Tailored Interventions to Increase Influenza Vaccination in Neighborhood Health Centers Serving the Disadvantaged

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1 Tailored Interventions to Increase Influenza Vaccination in Neighborhood Health Centers Serving the Disadvantaged Richard K. Zimmerman, MD, MPH, Mary Patricia Nowalk, PhD, RD, Mahlon Raymund, PhD, Melissa Tabbarah, PhD, MPH, David G. Hall, MD, J. Todd Wahrenberger, MD, Stephen A. Wilson, MD, and Edmund M. Ricci, PhD In the United States, influenza is responsible for more than deaths per year. 1 It is estimated that influenza vaccine prevents thousands of deaths each year, yet in the second quarter of 2002, the influenza vaccination rate was only 68% among adults aged 65 years and older. 2 Even lower vaccination rates among elderly minority populations have been reported, including rates of 47% for Hispanics and 52% for Blacks of non-hispanic origin. 3 For this reason, racial disparity in immunization rates is one of the areas targeted for elimination in the US Public Health Service s Healthy People 2010 objectives for the nation. 4 Moderate overall immunization rates and racial disparity in rates are perplexing, given that (1) Medicare covers influenza vaccine, (2) influenza vaccine is known to be efficacious, and (3) systematic reviews of effective methods to increase immunization rates have been published. 5,6 In our approach to the present study, we were influenced by the indepth analyses of barriers to prevention of Miller, Stange, Crabtree, and others, who have pointed out the complexity and diversity of primary-care practices and the importance of understanding the internal operating models and values of each practice. 7 9 They point to the need to tailor interventions to the practice to enhance success and continued use of the interventions We sought to implement tailored interventions to raise adult immunization rates in inner-city health centers. METHODS At each health center that served as a study site, we shared results from our earlier study of immunization barriers in inner-city health centers and conducted provider education on immunization, including discussions about types of interventions proven to be effective by systematic evidence reviews. 6 Centers were then encouraged to choose in- Objectives. We designed and evaluated interventions to increase adult immunizations within inner-city health centers. Methods. Interventions included reminders, standing orders, and walk-in flu shot clinics. Patients were surveyed and records evaluated. Results. Records from 1 center showed that immunization rates increased from 24% to 30% (P<.001) for patients aged 50 to 64 years and from 45% to 53% for patients aged 65 years and older (P <.001). Self-reported vaccination rates did not increase. In logistic regression analyses, the strongest predictor of vaccination among patients aged 50 to 64 years was the belief that unvaccinated persons will contract influenza (odds ratio [OR]=5.4; 95% confidence interval [CI]=2.4, 12.0). Among patients aged 65 years and older, the strongest predictor of vaccination was the belief that friends/relatives thought that they should be vaccinated (OR =9.7; 95% CI =4.2, 22.3). Conclusions. Tailored interventions can improve immunization rates at inner-city health centers. (Am J Public Health. 2003;93: ) terventions that staff believed would be most effective and feasible, given the unique characteristics of each center s operational systems, staffing patterns, and patient population. The impact of these tailored interventions was evaluated with a survey of patients about immunization and, where applicable, patients electronic medical records (EMRs) documenting administration of the influenza vaccine. Site Descriptions The intervention sites were faith-based neighborhood health centers that serve the disadvantaged in inner-city neighborhoods in Pittsburgh. The health centers are similar in that they are located in low-income urban neighborhoods and have similar missions. However, they differ somewhat in size, location, and insurance coverage of patients. Health Center A consists of 2 sister sites in the same organization serving different neighborhoods. One site is located in a primarily residential neighborhood, and the other is on a side street of a commercial district but within 1 to 2 blocks of public-housing highrise apartments. Health Center A has 6 fulltime equivalent (FTE) providers with 12 medical support staff divided between the 2 sites. This center served a total of 5610 persons in 2002, of whom 48% were Black, 25% were White, and 2% were Hispanic or other (25% were unreported). Health insurance coverage for patients at Health Center A is 22% uninsured, 33% Medicaid, and 45% private/ Medicare/other. Health Center B is a single site located in a mixed-use commercial district on a busy thoroughfare. It has 3.2 FTE providers with 2 medical support staff. This center served 3984 persons in 2002, of whom 45% were Black, 51% were White, 1% were Hispanic, and 3% were Asian. The insurance coverage of Health Center B patients is 16% uninsured, 37% Medicaid, and 47% private/medicare/other. Interventions Each health center implemented a multimodal approach that included patient-, provider-, and system-oriented interventions. These were chosen from the menu of options provided by the research team, on the basis of recommendations from the Task Force on Community Preventive Services. 6 Although all sites implemented standing orders, a provider reminder system, reduced-fee or free vaccines for patients, patient education posters, and staff education, differences in approach were apparent (Table 1). Furthermore, October 2003, Vol 93, No. 10 American Journal of Public Health Zimmerman et al. Peer Reviewed Research and Practice 1699

2 TABLE 1 Interventions by Site: Influenza Season Intervention Strategy Health Center A Health Center B Patient-oriented Posters in exam rooms Yes Yes Mailed reminder notice Yes No Free or low-cost vaccines to indigent Yes Yes Posters/fliers in community No Yes Provider-oriented Staff education Yes Yes Chart reminders EMR indicates date of last dose, Reminder card in or on chart prompt in vital signs screen System-oriented Standing orders to vaccinate by protocol Yes Yes On-site walk-in vaccinations Designated times Any time Off-site community vaccination clinics No Yes Note. EMR = electronic medical record. Health Center A received 580 doses and Health Center B received 250 doses of influenza vaccine free of charge from the county health department. Both centers received a fee for participating in the study. Immunization Rates Immunization rates for the and influenza seasons were defined in 2 distinct ways: (1) patient selfreporting on the survey and (2) number of doses divided by number of patients from EMRs. Total doses administered were collected from immunization logs. Survey Sample and response. Patients were randomly recruited from both health centers to participate in a telephone survey after the influenza season to assess vaccination status, impact of interventions, and patients attitudes and beliefs about adult immunizations. From patients more than 50 years of age (as of October 1, 2001) who had been seen in the last year, we randomly selected a sample using billing records from both health centers. This process resulted in a sample of 707 patients stratified by age group (50 to 64 years, 65 years and older). Of this sample, 59 were determined ineligible by medical professionals at the centers because they were deaf, homeless, had severe psychosis or dementia, or resided either in a nursing home or outside the Pittsburgh metropolitan area. Their exclusion left 648 patients for contact. Of those, 154 could not be reached, and 119 refused to participate, leaving 375 who completed the interview, for a response rate of 58% and a refusal rate of 18%. Questionnaire. The questionnaire was designed by a multidisciplinary team using an iterative process. It was based on the Triandis model for consumer decisionmaking, which draws upon the theory of reasoned action. This model considers facilitating conditions (e.g., the ease of travel for a flu shot) and behavioral intention. This factor consists of attitude about the activity (e.g., belief that getting a flu shot is wise), social influences (e.g., physician or family member recommends the flu shot), and the consequences of the activity (e.g., the flu shot prevents flu). The model accurately predicts a variety of behaviors, including exercise 18 and birth control/ fertility 17 behavior. It has been used in different cultural and economic situations. 17 In several analyses, Montano has shown the model to be internally consistent and externally valid when used for predicting influenza immunization (Cronbach α=0.79 to0.91). 16 The final questionnaire contained approximately 57 questions, depending on skip pattern, including multiple-choice items and Likert scale items. Each of the sampled patients was sent a personalized introductory letter and a letter from the respective site endorsing the project and encouraging participation. An honorarium was offered to encourage participation. Interviews were performed with computerassisted telephone interviewing (CATI). Use of CATI allowed for data entry during the interviews, directed the sequence of questioning, prevented skipped questions through automated skip patterns, and blocked illogical or out-of-range values. Trained interviewers conducted the telephone interviews between August and October 2002, before vaccine supplies for the next season were delivered. Statistical analysis. We calculated weights based on the achieved sample to account for different sampling fractions and stratification by age group and site. Chi-square tests were weighted and used to compare participants who did and did not receive the influenza vaccine for the variables of interest by age group. Frequency data are reported as weighted percentages only (i.e., reported sample sizes are unweighted). The McNemar test was used to evaluate yearly differences between vaccination rates overall and by site and age group. Logistic regression analyses were also weighted and performed to determine variables significantly associated with receipt of the influenza vaccine in the season by age group. All variables of P<.10 were included with the outcome variable in a forward selection procedure. Statistical significance was set at P<.05, and all statistical analyses were conducted with SAS software (SAS Institute Inc, Cary, NC). EMRs Sample. Health Center A uses an EMR system to record vaccinations in each patient chart. Patients without data from both EMR and billing records were excluded. In the influenza season, information on 363 patients was collected. This number increased to 467 patients for the season. Health Center B does not have an EMR system. EMR statistical analysis. Immunization dates, date of first visit, and date of most recent visit retrieved on October 8, 2002, and cleaned with FORTRAN 77 software (Free Software Foundation, Boston, Mass). Using dates of first and most recent visits, we created denominators for each influenza vaccina Research and Practice Peer Reviewed Zimmerman et al. American Journal of Public Health October 2003, Vol 93, No. 10

3 tion season. SAS software was used to calculate influenza immunization rates from September 1, 2000, to August 31, 2002, and the McNemar test was used to evaluate yearly differences between vaccination rates by age group for Health Center A. RESULTS Patient Survey Demographics. Demographic characteristics, with the exception of race, among patients who completed the survey did not vary by site. Health Center A had a significantly higher proportion of Black respondents than did Health Center B (57% vs 34%; P<.001). Demographic characteristics differed by age for marital status, annual household income, highest level of education completed, and employment status. Compared with patients aged 50 to 64 years (n=185), patients aged 65 years and older (n=190) were more frequently widowed (46% vs 15%) and less frequently single (8% vs 17%), married (28% vs 32%), or separated/divorced (18% vs 36%) (P<.001). Furthermore, patients aged 65 years and older reported annual household incomes less than $20000 (75% vs 56%; P=.009), fewer years of education (up to high school graduate, or technical or vocational school) (75% vs 53%; P<.001), and unemployed work status (88% vs 46%; P<.001). Overall, 210 respondents (53%) reported being vaccinated between September 2001 and March 2002 (i.e., during the influenza vaccination season). Despite the difference in racial distribution by site, vaccination rates did not vary by site. Vaccination rates were 58% for Health Center A and 49% for Health Center B (P=.114). Vaccination rates differed significantly by age, with older patients more frequently reporting being vaccinated (65%) than did younger patients (47%) (P<.001). Therefore, subsequent analyses were stratified by age. Demographic and health characteristics by age and vaccination status are shown in Table 2. Influences and rationale. The survey allowed patients to cite more than 1 source for hearing about the vaccine. No differences across age groups were found in how patients heard about the vaccine (P=.158), whether they received a letter from their physician regarding vaccination (P=.751), or whether they saw a poster advertising a flu shot clinic (a time set aside for administering influenza vaccines with no appointment needed) (P=.263). Within the 50- to 64-year age group, however, vaccinated patients reported hearing about the flu shot most frequently from medical professionals (65%), compared with 45% of unvaccinated patients (P<.001), whereas more frequent sources of information about the flu shot for unvaccinated patients were TV/radio (52% vaccinated vs 62% unvaccinated; P=.045) and friends/family (20% vaccinated vs 42% unvaccinated; P=.002). Among patients aged 65 years and older, vaccination status did not differ by source for hearing about the flu shot. Reasons mentioned for getting vaccinated differed by age group: flu prevention (50 to 64 years: 64%; 65 years and older: 83%), having a history of flu (50 to 64 years: 18%; 65 years and older: 8%), receiving a recommendation from a health professional (50 to 64 years: 14%; 65 years and older: 8%), to prevent others from getting the flu (50 to 64 years: 1%; 65 years and older: 1%), and other (50 to 64 years: 3%; 65 and older years: 0%) (P =.039). Interestingly, convenience and the vaccine being given free of charge were not reasons given for receiving the influenza vaccine within either age group. In addition, setting of vaccination did not differ between age groups (P =.775), and most vaccinations took place in a physician s office during a regular visit (50 to 64 years: 67%; 65 years and older: 63%), or other locations such as a flu shot clinic in the community (50 to 64 years: 19%; 65 years and older: 21%), the health department/other (50 to 64 years: 11%; 65 years and older: 10%), or a vaccine clinic at a physician s office (50 to 64 years: 3%; 65 years and older: 6%). The survey allowed patients to cite more than 1 reason for not getting vaccinated. Among the unvaccinated, patients differed significantly by age group in reasons for not getting vaccinated (P =.009). Unvaccinated patients aged 50 to 64 years attributed their behavior to believing that they were not likely to get the flu (33%), having had a previous adverse reaction to influenza vaccine (18%), fearing side effects (16%), not knowing it was needed (13%), forgetting (5%), lacking the time to get the shot (3%), being allergic to the vaccine (2%), believing that the flu shot causes the flu (1%), and other/unspecified reasons (8%). By contrast, unvaccinated patients aged 65 years and older attributed their behavior to having had a previous adverse reaction to influenza vaccine (33%), forgetting to get the shot (15%), fear of side effects (18%), believing that they were unlikely to get the flu (11%), not knowing the shot was needed (5%), being sick at the time the vaccine was recommended (5%), believing that the flu shot causes the flu (5%), and other/unspecified reasons (6%). Facilitators of and barriers to immunization. Participants were asked a series of questions to determine which factors of the Triandis model were related to vaccination status. Compared with patients aged 65 years and older, younger patients more frequently paid for the vaccine (11% vs 3%; P =.043) and less frequently had health insurance (81% vs 98%; P <.001). Within the 50- to 64-year age group, having health insurance was significantly associated with vaccination status (Table 3). The decision to get vaccinated was also influenced by specific attitudes, social influences, and perceived consequences particular to each age group (Table 3). Participants were asked to rate their level of trust in the health information they received from various sources. Compared with patients aged 65 years and older, patients aged 50 to 64 years more frequently reported trusting most or some information from friends/family (71% vs 56%; P =.003) and from newspapers/magazines (72% vs 57%; P =.003). Within each age group, vaccinated and unvaccinated participants trusted health information from their personal physicians, television/radio, friends/family, government, local churches/religious leaders, and newspapers/magazines with relatively equal frequency. No differences in whether patients felt that they could freely ask their physicians questions were found; nearly all felt that they could do so (50 to 64 years: 97%; 65 years and older: 98%). Interventions. Overall, self-reported immunization rates did not change significantly between the and the influenza season (56% in vs 57% in ; P =.807). Although October 2003, Vol 93, No. 10 American Journal of Public Health Zimmerman et al. Peer Reviewed Research and Practice 1701

4 TABLE 2 Demographic and Health Behavior Characteristics, by Age Group and Influenza Vaccination Status: Influenza Season Persons Aged 50 to 64 Years a Persons Aged 65 Years or Older a Unvaccinated Vaccinated Overall Unvaccinated Vaccinated Overall Variable (n = 96), % (no.) (n = 86), % (no.) (n = 185), % (no.) P (n = 65), % (no.) (n = 124), % (no.) (n = 190), % (no.) P Gender: female (referent: male) 61 (59) 60 (51) 61 (112) (49) 65 (79) 68 (129).155 Race White 54 (51) 45 (38) 50 (90) (31) 55 (65) 53 (97).130 Black 40 (39) 53 (46) 46 (87) (34) 40 (51) 44 (85)... Other 6 (6) 2 (2) 4 (8)... 0 (0) 4 (6) 3 (6)... Marital status Married 34 (32) 31 (26) 32 (59) (15) 31 (39) 28 (55).221 Never married 19 (18) 15 (13) 17 (31) (7) 7 (8) 8 (15)... Widowed 8 (8) 23 (20) 15 (28) (27) 48 (58) 46 (85)... Separated/divorced 39 (37) 31 (27) 36 (66) (16) 15 (18) 18 (34)... Employment status Unemployed (referent: work part or full time) 35 (34) 58 (50) 46 (86) (58) 87 (107) 88 (166).712 Education level Elementary/some high school 9 (8) 19 (16) 14 (26) (27) 33 (40) 36 (67).659 High school graduate/vocational or technical school 41 (39) 38 (33) 39 (72) (24) 39 (48) 38 (72)... Some college 23 (22) 24 (21) 23 (43) (7) 13 (17) 12 (24)... College graduate 28 (27) 19 (16) 24 (44) (7) 14 (18) 13 (26)... Annual household income, $ < (21) 35 (28) 29 (51) (27) 36 (39) 40 (66) (27) 25 (20) 27 (47) (20) 35 (37) 35 (57) (17) 22 (18) 20 (35) (6) 15 (18) 13 (24) (25) 18 (15) 23 (41)... 7 (4) 14 (16) 12 (21)... Self-rated health Excellent 23 (22) 10 (9) 18 (32) (11) 9 (11) 12 (22).226 Very good 21 (20) 20 (17) 20 (37) (14) 30 (37) 27 (52)... Good 30 (29) 30 (26) 30 (56) (20) 35 (44) 34 (64)... Fair/poor 25 (24) 40 (34) 32 (59) (20) 26 (31) 28 (51)... Frequency of physician visits Every 1 2 months 20 (19) 27 (23) 23 (43) < (16) 29 (36) 28 (53) times/year 20 (19) 49 (42) 33 (61) (21) 45 (55) 40 (76)... <2 times/year 60 (56) 24 (21) 44 (79) (28) 26 (32) 32 (60)... Time since last physical exam <1 year ago 67 (64) 76 (66) 71 (132) (46) 80 (95) 76 (141) years ago 23 (22) 9 (8) 16 (30) (11) 15 (17) 16 (29)... >2 years ago 10 (9) 15 (12) 13 (22) (7) 5 (7) 8 (14)... Smoking status Current smoker 31 (29) 28 (25) 30 (55) (21) 14 (17) 21 (38).013 Never smoker 35 (34) 28 (24) 32 (59) (19) 33 (41) 31 (60)... Former smoker 34 (33) 43 (37) 38 (71) (25) 53 (66) 48 (92)... Seatbelt usage Always 61 (58) 65 (55) 64 (116) (47) 72 (89) 73 (137).320 Sometimes 26 (25) 26 (22) 26 (47) (10) 22 (26) 19 (36)... Never 13 (12) 9 (7) 11 (19) (7) 6 (8) 7 (15)... Site: Health Center A (referent: Health Center B) 46 (49) 54 (50) 49 (99) (29) 52 (71) 47 (101).101 Note. All percentages are weighted and were obtained with SAS (SAS Institute Inc, Cary, NC). Subsample numbers are unweighted. P values were obtained by χ 2 test. a Vaccination status was unknown for 3 persons aged years and 1 person aged 65 years or older Research and Practice Peer Reviewed Zimmerman et al. American Journal of Public Health October 2003, Vol 93, No. 10

5 TABLE 3 Facilitators of and Barriers to Influenza Vaccination, by Age Group and Influenza Vaccination Status: Influenza Season Persons Aged 50 to 64 Years a Persons Aged 65 Years or Older a Unvaccinated Vaccinated Overall Unvaccinated Vaccinated Overall Variable (n = 96), % (no.) (n = 86), % (no.) (n = 185), % (no.) P (n = 65), % (no.) (n = 124), % (no.) (n = 190), % (no.) P Facilitating conditions I have health insurance 72 (68) 92 (79) 81 (149) < (63) 98 (122) 98 (186).526 I have problems paying for my medical treatment 40 (38) 37 (31) 39 (71) (13) 13 (17) 16 (30).196 It is easy for me to get to a place where I can get shots a 91 (85) 97 (83) 93 (170) (60) 94 (116) 93 (177).560 Attitudes a Getting a flu shot is a wise thing to do 59 (54) 98 (83) 78 (140) < (58) 98 (98) 84 (157) <.001 Getting a flu shot is more trouble than it s worth 45 (40) 3 (3) 24 (43) < (21) 3 (3) 14 (24) <.001 Social influences a My doctor thinks I should get the flu shot 63 (56) 95 (80) 79 (139) < (46) 99 (121) 93 (168) <.001 My friends/family think I should get the flu shot 42 (36) 75 (52) 57 (90) < (20) 80 (79) 64 (100) <.001 Perceived consequences a A person who does not get the flu shot will probably get the flu 20 (18) 54 (45) 37 (65) < (13) 52 (61) 41 (74) <.001 I believe the flu shot causes a person to get the flu 43 (38) 12 (10) 28 (49) < (21) 19 (22) 24 (43) The flu shot keeps a person from getting the flu 42 (37) 56 (48) 49 (87) (19) 64 (76) 54 (96) < I think that if a household member gets the flu, others are 75 (67) 78 (63) 76 (132) (46) 77 (88) 79 (134) more likely to get the flu Serious side effects from the flu shot are common 46 (38) 22 (17) 34 (56) (26) 35 (38) 40 (64) Getting a sore arm from the flu shot is common 67 (54) 57 (46) 62 (102) (34) 56 (67) 59 (102) Note. All percentages are weighted and were obtained with SAS (SAS Institute Inc, Cary, NC). Subsample numbers are unweighted. P values were obtained by χ 2 test. a Includes only subjects who gave Agree and Maybe/Sometimes responses and those whose vaccination status was known. Vaccination status was unknown for 3 persons aged years and 1 person aged 65 years or older. No differences occurred among these variables when stratified by site. patients aged 50 to 64 years showed an increasing trend in immunization rates (40% in , 47% in ; P =.08), patients aged 65 years and older did not (70% in , 66% in ; P =.122). Rates did not differ over time by site (Health Center A: 58% in , 61% in ; P =.473; Health Center B: 53% in , 52% in ; P =.868). At Health Center A, for which we also had EMR data (reported in this section), selfreported vaccination rates did not change over time within age groups (50 to 64 years: 43% in , 50% in ; P =.189; 65 years and older: 73% in , 71% in ; P =.754). Furthermore, despite differences in intervention strategies, few differences arose across sites. Sixty-three percent of Health Center A patients reported receiving a recommendation to get an influenza vaccination, compared with 53% of Health Center B patients (P =.052). Not surprisingly, more patients at Health Center A, which mailed flu vaccine reminders, reported receiving a letter than at Health Center B, which did not send reminders (37% vs 13%; P <.001). Logistic regression analyses. Preliminary analyses for both age groups revealed that receipt of the influenza vaccine in the previous year ( season) was strongly correlated with receipt of the influenza vaccine in the season (50 to 64 years: r=0.6; P<.001; 65 years and older: r=0.7; P <.001). Therefore, we chose to exclude this variable in the logistic regression analyses. Furthermore, although we tested the interactions of site and individual predictors in each model, none was significant. In logistic regression analyses specific to patients aged 50 to 64 years, variables positively associated with receiving the influenza vaccine in the season included believing that persons who do not get the flu shot will probably get the flu and having had the flu shot recommended by someone. The belief that the flu shot causes a person to get the flu was negatively associated with vaccination status (Table 4). Among patients aged 65 years and older, immunization during the season was influenced by beliefs of friends and family, self-rated health, and frequency of visits to a physician (Table 4). Number of Influenza Vaccine Doses Administered According to vaccination log data on all patients reported by sites, the number of influenza vaccinations administered at Health Center A increased 34%, from 797 doses in to 1071 in At Health Center B, the doses administered increased 114%, from 350 doses in to 750 in Rates of Influenza Vaccination From EMRs According to data from patient EMRs, vaccination rates at Health Center A increased from 24% in to 30% in among patients aged 50 to 64 years (P <.001) and from 45% in 2000 October 2003, Vol 93, No. 10 American Journal of Public Health Zimmerman et al. Peer Reviewed Research and Practice 1703

6 TABLE 4 Factors Associated With Receipt of Influenza Vaccine Logistic Regression Analyses, Stratified by Age Group: Influenza Season Triandis Model Factors by Age Group Odds Ratio (95% Confidence Interval) P Patients aged 50 to 64 years Belief that person who does not get flu shot will probably get the flu 5.4 (2.4, 12.0) <.001 Belief that flu shot causes a person to get the flu 0.2 (0.1, 0.4) <.001 Someone recommended the flu shot 4.0 (1.9, 8.5) <.001 Patients aged 65 years or older Belief that friends/family think subject should get the flu shot 9.7 (4.2, 22.3) <.001 Frequency of physician visits (referent: < 2 times/year) Every 1 2 months 2.9 (1.0, 8.5) times/year 3.9 (1.5, 10.1).004 Self-rated health (referent: fair/poor) Excellent 0.8 (0.2, 3.2).792 Very good 3.7 (1.2, 11.2).020 Good 1.8 (0.7, 5.0) to 53% in among patients aged 65 years and older (P <.001). DISCUSSION The purpose of this study was to develop and examine intervention strategies to increase influenza vaccination rates at inner-city health centers with racially mixed populations. At both sites, the number of doses administered increased. Although overall vaccination rates remained lower than national goals, racial disparities were eliminated through targeted interventions. We believe that individualization of interventions was important. Health centers have their own values and internal structures that serve unique communities. Consequently, staff were integral to the decisionmaking process for selecting intervention strategies. At Health Center A, the medical director noted, We have consistently tried to create an environment where flu and other immunizations are valued as a very effective means of promoting health. Among a menu of proven options, Health Center A established a nursing policy whereby staff could check immunization status as part of obtaining vital signs and vaccinate by standing order. Checking immunization status could be performed verbally with the patient or through an automatic reminder in the patient s EMR. At Health Center B, the medical director indicated that having staff focus on a problem in the context of applicable background information and appreciation for staff s contributions were key to success, because staff became engaged in solving a health issue facing their own community. Thus, the choice of interventions at each health center was based on that center s values and internal operating models, which, according to the analyses of barriers to prevention of Crabtree and others, 7 9 are essential aspects of success. Beliefs, Social Influence, and Trust In the 50- to 64-year age group, the belief that unvaccinated persons will contract influenza, the belief that the vaccine causes influenza, and the recommendation to get vaccinated were associated with influenza vaccination status. We urge focusing on these issues in patient education. Other studies have found that fear of adverse reactions, 20,21 concern that vaccination may actually cause disease, 20,22 and fear of the pain of injection and/or needles 20,23,24 lead many to decline vaccination. On the other hand, a study in Georgia found that the most important factor associated with influenza vaccination was recommendation by a health care provider. 25 In the 65-and-older age group, the belief that friends/family think that the subject should get vaccinated and visiting a physician more than twice a year were positively associated with vaccination. Age differences in the results were not unexpected, given the recent addition of 50- to 64-year-olds to national recommendations for annual influenza vaccination. 26,27 Another explanation may be differences in insurance coverage, because Medicare covers influenza immunization. We found no difference in vaccination by insurance coverage for patients aged 65 years or older, but among patients aged 50 to 64 years, vaccinated patients were more likely than unvaccinated patients to report having insurance coverage. The few published studies that have explored the role of trust in participation in medical treatment and/or research have indicated that Black patients have far less trust than do White patients The lack of racial disparity in our data may be explained in part by the high level of trust in their personal physicians reported by the respondents and by the fact that most respondents reported learning about immunization from their physicians. The faith-based nature of these neighborhood health centers, which were established specifically to serve disadvantaged populations, may contribute to their patients trust in those who treat them. Strengths and Limitations The strengths of this study include use of complementary methods to assess impact (i.e., data on doses administered and on patient reports), the use of CATI, and the study s realworld setting. Potential limitations of this study are the nonresponse rate and the modest sample size. It is, of course, impossible to know the extent, if any, to which the nonrespondents differ from respondents in regard to their knowledge and beliefs about influenza vaccine. This study included only 2 racial groups; therefore, results cannot be generalized to other ethnic groups. Because multimodal interventions were conducted, it is impossible to know which interventions were most important in changing immunization rates. Although the study was not a randomized controlled trial, the issue is not the effectiveness of the interventions, because effectiveness has already been demonstrated at the level of systematic evidence review. 6 Rather, the main issue is whether these interventions can be applied to disadvantaged communities and, if so, how this can be accomplished Research and Practice Peer Reviewed Zimmerman et al. American Journal of Public Health October 2003, Vol 93, No. 10

7 We observed a difference at Health Center A between vaccination rates reported by patients in the survey and recorded in the EMRs. Influenza vaccination is available at many community sites, and 24% of vaccinated patients at this site reported receiving the flu shot at a location other than their physician s office. Once this difference was taken into account, the rates nearly matched. The fact that only those with the financial resources to have a telephone were eligible for participation in the survey may have biased the data toward higher rates. On the other hand, EMRs include homeless persons, and 22% of the health center s patients are uninsured. Many such patients visit the clinic sporadically but would be included in the denominator. Also, the denominator could be affected by patients who entered or left the practice before the end of the season and by patients seen for consultations or in nursing home visits but whose immunization data would not be contained in the office records. Conclusions Tailored interventions based on proven strategies are an effective way to improve immunization rates among patients at inner-city health centers. Although vaccination rates differed by age, racial disparities were not observed. About the Authors Richard K. Zimmerman, Mary Patricia Nowalk, Mahlon Raymund, and Melissa Tabbarah are with the Department of Family Medicine and Clinical Epidemiology, University of Pittsburgh School of Medicine, Pittsburgh, Pa. Richard K. Zimmerman is also with the Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, as is Edmund M. Ricci. David G. Hall is with the East Liberty Family Health Care Center, Pittsburgh. J. Todd Wahrenberger is with the Northside Christian Health Center, Pittsburgh. Stephen A. Wilson is with the University of Pittsburgh Medical Center St. Margaret Family Practice Residency. Requests for reprints should be sent to Richard K. Zimmerman, MD, MPH, Department of Family Medicine and Clinical Epidemiology, University of Pittsburgh School of Medicine, 3518 Fifth Ave, Pittsburgh, PA ( zimmer@pitt.edu). This article was accepted May 30, Contributors R. K. Zimmerman was the project principal investigator and helped design interventions and analyses. M. P. Nowalk was the project manager and helped design interventions. M. Raymond was data manager for computer-assisted telephone interviewing (CATI) and immunization data and analyzed EMR immunization data. M. Tabbarah analyzed CATI data. D. G. Hall was head of interventions at Health Center A. J. T. Wahrenberger was head of interventions at Health Center B. S. A. Wilson was a co-investigator and helped design and implement interventions at Health Center B. E. M. Ricci was the overall grant principal investigator. Acknowledgments This project was funded by the Agency for Healthcare Research and Quality (grant P01 HS10864). Human Participant Protection This project was approved by the institutional review board of the University of Pittsburgh. References 1. National Center for Health Statistics. Early Release of Selected Estimates Based on Data From the First Quarter of 2002 NHIS. Available at: nchs/about/major/nhis/released htm. Accessed July 8, National Center for Health Statistics. Percent of adults aged 18 years and over who received influenza vaccine during the past 12 months, by age group and quarter: United States, Available at: figures04_1-4_3.htm. Accessed July 8, National Center for Health Statistics. 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J Health Polit Policy Law. 1998;23: October 2003, Vol 93, No. 10 American Journal of Public Health Zimmerman et al. Peer Reviewed Research and Practice 1705