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The American Journal of Managed Care November 2017
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Using the 4 Pillars to Increase Vaccination Among High-Risk Adults: Who Benefits?
Mary Patricia Nowalk, PhD, RD; Krissy K. Moehling, MPH; Song Zhang, MS; Jonathan M. Raviotta, MPH; Richard K. Zimmerman, MD, MPH; and Chyongchiou J. Lin, PhD
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Adam L. Sharp, MD, MS; Yi R. Hu, MS; Ernest Shen, PhD; Richard Chen, MD; Ryan P. Radecki, MD, MS; Michael H. Kanter, MD; and Michael K. Gould, MD, MS
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Using the 4 Pillars to Increase Vaccination Among High-Risk Adults: Who Benefits?

Mary Patricia Nowalk, PhD, RD; Krissy K. Moehling, MPH; Song Zhang, MS; Jonathan M. Raviotta, MPH; Richard K. Zimmerman, MD, MPH; and Chyongchiou J. Lin, PhD
Pneumococcal; tetanus, diphtheria, and pertussis; and influenza vaccination increased among high-risk adults in a 2-year study.
ABSTRACT

Objectives: To compare changes in vaccination rates (pneumococcal polysaccharide vaccine [PPSV]; tetanus, diphtheria, and pertussis [Tdap] vaccine; and influenza vaccine) among high-risk adults following an intervention (June 1, 2013, to January 31, 2015) that used the 4 Pillars Practice Transformation Program (4 Pillars Program). 

Study Design: Post hoc analysis of data from a randomized controlled cluster trial.

Methods: Eighteen primary care practices received staff education, guidance for using the 4 Pillars Program, and support for a practice immunization champion. Paired t tests were used to compare vaccination rates separately for those with diabetes, chronic lung or chronic heart disease, or other high-risk conditions. Student’s t tests were used to compare vaccination rates across high-risk conditions. Generalized estimating equation modeling was used to determine the likelihood of vaccination. 

Results: Based on International Classification of Diseases, Ninth Revision, Clinical Modification codes, 4737 patients aged 18 to 64 years were identified as having diabetes (n = 1999), chronic heart disease (n = 658), chronic lung disease (n = 1682), or another high-risk condition (n = 764). PPSV uptake increased by 12.2 percentage points (PP), Tdap vaccination increased by 11.4 PP, and influenza vaccination increased by 4.8 PP. In regression analyses, patients with diabetes (odds ratio [OR], 2.2; 95% CI, 1.80-2.73), chronic lung disease (OR, 1.50; 95% CI, 1.21-1.87), or chronic heart disease (OR, 1.32; 95% CI, 1.02-1.71) were more likely to receive PPSV than those without the respective high-risk condition. Those with diabetes (OR, 1.14; 95% CI, 1.01-1.28) or chronic lung disease (OR, 1.14; 95% CI, 1.01-1.30) were more likely to receive an influenza vaccine than those without the respective condition. The likelihood of Tdap vaccination was not significantly associated with any of the chronic conditions tested.

Conclusions: An intervention including the 4 Pillars Program was associated with significant increases in vaccination of high-risk adults. However, the overall uptake of recommended vaccines for those with high-risk conditions remained below national goals. 

Am J Manag Care. 2017;23(11):651-655
Takeaway Points
Using the 4 Pillars Practice Transformation Program (4 Pillars Program), primary care practices can achieve meaningful improvements in adult vaccination rates among high-risk adults younger than 65 years, who are historically a group with low vaccine uptake. 
  • There remains a gap between current vaccine uptake and national goals for high-risk adults. 
  • The 4 Pillars Program provides step-by-step guidance for implementing evidence-based strategies to improve vaccine uptake. 
  • In the program, those with diabetes were more likely to receive the pneumococcal polysaccharide vaccine than those with other high-risk conditions.
Adults with certain chronic medical conditions are at higher risk of complications from some vaccine-preventable diseases because these conditions are known to compromise the immune response to infection or increase vulnerability to the effects of infection.1-3 For example, among adults aged 18 to 64 years, rates of pneumococcal pneumonia are 3.0 to 9.8 times higher for those with chronic heart disease, lung disease, or diabetes compared with healthy adults; for invasive pneumococcal disease, rates are 3.6 to 7.7 times higher.3 Not only are vaccination rates for this group woefully low—20.3% for pneumococcal polysaccharide vaccine (PPSV) in 20144—and far from the Healthy People 2020 goal of 60%,5 there are significant disparities in rates by race,6 health insurance status, and frequency of contact with a medical provider.7 Although the 2013 influenza vaccination rate among high-risk adults (49.5%) was higher than that among those without high-risk conditions (32.9%),8 this value is also below the US goal of 70%.5 Tetanus, diphtheria, and pertussis (Tdap) vaccine uptake among all adults 19 years or older was 20.1% in 2014.4 

Recent research on interventions to improve vaccination among high-risk adults is scant. Two studies focused on specialized high-risk populations (patients on dialysis9 and American Indians with diabetes10). The interventions increased PPSV uptake to 65.5% and 92%, respectively, through extensive provider and patient education and outreach to patients, including home vaccination visits. Among patients on dialysis9 and veterans with spinal cord injuries,11 multicomponent interventions resulted in increases in influenza vaccine uptake of 4 to 5 percentage points (PP). 

We undertook a 2-year study of 18 primary care practices to test the effectiveness of an intervention designed to increase uptake of adult vaccines using the 4 Pillars Practice Transformation Program (4 Pillars Program). This program is a step-by-step guide for medical practices to implement evidence-based strategies for increasing vaccination rates in primary care or other outpatient settings.12 These strategies are applicable to many practice settings and populations. Overall findings from the randomized controlled cluster trial (RCCT) and pre-post studies have been published.13-15 The purpose of this study was to compare the effect of the intervention on adult PPSV, influenza, and Tdap vaccination rates and likelihood of vaccination among adults aged 18 to 64 years with the 3 most common high-risk medical conditions (diabetes, chronic lung disease, and chronic heart disease) in a post hoc analysis. 

METHODS 

The trial was approved by the Human Research Protection Office of the University of Pittsburgh. The methods have been published previously14 and are briefly presented herein. 

Sample Size and Sites 

Eligible primary care family medicine (FM) and internal medicine (IM) practices from a practice-based research network in Pittsburgh (FM PittNet), a clinical network in southwestern Pennsylvania (Community Medicine, Inc), and a safety-net clinical network in Houston were solicited for participation. When 25 sites (a sufficient number per sample size calculations for an RCCT) had agreed to participate, solicitation ceased. All sites used a common electronic health record (EHR), EpicCare. Eligibility requirements included having at least 100 patients 18 years or older, preliminary baseline vaccination rates less than 50% for at least 1 adult vaccine (influenza, pneumococcal, Tdap), and a willingness to make office changes to increase vaccination rates. Participating practices were stratified by location (urban, suburban, or rural) and discipline (FM or IM), then randomized. The practices in this analysis were the 18 private practices or residency sites in southwestern Pennsylvania and did not include 1 site in Pittsburgh, which dropped out, and 6 publicly funded practices in Houston, from which data on high-risk conditions were not available. 

4 Pillars Program and Intervention

The 4 Pillars Program14,15 is founded on 4 evidence-based16,17 key domains: Pillar 1: convenient vaccination services; Pillar 2: communication with patients about the importance of immunization and the availability of vaccines; Pillar 3: enhanced office systems to facilitate immunization; and Pillar 4: motivation through an office immunization champion (IC). The 4 Pillars Program includes background on the importance of protecting patients against vaccine-preventable diseases, barriers to increasing vaccination from both provider and patient perspectives, and strategies to eliminate those barriers. Practices were expected to implement strategies from each of the 4 pillars. 

The intervention was designed using the diffusion of innovations theory18 and included the 4 Pillars Program, provider education, and 1-on-1 coaching of the IC for each practice. The IC was responsible for using the 4 Pillars Program to guide the practice’s intervention activities, participating in the biweekly telephone call with a research liaison for coaching, ensuring that chosen strategies were being implemented, and working to maintain motivation of the staff. 

The overall study included a 2-year RCCT in which the year 1 controls were crossed over into active intervention and the year 1 intervention groups became maintenance groups after the first year.12-14 In this analysis, all patients from the 18 southwestern Pennsylvania sites were combined and vaccination among eligible high-risk patients was examined at the end of baseline (May 31, 2013) and the end of the intervention (January 31, 2015), at which time all sites had completed the intervention. The effects of the intervention among the types of high-risk conditions were compared in a post hoc analysis. 

Data Collection 

De-identified demographic data (date of birth, sex, race, health insurance coverage as a proxy for income); office visit dates; International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for high-risk conditions, including immune and autoimmune diseases, cancers, chronic kidney diseases, diabetes, chronic lung diseases, and chronic heart diseases (codes 42, 135, 141-208.91, 250.0-250.93, 279-279.9, 282.6-284, 288-288.2, 393-398.99, 402.0-404.93, 410-412, 141-141.9, 416-416.9, 428-428.9, 438-438.9, 446-446.7, 491-496, 500-505, 506.4, 506.9, 508-508.9, 510-510.9, 513-519.9, 571-572.8, 585-586, 710-710.9, and 714-714.9) (see eAppendix Table [eAppendix available at ajmc.com]); and vaccination data (vaccines given and dates) were derived from de-identified EHR data extractions. A longitudinal database was created with only those patients who were aged 18 to 64 years at baseline and who had a visit each year during the study period, creating the cohort of individuals for study. 

Statistical Analyses 

Descriptive analyses were performed for patient demographic characteristics (age, sex, race, health insurance, high-risk condition). Age was used as a continuous variable, and racial groupings were non-Hispanic white and nonwhite. Patients with more than 1 of the 3 high-risk conditions (diabetes, chronic lung disease, chronic heart disease) were included in each of their respective disease groups for analysis. PPSV and Tdap would typically be administered once during the project period; thus, PPSV and Tdap rates are presented as cumulative rates at the end of baseline (May 31, 2013) and end of the intervention (January 31, 2015). For influenza vaccination, the analytical periods were June 1, 2012, to May 31, 2013, for baseline, and June 1, 2014, to January 31, 2015, for the intervention year. Proportions were reported for categorical variables, and means and standard deviations were reported for continuous variables. The primary outcome measures were the cumulative PPSV and Tdap vaccination rates, influenza vaccination rates reported at the end of baseline and the end of the intervention, and PP differences. Student’s paired t tests were performed to test for 2-year differences in influenza vaccination rates and cumulative PPSV and Tdap vaccination rates. In addition, the weighted average vaccination rates were compared between high-risk conditions for each vaccine using Student’s t test.

Multilevel generalized estimating equation modeling, which accounts for the clustered nature of the data (ie, patients are clustered within practices), was conducted using vaccination status for each vaccine as the binary outcome variable. Those who received the PPSV or Tdap vaccine prior to the trial were excluded from the regression analyses. To determine which factors were related to PPSV, Tdap, and influenza vaccine uptake, the regression models also accounted for heterogeneity in demographic characteristics, including age, sex, race, and health insurance. Statistical significance of 2-sided tests was set at a type I error (alpha) equal to 0.05. All analytical procedures were performed using SAS version 9.4 (SAS Institute; Cary, North Carolina). 

RESULTS

Among the 4737 patients aged 18 to 64 years who had a high-risk condition, the average age was 52.1 ± 10.2 years, with 54.2% female patients, 8.2% nonwhite patients, and 65.4% who were privately insured (data not shown). In this cohort, 42.2% of patients had diabetes, 35.5% had chronic lung disease, 13.9% had chronic heart disease, and 16.1% had another high-risk condition. Overall, 366 (7.7%) had 2 or more high-risk conditions. 

Cumulative PPSV uptake at the end of intervention reached 55.7% for all high-risk patients. Specifically, 59% of those with chronic heart disease, 54% with chronic lung disease, 66% with diabetes, and 39% with another high-risk condition had received PPSV by the end of the intervention (Table 1). Overall cumulative pneumococcal vaccination rates significantly increased 12.2 PP from baseline; patients with diabetes had larger increases than those with chronic lung disease (P = .02), chronic heart disease (P = .032), or another high-risk condition (P = .009). Cumulative Tdap vaccination rates increased significantly for all high-risk patients by 11.4 PP from baseline, reaching nearly 50% at the end of the intervention. Vaccination rates for the various high-risk groups ranged from 46% to 51%. Only those with other high-risk conditions increased their rates significantly more than those with diabetes (12.7 PP vs 11.3 PP, respectively; P = .04). Annual influenza vaccination also increased significantly from baseline for those with diabetes, chronic lung disease, and other high-risk conditions, reaching 57% for all high-risk patients. There were no differences among high-risk groups for PP increases in influenza vaccination rates.

 
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