Evidence map›Paper›PMID 34492507›Full record

Observational studyPublic health2021

Protecting our vulnerable in the midst of the COVID-19 pandemic: lessons learnt from Malta.

Sarah Cuschieri, Victor Grech

Abstract readObservational Study
In one paragraph

Observational study in Public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

2 authors.

Sarah CuschieriAnatomy Department, Faculty of Medicine and Surgery, University of Malta, Malta. Electronic address: sarah.cuschieri@um.edu.mt.
Victor GrechMater Dei Hospital, Malta. Electronic address: victor.e.grech@gov.mt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesCOVID-19 is a health burden threatening the elderly and those with comorbidities. Malta is an ageing and cardiometabolic country. The study depicts how Malta protected the elderly and the effect of vaccination on this subpopulation. STUDY

designObservational study with quantitative analyses.

methodData were obtained from Malta's COVID dashboard, Institute for Health Metrics and Evaluation and Maltese newspapers. The case-fatality ratio (CFR) and Years of Life Lost (YLLs) for COVID were calculated. Comparisons were made between COVID-19 mortality and YLL to the pre-COVID leading mortality causes. Comparative observations were made between positive and mortality cases stratified by age groups in relation to the cumulative vaccination doses.

resultsThe elderly were cocooned during the first wave (CFR 0.98) through the Vulnerable Act, with only 10.80% of positive cases and seven out nine deaths above 65 years. The Vulnerable Act was not reinstated again, with 13.68% of positive cases and 91.34% (n = 369) of deaths above 65 years during the second wave (CRF 1.39). The elderly were given priority in COVID-19 vaccination rollout leading to an inverse relationship between positive cases/mortality and vaccination coverage.

conclusionThe elderly should be protected with timely restrictions to reduce morbidity, mortality and burden on healthcare systems. Vaccination is key to protecting the elderly, although mitigation measures, such as physical distancing, are still required to prevent the resurgence of infections and hospitalizations, especially in this group.

Indexed as

COVID-19PandemicsAgedCOVID-19 VaccinesHumansMaltaSARS-CoV-2COVID-19 VaccinesCoronavirusElderlyMaltaMorbidityMortalityVaccination

Identifiers

PMID34492507
PMCPMC8354801

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.