BB1726_Lecture4 (1)

7. John Snow and the 1854 Cholera Outbreak

• Background: English physician in the mid-19th century.

• Challenged prevailing notions: Opposed the miasma theory of disease, which posited that diseases were caused by bad air.

8. The 1854 Cholera Outbreak

• Description: A devastating cholera outbreak in Soho, London.

• Traditional Belief: Cholera thought to spread via "bad air."

• Snow's Hypothesis: Proposed that cholera was transmitted through contaminated water.

• Data Collection:

  • Conducted interviews with cholera victims.

  • Mapped cholera cases based on their proximity to water sources.

  • Noticed a high concentration of cases near the Broad Street pump.

• Visualization: Photos available from The Guardian that illustrate Snow's mapping of cases.

9. Data-Driven Discovery and Public Health Impact

• Key Discovery: Identified the Broad Street pump as the outbreak's epicenter.

• Observation: Areas with private water supplies had fewer cases of cholera.

• Action Taken: Successfully persuaded authorities to remove the handle from the pump, leading to a significant reduction in choleral cases.

• Legacy: Paved the way for modern epidemiology; demonstrated the importance of data collection and analysis in public health.

• Data Literacy: Snow's work exemplified the ability to gather and interpret data to solve real-world health issues, championing critical thinking and data analysis for impactful public health solutions.

10. Critical Thinking in Data Analysis

• Question Assumptions: Always investigate if there could be alternative explanations for the data.

• Evaluate Sources: Assess the reliability and potential biases of data sources.

• Analyze Patterns: Search for significant patterns, but avoid hasty conclusions about their meaning.

• Test Hypotheses: Critically assess different explanations of findings using data as Snow did.

• Ethics: Ensure that data interpretations respect ethical concerns such as privacy and consent.

11. What is Data?

• Definition: Information, especially facts or numbers, collected for analysis and decision-making; can also refer to electronic information stored in a computer.

12. Types of Data

• Data Includes:

  • Collected data

  • Derived data

  • Summaries of analyzed data

  • Various forms such as numbers, strings, images, and digital footprints.

15. Variables in Data

• Definition: Aspects that demonstrate variation from one subject or situation to another.

• Examples: Age, sex, ethnicity, diet, and blood sugar levels.

16. Variable Types

• Categorical (Qualitative):

  • Nominal: Multiple values without a specific order.

  • Binary: Data with two possible values.

• Numerical (Quantitative):

  • Discrete: Countable, distinct values.

  • Continuous: Any value within a certain range.

18. Examples of Binary Variables

• Examples include sex (male vs female), true/false questions, disease status, body weight categories (obese vs lean), and survival status.

21. Examples of Nominal Variables

• Nominal Variables include:

  • Smoking status (never, current, past smoker)

  • Drinking status

  • Genotypes (AA, AG, GG)

  • Blood group (ABO types)

26. Biomedical Research Designs

• Observational Design: Researcher observes variables without manipulation, collecting data as naturally occurs.

• Experimental Design: Researcher manipulates variables to examine effects on dependent variables, commonly using randomization and control groups to ensure reliability.

  • Examples of studies include clinical trials testing drugs with control groups receiving placebos.

36. Observational Design Types

• Cross-Sectional Studies: Capture a snapshot of data to identify relationships within a population.

• Case-Control Studies: Compare people with a disease against controls without the disease.

• Cohort Studies: Follow a group of healthy individuals over time to identify disease development and compare characteristics with those who develop the disease.