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.
Objectives: 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.
Study Design: 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.
Methods: 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.
Results: Intervention and control groups had similar ages in the pneumococcal older-adult cohort (mean age, 73.0 vs 73.4 years, respectively; P = .1255). The intervention group was older than the control group in the influenza cohort (mean age, 67.7 vs 66.4 years, respectively; P = .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.
Conclusions: A health plan–enabled, pharmacist-led intervention was effective in increasing the number of older adults receiving pneumococcal vaccination and individuals receiving influenza vaccination.
Am J Manag Care. 2021;27(10):In Press
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.
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.1 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.2 Nevertheless, adult vaccination rates remain low for most routinely recommended vaccines, and they are well below Healthy People 2020 targets.3
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.4 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.5 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% (P < .05).6 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.7 Three additional systematic reviews found that pharmacists played significant roles in screening, educating, and immunizing, thereby improving pneumococcal and influenza vaccination rates.8-10
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).11 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.12 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)13,14 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.15 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, χ2 and t 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.17,18 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 (Figure). 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 (Table 1). 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 (P = .1255). In the influenza cohort, the intervention group was older (67.7 [9.6] years) than the control group (66.4 [11.1] years) (P = .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 (P = .3582) and influenza (P = .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; P = .0047) and the influenza cohort (22.7% vs 28.4%, respectively; P = .0007).
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; P < .0001; and 33.5 [33.1] vs 22.9 [29.9] medications in the influenza cohort; P < .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]; P < .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 eAppendix Table 1 (eAppendix available at ajmc.com), 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.
As seen in Table 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) (P < .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 Table 3. 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 (P = .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 (P = .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) (P = .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 eAppendix Table 2, 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 Table 4. 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 (P = .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 (P = .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.8-10 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.19 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.20
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.20 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.21 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.22 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).23 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.
Author Affiliations: 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.
Source of Funding: Pharmacy Quality Alliance received an unrestricted educational grant by Pfizer Inc to conduct the study.
Author Disclosures: 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.
Authorship Information: 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).
Address Correspondence to: Richard L. Sheer, BA, Humana Healthcare Research Inc, 515 W Market St, Louisville, KY 40202. Email: firstname.lastname@example.org.
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