Evidence map›Paper›PMID 36469641›Full record

ArticleEmerging microbes & infections2023

Changes in the characteristics and outcomes of COVID-19 patients from the early pandemic to the delta variant epidemic: a nationwide population-based study.

Koichi Miyashita, Hironao Hozumi, Kazuki Furuhashi, Eiji Nakatani, Yusuke Inoue, Hideki Yasui, Masato Karayama, Yuzo Suzuki, Tomoyuki Fujisawa, Noriyuki Enomoto and 3 more

Open access · goldAbstract read
In one paragraph

Article in Emerging microbes & infections, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
4.8field-weighted citation impact, top 4% 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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 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

13 authors at 1 institution in 1 country.

Koichi MiyashitaSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Hironao HozumiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.ORCID 0000-0001-5439-1543
Kazuki FuruhashiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Eiji NakataniGraduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan.
Yusuke InoueSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Hideki YasuiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Masato KarayamaSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Yuzo SuzukiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.ORCID 0000-0001-6154-9791
Tomoyuki FujisawaSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Noriyuki EnomotoSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Naoki InuiDepartment of Clinical Pharmacology and Therapeutics, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Toshiyuki OjimaDepartment of Community Health and Preventive Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Takafumi SudaSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Hamamatsu University School of Medicine · JP

Funding

Wellcome Trust FC001033
6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has dramatically changed because of virus mutations, vaccine dissemination, treatment development and policies, among other factors. These factors have a dynamic and complex effect on the characteristics and outcomes of patients. Therefore, there is an urgent need to understand those changes and update the evidence. We used a large-scale real-world data set of 937,758 patients with COVID-19 from a nationwide claims database that included outpatients and inpatients in Japan to investigate the changes in their characteristics, outcomes and risk factors for severity/mortality from the early pandemic to the delta variant-predominant waves. The severity of COVID-19 was defined according to the modified World Health Organization clinical-progression ordinal scale. With changing waves, mean patient age decreased, and proportion of patients with comorbidities decreased. The incidences of "severe COVID-19 or death (i.e. ≥severe COVID-19)" and "death" markedly declined (5.0% and 2.9%, wild-type-predominant; 4.6% and 2.2%, alpha variant-predominant and 1.4% and 0.4%, delta variant-predominant waves, respectively). Across the wave shift, risk factors for ≥ severe COVID-19 and death, including older age, male, malignancy, congestive heart failure and chronic obstructive pulmonary disease, were largely consistent. The significance of some factors, such as liver disease, varied as per the wave. This study, one of the largest population-based studies on COVID-19, showed that patient characteristics and outcomes changed during the waves. Risk factors for severity/mortality were similar across all waves, but some factors were inconsistent. These data suggest that the clinical status of COVID-19 will change further with the coming epidemic wave.

Indexed as

COVID-19PandemicsHumansInpatientsMaleSARS-CoV-2COVID-19epidemiologynational database of health insurance claims and specific health checkups of JapanNDBrisk factors

Identifiers

PMID36469641
PMCPMC9788709
OpenAlexW4310780525

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.