Evidence map›Paper›PMID 37197177›Full record

ArticleChina CDC weekly2023

Collective and Individual Assessment of the Risk of Death from COVID-19 for the Elderly, 2020-2022.

Chaobao Zhang, Hongzhi Wang, Zilu Wen, Zhijun Bao, Xiangqi Li

Open access · diamondAbstract read
In one paragraph

Article in China CDC weekly, 2023. 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
1.4field-weighted citation impact, top 20% of its field
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, 8 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Chaobao ZhangShanghai Key Laboratory of Clinical Geriatric Medicine; Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Hongzhi WangShanghai Key Laboratory of Magnetic Resonance; Research Center for Artificial Intelligence in Medical Imaging, East China Normal University, Shanghai, China.
Zilu WenDepartment of Scientific Research, Shanghai Public Health Clinical Center, Shanghai, China.
Zhijun BaoShanghai Key Laboratory of Clinical Geriatric Medicine; Department of Geriatric Medicine, Huadong Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Xiangqi LiDepartment of Endocrinology and Metabolism, Gongli Hospital, Naval Medical University, Shanghai, China.
Second Military Medical University · CNShanghai Public Health Clinical Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Coronavirus disease 2019 (COVID-19) has had profound disruptions worldwide. For a population or individual, it is critical to assess the risk of death for making preventative decisions. Methods: In this study, clinical data from approximately 100 million cases were statistically analyzed. A software and an online assessment tool were developed in Python to evaluate the risk of mortality. Results: Our analysis revealed that 76.51% of COVID-19-related fatalities occurred among individuals aged over 65 years, with frailty-associated deaths accounting for more than 80% of these cases. Furthermore, over 80% of the reported deaths involved unvaccinated individuals. A notable overlap was observed between aging and frailty-associated deaths, both of which were connected to underlying health conditions. For those with at least two comorbidities, the proportion of frailty and the proportion of COVID-19-related death were both close to 75 percent. Subsequently, we established a formula to calculate the number of deaths, which was validated using data from twenty countries and regions. Using this formula, we developed and verified an intelligent software designed to predict the death risk for a given population. To facilitate rapid risk screening on an individual level, we also introduced a six-question online assessment tool. Conclusions: This study examined the impact of underlying diseases, frailty, age, and vaccination history on COVID-19-related mortality, resulting in a sophisticated software and a user-friendly online scale to assess mortality risk. These tools offer valuable assistance in informed decision-making.

Indexed as

COVID-19Risk evaluationSoftwareUnderlying disease

Identifiers

PMID37197177
PMCPMC10184470
OpenAlexW4377011944

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.