Analysis & Epidemiology

Department

Master's Program International Health & Social Management

Course unit code

IHSM-EU-HEM-M2.4

Number of ECTS credits allocated

Name of lecturer(s)

Dr. Mosenhauer Natacha, Priv. Doz. Dr. med. Borena Wegene, FH-Prof. Dr. Walch Siegfried, Univ.-Prof. Dr. Gondan-Rochon Matthias

Mode of delivery

Recommended optional program components

none

Recommended or required reading

Liu, C., Hu, B., Wang, Y., Pang, X., Guo, K., & Zhu, X. (2025). Enhancing social participation in older adults with noncommunicable diseases: Insights from system dynamics modeling. BMC Public Health, 26, Article 86. https://doi.org/10.1186/s12889-025-25741-2

World Health Organization. (2009). Systems thinking for health systems strengthening. Geneva: World Health Organization, download: https://iris.who.int/server/api/core/bitstreams/885c4703-060b-463e-ae06-a0fdc02dbd4e/content

CHMP (2010) Guideline on Missing Data in Confirmatory Clinical Trials
Wolbers M, Noci A, Delmar P, Gower-Page C, Yiu S, Bartlett JW. Standard and reference-based conditional mean imputation. Pharmaceutical Statistics. 2022;21(6):1246-1257. doi:10.1002/pst.2234

World Health Organization. (2022). Systems thinking for noncommunicable disease prevention policy: Guidance and case studies. Geneva: World Health Organization, download: https://www.who.int/europe/publications/i/item/WHO-EURO-2022-4195-43954-61946

CHMP (2015). Guideline on Adjustment for Baseline Covariates in Clinical Trials. London: European Medicines Agency.
Kabisch, M. (2011). Randomized controlled trials. Dtsch Ärztebl Int, 108, 663-668.
Röhrig, B. (2010). Sample size calculation. Dtsch Ärztebl Int, 107, 552-556.

CHMP (2019). Guideline on the Investigation of Subgroups in Confirmatory Clinical Trials. London: European Medicines Agency.
CHMP (2016). Guideline on Multiplicity Issues in Clinical Trials. London: European Medicines Agency.
Coxe, S. (2009). The analysis of count data. J Personality Assess, 91, 121-136.
Park, H. A. (2013). An introduction to logistic regression. J Kor Acad Nurs, 43, 154-164.

Hammer, G. P. (2009). Avoiding bias in observational studies. Dtsch Ärztebl Int, 106, 664-668.
Kuss, O. (2016). Propensity scores. Dtsch Ärztebl Int, 113, 597-603.
Ressning, M. et al. (2010). Data analysis of epidemiological studies. Dtsch Arztebl Int, 107, 187-192.
Röhrig, B. et al. (2009). Types of study in medical research. Dtsch Arztebl Int, 106, 262-268.

Greenland et al. (1999). Causal diagrams for epidemiologic research. Epidemiology, 199, 37-48.
Krieger, N. & Smith, G. D. (2016). The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology. International Journal of Epidemiology, 1787-1808.

Additional information about examination modalities

Course-immanent examination or final exam or combination of both examination types.

Level of course unit

Master

Year of study

Spring 2026

Semester when the course unit is delivered

Language of instruction

English

Learning outcomes of the course unit

By the end of this course, students will be able to:

-demonstrate a solid understanding of the role of epidemiology and its contribution to public health and evidence-informed decision-making
-understand key concepts, definitions, and applications of epidemiology and population health analysis
-explain how health outcomes emerge from complex interactions between biological, behavioral, social, and policy-related factors
-apply systems thinking approaches to analyze complex public health problems and visualize interdependencies (e.g., through causal models)
-differentiate between common disease classification schemes and patterns of disease occurrence at the population level
-critically assess public health surveillance systems, indicators, and risk measures
-understand core epidemiological study designs and principles of causal inference
-interpret epidemiological data and evaluate the strengths and limitations of empirical evidence
-reflect on how epidemiological evidence can be used to test hypotheses derived from conceptual or system-based models
critically question risk appraisals and public health interventions in light of complexity and real-world dynamics

Course contents

This course provides an integrated introduction to the analytical foundations of modern public health by combining systems thinking, epidemiology, and quantitative methods. It emphasizes how these perspectives jointly contribute to understanding complex health problems and informing evidence-based decision-making.

Planned learning activities and teaching methods

The course comprises an interactive mix of lectures, discussions and individual and group work.

Work placement(s)

none

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