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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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Updated: Jul 24, 2025

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Understanding COVID-19 Health Disparities With Birth Country and Language Data.

M Kumi Smith1, Kirsten R Ehresmann2, Gregory S Knowlton2

  • 1Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota.

American Journal of Preventive Medicine
|July 5, 2023
PubMed
Summary
This summary is machine-generated.

COVID-19 disparities in the U.S. are complex. Country of birth and preferred language reveal health risks masked by race/ethnicity, highlighting needs for targeted interventions.

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Disparities Research

Background:

  • Traditional race/ethnicity categories inadequately capture COVID-19 disparities in the U.S.
  • Nuanced understanding requires granular data beyond broad social classifications.

Purpose of the Study:

  • To analyze COVID-19 disparities using country of birth and preferred language.
  • To identify specific social groups with elevated COVID-19 risks.

Main Methods:

  • Analysis of data from 1,114,895 patients at a Midwestern health system (2019-2021).
  • Used age-adjusted generalized linear models to estimate risk ratios (RR).
  • Stratified analysis by race/ethnicity, country of birth, and preferred language.

Main Results:

  • Hispanic/Latino patients from Latin America/Caribbean preferring Spanish had highest infection/hospitalization RRs.
  • Black patients from sub-Saharan Africa had higher infection risk than Western counterparts.
  • Elevated hospitalization/death risks observed for specific European, Central Asian, Southeast Asian, and Pacific Islander groups; non-English speakers faced higher risks.

Conclusions:

  • Country of birth and preferred language identify vulnerable groups missed by traditional labels.
  • Routine collection of this data is crucial for targeted, culturally relevant interventions.
  • Findings underscore the need for culturally and linguistically tailored public health strategies.