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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Criteria for Causality: Bradford Hill Criteria - II01:28

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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Causality in Epidemiology01:21

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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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The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
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From cause and effect to causes and effects.

Joachim P Sturmberg1,2, James A Marcum3

  • 1School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Holgate, New South Wales, Australia.

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|February 13, 2023
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Most health outcomes stem from multiple interacting causes, not single factors. Embracing a "causes and effects" research approach is crucial for understanding complex health issues and developing personalized interventions.

Keywords:
heterogeneityphilosophy of sciencereductionismreductionist thinkingsystems thinking

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Area of Science:

  • Medical research methodology
  • Systems science in health
  • Clinical research design

Background:

  • Most health outcomes are influenced by multiple interacting causes.
  • Traditional medical research often uses binary, single-cause study designs.
  • The coronavirus disease 2019 pandemic highlighted the need for systemic approaches to complex health challenges.

Purpose of the Study:

  • To advocate for a shift from "cause and effect" to "causes and effects" in research.
  • To emphasize the limitations of reductionist thinking in understanding health and disease.
  • To promote the adoption of systemic research designs for complex health problems.

Main Methods:

  • Critique of traditional dichotomous research methodologies.
  • Proposal of a "causes and effects" framework.
  • Highlighting the need for research designs accommodating one-to-one, one-to-many, many-to-one, and many-to-many relationships.

Main Results:

  • One-to-one relationships suit traditional randomized control trials.
  • Complex relationships (one-to-many, many-to-one, many-to-many) require systemic research designs.
  • Systemic methodologies explain clinical outcome heterogeneity.

Conclusions:

  • A paradigm shift to "causes and effects" is necessary for modern medical research.
  • Embracing systemic research designs will improve understanding of health and disease variability.
  • This shift facilitates personalized interventions and advances health equity.