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Current Status
Course ended

Registration for the next run of this course opens in July 2024.
This course introduces the topic of linked health data analysis at an introductory to intermediate level.

Course details

This course introduces the topic of linked health data analysis at an introductory to intermediate level. It fills a gap in research training opportunities by combining the principles of health care epidemiology with hands-on practical exercises in the implementation of computing solutions. The module provides students with a theoretical grounding in the classroom on each topic, followed by a training session on the corresponding computing solutions. Students use de-identified linked data files in the hands-on exercises. The computing component of the module assumes a basic familiarity with computing syntax used in programs such as SPSS, SAS, STATA or R and methods of basic statistical analysis of fixed-format data files.

This course is worth 20 Masters level credits.

Who should attend?

This course is ideal for health and social care researchers, social scientists, clinical practitioners and health care managers who wish to improve their understanding of working with health-related routine data, and to gain the theory and skills needed to analyse linked health and social data.

Learning outcomes

By the end of the module, the student will be able to:

1. Critically appraise the theory of data linkage methods and features of comprehensive data linkage systems, sufficient to understand the sources and limitations of linked data sets.

2. Critically evaluate the principles of epidemiologic measurement and research methods for the conceptualisation and construction of numerators and denominators used in the analysis of health and social phenomena, including services utilisation and outcomes.

3. Critically appraise sources of measurement error in linked data, the difference between confounding and effect modification, and use of regression models in risk adjustment in health and social research.

4. Perform statistical analyses on linked longitudinal health and social data.

5. Conceptualise and perform the manipulation of large linked data files.

6. Construct syntax to prepare linked data files for analysis, derive exposure and outcome variables, relate numerators and denominators and produce results from statistical procedures.

Transferrable skills

1. Information technology
2. Database management
3. Statistical analysis
4. Information governance
5. Numeracy
6. Select and apply appropriate analysis or assessment techniques and tools.

Course dates





Students will have the option to attend the lectures entirely online or attend classroom-based lectures held at:

Population Data Science Research Institute
Data Science Building
Swansea University
Singleton Park

Taught by

Jointly delivered by Professor David Preen from the University of Western Australia and Dr Pete Arnold from Swansea University.

Taught by