Related Experiment Video

Updated: Dec 9, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.7K

Profile of Patients with Novel Coronavirus Disease 2019 (COVID-19) in Osaka Prefecture, Japan: A Population-Based

Taro Takeuchi1, Tomoka Imanaka2, Yusuke Katayama2

  • 1Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan.

Journal of Clinical Medicine
|September 15, 2020
PubMed

Insights

In Osaka, Japan, COVID-19 patients most commonly experienced fever. Older adults faced significantly higher mortality risks from the disease, highlighting a critical public health concern.

Area of Science:

  • Epidemiology
  • Public Health
  • Infectious Diseases

Background:

  • Limited data exists on the epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in Japan.
  • Understanding these characteristics is crucial for effective public health interventions and resource allocation.

Purpose of the Study:

  • To describe the epidemiological features of COVID-19 patients in Osaka Prefecture, Japan.
  • To identify factors associated with mortality among COVID-19 patients.

Main Methods:

  • Retrospective observational study utilizing publicly collected data from February 1 to May 31, 2020.
  • Analysis of polymerase chain reaction (PCR)-confirmed COVID-19 cases in Osaka Prefecture.
  • Cox proportional-hazards model applied to assess mortality risk factors, including age group and sex.

Main Results:

  • 5.7% of individuals tested for COVID-19 were positive. Fever (76.6%) and cough (44.3%) were the most frequent symptoms.
  • A total of 86 deaths were recorded among 1782 confirmed cases.
  • Mortality risk increased substantially with age: HRs ranged from 12.02 for ages 60-69 to 144.71 for ages ≥90, compared to ages 0-59.

Conclusions:

  • Fever is the predominant symptom of COVID-19 in Osaka, Japan.
  • Advanced age is a significant risk factor for mortality in COVID-19 patients in this region.
  • Findings underscore the need for targeted protective measures for elderly populations during COVID-19 outbreaks.

Keywords:
COVID-19JapanPCR testingelderlyepidemiology

More Related Videos

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.5K
Measuring Dengue Virus RNA in the Culture Supernatant of Infected Cells by Real-time Quantitative Polymerase Chain Reaction08:36

Measuring Dengue Virus RNA in the Culture Supernatant of Infected Cells by Real-time Quantitative Polymerase Chain Reaction

Published on: November 1, 2018

32.4K

Related Concept Videos

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
372
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
17.6K
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
1.3K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
395
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
773
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.1K