Adherence to Vaccination Policy among Public Health Professionals: Results of a National Survey in Italy

• Main Authors: Maria Teresa Montagna, Osvalda De Giglio, Christian Napoli, Fabrizio Fasano, Giusy Diella, Rosalba Donnoli, Giuseppina Caggiano, Silvio Tafuri, Pier Luigi Lopalco, Antonella Agodi, GISIO-SItI Working Group
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Vaccines
• Subjects: public health professional; healthcare workers; knowledge; recommended vaccinations; attitude; vaccination coverage
• Online Access: https://www.mdpi.com/2076-393X/8/3/379

Starting from 2013, the number of unvaccinated people alarmingly increased in Italy; therefore, in 2017 a new Vaccine National Plan was approved. Healthcare workers (HCWs), especially public health professionals (PHPs, i.e., workers in in the sector of hygiene and preventive medicine), have an important role in informing and promoting vaccinations. In this context, the Italian Study Group of Hospital Hygiene of the Italian Society of Hygiene, Preventive Medicine and Public Health (GISIO-SItI) conducted a national survey to assess knowledge, attitude, and practices towards recommended vaccinations among PHPs. The survey was conducted during October 2019 with an anonymous questionnaire distributed to PHPs attending the 52° SItI National Congress. Overall, 57.1% of operators answered correctly to all seven recommended vaccinations, 12.8% reported to be vaccinated for all seven recommended vaccinations, while 30% were naturally immunized. A higher immunization coverage was reported for anti-hepatitis B (88.9%) and measles (86.1%), and 81.3% of the participants reported being offered the influenza vaccination during the 2018/2019 season. The majority of our sample indicated that hepatitis B (95%) and influenza (93.7%) were the recommended vaccines for HCWs, while less was known regarding varicella, pertussis, diphtheria, and tetanus boosters every 10 years. PHPs who were vaccinated (or who intended to be vaccinated) were more likely to recommend vaccinations to their patients and provided a reassuring example to those hesitant patients. Finally, this is the first study that identified good algorithms (using the techniques of machine learning as Random Forest and Deep Learning) to predict the knowledge of PHPs regarding recommended vaccinations with possible applications in other national and international contexts.