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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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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,...
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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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Strategies for Assessing and Addressing Confounding01:25

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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The Lines That Held Us: Assessing Racial and Socioeconomic Disparities in SARS-CoV-2 Testing.

Chu J Hsiao1,2, Aditi G M Patel3,4, Henrietta O Fasanya2

  • 1Department of Anthropology, College of Liberal Arts and Sciences, University of Florida, Gainesville, FL.

The Journal of Applied Laboratory Medicine
|July 9, 2021
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Summary

Black race was linked to higher SARS-CoV-2 test positivity. Socioeconomic status impacts test positivity differently based on race, indicating race-specific effects on COVID-19 outcomes.

Keywords:
SARS-CoV-2 testingdisparitiesracesocioeconomic status

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Area of Science:

  • Public Health
  • Epidemiology
  • Sociology

Background:

  • Racial disparities in SARS-CoV-2 (the virus that causes COVID-19) prevalence are evident.
  • Race is understood as a sociocultural construct, prompting an investigation into the influence of sociocultural factors on these disparities.

Purpose of the Study:

  • To examine the association between race, socioeconomic factors, and SARS-CoV-2 test positivity.
  • To explore how socioeconomic status (SES) modifies the relationship between race and COVID-19 test results.

Main Methods:

  • A cross-sectional study linked laboratory data (Feb 29–May 15, 2020) with 2018 American Community Survey (ACS) socio-demographic data across multiple US states.
  • Race was utilized as a proxy for racism, not biological differences.
  • Statistical analyses, including odds ratios (OR), assessed associations between race, SES variables, and SARS-CoV-2 test results.

Main Results:

  • Of 126,452 patients, 15.0% tested positive for SARS-CoV-2.
  • Black race was significantly associated with increased odds of a positive test (OR 2.11).
  • Higher SES increased positive test odds for White patients but decreased them for Black patients. Black patients with positive tests predominantly lived in densely populated areas.

Conclusions:

  • Black race is associated with higher SARS-CoV-2 positivity.
  • The impact of socioeconomic status on COVID-19 test positivity is race-specific.
  • Sociocultural factors, including racism and residential density, play a role in observed racial disparities in SARS-CoV-2 prevalence.