The impact of COVID-19 first wave on long term care facilities of an Italian Province: an historical reference

  1. Andrea Tramarin
  2. Nicola Gennaro
  3. Giancarlo Dal Grande
  4. Luciana Bragagnolo
  5. Maria Rosa Carta
  6. Davide Giavarina
  7. Michela Pascarella
  8. Mario Rassu
  9. Antonio Matteazzi
  10. Giampaolo Stopazzolo

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The coronavirus disease 2019 (COVID-19) pandemic will leave a profound imprint in the collective memory of humanity. In Italy, Long-Term Care Facilities (LTCFs) have seen a disproportionally high number of deaths during and the COVID-19 pandemic and, certainly, they may be considered as its epicenter. Aiming to leave a symbolic mark of what the pandemic did in these care settings, we report on an outbreak in a single LTCF where, 53 out of 64 residents, resulted infected. Our narration is based on an epidemiological field investigation together with a calendar of passages through the stages of disease in the infected population. We found an age-gradient in all clinical and epidemiological variables explored such as symptoms onset, illness severity, recovery from symptoms and deaths. According to the disease staging, 26 (49%) were asymptomatic; 9 (17%) had a mild disease; 7 (13%) a moderate stage and 11 (21%) a severe illness severity of whom 10 died. For a more comprehensive description of the impact of the pandemic on LTCFs, we compared the standard mortality ratio (SMR) in the first six months of 2020 to that of 2018 and 2019 in all the 34 facilities of the Vicenza province. Overall, there was a SMR higher 60% than the equivalent period of the previous years.

Article activity feed

  1. Version published to 10.4081/gc.2021.9654
  2. ScreenIT

    SciScore for 10.1101/2020.10.21.20216705: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, using data with the caveats that definitions used and difficulties in comparing data, it has been estimated that the share of total COVID-19 associated deaths in residents of LTCFs is 47% (based on 26 countries) [15]. In Ontario, Canada, the population in LTCFs represented over 80% of deaths from probable cases of COVID-19 [16]. A report based on different sources of data and from different regions of the world estimated a CFR of 14.8% in the age class > 80. [17]. The SMR calculated in the subset of LTCFs of the Vicenza Province is up to 60%. The comparison of CFR and age of death between the group of 8 LTCF and the CSF were similar. The median age of deceased residents was 90 years old in the 9 LTCF and 92 years old in the SCF, the CFR was 23.3% while in SCF was equal to 22.4%. We found age gradient in all dimensions considered (i.e. symptoms, illness severity, time of recovery, clearance of the virus, immune response and CFR). We did not find a positive correlation of comorbidity with death and it may be due to the small sample of the population studied. A meta-analysis evidenced that hypertension, diabetes, chronic obstructive pulmonary disease (COPD), cardiovascular disease and cerebrovascular disease are major risk factors associate with Covid-19 adverse outcome. Liver disease, malignancy or renal disease had no correlation with death [19]. In our population, over 40% of residents were affected by a chronic renal disease. This unusual frequency of such disease i...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.10.21.20216705v1 on medRxiv