David T. Liss, PhD

 To examine differences in health care costs associated with choice of second-line antidiabetes medication (ADM) for commercially insured adults with type 2 diabetes.

Retrospective cohort study with multiple pretests and posttests.

 Included patients initiated second-line ADM therapy between 2011 and 2015, with variable follow-up through 2017. The 6 index medication classes were sulfonylureas, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 RAs), basal insulin, sodium-glucose cotransporter-2 (SGLT-2) inhibitors, and thiazolidinediones (TZDs). Multivariable regression models compared between-class changes in adjusted quarterly costs after second-line ADM initiation.

 The study cohort included 34,963 adults. Most were prescribed a sulfonylurea (46.0%) or DPP-4 inhibitor (30.4%). Adjusted quarterly index medication costs were significantly higher for all patients receiving nonsulfonylurea medications, ranging from $108 (95% CI, $99-$118) for TZDs to $742 (95% CI, $720-$765) for GLP-1 RAs. Changes in quarterly total health care costs were significantly higher for all nonsulfonylurea classes. Conversely, changes in quarterly nonpharmacy medical costs were significantly lower for patients receiving DPP-4 inhibitors (–$67; 95% CI, –$92 to –$43), GLP-1 RAs (–$43; 95% CI, –$85 to –$1), and SGLT-2 inhibitors (–$46; 95% CI, –$87 to –$6); changes in all other quarterly costs besides the index medication were significantly lower for patients receiving DPP-4 inhibitors (–$60; 95% CI, –$94 to –$26) and SGLT-2 inhibitors (–$113; 95% CI, –$169 to –$57).

 The higher cost of nonsulfonylurea medications was the main driver of relative increases in total costs. Relative decreases in nonpharmacy medical costs among patients receiving newer ADM classes reflect these medications’ potential value.

Among commercially insured adults starting second-line medications for type 2 diabetes, the higher cost of nonsulfonylurea medications was the main driver of relative increases in total health care costs. However, there were relative decreases in nonpharmacy medical costs among patients receiving dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose cotransporter-2 inhibitors.

Findings reflect the higher up-front cost, but also the potential benefits, of newer medication classes.

Consideration of medications’ impacts on both medical and pharmacy costs may be useful in pharmacy benefit design.

More research is needed on medications’ benefits, tolerability, and harms in particular patient subgroups.

Although considerable progress has been made in preventing diabetes complications,

 the annual cost of care for Americans with type 2 diabetes increased more than 70% during a recent 10-year period.

 Increasing costs of diabetes care stem in large part from increased diagnosis and prevalence of diabetes, in addition to increased costs of outpatient services, supplies, and outpatient medications,

 and newer antidiabetes medications (ADMs).

Metformin is widely recommended as first-line pharmacologic therapy for type 2 diabetes.

 When a second-line ADM is required after metformin, nearly half of patients receive sulfonylureas,

 which have demonstrated glycemic-lowering effects similar to those of newer alternatives at much lower cost.

 Despite sulfonylureas’ low price, other second-line ADMs may offer value through improved patient outcomes that generate downstream cost savings. Sulfonylureas may increase the risk of hypoglycemia-related hospital admissions (and related costs),

 whereas other second-line ADMs may reduce downstream costs due to varying glycemic effects,

 or potentially preventing cardiovascular events

Cost-effectiveness analyses can provide important information about the value of different second-line ADMs. However, prior cost-effectiveness analyses have modeled disease progression and medication adherence

 based on randomized trials in high-risk populations

 or trials including many participants from outside the United States.

 Real-world evidence in American populations can help health care stakeholders in the United States evaluate whether the higher up-front costs of newer ADMs may be offset by downstream impacts on other health care costs.

In this study, we examined differential changes in quarterly health care costs associated with clinicians’ choice of second-line ADM class in a commercially insured cohort of US adults, following the use of metformin alone. In addition to evaluating between-class changes in second-line ADM costs, total health care costs, nonpharmacy medical costs, and all other costs besides the second-line ADM, we explored between-class changes for several secondary cost measures.

 our study team extracted and evaluated data from national administrative databases provided by a large commercial health payer. We used health plan enrollment files, medical inpatient and ambulatory claims, and pharmacy claims from the period between January 1, 2011, and December 31, 2017, to evaluate outcomes for a large cohort of commercially insured US adults with type 2 diabetes. Due to our use of coded, nonidentifiable data, Northwestern University’s institutional review board judged this work as non–human subjects research.

This retrospective new-user cohort study used a 6-group pretest-posttest design, with multiple pretests and posttests, and a patient-quarter unit of analysis. We compared costs between classes of patients’ initial second-line ADM (described henceforth as the index medication), following the use of metformin alone. The 6 index medication classes under study were sulfonylureas, dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 RAs), basal insulin, sodium-glucose cotransporter-2 (SGLT-2) inhibitors, and thiazolidinediones (TZDs). These index medication classes include all 5 major drug classes being tested in an ongoing comparative effectiveness trial of leading second-line ADMs,

 as well as the SGLT-2 inhibitor class that was more recently brought to market. 

) lists included medications within each included class.

The study cohort included adult commercial health plan enrollees with type 2 diabetes, with metformin treatment augmented by a second-line ADM. Patients met this case definition by fulfilling 4 criteria, where their “index date” was defined as the date of the initial pharmacy claim for their index medication: (1) at least 1 medical claim with an 

International Classification of Diseases, Ninth Revision

) diabetes diagnosis code for type 2 diabetes on or before the index date; (2) at least 1 pharmacy claim for metformin during the 180-day periods before and after the index date; (3) at least 1 pharmacy claim for an included index medication class between January 2011 and September 2015; and (4) no prior claims for any other included index medication class. Patient-quarters preceding the index date were categorized as the baseline period. The 

 provides additional details on our methods and identification of study periods.

Included patients were required to have continuous health plan enrollment for at least 2 calendar quarters preceding and following the index date, guaranteeing at least 4 patient-quarters of data. To eliminate bias in baseline period comorbidity measurement attributable to differences between 

 we included only patients whose index date preceded the October 2015 transition to 

We excluded patients who had ever had any diagnostic codes for type 1 diabetes. Due to expected high costs that were not attributable to the ADMs under study, we excluded patients with a major organ transplant or end-stage renal disease diagnosis during the baseline period. We also excluded patients with major organ transplant during follow-up—other than kidney or heart transplant, which could potentially be related to the index medications under study—and women with pregnancy/childbirth during follow-up or the last 2 baseline quarters. Patients were censored from analysis at the time of commercial health plan disenrollment or transition to Medicare Advantage enrollment.

We calculated cost outcomes for each included patient-quarter. Costs of each claim were allocated to individual service categories using methods previously employed for commercial health plan data.

 All study outcomes represent the total amount of plan-defined costs for relevant covered benefits, including both health plan payments and plan-defined patient responsibilities (ie, deductibles, co-pays, and coinsurance) for paid claims.

We examined 4 primary outcomes: quarterly costs of the index medication, quarterly total health care costs, quarterly nonpharmacy medical costs (ie, total costs excluding all pharmacy claims), and all other quarterly costs besides the index medication (ie, total costs excluding index medication claims). The last 3 outcomes were winsorized at the 95th percentile (ie, across all included patient-quarters, costs above the 95th percentile were transformed to 95th percentile costs) to reduce the influence of outlier observations.

There were several secondary measures of quarterly costs, including pharmacy claims for other (ie, nonindex) diabetes medications, pharmacy claims for all diabetes medications, total pharmacy claims, and diabetes supplies. Outpatient cost measures included costs of primary care office visits, endocrinology office visits, total office visits, diabetes education, and pathology/laboratory costs. We additionally examined hospital cost data separately for emergency department visits (including observation stays) and inpatient admissions.

We defined several covariate measures for multivariable regression modeling. Patient characteristics included sex, age, race/ethnicity (imputed), year of initiating index medication, US Census region, total health care costs during the last 2 baseline quarters, and most recent glycated hemoglobin (A1C) level. We additionally defined health plan characteristics for plan structure (eg, health maintenance organization or preferred provider organization), self-funded (ie, administrative services only) employer plans, and high-deductible plans. Because provider characteristics can influence the choice of index medication,

 the prescribing clinician’s area of clinical training was also defined. To enable adjustment for variation in costs and care delivery patterns across regions and over time, we collected data on hospital referral region

 and observation year. CMS Hierarchical Condition Categories risk adjustment variables described patients’ overall morbidity burden.

Statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc) with α = 0.05. Descriptive statistics were computed for all baseline characteristics. We also calculated quarterly measures of total health care costs, by index medication class, adjusted for medical care inflation to 2011 levels.

We used multivariable regression analyses to adjust for potential confounders of each primary and secondary outcome. Generalized estimating equations accounted for longitudinal patient-level clustering of quarterly observations.

 In our main analysis, models used an identity link and normal distribution to estimate changes in US$ relative to sulfonylureas, an approach shown to provide valid inference on differences in mean costs in large samples.

 Each regression model adjusted for all covariates described above.

In each regression model, the key independent variables were interactions between the follow-up period and each of the 5 nonsulfonylurea index medication classes. These interaction terms represent the differential change in costs during follow-up (vs baseline) by index medication class (referent: change in costs for patients receiving sulfonylureas). Key independent variables and baseline patient characteristics remained fixed over the entire follow-up period. This modeling approach focused inference on clinicians’ initial choice of index medication while effectively disregarding any subsequent changes in second-line ADM use (eg, switching from a sulfonylurea to a DPP-4 inhibitor).

We conducted a sensitivity analysis where regression models for our 4 primary outcomes omitted the categorical covariate for most recent A1C level, which had a high rate of missingness due to limited availability of laboratory testing results (although most patients had recent laboratory claims for A1C tests, test results are typically not available from administrative data). We also conducted multiple sensitivity analyses that adjusted for the same covariates as the main adjusted analysis. First, we investigated proportional differences in cost changes under varying distributional assumptions that are often employed in cost analyses (ie, log link, gamma distribution; outlier costs not winsorized). Second, we repeated the analytic approach from our primary analysis, restricted to the following subgroups: (1) subjects whose follow-up period began no earlier than January 2013 (ie, when SGLT-2 inhibitors were introduced to the US market), and (2) “adherent” subjects who, during at least the first 6 months of follow-up, used no other diabetes medications (besides metformin and the index medication) and filled prescriptions for the index medication for at least 80% of included days’ supply. These 2 subgroups comprised 63.8% and 44.6% of the final cohort, respectively.

The final cohort included 34,963 commercially insured adults with type 2 diabetes (

 presents numbers of patients meeting individual inclusion/exclusion criteria). Included patients contributed a mean of 8.0 patient-quarters during the baseline period (median, 7; range, 2-18) and 11.1 patient-quarters during follow-up (median, 10; range, 2-26).

More than three-fourths of included patients received either a sulfonylurea (n = 16,096; 46.0%) or DPP-4 inhibitor (n = 10,635; 30.4%) as their index medication (

). Fewer patients were prescribed a GLP-1 RA (n = 2874; 8.2%), basal insulin (n = 1618; 4.6%), SGLT-2 inhibitor (n = 2475; 7.1%), or TZD (n = 1265; 3.6%). A majority of included patients were male (59.3%), were non-Hispanic White (65.8%), and received coverage through a self-funded employer health plan (52.2%). During the 2 quarters preceding the index date, patients whose index medications were either basal insulin or GLP-1 RAs tended to have higher baseline total health care costs: A higher proportion of patients started on basal insulin (9.0%) were in the highest baseline cost category, and a smaller proportion of patients started on a GLP-1 RA (37.6%) were in the lowest baseline cost category. Conversely, baseline total health care costs tended to be lower among patients whose index medication was a sulfonylurea or TZD (

 presents inflation-adjusted mean total quarterly health care costs (to 2011 levels), by index medication class, for the last 2 years of the baseline period and first 3 years of follow-up. In all 6 index medication classes, costs were highest during the first follow-up quarter. Later in the first follow-up year, mean total health care costs declined and then stabilized at a level higher than that of the baseline period. Mean costs then increased modestly but consistently for several index medication classes.

Mean total adjusted health care costs for patients prescribed sulfonylurea as their index medication were $1595 per quarter during the baseline period and grew by $252 per quarter during follow-up. In multivariable model estimates of adjusted differential changes in our primary outcomes, higher costs of nonsulfonylurea medications seemed to be the main driver of growth in total costs during follow-up (

). After initiating second-line ADM therapy, per-person per-quarter index medication costs were significantly higher for all patients receiving a nonsulfonylurea index medication, ranging from an additional $108 (95% CI, $99-$118) for TZDs to $742 (95% CI, $720-$765) for GLP-1 RAs. The change in quarterly total costs was similarly higher for all nonsulfonylurea classes, ranging from an additional $107 (95% CI, $32-$182) for TZDs to $664 (95% CI, $601-$726) for GLP-1 RAs.

However, patients receiving some index medication classes experienced relative decreases in other adjusted primary outcomes during follow-up. Compared with patients receiving sulfonylureas, the change in quarterly nonpharmacy medical costs was significantly lower for patients receiving DPP-4 inhibitors (–$67; 95% CI, –$92 to –$43), GLP-1 RAs (–$43; 95% CI, –$85 to –$1), and SGLT-2 inhibitors (–$46; 95% CI, –$87 to –$6). The change in all other quarterly costs besides the index medication (ie, after adding back all other pharmacy expenditures) was significantly lower for patients receiving DPP-4 inhibitors (–$60; 95% CI, –$94 to –$26) and SGLT-2 inhibitors (–$113; 95% CI, –$169 to –$57) but significantly higher for patients receiving basal insulin ($186; 95% CI, $106-$267). In the sensitivity analysis where we did not adjust for most recent A1C level, inference was identical for all primary outcomes (results available from authors upon request). In sensitivity analyses adjusting for the same covariates as the main adjusted analysis, inference on index medication costs and total health care costs was largely consistent with results of the main adjusted analysis (

In secondary outcome analysis, changes in adjusted office visit costs (primary care and total) were significantly lower for patients receiving a DPP-4 inhibitor, a GLP-1 RA, or an SGLT-2 inhibitor index medication compared with sulfonylureas (

); however, these relative decreases did not exceed $15 per quarter in either the main or sensitivity analyses. There was also some evidence of relative decreases in costs for hospital encounters, but results were not consistent across analyses. For example, in our main analysis, the change in quarterly inpatient costs was lower for patients receiving a DPP-4 inhibitor (–$129; 95% CI, –$230 to –$29) index medication vs sulfonylureas; however, in some sensitivity analyses, significant relative reductions were observed for either inpatient costs or emergency department costs (but not both inpatient and emergency costs). In the main analysis, patients receiving basal insulin as their index medication had a significant relative increase in quarterly costs of nonindex diabetes medications ($105; 95% CI, $72-$137), diabetes supplies ($16; 95% CI, $15-$17), primary care office visits ($8; 95% CI, $4-$12), and total office visits ($19; 95% CI, $11-$27) vs sulfonylureas; inference on these 4 outcomes was consistent across sensitivity analyses for those receiving basal insulin.

In this cohort of commercially insured adults with type 2 diabetes who were prescribed a second-line ADM, patients who initiated a sulfonylurea—the most common and least costly option—experienced a $252 increase in mean total adjusted health care costs per quarter. Patients initiating nonsulfonylurea medications experienced relatively greater increases in total costs, reaching as high as an additional $664 per quarter for patients receiving GLP-1 RAs. However, compared with patients initiating sulfonylureas, those initiating the 3 newest ADM classes (DPP-4 inhibitors, GLP-1 RAs, SGLT-2 inhibitors) had relative decreases in nonpharmacy medical costs. These relative decreases in nonpharmacy medical costs (ie, total costs excluding all pharmacy claims) were at least partially attributable to factors observed in secondary analysis, such as relative decreases in office visit costs among patients initiating these newer ADM classes. Patients initiating basal insulin experienced relative increases in quarterly costs excluding the index medication, which were driven by relative increases in costs of nonindex diabetes medications, diabetes supplies, and office visits.

These results demonstrate the higher up-front cost, but also potential value, of nonsulfonylurea medications. The observed relative decreases in nonpharmacy medical costs may conceivably reflect improvements in outcomes observed in prior studies such as reduced cardiovascular disease risk,

 Relative decreases in medical costs might also be attributable to pleiotropic effects of newer ADM classes such as GLP-1 RAs

 although a longer follow-up period may be required for such impacts to be observed. Cumulatively or individually, these potential benefits of newer ADM classes could contribute to relative reductions in patient morbidity, mortality, and medical costs or—if desirable impacts such as weight loss occur early in the medication course—possibly even outcomes such as increased medication adherence.

Current recommendations that clinicians and patients collaborate on personalized prescribing decisions provide a strong rationale for this study of real-world data; however, although we are confident that our regression modeling approach was methodologically appropriate, it provides little context on changes in patients’ medication regimens after the index date. Rather, our results (including findings on index medication costs) reflect real-world patterns of clinical practice and medication use, in which many included patients had incomplete refill adherence or discontinued the index medication during follow-up. To aid prescribing decisions going forward, it is important that real-world data be used to identify which medications have the greatest potential benefits, tolerability, and harms in particular patient subgroups. Furthermore, considering both medical and pharmacy costs may prove useful for payers using or considering a pharmacy benefit carve-out design.

This study has some notable limitations. Despite the cohort’s large size and the consistency of results across sensitivity analyses, we cannot assume that index ADM choice was the sole reason for observed differences during follow-up. Although multivariable regression models adjusted for covariates, we did not have data on some predictors of costs and diabetes-related complications, including duration of diabetes

 or health behaviors such as physical activity. Even after adjustment for morbidity burden, our analyses may not have fully accounted for potential selection bias in groups such as patients receiving basal insulin or those whose index medication was prescribed by a specialist (ie, an endocrinologist). Among patients who were censored at the time of health plan disenrollment, we did not have information on the reason for disenrollment, which could have included events such as change in employment, loss of employment, switching to another commercial insurer, or death. The study cohort was identified and evaluated at a relatively early point in the diabetes disease course (ie, when starting a second-line ADM), and patients may have been at relatively low risk of medication-related complications that can lead to additional costs. Additional research is needed to understand the extent to which nonsulfonylurea medications may reduce costs in higher-risk, higher-cost diabetes populations.

A notable strength of our study is its use of commercial health plan claims, reflecting actual plan costs and patient responsibilities. Nevertheless, our outcome data on medication costs represent contracted prescription drug prices; these prices do not reflect prescription drug rebates negotiated between pharmaceutical manufacturers and pharmacy benefit managers, which ultimately decrease the net cost of brand-name medications.

 As a result, our findings on index medication costs may effectively overstate the net costs of many nonsulfonylurea ADM classes. We were additionally able to link health care claims data with laboratory results for some—but not all—patients, and multivariable regression models adjusted for patients’ most recent A1C results. A sensitivity analysis found no differences in inference when models did not adjust for A1C.

In a large cohort of commercially insured adults with type 2 diabetes who were prescribed second-line ADM augmenting metformin monotherapy, the higher cost of nonsulfonylurea medications was the main driver of relative increases in total health care costs during follow-up. However, there were relative decreases in nonpharmacy medical costs among patients receiving DPP-4 inhibitors, GLP-1 RAs, and SGLT-2 inhibitors, reflecting the potential value and benefits of these newer ADM classes. Additional research is needed to investigate real-world outcomes of different second-line ADMs in high-risk, high-cost diabetes populations and to enable clinicians and patients to make more informed, patient-centered prescribing decisions.

The authors wish to acknowledge the UnitedHealth Group Enterprise Research and Development team for ensuring access to the data. They also thank Cassandra Aikman, MPH, of Northwestern University, and Sanya Pasricha, BS, of UnitedHealth Group, for project management support.

 Division of General Internal Medicine and Geriatrics (DTL, NL, MJO, AJC, SAH, RTA) and Division of Endocrinology, Metabolism, and Molecular Medicine (AW, RTA), Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL; Institute of Public Health and Medicine, Northwestern University Feinberg School of Medicine (DTL, RHK, MJO, AW, SAH, RTA), Chicago, IL; Applied Research, UnitedHealth Group (EDP), Minnetonka, MN.

 This work and the evaluation team were supported through a grant to Northwestern University from UnitedHealthcare Services.

Dr Liss’ work on the manuscript was supported through a grant from UnitedHealthcare Services, which is the insurer that paid the health care claims of the patients included in this study and, as such, has a financial interest in the manuscript’s subject matter. Dr Wallia has received grants from Novo Nordisk and Eli Lilly. Dr Parker is employed by and owns stock in UnitedHealth Group. 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 (DTL, RHK, MJO, AW, RTA); acquisition of data (RHK, NL, MJO, AJC, EDP, RTA); analysis and interpretation of data (DTL, RHK, NL, MJO, AW, AJC, EDP, RTA); drafting of the manuscript (DTL, RHK, MJO, AJC, SAH, EDP); critical revision of the manuscript for important intellectual content (DTL, RHK, MJO, AW, SAH, EDP, RTA); statistical analysis (DTL, RHK, NL); obtaining funding (RTA); administrative, technical, or logistic support (SAH); and supervision (DTL).

 David T. Liss, PhD, Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, 750 N Lake Shore Dr, 10th Floor, Chicago, IL 60611. Email: david.liss@northwestern.edu.

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Adults with type 2 diabetes who received nonsulfonylurea medications had relative increases in total costs. Receiving newer medication classes led to relatively decreased medical costs.

Costs for Commercially Insured Adults Prescribed Second-line Diabetes Medications

Appropriate vaccinations are important for patients with rheumatoid arthritis (RA), who are often treated with highly immunosuppressive therapies that increase their risk of infection. However, rates of vaccination among patients with RA are below optimal levels.

We conducted a patient survey to assess self-reported vaccination status and to compare that status with electronic health record (EHR) data.

We recruited randomly selected patients with RA in an academic practice in 2013. Eligible participants had a diagnosis of RA, at least 1 visit to a rheumatology clinic in each of the previous 2 years, were 18 years or older, and had English listed as their preferred language. The survey included the following domains: a) patient self-reported receipt of influenza, pneumococcal (PNVX), and herpes zoster (HZVX) vaccinations; b) attitudes about these vaccines, including reasons for unvaccinated status, if applicable; and c) provider recommendations about these vaccines.

Based on participants’ self-report, we found a high vaccination rate for influenza during the previous season (79.4%), a moderate rate of any previous vaccination for pneumococcus (53.9%), and a very low rate of any previous vaccination for herpes zoster (7.8%). If we assume that all self-reports are accurate and we include vaccinations recorded in the EHR that were not reported by patients, the vaccination rates were approximately 8% to 9% higher for PNVX and HZVX.

Vaccination rates are low among patients with RA based on self-report data. Further research is needed to investigate system-level barriers to vaccination and the impact of evidence-based, provider-level interventions on vaccination rates. 

	Vaccination rates are low among patients with rheumatoid arthritis based on self-report data. 

		Lack of physician recommendation, as recalled by the patient, appears to be the most important factor for unvaccinated status. Only three-fourths of patients said their doctors had discussed their increased risk of infection, and only two-thirds said their doctors discussed the importance of vaccinations. 

		Patient lack of knowledge or negative attitudes toward vaccinations appear to be less important contributors to suboptimal vaccination rates. 

		Further research is needed to investigate system-level barriers to vaccination and the impact of evidence-based provider-level interventions on vaccination rates.

Patients with autoimmune rheumatic diseases have a risk of infection approximately double that of age- and gender-matched controls.

 Patients with rheumatoid arthritis (RA) are at an increased risk for infection due both to inherent dysregulation of the immune system and, for many, the added effect of chronic immunosuppressive therapy. For example, the adjusted hazard ratio for contracting herpes zoster in patients with RA is 1.9 compared with healthy controls.

Because of their increased risk of infection, the Advisory Committee on Immunization Practices (ACIP) recommends that immunocompromised adults receive an annual influenza vaccination (INFVX), as well as the pneumococcal vaccination (PNVX) with the 13-strain pneumococcal conjugated vaccine (PCV-13) followed by the 23-strain pneumococcal polysaccharide vaccine (PPSV-23).

 The recommendations are more complex for herpes zoster vaccination (HZVX), which uses a live virus and has historically been considered contraindicated in patients who are severely immunosuppressed. Currently, ACIP recommends HZVX for all individuals 60 years or older.

 However, a recent study found that HZVX had even higher efficacy (ie, 70% risk reduction) among healthy adults aged 50 to 59 years

; HZVX (Zostavax) has FDA approval for use in adults 50 years or older.

 ACIP states that HZVX is safe for patients who are taking less than 20 mg/day of prednisone and/or low doses of methotrexate (≤0.4 mg/kg/week), azathioprine (≤3.0 mg/kg/day) or 6-mercaptopurine (≤1.5 mg/kg/day).

 If a physician plans to initiate a strong immunosuppressant, ACIP recommends the patient be given the HZVX at least 14 days prior to starting the medication.

 This recommendation has been challenged by findings from a recent analysis of Medicare patients with immune-mediated disease who were given HZVX. There was no increase in herpes zoster infections during the 42 days after HZVX, even among patients receiving biologic immunosuppression, whereas those vaccinated had a lower incidence of zoster over a median 2-year follow-up.

Vaccination rates remain low among RA patients, despite their high risk of infection, strong evidence of vaccine safety and efficacy, and recommendations from national organizations. A recent large study found that only 25% of RA patients were adherent to current vaccine recommendations.

 Studies have shown adherence rates among RA patients ranging from 10% to 34% for INFVX,

 The reasons for these low vaccination rates are not entirely clear.

At our institution, the Northwestern Memorial Medical Center in Chicago, vaccination rates for patients with RA measured using electronic health record (EHR) data have been similar to national data. However, EHR vaccination data may be incomplete because many patients receive vaccinations outside of their rheumatologist’s office and their primary care clinician may practice in a different health system. The completeness of EHR data on vaccinations is also limited, as many patients may get vaccinated in pharmacies and retail stores with no automated way to transfer documentation to their healthcare team.

 Therefore, at the start of a quality improvement research project to improve vaccination rates, we conducted a telephone survey to capture patients’ self-report of vaccination status and to better understand vaccination behavior and attitudes of patients with RA.

This study was conducted at an academic rheumatology clinic from July through September 2013 and was approved by the Northwestern University Institutional Review Board. Structured query language was used to identify all eligible patients from EHR data. Patients were eligible if they had a diagnosis of RA (

International Classification of Diseases, Ninth Revision, Clinical Modification

 codes 714.0-714.9) in the problem list, at least 1 visit to the study clinic in each of the previous 2 years, English listed as their preferred language, and were aged at least 18 years. A total of 1119 patients were identified as eligible based on EHR data. Query output included patient contact information for recruitment. A random number was generated for each patient on the list, and the list was sorted by this number to ensure that a random sample of patients would be recruited.

Our goal was to complete 100 interviews. Assuming a 50% participation rate based on previous work with a similar patient population, we initially selected the first 200 patients for recruitment. Manual chart reviews were done prior to recruitment to confirm that the patients had a diagnosis of RA. After initial chart reviews found that some patients did not have a confirmed diagnosis of RA, we selected an additional 50 patients for possible recruitment. We stopped chart reviews and recruitment of new subjects once we reached 100 completed interviews. Of the 245 manual charts we reviewed, 48 did not have an RA diagnosis, resulting in 197 patients whom we attempted to recruit.

A study interviewer called potential respondents up to 6 times and invited them to participate in a 10-minute structured telephone interview. We varied the time of day for the calls to maximize the chance of contact and to decrease selection bias. A message was left on identified voicemails on the second and fifth attempts asking patients to return the call if they were interested in participating in the study. Patients were offered a $10 gift card for completing the survey.

The survey included published items from the Behavioral Risk Factor Surveillance System survey

 and items developed for this project by the study team. The survey had 22 to 27 items (depending on skip patterns) and addressed the following content areas: a) self-reported receipt of INFVX, PNVX, and HZVX; b) attitudes about these vaccines, including reasons for being unvaccinated, if applicable; and c) provider recommendations about these vaccines. Additionally, if participants reported receipt of a vaccination, we asked for details on the type of setting/place they had received each vaccine. If a participant reported ever receiving an influenza vaccination, we asked if they had been vaccinated during the previous year’s flu season (2012-2013). As HZVX is contraindicated for some immunosuppressed patients, we included an item inquiring whether a provider had ever recommended against receipt of this vaccine. Participants responded to several items regarding attitudes toward vaccinations on a 4-point Likert scale. Open-ended questions were asked to elicit participants’ reasons for not being vaccinated. Interviewers coded responses to open-ended items about reasons for not being vaccinated into the following predefined categories if appropriate: a) no one ever recommended vaccine, b) did not think they needed vaccine, and c) do not like vaccinations/do not think safe. Responses that could not be coded into the above categories were reviewed by 2 authors (DB and DS) and an additional category—d) MD/pharmacist recommended against vaccination&mdash;was added. If more than 1 reason was mentioned, multiple responses were recorded. Demographic items were asked at the conclusion of the survey.

We reviewed EHRs of consented participants who completed the telephone interview to identify whether they were receiving an immunosuppressive drug for treatment of RA, the presence of comorbid conditions, and documented immunizations (administered in the clinic and/or historical vaccinations). We classified patients as receiving immunosuppressive medications if the current medication list included any biologic agents, corticosteroids, methotrexate, leflunomide, or azathioprine; hydroxychloroquine and sulfasalazine were not considered immunosuppressive agents. Comorbid conditions were identified from the participants’ problem list and included cancer, chronic pulmonary disease, coronary artery disease, chronic heart failure, peripheral vascular disease, severe chronic liver disease, diabetes with end organ damage, renal failure, and dementia.

The survey response rate was calculated using guidelines from the American Association for Public Opinion Research.

 Survey responses were summarized using descriptive statistics. We calculated kappa statistics to analyze the agreement between self-reported vaccination and vaccination documentation in EHR data; for these analyses, if a respondent said he or she did not know whether they had been vaccinated, this was categorized as a “no” response.

Of the 197 patients we attempted to recruit, 13 had no valid telephone number, 19 refused participation, 63 were not reached after 6 attempts, and 102 patients enrolled in the study and completed the interview (55.4% response rate). Most participants were female (85.3%) and the mean age was 57.8 years (

). A total of 91.2% of participants were prescribed immunosuppressive therapy based on chart review; 85.2% of participants reported they were taking immunosuppressive therapy.

Most participants (90.2%) reported that they had received INFVX at least once; 79.4% reported receiving INFVX during the 2012-2013 season (

). Of the 81 participants who self-reported INFVX in the last year, only 28 (34.6%) had INFVX recorded in the EHR. This low rate of INFVX documentation in the EHR is consistent with the variety of settings in which patients reported receiving INFVX (

). Of the 21 patients who said they had not received INFVX in the previous year (or were unsure), only 1 (5%) had an INFVX documented in the EHR.

Approximately half of participants (53.9%) reported that they had received PNVX at least once. Only 32 of the 55 (58.2%) who reported getting PNVX had a PNVX documented in the EHR; conversely, of the 47 who said they had never received PNVX, 9 (19.1%) had a PNVX documented in the EHR. If either self-reported or EHR-documented PNVX were included, the rate of receiving 1 or more PNVX was 62.7%.

Only 8 patients (7.8%) reported receiving HZVX; of these, only 1 (12.5%) was documented in the EHR. Of the 94 who said they had not received HZVX, 8 (8.5%) had a documented HZVX in the EHR. Among patients who reported HZVX, 87.5% said they received it at a doctor’s office.

When participants were asked, “How important do you think it is to get vaccinations to prevent infections?” 16.3% said it was somewhat important and 65.3% said it was very important to get vaccinations. However, 15.3% thought vaccines were not important at all. Of note, of the 18 participants who did not feel it was important to be vaccinated, all but 1 were taking immunosuppressive medications at the time of the survey, including 9 taking tumor necrosis factor inhibitors and 12 taking methotrexate. When participants were asked how much they worried about getting infections because of the medications they take for RA, 20% responded “somewhat” and 21% responded “a great deal.”

Participants’ reasons for not being vaccinated varied by type of vaccine. The majority of participants who had not received PNVX or HZVX reported that it had never been recommended to them (

). Patient dislike or distrust of vaccination was more common for INVFX than for PNVX or HZVX. Of the 10 patients who had never been vaccinated for influenza, 7 stated that this was because they did not like or trust the vaccine.

Patient Perceptions of Provider Communication Regarding Vaccines

Three-fourths of respondents said that a physician had told them they had an increased risk of infection (

); however, only 64% recalled being told the importance of vaccines by their providers. Communication about vaccinations also varied by vaccination type (Table 5). Nearly all (96.1%) participants reported that their provider had recommended INFVX, and only 2 patients reported that one of their providers had recommended against INFVX. Only 60.8% said they were told to get PNVX, and only 16.7% reported that HZVX had been recommended (Table 5). Thirteen participants (12.8%) reported they were told not to get HZVX; of these, only 1 patient was eligible for this vaccine (ie, not on biologic therapy).

Based on participants’ self-report, we found a high vaccination rate for influenza during the previous season (79.4%), a moderate rate of any previous vaccination for pneumococcus (53.9%), and a very low rate of any previous vaccination for herpes zoster (7.8%). If we assume that all self-reports are accurate and we include vaccinations recorded in the EHR that were not reported by patients, the vaccination rates were approximately 8% to 9% higher for PNVX and HZVX (Table 2).

A variety of factors likely contribute to suboptimal vaccination rates for patients with RA; lack of physician recommendation, as recalled by the patient, appears to be the most important. Only three-fourths of patients said their doctors had discussed their increased risk of infection, and only two-thirds said their doctors discussed the importance of vaccinations (Table 5). Although almost all participants reported they had been told to be vaccinated for INFVX, less than two-thirds had been advised to get PNVX and only 1 in 6 had been advised to have HZVX. These rates of recommendations correspond well to the self-reported vaccination rates; when patients who said they had not received a PNVX or HZVX were asked why, the most common response was that it was not recommended by their provider.

The reasons why physicians fail to recommend vaccinations for immunocompromised patients are not fully understood. Some failures may occur because patients with RA are often cared for by both a rheumatologist and a primary care physician. The rheumatologist may see preventive care as the responsibility of the primary care physician, but primary care physicians may not be knowledgeable about guidelines for vaccination of immunosuppressed patients or may feel this is the responsibility of the rheumatologist prescribing the immunosuppressants.

A 2009 German study found that only 65% of patients with RA were vaccinated against influenza despite 95% patient awareness of the vaccine’s availability. This was thought to be due to the fact that in Germany, the rheumatologist “delegates” vaccinations to the general practitioner.

Similarly, a 2009 study in Ireland found that approximately half of rheumatologists did not advise influenza vaccination due to insufficient time during the visit, and they also felt vaccination was the responsibility of the general practitioner.

 However, it is not certain whether these European findings are generalizable to the United States. General practitioners in European countries tend to have a more primary role in patients’ care and serve as gatekeepers to specialty services.

 In contrast, in the United States, rheumatologists often share the responsibility of primary care for their patients’ overall health (eg, monitoring cardiovascular risk, blood glucose, malignancy screening), recognizing that many comorbid conditions have a higher prevalence with autoimmune disease. It may be appropriate for specialty societies to address shared responsibility for vaccination and to issue guidance statements to help avoid situations in which responsibility is not clear.

Patients with rheumatoid arthritis self-report a moderate rate of any previous pneumococcal vaccination (54%) and a very low rate of herpes zoster vaccination (8%).

David T. Liss, PhD

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