Modern healthcare systems generate vast volumes of data every day, but much of this data is underutilised due to complexity, inconsistency, and bias. The Using Routine Data for Public Health module equips you with the practical skills and theoretical understanding needed to unlock the potential of routine health data and use it to inform impactful public health decisions.
Over 12 weeks, you will engage with real-world datasets—including Cancer Registry, and the National Survey of Sexual Attitudes and Lifestyles—and learn how to estimate key epidemiological measures such as incidence, prevalence, mortality, and case fatality. You will explore disease coding frameworks (ICD-10 and SNOMED), build phenotyping algorithms, and tackle the challenges of data bias, linkage, and validation.
This module is delivered through interactive lectures, practical computer lab sessions, and guest seminars from experts actively working in public health data. You’ll develop vital skills in R programming, critical appraisal, and conducting transparent, reproducible research—while growing in confidence and digital fluency.
Assessments are authentic and designed to reflect real-world practice, including a poster and pre-recorded oral presentation (30%) to communicate your findings with clarity and impact, and a written report (70%) critically appraising your approach, with a reflective summary of your learning journey.
Whether you are preparing for a role in epidemiology, health informatics, or public health policy, this module offers hands-on experience with large-scale datasets and the analytical techniques needed to generate reliable insights.
You should choose Using Routine Data for Public Health if you want to learn how to turn complex health data into actionable knowledge, to become a skilled, confident contributor to tomorrow’s data-driven healthcare solutions.