Vaccine Hesitancy and Immunization Patterns in Central and Eastern Europe: Sociocultural, Economic, Political, and Digital Influences Across Seven Countries
Bibliographic record
Abstract
Donata Kurpas,1 Dorota Stefanicka–Wojtas,1 Aneta Soll–Morka,1,2 Katarzyna Lomper,1 Bartosz Uchmanowicz,1 Beata Blahova,3 Aelita Bredelytė,4 Gheorghe Gindrovel Dumitra,5 Vladimíra Hudáčková,6 Katerina Javorska,7 Zoltán Juhász,8 Stanisław Manulik,1 András Mohos,8,9 Egidijus Skarbalius,10 Victoria I Tkachenko,11 Izabella Uchmanowicz1 1Department of Nursing, Wroclaw Medical University, Wrocław, Poland; 2Department of Family Medicine and Public Health, University of Opole, Opole, Poland; 3EURIPA, Slovak Society of General Practitioners, Krompachy, Slovak Republic; 4Department of Nursing, Klaipeda University, Klaipeda, Lithuania; 5Family Medicine Department, University of Medicine and Pharmacy of Craiova, Craiova, Romania; 6The London School of Hygiene and Tropical Medicine, University of London, London, UK; 7Department of Preventive Medicine, Charles University, Hradec Králové, Czech Republic; 8Department of Family Medicine, University of Szeged, Albert Szent-Györgyi Medical School, Szeged, Hungary; 9Department of Family Medicine, Semmelweis University, Budapest, Hungary; 10Department of Public Health, Klaipeda University, Klaipeda, Lithuania; 11Department of Internal Medicine and Training Centre of Family Medicine, Bogomolets National Medical University, Kyiv, UkraineCorrespondence: Dorota Stefanicka–Wojtas, Email dorota.stefanicka-wojtas@umw.edu.plBackground/Objectives: Vaccination programs are essential for preventing infectious diseases, yet the effectiveness of these programs varies significantly across Central and Eastern European countries due to diverse socio-economic, cultural, and political influences. This study examines vaccination trends in Hungary, Slovakia, Romania, the Czech Republic, Poland, Ukraine, and Lithuania, focusing on misinformation, regional healthcare disparities, and socio-cultural factors on vaccination rates.Methods: A comprehensive review of national policies, vaccination rates, and factors influencing vaccine hesitancy was conducted across seven Central and Eastern European countries. Input from local health stakeholders and national data sources was analysed to contextualize vaccination patterns and challenges.Results: Significant cross-country variation was observed. Hungary and the Czech Republic reported consistently high coverage of mandatory childhood vaccinations, while Romania and Ukraine experienced severe declines in uptake, leading to outbreaks of measles and polio. Slovakia demonstrated low COVID-19 vaccination willingness, and Poland recorded a sharp increase in formal vaccine refusals. Conversely, Lithuania implemented successful campaigns that improved uptake, particularly for influenza and pneumococcal vaccines. Differences were influenced by healthcare system structure, public trust, exposure to misinformation, and digital communication strategies.Conclusion: Addressing vaccine hesitancy requires targeted, context-sensitive communication and digital literacy programs. Additionally, policy reforms to enhance accessibility, particularly in rural areas, and real-time monitoring systems can strengthen vaccination rates. Cross-border collaboration and tailored public health campaigns addressing cultural and socio-economic challenges are necessary to improve immunization coverage in these regions.Keywords: vaccination trends, vaccine hesitancy, public health, Central and Eastern Europe
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".