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Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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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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Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Related Experiment Video

Updated: May 10, 2025

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
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Attitudes regarding polygenic risk testing for lung cancer: a mixed-methods study.

Hoda Badr1, Jinyoung Byun1,2, Melinda C Aldrich3,4,5

  • 1Department of Medicine, Baylor College of Medicine, Houston, TX, United States.

Annals of Behavioral Medicine : a Publication of the Society of Behavioral Medicine
|April 22, 2025
PubMed
Summary

Patient interest in lung cancer polygenic risk scores (PRS) is influenced by knowledge, perceived benefits, and barriers like privacy concerns and psychological burden. Tailored communication strategies are needed for equitable uptake.

Keywords:
attitudeshealth belief modellung cancerpolygenic risk

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Area of Science:

  • Genomics and Precision Medicine
  • Cancer Prevention and Early Detection
  • Health Disparities Research

Background:

  • Polygenic risk scores (PRS) show potential for lung cancer early detection and personalized treatment.
  • Understanding patient interest in PRS-based genetic testing is crucial but currently limited.
  • Sociodemographic and personal risk factors may influence attitudes towards PRS.

Purpose of the Study:

  • To explore knowledge, attitudes, perceived benefits, and barriers related to lung cancer PRS.
  • To investigate patient preferences for receiving PRS results.
  • Grounded in the health belief model, this study examined factors influencing PRS uptake.

Main Methods:

  • Mixed-methods study involving 141 participants from a comprehensive cancer center.
  • Quantitative assessment of knowledge, attitudes, benefits, and barriers.
  • Qualitative exploration of psychological impacts and result preferences.

Main Results:

  • Limited participant knowledge of PRS despite recognizing lung cancer severity.
  • Concerns regarding privacy, psychological impact, and result utility decreased interest in PRS testing.
  • Women and African Americans reported distinct concerns about psychological effects, stigma, and familial consequences.

Conclusions:

  • Perceived benefits and barriers to PRS testing are influenced by sociodemographic and personal risk factors.
  • Stigma, psychological burden, and privacy concerns significantly shape attitudes toward PRS.
  • Improving risk communication, ensuring data privacy, and providing actionable guidance are key to enhancing PRS uptake and equitable access.