Examining factors associated with vaccine hesitancy against Covid-19 among adults in Europe: a secondary analysis of cross-sectional survey data

Examining factors associated with vaccine hesitancy against Covid-19 among adults in Europe: a secondary analysis of cross-sectional survey data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Examining factors associated with vaccine hesitancy against Covid-19 among adults in Europe: a secondary analysis of cross-sectional survey data Megumi Nagase This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3490587/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2024 Read the published version in BMC Research Notes → Version 1 posted 8 You are reading this latest preprint version Abstract Objective This study aimed to explore the factors associated with Covid-19 vaccine hesitancy in Europe among adults by using the Socio-Ecological Model. Results This cross-sectional study used secondary data collected from respondents residing in 27 EU countries at the time of May 2021. The outcome was vaccine hesitancy against Covid-19, and the total sample size of 23,606 was analysed by binary logistic regression, as well as McKelvey and Zavonoia’s R 2 . After adding each level of variables, the model found the significant and increased association with vaccine hesitancy in younger age groups (21-39 years and 40-60 years vs 65 years+), who left full-time education at a young age (16-19 years), those with manual jobs, those with children at home, individuals residing in small towns, and norms related to the vaccine. Together, the levels explained 49.4% of the variance associated with vaccine hesitancy, and the addition to each variable layer increased the variance. This highlights the need to consider broad factors at multiple levels to enhance vaccine acceptance and uptake. vaccine hesitancy Europe Socio-Ecological Model Covid-19 SARS-CoV-2 Introduction As of October 18 st 2023, more than 771 million people worldwide have been infected with SARS-CoV-2, and nearly 6.9 million people have died due to this highly infectious disease(1). In March 2020, soon after the declaration of the pandemic by the World Health Organization (WHO), Italy emerged as a major hot spot with the second-largest number of confirmed cases in the world (2, 3). After that, Spain and France recorded dramatic increases in new confirmed cases and deaths (4). In response to the disastrous spread of Covid-19 across Europe, the WHO recognized that, with the exception of China, Europe had become the new epicentre of the pandemic, with more reported cases and deaths than the rest of the world combined (4). Despite the urgent need to prevent the further spread of Covid-19, however, European countries faced persistent vaccine hesitancy and anti-vaccine movement. According to a report from Eurofound (5) published in April 2021, 27% of European Union (EU) adult residents were vaccine-hesitant as they stated that they were either “very unlikely” or “rather unlikely” to receive the Covid-19 vaccine. Given these considerations, and the continuous need to tackle vaccinate hesitancy for the next disease outbreak, it is imperative to further examine the attitude among people in high-income countries such as Europe and the potential drivers of both their willingness and unwillingness to be vaccinated against Covid-19. Thus, to gain a better understanding of Covid-19 vaccine hesitancy, this study aims to explore the factors associated with individuals’ vaccine hesitancy against Covid-19 among adults in Europe. Methods Theoretical considerations This study applied the Socio-Ecological Model (SEM), which proposes that individual behaviour is shaped by factors at different levels ranging from people to groups to their socio-physical milieus (6). The most commonly used SEM (7) comprises five different socio-ecological layers: the intrapersonal, interpersonal, institutional, community, and policy levels. However, we adopted the four-level model that is used by the Centers of Disease Control and Prevention (8), which includes the constructs that are relevant to the dataset. In this model, the they start at the individual level and spread out to the relationship , community , and societal levels. Data source and study design The primarily used source was cross-sectional survey data from Flash Eurobarometer 494: Attitudes on vaccination against Covid-19 published by the European Commission in July 2021 (9). All the respondents were aged 15 years and over. With the exception of Luxembourg, Cyprus, and Malta, each of the 27 EU countries included a sample size of approximately 1,000 citizens. After the removal of missing values and the untargeted population group aged between 15 and 19, the final sample size was set to 23,606. In addition to that, the dataset combined external sources to expand the variety of societal variables based on the respondent´s country of origin. Those added variables include the country´s gross national income (GNI) per capita (10), the most commonly accepted religion (11), political spectrum (12), and geographical region of the country (13). Outcome variables The outcome variable of interest was Covid-19 vaccine hesitancy. To determine whether the respondent is vaccine-hesitant or not, this study used the WHO definition that articulates vaccine hesitancy as “a delay in acceptance or refusal of vaccines despite availability of vaccine services.” Based on that, responses to the question “When would you like to get vaccinated?” were categorized as follows: those who answered with “sometime in 2021,” “later,” “don’t know,” and “never” were classified as vaccine-hesitant group, while responses such as “I have already been vaccinated” and “as soon as possible” were placed in the vaccine-acceptant group. Exposure variables Exposure variables consist of four groups; individual, relationship, community, and societal levels. The individual category included each respondent’s gender, age, age when they stopped full-time education, and occupational status. In addition, questions that reflect their beliefs and attitudes regarding getting vaccinated were included as an extension variable at the individual level, such as whether they agree or disagree on “All in all, the benefits of Covid-19 vaccines outweigh the possible risks," or “You can avoid being infected by Covid-19 without being vaccinated." Overall, those questions were considered to project respondents´ perceived benefits, safety and utility of the vaccine and their subjective civic norm about getting vaccinated. The number of people aged 15 years and older and the number of children younger than 15 in the household were identified as the relationship variables. The community level of variable examined respondents’ type of residence. Finally, the societal level considered their country’s cultural, social, and political characteristics as mentioned in the section of date source above. Statistical analysis Stata version 17 was employed for analysis, and statistical significance was defined as 0.05 and 0.01. All variables, including dependent and independent variables, were categorized (Table 1). Unweighted univariate analysis, Pearson´s chi-square test, and multivariate binary logistic regression were performed to assess the association between the outcome variables and explanatory variables. These levels were introduced in a stepwise regression model. Finally, the analysis reported the value of McKelvey-Zavoina pseudo-R 2 (14, 15) each level of the logistic regression model to investigate the variance in the Covid-19 vaccine uptake explained by each level of variables. While in logistic regression, pseudo-R 2 does not estimate the variance explained by variables as it does in ordinary least squares (OLS) regression, the McKelvey-Zavoina R 2 provides a value that can be interpreted in a way similar to the OLS regression R 2 (14-16). Results Table 1 presents the results of unweighted univariate analysis and illustrates the characteristics of the respondents. Overall, 32.3% of respondents were found as vaccine-hesitant. The proportions of females and males were close to equal, and the largest age group was 40-64 years, accounting for 45.9% of the total respondents. Over half of them finished their full-time education at age of 20 or older, and nearly 30% completed it between ages 16 and 19. The largest occupational group was employee group, which consisted half of the respondents, followed by unemployed (30.5%), self-employed (11.8%), and manual worker groups(5.9%). Regarding the individuals’ beliefs, 79.1% of respondents believed in the benefits and safety of the Covid-19 vaccine. However, only 51.6% believed in the efficacy of the vaccine. Furthermore, 60.1% of the respondents believed that getting vaccinated against Covid-19 is a civic duty. Most respondents (70.7%) lived in a household without children under 15 years. Bivariate analysis was stratified by vaccine uptake attitude. All the listed variables showed correlations with the outcomes, with a p-value of less than 0.01, except the community level (p-value = 0.017). In the vaccine-hesitant group, there was a slightly higher proportion of females (53.6%), and the 21-39 age group displayed the highest rate of vaccine hesitancy (49.3%). In contrast, 86.2% of respondents in the oldest age group (65 years and above) expressed willingness to be vaccinated. The number of adults and children in the household also correlated with vaccine hesitancy. The smaller proportion of households without children under 15 years showed vaccine hesitancy at 27.6%, but this changed when people lived with one child (40.6%) or more than two children (41.8 %), as they became more hesitant to the vaccine. Concerning societal factors, vaccine hesitancy was higher in the lower-income group (37.2%) than in the higher-income group (23.8%). The different religious groups from orthodox-dominant countries showed a particularly high tendency to be vaccine hesitant, amounting to almost half of the group. In addition, the proportion of vaccine hesitancy was almost twice as common among respondents from the eastern region (42.7%) compared to those from the western region (24.5%). Table 2 provides the findings from the binary logistic regression analyses along with the McKelvey and Zavoina’s R 2 . The unadjusted odds ratio (OR) showed that the following factors were consistently linked to a higher likelihood of vaccine hesitancy when compared to the reference group: (I) age groups 40-64 years and 21-39 years, (II) those who stopped full-time education between ages 16 and 19, (II) people employed in a manual work, (IV) individuals who disagreed with the benefits, safety, and norms associated with vaccination, (V) households with more than one child, (VI) individuals residing in small towns. Moreover, with decreasing age group, the odds of vaccine hesitancy increased with compared to the 65+ age group. Although the analysis indicated that the presence of children in the household showed a statistical significance, the odds of vaccine hesitancy changed only minimal with increasing number of children in the household. This implies that the presence of child itself is the significant factor, rather than the number of children in a household. At the societal level, variables such as lower GNI, protestant or orthodox groups, and residence in eastern Europe emerged as robust underlying factors associated with a higher likelihood of vaccine hesitancy. Conversely, two-adult households, individuals currently pursuing education, and political spectrum showed no statistical significance in any of the analytical models. Employee status and disagreement with the efficacy of vaccination consistently showed a negative association with the outcome. The belief variables had the greatest R 2 (R 2 =0.423), followed by other individual demographic variables (R 2 =0.108), societal-level variables (R 2 =0.074), and relationship-level variables (R 2 =0.026). The smallest effect was observed at the community level (R 2 =0.001). Additionally, starting at the individual level and adding to each layer of variables resulted in an increase in the variance explained in relation to the outcome. Discussion This study examined the factors associated with Covid-19 vaccine hesitancy among adults in Europe by using the SEM. The findings of this analysis demonstrated that younger age, residence in small towns, completion of full-time education between the ages of 16 and 19, manual worker status, disagreement with the benefits, safety and norms related to vaccination, and the presence of children in the household were significantly linked to vaccine hesitancy against Covid-19. The consistent contribution of younger populations and residing in small towns to vaccine hesitancy corresponded with existing literature focused on the attitude in high-income countries, including Germany, Austria and France (17-22). However, it is worth noting that although this study did not show a consistent association between the education and the outcome, existing studies have indeed found that having a shorter education period, such as a high school education or less, has a significant association with vaccine hesitancy (17, 19, 23-25). This discrepancy might be because the variable does not necessarily reflect the respondents’ educational level or their schooling years in the data set, as other studies often do. The analysed results also highlights that the perceived benefits, safety, efficacy, and norms of getting vaccinated against Covid-19 had the most potent effect on an individual’s vaccine uptake attitude, among other variables (19, 21). This strong effect of people´s perceptions regarding the vaccine underscores the importance of psychological factors and effective health communication strategies to counteract misinformation when tackling vaccine hesitancy in a large population. Finally, the findings emphasize that belief factors and individual-level variables have a statistically significant association with the vaccine hesitancy. At the same time, other levels of variables, including relationship-, community-, and societal-level variables, also demonstrate either entire or partial significance. However, when looking at the difference in R 2 at each level, the effects of each level varied, with beliefs having the greatest effect, followed by individual demographical variables, then the societal, relationship, and community levels. The varying effects at each level would be contingent on the available data and the methodology employed in selecting and categorizing the data into each level. Conclusion The reluctance of a considerable number of individuals to be vaccinated can significantly disrupt population efforts to achieve herd immunity (26, 27), increasing the risk of the next outbreak in future (28). When developing effective measures to enhance vaccine acceptance and uptake, the results of this study suggest that factors at various levels surrounding individuals determine vaccine hesitancy. Therefore, accounting for the factors across multiple levels is crucial to boost vaccine acceptance and uptake. Limitations This study finds two major limitations in its analysis. First, only unweighted analysis was performed. Secondly, clustering issues were not accounted when gathering the respondents from 27 different countries into one large. List of abbreviations WHO World Health Organization SEM Socio-Ecological Model GNI Gross National Income OLS Ordinary Least Squares OR Odds ratio MN Megumi Nagase Declarations Ethics approval and consent to participate The study did not require ethical approval as it used publicly available data. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analysed during the current study are available in the GESIS repository, https://search.gesis.org/research_data/ZA7771 Competing interests The authors declare that they have no competing interests. Funding Not applicable Authors' contributions Not applicable Acknowledgements This study was produced and submitted as part of the requirements for the degree of Master of Public Health at New York University, School of Global Public Health in May 2022. The author expresses gratitude to Dr. David Abramson, PhD, MPH (New York University) for supervising the research process. The author is also grateful to Professor Ralf Weigel, MD, MSc, PhD (Witten/Herdecke University) for reviewing this work. Authors´ contributions MN conceived this work, analyzed the data, and wrote the manuscript. References World Health Organization. COVID-19 vaccine tracker and landscape 2021 [Available from: https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines. Saglietto A, D'Ascenzo F, Zoccai GB, De Ferrari GM. COVID-19 in Europe: the Italian lesson. Lancet. 2020;395(10230):1110-1. Russo L, Anastassopoulou C, Tsakris A, Bifulco GN, Campana EF, Toraldo G, et al. Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach. PLoS One. 2020;15(10):e0240649. BBC. Coronavirus: Europe now epicentre of the pandemic, says WHO 2020 [Available from: https://www.bbc.com/news/world-europe-51876784. Eurofound. Living, working and COVID-19 (Update April 2021): Mental health and trust decline across EU as pandemic enters another year. 2021. Urie Bronfenbrenner. Ecological systems theory Jessica Kingsley Publishers; 1992. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351-77. Centers for Disease Control and Prevention. The Social-Ecological Model: A Framework for Prevention [Available from: https://www.cdc.gov/violenceprevention/about/social-ecologicalmodel.html. European Comission. Eurobarometer 494: Attitudes on vaccination against Covid-19. 2021. World Bank. GNI per capita, PPP - (current international $) European Union 2020 [Available from: https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD?locations=EU. Central Intelligence Agency. The World Factbook [Available from: https://www.cia.gov/the-world-factbook/. University Carlos III of Madrid. EU Political Barometer 2022 [Available from: https://eupoliticalbarometer.uc3m.es/. University of Minnesota Libraries. World Regional Geography: People, Places and Globalization2016. Kumar S, Quinn SC, Kim KH, Musa D, Hilyard KM, Freimuth VS. The social ecological model as a framework for determinants of 2009 H1N1 influenza vaccine uptake in the United States. Health Educ Behav. 2012;39(2):229-43. Richard D. McKelvey ZW. A statistical model for the analysis of ordinal level dependent variables. The Journal of Mathematical Sociology. 1975:103-20. UCLA Statisctial Methods and Data Analytics. FAQ: What are pseudo R squares? 2011 [Available from: https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/. Yasmin F, Najeeb H, Moeed A, Naeem U, Asghar MS, Chughtai NU, et al. COVID-19 Vaccine Hesitancy in the United States: A Systematic Review. Front Public Health. 2021;9:770985. El-Far Cardo A, Kraus T, Kaifie A. Factors That Shape People's Attitudes towards the COVID-19 Pandemic in Germany-The Influence of MEDIA, Politics and Personal Characteristics. Int J Environ Res Public Health. 2021;18(15). Aw J, Seng JJB, Seah SSY, Low LL. COVID-19 Vaccine Hesitancy-A Scoping Review of Literature in High-Income Countries. Vaccines (Basel). 2021;9(8). Schernhammer E, Weitzer J, Laubichler MD, Birmann BM, Bertau M, Zenk L, et al. Correlates of COVID-19 vaccine hesitancy in Austria: trust and the government. J Public Health (Oxf). 2022;44(1):e106-e16. Al-Jayyousi GF, Sherbash MAM, Ali LAM, El-Heneidy A, Alhussaini NWZ, Elhassan MEA, et al. Factors Influencing Public Attitudes towards COVID-19 Vaccination: A Scoping Review Informed by the Socio-Ecological Model. Vaccines (Basel). 2021;9(6). Detoc M, Bruel S, Frappe P, Tardy B, Botelho-Nevers E, Gagneux-Brunon A. Intention to participate in a COVID-19 vaccine clinical trial and to get vaccinated against COVID-19 in France during the pandemic. Vaccine. 2020;38(45):7002-6. Kempe A, Saville AW, Albertin C, Zimet G, Breck A, Helmkamp L, et al. Parental Hesitancy About Routine Childhood and Influenza Vaccinations: A National Survey. Pediatrics. 2020;146(1). Reno C, Maietti E, Fantini MP, Savoia E, Manzoli L, Montalti M, et al. Enhancing COVID-19 Vaccines Acceptance: Results from a Survey on Vaccine Hesitancy in Northern Italy. Vaccines (Basel). 2021;9(4). Robertson E, Reeve KS, Niedzwiedz CL, Moore J, Blake M, Green M, et al. Predictors of COVID-19 vaccine hesitancy in the UK household longitudinal study. Brain Behav Immun. 2021;94:41-50. Kennedy J. Vaccine Hesitancy: A Growing Concern. Paediatr Drugs. 2020;22(2):105-11. Dubé E, Laberge C, Guay M, Bramadat P, Roy R, Bettinger J. Vaccine hesitancy: an overview. Hum Vaccin Immunother. 2013;9(8):1763-73. Hall V, Banerjee E, Kenyon C, Strain A, Griffith J, Como-Sabetti K, et al. Measles Outbreak - Minnesota April-May 2017. MMWR Morb Mortal Wkly Rep. 2017;66(27):713-7. Tables Table 1. Unweighted univariate analysis results of data from the Flash Eurobarometer 494: Attitudes on vaccination against Covid-19 (July 2021), N=23,606 Individual Level N ( %) Total 23,606 (100) Gender Male 11,438 (48.56) Female 12,115 (51.44) Age 21 – 39 8,342 (35.34) 40 – 64 10,840 (45.92) 65 and over 4,424 (18.74) Age when stopped full-time education Up to 15 years 756 (3.49) 16 – 19 7,446 (34.36) 20 years + 11,672 (53.86) Currently studying 1,797 (8.29) Occupational status Self-employed 2,663 (11.79) Employee 11,695 (51.80) Manual worker 1,331 (5.89) Unemployed 6,890 (30.52) Individual- beliefs “Benefits of Covid vaccines outweigh possible risks” Agree 17,430 (79.05) Disagree 4,619 (20.95) "You can avoid being infected by Covid-19 without being vaccinated" Agree 11,019 (51.56) Disagree 10,351 (48.44) "Everyone should get vaccinated against Covid-19, it is a civic duty" Agree 13,634 (60.61) Disagree 8,859 (39.39) Relationship Level # of adults aged ≥ 15 in a household 1 5,614 (25.20) 2 10,108 (45.37) 3+ 6,558 (29.43) # of children aged <15 in a household 0 15,823 (70.70) 1 3,812 (17.03) 2+ 2,745 (12.27) Community Level Type of residence Rural area 5,798 (24.56) Small or medium-sized town 9,441 (39.99) Large town/city 8,367 (35.44) Total 23,606 (100) Societal Level GNI per capita Above EU average 8,521 (36.10) Below EU average 15,085 (63.90) Religion Catholic 13,837 (58.62) Protestant 3,660 (15.50) Orthodox 2,364 (10.01) Unspecified or None 3,745 (15.86) Political spectrum Centre left 4,707 (19.94) Centre right 4,879 (20.67) Right 14,020 (59.39) Geographical region Western Europe 13,262 (56.18) Eastern Europe 10,344 (43.82) Outcome Vaccine-hesitant 7,125 (32.27) Vaccine-acceptant 14,953 (67.73) Table 2. Multivariate binary logistic regression analysis results and McKelvey and Zavoina’s R 2 , Flash Eurobarometer 494: Attitudes on vaccination against Covid-19 (July 2021) M1 M2 M3 M4 M5 (full) Unadjusted OR Individual Gender Male (ref) (ref) (ref) (ref) (ref) (ref) Female 1.15** 1.05 1.07 1.07 1.08 1.19** Age (years) 65+ (ref) (ref) (ref) (ref) (ref) (ref) 40-64 2.77** 2.24** 2.09** 2.10** 2.08** 2.66** 21-39 5.72** 4.12** 3.76** 3.76** 3.75** 5.40** Education age 20+ (ref) (ref) (ref) (ref) (ref) (ref) 16-19 1.27** 1.16* 1.16* 1.16* 1.19* 1.16** Up to 15 1.16 1.17** 1.10 1.11 1.33 0.92 Studying 0.91 0.98 0.93 0.93 0.98 1.34** Occupation Unemployed (ref) (ref) (ref) (ref) (ref) (ref) Employee 0.90* 0.87* 0.83* 0.83* 0.83* 1.46** Self-employed 1.28** 1.15 1.13 1.13 1.05 1.80** Manual worker 1.66** 1.53** 1.43** 1.43* 1.26* 2.93** Beliefs Benefits/safety Agree (ref) (ref) (ref) (ref) (ref) Disagree 6.11** 6.38** 6.40** 6.33** 6.45** Efficacy Agree (ref) (ref) (ref) (ref) (ref) Disagree 0.42** 0.43** 0.43** 0.43** 0.39** Subjective norm Agree (ref) (ref) (ref) (ref) (ref) Disagree 4.99** 5.00** 5.01** 4.69** 5.13** Relationship # of adults in a household 1 (ref) (ref) (ref) (ref) 2 1.00 1.00 0.98 0.95 3+ 1.15* 1.16* 1.05 1.23** # of children in a household 0 (ref) (ref) (ref) (ref) 1 1.31** 1.31** 1.24** 1.79** 2+ 1.30** 1.30** 1.28** 1.88** Community Residence Large city (ref) (ref) (ref) Small town 1.31** 1.16* 1.10** Rural area 1.30** 1.13 1.88** M1 M2 M3 M4 M5 (full) Unadjusted OR Societal GNI Above EU average (ref) (ref) Below EU average 1.22* 1.90** Religion Unspecified (ref) (ref) Protestant 1.44** 1.10 Catholic 1.11 1.09* Orthodox 2.69** 2.40** Political spectrum Right (ref) (ref) Centre-right 0.91 1.10* Centre-left 1.02 1.14** Region Western Europe (ref) (ref) Eastern Europe 1.56** 2.30** R 2 for each level 0.108 0.423 0.026 0.001 0.074 - Accumulated R2 - 0.465 0.470 0.471 0.494 - N Observations 19,583 16,899 16,128 16,128 16,128 - * p-value <0.05, ** p-value <0.01 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2024 Read the published version in BMC Research Notes → Version 1 posted Editorial decision: Revision requested 17 Nov, 2023 Reviews received at journal 10 Nov, 2023 Reviewers agreed at journal 03 Nov, 2023 Reviewers invited by journal 30 Oct, 2023 Editor invited by journal 30 Oct, 2023 Editor assigned by journal 30 Oct, 2023 Submission checks completed at journal 30 Oct, 2023 First submitted to journal 25 Oct, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3490587","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":244079154,"identity":"c446a75f-34c1-4397-87f8-a08629a9fee3","order_by":0,"name":"Megumi Nagase","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYNACAxijgoGBDYaI1HKGaC0wwNgGpvBr0W1vvyZ1o4Ahj79/8eMPH+cdzuNj4DF7wFBmg1OL2ZkzZdI5BgzFEjeemUnO3Ha4mI2Bx9yA4Vwabi03ctJAWhIbbhwwY+bddjixTf7tNgnGtsO4tdx/A9Ey/8bxz5//zgFqYeAFafmPxxb2Y2AtG873GEgzNsC1HMDjlxxm6xwDicSNN3jKJHuOpQO18H+TSDiXjFvL8eMPb+f8sUmcd/745g8/aqwT5zewpUl8KLPDqYWBgQcUjxJAlIAkmIBdLRSwP4DQ/LhdPwpGwSgYBSMcAACrs1U30e6IZwAAAABJRU5ErkJggg==","orcid":"","institution":"Witten/Herdecke University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Megumi","middleName":"","lastName":"Nagase","suffix":""}],"badges":[],"createdAt":"2023-10-25 13:14:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3490587/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3490587/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13104-024-06739-2","type":"published","date":"2024-03-19T15:03:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53403797,"identity":"a434a362-aa75-43c1-aea0-0500a4f50c9f","added_by":"auto","created_at":"2024-03-25 15:14:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":321326,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3490587/v1/a4b04c82-b7e4-4fb9-827b-e4e2227075c0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Examining factors associated with vaccine hesitancy against Covid-19 among adults in Europe: a secondary analysis of cross-sectional survey data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs of October 18\u003csup\u003est\u003c/sup\u003e 2023, more than 771 million people worldwide have been infected with SARS-CoV-2, and nearly 6.9 million people have died due to this highly infectious disease(1). \u003c/p\u003e\n\u003cp\u003eIn March 2020, soon after the declaration of the pandemic by the World Health Organization (WHO), Italy emerged as a major hot spot with the second-largest number of confirmed cases in the world (2, 3). After that, Spain and France recorded dramatic increases in new confirmed cases and deaths (4). In response to the disastrous spread of Covid-19 across Europe, the WHO recognized that, with the exception of China, Europe had become the new epicentre of the pandemic, with more reported cases and deaths than the rest of the world combined (4).\u003c/p\u003e\n\u003cp\u003eDespite the urgent need to prevent the further spread of Covid-19, however, European countries faced persistent vaccine hesitancy and anti-vaccine movement. According to a report from Eurofound (5) published in April 2021, 27% of European Union (EU) adult residents were vaccine-hesitant as they stated that they were either \u0026ldquo;very unlikely\u0026rdquo; or \u0026ldquo;rather unlikely\u0026rdquo; to receive the Covid-19 vaccine. Given these considerations, and the continuous need to tackle vaccinate hesitancy for the next disease outbreak, it is imperative to further examine the attitude among people in high-income countries such as Europe and the potential drivers of both their willingness and unwillingness to be vaccinated against Covid-19. Thus, to gain a better understanding of Covid-19 vaccine hesitancy, this study aims to explore the factors associated with individuals\u0026rsquo; vaccine hesitancy against Covid-19 among adults in Europe.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eTheoretical \u003c/em\u003e\u003cem\u003econsiderations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study applied the Socio-Ecological Model (SEM), which proposes that individual behaviour is shaped by factors at different levels ranging from people to groups to their socio-physical milieus (6). The most commonly used SEM (7) comprises five different socio-ecological layers: the intrapersonal, interpersonal, institutional, community, and policy levels. However, we adopted the four-level model that is used by the Centers of Disease Control and Prevention (8), which includes the constructs that are relevant to the dataset. In this model, the they start at the \u003cem\u003eindividual \u003c/em\u003elevel and spread out to the \u003cem\u003erelationship\u003c/em\u003e, \u003cem\u003ecommunity\u003c/em\u003e, and \u003cem\u003esocietal \u003c/em\u003elevels.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData source and study design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe primarily used source was cross-sectional survey data from Flash Eurobarometer 494: Attitudes on vaccination against Covid-19\u003csup\u003e \u003c/sup\u003epublished by the European Commission in July 2021 (9). All the respondents were aged 15 years and over. With the exception of Luxembourg, Cyprus, and Malta, each of the 27 EU countries included a sample size of approximately 1,000 citizens. After the removal of missing values and the untargeted population group aged between 15 and 19, the final sample size was set to 23,606. In addition to that, the dataset combined external sources to expand the variety of societal variables based on the respondent\u0026acute;s country of origin. Those added variables include the country\u0026acute;s gross national income (GNI) per capita (10), the most commonly accepted religion (11), political spectrum (12), and geographical region of the country (13).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcome variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome variable of interest was Covid-19 vaccine hesitancy. To determine whether the respondent is vaccine-hesitant or not, this study used the WHO definition that articulates vaccine hesitancy as \u0026ldquo;a delay in acceptance or refusal of vaccines despite availability of vaccine services.\u0026rdquo; Based on that, responses to the question \u0026ldquo;When would you like to get vaccinated?\u0026rdquo; were categorized as follows: those who answered with \u0026ldquo;sometime in 2021,\u0026rdquo; \u0026ldquo;later,\u0026rdquo; \u0026ldquo;don\u0026rsquo;t know,\u0026rdquo; and \u0026ldquo;never\u0026rdquo; were classified as vaccine-hesitant group, while responses such as \u0026ldquo;I have already been vaccinated\u0026rdquo; and \u0026ldquo;as soon as possible\u0026rdquo; were placed in the vaccine-acceptant group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExposure variables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eExposure variables consist of four groups; individual, relationship, community, and societal levels.\u003c/p\u003e\n\u003cp\u003eThe individual category included each respondent\u0026rsquo;s gender, age, age when they stopped full-time education, and occupational status. In addition, questions that reflect their beliefs and attitudes regarding getting vaccinated were included as an extension variable at the individual level, such as whether they agree or disagree on \u0026ldquo;All in all, the benefits of Covid-19 vaccines outweigh the possible risks,\u0026quot; or \u0026ldquo;You can avoid being infected by Covid-19 without being vaccinated.\u0026quot; Overall, those questions were considered to project respondents\u0026acute; perceived benefits, safety and utility of the vaccine and their subjective civic norm about getting vaccinated. The number of people aged 15 years and older and the number of children younger than 15 in the household were identified as the relationship variables. The community level of variable examined respondents\u0026rsquo; type of residence. Finally, the societal level considered their country\u0026rsquo;s cultural, social, and political characteristics as mentioned in the section of date source above. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStata version 17 was employed for analysis, and statistical significance was defined as 0.05 and 0.01. All variables, including dependent and independent variables, were categorized (Table 1). Unweighted univariate analysis, Pearson\u0026acute;s chi-square test, and multivariate binary logistic regression were performed to assess the association between the outcome variables and explanatory variables. These levels were introduced in a stepwise regression model. Finally, the analysis reported the value of McKelvey-Zavoina pseudo-R\u003csup\u003e2\u003c/sup\u003e (14, 15) each level of the logistic regression model to investigate the variance in the Covid-19 vaccine uptake explained by each level of variables. While in logistic regression, pseudo-R\u003csup\u003e2\u003c/sup\u003e does not estimate the variance explained by variables as it does in ordinary least squares (OLS) regression, the McKelvey-Zavoina R\u003csup\u003e2\u003c/sup\u003e provides a value that can be interpreted in a way similar to the OLS regression R\u003csup\u003e2\u003c/sup\u003e (14-16).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eTable 1\u003c/u\u003e presents the results of unweighted univariate analysis and illustrates the characteristics of the respondents. Overall, 32.3% of respondents were found as vaccine-hesitant. The proportions of females and males were close to equal, and the largest age group was 40-64 years, accounting for 45.9% of the total respondents. Over half of them finished their full-time education at age of 20 or older, and nearly 30% completed it between ages 16 and 19. The largest occupational group was employee group, which consisted half of the respondents, followed by unemployed (30.5%), self-employed (11.8%), and manual worker groups(5.9%). Regarding the individuals\u0026rsquo; beliefs, 79.1% of respondents believed in the benefits and safety of the Covid-19 vaccine. However, only 51.6% believed in the efficacy of the vaccine. Furthermore, 60.1% of the respondents believed that getting vaccinated against Covid-19 is a civic duty. Most respondents (70.7%) lived in a household without children under 15 years.\u003c/p\u003e\n\u003cp\u003eBivariate analysis was stratified by vaccine uptake attitude. All the listed variables showed correlations with the outcomes, with a p-value of less than 0.01, except the community level (p-value = 0.017). In the vaccine-hesitant group, there was a slightly higher proportion of females (53.6%), and the 21-39 age group displayed the highest rate of vaccine hesitancy (49.3%). In contrast, 86.2% of respondents in the oldest age group (65 years and above) expressed willingness to be vaccinated. The number of adults and children in the household also correlated with vaccine hesitancy. The smaller proportion of households without children under 15 years showed vaccine hesitancy at 27.6%, but this changed when people lived with one child (40.6%) or more than two children (41.8 %), as they became more hesitant to the vaccine. Concerning societal factors, vaccine hesitancy was higher in the lower-income group (37.2%) than in the higher-income group (23.8%). The different religious groups from orthodox-dominant countries showed a particularly high tendency to be vaccine hesitant, amounting to almost half of the group. In addition, the proportion of vaccine hesitancy was almost twice as common among respondents from the eastern region (42.7%) compared to those from the western region (24.5%).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eTable 2\u003c/u\u003eprovides the findings from the binary logistic regression analyses along with the McKelvey and Zavoina\u0026rsquo;s R\u003csup\u003e2\u003c/sup\u003e. The unadjusted odds ratio (OR) showed that the following factors were consistently linked to a higher likelihood of vaccine hesitancy when compared to the reference group: (I) age groups 40-64 years and 21-39 years, (II) those who stopped full-time education between ages 16 and 19, (II) people employed in a manual work, (IV) individuals who disagreed with the benefits, safety, and norms associated with vaccination, (V) households with more than one child, (VI) individuals residing in small towns. Moreover, with decreasing age group, the odds of vaccine hesitancy increased with compared to the 65+ age group. Although the analysis indicated that the presence of children in the household showed a statistical significance, the odds of vaccine hesitancy changed only minimal with increasing number of children in the household. This implies that the presence of child itself is the significant factor, rather than the number of children in a household. At the societal level, variables such as lower GNI, protestant or orthodox groups, and residence in eastern Europe emerged as robust underlying factors associated with a higher likelihood of vaccine hesitancy.\u003c/p\u003e\n\u003cp\u003eConversely, two-adult households, individuals currently pursuing education, and political spectrum showed no statistical significance in any of the analytical models. Employee status and disagreement with the efficacy of vaccination consistently showed a negative association with the outcome.\u003c/p\u003e\n\u003cp\u003eThe belief variables had the greatest R\u003csup\u003e2\u003c/sup\u003e (R\u003csup\u003e2\u003c/sup\u003e=0.423), followed by other individual demographic variables (R\u003csup\u003e2\u003c/sup\u003e=0.108), societal-level variables (R\u003csup\u003e2\u003c/sup\u003e=0.074), and relationship-level variables (R\u003csup\u003e2\u003c/sup\u003e=0.026). The smallest effect was observed at the community level (R\u003csup\u003e2\u003c/sup\u003e=0.001). Additionally, starting at the individual level and adding to each layer of variables resulted in an increase in the variance explained in relation to the outcome.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the factors associated with Covid-19 vaccine hesitancy among adults in Europe by using the SEM. The findings of this analysis demonstrated that younger age, residence in small towns, completion of full-time education between the ages of 16 and 19, manual worker status, disagreement with the benefits, safety and norms related to vaccination, and the presence of children in the household were significantly linked to vaccine hesitancy against Covid-19.\u003c/p\u003e\n\u003cp\u003eThe consistent contribution of younger populations and residing in small towns to vaccine hesitancy corresponded with existing literature focused on the attitude in high-income countries, including Germany, Austria and France (17-22). However, it is worth noting that although this study did not show a consistent association between the education and the outcome, existing studies have indeed found that having a shorter education period, such as a high school education or less, has a significant association with vaccine hesitancy (17, 19, 23-25). This discrepancy might be because the variable does not necessarily reflect the respondents\u0026rsquo; educational level or their schooling years in the data set, as other studies often do.\u003c/p\u003e\n\u003cp\u003eThe analysed results also highlights that the perceived benefits, safety, efficacy, and norms of getting vaccinated against Covid-19 had the most potent effect on an individual\u0026rsquo;s vaccine uptake attitude, among other variables (19, 21). This strong effect of people\u0026acute;s perceptions regarding the vaccine underscores the importance of psychological factors and effective health communication strategies to counteract misinformation when tackling vaccine hesitancy in a large population.\u003c/p\u003e\n\u003cp\u003eFinally, the findings emphasize that belief factors and individual-level variables have a statistically significant association with the vaccine hesitancy. At the same time, other levels of variables, including relationship-, community-, and societal-level variables, also demonstrate either entire or partial significance. However, when looking at the difference in R\u003csup\u003e2\u003c/sup\u003e at each level, the effects of each level varied, with beliefs having the greatest effect, followed by individual demographical variables, then the societal, relationship, and community levels. The varying effects at each level would be contingent on the available data and the methodology employed in selecting and categorizing the data into each level.\u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThe reluctance of a considerable number of individuals to be vaccinated can significantly disrupt population efforts to achieve herd immunity (26, 27), increasing the risk of the next outbreak in future (28). When developing effective measures to enhance vaccine acceptance and uptake, the results of this study suggest that factors at various levels surrounding individuals determine vaccine hesitancy. Therefore, accounting for the factors across multiple levels is crucial to boost vaccine acceptance and uptake.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study finds two major limitations in its analysis. First, only unweighted analysis was performed. Secondly, clustering issues were not accounted when gathering the respondents from 27 different countries into one large.\u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.686746987951807%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;WHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.3132530120482%\" valign=\"top\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.686746987951807%\" valign=\"top\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.3132530120482%\" valign=\"top\"\u003e\n \u003cp\u003eSocio-Ecological Model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.686746987951807%\" valign=\"top\"\u003e\n \u003cp\u003eGNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.3132530120482%\" valign=\"top\"\u003e\n \u003cp\u003eGross National Income\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.686746987951807%\" valign=\"top\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.3132530120482%\" valign=\"top\"\u003e\n \u003cp\u003eOrdinary Least Squares\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.686746987951807%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.3132530120482%\" valign=\"top\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.686746987951807%\" valign=\"top\"\u003e\n \u003cp\u003eMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.3132530120482%\" valign=\"top\"\u003e\n \u003cp\u003eMegumi Nagase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study did not require ethical approval as it used publicly available data.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the GESIS repository, https://search.gesis.org/research_data/ZA7771\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis study was produced and submitted as part of the requirements for the degree of Master of Public Health at New York University, School of Global Public Health in May 2022. The author expresses gratitude to Dr. David Abramson, PhD, MPH (New York University) for supervising the research process. The author is also grateful to Professor Ralf Weigel, MD, MSc, PhD (Witten/Herdecke University) for reviewing this work.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026acute; contributions\u003c/p\u003e\n\u003cp\u003eMN conceived this work, analyzed the data, and wrote the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. COVID-19 vaccine tracker and landscape 2021 [Available from: https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines.\u003c/li\u003e\n\u003cli\u003eSaglietto A, D\u0026apos;Ascenzo F, Zoccai GB, De Ferrari GM. COVID-19 in Europe: the Italian lesson. Lancet. 2020;395(10230):1110-1.\u003c/li\u003e\n\u003cli\u003eRusso L, Anastassopoulou C, Tsakris A, Bifulco GN, Campana EF, Toraldo G, et al. Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach. PLoS One. 2020;15(10):e0240649.\u003c/li\u003e\n\u003cli\u003eBBC. Coronavirus: Europe now epicentre of the pandemic, says WHO 2020 [Available from: https://www.bbc.com/news/world-europe-51876784.\u003c/li\u003e\n\u003cli\u003eEurofound. Living, working and COVID-19 (Update April 2021): Mental health and trust decline across EU as pandemic enters another year. 2021.\u003c/li\u003e\n\u003cli\u003eUrie Bronfenbrenner. Ecological systems theory Jessica Kingsley Publishers; 1992.\u003c/li\u003e\n\u003cli\u003eMcLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351-77.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. The Social-Ecological Model: A Framework for Prevention [Available from: https://www.cdc.gov/violenceprevention/about/social-ecologicalmodel.html.\u003c/li\u003e\n\u003cli\u003eEuropean Comission. Eurobarometer 494: Attitudes on vaccination against Covid-19. 2021.\u003c/li\u003e\n\u003cli\u003eWorld Bank. GNI per capita, PPP - (current international $) European Union 2020 [Available from: https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD?locations=EU.\u003c/li\u003e\n\u003cli\u003eCentral Intelligence Agency. The World Factbook [Available from: https://www.cia.gov/the-world-factbook/.\u003c/li\u003e\n\u003cli\u003eUniversity Carlos III of Madrid. EU Political Barometer 2022 [Available from: https://eupoliticalbarometer.uc3m.es/.\u003c/li\u003e\n\u003cli\u003eUniversity of Minnesota Libraries. World Regional Geography: People, Places and Globalization2016.\u003c/li\u003e\n\u003cli\u003eKumar S, Quinn SC, Kim KH, Musa D, Hilyard KM, Freimuth VS. The social ecological model as a framework for determinants of 2009 H1N1 influenza vaccine uptake in the United States. Health Educ Behav. 2012;39(2):229-43.\u003c/li\u003e\n\u003cli\u003eRichard D. McKelvey ZW. A statistical model for the analysis of ordinal level dependent variables. The Journal of Mathematical Sociology. 1975:103-20.\u003c/li\u003e\n\u003cli\u003eUCLA Statisctial Methods and Data Analytics. FAQ: What are pseudo R squares? 2011 [Available from: https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/.\u003c/li\u003e\n\u003cli\u003eYasmin F, Najeeb H, Moeed A, Naeem U, Asghar MS, Chughtai NU, et al. COVID-19 Vaccine Hesitancy in the United States: A Systematic Review. Front Public Health. 2021;9:770985.\u003c/li\u003e\n\u003cli\u003eEl-Far Cardo A, Kraus T, Kaifie A. Factors That Shape People\u0026apos;s Attitudes towards the COVID-19 Pandemic in Germany-The Influence of MEDIA, Politics and Personal Characteristics. Int J Environ Res Public Health. 2021;18(15).\u003c/li\u003e\n\u003cli\u003eAw J, Seng JJB, Seah SSY, Low LL. COVID-19 Vaccine Hesitancy-A Scoping Review of Literature in High-Income Countries. Vaccines (Basel). 2021;9(8).\u003c/li\u003e\n\u003cli\u003eSchernhammer E, Weitzer J, Laubichler MD, Birmann BM, Bertau M, Zenk L, et al. Correlates of COVID-19 vaccine hesitancy in Austria: trust and the government. J Public Health (Oxf). 2022;44(1):e106-e16.\u003c/li\u003e\n\u003cli\u003eAl-Jayyousi GF, Sherbash MAM, Ali LAM, El-Heneidy A, Alhussaini NWZ, Elhassan MEA, et al. Factors Influencing Public Attitudes towards COVID-19 Vaccination: A Scoping Review Informed by the Socio-Ecological Model. Vaccines (Basel). 2021;9(6).\u003c/li\u003e\n\u003cli\u003eDetoc M, Bruel S, Frappe P, Tardy B, Botelho-Nevers E, Gagneux-Brunon A. Intention to participate in a COVID-19 vaccine clinical trial and to get vaccinated against COVID-19 in France during the pandemic. Vaccine. 2020;38(45):7002-6.\u003c/li\u003e\n\u003cli\u003eKempe A, Saville AW, Albertin C, Zimet G, Breck A, Helmkamp L, et al. Parental Hesitancy About Routine Childhood and Influenza Vaccinations: A National Survey. Pediatrics. 2020;146(1).\u003c/li\u003e\n\u003cli\u003eReno C, Maietti E, Fantini MP, Savoia E, Manzoli L, Montalti M, et al. Enhancing COVID-19 Vaccines Acceptance: Results from a Survey on Vaccine Hesitancy in Northern Italy. Vaccines (Basel). 2021;9(4).\u003c/li\u003e\n\u003cli\u003eRobertson E, Reeve KS, Niedzwiedz CL, Moore J, Blake M, Green M, et al. Predictors of COVID-19 vaccine hesitancy in the UK household longitudinal study. Brain Behav Immun. 2021;94:41-50.\u003c/li\u003e\n\u003cli\u003eKennedy J. Vaccine Hesitancy: A Growing Concern. Paediatr Drugs. 2020;22(2):105-11.\u003c/li\u003e\n\u003cli\u003eDub\u0026eacute; E, Laberge C, Guay M, Bramadat P, Roy R, Bettinger J. Vaccine hesitancy: an overview. Hum Vaccin Immunother. 2013;9(8):1763-73.\u003c/li\u003e\n\u003cli\u003eHall V, Banerjee E, Kenyon C, Strain A, Griffith J, Como-Sabetti K, et al. Measles Outbreak - Minnesota April-May 2017. MMWR Morb Mortal Wkly Rep. 2017;66(27):713-7.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Unweighted univariate analysis results of data from the Flash Eurobarometer 494: Attitudes on vaccination against Covid-19 (July 2021), N=23,606\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIndividual\u0026nbsp;Level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003eN\u0026nbsp;(\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e23,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e11,438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(48.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e12,115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(51.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e21\u0026nbsp;\u0026ndash; 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e8,342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(35.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e40\u0026nbsp;\u0026ndash; 64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e10,840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(45.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e65\u0026nbsp;and over\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e4,424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(18.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;when\u0026nbsp;stopped\u0026nbsp;full-time education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eUp\u0026nbsp;to\u0026nbsp;15\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e16\u0026nbsp;\u0026ndash; 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e7,446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(34.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e20\u0026nbsp;years\u0026nbsp;+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e11,672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(53.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eCurrently\u0026nbsp;studying\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e1,797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(8.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e2,663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(11.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eEmployee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e11,695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(51.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eManual\u0026nbsp;worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e1,331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(5.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e6,890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(30.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndividual-\u0026nbsp;beliefs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ldquo;Benefits\u0026nbsp;of\u0026nbsp;Covid\u0026nbsp;vaccines\u0026nbsp;outweigh\u0026nbsp;possible\u0026nbsp;risks\u0026rdquo;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e17,430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(79.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e4,619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(20.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026quot;You\u0026nbsp;can\u0026nbsp;avoid\u0026nbsp;being\u0026nbsp;infected\u0026nbsp;by Covid-19\u0026nbsp;without\u0026nbsp;being vaccinated\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e11,019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(51.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e10,351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(48.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026quot;Everyone\u0026nbsp;should\u0026nbsp;get vaccinated\u0026nbsp;against Covid-19,\u0026nbsp;it\u0026nbsp;is\u0026nbsp;a\u0026nbsp;civic\u0026nbsp;duty\u0026quot;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e13,634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(60.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e8,859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(39.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRelationship\u0026nbsp;Level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e#\u0026nbsp;of\u0026nbsp;adults aged\u0026nbsp;\u0026ge;\u0026nbsp;15\u0026nbsp;in\u0026nbsp;a household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e5,614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(25.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e10,108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(45.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e6,558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(29.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e# of\u0026nbsp;children\u0026nbsp;aged\u0026nbsp;\u0026lt;15\u0026nbsp;in a\u0026nbsp;household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e15,823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(70.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e3,812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(17.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e2,745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(12.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCommunity\u0026nbsp;Level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eRural\u0026nbsp;area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e5,798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(24.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSmall\u0026nbsp;or\u0026nbsp;medium-sized\u0026nbsp;town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e9,441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(39.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.210191082802545%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLarge\u0026nbsp;town/city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.05095541401274%\" valign=\"top\"\u003e\n \u003cp\u003e8,367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e(35.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e23,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSocietal Level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNI\u0026nbsp;per capita\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eAbove\u0026nbsp;EU\u0026nbsp;average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e8,521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(36.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eBelow\u0026nbsp;EU\u0026nbsp;average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e15,085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(63.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eCatholic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e13,837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(58.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eProtestant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3,660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(15.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eOrthodox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2,364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(10.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eUnspecified\u0026nbsp;or\u0026nbsp;None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3,745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(15.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolitical\u0026nbsp;spectrum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eCentre\u0026nbsp;left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4,707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(19.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eCentre\u0026nbsp;right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4,879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(20.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e14,020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(59.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical\u0026nbsp;region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eWestern\u0026nbsp;Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e13,262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(56.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eEastern\u0026nbsp;Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e10,344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(43.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eOutcome\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eVaccine-hesitant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e7,125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(32.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.1592356687898089%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.89171974522293%\" valign=\"top\"\u003e\n \u003cp\u003eVaccine-acceptant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.21019108280255%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e14,953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.420382165605096%\" valign=\"top\"\u003e\n \u003cp\u003e(67.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.3184713375796178%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Multivariate binary logistic regression analysis results and McKelvey and Zavoina\u0026rsquo;s R\u003csup\u003e2\u003c/sup\u003e, Flash Eurobarometer 494: Attitudes on vaccination against Covid-19 (July 2021)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003eM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003eM5\u0026nbsp;(full)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003eUnadjusted\u0026nbsp;OR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIndividual\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e1.15**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.19**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e40-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e2.77**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e2.24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e2.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e2.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e2.08**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e2.66**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e21-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e5.72**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e4.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e3.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e3.76**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e3.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e5.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u0026nbsp;age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e20+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e16-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e1.27**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e1.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.16**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eUp\u0026nbsp;to 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e1.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eStudying\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.34**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eEmployee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e0.90*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e0.87*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e0.83*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e0.83*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e0.83*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eSelf-employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e1.28**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.80**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eManual\u0026nbsp;worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e1.66**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e1.53**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.43*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.26*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e2.93**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBeliefs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenefits/safety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e6.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e6.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e6.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e6.33**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e6.45**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEfficacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e0.42**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e0.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e0.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubjective\u0026nbsp;norm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eAgree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eDisagree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e4.99**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e5.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e5.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e4.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e5.13**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRelationship\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e# of adults in a\u0026nbsp;household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.15*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.23**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e# of children in a\u0026nbsp;household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.24**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.79**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.28**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.88**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCommunity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eLarge city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eSmall town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.263482280431433%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u0026nbsp;area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.559322033898304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.01848998459168%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.480739599383668%\" valign=\"top\"\u003e\n \u003cp\u003e1.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.097072419106317%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.10015408320493%\" valign=\"top\"\u003e\n \u003cp\u003e1.88**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003eM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eM5\u0026nbsp;(full)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eUnadjusted\u0026nbsp;OR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSocietal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Above\u0026nbsp;EU\u0026nbsp;average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eBelow\u0026nbsp;EU average\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e1.22*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.90**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Protestant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e1.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Catholic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Orthodox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e2.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e2.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolitical spectrum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Centre-right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.10*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Centre-left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e1.14**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eAccumulated\u0026nbsp;R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003eN\u0026nbsp;Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.846153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e19,583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003e16,899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e16,128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e16,128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e16,128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.076923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* p-value \u0026lt;0.05, ** p-value \u0026lt;0.01\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"vaccine hesitancy, Europe, Socio-Ecological Model, Covid-19, SARS-CoV-2","lastPublishedDoi":"10.21203/rs.3.rs-3490587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3490587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective\u003c/p\u003e\n\u003cp\u003eThis study aimed to explore the factors associated with Covid-19 vaccine hesitancy in Europe among adults by using the Socio-Ecological Model.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study used secondary data collected from respondents residing in 27 EU countries at the time of May 2021. The outcome was vaccine hesitancy against Covid-19, and the total sample size of 23,606 was analysed by binary logistic regression, as well as McKelvey and Zavonoia’s R\u003csup\u003e2\u003c/sup\u003e. After adding each level of variables, the model found the significant and increased association with vaccine hesitancy in younger age groups (21-39 years and 40-60 years vs 65 years+), who left full-time education at a young age (16-19 years), those with manual jobs, those with children at home, individuals residing in small towns, and norms related to the vaccine. Together, the levels explained 49.4% of the variance associated with vaccine hesitancy, and the addition to each variable layer increased the variance. This highlights the need to consider broad factors at multiple levels to enhance vaccine acceptance and uptake.\u003c/p\u003e","manuscriptTitle":"Examining factors associated with vaccine hesitancy against Covid-19 among adults in Europe: a secondary analysis of cross-sectional survey data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-01 11:56:40","doi":"10.21203/rs.3.rs-3490587/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2023-11-17T13:37:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-11-10T10:37:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39041fe7-e42b-4320-8f0b-235e074399c1","date":"2023-11-03T08:13:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-10-30T16:52:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-10-30T16:46:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-10-30T14:40:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-10-30T14:40:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2023-10-25T13:02:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4ec3303-4df3-436c-9e74-7a681dbbf9f2","owner":[],"postedDate":"November 1st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-25T15:09:26+00:00","versionOfRecord":{"articleIdentity":"rs-3490587","link":"https://doi.org/10.1186/s13104-024-06739-2","journal":{"identity":"bmc-research-notes","isVorOnly":false,"title":"BMC Research Notes"},"publishedOn":"2024-03-19 15:03:03","publishedOnDateReadable":"March 19th, 2024"},"versionCreatedAt":"2023-11-01 11:56:40","video":"","vorDoi":"10.1186/s13104-024-06739-2","vorDoiUrl":"https://doi.org/10.1186/s13104-024-06739-2","workflowStages":[]},"version":"v1","identity":"rs-3490587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3490587","identity":"rs-3490587","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}