Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data
<b>Background:</b> Seasonal influenza currently remains a major public health concern for the community and, in particular, the health care worker (HCW). According to the World Health Organization, HCWs are among the high-risk categories for which vaccination is recommended, due to the d...
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MDPI AG
2025-03-01
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| author | Matteo Ratti Riccardo Rescinito Domenico Gigante Alberto Lontano Massimiliano Panella |
| author_facet | Matteo Ratti Riccardo Rescinito Domenico Gigante Alberto Lontano Massimiliano Panella |
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| description | <b>Background:</b> Seasonal influenza currently remains a major public health concern for the community and, in particular, the health care worker (HCW). According to the World Health Organization, HCWs are among the high-risk categories for which vaccination is recommended, due to the derived absenteeism, productivity loss, and high probability of transmitting the disease to vulnerable individuals or patients. Therefore, an HCW vaccination policy should be adopted by every health care provider. There is growing evidence that a time effect of the vaccination event is probable, which may influence vaccine effectiveness. We designed and conducted an observational study to investigate the time to anti-influenza vaccination event of different categories of HCWs belonging to different occupational settings in a tertiary hospital during three seasons in order to retrieve some insight about HCW prioritization when designing vaccination campaigns. <b>Materials and Methods:</b> We retrospectively analyzed the results of two HCW anti-influenza vaccination campaigns (2022 and 2023) to assess any difference regarding job typology and unit typology (critical care, surgical, medical, service). We first fitted a classic Cox proportional hazard model and then an AI random forest model to assess variable importance. We used R, RStudio, and the survex package. <b>Results:</b> Overall, other HCWs reported a lower vaccination rate compared to nurses (HR 0.77; 95%CI 0.62–0.97), and service unit personnel appeared to more likely be vaccinated (HR 1.42; 95%CI 1.01–1.99) compared to those belonging to the critical care units. As expected, older workers tended to be vaccinated more frequently (HR 1.70 for the (46, 65] category compared to the younger one; 95%CI 1.39–2.09). The variable importance analysis showed consistent superiority of the ward typology and age category variables with respect to time. During the entire timeline, the ward typology appeared to be more important than the HCW typology. <b>Conclusions:</b> Our results suggest a prioritization policy based firstly on the unit typology followed by the job typology for HCW anti-influenza campaigns. |
| format | Article |
| id | doaj-art-ee60e208b414407b987e72a46bc22daa |
| institution | OA Journals |
| issn | 2039-439X 2039-4403 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Nursing Reports |
| spelling | doaj-art-ee60e208b414407b987e72a46bc22daa2025-08-20T01:48:49ZengMDPI AGNursing Reports2039-439X2039-44032025-03-011538710.3390/nursrep15030087Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event DataMatteo Ratti0Riccardo Rescinito1Domenico Gigante2Alberto Lontano3Massimiliano Panella4Department of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, ItalyDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, ItalyDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, ItalyAzienda Ospedaliero Universitaria Maggiore della Carità, 28100 Novara, ItalyDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy<b>Background:</b> Seasonal influenza currently remains a major public health concern for the community and, in particular, the health care worker (HCW). According to the World Health Organization, HCWs are among the high-risk categories for which vaccination is recommended, due to the derived absenteeism, productivity loss, and high probability of transmitting the disease to vulnerable individuals or patients. Therefore, an HCW vaccination policy should be adopted by every health care provider. There is growing evidence that a time effect of the vaccination event is probable, which may influence vaccine effectiveness. We designed and conducted an observational study to investigate the time to anti-influenza vaccination event of different categories of HCWs belonging to different occupational settings in a tertiary hospital during three seasons in order to retrieve some insight about HCW prioritization when designing vaccination campaigns. <b>Materials and Methods:</b> We retrospectively analyzed the results of two HCW anti-influenza vaccination campaigns (2022 and 2023) to assess any difference regarding job typology and unit typology (critical care, surgical, medical, service). We first fitted a classic Cox proportional hazard model and then an AI random forest model to assess variable importance. We used R, RStudio, and the survex package. <b>Results:</b> Overall, other HCWs reported a lower vaccination rate compared to nurses (HR 0.77; 95%CI 0.62–0.97), and service unit personnel appeared to more likely be vaccinated (HR 1.42; 95%CI 1.01–1.99) compared to those belonging to the critical care units. As expected, older workers tended to be vaccinated more frequently (HR 1.70 for the (46, 65] category compared to the younger one; 95%CI 1.39–2.09). The variable importance analysis showed consistent superiority of the ward typology and age category variables with respect to time. During the entire timeline, the ward typology appeared to be more important than the HCW typology. <b>Conclusions:</b> Our results suggest a prioritization policy based firstly on the unit typology followed by the job typology for HCW anti-influenza campaigns.https://www.mdpi.com/2039-4403/15/3/87seasonalinfluenzavaccinationartificial intelligencehealth care workersoccupational safety |
| spellingShingle | Matteo Ratti Riccardo Rescinito Domenico Gigante Alberto Lontano Massimiliano Panella Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data Nursing Reports seasonalinfluenza vaccination artificial intelligence health care workers occupational safety |
| title | Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data |
| title_full | Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data |
| title_fullStr | Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data |
| title_full_unstemmed | Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data |
| title_short | Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data |
| title_sort | influenza vaccination of nurses and other health care workers in different occupational settings a classic and ai mixed approach for time to event data |
| topic | seasonalinfluenza vaccination artificial intelligence health care workers occupational safety |
| url | https://www.mdpi.com/2039-4403/15/3/87 |
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