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.

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.

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.

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.

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

Data Includes:
- Collected data
- Derived data
- Summaries of analyzed data
- Various forms such as numbers, strings, images, and digital footprints.

Definition: Aspects that demonstrate variation from one subject or situation to another.
Examples: Age, sex, ethnicity, diet, and blood sugar levels.

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.

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

Nominal Variables include:
- Smoking status (never, current, past smoker)
- Drinking status
- Genotypes (AA, AG, GG)
- Blood group (ABO types)

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.

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.