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Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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:
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:
Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...

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Related Experiment Video

Updated: Jun 22, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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Published on: May 17, 2019

[Clinical databases in social epidemiological research].

Merete Osler1

  • 1Forskningscenter for Forebyggelse og Sundhed, Glostrup Hospital, DK-2600 Glostrup. m.osler@dadlnet.dk

Ugeskrift for Laeger
|June 3, 2009
PubMed
Summary

Danish researchers leverage unique personal ID numbers to link health and socioeconomic data. Studies reveal social inequalities impact colorectal cancer prognosis, with future research exploring this in other cancers and surgeries.

Area of Science:

  • Public Health
  • Epidemiology
  • Health Services Research

Background:

  • Denmark enables data linkage across medical and socioeconomic databases using individual person ID numbers.
  • This facilitates research on the influence of social determinants on healthcare outcomes.

Purpose of the Study:

  • To investigate the impact of social factors on healthcare-related outcomes.
  • To examine social inequalities in the prognosis of colorectal cancer.

Main Methods:

  • Utilizing linked national databases including medical records and socioeconomic data.
  • Employing individual person ID numbers for comprehensive data integration.

Main Results:

  • Established evidence of social inequalities affecting colorectal cancer prognosis.

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Conclusions:

  • Social position significantly influences patient outcomes, as demonstrated in colorectal cancer.
  • Future research will extend this investigation to other cancer types and common surgical procedures like hysterectomy.