin (A10BX).

In this study, the MPR was calculated as the ratio of the number of days of prescribed medication divided by the total number of days in each study year. Refilled medication days for oral antihyperglycemic medications were also included. The days when patients were prescribed oral antihyperglycemic medications during a hospital stay were excluded from the denominator in the MPR calculation. In addition, patients might receive multiple medications for different numbers of days during a visit; the number of the longest days of the prescribed medications (usually for chronic conditions such as diabetes) in a visit is reported in the NHI claims data. Patients were considered to be adherent to oral antihyperglycemic medications if the MPR was equal to or larger than 80%. This cutoff point has been widely used in previous studies to categorize medication adherence.

In addition, the duration of diabetes was divided into 2 categories: less than 5 years (the second to fourth year after the initial diagnosis) and 5 years or more (the fifth to seventh year after the initial diagnosis). Effect modification for the duration of diabetes was examined by the inclusion of an interaction term for medication adherence and the duration of diabetes.

Several confounding factors were controlled for in the regression models, including yearly time-dependent variables and time-independent variables. The time-dependent variables in these models included the characteristics of patients and healthcare providers. The characteristics of patients included age, sex, number of physician visits, hospitalizations in the previous year, diabetes complication severity index (DCSI),

 chronic illness with complexity (CIC) index,

 intensity of the diabetes drug regimen, the average number of medications per prescription, and enrollment in the NHI diabetes pay-for-performance (P4P) program.

The DCSI contained 7 categories of complications: cardiovascular complications, nephropathy, retinopathy, peripheral vascular disease, stroke, neuropathy, and metabolic disorders. The CIC index was used to adjust for comorbidity of patients with multiple chronic diseases. This index contained information regarding non-diabetes physical illness complexity (including cancers, as well as gastrointestinal, musculoskeletal, and pulmonary diseases), diabetes-related complexity, microvascular complications, and mental illness/substance abuse complexity. We excluded diabetes-related complexity to avoid the duplication of the comorbidity effect presented by the DCSI index. In the analysis, we counted the number of comorbidities based on the CIC. The intensity of the diabetes drug regimen was indicated whether study subjects used oral monotherapy or oral combination therapy. The average number of medications per prescription was stratified by the mean number of medications per prescription (

3 medications, <3 medications). The healthcare provider characteristics included the accreditation level of the hospital most frequently visited for diabetes care (medical center, regional hospitals, district hospitals, clinics)

 and specialty of the physician most frequently seen for diabetes care (metabolism endocrinologist, others). The time-independent variable was the subject’s sex.

Generalized estimating equations (GEEs) were used with various proper distributions. The analysis accounted for the intraclass correlation between repeated observations of the same subject.

 Based on the characteristics of the variables used herein, the likelihood of hospitalization and ED visits was analyzed using a binominal distribution. Additionally, values from the healthcare expenses were skewed to the right. Therefore, we used the GEE model with a logarithmic link function and a gamma distribution to analyze the skew of the healthcare expense data. A similar approach has been used in the econometric literature to assess healthcare costs.

In addition to these models, we conducted 3 sensitivity analyses to improve the robustness of this study. First, the diabetes and cardiovascular/cerebrovascular-related hospitalizations was originally based on Lau and Nau’s definition (2004).

 Because this definition only includes specific aspects of health conditions that are related to diabetes, we may have underestimated or overestimated the results of our study. Therefore, we examined the effects of medication adherence on healthcare outcomes and expenses based on more general definitions of diabetes-related conditions, including the definitions made by Sokol et al (2005)

. Second, we used various MPR cut-off points to examine the stability of the association between MPR and healthcare outcomes as well as expenses. Finally, because the patients with diabetes were not randomly assigned to the medication-adherent group or nonadherent group, we used a PSM approach to minimize the selection bias and assigned patients with diabetes to the nonadherent group.

We employed the caliper matching method (also known as the greedy algorithm) with 1-to-1 matching between the adherent and nonadherent groups based on the propensity score. A total of 7728 patients and 46,368 patient-years were included in the matched analysis. The analyses were performed using SAS version 9.1.3 (SAS Institute, Cary, North Carolina) and Stata 9.1 (Stata Corp, College Station, Texas).

presents the baseline characteristics of the study subjects in the year being diagnosed with T2DM. In terms of basic characteristics, the mean age of subjects was 55.56 years. The average number of physician visits for any condition in the previous year was 23.77 and the rate of hospitalization in the previous year was 15.78%. Approximately 61.03% of the study sample had a DCSI score of 0, whereas 15.47% had a score of 2 or higher. In addition, 61.85% of the study sample had an average of more than 3 medications per prescription.

 presents the trends for interest variables. The average MPR slightly decreased from 75.21% in the first year after diagnosis of diabetes to 75.14% in the second year and then increased in the subsequent years to 88.87% in the last year of the study period. The proportion of patients who were considered to be adherent to oral antihyperglycemic medications (MPR >80%) increased during the study period. The rates of hospitalization for diabetes or cardiovascular/cerebrovascular conditions decreased from 9.04% in the first year after diagnosis of diabetes to 8.39% in the second year, respectively. These rates then increased in the subsequent years to 11.74% in the last year of the study period. The rate of ED visits for diabetes or cardiovascular/cerebrovascular conditions decreased from 6.25% in the first year to 5.40% in the second and third year and then increased steadily during the subsequent years. Additionally, the mean drug expenses for oral antihyperglycemic medications, the average expenses for diabetes or cardiovascular/cerebrovascular-related hospitalizations and ED visits, and total healthcare expenses increased during the study period.

Relationship Between Medication Adherence and Healthcare Outcomes

 presents the results of the GEE models concerning the relationship between medication adherence and healthcare outcomes. The first model examined the relationship between medication adherence and healthcare outcomes (Model 1). In the second model (Model 2), the interaction of medication adherence and the duration of diabetes was incorporated. In Model 1, patients with better medication adherence were less likely to be hospitalized for diabetes or cardiovascular/cerebrovascular-related conditions (odds ratio [OR] = 0.74, 95% confidence interval [CI] = 0.69-0.78). Similarly, patients with better medication adherence were less likely to have ED visits for diabetes or cardiovascular/ cerebrovascular-related conditions based on Model 1 (OR = 0.78, 95% CI = 0.73-0.84). In Model 2, the interaction term revealed that the duration of diabetes significantly moderated the relationship between medication adherence and hospitalization (OR = 0.86, 95% CI = 0.76-0.97) but did not moderate the relationship between medication adherence and ED visits (OR = 0.91, 95% CI = 0.79-1.05).

Relationship Between Medication Adherence and Healthcare Expenses

Results from the GEE models concerning the relationship between adherence and healthcare expenses are presented in 

Patients in the adherent group had higher drug expenses for oral antihyperglycemic medications than did patients in the nonadherent group (β = 0.52,  

 <.001). However, patients in the adherent group had lower expenses for hospitalizations and ED visits for diabetes or cardiovascular/cerebrovascular-related conditions than did patients in the nonadherent group (β =—0.56, 

 <.001). The results indicated that adherence to oral antihyperglycemic medications was positively associated with total healthcare expenses for any condition (β = 0.09, 

<.001). In addition, we found that the duration of diabetesmoderated the relationship between medication adherence and total healthcare expenses (β = —0.16, 

 <.001). These results indicated thatduring the 4 years following diagnosis, patients who had good medication adherence tended to have higher healthcare expenses than patients who had poor medication adherence. However, this relationship was attenuated at 5 years afterinitial diabetes diagnosis.

. First, while using the definitions made by Sokol et al (2005)

, we found the results were similar to those obtained by using the definition of Lau and Nau (2004)

). Second, while using various cut-off points for MPR (70% and 90% respectively), we found stable results concerning the association between MPR and healthcare outcomes and expenses (

). Finally, the PSM process yielded a new study sample with similar distribution of the characteristics between the adherent and nonadherent  groups, and generated estimates similar to the original regression models (

). In summary, these sensitivity analyses indicated that the results from the study were robust.

The objectives of this study were to examine the association between medication adherence and healthcare outcomes and expenses using a longitudinal study design. The results indicated that better medication adherence was associated with decreased hospitalizations and ED visits for diabetes or cardiovascular/cerebrovascular-related conditions. We also found that medication adherence was negatively associated with healthcare expenses that were due to diabetes or cardiovascular/cerebrovascular-related hospitalizations and ED visits, but it was positively associated with the patient’s total healthcare expenses. Nevertheless, the adherence-related differences in total healthcare expenses were observed to decrease beginning 5 years after the onset of diabetes.

Our findings support previous studies suggesting that there is a significant association between medication adherence and decreased hospitalizations

Medication adherence may lead to better glycemic control,

which further reduces the need for hospitalizations and ED visits by decreasing the incidences of microvascular and macrovascular complications. Moreover, we found that the relationship between better medication adherence and decreased hospitalizations was stronger a certain number of years after the onset of diabetes. Given the chronic nature of diabetes, the duration of diabetes has been an increased risk of the development of complications, such as microalbuminuria

 A longer follow-up study is needed to further explore the relationship between medication adherence and the occurrence of complications.

Consistent with most previous studies, we found that higher medication adherence was associated with increased drug expenses

 but was also associated with lower diabetes-related hospitalization or ED visit expenses.

 Studies regarding the effects of adherence to oral antihyperglycemic medications on total healthcare expenses are limited, and the results tend to be inconclusive.

 Some studies have found that increased medication adherence is associated with lower total healthcare expenses,

 while others have found the opposite relationship.

 Our study found that better  medication adherence was associated with increased total healthcare expenses. This result implies that reduced expenses for diabetes-related hospitalizations and ED visits did not offset the higher expenses associated with oral antihyperglycemic medications or physician visits. A potential explanation for this is that because of the high accessibility to healthcare, the low cost-sharing for patients, and the fee-for-service reimbursement for physicians in Taiwan, patients are typically prescribed medications for fewer days than they need and thus are required to make a return visit to the physician. The higher expenses associated with physician visits significantly contributed to the increased total expenses for patients who had better medication adherence. However, the positive relantionship between medication adherence and total healthcare expenses was attenuated after 5 years following the onset of diabetes. Therefore, the effects of medication adherence may be more pronounced with longer duration of diabetes. A longer follow-up study and further exploration are needed.

Previous studies in the United States have found that a higher level of prescription drug cost-sharing was associated with lower medication adherence.

In the present study, the mean value of MPR for patients with newly diagnosed diabetes ranged from 75.21 to 88.87% throughout the study period. An MPR of 80% is frequently considered to indicate good adherence for patients.

 The mean value of MPR in Taiwan was higher than that in some studies conducted in other countries, such as the United States

 This difference may be due to Taiwan’s universal health insurance, which provides comprehensive medication coverage and involves a low drug cost-sharing requirement. However, we found that variation in MPR values still exists in a setting with low drug costsharing, which is similar to VBID in the United States. We suggest that non-financial barriers to medication adherence might also be important for improving medication adherence for patients with diabetes. Increasing the patients’ health literacy (such as their awareness of the importance of adherence), enhancing the continuity of care between physicians and patients, and the development of a pay-for-performance program to promote medication adherence are some strategies for healthcare policy makers to consider.

It should be noted that the present study has limitations. First, this study did not include some unobserved (such as health literacy) or unavailable characteristics (such as socioeconomic status, severity of illness) in the regression models. Some of these characteristics might influence both medication use and their healthcare outcomes and expenses. For example, patients with a higher education level might be more compliant and would have better healthcare outcomes and lower healthcare expenses, which might result in an overestimation of the effects of medication adherence. Nevertheless, this study employed a longitudinal study design which might account for the unobserved time-invariant characteristics, and this concern might be mitigated. In addition, we included several proxy indicators for severity of illness in the regression models, namely DCSI score, CIC index count, number of physician visits, and hospitalization in the previous year, which might also lessen the bias due to confounders not incorporated in the model. Second, the determination of medication adherence using claims data may have resulted in an overestimation of medication adherence, although MPR calculation based on claims data is a common measure in the literature for quantifying medication adherence.

 Third, due to the lack of information, this study did not incorporate more appropriate outcome measures for diabetes care, such as A1C levels. Finally, there are certain unique aspects of Taiwan’s healthcare system that may make it difficult to generalize the results of this study to other populations.

Articles

POLICY

Adherence to medication can improve healthcare outcomes but is associated with higher total healthcare expenses, especially during the years immediately following the onset of diabetes.

Does Medication Adherence Lead to Lower Healthcare Expenses for Patients With Diabetes?

We sought to evaluate trends in door-to-balloon (D2B) times and false activation rates for the cardiac catheterization laboratory (CCL) in patients presenting to the emergency department (ED) with acute ST-elevation myocardial infarction (STEMI). In patients with STEMI, national efforts have focused on reducing D2B times for primary percutaneous coronary intervention (P-PCI). This emphasis on time-to-treatment may increase the rate of false CCL activations and unnecessary healthcare utilization.

 Retrospective quality improvement chart review.

 We examined all emergent CCL activations for P-PCI between 2007 and 2011 at the University of Michigan Hospital. False activation was defined as emergent CCL activation when the patient did not require CCL care or emergent cardiology evaluation in the ED. Pre-hospital or ED false activation rates and mean D2B time were retrospectively determined by chart review.

 The CCL was activated 717 times for suspected STEMI. The number of CCL activations increased from 96 in 2007 to 190 in 2011. False CCL activations accounted for 28% of all  prehospital and 29% of all ED activations. The false activation rate increased from 15% of all cases in 2007 to 40% of all cases in 2011. The median D2B time decreased from 67 minutes in 2007 to  55 minutes in 2011.

 Over a 5-year period with a strong emphasis on reducing D2B times, there has been an increased CCL false activation rate for P-PCI.

This study examined the trend in false activation of the cardiac catheterization laboratory for patients with ST-elevation myocardial infarction (STEMI) since the introduction of national door-to-balloon initiatives.

		Activation of the cardiac catheterization laboratory for primary percutaneous coronary intervention increased steadily since 2007, with up to 40% of activations not requiring emergent cardiac intervention.

		Identifying measures to reduce false cardiac catheterization laboratory activation without impacting door-to-balloon times is important.

The American College of Cardiology/American Heart Association (ACC/AHA) guidelines for management of ST-elevation myocardial infarction (STEMI) recommend that patients should be treated with primary percutaneous coronary intervention (P-PCI) within 90 minutes of first medical contact.

 A national effort was started by the ACC in 2006 to reduce door-to-balloon (D2B) times, defined as the time from patient arrival at the hospital emergency department (ED) to reperfusion in the cardiac  catheterization laboratory (CCL).

 This process included encouraging emergency medical service (EMS) providers (primarily paramedics) and ED physicians to activate the CCL prior to consultation with a staff cardiologist.

 Hospital D2B times are publically reported, so clinicians may feel pressure to rush and activate the CCL before a review of the patient’s presenting condition can be completed, and occasionally CCL may be activated when the patient does not have STEMI. While there is increasing awareness of false CCL activation, the change in false CCL activation over time has not been assessed since the  ACC initiated the D2B Alliance campaign.

Our objective was to describe the trends in the rate of false CCL activation and D2B time at our institution over the 5 years following the national D2B initiative. We hypothesized that as D2B times have fallen, the rate of false CCL activations has increased.

Between January 1, 2007, and December 31, 2011, all adult patients with suspected STEMI that presented to the University of Michigan Health System ED were reviewed. This study was approved by the institutional review board at the University of Michigan, a large academic suburban hospital. Data were initially collected retrospectively in a quality improvement database by 1 author (JB) and then verified through the group paging system by another author (GDB). Additional data were abstracted from the chart using a predefined set of data elements by 2 authors (GDB, AK). Selected cases werereviewed by both abstractors, with any discrepancy resolved by consensus.

For the purpose of this study, a false activation is defined as one in which the CCL was activated by EMS or the ED for new STEMI or left bundle branch block (LBBB), but the patient was determined by the interventional cardiologist to not require emergent transfer from the ED to the CCL for P-PCI. To be as inclusive as possible for other reasons for emergent activation of the CCL, patients deemed too unstable to transfer to the CCL, who refused cardiac catheterization, who presented with cardiac arrest, who died prior to arrival in the CCL, or who were taken to the CCL emergently for any other indication, were defined as true CCL activations. Findings in the CCL did not influence the determination of a true or false CCL activation. The decision to not take a patient to the CCL was made initially by the on-call interventional cardiology fellow and verified in all cases by the interventional cardiology attending.

The primary outcome was the rate of false CCL activation, defined as the number of false CCL activations divided by the total number of CCL activations. Discharge diagnoses, peak serum troponin levels, and results of any cardiac catheterization during the index hospitalization were reviewed for all patients and compared with the initial decision to proceed or not proceed emergently to the CCL. Patients were classified by the year they presented to the ED and by CCL activation location (pre-hospital or ED). Other variables examined included age and gender. The D2B time was calculated in minutes as the time from ED arrival until initial device deployment for reperfusion, when appropriate.

Logistic regression analysis was conducted to demonstrate the relationship between year of presentation, location of CCL activation, and true/false CCL activation, as well as D2B time and year of presentation. Adjusted odds ratios were calculated by incorporating the patient’s age and gender into the logistic regression analysis, but these did not impact the final model and so are not presented. Model fit was assessed with the Hosmer-Lemeshow goodness of fit test.

 Logistic regression analysis was also conducted to demonstrate the relationship between year of presentation and location of CCL activation independent of true/false CCL activation status. Statistical analyses were conducted with the use of SPSS version 19 (Armonk, New York).

Between January 1, 2007, and December 31, 2011, the CCL was activated 717 times for possible STEMI. The average patient age was 61.4 (standard deviation [SD] 14.4) years and 72.4% were male. Pre-hospital CCL activations ranged from 33% in 2007 to 52% in 2011. For those patients who received P-PCI, the median D2B time decreased from 67 (SD 37.2) minutes in 2007 to 55 (SD 33.6) minutes in 2011 (3.74 min/year, 95% confidence interval [CI] 1.36-6.11 min/year, 

There were 513 (72%) true CCL activations and 204 (28%) false CCL activations. Of the true CCL activations, 73 (14%) patients were never taken to the CCL due to pulseless electrical activity, ventricular fibrillation, hemodynamic instability, refusal, or death. Yearly false CCL activation rates ranged from 15% in 2007 to 40% in 2011, a 37% per year increase in the odds of false CCL activation (95% CI 21-56%, 

 <.001, Figure). Of the 717 CCL activations, 315 (44%) were pre-hospital activations (33% in 2007 to 52% in 2011, 

). False CCL activations were similar for ED versus pre-hospital location (odds ratio 1.12, 95% CI0.62-2.07, 

A reason for not proceeding with emergent cardiac catheterization was documented in the medical chart for 181 of the 204 (89%) false activation cases. These included electrocardiogram (ECG) unchanged compared with prior ECG (27%), non-STEMI (13%), early repolarization (13%), atrial fibrillation or atrial flutter (11%), ECG reinterpretation as a prior MI (6%), pericarditis (4%), prolonged chest pain (4%), supraventricular tachycardia (1%), and other etiologies (10%). LBBB was present in 39 (5%) patients, 27 (11%) with false CCL activations and 12 (2.6%) with true CCL activations. Of the 717 CCL activations, 471 (66%) were transferred to the CCL, declining from 76% in 2007 to 53% in 2011 (Table). STEMI was confirmed based on coronary angiography in 385 (88%) of those transferred to the CCL.

Angiography was subsequently performed in 30/204 (15%) patients with false CCL activation during their hospitalization and 15 (7%) were treated with PCI. Of these 15 cases, 3 had evidence of subtotal or complete coronary occlusion consistent with STEMI; the remainder had nonocclusive disease that was amenable to PCI. No documented chest pain was found for 68 (33%) patients with false activation and 153 (75%) had negative cardiac biomarkers during hospitalization.

The major finding of our study is that there has been a striking increase in CCL activations between 2007 and 2011, an increase primarily driven by a marked rise in false CCL activations. There was a 300% increase in pre-hospital activation rates compared with a 50% increase in ED activation rates. While we cannot directly link the increase in false CCL activations with the initiation of national D2B efforts, their concurrence is notable.

Some centers have reported false CCL activation rates notably lower than ours. A recent report of CCL activations from a STEMI network of 14 PCI-capable hospitals and 85 referral hospitals demonstrated a 15% false CCL activation rate in 2009.

 Another report from Minneapolis described a 9.2% false activation rate between 2003 and 2006, before the ACC introduced the D2B Alliance campaign.

 A group from South Korea reported a 10% false CCL activation rate after initiating a single page system for STEMI patients between 2009 and 2011.

Other groups, however, have reported experiences similar to ours. McCabe and colleagues describe a 36% false CCL activation rate at 2 urban hospitals in California between 2008 and 2011.

 Kontos and colleagues also report a 24% false CCL activation rate at their center between 2006 and 2008.

 Mixon and colleagues report a 28% false activation rate in their 6-county Texas cohort between 2009 and 2010.

 Although these studies did not report on changes over time, they do describe that the reported finding of frequent false CCL activation is not unique to 1 individual center. This is likely occurring at many centers across the United States.

While some might argue that an increase in false CCL activations is the expected cost of efforts to decrease D2B times, it is not clear whether this assumption is valid. Despite a 37-minute decline in the median D2B time between 2003 and 2008, there has been no decline in mortality for STEMI patients in Michigan.

 While reperfusion therapy delays are associated with worse outcomes,

 inaccurate diagnosis also has potential clinical repercussions. When the CCL is activated emergently, resources must be collected and CCL prepared for a potential patient. During off hours, this often requires bringing in a full CCL team to begin preparing the CCL. During regular business hours, other patients’ cases are often delayed in order to create availability in the CCL for the emergent case.

High rates of false CCL activation can have negative effects on providers. There is reasonable concern for the “boy who cried wolf” phenomenon, sleep deprivation affecting clinical activities on the subsequent day, and potential lost productivity when CCL space is being prepared for a false CCL activation.

When the national effort to reduce D2B times was started in 2006, our institution made significant changes to expedite care of patients with chest pain. In 2007, there was a large process shift from having cardiology consulted in the ED to having EMS or an ED physician activate the CCL independently. To ensure lower D2B times, the CCL staff and ED leadership encouraged ED physicians to activate the CCL “liberally,” including any patient with a reasonable cardiac presentation who presented with a positive troponin and/or concerning ECG changes. Since that time, a team of ED and CCL providers have met regularly to review cases and develop protocols.

Noting the increase in false activations, a multi-disciplinary group of ED and cardiology physicians, nurses, quality improvement staff, and EMS representatives have met to develop proposed changes to maintain low D2B times while decreasing false activation rates. Changes include making urgent cardiology consultation available in the ED for cases without a clear-cut STEMI presentation. We are also considering many of the suggestions put forth by Mixon and colleagues, which include EMS and ED education around ECG interpretation, investigating opportunities for ECG transmission for review, minimizing pre-hospital activation in the setting of LBBB, using a more formalized review process, and emphasizing the importance of chest pain in the clinical presentation.

 The importance of a multi-disciplinary team with strong partnership between cardiology, ED, and EMS  providers must be emphasized.

Our study has a few limitations. First, this is a single-center report and may or may not be generalizable to other hospitals or systems. However, a few recent reports have described similarly high rates of false CCL activation.

Second, retrospective data collection might induce a certain degree of bias.Third, D2B times are the results of a number of system effects, including access to rapid ECG on ED arrival, expedited ECG review by ED staff, rapid notification of the CCL team by the EMS or ED staff, and expedited movement of a patient from the ED to the CCL. While all of these were focused on during the 5-year study period and likely influenced the changing mean D2B time, we were not able to quantify them for this analysis. Care must be taken to not confuse the presented odds ratios with relative risk in this study, as the rate of false activation was not a rare event. Last, the reason for CCL cancellation was inferred from the ED admission and discharge documentation. While there often was not direct documentation from the cardiology consultant as to their reasoning for not pursuing P-PCI, we explored the ED  documentation, subsequent inpatient documentation, any subsequent CCL documentation, and hospital discharge documentation to explore the reasons for emergent CCL cancellation.

In summary, although D2B times have decreased since 2007, the rate of false activations has risen. Quality improvement initiatives aimed at decreasing the false activation rate of the CCL for STEMI patients are needed. Studies demonstrating effective tools and healthcare system interventions to decrease false CCL activation rates while maintaining low D2B times would be beneficial. 

From Division of Cardiovascular Medicine, Department of Internal Medicine (GDB, SJC, ERB, HSG), Department of Internal Medicine (AK), Department of Emergency Medicine (JSD, SLK), and Quality Improvement Office (JB), University of Michigan Health System, Ann Arbor, MI.

 Dr Bates reports board membership of the AHA Mission Lifeline Science Task Force. The other authors (GDB, AK, JSD, SLK, JB, SJC, HSG) 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 (GDB, JSD, SJC, HSG); acquisition of data (GDB, AK, SLK, JB); analysis and interpretation of data (GDB, AK, JSD, SLK, ERB, HSG); drafting of the manuscript (GDB); critical revision of the manuscript for important intellectual content (GDB, AK, JSD, JB, SJC, ERB, HSG); statistical analysis (GDB, SLK); administrative, technical, or logistic support (JB); and supervision (SLK, SJC, ERB).

 Geoffrey D. Barnes, MD, CVC Cardiovascular Medicine, 1500 E Medical Center Dr, Ann Arbor, MI 48109-5853. E-mail:gbarnes@umich.edu.1. Antman EM, Hand M, Armstrong PW, et al. 2007 Focused Update of the ACC/AHA 2004 Guidelines for the Management of Patients With ST-Elevation Myocardial Infarction: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the Canadian Cardiovascular Society endorsed by the American Academy of Family Physicians: 2007 Writing Group to Review New Evidence and Update the ACC/AHA 2004   Guidelines for the Management of Patients With ST-Elevation Myocardial Infarction, Writing on Behalf of the 2004 Writing  Committee. Circulation. 2008;117(2):296-329.

2. O’Gara PT, Kushner FG, Ascheim DD, et al. 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines [published online December 17, 2012]. Circulation.

3. Bradley EH, Herrin J, Wang Y, et al. Strategies for reducing the door-to-balloon time in acute myocardial infarction. N Engl J Med. 2006;355(22):2308-2320.

4. Diercks DB, Kontos MC, Chen AY, et al. Utilization and impact of pre-hospital electrocardiograms for patients with acute ST-segmentelevation myocardial infarction: data from the NCDR (National Cardiovascular Data Registry) ACTION (Acute  oronary Treatment and Intervention Outcomes Network) Registry. J Am Coll Cardiol. 2009;53(2):161-166.

TRENDS FROM THE FIELD

Between 2005 and 2011, rates of cardiac catheterization laboratory false activation doubled while mean door-to-balloon times for primary PCI declined.

False Activation of the Cardiac Catheterization Laboratory for Primary PCI

Opioid misuse and abuse are growing concerns among the medical and public health communities.

 To examine the prevalence of indicators for potential opioid misuse in a large, commercially insured adult population.

 We adapted existing indicators developed by expert panels to include having overlapping opioid prescriptions, overlapping opioid and benzodiazepine prescriptions, long-acting/ extended release (LA/ER) opioids for acute pain,and high daily doses of opioids (>100 morphine milligram equivalents). These indicators were assessed among continuously enrolled individuals aged 18-64 years from the 2009 Truven Health MarketScan databases. Analyses were stratified by sex.

 We identified 3,391,599 eligible enrollees who received at least 1 opioid prescription. On average, enrollees obtained 3.3 opioid prescriptions, and the average annual days of supply was 47 days. Twice as many enrollees received opioid prescriptions for acute pain as for chronic pain. About a quarter of the enrollees had at least 1 indicator of either potential misuse by patients or inappropriate prescription practices by providers. About 15% of enrollees had high daily doses;7.8% had opioid overlap; and 7.9% had opioid and benzodiazepine overlap. Among those prescribed LA/ER opioids, 24.3% were treated for acute pain. Overlap indicators were more common among women.

Our findings underscore the critical need to develop programs aimed at promoting appropriate use of opioids. Retrospective opioid utilization reviews similar to our analyses can potentially help managed care organizations and healthcare providers improve patient care and reduce the risk of adverse outcomes related to these medications.

Opioid misuse and abuse are growing concerns in the United States.

Using a large, fully integrated commercial claims database, we found a substantial prevalence of indicators of potential misuse by patients or inappropriate prescription practice by providers.

Our findings underscore the critical need to develop programs to promote appropriate use of opioids.

Managed care organizations and other insurers might help monitor opioid use and reduce the risk of adverse outcomes through retrospective opioid utilization reviews similar to our analyses.

The problem of abuse and overdose of prescription drugs has emerged as a major public health issue in the United States.

 In 2010, drug overdoses killed 38,329 Americans, more than the number killed in motor vehicle traffic crashes.

 Misuse or abuse of prescription drugs also led to 1.2 million emergency department (ED) visits, compared with 1.0 million ED visits related to illicit drugs such as heroin and cocaine.

 Opioid analgesics alone or in combination with benzodiazepines or other drugs  accounted for  nearly half of the drug overdose deaths and more than three-fourths of prescription drug—related ED visits in 2009.

The majority of prescription opioids used for nonmedical reasons are diverted from prescriptions originally written for therapeutic use.

 Yet many opioid abusers obtain them directly from a doctor and/or pharmacy, often by fabricating pain symptoms, forging prescriptions, and engaging in doctor and pharmacy shopping (ie, obtaining multiple prescriptions from multiple providers or pharmacies).

 Providers and payers may be in a position to help enhance the safety of prescribed  opioids and protect susceptible patients from becoming addicted to or overdosing  on opioids. Among patients who were prescribed opioids, those receiving multiple opioid prescriptions, overlapping opioids, overlapping opioids and benzodiazepines, and opioids at high dosage levels are at greater risk for abuse and overdose.

Despite the well-documented associations between usage patterns and risk, relatively little is known about the current prescription practices for opioid analgesics and the extent to which they might be contributing to the rising prescription drug abuse problem. A small number of studies using administrative data from a limited number of health plans have described general opioid use (eg, number of opioid prescriptions received, average daily dose, total days of supply) and/or potential misuse (eg, high daily dosage, overlapping opioids, overlapping opioids and benzodiazepines).

 However, these studies have generally limited their analyses to long-term (

90 days) opioid users9,10 and/or populations with specific types of  noncancer pain (eg, back pain, headache).

This study examines indicators of potential misuse by patients or inappropriate prescription practices by providers using one of the largest fully integrated commercial claims databases in the United States. The population of interest includes all adults with at least 1 opioid prescription regardless of their length of opioid use or indication for use with the exception of pain associated with cancer. Additional analysis reports differences in these indicators between men and women. Our results could be used to inform the monitoring of opioid use and to promote efforts to improve appropriate prescription practices by providers.

We conducted secondary data analyses of the 2009 Truven Health MarketScan Commercial Claims and Encounters databases, which consisted of data from approximately 100 payers and health plans for about 50 million individuals from all 50 states.

 MarketScan contains standardized, fully integrated, enrollee-level, de-identified claims across inpatient, outpatient, and prescription drug services. Our analysis drew data primarily from the pharmaceutical claims, which included outpatient drug name, class, dosage, and quantity for about 17.8 million (10.3 million females and 7.5 million males) enrollees aged 18 to 64 years. In addition, the outpatient service claims and inpatient admission records were used to identify the underlying pain diagnoses related to opioid use. Inpatient admission records were necessary because some opioid prescriptions were prescribed to enrollees at discharge.

 We identified 13,097,589 opioid prescriptions for enrollees who were aged 18 to 64 years, were continuously enrolled in 2009, and did not have a cancer diagnosis in their outpatient or inpatient service claims (

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes including 338.3, 140-172.9, 174-215.9, 217-229.10, and 235-239.9. A list of analyzed opioids is provided in 

. Buprenorphine was excluded due to its primary use for treatment of opioid dependence. We excluded 1,216,388 (9.3%) opioid prescriptions that lacked the enrollee identification (ID) or dispensing information necessary for the calculation of predetermined outcome indicators. An additional 770,446 (5.9%) refill prescriptions were excluded due to inability to identify their original diagnoses. This selection process resulted in 11,110,755 (84.8%) opioid prescriptions filled by 3,391,599 enrollees as our overall study population.

 To calculate a subset of outcome indicators that are specific to certain types of pain, we linked opioid prescription claims to the diagnoses on outpatient or inpatient service claims by matching enrollee ID and the date of service. Consistent with the existing literature,

 we linked opioid prescriptions to the outpatient services or inpatient discharges that occurred within 14 days of the prescription dispensing dates. If multiple outpatient or inpatient records existed within this interval, we linked to the ones that occurred on the day closest to the drug dispensing dates. When inpatient and outpatient dates of service overlapped, we used the outpatient claims for the linkage. Prescription refills were assigned the diagnoses on the original prescriptions. We successfully linked 7,714,067 (69.4%) of the 11,110,755 opioid prescriptions to diagnoses for 2,833,224 enrollees (85.3% of the overall study population). Of the remaining 30.6% of prescriptions, 28.0% could not be linked because the outpatient services or inpatient admissions had occurred more than 14 days prior to the prescription dispensing date or in 2008; 2.6% could not be linked because MarketScan did not have the enrollee’s outpatient service claims.

We adapted outcome indicators from clinical guidelines and those developed previously by expert panels.

 These indicators captured both general opioid use as well as potential misuse by patients or inappropriate prescription practices by providers.

At the enrollee level, indicators of opioid general use included the total number of opioid prescriptions obtained, total days of supply, and medical diagnoses (eg, acute pain, chronic pain, or both) associated with opioid prescriptions. Acute pain and chronic pain diagnoses were based on

, footnotes b and c). Benzodiazepine and muscle relaxant use in combination with opioids was also examined. Indicators of potential misuse or inappropriate prescription practices consisted of (1) opioid overlap, defined as opioid prescriptions that overlap by 7 or more days (including early refills); (2) opioid and benzodiazepine overlap, defined as opioid and benzodiazepine prescriptions that overlap by 7 or more days; (3) long acting/extended release (LA/ER) opioid prescriptions written for acute pain conditions; (4) high daily opioid dosage, defined as a prescribed daily dose of 100 morphine milligram equivalents (MMEs) or greater (MME conversion factors are provided in Table 1); and (5) opioid dose escalation, measured as having a 50% or greater increase in mean MMEs per month twice consecutively during the year.

At the prescription level, indicators of opioid general use included the number of days of supply and the prescribed daily doses for opioid prescriptions for acute, chronic, and back pain. Back pain included both acute and chronic back pain and was based on 

 codes recommended by the American College of Occupational and Environmental Medicine practice guidelines.20 Indicators of potential misuse or inappropriate prescription practices included the proportion of opioid prescriptions involved in opioid overlap or opioid and benzodiazepine overlap, similar to the enrolleelevel analysis, and 3 indicators specific to LA/ER prescriptions given their elevated risk for addiction and abuse: (1) overlapping LA/ER opioid prescriptions; (2) LA/ER prescriptions written for acute pain; and (3) LA/ER prescriptions obtained by an opioid-naïve person, defined as a person who had no use of an opioid for at least 60 days prior to LA/ER drug initiation.

We calculated the distributions of various levels of usage among all enrollees receiving an opioid prescription overall, by sex, and by pain type. The prevalences of indicators of potential misuse by patients or inappropriate prescription practices by providers were calculated as both percentages of enrollees and percentages of prescriptions. We used x

 tests for comparisons by sex. Because of the large sample sizes, differences by sex might have been statistically significant (

 <.05) but not clinically meaningful. Therefore, we only provide test results for sex differences that were considered meaningful.

Among the overall study population of opioid recipients, 59% were female (Table 2). The mean age of opioid recipients was 44.7 years among males and 43.2 years among females. Males and females received comparable amount of opioids, as measured by the number of prescriptions and total days of supply received per person in 2009. More than half of all recipients had only 1 opioid prescription; fewer than one-third of recipients (28.3%) had 3 or more opioid prescriptions. Notably, 2.3% of enrollees had 20 or more opioid prescriptions during the data year—with nearly 0.1% of enrollees receiving 50 or more opioid prescriptions (data not shown).

More than three-fourths of the recipients (78.5%) received fewer than 30 days of supply of opioids; about 12% of recipients received more than 90 days of supply of opioids. Among all opioid recipients, 16.3% also filled a benzodiazepine prescription during the data year, and 10.3% filled 2 or more such prescriptions. A higher proportion of females than males received both an opioid and benzodiazepine (19.0% vs 12.5%; 

We were able to identify the underlying medical diagnoses for opioid prescriptions for 82.3% of the overall study population. About 27.9% of the recipients obtained opioids for acute pain conditions only; 13.7% received opioids for chronic pain conditions only; and 24.2% obtained opioids for both acute and chronic pain conditions. Another 17.7% of the recipients received opioid prescriptions for diagnoses not included in the lists of acute or chronic pain conditions. Nearly a quarter of opioid recipients showed at least 1 indicator (

). The most common indicator was having high daily doses; an estimated 15.0% of the study population had daily doses of 100 MMEs of higher. The prevalence of opioid overlap and opioid and benzodiazepine overlap was 7.8% and 8.0%, respectively. About 0.9% of enrollees received LA/ER opioids for acute pain, and 1.2% had opioid  dose escalation. A higher percentage of females versus males (9.1% vs 6.3%; 

 <.01) had 1 or more incidents of opioid and benzodiazepine overlap. A higher percentage of females versus males also had 1 or more incidents of high daily dose of opioids (16.2% vs 13.2%; 

 <.01). Overall, females were more likely than males to have any type of indicator (26.7% vs 22.4%; 

Among the 7,714,067 prescriptions that were linked to diagnoses, about a quarter of them were written for acute pain conditions. A slightly higher proportion (28.8%) were for chronic pain, and 15.6% were associated with both acute and chronic pain (

). The remaining 30.2% of the prscriptions were linked to diagnoses not included in the lists of acute or chronic pain conditions. In addition, about 6.9% of the opioid prescriptions were written for either acute or chronic back pain conditions.

The median days of supply for acute pain, chronic pain, and back pain were 5, 15, and 20 days, respectively. For acute pain, 22.8% of prescriptions were written for 10 or more days, and 9.5% were written for 30 or more days. For chronic pain, more than one-third of the prescriptions were for 30 or more days. The duration distribution for back pain, whether acute or chronic, was similar to that for chronic pain. The distributions for the days of supply for acute pain, chronic pain, and back pain were similar for males and females.

The median daily opioid dose for prescriptions for acute pain was similar for both sexes at 40 MME. The median daily dose for opioid prescriptions for chronic pain was higher for males (40.0 MME) than for females (37.5 MME). Among both acute pain and chronic pain prescriptions, 11.8% to 14.0% had a daily dose of 100 MME or greater; 1.9% to 4.5% of prescriptions were for 200 MME or greater.

Roughly one-fifth of opioid prescriptions overlapped with other opioid prescriptions, and 15.1% overlapped with a  benzodiazepine prescription (

). Opioid prescriptions written for female versus male recipients were significantly more likely to overlap with 1 or more benzodiazepine prescriptions (17.1% vs 12.4%; 

 <.05). Among LA/ER opioid prescriptions, nearly a quarter overlapped with other LA/ER opioid prescriptions; more than 6% of LA/ER prescriptions were written for acute pain conditions; and 2.6% were obtained by opioid-naïve patients.

In 2009, more than 11 million opioid prescriptions were written for 3.4 million (or 19.1%) of the 17.8 million adults without cancer diagnoses, with continuous enrollment, and with at least 1 outpatient drug claim in the MarketScan Commercial Claims and Encounters databases. Most patients obtained single-opioid prescriptions without also getting prescriptions for benzodiazepines. Although more patients were treated for acute pain, more prescriptions were written for chronic pain. The majority of prescriptions were written for fewer than 30 days, and the average prescription provided roughly 60 MMEs per day. There were signs of potential opioid misuse by patients or providers among the study population: nearly a quarter of patients had at least 1 indicator of potential misuse of opioids and more than 5% had 2 or more indicators of potential misuse.

Patients with misuse indicators probably account for a disproportionate share of adverse outcomes associated with opioid use. Increased numbers of opioid prescriptions, overlapping or early refill prescriptions, dose escalation, and more days of supply of opioids have all been associated with increased risk of clinically recognized abuse.

Higher daily dose has been associated with misuse and overdoses.

 Opioid dependence has been associated with concurrent opioid and psychotropic drug use.

 Simultaneous prescribing of opioids and benzodiazepines, although it might be appropriate in some cases, has been associated with multiple-provider episodes, also known as doctor shopping.

Acute pain is not an indication for an LA/ER opioid, and such use is considered inappropriate by clinical guidelines.

 Many LA/ER opioids carry warnings against initiation among opioidnaïve patients.

Other patterns in prescribing for specific types of pain have not been previously flagged as indicators of possible misuse but run counter to the recommendations of some advisory groups.For example, the New York City Department of Health has recommended no more than a 7-day supply for acute pain.

 However, in this study 22.8% of opioid prescriptions for acute pain were for 10 or more days, and 9.5% were for 30 or more days. The American College of Occupational and Environmental Medicine practice guidelines only recommend opioids on a limited basis for treatment of severe, acute low back pain, with treatment to last no more than 2 weeks.

 In this study, 42.3% of opioid prescriptions for back pain were for 30 days or more, clearly much more than recommended.

Comparisons of our findings with the published literature were difficult because of use of different indicators or different study populations. In 1 managed care population with chronic noncancer pain during 1999 through 2005, 3.5% of personyears of opioid use were at dosages of 100 MME or more,

 and in a population of veterans with chronic pain during 2008, 3.4% of those treated with opioids were at dosages of 180 MME or more.26 This compares with 14.0% of prescriptions for chronic pain over 100 MME and 4.5% over 200 MME in this study. In Massachusetts in 2006, 6.9% of patients treated with Schedule II drugs had early refills,27 compared with 7.8% for any opioid overlap in this study.

A quarter of opioid recipients with commercial insurance had at least 1 indicator of potential misuse by patients or inappropriate prescription practices by providers.

August 2013

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