Evidence map›Paper›PMID 36679877›Full record

ArticleVaccines2022

Vaccination Strategies against Seasonal Influenza in Long Term Care Setting: Lessons from a Mathematical Modelling Study.

Matteo Ratti, Diego Concina, Maurizio Rinaldi, Ernesto Salinelli, Agnese Maria Di Brisco, Daniela Ferrante, Alessandro Volpe, Massimiliano Panella

Abstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Modeling the Impacts of Antiviral Prophylaxis Strategies in Mitigating Seasonal Influenza Outbreaks in Nursing Homes.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Matteo RattiDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy.ORCID 0000-0002-9273-9114
Diego ConcinaDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy.
Maurizio RinaldiDepartment of Pharmaceutical Science (DSF), Università del Piemonte Orientale, 28100 Novara, Italy.ORCID 0000-0003-1346-1634
Ernesto SalinelliDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy.ORCID 0000-0002-9591-7289
Agnese Maria Di BriscoDepartment of Studies for Economics and Business (DiSEI), Università del Piemonte Orientale, 28100 Novara, Italy.ORCID 0000-0003-4439-9589
Daniela FerranteDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy.ORCID 0000-0003-4929-3759
Alessandro VolpeDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy.
Massimiliano PanellaDepartment of Translational Medicine (DiMeT), Università del Piemonte Orientale, 28100 Novara, Italy.ORCID 0000-0003-1880-7198

Funding

University of Eastern Piedmont Amadeo Avogadro FAR-2019 Local funds for research
6 · The paper itself

Abstract

backgroundseasonal influenza in nursing homes is a major public health concern, since in EU 43,000 long term care (LTC) facilities host an estimated 2.9 million elderly residents. Despite specific vaccination campaigns, many outbreaks in such institutions are occasionally reported. We explored the dynamics of seasonal influenza starting from real data collected from a nursing home located in Italy and a mathematical model. Our aim was to identify the best vaccination strategy to minimize cases (and subsequent complications) among the guests. MATERIALS AND

methodsafter producing the contact matrices with surveys of both the health care workers (HCW) and the guests, we developed a mathematical model of the disease. The model consists of a classical SEIR part describing the spreading of the influenza in the general population and a stochastic agent based model that formalizes the dynamics of the disease inside the institution. After a model fit of a baseline scenario, we explored the impact of varying the HCW and guests parameters (vaccine uptake and vaccine efficacy) on the guest attack rates (AR) of the nursing home.

resultsthe aggregate AR of influenza like illness in the nursing home was 36.4% (ward1 = 56%, ward2 = 33.3%, ward3 = 31.7%, ward4 = 34.5%). The model fit to data returned a probability of infection of the causal contact of 0.3 and of the shift change contact of 0.2. We noticed no decreasing or increasing AR trend when varying the HCW vaccine uptake and efficacy parameters, whereas the increase in both guests vaccine efficacy and uptake parameter was accompanied by a slight decrease in AR of all the wards of the LTC facility.

conclusionfrom our findings we can conclude that a nursing home is still an environment at high risk of influenza transmission but the shift change room and the handover situation carry no higher relative risk. Therefore, additional preventive measures in this circumstance may be unnecessary. In a closed environment such as a LTC facility, the vaccination of guests, rather than HCWs, may still represent the cornerstone of an effective preventive strategy. Finally, we think that the extensive inclusion of real life data into mathematical models is promising and may represent a starting point for further applications of this methodology.

Indexed as

health care workersinfluenzalong term care facilitiesmathematical modelling of infectious diseases

Identifiers

PMID36679877
PMCPMC9861048

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.