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Unit information: Introduction to Epidemiology and Statistics in 2018/19

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Unit name Introduction to Epidemiology and Statistics
Unit code BRMSM0001
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Kipping
Open unit status Not open




School/department Bristol Medical School
Faculty Faculty of Health Sciences


The aims of this unit are to:
  1. Give students an appreciation of the uses of epidemiology, and the role of statistical methods in epidemiology and public health.
  1. Convey an understanding of measures of disease frequency, measures of effect, and measures of public health impact
  2. Convey an understanding of sampling variation, how to quantify uncertainty using confidence intervals, and appropriate and inappropriate interpretations of p values
  3. Introduce randomized and non-randomized (observational) study designs and explain their strengths and limitations
  4. Introduce the concept of confounding, and explain how it can be addressed during study design and through statistical analysis.
  5. Explain how selection and information bias can occur in epidemiological studies
  6. Explain the importance of effect modification, and how it can be examined in statistical analyses
  7. Give students knowledge of the uses of regression models to estimate exposure effects after controlling for confounders, and for multivariable prediction
  1. Introduce linear regression for analyses of numerical outcomes, and logistic regression for analyses of binary outcomes
  1. Introduce students to deriving causal inferences from epidemiological studies

Intended learning outcomes

By the end of this unit, students should be able to:

  1. Calculate and interpret measures of disease frequency, measures of association and effect, and measures of public health impact
  2. Describe the principles of, and evaluate the strengths and limitations of, different study designs for addressing a specific epidemiological question
  3. Explain sampling variation, and use confidence intervals and p-values in interpreting the results of statistical analyses
  4. Explain the concept of confounding and apply methods to address it
  5. Explain the concept of effect modification and apply methods to address it
  6. Explain how selection and information biases can affect different types of study
  7. Use Stata software to fit linear and logistic regression models, and interpret output from these
  8. Evaluate the strength of evidence supporting a causal link between an exposure and an outcome

Teaching details

There will be 10 teaching weeks, plus reading week and revision week.

Face to face teaching in the form of campus based lectures (20 hours) and tutorials (30 hours). Directed and self-directed learning (150 hours) will include reading, quizzes, multi-media learning and preparation for assessment.

Assessment Details

Assessment for learning will be provided via a weekly student-completed quiz. 5 Multi-Choice Questions (MCQs) will be available after teaching. Feedback in the form of model answers will be provided. (ILOs 1- 8). Quizzes are self-directed learning and will not contribute to final unit mark.

There will be a progress test in week 6. This will comprise unseen MCQs and short-answer questions in exam conditions (1 hour). It will prepare students for the final summative assessment but will be based on first 5 weeks’ material only. Feedback in the form of correct answers, with explanation, will be provided. The progress test is for student learning and does not contribute to the final mark for the unit (ILOs 1- 8).

Summative exam at end of unit. A 2-hour closed book exam will comprise MCQs and short-answer (100% of total unit mark) (ILOs 1-8). A mark of 50% is required to pass the unit.

Reading and References

There is no compulsory unit text book.

Recommended reading:

  1. Webb, P., Bain, C., Page, A. (2017) Essential Epidemiology 3rd ed. Cambridge University Press
  2. Rothman, K., Lash, T., Greenland, S., Williams, L.(2008) Modern Epidemiology 3rd ed. Wolters Kluwer
  3. Kirkwood, B.R., Sterne, J. (2010) Essential Medical Statistics. Blackwell.
  4. Petrie, A., Sabin, C. (2009) Medical Statistics at a Glance. 3rd ed. Wiley-Blackwell.