Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data.
Background: 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,...
| Publicado en: | Nursing Reports Vol. 15; no. 3; pp. 87 - 102 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184102314&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184102314 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2039439X EGNT jtl: Nursing Reports issn: 2039439X maglogo: N pubinfo: dt: Mar2025 vid: 15 iid: 3 pid: 97109 pub: MDPI artinfo: ui: 184102314 184102314 184102314 10.3390/nursrep15030087 184102314 ppf: 87 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Influenza Vaccination of Nurses and Other Health Care Workers in Different Occupational Settings: A Classic and AI Mixed Approach for Time-to-Event Data. aug: au: Ratti, Matteo Rescinito, Riccardo Gigante, Domenico Lontano, Alberto Panella, Massimiliano affil: Department of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy sug: subj: Nursing Staff, Hospital Health Personnel Influenza Prevention and Control Influenza Vaccine Therapeutic Use Work Environment Tertiary Health Care Human Nonexperimental Studies Retrospective Design Cox Proportional Hazards Model Data Analysis Software Confidence Intervals Descriptive Statistics Age Factors Occupational Safety ab: Background: 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. Materials and Methods: 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. Results: 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. Conclusions: Our results suggest a prioritization policy based firstly on the unit typology followed by the job typology for HCW anti-influenza campaigns. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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