Increasing Influenza Vaccination Rates in People With Chronic Illness
A systematic review of measures in primary care
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Background: The safety and efficacy of influenza vaccination for the chronically ill are clearly supported by the evidence, yet vaccination rates in this vulnerable population remain low. This leads to many avoidable hospitalizations and deaths in Germany every year. The goal of this systematic review is to identify measures in primary care medicine that can be used to increase influenza vaccination rates among the chronically ill.
Methods: This review was carried out as recommended in the PRISMA statement. A systematic literature search was performed. Only randomized, controlled trials were included in the analysis. Details can be found in the study protocol (PROSPERO, CRD42018114163).
Results: 15 trials were included in the analysis. Training sessions for medical practice teams focusing on a particular disease raised the vaccination rates by as much as 22%. A financial incentive had the greatest effect (relative risk [RR]: 2.79; 95% confidence interval: [1.18; 6.62]). Reminders via text message yielded a maximum 3.8% absolute increase in vaccination rates. Complex interventions were not found to be of any greater benefit than simple ones.
Conclusion: A variety of approaches can be effective. Focusing training sessions for medical practice teams on certain diseases may be of greater benefit than vaccination-centered training sessions. Reminder systems for doctors should be more reliably implemented. Simple strategies are perhaps the most suitable ones in the heterogeneous population of chronically ill persons. The limitations of this systematic review include the heterogeneity of the studies that we examined and the small number of studies in each category.
In the 2017/2018 influenza season, around 45 000 patients were admitted to hospital for treatment of seasonal influenza in Germany alone; 1674 deaths due to this disease were reported to the Robert Koch Institute (e1). The course of the illness and the risk of hospitalization or death vary depending on the general health status of the individual patient. The young, the elderly, and the chronically ill are especially vulnerable (1, e1). For instance, a study published in 2017 showed that 86% of patients admitted to the hospital with an “influenza-like illness” (ILI) had at least one chronic (≥ 65 years) medical condition. The prognosis was poor despite adequate treatment: more than 30% of the patients needed intensive care, and 14.8% of them died in the hospital (1). Besides the individual consequences, the additional consultations and hospitalizations caused by seasonal influenza represent a heavy burden on the entire healthcare system. Chronically ill patients have frequent contacts with their primary care physicians (e2), who therefore carry a large share of the extra load (2).
The treatment of influenza is primarily symptomatic; in the early stage, the population at risk should also be given antiviral drugs (3). The most effective way of combating the virus is prophylactic vaccination, shown in many studies to be efficacious and safe, particularly with reference to the chronically ill (4, e3–e6).
A vaccination rate of 90% among the chronically ill is necessary to attain herd immunity (5). However, this quota is rarely achieved. Depending on country, population, and age group, rates of between 9% and 70% have been observed (6–8). In Germany, the vaccination rate in chronically ill patients below 60 years of age was 24.1% in the season 2012/2013 and 22.6% in 2013/2014 (9). Among those over 60, the rate was 43.6% in 2010/2011, but only 34.8% in 2016/2017 (e7). Consequently, it is essential to improve vaccination rates, especially in high-risk populations. Given their coordinating role in the healthcare system and their professional focus, primary care physicians would appear especially well suited to implement these interventions.
The aim of our study was to evaluate measures taken to increase the rates of vaccination against seasonal influenza among people with chronic illness in the primary care setting.
This systematic review was conducted in accordance with the PRISMA Statement for systematic reviews (e8). A detailed description of the methods can be found in the study protocol (PROSPERO CRD42018114163) and in the eMethods (10). We included randomized controlled trials that had been carried out by or were focused on primary care physicians and which examined strategies to increase the rate of vaccination against influenza in the chronically ill. The systematic literature survey (eBox) was performed in October 2018.
The Cochrane risk-of-bias tool was used to assess the risk of bias in the studies included for analysis (e9). After assigning the individual criteria 0 points for high, 1 point for unclear, and 3 points for low risk of bias, we summed the risk scores to form a quality factor (QF; modified according to ), which served to enhance comparability. On the basis of this factor the quality of each trial was rated as low (0–10 points), moderate (11–14 points), or high (at least 15 points).
We preferentially extracted adequately corrected results (e10) from cluster-randomized studies. Whenever possible, intention-to-treat analyses were carried out. Owing to the high methodological, clinical, and statistical heterogeneity of the trials, meta-analysis with calculation of a pooled effect estimator was not a suitable means of synthesizing our data.
Fifteen trials were included for analysis (Figure 1) (12–26). The populations studied varied widely. At least 77% of the patients included were below 65 years of age. Eleven of the trials had investigated patients with cardiovascular disease, while the remaining four focused on chronic obstructive pulmonary disease (COPD) or asthma. There were no studies on patients with mental illness. The categorization of the trials is shown in Table 1, their characteristics in Table 2 and the eTable. Besides their methodological and clinical heterogeneity, the trials exhibited marked statistical heterogeneity: assessed according to Higgins and Thompson (e11), the heterogeneity of the studies was I2 = 87 % for those focusing on training of office teams, I2 = 85% for those on enhancing the competence of medical professionals, and I2 = 94% for those on sending text-message reminders.
The quality of the trials included for analysis also varied widely (for details see the eFigure). Blinding of outcome assessment was mostly assessed as bearing a low risk of bias, because many authors used objective recording methods. Assessment of the risk of incomplete outcome data or selective outcome reporting was often not possible because study protocols were not available.
Effectiveness of interventions
Patient-centered studies tended to have larger samples. Overall, studies focused on medical personnel were of higher quality (Figure 2).
Interventions focussed on medical personnel
Training for office teams was examined in three trials. COPD care packages, investigated by Markun et al. (18) and Zwar et al. (26), demonstrated significant positive effects (relative risk [RR] 1.35, 95% confidence interval [1.14; 1.59] and RR 1.29 [1.03; 1.62], respectively), with control group vaccination rates of 61.6% and 49.1%. Siriwardena et al. (23) achieved improvement of the vaccination rate by 22% in patients post splenectomy by means of a vaccination-centered workshop, but the sample was very small (n = 169 patients), so the effect size was not significant (RR 1.08 [0.90; 1.25]). Only slight, non-significant improvements were achieved in patients with coronary heart disease or diabetes mellitus (RR 1.02 [1.00; 1.04]); however, very high proportions of patients were vaccinated even without intervention (72.5 % and 70.2 %, respectively). Studies in this category were of moderate to high quality (QF 12 to 18).
Automatic reminder systems for physicians with printed vaccination recommendations on documentation forms proved effective in the trial by Chambers et al. (15) (RR 1.86; p = 0.001). The vaccination rate was lower among participants whose physicians received reminders for only half of their patients. For unknown reasons, digital reminders were also generated for only half of the patients included in the trial by Tierney et al. (25), leading to a vaccination rate 2.2% lower than in the control group (RR 0.95; [0.67; 1.35]). In both cases, the control group vaccination rate was around 20%. Studies in this category were of moderate to high quality (QF 14 to 15).
Programs to enhance the competence of medical professionals were generally complex interventions of low to moderate quality (QF 9 to 12). Beck at al. (14) achieved a significantly positive result by treating patients in the framework of structured group visits under the supervision of medical professionals (RR 1.27 [1.11; 1.46]). In contrast, a study by Hermiz et al. (16) showed that home visits by a community nurse in contact with the responsible primary care physician resulted in a vaccination rate 7.4% lower than in the control group (RR 0.89 [0.70; 1.12]); there were problems with the quality of implementation and the communication between nurse and physician. In both cases, the control group vaccination rate was around 65%.
Altogether, simple interventions achieved greater effects and were more likely to produce significant results than complex interventions.
Interventions focussed on patients
The effect of sending text-message reminders to patients was investigated in two high-quality studies (QF 17 to 18) by Herrett et al. (17) and Regan et al. (22). The former had a vaccination rate of 50% in the control group and were able to achieve an absolute increase of only 1.7% (RR 1.05 [1.00; 1.11]), while the latter attained a significant absolute increase of 3.8% over the rate of 9.1% in the control group (RR 1.41 [1.22; 1.63]).
Personalized postcard reminders were used by Spaulding et al. (24) in a high-quality study (QF 16) and by Baker et al. (13) in a low-quality study (QF 10). Compared with the 9% figure in the control group, Spaulding et al.’s personalized postcard raised the vaccination rate by 16.1% (RR 2.77 [2.05; 3.75]). Baker et. al. found that only a personalized postcard achieved a significant result in both subgroups, with generally greater effect sizes (RR 1.09 and 1.11; p <0.05) than for a non-personalized postcard (RR 1.05 and 1.07; p >0.05). The control group vaccination rate was 35.8 for patients under 65 and 50.7% for those over 65.
Studies in which the patients received reminder letters were of low quality (QF 7 to 10). The letters were personalized and featured an educational element. Only in their younger subpopulation did Baker et al. (13) achieve a significant effect (RR 1.09; p <0.05). In the study by Moran et al. (19), a single reminder letter reduced the vaccination rate by 0.5% in comparison with the control group (RR 0.99 [0.60; 1.63]). Mullooly et al. (21) found a significant increase of 8.8% in the vaccination rate compared with 30.1% in the control group (RR 1.29 [1.15; 1.45]).
Moran et al. (20) investigated the effectiveness of sending an educational brochure and providing a financial incentive, alone and in combination, in a study of moderate quality (QF 13). All three interventions achieved statistically significant improvements over the vaccination rate of 9.4% in the control group. The combination of brochure and financial incentive (RR 2.78 [1.13; 6.87]) was superior to the brochure alone (RR 2.53 [1.04; 6.15]), but not to the financial incentive alone (RR 2.79 [1.18; 6.62]).
Ahmed et al. (12) and Moran et al. (19) compared sending two reminders to each patient with sending only one reminder. In the former study, the vaccination rate fell by 5.8% in 18- to 49-year-olds and rose by 4.6% in 50- to 64-year-olds (RR 0.94 [0.84; 1.05] and RR 1.08 [1.00; 1.15], respectively). In the latter, the rate went down by 6.1% (RR 0.80 [0.46; 1.38]).
The identified interventions designed to increase the vaccination rates for seasonal influenza among people with chronic illness in the primary care setting are heterogeneous. We found that both interventions focused on patients and interventions focussed on medical personnel have been implemented successfully.
Interventions focussed on medical personnel
Both Kovacs et al. (11) and Forsetlund et al. (27) conjectured that training programs alone can exert only limited influence on complex behavior patterns. Among the studies we analyzed, training courses for office teams that were oriented on the management of a particular disease (18, 26) achieved better results than a vaccination-centered approach (23). Consequently, training programs with an obvious bearing on routine practice may be effective; however, the effect should not be overestimated.
Reminder systems for physicians were effective only when the messages were generated for all eligible patients (15, 25). Therefore, any such system needs to be highly reliable. A related approach involves standardized checklists: these are intended to help assess the indication for vaccination and were found by Mendu et al. (28) and Merkel et al. (29) to have a positive effect on vaccination rates.
Group visits to enhance the competence of medical professionals showed positive effects (14), whereas cooperation between community nurses and primary care physicians may be difficult (16). A related approach is the blanket delegation of vaccination—indications and implementation—by physicians to their office nurses (30). Overall, there is broad evidence for the efficacy of extending the competence of medical professionals (31); indeed, this is now common practice in countries such as the UK and the USA (32).
Interventions focussed on patients
Text-message reminders to remind patients about vaccination, investigated in two recent high-quality studies (17, 22), showed no advantage over letters or postcards. Both studies found that such reminders achieved only slight increases in vaccination rates. The great strength of this type of intervention is its wide scope; the two studies analyzed here had extremely large sample sizes (102 257 and 6245 patients respectively). Consequently, there is sufficient evidence for the positive effect of text-message reminders in increasing chronically ill patients’ adherence to their physicians’ recommendations (e12).
Overall, the implementation of patient reminder systems in Germany seems inadequate (33); universal coverage would be desirable. Written reminders addressed directly to patients can also be effective in Germany, as shown by Schulte et al. (34) in a sample of patients with chronic kidney disease.
A number of publications described the use of financial incentives as an element of influenza vaccination campaigns (e13). Like other researchers before us (20), we found that this type of intervention had the greatest effect.
A personal recommendation by the patient’s own doctor has been described as a powerful predictor of subsequent vaccination (e14, e15). The credibility of the source is also a major factor in the assessment of the indications for vaccination and the associated risks following an educational program (e16). Personalized invitations to attend for screening colonoscopy showed a positive effect in Germany (35, 36). Our findings do not permit any clear conclusions with regard to the respective effects of personalized and non-personalized invitations, but overall one can by all means assume a benefit of personalization.
Educational interventions have previously been described as an appropriate way of filling gaps in knowledge (e17) and improving vaccination rates (e18). Addition of an educational element to patient reminders yielded indifferent results in our study, however, leading to neither an increase nor a decrease in vaccination rates.
Complexity of interventions
In the studies we investigated, simple interventions displayed higher quality and wider-reaching effects than complex interventions. A second reminder had no additional effect, and the inclusion of an educational element did not necessarily improve the end result. The combination of an educational brochure and a material incentive was not significantly superior to either measure alone. All this supports the previous observation that complex interventions do not automatically generate greater effects (11, 31, e19). Simple approaches may therefore prove eminently suitable means of improving the vaccination rate in large populations with heterogeneous attitudes and requirements.
Authors have described promising interventions to increase the vaccination rates of at-risk populations in settings other than primary care and outpatient clinics. One suitable environment may be the emergency room, where the proportion of patients at risk is much higher than in the total population (37). Rimple et al. (38) increased the influenza vaccination rate of high-risk patients in a surgical emergency department from 16% to 83% by screening all patients for indications. Screening before certain operations or in the presence of particular clinical constellations in the inpatient setting, as reported by Gurfinkel et al. (39), also led to higher vaccination rates. As outlined by Schulte et al. (34), vaccination programs in Germany could in future be implemented largely by health insurance funds or federal institutions.
Although our search covered the four major relevant databases and the gray literature, pertinent publications may have been overlooked. The quality of the studies we evaluated was variable, and some had very small samples. Since we found no significantly negative results, publication bias cannot be ruled out. The Pearson correlation coefficient was significantly negative for the control group vaccination rate and the RR per study (r = −0:650; p = 0.01); consequently, our results could also suffer from ceiling effects (40). In some cases we had to use the results of cluster-randomized studies without sufficient correction, thus impairing quality and power. The studies were highly heterogeneous in terms of patients, country, healthcare system, and interventions, which particularly limits the external validity of our findings.
The project was not supported by external funding.
Conflict of interest statement
Prof. Schelling has received consultancy fees from Pfizer, MSD, GlaxoSmithKline, and Sanofi; lecture fees from Pfizer, MSD, and GlaxoSmithKline; and third-party funding for a project of his own initiation from Pfizer.
Dr. Sanftenberg has received payment for an expert opinion from the Society for Promotion of Vaccination Medicine (Gesellschaft zur Förderung der Impfmedizin).
The remaining authors declare that no conflict of interest exists.
Manuscript received on 2 April 2019, revised version accepted on
28 June 2019
Translated from the original German by David Roseveare
Dr. rer. nat. Linda Sanftenberg
Institut für Allgemeinmedizin
Klinikum der Ludwig-Maximilians-Universität München
80336 München, Germany
Cite this as
Sanftenberg L, Brombacher F, Schelling J, Klug SJ, Gensichen J: Increasing influenza vaccination rates in people with chronic illness—a systematic review of measures in primary care. Dtsch Arztebl Int 2019; 116: 645–52.
For eReferences please refer to:
eMethods, eBox, eTable, eFigure:
Chair of Epidemiology, Faculty for Sport and Health Sciences, Technical University of Munich, Munich: Prof. Dr. rer. nat. et med. habil. Stefanie J. Klug, MPH
|1.||Tanriover MD, Bagci Bosi T, Ozisik L, et al.: Poor outcomes among elderly patients hospitalized for influenza-like illness. Curr Med Res Opin 2018; 34: 1201–7 CrossRef MEDLINE|
|2.||Fleming DM, Taylor RJ, Haguinet F, et al.: Influenza-attributable burden in United Kingdom primary care. Epidemiol Infect 2016; 144: 537–47 CrossRef MEDLINE PubMed Central|
|3.||Lehnert R, Pletz M, Reuss A, Schaberg T: Antiviral medications in seasonal and pandemic influenza—a systematic review. Dtsch Arztebl Int 2016; 113: 799–807 VOLLTEXT|
|4.||Restivo V, Costantino C, Bono S, et al.: Influenza vaccine effectiveness among high-risk groups: a systematic literature review and meta-analysis of case-control and cohort studies. Hum Vaccin Immunother 2018; 14: 724–35 CrossRef MEDLINE PubMed Central|
|5.||Plans-Rubió P: The vaccination coverage required to establish herd immunity against influenza viruses. Prev Med 2012; 55: 72–7 CrossRef MEDLINE|
|6.||Hofstetter AM, Camargo S, Natarajan K, Rosenthal SL, Stockwell MS: Vaccination coverage of adolescents with chronic medical conditions. Am J Prev Med 2017; 53: 680–8 CrossRef MEDLINE|
|7.||Nitsch-Osuch A, Gołębiak I, Wyszkowska D, et al.: Influenza vaccination coverage among Polish patients with chronic diseases. Adv Exp Med Biol 2017; 968: 19–34 CrossRef MEDLINE|
|8.||Byeon KH, Kim J, Choi B, Choi BY: The coverage rates for influenza vaccination and related factors in Korean adults aged 50 and older with chronic disease: based on 2016 community health survey data. Epidemiol Health 2018; 40: e2018034 CrossRef MEDLINE PubMed Central|
|9.||Bödeker B, Remschmidt C, Schmich P, Wichmann O: Why are older adults and individuals with underlying chronic diseases in Germany not vaccinated against flu? A population-based study. BMC Public Health 2015; 15: 618 CrossRef MEDLINE PubMed Central|
|10.||Sanftenberg L, Brombacher F, Gensichen J: A systematic review on strategies for increasing vaccination rates against influenza in patients with chronic diseases in primary care. PROSPERO 2018 CRD42018114163.|
|11.||Kovacs E, Strobl R, Phillips A, et al.: Systematic review and meta-analysis of the effectiveness of implementation strategies for non-communicable disease guidelines in primary health care. J Gen Intern Med 2018; 33: 1142–54 CrossRefMEDLINE PubMed Central|
|12.||Ahmed F, Friedman C, Franks A, et al.: Effect of the frequency of delivery of reminders and an influenza tool kit on increasing influenza vaccination rates among adults with high-risk conditions. Am J Manag Care 2004; 10: 698–702.|
|13.||Baker AM, McCarthy B, Gurley VF, Yood MU: Influenza immunization in a managed care organization. J Gen Intern Med 1998; 13: 469–75 CrossRef MEDLINE PubMed Central|
|14.||Beck A, Scott J, Williams P, et al.: A randomized trial of group outpatient visits for chronically ill older HMO members: The Cooperative Health Care Clinic. J Am Geriatr Soc 1997; 45: 543–9 CrossRef MEDLINE|
|15.||Chambers CV, Balaban DJ, Carlson BL, Grasberger DM: The effect of microcomputer-generated reminders on influenza vaccination rates in a university-based family practice center. J Am Board Fam Pract 1991; 4: 19–26.|
|16.||Hermiz O, Comino E, Marks G, Daffurn K, Wilson S, Harris M: Randomised controlled trial of home based care of patients with chronic obstructive pulmonary disease. BMJ 2002; 325: 938 CrossRef MEDLINE PubMed Central|
|17.||Herrett E, Williamson E, van Staa T, et al.: Text messaging reminders for influenza vaccine in primary care: a cluster randomised controlled trial (TXT4FLUJAB). BMJ Open 2016; 6: ce010069 CrossRef MEDLINE PubMed Central|
|18.||Markun S, Rosemann T, Dalla-Lana K, Steurer-Stey C: Care in chronic obstructive lung disease (CAROL): a randomised trial in general practice. Eur Respir J 2018; 51: 1701873 CrossRef MEDLINE|
|19.||Moran WP, Nelson K, Wofford JL, Velez R: Computer-generated mailed reminders for influenza immunization: a clinical trial. J Gen Intern Med 1992; 7: 535–7 CrossRef MEDLINE|
|20.||Moran WP, Nelson K, Wofford JL, Velez R, Case LD: Increasing influenza immunization among high-risk patients: education or financial incentive? Am J Med 1996; 101: 612–20 CrossRef|
|21.||Mullooly JP: Increasing influenza vaccination among high-risk elderly: a randomized controlled trial of a mail cue in an HMO setting. Am J Public Health 1987; 77: 626–7 CrossRef MEDLINE|
|22.||Regan AK, Bloomfield L, Peters I, Effler PV: Randomized controlled trial of text message reminders for increasing influenza vaccination. Ann Fam Med 2017; 15: 507–14 CrossRef PubMed Central PubMed Central|
|23.||Siriwardena NA, Rashid A, Johnson MRD, Dewey ME: Cluster randomised controlled trial of an educational outreach visit to improve influenza and pneumococcal immunisation rates in primary care. Br J Gen Pract 2002; 52: 735–40.|
|24.||Spaulding SA, Kugler JP: Influenza immunization: the impact of notifying patients of high-risk status. J Fam Pract 1991; 33: 495–8.|
|25.||Tierney WM, Overhage JM, Murray MD, et al.: Can computer-generated evidence-based care suggestions enhance evidence-based management of asthma and chronic obstructive pulmonary disease? A randomized, controlled trial. Health Serv Res 2005; 40: 477–97 CrossRef MEDLINE PubMed Central|
|26.||Zwar NA, Bunker JM, Reddel HK, et al.: Early intervention for chronic obstructive pulmonary disease by practice nurse and GP teams: a cluster randomized trial. Fam Pract 2016; 33: 663–70 CrossRef MEDLINE|
|27.||Forsetlund L, Bjørndal A, Rashidian A, et al.: Continuing education meetings and workshops: effects on professional practice and health care outcomes. Cochrane Database Syst Rev 2009; 2: CD003030 CrossRef MEDLINE|
|28.||Mendu ML, Schneider LI, Aizer AA, et al.: Implementation of a CKD checklist for primary care providers. Clin J Am Soc Nephrol 2014; 9: 1526–35 CrossRef MEDLINE PubMed Central|
|29.||Merkel PA, Caputo GC: Evaluation of a simple office-based strategy for increasing influenza vaccine administration and the effect of differing reimbursement plans on the patient acceptance rate. J Gen Intern Med 1994; 9: 679–83 CrossRef MEDLINE|
|30.||Goebel LJ, Neitch SM, Mufson MA: Standing orders in an ambulatory setting increases influenza vaccine usage in older people. J Am Geriatr Soc 2005; 53: 1008–10 CrossRef MEDLINE|
|31.||Shekelle PG, Stone EG, Maglione MA, et al.: Interventions that increase the utilization of Medicare-funded preventive services for persons age 65 and older. U.S. Department of Health and Human Services, 2003. www.rand.org/pubs/reprints/RP1229.html (last accessed on 1 August 2019).|
|32.||Tanner M: The grass is not always greener: a look at national health care systems around the world. Cato J 2008; 613: 1–48 CrossRef|
|33.||Schelling J, Thorvaldsson I, Sanftenberg L: Elektronische Impfmanagementsysteme in der Praxis zur Verbesserung der Impfquoten. Bundesgesundheitsbl 2019; 1 CrossRef MEDLINE|
|34.||Schulte K, Schierke H, Tamayo M, et al.: Strategies for improving influenza vaccination rates in patients with chronic renal disease—results from two randomized controlled trials and a prospective interventional study. Dtsch Arztebl Int 2019; 116: 413–9. VOLLTEXT|
|35.||Bauer A, Riemann JF, Seufferlein T, et al.: Invitation to screening colonoscopy in the population at familial risk for colorectal cancer—a cluster-randomized study aimed at increasing participation rates. Dtsch Arztebl Int 2018; 115: 715–22 VOLLTEXT|
|36.||Hoffmeister M, Holleczek B, Zwink N, Stock C, Stegmaier C, Brenner H: Screening for bowel cancer: increasing participation via personal invitation—a randomized intervention study. Dtsch Arztebl Int 2017; 114: 87– 93 VOLLTEXT|
|37.||Hiller KM, Sullivan D: Influenza vaccination in the emergency department: are our patients at risk? J Emerg Med 2009; 37: 439–43 CrossRef MEDLINE|
|38.||Rimple D, Weiss SJ, Brett M, Ernst AA: An emergency department-based vaccination program: overcoming the barriers for adults at high risk for vaccine-preventable diseases. Acad Emerg Med 2006; 13: 922–30 CrossRef CrossRef|
|39.||Gurfinkel EP, La Leon de Fuente R, Mendiz O, Mautner B: Flu vaccination in acute coronary syndromes and planned percutaneous coronary interventions (FLUVACS) Study. Eur Heart J 2004; 25: 25–31 CrossRef MEDLINE|
|40.||Thomas RE, Lorenzetti DL: Interventions to increase influenza vaccination rates of those 60 years and older in the community. Cochrane Database Syst Rev 2014; 7: CD005188 CrossRef PubMed Central|
|e1.||Robert Koch-Institut (RKI): Bericht zur Epidemiologie der Influenza in Deutschland, Saison 2017/18. Berlin 2018.|
|e2.||Katz A, Martens P, Chateau D, Bogdanovic B, Koseva I: Do primary care physicians coordinate ambulatory care for chronic disease patients in Canada? BMC Fam Pract 2014; 15: 148 CrossRef MEDLINE PubMed Central|
|e3.||Bekkat-Berkani R, Wilkinson T, Buchy P, et al.: Seasonal influenza vaccination in patients with COPD: a systematic literature review. BMC Pulm Med 2017; 17: 79 CrossRef MEDLINE PubMed Central|
|e4.||Jaiwong C, Ngamphaiboon J: Effects of inactivated influenza vaccine on respiratory illnesses and asthma-related events in children with mild persistent asthma in Asia. Asian Pac J Allergy Immunol 2015; 33: 3–7.|
|e5.||Kadoglou NPE, Bracke F, Simmers T, Tsiodras S, Parissis J: Influenza infection and heart failure—vaccination may change heart failure prognosis? Heart Fail Rev 2017; 22: 329–36 CrossRef MEDLINE PubMed Central|
|e6.||Remschmidt C, Wichmann O, Harder T: Influenza vaccination in patients with end-stage renal disease: systematic review and assessment of quality of evidence related to vaccine efficacy, effectiveness, and safety. BMC Med 2014; 12: 244 CrossRef MEDLINE PubMed Central|
|e7.||Robert Koch-Institut (RKI): Aktuelles aus der KV-Impfsurveillance – Impfquoten ausgewählter Schutzimpfungen in Deutschland. Epid Bull 2018; 1 .|
|e8.||Moher D, Liberati A, Tetzlaff J, Altman DG, the PRISMA Group: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009; 6: e1000097 CrossRef MEDLINE PubMed Central|
|e9.||Cochrane Deutschland, Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften – Institut für Medizinisches Wissensmanagement: Bewertung des Biasrisikos (Risiko systematischer Fehler) Studien: Ein Manual für die Leitlinienerstellung. 1. Auflage 2016.|
|e10.||Donner A, Piaggio G, Villar J: Meta-analyses of cluster randomization trials. Power considerations. Eval Health Prof 2003; 26: 340–51 CrossRef MEDLINE|
|e11.||Higgins JPT, Thompson SG: Quantifying heterogeneity in a meta-analysis. Stat Med 2002; 21: 1539–58 CrossRef MEDLINE|
|e12.||Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS: Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res 2015; 17: e52 CrossRef MEDLINE PubMed Central|
|e13.||Mustafa M, Al-Khal A, Al Maslamani M, Al Soub H: Improving influenza vaccination rates of healthcare workers: a multipronged approach in Qatar. East Mediterr Health J 2017; 23: 303–10 CrossRef|
|e14.||Betsch C, Wicker S: Personal attitudes and misconceptions, not official recommendations guide occupational physicians‘ vaccination decisions. Vaccine 2014; 32: 4478–84 CrossRef MEDLINE|
|e15.||Harrison N, Poeppl W, Herkner H, et al.: Predictors for and coverage of influenza vaccination among HIV-positive patients: a cross-sectional survey. HIV Med 2017; 18: 500–6 CrossRef MEDLINE|
|e16.||Haase N, Betsch C, Renkewitz F: Source credibility and the biasing effect of narrative information on the perception of vaccination risks. J Health Commun 2015; 20: 920–9 CrossRef MEDLINE|
|e17.||Ho HJ, Chan YY, Ibrahim MAB, Wagle AA, Wong CM, Chow A: A formative research-guided educational intervention to improve the knowledge and attitudes of seniors towards influenza and pneumococcal vaccinations. Vaccine 2017; 35: 6367–74 CrossRef MEDLINE|
|e18.||Altay M, Ateş İ, Altay FA, Kaplan M, Akça Ö, Özkara A: Does education effect the rates of prophylactic vaccination in elderly diabetics? Diabetes Res Clin Pract 2016; 120: 117–23 CrossRef MEDLINE|
|e19.||Bodenheimer T, Wagner EH, Grumbach K: Improving primary care for patients with chronic illness: The Chronic Care Model, part 2. JAMA 2002; 288: 1909–14 CrossRef MEDLINE|
|e20.||World Health Organization: Noncommunicable diseases and their risk factors. www.who.int/ncds/en/ (last accessed on 27 March 2019).|
|e21.||American Association of Family Physicians: Primary care policies: Definition #1 – Primary care. www.aafp.org/about/policies/all/primary-care.html (last accessed on 22 November 2018).|
|e22.||United Nations Statistics Division: Methodology: standard country or area codes for statistical use. Developed regions. www.unstats.un.org/unsd/methodology/m49/ (last accessed on 31 January 2019).|
|e23.||Schulz KF, Altman DG, Moher D, for the CONSORT Group: CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ 2010; 340: c332 CrossRef MEDLINE PubMed Central|
|e24.||Randolph R: CONSORT 2010 Checklist. www.elsevier.com/__data/promis_misc/CONSORT-2010-Checklist.pdf (last accessed on 1 August 2019).|
|e25.||Divine GW, Brown JT, Frazier LM: The unit of analysis error in studies about physicians’ patient care behavior. J Gen Intern Med 1992; 7: 623–9 CrossRef MEDLINE|
|e26.||Cochrane Effective Practice and Organisation of Care Review Group (EPOC): Data collection checklist. Institute of Population Health, University of Ottawa, 2002. https://methods.cochrane.org/sites/methods.cochrane.org.bias/files/public/uploads/EPOC%20Data%20Collection%20Checklist.pdf (last accessed on 1 August 2019)|
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