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Introduction to Epidemiology, Statistics and Health Data for Public Health

Overview

  • Credit value: 30 credits at Level 6
  • Convenor: to be confirmed
  • Assessment: a 1000-word data analysis and write-up (40%) and 1000-word public health briefing note/health data audit plus five-minute presentation with five-minute Q&A (60%)

Module description

In this module we introduce epidemiological concepts, biostatistics and health data literacy. Public health practice depends on the ability to measure health and disease in populations, understand their causes and use data to guide decision-making. As health systems increasingly rely on large-scale routinely collected data, public health professionals must be equipped to understand how such data are generated, what their limitations are, and how to use them responsibly and effectively.

The module also reflects the growing importance of data ethics and information governance as professional competencies, ensuring you are aware of the responsibilities that come with using health data.

Indicative syllabus

Epidemiology foundations

  • Measures of disease frequency, association and impact; standardisation
  • Epidemiological study designs and their appropriate use
  • Threats to validity: chance, bias, confounding and causality
  • Prevention strategies and the principles of screening

Biostatistics foundations

  • Probability and distributions; descriptive statistics and exploratory data analysis
  • Statistical inference: confidence intervals, hypothesis testing and common statistical tests
  • Introduction to linear and logistic regression in R software; interpreting and communicating results under uncertainty

Health data in practice

  • The health data landscape: to include NHS administrative data, disease registries, national surveys and linked datasets
  • Data quality in routinely collected data: coding systems, missing data and record linkage
  • Information governance and ethics: to include GDPR, Caldicott Principles, consent, data ownership and algorithmic fairness
  • Applied case studies: communicable disease surveillance, cancer registries, health inequalities monitoring

Learning objectives

By the end of this module, you will be able to:

  • define and calculate core measures of disease frequency (incidence, prevalence) and association (relative risk, odds ratio, risk difference)
  • describe and compare the main epidemiological study designs (ecological, cross-sectional, cohort, case-control and randomised controlled trial) including their strengths and limitations
  • identify and explain the principal threats to validity in epidemiological research: chance, bias and confounding, and describe approaches to address them
  • apply basic concepts of statistical inference including probability, confidence intervals, hypothesis testing and regression to interpret quantitative evidence in a public health context
  • critically appraise published epidemiological evidence, assessing the validity of methods and the appropriateness of conclusions, including causal claims
  • identify major sources of health data in the UK, discuss their relevance for public health and recognise common data quality issues such as missing data, miscoding and linkage errors
  • explain the key principles of information governance, data protection (including GDPR) and research ethics as they apply to health data
  • communicate quantitative health evidence clearly, and demonstrate awareness of the ethical and social responsibilities involved in using health data.