David P. Nau, PhD

To evaluate the impact of a collaborative effort of a Medicare Advantage and prescription drug (MAPD) plan and community pharmacies to improve vaccination rates for pneumonia and influenza.

 This quasiexperimental, cluster-randomized intervention study used MAPD data to assess the impact of community pharmacists on vaccination rates. Pharmacies in specific regions (districts) were randomly assigned to intervention or control groups. Intervention pharmacies received reports of patients with a gap in influenza (aged 19-89 years) and/or pneumococcal (aged 65-89 years) vaccinations based on medical and pharmacy claims history. Vaccine-naïve patients were offered vaccinations.

 The vaccination rates for the previously vaccine-naïve patients utilizing intervention and control pharmacies were compared 6 months post randomization. Inverse probability weighted hierarchical generalized linear models determined the odds of receiving pneumonia and influenza vaccinations for intervention and control groups, controlling for baseline clinical and demographic characteristics.

 Intervention and control groups had similar ages in the pneumococcal older-adult cohort (mean age, 73.0 vs 73.4 years, respectively; 

= .1255). The intervention group was older than the control group in the influenza cohort (mean age, 67.7 vs 66.4 years, respectively; 

= .0006). Slightly more than half of each cohort were women, and the proportion of women was not significantly different between the intervention and control groups in each cohort. In multivariable analyses, intervention pharmacies were associated with higher odds of delivering pneumococcal (odds ratio [OR], 1.91; 95% CI, 1.26-2.87) and influenza (OR, 2.18; 95% CI, 1.37-3.46) vaccinations than control pharmacies.

 A health plan–enabled, pharmacist-led intervention was effective in increasing the number of older adults receiving pneumococcal vaccination and individuals receiving influenza vaccination.

Patients whose pharmacies receive notification of their immunization gap have twice the odds of receiving immunizations compared with those whose pharmacies do not receive the notification.

A Medicare Advantage health plan provided community pharmacists with selective lists of patients who have an immunization gap for pneumococcal or influenza vaccinations, followed by confirmation from patients and an invitation to receive the needed vaccination at the pharmacy.

The intervention had a significant impact on the rate of pneumococcal and influenza vaccinations. During the 6-month postintervention time period, the odds of receiving either a pneumococcal vaccination or an influenza vaccination for the intervention group were more than double that of the control group (both 

Vaccine-preventable diseases such as influenza and pneumococcal infections can lead to significant morbidity and mortality. Influenza vaccines are effective in preventing hospitalization and death in individuals with high-risk medical conditions.

 Similarly, pneumococcal vaccines can prevent approximately 16,000 deaths annually and can save $1.8 billion in health care costs related to hospitalizations, outpatient visits, and medications used to treat older adults (≥ 65 years) with pneumonia.

 Nevertheless, adult vaccination rates remain low for most routinely recommended vaccines, and they are well below Healthy People 2020 targets.

Myriad strategies have been described to close gaps in vaccination rates. A systematic review found that increasing community demand (eg, patient education, educational letters/flyers), enhancing vaccination access (eg, home visits, low-cost or free vaccines), and provider- or system-based interventions (eg, financial incentives, reminders, educational outreach) were associated with improvements in influenza vaccination rates.

 Among examples of provider-based interventions, pharmacist-driven interventions have improved immunization rates. For example, a statewide, pharmacist-driven pneumococcal vaccination educational outreach program led to higher vaccination rates and lower pneumococcal infection rates during the study period.

 Another pharmacist-managed vaccination campaign in high-risk patients at a rural primary care clinic substantially increased influenza vaccination rates among patients 65 years and older, from 44.6% to 70.9% (

 A peer-to-peer outreach intervention consisting of an educational mailing followed by tracking and outreach by senior volunteers was effective in increasing influenza and pneumococcal vaccination rates.

 Three additional systematic reviews found that pharmacists played significant roles in screening, educating, and immunizing, thereby improving pneumococcal and influenza vaccination rates.

Although there have been improvements in vaccination rates over time, only 59% of older adults have received at least 1 of the pneumococcal vaccinations, as seen in the pneumococcal vaccination rates of Medicare beneficiaries published by the CDC (2009-2017).

 Furthermore, a significant gap still exists regarding influenza vaccination within this population, with approximately 45% of Medicare beneficiaries receiving the influenza vaccine during the 2017-2018 season.

 In an attempt to continue to improve the pneumococcal and influenza vaccination rates among individuals enrolled in Medicare Advantage and prescription drug (MAPD) plans, this pilot study designed, implemented, and evaluated a program that identified and communicated immunization gaps to community pharmacists in a pharmacist-led intervention regarding these vaccinations.

This quasiexperimental, cluster-randomized intervention study examined the impact of providing community pharmacists with information related to patient history of pneumococcal and influenza vaccinations based on both medical and prescription claims. Pharmacies in specific regional areas (districts) consisting of approximately 25 to 30 pharmacies with comparable patient characteristics were randomly divided into 2 groups, with a balanced distribution of urban/rural, age, and sex. The intervention group consisted of 1 urban and 1 rural district in the Lexington/Frankfort, Kentucky, area, and the control group consisted of 1 urban and 1 rural district in the Louisville, Kentucky, area. The study was reviewed and approved by the Chesapeake and Advarra (formerly Schulman) institutional review boards.

Data were provided by Humana Pharmacy Solutions for individuals enrolled in MAPD plans with Humana Inc and who filled prescriptions at the participating pharmacies. Pneumococcal and influenza quality measures were configured into a cloud-based performance information management platform (Electronic Quality Improvement Platform for Plans & Pharmacies [EQuIPP]), tested, and implemented to report rates of immunizations. Patients were attributed to specific pharmacies based on where they filled a majority of their prescriptions. In the event of a “tie,” the patient was assigned to the pharmacy where they filled a prescription most recently.

Pharmacies designated in the intervention group received, via the EQuIPP application, active reporting of individuals who had a gap in influenza and/or pneumococcal vaccinations from their medical and pharmacy claims history. Active reporting included both monthly performance tracking for the immunization measures and patient-level details regarding those who had not received the applicable immunization during the measurement period. Patients were queued to receive a consultation from the pharmacist to determine if a vaccination was appropriate when they came into the pharmacy to fill a prescription (ie, the pharmacist asked whether they had received the vaccination that was not documented in claims, and if not, shared the indications as stated in the guidelines and confirmed there were no contraindications). Training for pharmacists was conducted via on-demand teleconference beginning December 23, 2016. Control group pharmacies received passive reporting, which was standard of care at the time of this study (ie, pharmacies had access to patient information but were not messaged directly).

The intervention began on January 1, 2017, defined as the index date, and lasted 4 months. The impact of the intervention on vaccination rates was assessed by conducting a retrospective data analysis based on the administrative claims data in the Humana Research Database (Louisville, Kentucky). Differences in the proportion of patients aged 65 to 89 years in intervention and control groups receiving a pneumococcal vaccination, as well as those aged 19 to 89 years receiving an influenza vaccination, during the 6 months post index (January 1, 2017, to June 30, 2017) were evaluated. A period of 6 months was used to capture the lag effect of the intervention on members who chose not to be vaccinated the day they were invited.

The study population for the pneumococcal vaccination analysis was composed of individuals aged 65 to 89 years with both medical and prescription drug coverage in the MAPD plan who filled prescriptions in participating pharmacies. The study population for the influenza vaccination analysis was composed of individuals aged 19 to 89 years with both medical and prescription drug coverage in the MAPD plan who filled prescriptions in participating pharmacies. Of note, the quality measure included patients as young as 6 months; however, there were no nonadult patients enrolled in this particular MAPD plan who filled prescriptions in participating pharmacies.

The analysis of pneumococcal vaccination in older adults included individuals who were pneumococcal vaccine naïve, aged 65 to 89 years, and continuously enrolled for 12 months preindex and 6 months post index, and had least 1 prescription fill between January 1, 2016, and December 31, 2016, at a participating pharmacy. Individuals with a documented pneumococcal vaccination (ie, PCV-13 or PPSV23)

 during the 12 months preindex were excluded from the pneumococcal measures, in accordance with the Consumer Assessment of the Healthcare Providers and Systems (CAHPS) survey.

 The look-back period to determine vaccination-naïve status was extended up to 5 years for individuals aged 65 to 69 years on January 1, 2017, preintervention, provided that the individual was enrolled with the health plan for that duration. For those who were not enrolled for 5 years preintervention, all available data were used (minimum 12 months). The influenza cohort included individuals without documentation of an influenza vaccine during July 1, 2016, to December 31, 2016; aged 19 to 89 years; and continuously enrolled for 12 months preindex and 6 months post index, with at least 1 prescription fill between January 1, 2016, and December 31, 2016, at a participating pharmacy. Individuals were excluded from both cohorts if they had a cumulative stay in a long-term care facility for at least 90 days during the study period.

The Humana Research Database, which contains claims data for individuals enrolled in Humana’s fully insured commercial and Medicare plans, was utilized for this study, along with data feeds from the participating community pharmacies. Patient enrollment data included information on demographics and coverage start and end dates. Medical claims data included information on physician visits, outpatient visits, hospital inpatient stays, and vaccinations administered in a practitioner’s office setting. Pharmacy claims data included information on prescription utilization, such as the specific medication filled, prescription fill date, quantity dispensed, and days’ supply.

The primary outcomes of interest were the proportion of individuals aged 65 to 89 years who received a pneumococcal vaccination and the proportion of individuals aged 19 to 89 years who received an influenza vaccination. The outcomes were calculated for the overall 6-month postindex time period.

Several additional variables were analyzed to compare the demographic and clinical characteristics of the intervention and control groups. The Deyo-Charlson Comorbidity Index was constructed from 

International Classification of Diseases, Tenth Revision, Clinical Modification

 diagnosis codes based on the methodology of Deyo and colleagues.16 Relevant codes were identified for the year prior to index (2016). Baseline health care resource utilization variables were constructed by separately counting the number of inpatient, emergency center, and outpatient visits for each patient during the year prior to the index date. The number of prescription fills during the preindex year was also tabulated for each patient. Patients who resided in Kentucky were assigned to the South region and patients from neighboring Indiana were assigned to the Midwest region based on US Census Bureau regional assignments. Age, gender, and race were derived from the enrollment files.

The baseline demographic and clinical characteristics were compared across intervention and control groups using descriptive statistics (ie, χ

 tests). Inverse probability of treatment weighting (IPTW) was used to adjust for baseline differences in the intervention and control groups. Standardized differences before and after adjustment were compared to evaluate balance on baseline patient characteristics in the weighted study cohorts, with a difference of 0.2 or less indicating small differences between the 2 groups.

 IPT-weighted hierarchical generalized linear models with a logit link and a binary distribution were constructed to examine the relationship of intervention status with immunization status. Individual pharmacies were included as a random effect to the models to account for potential clustering. Odds ratios (ORs) and 95% CIs were evaluated to determine statistical significance of relationships. The intraclass correlation coefficient was calculated, and the proportion of variability in the outcome accounted for by differences between the pharmacies, as opposed to differences among patients in the intervention and control groups, was reported. All analyses were performed using SAS Enterprise Guide Version 7.1.1 (SAS Institute).

After applying inclusion and exclusion criteria for the pneumococcal and influenza cohorts, the final sample sizes were 2135 and 2798, respectively (

). Of the individuals in the sample, 46.5% (n = 993) of the pneumococcal cohort and 44.1% (n = 1233) of the influenza cohort were attributed to intervention pharmacies. Upon comparison, the demographic characteristics of the intervention and control groups across the 2 cohorts exhibited similar patterns (

). The intervention and control groups in the pneumococcal older-adult cohort had similar ages, with a mean (SD) age of 73.0 (5.6) years for the intervention group and 73.4 (5.8) years for the control group (

= .1255). In the influenza cohort, the intervention group was older (67.7 [9.6] years) than the control group (66.4 [11.1] years) (

= .0006). The proportion of women in the total cohort was just more than half and did not differ significantly between intervention and control groups in the pneumococcal (

= .6836) cohorts. The geographic distribution was significantly different between intervention and control groups, likely due to the fact that the greater Louisville area (control group) included a portion of patients residing in southern Indiana (Midwestern region). The proportion of individuals qualifying for low-income subsidy status was lower in the intervention group vs the control group for both the pneumococcal cohort (16.6% vs 21.5%, respectively; 

= .0047) and the influenza cohort (22.7% vs 28.4%, respectively; 

A comparison of clinical characteristics indicated a few differences between the intervention and control groups for both immunization cohorts. The intervention group of both cohorts had a higher average number of prescriptions during the prior year than the control group (mean [SD], 25.0 [28.6] vs 18.3 [26.3] medications in the pneumococcal cohort; 

< .0001; and 33.5 [33.1] vs 22.9 [29.9] medications in the influenza cohort; 

< .0001). The Deyo-Charlson Comorbidity Index score was lower in the intervention group relative to the control group for both cohorts (mean [SD], 1.3 [1.8] vs 1.7 [2.2]; 

< .0001 for the pneumococcal cohort). The proportion of patients with specific comorbidities was similar between the intervention and control groups in the pneumococcal cohort, with the exception of diabetes, heart failure, and chronic obstructive pulmonary disease (COPD), where a lower proportion of the intervention group had these comorbidities. The pattern was similar in the influenza cohort. After applying IPTW, as can be seen in 

), standardized differences between the intervention and control groups generally decreased, and they indicated that the groups were well balanced, with only region having a difference greater than 0.2.

, during the 6-month postintervention period (January 1, 2017, to June 30, 2017), the proportion of older adults receiving pneumococcal vaccinations was higher in the intervention group (16.1%; n = 160) vs the control group (10.2%; n = 116) (

< .0001). In both the intervention group and control groups, approximately one-fourth of vaccinations were administered in the pharmacy and three-fourths were administered in the practitioner’s office. The number of patients receiving vaccinations declined each month from January until June for both groups.

Results for the generalized linear model estimating the impact of the intervention on pneumococcal vaccination in older adults are reported in 

. Unadjusted results indicated that patients in the intervention group had 1.70 times higher odds of receiving a pneumococcal vaccination than patients in the control group (

= .0126). After adjusting for demographic and clinical characteristics, the odds of receiving a vaccination were 1.91 times higher than the intervention group vs the control group (

= .0021). The effect of individual pharmacies accounted for 14% and 12% of the variation in the outcome in the unadjusted and adjusted models, respectively.

During the 6-month postintervention time period, the proportion of the influenza cohort receiving influenza vaccinations was higher in the intervention group (7.0%; n = 86) vs the control group (3.9%; n = 61) (

= .0003). In the intervention group, 41.9% (n = 36) of the vaccinations were received in a pharmacy and 58.1% (n = 85) of the vaccinations were received in the practitioner’s office, whereas for the control group only 31.1% (n = 19) of the vaccinations were received in store vs 68.9% (n = 42) received in the practitioner’s office. After IPTW was applied, as seen in 

, standardized differences between the 2 groups generally decreased and suggested that the groups were balanced, where only region and number of prescription fills showed differences greater than 0.2.

Results for the generalized linear model for the influenza vaccination are reported in 

. The unadjusted logistic regression results indicated that patients in the intervention group had 1.99 higher odds than the control group of receiving an influenza vaccination (

= .0030). After adjusting for demographic and clinical characteristics, the odds of receiving a vaccination were 2.18 times higher for the intervention group vs the control group (

= .0010). The presence of COPD increased the odds of receiving the influenza vaccination (OR, 1.67; 95% CI, 1.09-2.55). Individual pharmacies accounted for 11% of variability in the outcome in both models.

This quasi-experimental study found that a health plan–enabled pharmacist-led intervention was effective at increasing the odds that older adults aged 65 to 89 years receive the pneumococcal vaccination and the odds that individuals aged 19 to 89 years receive the influenza vaccination. Patients in the intervention group had twice the odds as patients in the control group of receiving a vaccination in both the pneumococcal and the influenza cohorts. These results suggest that pharmacists have a direct impact on immunization rates when provided with relevant data to support immunization gap closure.

The findings of this study are congruent with those of 3 systematic reviews, which found that pharmacy-based immunization programs increase the rate of adult immunizations.

 However, their impact varied widely, and a large number of the studies were not randomized. Another review reported on numerous case studies and observational analyses that demonstrated a positive impact of community pharmacists on immunization rates.

 However, this is 1 of only 2 studies of which we are aware that used a cluster randomized design to evaluate the impact of a health plan providing selective lists of patients to pharmacies.

Making the gaps in immunizations directly visible to the pharmacist may have contributed significantly to the success of this program. This relied on the completion of several critical steps. First, immunization gaps from administrative claims at the health plan were linked to pharmacies where the patients filled their prescriptions. Second, the gap information was shared with the specific community pharmacy where patients filled their prescriptions, via the EQuIPP platform. Third, once received, the pharmacist needed to confirm with patients that they had not received their immunization before offering it to them. Training ensured that the pharmacists understood these steps and acted upon them appropriately.

Pharmacist-led interventions may also be successful due to the convenience of visiting a pharmacy. Patients may visit a pharmacy to fill a prescription more frequently than they go to a physician’s office, and an individual’s pharmacy may be more conveniently located than the physician’s office. In addition, in-person contact may be preferred to a telephonic intervention, an example of which reported no impact.

 The acceptance of community pharmacists as immunizers is widespread, and all states allow pharmacists to provide at least some adult immunizations. In fact, more than 376,000 pharmacists have been certified as immunizers.

 Pharmacists are more geographically dispersed across Census tracts than are primary care physicians, increasing the availability of vaccine providers in areas with inadequate primary care provider coverage.

 Thus, a pharmacist-led in-person intervention has the potential to reach many more individuals than messaging from physician offices alone, regardless of where the patient chooses to receive the vaccination.

Notably, the number of prescription fills during baseline was significantly higher in the intervention group than in the control group for both the pneumococcal and influenza cohorts. This finding could imply that individuals in the intervention group frequented the pharmacy more regularly than individuals in the control group; thus, they may have had greater exposure to a pharmacist-led intervention, increasing the measured impact of the intervention. Nevertheless, the impact of the intervention remained significant even after adjusting for baseline differences.

Because this study used data from patients enrolled in 1 Medicare Advantage plan and was limited to pharmacies from 1 pharmacy chain in or near the state of Kentucky, generalization to the entire Medicare population should be done with caution. Additional intervention pilot programs in different regions, with different health plans and different retail pharmacies, would shed light on whether the impact of the intervention holds across alternative scenarios. Individuals filling their prescriptions via mail-order pharmacies would not be covered under this outreach plan. For these patients, the impact of telephonic or digital outreach, triggered by mail-order refills, might not be as high, because it would require initiative on the part of the patient to visit their pharmacy or physician’s office to receive a vaccination. The influence of temporal effects should also be evaluated in future studies by timing interventions during the fall months when vaccination rates tend to peak.

Considering the ongoing COVID-19 pandemic, the generalizability of findings in the current environment is unknown, because decreased access due to physical distancing and transportation reductions, as well as concerns about COVID-19 exposure, may hinder in-person pharmacist immunization programs. However, given the significant immunization service interruptions, opportunities for future health plan–pharmacy partnerships for COVID-19 vaccine administration and catch-up vaccinations that were missed during the pandemic should be considered.

Additionally, limitations common to studies using administrative claims data apply to this study. These include lack of comprehensive information regarding whether or not a patient received a pneumococcal or influenza vaccination. The database used was composed of claims submitted for payment. Therefore, any vaccinations received but not submitted for reimbursement were not captured. However, this intervention program enabled the pharmacist to learn directly from the patient whether the vaccinations were received. Lastly, for this study, individuals with either a PCV-13 or a PPSV23 vaccination at any time preindex (up to 5 years) were excluded from the pneumococcal measures in accordance with the CAHPS survey, which asked only whether patients had previously received a pneumococcal vaccination. However, guidelines at the time of the study recommended the PCV-13 followed 12 months later by the PPSV23 vaccination (note that more recently updated guidelines in 2019 recommend PPSV23, with shared decision-making used for PCV-13).

 As such, there may have been patients indicated for one of the pneumococcal vaccinations among those excluded according to the exclusion criteria.

This prospective pilot intervention study found that a pharmacist-led intervention was effective in increasing the number of older adults aged 65 to 89 years receiving the pneumococcal vaccination and individuals aged 19 to 89 years receiving the influenza vaccination. Patients in the intervention group were twice as likely as patients in the control group to receive a vaccination in both the pneumococcal older-adult cohort and the influenza cohort. Given the modest increase in absolute terms, however, continued efforts are needed to explore improvements in the vaccination rate. 

 Humana Healthcare Research Inc (RLS, MKP), Louisville, KY; Rx Quality Solutions (DPN), Ada, OH; Pharmacy Quality Solutions Inc (ND), Durham, NC; Rite Aid (JM), Camp Hill, PA; Premier Inc (ADB), Charlotte, NC; Pharmacy Quality Alliance (MP, PJC), Alexandria, VA.

 Pharmacy Quality Alliance received an unrestricted educational grant by Pfizer Inc to conduct the study.

 Mr Sheer and Dr Pasquale are employed by Humana Healthcare Research Inc, a wholly owned subsidiary of Humana Inc, that was paid by Pharmacy Quality Alliance, which received an unrestricted educational grant from Pfizer Inc to conduct this study. This study was conducted independent of health plan involvement. Dr Dorich is employed by Pharmacy Quality Solutions Inc, which operates the EQuIPP dashboard that was utilized in the study by pharmacists as the intervention tool. Dr Campbell has received grants from the Community Pharmacy Foundation, Merck, and Pfizer, and at the time of the study was employed by Pharmacy Quality Alliance, which received an unrestricted educational grant from Pfizer to conduct this study. Pharmacy Quality Alliance established Pharmacy Quality Solutions Inc as a for-profit, jointly owned company and holds a 51% share of the company. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (RS, DPN, ND, ADB, MP, MKP); acquisition of data (JM); analysis and interpretation of data (RS, DPN, MP, PJC, MKP); drafting of the manuscript (RS, DPN, MP, MKP); critical revision of the manuscript for important intellectual content (RS, ADB, MP, PJC, MKP); statistical analysis (RS); provision of patients or study materials (JM); obtaining funding (ADB, MP); administrative, technical, or logistic support (ND, JM, PJC, MKP); and supervision (ND, JM, MKP).

Richard L. Sheer, BA, Humana Healthcare Research Inc, 515 W Market St, Louisville, KY 40202. Email: rsheer1@humana.com.

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Articles

Patients whose pharmacy receives notification of their immunization gap have twice the odds of receiving immunizations compared with those whose pharmacy does not receive the notification.

Medicare Advantage–Pharmacy Partnership Improves Influenza and Pneumococcal Vaccination Rates

Adherence to therapy in patients with type 2 diabetes mellitus is contingent upon a number of variables, including variables specific to the patient, to the provider, and to the treatment. While treatment selection will involve consideration to maximize effectiveness and minimize side effects, the physician must also take into account the priorities and preferences of each individual patient. For some patients, the risk of weight gain may exert a significant influence on adherence, while for others the risk of hypoglycemia or the cost of medications may be more important factors. It is incumbent upon physicians to discuss these issues with patients and to develop a patient-centric treatment plan to achieve optimal adherence and therapeutic outcomes. The nature of the clinical setting can also influence the likelihood of patient adherence to treatment. A multidisciplinary team approach to diabetes management has been shown to improve outcomes and to have a neutral or beneficial effect on costs. The treatment plan itself plays an additional role in the likelihood of a patient adhering to treatment. Less complex treatment regimens with fewer pills are associated with higher rates of adherence, as are fixed-dose combinations for those patients requiring combination therapy. Frequency and timing of dosing are also important aspects of adherence, as once-daily dosing is associated with higher rates of adherence than twice-daily dosing for anti-hyperglycemic medications.

The effective management of type 2 diabetes mellitus (T2DM) is a multifaceted effort involving lifestyle interventions (eg, diet and exercise) and, for most patients, drug therapy. Regardless of the intervention made, adherence is critical to achieving treatment goals. In fact, a connection between adherence to medications and glycated hemoglobin (A1C) has been shown: when adherence increases, A1C decreases. Higher adherence likely leads to better treatment effectiveness.

 Yet despite the importance of adherence to achieving treatment goals, there is room for improvement with regard to the current level of adherence to treatments among patients with T2DM.

 Focusing on drug therapy, in patients taking oral medications for glycemic control, the rate of adherence has been reported to be as low as 65%.

The challenges to patient adherence to prescribed treatment for T2DM are multiple and varied, may be mutually reinforcing, and may be difficult to overcome. Among the many reasons for nonadherence in T2DM, the most common ones relate to fear of treatment side effects (particularly weight gain and hypoglycemia), needle anxiety (for injectable therapies), inconvenience or complexity of a prescribed treatment regimen, inaccessibility to treatment arising from cost or formulary issues, and poor levels of patient knowledge about the use and importance of therapies for glycemic control.

Oral and injectable non-insulin therapies represent a broad array of treatments and drug classes which are commonly used before insulin is initiated. Strategies to improve adherence to oral and injectable non-insulin therapies must address each of the areas of potential adherence deficit, and these can range from the relatively straightforward—eg, optimizing therapies to avoid or minimize side effects, prescribing medications with easier-to-use formulations and simpler dosing schedules&mdash;to more complex approaches that may require changes in the way T2DM is treated by clinicians as well as cooperation on the part of third-party payers. The potential benefits of achieving higher rates of treatment adherence in T2DM include improved patient outcomes and cost savings.

 This article will focus both on the potential causes of poor adherence in T2DM and strategies to improve adherence to oral and injectable non-insulin therapies. Potential causes for poor adherence to insulin therapy in patients with T2DM along with strategies for improving adherence will be discussed in the third article in this supplement.

Impact of Patient-Related Factors on Adherence to Drug Therapy

A key therapeutic challenge in managing T2DM—and one with particular relevance to adherence&mdash;is the need to balance therapeutic efficacy with tolerability. The dual goals of achieving A1C target and avoiding or minimizing side effects that may impact tolerability and promote poor adherence need to be taken into consideration when formulating a treatment plan for patients with T2DM. The situation is complicated by the fact that unlike certain other diseases, in which disease management and therapeutic intervention may occur during a discrete period of time of illness, the task for patients with T2DM of achieving glycemic control is usually one that extends for the duration of a patient’s life. Ultimately, the patient is responsible for the day-to-day management of T2DM, including adherence to lifestyle changes as well as medications. Psychosocial factors can weigh heavily on patients and have a meaningful impact on the likelihood of treatment adherence. An international study of patients with diabetes and their providers found that between two-thirds and three-fourths of providers reported that their patients had psychosocial barriers that interfered with their adherence to therapy.

Among the barriers to adherence in T2DM, patient fear of side effects such as weight gain, increased risk of cardiovascular events, and hypoglycemia are among the most influential factors. The frequency of these side effects, and their impact on adherence, is illustrated by a survey of 2074 patients with T2DM receiving at least 1 oral antidiabetic drug (OAD) and no insulin therapy.

 Nearly 72% of respondents had experienced a tolerability issue in the 2 weeks prior to the survey and half had experienced more than 2 tolerability issues in that time. Symptoms related to hypoglycemia were the most common, affecting 57% of patients with T2DM, while other common issues included constipation and/or diarrhea (28%), headaches (26%), weight gain (23%), and fluid retention (21%). A limited but statistically significant correlation was observed between the number of tolerability issues experienced by a patient and their likelihood of nonadherence to treatment (

 <.01). Moreover, for every additional tolerability issue experienced by a patient, the risk of medication nonadherence increased by 28% (

The confluence of efficacy, side effects, and adherence was explored in the Real-Life Effectiveness and Care Patterns of Diabetes Management (RECAP-DM) study, an observational study conducted in 6 European countries. RECAP-DM included patients who had failed metformin monotherapy and subsequently added a sulfonylurea or thiazolidinedione to their existing metformin therapy.

 The study sought to evaluate the relationships between the occurrence of hypoglycemia, patient satisfaction with treatment, and poor adherence. Data from 1709 patients with T2DM participating in the study showed that within the previous 12 months, 38% of patients receiving combination therapy had experienced hypoglycemic symptoms. Moreover, only 28% of patients were found to be below the International Diabetes Federation A1C goal of 6.5%. Compared with patients who had not experienced hypoglycemic symptoms, those who had experienced them rated the efficacy, convenience, and satisfaction with treatment significantly lower, and also gave a lower rating to the side-effect profile of their regimen (all 

 <.0001). Patients with hypoglycemic symptoms were also significantly more likely to report barriers to treatment adherence (eg, being bothered by medication side effects). The impact of hypoglycemia on glycemic management is further reflected in the fact that patients with more severe hypoglycemic events were less likely to be at A1C goal (

). The authors suggest that reducing the incidence and severity of hypoglycemic events might help improve treatment adherence and glycemic control.

In reality, the challenge of balancing efficacy and tolerability in T2DM treatment is even more complex since successful disease management should also take into account the perceptions and preferences of the patients themselves. In an ideal world, patients would prefer those treatments that were the most measurably effective and tolerable. However, patients may have different priorities and may also be influenced by factors that they themselves underestimate as being influential on their adherence behavior. For example, in a 2009 survey patients with T2DM in the United States and the United Kingdom receiving OADs were asked a series of questions, the purpose of which was to determine the influence of treatment effectiveness and side effects on adherence to therapy.

 Subgroup analyses revealed several key differences in priorities. For example, weight gain was twice as important to women compared with men, while for those without a college education, cardiovascular risk was the most important factor.

Newer medications may help address medication-related weight gain. Glucagon-like peptide-1 (GLP-1)-receptor agonists represent a newer generation of medications for glycemic control that have the advantage of being associated with moderate weight loss, which might be expected to lower the risk of nonadherence.

 However, it is not clear how patients will balance the lower risk of weight gain against fears of self-injection or the costs of this class of medications. Nonetheless, it may be an appropriate option for patients who are concerned about weight gain.

In summary, the calculation necessary to achieve maximal treatment efficacy and adherence while minimizing side effects is a nuanced one that requires evaluating the benefits and limitations of particular treatments along with an understanding of the preferences and inclinations of the patients who must take them.

Practitioner-Related Factors in Treatment Adherence

If patients are to adhere to treatment, and treatment outcomes are to be optimized, it is important that treatment be customized to individual patients and modified to meet their evolving clinical requirements. There are currently about 10 different classes of antihyperglycemic agents on the market (depending on the classification scheme used), including agents administered orally or parenterally (including injectables); each class has its own advantages and disadvantages.

 The emergence of a diverse armamentarium of treatment options for diabetes provides additional tools for clinicians; however, the diversity of treatment options can make the management of T2DM more complex and difficult, particularly in the primary care setting where time is short and the clinical agenda is long. Additionally, gaps in knowledge of diabetes management among some primary care physicians contribute to the clinical challenges of managing T2DM.

 Improvements both in continuing medical education for physicians who manage patients with T2DM and in educational initiatives for patients who have T2DM can help address these gaps in knowledge.

Other factors beyond knowledge gaps influence the success of diabetes treatment. The Diabetes Outcome Study, which examined factors that were associated with poorer A1C control in primary care clinics, found that single-specialty practices were more successful than multispecialty practices, solo practices, and academic settings in achieving glycemic targets. The same study also found that practices that included nurse practitioners or physician assistants were more successful in helping patients meet their A1C targets.

It may, ultimately, be the case that the best solution for achieving clinical outcomes for patients with diabetes involves the use of multidisciplinary treatment teams. Multidisciplinary teams have been repeatedly shown to improve outcomes and have typically demonstrated beneficial or neutral effects on costs.

 Scanlon et al compared the impact of multidisciplinary teams on diabetes outcomes and costs in a Medicaid- and Medicare-enrolled patient population. Significant improvements were observed for A1C, body mass index (BMI), and blood pressure among patients with higher baseline levels for each of those variables, while no significant differences were seen in treatment costs compared with patients not receiving treatment from a multidisciplinary team.

 An Australian study by Zwar et al found that the implementation of multidisciplinary care in patients with T2DM resulted in significant reductions in A1C (from 8.3% to 7.8%; 

 <.001) for those patients who had A1C levels greater than 7%, and also significantly increased adherence to outcomes guidelines for A1C, blood pressure, and total cholesterol (all 

A different approach was taken in a French study which evaluated the utility of a multidisciplinary team intervention by 1) providing a group of 73 volunteer general practitioners who treated T2DM with an 8-page guideline booklet for the appropriate treatment of T2DM and 2) conducting 2 subsequent audits of outcomes. The first audit, conducted in the year following the provision of guidelines, found generally poor guideline adherence and subpar patient outcomes, including inadequate screening for complications, underestimation of the role of education, and insufficient control of hypertension.

 After the first audit, a multidisciplinary team was given the task of evaluating the clinical deficits it observed and making recommendations for their improvement. Recommended interventions for improvement included the implementation of educational programs and the creation of protocols for screening and treating retinopathy, nephropathy, and foot complications. The following year, a second audit was conducted which demonstrated that the interventions had resulted in significant improvements, including decreases in A1C levels, reductions in foot complications, increases in prescription of antihypertensive medications, and reductions in severe complications.

It should be noted that physicians themselves recognize the need for a multidisciplinary team approach to the treatment of diabetes, and also recognize the challenges that a single practitioner faces when treating diabetes.

 Thus, success in optimizing outcomes would appear to be at least partially contingent on the structure of the clinical setting in which T2DM is treated and on the diversity of clinical expertise involved in the provision of care.

In addition to the strategies already discussed, programs that incentivize providers for quality improvements (eg, pay-for-performance [P4P] programs) also have the potential to impact patient adherence. Given the importance of adherence to achieving treatment goals and the current levels of adherence to treatment for T2DM, financial rewards or penalties tied to clinical outcomes may provide additional motivation for providers to help improve patient adherence to treatment for T2DM. Providers may help encourage adherence by several means, including educating patients regarding the consequences of hyperglycemia and the importance of adherence to lifestyle modifications as well as medications, and selecting medications that have favorable side effect profiles and convenient dosage and administration requirements.

Foels and Hewner evaluated the effect of a quality improvement initiative combining P4P with physician education on the percentage of patients with diabetes meeting specific process and outcome measures (eg, date and value of A1C) and physicians’ scores on composite adherence to guidelines (scored on a 10-point scale). Patient records and physician adherence to guidelines were evaluated in 6-month cycles; the results were discussed with physicians to help identify opportunities for improvement. The physician education component of the program focused on helping physicians formulate a systematic approach to the care of patients with T2DM. From the first cycle to the ninth cycle, the proportion of patients achieving an A1C less than 7% increased from 39.0% to 53.5%, and physicians’ scores on composite adherence to guidelines increased from 4.3 to 6.2.

To investigate the effect of a P4P program on how frequently patients with diabetes received certain quality-of-care interventions, and the relationship between receipt of these interventions and the likelihood of hospitalization, Chen et al conducted a longitudinal study of patients enrolled in a preferred provider organization between January 1, 1999, and December 31, 2006. Patients who had a claim for at least 2 A1C tests and 1 low-density lipoprotein cholesterol (LDL-C) test during 1 year were considered to have received quality care. Patients who saw physicians participating in the P4P program were significantly more likely to receive quality care compared with patients who saw physicians not participating in the P4P program (odds ratio, 1.16; 95% confidence interval [CI], 1.11-1.22; 

 <.001). Patients who received quality care were significantly less likely to be hospitalized in the following year compared with patients who did not receive quality care (incident rate ratio, 0.80; 95% CI, 0.78-0.83; 

Impact of Medication Formulations and Administration on Adherence

Factors related to the medications themselves can also influence adherence. A greater number of daily doses of medication or a more complex regimen involving more than 1 medication can decrease adherence.

 A large-scale study based on data from a pharmacy claims database for a large MCO compared rates of adherence in nearly 17,000 patients with T2DM whose disease was poorly controlled on metformin or on a thiazolidinedione, and who switched to oral rosiglitazone-metformin combination therapy either as a fixed-dose combination (FDC) or loose-pill combination (LPC) regimen.

 Patients switching to the FDC were found to have significantly better improvements in treatment adherence measured by medication possession ratio (MPR) compared with those who switched to LPC (—12.4% vs –4.6%; difference in MPR improvement = 7.8%; 

 <.001). Similarly, a retrospective analysis of data from 2 very large health plan databases found improved adherence in patients switching from sulfonylurea or rosiglitazone monotherapy to rosiglitazone/glimepiride FDC compared with a switch to sulfonylurea/rosiglitazone LPC therapy.

A 2011 systematic literature review analyzed studies of oral FDC therapies and LPC therapies in patients with T2DM to determine the relationship between particular formulations and adherence, patient satisfaction, and treatment costs.

 Based on an analysis of 7 studies, the authors observed that among patients initiating combination therapy, FDC therapies were associated with adherence rates 10% to 13% higher than those seen with LPC therapies. Switching from monotherapy to LPC therapy resulted in a 10% drop in adherence, compared with a 1.5% drop in adherence for those switching from monotherapy to FDC therapy (

 <.001). Switching from LPC to FDC therapy was associated with adherence increases ranging from 3.5% to 12.4%. The authors further found that 4 out of 5 randomized controlled trials showed greater patient satisfaction with FDC therapy, while limited data suggested that FDC therapies were associated with cost savings.

The number of pills, however, may exert a smaller influence on adherence than the frequency of dosing. A study comparing once-daily dosing of glipizide oral therapy with twice-daily dosing found a 60% adherence rate for the former compared with 52% for the latter (

 <.027) despite the fact that the once-daily regimen required the administration of more pills at the time of dosing.

 The influence of frequency of dosing was also the subject of a retrospective observational study from the United Kingdom that compared adherence to immediate release (IR) versus sustained release (SR) metformin in patients with T2DM. IR metformin is dosed at least twice daily, while SR metformin is dosed once daily. The investigators found that switching from IR to SR metformin was associated with an increase in adherence from 62% to 81% (

 One adherence-related benefit of the metformin SR formulation is its potential to reduce the gastrointestinal side effects that impact many patients who take metformin, since gastrointestinal side effects result in high rates of discontinuation.

Currently available GLP-1 agonists include liraglutide, which requires once daily injection at any time of the day regardless of timing with meals, and exenatide, which requires twice-daily injection within the 60 minutes preceding the morning and evening meals.

 Longer-acting GLP-1 agonists, requiring only 1 injection per week, are currently in development, and a form of exenatide requiring only 1 injection per week was approved by the FDA in January 2012.

 The impact of weekly versus daily injections on adherence remains to be determined.

Adherence to therapy in T2DM is influenced by many variables, including treatment side effects and medication formulations. Practitioner-related factors such as the structure of the clinical setting and the particular personnel employed for disease management also impact outcomes. Adherence is also influenced by patients’ preferences and priorities. Formulation of a highly effective treatment regimen to which the patient can be adherent should include discussion with the patient regarding treatment effectiveness, side effects, dosing regimen, and costs upon treatment initiation. As patients typically will require treatment intensification, it is important that clinicians also discuss effectiveness, side effects, and costs of therapy when adjusting the treatment regimen. It is, furthermore, advisable for physicians to educate their patients with T2DM about the consequences of hyperglycemia and key strategies for effective disease management, in addition to apprising patients of expected patterns of therapy escalation, the importance of blood glucose monitoring, and the necessity of maintaining regular appointments in order to achieve glycemic control.

Because of the complexity of diabetes and its comorbidities, a multidisciplinary team approach to managing diabetes is likely to be the most effective one and has been shown to be cost effective or cost neutral. Achieving adherence in order to optimize outcomes within the strictures of cost controls requires cooperation between patients, providers, and third-party payers so that proven treatment strategies can be fully and properly implemented. 

 Pharmacy Quality Alliance, Fairfax Station, VA.

 Funding for the development of this supplement was provided by Novo Nordisk.

 Dr Nau reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this supplement.

 Concept and design; drafting of the manuscript; critical revision of the manuscript for important intellectual content; supervision.

 David P. Nau, PhD, Pharmacy Quality Alliance, 9687 South Run Oaks Dr, Fairfax Station, VA, 22039. E-mail: 

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David P. Nau, PhD

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