Dr Joanne Demmler
Lecturer in Health Informatics
Swansea University Medical School
Telephone: (01792) 295674
Room: Director Cellular Office - 201
Data Science Building
Singleton Campus

Areas of Expertise

  • Population Health Research
  • SAIL Databank
  • Data Linkage
  • SQL
  • R
  • GIS
  • Spatial Data Analysis


  1. & Residential Moving and Preventable Hospitalizations. PEDIATRICS 138(1), e20152836-e20152836.
  2. & Educational Attainment at Age 10–11 Years Predicts Health Risk Behaviors and Injury Risk During Adolescence. Journal of Adolescent Health
  3. & Epilepsy and deprivation, a data linkage study. Epilepsia, n/a-n/a.
  4. Holocene book review: An Introduction to R for Spatial Analysis and Mapping. The Holocene 25(9), 1533-1533.
  5. & Oxygen stable isotope ratios from British oak tree-rings provide a strong and consistent record of past changes in summer rainfall. Climate Dynamics

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  • PMIM102 Scientific Computing and Health Care

    The module aims at raising the awareness of students about scientific computing. It provides a brief overview of computation and focuses on the computational needs and workflows that health data scientists most often employ. Students will also learn about the professional context within which health data scientists operate.

  • PMIM202 Health Data Modelling

    Health data scientists are expected to work with diverse data sources. However, due to the abstraction offered by modern database management systems, these data sources can be treated similarly through a set of standardised operations. The objective of this module is to raise the awareness of students about the process of data modelling and the key operations involved in the data processing of large and diverse datasets.

  • PMIM302 Introductory Analysis of Linked Health Data

    This module 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 or STATA and methods of basic statistical analysis of fixed-format data files.