Evidence map›Paper›PMID 38013738›Full record

ArticleInfectious medicine2023

Hospital bed capacity across in Tunisia hospital during the first 4 waves of the COVID-19 pandemic: A descriptive analysis.

Slimane BenMiled, Chiraz Borgi, Mohamed Hsairi, Naoufel Somrani, Amira Kebir

Abstract read
In one paragraph

Article in Infectious medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

5 authors.

Slimane BenMiledBio-(Informatic, Mathematics and Statistic) BIMS-Lab LR09-IPT16, Institut Pasteur de Tunis, University of Tunis el Manar, Tunis 1002, Tunisia.
Chiraz BorgiMinistry of Health, Tunis 1002, Tunisia.
Mohamed HsairiDepartment of Epidemiology and Preventive Medicine, Faculty of Medecin, Tunis El Manar University, Tunis 1002, Tunisia.
Naoufel SomraniMinistry of Health, Tunis 1002, Tunisia.
Amira KebirBio-(Informatic, Mathematics and Statistic) BIMS-Lab LR09-IPT16, Institut Pasteur de Tunis, University of Tunis el Manar, Tunis 1002, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In March 2020, the WHO declared COVID-19 as a pandemic, and Tunisia implemented a containment and targeted screening strategy. The country's public health policy has since focused on managing hospital beds. Methods: The study analyzed the bed occupancy rates in public hospitals in Tunisia during the pandemic. The evolution of daily cases and nonpharmaceutical interventions (NPI) actions undertaken by the Tunisian Government were also analyzed. The study used 3 indices to assess bed flexibility: Ramp duration until the peak, ramp growth until the peak, and ramp rate until the peak. The study also calculated the time shift at the start and peak of each wave to evaluate the government's response efficacy. Results: The study found that the evolution of the epidemic in Tunisia had 2 phases. The first phase saw the pandemic being controlled due to strong NPI actions, while the second phase saw a relaxation of measures and an increase in wave intensity. ICU bed availability followed the demand for beds, but ICU bed occupancy remained high, with a maximum of 97%. The government's response in terms of bed distribution and reallocation was slow. The study found that the most deadly wave by ICU occupied bed was the third wave due to a historical variant, while the fifth wave due to the delta variant was the most deadly in terms of cumulative death. Conclusions: The study concluded that decision-makers could use its findings to assess their response capabilities in the current pandemic and future ones. The study highlighted the importance of flexible and responsive healthcare systems in managing pandemics.

Indexed as

Bed occupancyDaily casesDaily deathRamp duration until the peak (RDUP)Ramp growth until the peak (RGUP)Ramp rate until the peak (RRUP)

Identifiers

PMID38013738
PMCPMC10204889

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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.