Spatial epidemiology

The course is an elective course within the Master’s Programme of Applied Biostatistics (M2STA). The course can be also offered as a free-standing course on advanced level.

The entry requirements of the course include a professional degree/Bachelor's degree of at least 180 credits in health sciences, natural sciences, economics, or engineering, and a course in statistics or quantitative methods of at least 7,5 credits. Further, R programming of at least 2 credits or equivalent, a course in regression analysis of at least 2 credits or equivalent, English B/English 6 or equivalent, and Matematik 3b/3c or equivalent are required.

The course introduces important concepts, methods, and applications in spatial epidemiology. It focuses on understanding how health outcomes, exposures, and determinants can vary across geographic areas, as well as on applying appropriate analytical methods to spatially structured data.

A fundamental part of the course is working with spatial data, including spatial data structures, coordinate systems, and methods for describing and visualizing spatial patterns in health-related variables. The course also covers exploratory spatial data analysis, including methods for assessing clustering and spatial autocorrelation.

The course introduces concepts related to spatial smoothing and mapping of diseases at the area level, with an emphasis on methods used to stabilize frequencies and support interpretation in small-area epidemiology. In addition, the course discusses methods that account for spatial dependence and/or heterogeneity, illustrated with examples relevant to epidemiology and public health.

On successful completion of the course the student will be able to:

Knowledge and understanding

• describe fundamental concepts in spatial epidemiology, including the role of geographic location in health research and the interpretation of spatial patterns of health-related outcomes.
• explain key ideas underlying spatial dependence and heterogeneity and spatial clustering and describe why these phenomena require specific analytical approaches.

Competence and skills

• prepare spatial data for analysis and apply exploratory techniques to identify spatial patterns, including assessment of clustering and correlation.
• implement selected statistical methods to analyze spatially referenced health data and interpret related spatial findings in relation to epidemiological questions.

Judgement and approach

• reflect on different forms of bias in spatial data, with a particular focus on issues related to aggregation, measurement, and missing data.

The teaching consists of lectures, practical computer-based sessions, interactive discussions, and seminars.

Language of instruction: English

If a student who has been failed twice for the same examination element wishes to change examiner before the next examination session, such a request is to be granted unless there are specific reasons to the contrary (Chapter 6 Section 22 HF).

If a student has received a certificate of disability study support from the University of Gothenburg with a recommendation of adapted examination and/or adapted forms of assessment, an examiner may decide, if this is consistent with the course’s intended learning outcomes and provided that no unreasonable resources would be needed, to grant the student adapted examination and/or adapted forms of assessment.

If a course has been discontinued or undergone major changes, the student must be offered at least two examination sessions in addition to ordinary examination sessions. These sessions are to be spread over a period of at least one year but no more than two years after the course has been discontinued/changed. The same applies to placement and internship (VFU) except that this is restricted to only one further examination session.

If a student has been notified that they fulfil the requirements for being a student at Riksidrottsuniversitetet (RIU student), to combine elite sports activities with studies, the examiner is entitled to decide on adaptation of examinations if this is done in accordance with the Local rules regarding RIU students at the University of Gothenburg.

The grading scale comprises: Pass with distinction (VG), Pass (G) and Fail (U). The active participation in a seminar (1.0 credits) is graded Pass or Fail, while the individual written exam (2.5 credits) is graded Pass with Distinction, Pass, or Fail.

To receive a Pass grade for the course, students must receive a Pass grade for both the seminar and the written exam. To receive a grade of Pass with Distinction for the course, students must receive a grade of Pass for the seminar and a grade of Pass with Distinction for the individual written exam.

The course evaluation is carried out in the form of an anonymous questionnaire. A compilation of the questionnaire is done by the course coordinator. The results of and possible changes to the course will be shared with students who participated in the evaluation and students who are starting the course.

Each student needs access to a laptop (at least 8 GB RAM recommended) with R and RStudio (or other user interface for R) installed. In addition, installation of the open-source software GeoDa is required. Expected prior knowledge of statistics includes understanding the principles of statistical hypothesis testing, construction of confidence intervals, understanding the concept of p-values, and good knowledge of linear regression.