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

Areas of Expertise

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

Publications

  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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Teaching

  • PMI302 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, STATA or R and methods of basic statistical analysis of fixed-format data files. The main lecturers will be Professors Tom Briffa and Jane Hayward of the University of Western Australia. The module co-ordinator is Dr Joanne Demmler.

  • PMI602 Advanced Analysis of Linked Health Data

    This module is taught at an intermediate to advanced level and assumes that students have completed PMIM302 Introductory Analysis of Linked Health Data or have equivalent knowledge. Advanced principles of health care epidemiology are combined with hands-on practical exercises in the implementation of computing solutions. The module provides students with a theoretical grounding on each topic, followed by a training session on the corresponding computing solutions. Students use de-identified data files in the hands-on exercises. The computing component of the module assumes a basic competence in the preparation of computing syntax for programs such as SPSS, SAS, STATA or R and familiarity with the statistical analysis of linked data files at an introductory to intermediate level.

  • PMIM102 Scientific Computing and Health Care

    The module aims to raise the awareness of students about scientific computing in the field of health data science. It focuses on basic software development workflows, tools, and skills that health data scientists most often employ. Students will also learn about the professional context within which health data scientists operate. This is a core / compulsory module and worth 20 Masters level credits. Module leader is Dan Thayer

  • PMIM202 Health Data Modelling

    Health data scientists are expected to work with diverse data sources. Modelling data encompasses setting up database models and analysing the data using statistical models. The objective of this module is to raise the awareness of students about the various processes of data modelling and the key operations involved in the data processing of large and diverse datasets. This is a core / compulsory module and worth 20 Masters level credits. Module leader is Dr Joanne Demmler

  • 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, STATA or R and methods of basic statistical analysis of fixed-format data files. This is a core / compulsory module and worth 20 Masters level credits. The main lecturers will be Professors Tom Briffa and Jane Hayward of the University of Western Australia. The module co-ordinator is Dr Joanne Demmler.

  • PMIM602 Advanced Analysis of Linked Health Data

    This module is taught at an intermediate to advanced level and assumes that students have completed PMIM302 Introductory Analysis of Linked Health Data or have equivalent knowledge. Advanced principles of health care epidemiology are combined with hands-on practical exercises in the implementation of computing solutions. The module provides students with a theoretical grounding on each topic, followed by a training session on the corresponding computing solutions. Students use de-identified data files in the hands-on exercises. The computing component of the module assumes a basic competence in the preparation of computing syntax for programs such as SPSS, SAS, STATA or R and familiarity with the statistical analysis of linked data files at an introductory to intermediate level. This is a core / compulsory module and worth 20 Masters level credits. The main lecturer will be Professors David Preen of the University of Western Australia. The module co-ordinator is Dr Joanne Demmler.