Evidence map›Paper›PMID 37529569›Full record

ArticleJournal of applied statistics2023

A continuous age-specific standardized mortality ratio for estimating the unascertained rates in the early epidemic of COVID-19 in different regions.

Peipei Du, Peihua Cao, Xiaodong Yan, Daihai He, Xiaotong Zhang, Weixiang Chen, Jiawei Luo, Ziqian Zeng, Yaolong Chen, Lin Yang and 2 more

Abstract read
In one paragraph

Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Peipei DuSchool of Public Health, Chengdu Medical College, Chengdu, People's Republic of China.ORCID https://orcid.org/0000-0001-6582-8384
Peihua CaoClinical Research Center, Zhujiang Hospital, Southern Medical University, Guangzhou, People's Republic of China.ORCID https://orcid.org/0000-0003-4269-3899
Xiaodong YanZhongtai Securities Institute for Financial Studies, Shandong University, Jinan, People's Republic of China.
Daihai HeDepartment of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, People's Republic of China.ORCID https://orcid.org/0000-0003-3253-654X
Xiaotong ZhangCollege of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, People's Republic of China.
Weixiang ChenCollege of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, People's Republic of China.
Jiawei LuoWest China Biomedical Big Data Center, West China Clinical Medical College of Sichuan University, Chengdu, People's Republic of China.
Ziqian ZengSchool of Public Health, Chengdu Medical College, Chengdu, People's Republic of China.
Yaolong ChenEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, People's Republic of China.
Lin YangSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, People's Republic of China.ORCID https://orcid.org/0000-0002-5964-3233
Shu YangCollege of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, People's Republic of China.ORCID https://orcid.org/0000-0002-1859-6362
Xixi FengSchool of Public Health, Chengdu Medical College, Chengdu, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The difference in age structure and aging population level was an important factor that caused the difference in COVID-19's case fatality rate (CFR) in various regions. To eliminate the age effect on estimating the CFR of COVID-19, our study applied nonlinear logistic model and maximum likelihood method to fit the age-fatality curves of COVID-19 in different countries and regions. We further computed the standardized mortality ratio from the age-fatality curves of COVID-19 in the above regions and found that the risk of COVID-19 death in Wuhan was of a moderate level, while the non-Hubei region was even lower, compared with other regions. Regarding the disparity of CFRs among different regions in the country, we believed that there might be an unascertained phenomenon in high-endemic regions. Based on age-fatality rate curves, we estimated unascertained rates in cities with severe epidemics such as Wuhan and New York, and it was found that the total unascertained rates in Wuhan and New York were 81.6% and 81.2%, respectively. Meanwhile, we also found that the unascertained rates varied greatly with age.

Indexed as

age-specificcase fatality rateCOVID-19nonlinear logistic modelStandardized mortality ratio

Identifiers

PMID37529569
PMCPMC10388831

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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.