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Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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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:  
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Statistical Methods for Analyzing Epidemiological Data01:25

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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:
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Factors Affecting the Risk of Infection01:26

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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
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Assumptions of Survival Analysis01:15

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

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Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
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When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
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Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
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Demographic variables associated with Covid-19 mortality.

José Manuel Madrazo Cabo1, Nuvia Adriana Monter Valera1, Edith Jocelyn Hernández Sánchez1

  • 1Bioethics Center, Autonomous Popular University of the State of Puebla, Puebla, Mexico.

Journal of Public Health Research
|December 7, 2020
PubMed
Summary

Countries with progressive population pyramids experienced lower Covid-19 mortality rates, especially when implementing widespread screening. This suggests population structure and screening strategies significantly impact disease outcomes.

Keywords:
Covid-19countryisolationmortalitypopulation pyramid

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Area of Science:

  • Epidemiology
  • Public Health
  • Demography

Background:

  • Coronavirus disease 2019 (Covid-19) is a betacoronavirus with zoonotic origins.
  • Cellular entry involves the S protein binding to angiotensin-converting enzyme receptors.
  • Transmission occurs via direct contact and respiratory droplets, disproportionately affecting older adults and those with chronic conditions.

Purpose of the Study:

  • To investigate the association between population pyramid structure, Gross Domestic Product (GDP), isolation strategies, and Covid-19 mortality.
  • To compare factors in countries with the highest and lowest Covid-19 mortality rates.

Main Methods:

  • Analysis of population structure using population pyramids.
  • Evaluation of national isolation and Covid-19 screening strategies.
  • Comparison of Gross Domestic Product (GDP) across nations.

Main Results:

  • A statistically significant association was found between regressive population pyramids and higher Covid-19 mortality rates (p<0.001).
  • Countries with progressive population pyramids showed significantly lower mortality when implementing extensive population screening (p<0.036).

Conclusions:

  • Progressive population pyramid structures are linked to better Covid-19 outcomes.
  • Enhanced population screening in countries with progressive demographics leads to a significant reduction in mortality rates.