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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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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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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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Racial Disparities in Coronavirus Disease 2019 (COVID-19) Mortality Are Driven by Unequal Infection Risks.

Jon Zelner1,2, Rob Trangucci3, Ramya Naraharisetti1,2

  • 1Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.

Clinical Infectious Diseases : an Official Publication of the Infectious Diseases Society of America
|November 22, 2020
PubMed
Summary

Racial disparities in COVID-19 outcomes are significant, with Black individuals facing disproportionately higher infection and mortality rates compared to White individuals. These disparities are primarily driven by increased exposure risks, not differences in case-fatality rates.

Keywords:
COVID-19SARS-CoV-2disparitiessocial epidemiology

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Area of Science:

  • Epidemiology
  • Public Health
  • Health Disparities

Background:

  • Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over 9 million cases and 230,000 deaths in the US by November 2020.
  • The coronavirus disease 2019 (COVID-19) epidemic in the US has been characterized by significant geographic, racial/ethnic, age, and socioeconomic disparities in exposure and mortality.

Purpose of the Study:

  • To estimate age-specific incidence and mortality rates by race/ethnic group using individual-level COVID-19 data from Michigan.
  • To analyze the drivers of racial disparities in COVID-19 incidence and mortality.

Main Methods:

  • Utilized individual-level COVID-19 incidence and mortality data from the state of Michigan.
  • Employed hierarchical Bayesian regression models for data analysis.
  • Validated model results using posterior predictive checks.

Main Results:

  • Black individuals experienced COVID-19 incidence and mortality rates over five times higher than White individuals.
  • Age-standardized incidence for Black individuals was 1626/100,000 population, and mortality was 244/100,000.
  • Disparities in mortality were primarily driven by higher infection rates across all age groups, particularly in older adults, rather than differences in case-fatality rates.

Conclusions:

  • Racial disparities in COVID-19 mortality in Michigan are substantially influenced by variations in household, community, and workplace exposure.
  • Findings highlight the critical role of exposure in exacerbating COVID-19 racial inequities.