Abstract
Introduction:
Healthcare workers risk of exposure to the influenza virus in their work, is a high-risk group for flu infections. Thus WHO recommends prioritizing flu vaccination for them–an approach adopted by >40 countries and/or regions worldwide.
Methods:
Cross-sectional studies on influenza vaccination rates among healthcare workers were collected from PubMed, EMBASE, CNKI, and CBM databases from inception to February 26, 2023. Influenza vaccination rates and relevant data for multiple logistic regression analysis, such as odds ratios (OR) and 95% confidence intervals (CI), were extracted.
Results:
A total of 92 studies comprising 125 vaccination data points from 26 countries were included in the analysis. The meta-analysis revealed that the overall vaccination rate among healthcare workers was 41.7%. Further analysis indicated that the vaccination rate was 46.9% or 35.6% in low income or high income countries. Vaccination rates in the Americas, the Middle East, Oceania, Europe, Asia, and Africa were 67.1, 51.3, 48.7, 42.5, 28.5, and 6.5%, respectively. Influencing factors were age, length of service, education, department, occupation, awareness of the risk of influenza, and/or vaccines.
Conclusion:
The global influenza vaccination rate among healthcare workers is low, and comprehensive measures are needed to promote influenza vaccination among this population.
Systematic review registration:
www.inplysy.com, identifier: 202350051.
Introduction
The World Health Organization (WHO) reports that the flu causes 3 to 5 million severe cases and contributes to 290,000 to 650,000 respiratory disease-related deaths globally p.a (1). Thus flu imposes a substantial impact on both public health and the economy, i.e., the flu resulted in 145,000 deaths, 9.459 million hospitalizations, and 81.536 million hospitalization days due to lower respiratory tract infections (LRTIs), with the flu accounting for 11.5% of LRTI cases in 2017 (2). This aligns with that indirect costs accounted for 88% of the overall economic burden of flu in the 18–64 age group, with 75% of direct costs attributed to hospitalization. Additionally, the costs associated with flu increase with age and the presence of underlying diseases within the 18–64 age group (3).
Annual flu vaccination is widely recognized as an effective preventive measure against the flu. Evidence from a systematic review of randomized controlled trials indicates that inactivated flu vaccines administered to healthy adults can prevent 59% of laboratory-confirmed flu cases, furthermore, when the vaccine strains closely match the circulating flu virus strains, it has been shown to reduce the incidence of influenza-like illness (ILI) by 42% (4).
Healthcare workers face a significant risk of exposure to the flu virus in their daily work, making them a high-risk group for flu infections. A meta-analysis revealed that the incidence of lab-confirmed flu among non-vaccinated healthcare workers was 18.7%, which is 3.4 times higher than the rate observed in healthy adults (5). When healthcare workers contract the flu, it can lead to heightened absenteeism, causing disruptions in medical services and a greater risk of hospital-acquired infections. Furthermore, continuing to work while infected can potentially facilitate the transmission of the flu to other individuals, particularly their family members.
Influenza vaccination is the most significant prevention measure. Recognizing the importance of protecting healthcare workers and preventing the spread of flu, WHO recommends that healthcare workers be given priority for flu vaccination. This recommendation has been adopted by over 40 countries and regions worldwide. However, vaccination coverage exhibited significant variations from one country to another (6), and in some instances, it was notably low (7). In this current systematic review, our objective is to examine the influenza vaccination rates among healthcare workers and the factors that impact their adherence to flu vaccination.
Methods
Study type
This meta-analysis included cross-sectional studies that reported the seasonal influenza vaccination rate among healthcare workers.
Study population
The study population consisted of healthcare workers and healthcare professionals directly involved in providing health services globally.
Outcome measures
The primary outcome measure of interest was the seasonal influenza vaccination rate, which was defined as the percentage of vaccinated individuals among the total survey population.
Inclusion criteria
To be included in this meta-analysis, studies had to meet the following criteria:
Studies reporting the seasonal influenza vaccination rate among healthcare workers and/or its influencing factors.
The study population included healthcare workers and healthcare professionals directly involved in providing health services globally.
Studies provided specific information on sample size, vaccination rates, and the number of vaccinated individuals within a given year.
Studies were published in either Chinese or English.
The study design was cross-sectional.
Exclusion criteria
The following criteria were used to exclude studies from this meta-analysis:
Studies reporting on types of influenza vaccines other than seasonal influenza vaccines.
Studies that did not report key data such as sample size, vaccination rates, and the number of vaccinated individuals, or studies that did not specify the vaccination year or only reported combined vaccination rates for multiple years.
Studies that focused solely on healthcare institutions or the overall population of a country, without specific data on healthcare workers.
Duplicate publications, where the same study was published in multiple sources.
Studies with logical errors or inconsistencies in the reported data.
Literature search strategy
Computer-based searches were performed in multiple databases, including PubMed, EMBASE, CNKI, CBM, Wanfang, and VIP. The search aimed to identify cross-sectional studies that reported the seasonal influenza vaccination rate among healthcare workers. The search was conducted from the inception of each database up to February 26, 2023. The search strategy utilized a combination of subject terms and free-text terms, Search, terms like “Influenza Vaccine*,” “Flu Vaccine*,” “Influenza Virus Vaccine*,” “Universal Influenza Vaccine*,” “Universal Flu Vaccine*,” “Immunization Coverage*” and “Vaccination Coverage*” were utilized. This comprehensive search strategy was designed to capture relevant studies and gather a wide range of literature on the seasonal influenza vaccination rate among healthcare workers (Supplementary Table S1).
Literature screening and data extraction
The identified literature was imported into Endnote literature management software, and duplicate records were removed. Two researchers independently screened the literature and performed data extraction. In cases of discrepancies, a third senior researcher was consulted for discussion and to reach a consensus. Initially, the title and abstract of each article were reviewed to exclude obviously irrelevant studies. Subsequently, the full text of the remaining articles was thoroughly examined to determine their eligibility for inclusion in the meta-analysis.
Data extraction encompassed various key aspects, including the first author’s name, publication year, survey region, sampling location, study population, vaccination time, sample size, number of vaccinated individuals, and relevant data from multiple logistic regression analysis, such as odds ratios (ORs), 95% confidence intervals (CIs), and reference objects. This rigorous screening and data extraction process ensured that relevant and reliable information was obtained from the selected studies for further analysis.
Evaluation of bias risk in included studies
To assess the methodological quality of the included cross-sectional studies, a checklist was developed based on recommended guidelines. This checklist incorporated items from the cross-sectional study quality evaluation tool endorsed by the Agency for Healthcare Research and Quality (AHRQ) and the JBI Analytic Cross-Sectional Study Quality Evaluation Scale.
The checklist consisted of nine key items aimed at evaluating the potential biases in the included studies. These items included:
Clearly stating the source of data (e.g., survey, literature review).
Clearly defining the inclusion criteria for the study population.
Providing detailed descriptions of the study population and study site.
Offering an explanation for the exclusion of certain study subjects from the analysis.
Summarizing the patient response rate and data collection completeness.
Explaining how missing data was handled during the analysis if the research data was incomplete or had missing values.
Describe how confounding was assessed and/or controlled.
Whether to use effective and credible methods to measure outcome indicators.
Whether the data analysis method is appropriate.
By systematically assessing these aspects, the checklist enabled a comprehensive evaluation of the methodological quality of the cross-sectional studies. This evaluation helped to identify any potential biases that may have influenced the study results and ensured the reliability of the findings.
Data analysis
The data extraction and analysis were performed using Excel 2016 and STATA 12.0 software. To assess publication bias, Egger’s test and funnel plot were utilized. A significance level of 0.05 or 0.01 was considered statistically significant. Given the anticipated heterogeneity, a random-effects model was employed for the analysis. Sensitivity analysis was conducted to assess the robustness and reliability of the overall vaccination rate estimate. Additionally, subgroup analysis was performed to explore potential sources of heterogeneity.
For the analysis of vaccination rates, the formula used was as follows:
Influenza vaccine vaccination rate = number of vaccinators / sample size.
The standard error of the rate was calculated using the formula:
Standard error of rate = sqrt (rate × (1-rate) / sample size).
When adequate data were available from the included articles, the random effects model was utilized to estimate the odds ratios (OR) of the influencing factors. This approach allowed for a comprehensive assessment of the relationship between the influencing factors and the vaccination rates.
These analytical methods were employed to ensure a comprehensive evaluation of the data and to derive reliable and robust outcomes from the study. By utilizing these methods, we aimed to provide accurate and valid insights into the influencing factors of influenza vaccination rates among healthcare workers.
Results
During the literature screening process
A comprehensive search of relevant articles yielded a total of 6,502 records. Following the screening process, 92 cross-sectional studies were considered eligible for inclusion in the analysis. The detailed process and results of the literature screening are presented in Figure 1. These 92 studies encompassed 125 data points on influenza vaccination, with sample sizes ranging from 106 to 8,975 participants. The reported vaccination rates varied between 3.1 and 99.6%. The studies were conducted in 26 countries across Asia, Europe, the Americas, Africa, Oceania, and the Middle East, providing a diverse geographical representation.
Figure 1
It is summarized that the key characteristics of the included studies, including their basic information and vaccination data (Table 1). The evaluation of literature quality resulted in an average score of 7.86 points. Among the included articles, one was rated as low-quality, 30 as medium-quality, and 61 as high-quality studies.
Table 1
| Study | Sampling location | Population | Vaccination time | Study region | Sample size | Vaccination population | Quality score |
|---|---|---|---|---|---|---|---|
| Sheng et al. (8) | Internet survey | Nurses | 2017 | Mainland China | 773 | 31 | 8 |
| Liu et al. (9) | Community health centers | All HCWs | 2018 | Mainland China | 1,359 | 424 | 9 |
| Wang et al. (10) | Hospital | All HCWs | 2012 | Mainland China | 569 | 171 | 9 |
| Gao et al. (11) | Hospital | All HCWs | 2013 | Mainland China | 369 | 51 | 8 |
| Liu et al. (12) | Hospital | Nurses | 2018 | Mainland China | 299 | 68 | 9 |
| Yang and Chen (13) | Hospital | Nurses | 2013 | Mainland China | 650 | 284 | 9 |
| Gan et al. (14) | Community health centers | All HCWs | 2018 | Mainland China | 106 | 24 | 9 |
| Wang (15) | Hospital | All HCWs | 2007 | Mainland China | 199 | 15 | 6 |
| Bu et al. (16) | Hospital | All HCWs | 2012 | Mainland China | 1,521 | 98 | 8 |
| Yang et al. (17) | Hospital | All HCWs | 2016 | Mainland China | 1941 | 107 | 9 |
| Wang et al. (18) | Internet survey | Nurses | 2017 | Mainland China | 510 | 16 | 8 |
| Zhang et al. (19) | Hospital | All HCWs | 2017 | Mainland China | 943 | 131 | 9 |
| Kong et al. (20) | Hospital/Community health centers /CDC | All HCWs | 2019 | Mainland China | 8,975 | 2,241 | 9 |
| Ma et al. (21) | Hospital | All HCWs | 2017 | Mainland China | 3,260 | 226 | 8 |
| Gan et al. (22) | Influenza sentinel surveillance hospital/Hospital | All HCWs | 2018 | Mainland China | 1,412 | 237 | 8 |
| James et al. (23) | Hospital | All HCWs | 2016 | Sierra Leone | 706 | 46 | 8 |
| Liu et al. (24) | Internet survey | All HCWs | 2018 | Mainland China | 4,078 | 472 | 9 |
| Hosamirudsari et al. (25) | Hospital | All HCWs | 2015 | Iran | 378 | 218 | 7 |
| Alhammadi et al. (26) | Hamad Medical Corporation | All HCWs | 2013 | Qatar | 230 | 151 | 9 |
| Boey et al. (27) | Hospital/Nursing homes | All HCWs | 2014 | Belgium | 450 | 334 | 9 |
| Barbadoro et al. (28) | National Health Surveys. | All HCWs | 2012 | Italy | 5,336 | 1,110 | 6 |
| Wong et al. (29) | Hospital | Nurses | 2017 | Hong Kong | 708 | 309 | 5 |
| Kyaw et al. (30) | Hospital | All HCWs | 2015 | Singapore | 3,873 | 3,191 | 9 |
| Rabensteiner et al. (31) | Health Service | All HCWs | 2015 | Italy | 4,091 | 425 | 9 |
| Garcell and Ramirez (32) | Hospital | All HCWs | 2012 | Qatar | 325 | 231 | 6 |
| Esposito et al. (33) | University Hospital | All HCWs | 2006 | Italy | 2,143 | 432 | 9 |
| Hudu et al. (34) | Hospital | All HCWs | 2013 | Malaysia | 527 | 271 | 7 |
| Costantino et al. (35) | University | Medical residents | 2011 | Italy | 2,506 | 299 | 9 |
| Jimenez-Garcia et al. (36) | National Health Surveys. | All HCWs | 2003 | Spain | 518 | 102 | 8 |
| Von Perbandt et al. (37) | Hospital | All HCWs | 2014 | Switzerland | 200 | 30 | 8 |
| Haridi et al. (38) | Medical City | All HCWs | 2014 | Saudi Arabia | 447 | 394 | 9 |
| Sočan et al. (39) | Slovenian Medical Chamber | Physicians and dentists | 2009 | Slovenia | 1718 | 890 | 8 |
| Domínguez et al. (40) | Healthy primary facilities | All HCWs | 2011 | Spain | 1749 | 887 | 9 |
| Rehmani and Memon (41) | Hospital | All HCWs | 2008 | Saudi Arabia | 512 | 176 | 9 |
| Kan et al. (42) | Hospital | Nurses | 2011 | Mainland China | 895 | 295 | 9 |
| Kent et al. (43) | Public Health Directorates | All HCWs | 2007 | America | 1,203 | 871 | 9 |
| Hagemeister et al. (44) | Hospital | All HCWs | 2012 | Germany | 675 | 286 | 7 |
| Castilla et al. (45) | Hospital | All HCWs | 2008 | Spain | 1965 | 1,203 | 8 |
| Ball et al. (46) | National opt-in Internet panels | All HCWs | 2012 | America | 1944 | 1,400 | 7 |
| Black et al. (47) | National opt-in Internet panels | All HCWs | 2013 | America | 1882 | 1,415 | 7 |
| Black et al. (48) | National opt-in Internet panels | All HCWs | 2014 | America | 1914 | 1,480 | 7 |
| Black et al. (49) | National opt-in Internet panels | All HCWs | 2015 | America | 2,258 | 1784 | 7 |
| Black et al. (50) | National opt-in Internet panels | All HCWs | 2016 | America | 2,438 | 1916 | 7 |
| Black et al. (51) | National opt-in Internet panels | All HCWs | 2017 | America | 2,265 | 1776 | 7 |
| CDC (52) | National opt-in Internet panels | All HCWs | 2010 | America | 1931 | 1,226 | 7 |
| Ball at al. 2012 (53) | National opt-in Internet panels | All HCWs | 2011 | America | 2,348 | 1,571 | 7 |
| Tanguy et al. (54) | Hospital | All HCWs | 2009 | France | 532 | 119 | 5 |
| Amodio et al. (55) | University Hospital | Medical residents | 2009 | Italy | 202 | 44 | 8 |
| Hakim et al. (56) | Hospital | All HCWs | 2018 | Egypt | 3,534 | 1,087 | 9 |
| Hussain et al. (57) | Hospital | All HCWs | 2013 | Canada | 896 | 654 | 7 |
| Tagajdid et al. (58) | Hospital | All HCWs | 2011 | Morocco | 721 | 122 | 6 |
| Dorribo et al. (59) | University Hospital | All HCWs | 2009 | Switzerland | 472 | 245 | 9 |
| Bazán et al. (60) | Hospital/Health centers | All HCWs | 2010 | Peru | 672 | 544 | 9 |
| Yi et al. (61) | Internet survey | All HCWs | 2019 | Mainland China | 4,366 | 2,927 | 8 |
| Sánchez-Payá et al. (62) | University Hospital | All HCWs | 2010 | Spain | 3,126 | 762 | 8 |
| Yu et al. (63) | Internet survey | Nurses | 2017 | Mainland China | 4,153 | 257 | 8 |
| Groenewold et al. (64) | Nursing homes | Nurses | 2004 | America | 2,873 | 107 | 6 |
| Hajiabdolbaghi et al. (65) | Hospital | All HCWs | 2019 | Iran | 637 | 189 | 5 |
| Dubnov et al. (66) | Hospital | All HCWs | 2004 | Israel | 256 | 42 | 7 |
| Buxmann et al. (67) | Hospital | All HCWs | 2016 | Germany | 124 | 49 | 9 |
| Khazaeipour et al. (68) | University Hospital | All HCWs | 2008 | Iran | 139 | 93 | 7 |
| Lu and Euler (69) | National Health Surveys. | All HCWs | 2006 | America | 484 | 226 | 6 |
| Domínguez et al. (70) | Hospital | All HCWs | 2011 | Spain | 1749 | 886 | 8 |
| Toledo et al. (71) | community health centers | Pharmacists | 2013 | Spain | 463 | 116 | 9 |
| Loulergue et al. (72) | Medical departments | All HCWs | 2006 | France | 395 | 204 | 8 |
| Madewell et al. (73) | Hospital | All HCWs | 2018 | America | 706 | 393 | 8 |
| Harrison et al. (74) | Hospital | Nurses | 2013 | Austria | 107 | 45 | 8 |
| Petek and Kamnik-Jug (75) | Primary care centers | All HCWs | 2014 | Slovenia | 250 | 30 | 9 |
| Murray and Skull (76) | Hospital | All HCWs | 1999 | Australia | 269 | 131 | 7 |
| Mojamamy et al. (77) | Primary care centers | All HCWs | 2015 | Saudi Arabia | 368 | 320 | 7 |
| Vírseda et al. (78) | University Hospital | All HCWs | 2009 | Spain | 527 | 262 | 8 |
| Amani et al. (79) | Hospital/community health centers | All HCWs | 2019 | Egypt | 980 | 131 | 9 |
| Hämäläinen et al. (80) | University Hospital | All HCWs | 2015 | Finland | 985 | 586 | 7 |
| Khazaeipour et al. (81) | University Hospital | All HCWs | 2008 | Iran | 139 | 93 | 9 |
| Jiang et al. (82) | Hospital | All HCWs | 2019 | Mainland China | 2,974 | 713 | 8 |
| Fan et al. (83) | Hospital | All HCWs | 2019 | Mainland China | 6,654 | 1,037 | 7 |
| Yan et al. (84) | Hospital | All HCWs | 2019 | Mainland China | 1,332 | 614 | 7 |
| Li et al. (85) | Hospital | All HCWs | 2020 | Mainland China | 4,135 | 2,460 | 9 |
| Zhang et al. (86) | Hospital | All HCWs | 2019 | Mainland China | 775 | 255 | 9 |
| Wu et al. (87) | Hospital | All HCWs | 2018 | Mainland China | 3,507 | 413 | 8 |
| Lv et al. (88) | Community health centers | All HCWs | 2018 | Mainland China | 1,483 | 216 | 8 |
| Fan et al. (89) | Hospital | All HCWs | 2020 | Mainland China | 769 | 670 | 9 |
| Lei et al. (90) | Influenza sentinel surveillance Hospital | All HCWs | 2020 | Mainland China | 1854 | 419 | 9 |
| Ma et al. (91) | Internet survey | All HCWs | 2021 | Mainland China | 1,697 | 600 | 9 |
| Papageorgiou et al. (92) | Health care services institutions | All HCWs | 2019 | Cyprus | 962 | 306 | 8 |
| Ajejas Bazán et al. (93) | Public Health Directorates | All HCWs | 2020 | Spain | 832 | 590 | 8 |
| Bertoni et al. (94) | Cancer research institute | All HCWs | 2020 | Italy | 579 | 334 | 8 |
| Marinos et al. (95) | Athens Medical Association | All HCWs | 2020 | Greece | 1993 | 1,523 | 7 |
| Shi et al. (96) | Hospital/Community health centers | All HCWs | 2020 | Mainland China | 2,192 | 868 | 8 |
| Jędrzejek and Mastalerz-Miga (97), | Hospital | All HCWs | 2019 | Poland | 165 | 101 | 8 |
| Costantino et al. (98) | Community health centers | Pharmacists | 2020 | Italy | 1,450 | 841 | 7 |
| Ogliastro et al. (99) | University Hospital | All HCWs | 2021 | Italy | 4,753 | 1,423 | 4 |
Basic information of literatures of included studies.
Influenza vaccination rate and subgroup analysis
The meta-analysis included a total of 92 cross-sectional studies, and a random effects model was employed. The analysis revealed that the global influenza vaccination rate among healthcare workers was 41.7% (95% CI [35.7, 47.7%)]. However, it is noted that significant heterogeneity was observed among the studies (I2 = 99.9%, p < 0.001). To further explore the sources of heterogeneity, subgroup analyzes were conducted based on the country’s level of development, geographic region, and time of vaccination.
The countries included in the analysis were categorized as low income or high income according to their economic levels. It was revealed that the influenza vaccination rate among healthcare workers in developed or developing countries was 46.9% or 35.6%. Furthermore, the study regions were classified into Asia, Europe, America, Africa, Oceania, and the Middle East based on their geographical locations. Subgroup analysis revealed that America had the highest vaccination rate at 67.1%, followed by the Middle East, Oceania, Europe, and Asia with rates of 51.3, 48.7, 42.5, and 28.5%, respectively. Africa had the lowest vaccination rate at 6.5%. The study periods were divided based on the occurrence of the H1N1 influenza pandemic (March 2009 to August 2010) and the COVID-19 epidemic (from the end of December 2019). The vaccination rates were separately analyzed for different periods: before 2009, 2009–2012, 2013–2016, 2017–2019, and 2020-present. The subgroup analysis showed that the highest vaccination rate was observed since 2020 at 52.8%, followed by the period of 2009–2012 at 46.7%, 2013–2016 at 46.5%, before 2009 at 39.4%, and the lowest rate was during 2017–2019 at 31.4%.
Despite the subgroup analysis, there remained high heterogeneity in the vaccination rates within each subgroup, indicating that the level of economic development, geographical location, and different vaccination periods were not the primary sources of heterogeneity. The detailed results of the subgroup analysis can be found in Table 2.
Table 2
| Groups | Reference(n) | Test of heterogeneity result | Meta-analysis results | |||
|---|---|---|---|---|---|---|
| P | I2(%) | Effect model | Rate (%) | 95% CI | ||
| Economic development levels | ||||||
| Developing country | 67 | <0.001 | 99.9 | Random | 46.9 | (38.0, 55.9%) |
| Developed country | 58 | <0.001 | 99.8 | Random | 35.6 | (30.1, 41.1%) |
| Geographic region | ||||||
| Asia | 45 | <0.001 | 99.8 | Random | 28.5 | (23.2, 33.8%) |
| Europe | 45 | <0.001 | 99.9 | Random | 42.5 | (31.2, 53.8%) |
| America | 17 | <0.001 | 99.9 | Random | 67.1 | (48.9, 85.4%) |
| Africa | 1 | – | – | Random | 6.5 | (4.7, 8.3%) |
| Oceania | 1 | – | – | Random | 48.7 | (42.7, 54.7%) |
| Middle East | 16 | <0.001 | 99.6 | Random | 51.3 | (38.1, 64.5%) |
| Vaccination time | ||||||
| ~2008 | 13 | <0.001 | 99.8 | Random | 39.4 | (21.9, 56.8%) |
| 2009–2012 | 28 | <0.001 | 99.8 | Random | 46.7 | (37.9, 55.6%) |
| 2013–2016 | 33 | <0.001 | 99.8 | Random | 46.5 | (35.8, 57.2%) |
| 2017–2019 | 39 | <0.001 | 100.0 | Random | 31.4 | (18.5, 44.3%) |
| 2020~ | 12 | <0.001 | 99.7 | Random | 52.8 | (41.9, 63.8%) |
| Total | 125 | <0.001 | 99.9 | Random | 41.7 | (35.7, 47.7%) |
Influenza vaccination rate of HCWs in different groups.
Publication bias test
A funnel plot was generated using the 125 vaccination rate data included in the study (Figure 2), which showed that the scatter was relatively dispersed and roughly symmetrical. The Egger’s test confirmed that there was no significant publication bias in the studies (t = −0.33, p = 0.741), indicating that this study had low publication bias.
Figure 2
Sensitivity analysis
A sensitivity analysis was performed by systematically excluding individual studies from the meta-analysis. The results indicated that the effect size remained consistent, ranging from 41 to 43%, even when each study was removed, suggesting that the meta-analysis findings were robust and stable (Supplementary Table S2).
Factors influencing influenza vaccination
A total of 32 factors were identified from the included studies that significantly influenced healthcare workers’ uptake of influenza vaccine. Several factors played a significant role in influencing vaccination uptake among healthcare workers, including age, length of employment, education level, department of work, occupation, presence of chronic diseases, perception of being at risk of infection, belief in vaccine effectiveness, willingness to receive vaccination, recommendation of influenza vaccine to patients, previous COVID-19 vaccination, participation in influenza or influenza vaccine training and health education, and knowledge of vaccination timing.
Compared with the younger age group, the middle-aged and older adult groups were more likely to receive the vaccine. Healthcare workers with more than 10 years of experience were more likely to be vaccinated than those with less than 10 years of experience. Non-clinical staff were more likely to receive the vaccine than clinical staff. Among healthcare workers who had chronic diseases, perceived themselves to be at high risk of infection, believed in the effectiveness of the vaccine, had the willingness to receive the vaccine, recommended the vaccine to patients, had previous COVID-19 vaccination, and had knowledge of vaccination timing, were more likely to receive the influenza vaccine.
Subgroup analysis of influencing factors showed that gender, marital status, professional title, perception of vaccine safety, source of vaccine information, and whether the workplace provided free vaccines may also be factors influencing healthcare workers’ uptake of influenza vaccine. The detailed findings of these significant factors are summarized in Table 3.
Table 3
| Factor | Test of heterogeneity result | Meta-analysis results | P | |||
|---|---|---|---|---|---|---|
| P | I2(%) | Effect model | OR | OR95%CI | ||
| Sex | <0.001 | 78.5 | Random | 1.197 | (0.987, 1.452) | 0.068 |
| Female | <0.001 | 71.3 | Random | 0.960 | (0.787, 1.171) | 0.687 |
| Male | 0.001 | 85.3 | Random | 1.656 | (1.289, 2.127) | <0.001 |
| Age | <0.001 | 95.1 | Random | 1.700 | (1.600, 1.807) | <0.001 |
| Younger age | 0.001 | 70.6 | Random | 1.575 | (1.104, 2.247) | 0.012 |
| Middle-aged | <0.001 | 91.0 | Random | 2.278 | (1.790, 2.900) | <0.001 |
| Older adult | <0.001 | 90.5 | Random | 2.824 | (1.669, 4.779) | <0.001 |
| Whole population | 0.001 | 77.6 | Random | 1.018 | (1.002, 1.034) | 0.030 |
| Length of service | <0.001 | 93.7 | Random | 1.286 | (1.179, 1.402) | <0.001 |
| ≤10 | <0.001 | 90.6 | Random | 1.214 | (0.888, 1.659) | 0.224 |
| 11–30 | <0.001 | 81.6 | Random | 1.397 | (1.203, 1.622) | <0.001 |
| >30 | <0.001 | 84.9 | Random | 1.414 | (0.775, 2.582) | 0.259 |
| Other | 0.373 | 0.0 | Random | 1.009 | (0.999, 1.018) | 0.075 |
| Education level | <0.001 | 73.1 | Random | 0.837 | (0.723, 0.969) | 0.017 |
| College degree or below | <0.001 | 76.5 | Random | 0.721 | (0.582, 0.895) | 0.003 |
| Bachelor degree | 0.154 | 37.9 | Random | 0.829 | (0.666, 1.033) | 0.095 |
| Master degree or above | 0.005 | 70.2 | Random | 1.076 | (0.809, 1.431) | 0.616 |
| Marital status | 0.054 | 44.6 | Random | 1.139 | (0.976, 1.329) | 0.100 |
| Married/Cohabitant | 0.027 | 60.4 | Random | 1.096 | (0.854, 1.406) | 0.473 |
| Separated/Divorced | 0.922 | 0.0 | Random | 1.086 | (0.896, 1.318) | 0.400 |
| Widowed | 0.716 | 0.0 | Random | 1.583 | (1.162, 2.158) | 0.004 |
| Professional title | 0.058 | 41.6 | Random | 1.123 | (0.992, 1.270) | 0.066 |
| Associate senior or above | 0.015 | 67.6 | Random | 1.238 | (0.939, 1.633) | 0.130 |
| Middle | 0.434 | 0.0 | Random | 1.139 | (1.027, 1.264) | 0.014 |
| Primary | 0.110 | 60.8 | Random | 1.059 | (0.581, 1.933) | 0.851 |
| No title | 0.857 | 0.0 | Random | 0.762 | (0.481, 1.208) | 0.248 |
| Department | <0.001 | 77.2 | Random | 1.435 | (1.148, 1.794) | 0.241 |
| Clinical | <0.001 | 89.6 | Random | 1.177 | (0.896, 1.546) | 0.002 |
| Non-clinical | <0.001 | 85.2 | Random | 1.781 | (1.243, 2.551) | 0.002 |
| Occupation | <0.001 | 86.7 | Random | 1.757 | (1.503, 2.055) | <0.001 |
| Nursing staff | <0.001 | 93.1 | Random | 1.371 | (1.006, 1.868) | 0.046 |
| Others | 0.016 | 49.3 | Random | 1.397 | (1.160, 1.682) | <0.001 |
| Clinician | <0.001 | 80.0 | Random | 2.365 | (1.868, 2.993) | <0.001 |
| Hospital level | <0.001 | 88.9 | Random | 0.941 | (0.660, 1.340) | 0.734 |
| Primary | 0.002 | 84.5 | Random | 1.315 | (0.907, 1.907) | 0.148 |
| Secondary | 0.033 | 70.6 | Random | 0.618 | (0.376, 1.015) | 0.057 |
| Have children at home | 0.465 | 0.0 | Random | 1.024 | (0.907, 1.155) | 0.706 |
| Have old people at home | 0.047 | 62.3 | Random | 1.347 | (0.987, 1.838) | 0.060 |
| Have chronic medical condition | 0.399 | 4.5 | Random | 1.707 | (1.441, 2.021) | <0.001 |
| They consider themselves to be at high risk of infection | <0.001 | 87.7 | Random | 1.981 | (1.256, 3.126) | 0.003 |
| Think the vaccine is effective | <0.001 | 87.7 | Random | 2.101 | (1.249, 3.534) | 0.005 |
| Whether the vaccine is safe | <0.001 | 87.1 | Random | 1.413 | (0.921, 2.169) | 0.113 |
| Safe | <0.001 | 90.2 | Random | 1.619 | (1.008, 2.601) | 0.046 |
| Unsafe | 0.440 | 0.0 | Random | 0.741 | (0.349, 1.577) | 0.437 |
| Support HCWs to receive influenza vaccination | <0.001 | 95.2 | Random | 2.279 | (0.824, 6.308) | 0.113 |
| Worried about vaccine side effects | 0.041 | 76.0 | Random | 0.693 | (0.312, 1.537) | 0.367 |
| That vaccines cause the flu | 0.074 | 68.7 | Random | 0.834 | (0.443, 1.570) | 0.575 |
| Protect patients | 0.011 | 84.4 | Random | 2.154 | (0.971, 4.778) | 0.059 |
| Willing to vaccinate | 0.792 | 0.0 | Random | 4.104 | (2.421, 6.956) | <0.001 |
| Whether to recommend vaccines to patients | <0.001 | 86.1 | Random | 2.193 | (1.315, 3.658) | 0.003 |
| No | 0.293 | 9.5 | Random | 1.320 | (0.877, 1.986) | 0.183 |
| Yes | <0.001 | 86.1 | Random | 2.739 | (1.524, 4.922) | 0.001 |
| COVID-19 vaccination | 0.001 | 91.2 | Random | 5.922 | (1.136, 30.876) | 0.035 |
| Have participated in flu or flu vaccine training, health promotion | 0.003 | 89.0 | Random | 0.773 | (0.259, 0.309) | 0.645 |
| Yes | .. | .. | Random | 1.288 | (1.034, 1.604) | 0.024 |
| No | .. | .. | Random | 0.420 | (0.420, 0.840) | 0.014 |
| Sources of information | <0.001 | 84.5 | Random | 1.060 | (0.814, 1.380) | 0.666 |
| People around me | 0.099 | 63.3 | Random | 1.174 | (0.714, 1.928) | 0.527 |
| Mass media | <0.001 | 92.4 | Random | 0.665 | (0.320, 1.382) | 0.275 |
| Professional organization or publication | 0.245 | 24.2 | Random | 1.301 | (1.113, 1.520) | 0.001 |
| Know the vaccination time | <0.001 | 85.2 | Random | 2.224 | (1.165, 4.244) | 0.015 |
| Know the vaccine priority groups | <0.001 | 84.7 | Random | 1.327 | (0.857, 2.053) | 0.205 |
| Know that the vaccine is the most effective way to prevent flu | <0.001 | 88.7 | Random | 1.031 | (0.310, 3.432) | 0.960 |
| Know that the vaccine is given once a yea | <0.001 | 88.5 | Random | 1.028 | (0.612, 1.729) | 0.916 |
| Work units participate in the influenza sentinel network | 0.850 | 0.0 | Random | 0.920 | (0.698, 1.213) | 0.555 |
| Free vaccination at workplace | <0.001 | 98.9 | Random | 0.746 | (0.317, 1.756) | 0.502 |
| Yes | <0.001 | 98.3 | Random | 1.533 | (0.525, 4.479) | 0.435 |
| No | <0.001 | 99.4 | Random | 0.644 | (0.087, 4.777) | 0.667 |
| Unclear | <0.001 | 94.6 | Random | 0.279 | (0.088, 0.886) | 0.030 |
| Have vaccination sites at workplace | <0.001 | 91.5 | Random | 1.377 | (0.764, 2.480) | 0.287 |
| Workplace attitudes toward influenza vaccination of medical staff | <0.001 | 95.7 | Random | 0.897 | (0.498, 1.617) | 0.718 |
| Ask for or support encouragement | <0.001 | 94.0 | Random | 1.602 | (0.944, 2.717) | 0.081 |
| Do not require or encourage | 0.725 | 0.0 | Random | 0.300 | (0.207, 0.435) | <0.001 |
| Unclear | .. | .. | Random | 0.090 | (0.038, 0.216) | <0.001 |
Factors associated with influenza vaccination rates among health care workers.
Reasons for accepting or refusing influenza vaccination
Among the 92 studies included, 47 studies reported on the reasons why healthcare workers chose to get vaccinated against influenza, while 55 studies reported on the reasons for refusing vaccination. The comprehensive data are summarized in Table 4, providing insights into the factors that influenced healthcare workers’ decisions to either receive or decline influenza vaccination.
Table 4
| Reasons for refusing | Reference(n) | Reasons for accepting | Reference(n) |
|---|---|---|---|
| 1. The vaccine is considered to have poor or limited preventive effect | 43 | 1. Protect myself | 30 |
| 2. Concerns about adverse reactions or vaccine quality | 38 | 2. Protect my family, patients, and people around me | 26 |
| 3. I’m too busy at work to have time | 33 | 3. Worried about spreading it to the people around me | 19 |
| 4. They are considered to be in good physical condition or have strong immunity and do not need vaccination | 23 | 4. Vaccines are free or cheap | 17 |
| 5. Think the flu is mild and will not cause serious illness | 21 | 5. The vaccine is considered effective in preventing influenza and its complications | 16 |
| 6. Vaccines are out-of-pocket or too expensive | 20 | 6. Consider myself at high risk for the flu and its complications | 13 |
| 7. Vaccinations are inconvenient or lacking | 19 | 7. A work organization or employer requires or performs professional obligations | 13 |
| 8. There are contraindications to vaccination | 16 | 8. Recommended or influenced by leaders, colleagues, relatives and friends | 12 |
| 9. Do not know about influenza vaccination and related information | 14 | 9. Vaccination sites are available or readily available in the workplace | 11 |
| 10. Not considered to be at high risk of catching the flu | 14 | 10. That flu is a serious illness with serious effects | 10 |
| 11. Adverse reactions after vaccination (e.g., flu-like symptoms, pain at injection site) | 12 | 11. Avoid infection affecting my work | 8 |
| 12. Not knowing when and where to get flu shots | 11 | 12. It is recommended by government health authorities or the technical guidelines for influenza vaccines | 7 |
| 13. Forget to vaccinate | 11 | 13. Old age, underlying disease or chronic disease, fear of complications after infection | 7 |
| 14. Fear of injection | 8 | 14. Believe in the safety of flu vaccines | 4 |
| 15. It is considered easy to treat with drugs or prevent with hygiene measures or other drugs | 8 | 15. Doctor’s recommendation | 3 |
| 16. Concerned about the safety of vaccines | 7 | 16. I had the flu last season | 3 |
| 17. Being pregnant or lactating | 7 | 17. Participate in multidisciplinary campaigns or influenza vaccination campaigns | 2 |
| 18. Requires annual vaccinations or immunization procedures | 5 | 18. Have a history of influenza vaccination | 2 |
| 19. Vaccination is not mandatory or recommended by the workplace | 5 | 19. Familiarize with flu vaccination | 1 |
| 20. Does not believe in or oppose vaccination | 5 | 20. Flu infections take an economic toll | 1 |
| 21. Personal choice, reduce drug use | 4 | ||
| 22. There is no awareness of getting the flu vaccine | 1 | ||
| 23. Had the flu this year and do not need to get vaccinated | 1 |
Self-reported reasons for accepting or refusing influenza vaccination in healthcare workers.
Discussion
The present study encompasses a broad range of countries, including 26 nations across 7 different regions. The meta-analysis findings indicate a relatively low global influenza vaccination rate among healthcare personnel, estimated at 41.7%. Subgroup analysis reveals a notable disparity between developed and developing countries, with higher vaccination rates observed in the former. Among regional subgroups, the Americas exhibit the highest vaccination rate, followed by the Middle East, Oceania, and Europe, while Africa demonstrates the lowest rate. These results suggest that variations in socio-economic development, vaccine accessibility, cost, healthcare service standards, healthcare personnel’s knowledge regarding influenza and influenza vaccines, as well as disparities in awareness of preventive healthcare and vaccination, contribute to the observed differences in influenza vaccination rates across countries. This is consistent with a previous report, which highlights that while Chinese clinical workers possess extensive knowledge about disease diagnosis and treatment, their understanding of health maintenance and disease prevention is comparatively lacking (22).
Subgroup analysis based on vaccination time reveals that rate is gradually increased over the period of 14 years, suggesting that the H1N1 influenza pandemic in 2009 and the subsequent COVID-19 epidemic have played a role in promoting the seasonal influenza vaccination rate among healthcare personnel, likely due to increased awareness of the contagious nature of these diseases (95, 99). However the influenza vaccination rate gradually declined since 2009 pandemic, which aligns with the decreasing impact of the influenza outbreak. However, the occurrence of the COVID-19 epidemic led to a surge in the influenza vaccination, reaching its highest level. This could be attributed to heightened focus on self-protection during the influenza season, increased awareness of the importance of influenza vaccines, and a general promotion of vaccination practices.
The analysis of influencing factors reveals that several characteristics contribute to the higher likelihood of healthcare personnel receiving influenza vaccinations, including age, tenure, education level, professional designation (clinical doctors compared to nurses), and their inclination to recommend influenza vaccines to patients. These findings are in line with studies conducted in China (21, 22) and Cyprus (92), which similarly indicate that doctors are more likely to be vaccinated compared to nurses. This discrepancy may be due to doctors increased exposure to influenza patients due to their longer experience in the field, resulting in a stronger sense of identification as a high-risk group for influenza infection. Consequently, doctors exhibit heightened attention and awareness regarding influenza-related knowledge and information on influenza vaccines.
A study conducted in Spain focused on healthcare personnel in the armed forces, the proportion of vaccinated individuals increased with age and years of service in the 2016–2017 season, but the vaccination rate among younger/middle-ranking officers actually surpassed that of the older adult, indicating a notable shift in vaccination behavior in the 2019–2020 season (93). Such outcome could be attributed to the evolving health knowledge system, which now places greater emphasis on disease prevention and health maintenance. In another survey conducted among nurses in North-eastern China, showing an inverse correlation between vaccination and flu among nurses, maybe due to lack of knowledge among these nurses regarding influenza vaccines, necessitating further education and awareness campaigns to emphasize the importance of vaccination.
Our present findings offer valuable insights for promoting flu vaccination, particularly among healthcare workers. This may involve strategies such as cost reduction or even the implementation of mandatory vaccination policies for specific high-risk population groups. Furthermore, our current data could serve as a foundation for future studies and investments in healthcare worker well-being. Our data underscores the critical importance of flu vaccination for these healthcare workers, who often find themselves in more vulnerable conditions, serving both the older adult and other high-risk groups. This relevance is further emphasized by the ongoing threat of viral mutation and the persistence of long-term consequences from COVID-19, even though it is no longer classified as a pandemic. Hence, our present data strongly underscores the critical importance of flu vaccination for healthcare workers, especially those in more vulnerable roles, such as caring for the older adult and other high-risk groups. This relevance is further accentuated by the context of the ongoing COVID-19 outbreak, even if it is no longer considered a pandemic. The continuous viral mutation and the lingering presence of long-term COVID-19 complications make this vigilance particularly vital.
In conclusion, the influenza vaccination rate among healthcare workers globally remains low. To address this issue effectively, it is crucial to implement comprehensive measures that promote influenza vaccination among this population, as well as the general public. Efforts should be focused on raising awareness about the importance of vaccination, providing accessible and convenient vaccination services, and enhancing education regarding influenza and its prevention. By implementing these measures, we can strive to improve the influenza vaccination rates among healthcare workers and the wider population, leading to better overall public health outcomes.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
Author contributions
JF: Conceptualization, Funding acquisition, Supervision, Writing – original draft, Writing – review & editing. SX: Data curation, Formal analysis, Writing – original draft. YL: Data curation, Visualization, Writing – review & editing. XM: Data curation, Formal analysis, Software, Writing – review & editing. JC: Data curation, Formal analysis, Investigation, Writing – review & editing. CF: Conceptualization, Project administration, Validation, Writing – review & editing. SB: Conceptualization, Project administration, Writing – review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The study was supported by Health Industry Scientific Research Management Project of Gansu Province [Grant No. GSWSKY2021-058], Wu Jieping Medical Foundation Project [Grant No. 320.6750.2022-20-21] and Natural Science Foundation of Gansu Province (22JR5RA589).
Acknowledgments
We are grateful to the institutions and individuals who have helped us throughout the research process.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1295464/full#supplementary-material
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Summary
Keywords
influenza vaccine, vaccination rate, healthcare workers, influencing factors, meta-analysis
Citation
Fan J, Xu S, Liu Y, Ma X, Cao J, Fan C and Bao S (2023) Influenza vaccination rates among healthcare workers: a systematic review and meta-analysis investigating influencing factors. Front. Public Health 11:1295464. doi: 10.3389/fpubh.2023.1295464
Received
16 September 2023
Accepted
19 October 2023
Published
06 November 2023
Volume
11 - 2023
Edited by
Aida Bianco, University Magna Graecia of Catanzaro, Italy
Reviewed by
Marco Del Riccio, University of Florence, Italy; Maria Gabriella Verso, University of Palermo, Italy
Updates
Copyright
© 2023 Fan, Xu, Liu, Ma, Cao, Fan and Bao.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Shisan Bao, profbao@hotmail.comChunling Fan, 306686224@qq.com
†These authors have contributed equally to this work and share first authorship
Disclaimer
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