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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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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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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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Prevalence and Incidence01:08

Prevalence and Incidence

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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...
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Vaccinations01:51

Vaccinations

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Overview
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Contingency Table01:29

Contingency Table

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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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Related Experiment Video

Updated: Jul 17, 2025

In Vivo Mouse Model of Spinal Implant Infection
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COVID-19 Infection Rates in Vaccinated and Unvaccinated Inmates: A Retrospective Cohort Study.

Luke Ko1, Gary Malet2, Lisa L Chang3

  • 1Biomedical Sciences Pathway Program, California High School, San Ramon, USA.

Cureus
|September 8, 2023
PubMed
Summary
This summary is machine-generated.

COVID-19 infection rates were low in both vaccinated and unvaccinated individuals in California prisons. The bivalent vaccine showed minimal difference in preventing infection, especially in older age groups.

Keywords:
breakthrough infectionscovid-19covid-19 vaccinationherd immunitynatural immunityomicron variantpandemicpublic healthsars-cov-2 (severe acute respiratory syndrome coronavirus -2)viral infection

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Area of Science:

  • Epidemiology
  • Infectious Diseases
  • Public Health

Background:

  • COVID-19 (Coronavirus Disease 2019) remains prevalent with Omicron subvariants causing infections in vaccinated and previously infected individuals.
  • Understanding immunity's impact is crucial for public health strategies, particularly in congregate settings like prisons.

Purpose of the Study:

  • To analyze COVID-19 infection rates in vaccinated versus unvaccinated populations within California state prisons.
  • To assess the effectiveness of COVID-19 bivalent vaccines against infection in a high-density setting.

Main Methods:

  • Analysis of COVID-19 surveillance data from 33 California state prisons (January-July 2023).
  • Inclusion of 96,201 individuals, primarily male.
  • Calculation of infection incidence rates comparing bivalent-vaccinated and unvaccinated groups across different age demographics.

Main Results:

  • Overall infection rates were 3.24% in bivalent-vaccinated and 2.72% in unvaccinated groups (absolute risk difference: 0.52%).
  • In individuals aged 50+, rates were 4.07% (vaccinated) and 3.1% (unvaccinated) (absolute risk difference: 0.97%).
  • In individuals aged 65+, rates were 6.45% (vaccinated) and 4.5% (unvaccinated) (absolute risk difference: 1.95%), with no statistically significant difference.

Conclusions:

  • Low COVID-19 infection rates observed in both groups, potentially due to combined vaccine and natural immunity.
  • Bivalent vaccination did not significantly reduce infection risk compared to unvaccinated individuals, particularly in older age groups.
  • Further research is needed to understand vaccine effectiveness against evolving variants and inform future vaccine development.