**This document includes indicative outline information for each module from last year. Details are subject to change for future iterations of the course.**

## Principles of Epidemiology

**Module Aims**:

This core module covers principles of both descriptive and analytical epidemiology. Students will learn how to describe the distribution and determinants of health-related states and events in a population, the main approaches to studying the relationship between exposures and outcomes and their principal applications to the control of diseases and other health problems.

**Module Learning Outcomes**:

By the end of the module, students should be able to:

- Describe and discuss the application and scope of epidemiological research
- Define, calculate and interpret epidemiological measures, including measures of disease frequency, measures of association and measures of population impact
- Plan, discuss and evaluate epidemiological study designs and analytical strategies
- Describe the uses and limitations of different data sources in the context of epidemiological measures
- Express and describe the key concepts, implications and approaches to address random error, bias, and confounding in the context of epidemiological research and study design
- Discuss the concepts of reliability and validity
- Evaluate the results and interpretation of published epidemiological research
- Describe strategies for causal inference in epidemiological research

**Pre-requisites**:

Fluent numeracy and a good understanding of elementary algebra (e.g. rearranging equations, graphical interpretation of a linear equation in two variables, simultaneous linear equations when solution is unique, quadratic equations), logarithms (and performing algebraic operations on logarithmic scale), summation notation (∑), and probability (including performing simple probability operations). Familiarity with scientific notation, and with performing automated calculations (e.g. in Excel, R, or equivalent).

**Teaching Strategy**:

The module will be delivered using a combination of lectures, workshops, group discussion and experiential learning. Some reading will be required prior to some sessions.

**Assessment**:

A timed assessment, approximately 1 week after the end of the module, consisting of structured questions on core epidemiological principles. (100% of module grade)

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## Principles of Biostatistics

**Module Length**: 9 days over 6 weeks

**Module Aims**: This core module aims to provide students with the necessary knowledge and biostatistical skills to be able to interpret and conduct basic statistical analyses of population health data.

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**Module Learning Outcomes**:

By the end of the module, students should be able to:

- Understand sampling variation in the context of population health studies
- Use R to manipulate data and to apply and interpret the output of commonly used statistical procedures
- Select, perform and interpret appropriate descriptive analyses of population health data
- Select, apply and interpret common regression models for the statistical analysis of population health data
- Perform standard sample size and power calculations.

**Pre-requisites**: Fluent numeracy and a good understanding of elementary algebra (e.g. rearranging equations, graphical interpretation of a linear equation in two variables, simultaneous linear equations when the solution is unique, quadratic equations), logarithms (and performing operations on logarithmic scale), summation notation (∑), and probability (including performing simple probability operations). Familiarity with scientific notation, and with performing automated calculations (e.g. in excel, R, or equivalent).

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**Teaching Strategy**: The module will be delivered using a combination of lectures, class discussion, small-group exercises, and computer practical. Some reading may be required prior to some sessions.

**Assessment**

Students will be assessed individually upon this module via a timed open book assessment approximately 1 week after the end of the module, consisting of a data analysis task and associated report. Dataset to be given to students 48 hours in advance of assessment. ** **

## Statistics for Health Data Science

**Module Length and Dates**: 8 days over 5 weeks

**Module Aims**: This core module aims to provide students with a knowledge of the fundamentals of statistical theory and some experience of analysing data using statistical software.

**Module Learning Outcomes**:

By the end of the module, students should be able to:

- compare and contrast frequentist and Bayesian statistical theory
- choose appropriate models and methods for analysing a given dataset
- explain the assumptions made by these models and methods
- interpret the results obtained from application of these methods
- describe how the parameters of these models are estimated
- fit these models and apply these methods using R, and interpret the output
- perform simple likelihood and Bayesian calculations
- write simple R programs to determine repeated-sampling properties of estimators by simulation (and understand such programs).

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**Pre-requisites**: Good understanding of the concepts of integration and differentiation, logarithms and exponents, and matrix inversion. Ability to perform simple manipulation of vectors and matrices (e.g. addition and multiplication). Good knowledge of basic concepts of probability theory, e.g. probability density functions, marginal and conditional distributions, random variables, expectation and variance, and important parametric distributions (especially the normal, binomial and Poisson distributions). There will be a brief review of probability theory at the beginning of the module, but you may struggle with this module if you are not already fairly comfortable with these concepts. See the content of session 2 (`Probability Theory’) described below and consider the optional reading listed there as possible preliminary reading for this module.

**Teaching Strategy**: Lectures, computer practicals and mathematical exercises. Some reading is required.

**Assessment**: The module will be assessed in two parts. Part one (25% of module grade) is a timed, open-book test involving calculations, interpretation of the results, and interpretation of concepts taught in the module. Part two (75% of module grade) will involve a data set and a research question. Students will use R to analyse this data set, and then write a clear and detailed report describing the methods they used, the results they obtained, their interpretation of these results, and their conclusions.

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## Applied Data Analysis

** ****Module Length: **Ten 3-hour sessions

**Module Aims: **

This core module aims to equip students to competently handle data. We will be using the R language and environment for statistical computing and graphics. We will teach:

- Why reproducible research is important, and how to make your own analysis reproducible
- The basics of coding in R
- How to import data into R
- How to clean and manipulate data
- How to summarise data
- How to identify and rectify errors in analysis
- Data visualisation

We will practise some concepts learnt earlier in term e.g. trial design, age standardisation, routine data, chance, bias, confounding, measures of risk.

**Module learning outcomes:**

By the end of the module, all students should be able to:

- Explain why reproducible research is important and conduct their own research in a reproducible way
- Import, input, clean, check, summarise, and manipulate raw data
- Prepare data for analysis
- Understand how to present data using appropriate visualisation

**Pre-requisites: **

*R and R Studio installed on computersBasic skills in mathematicsUnderstand the principles of direct standardisation of incidence rates*

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**Teaching strategy:**

The material is work-book based, allowing students to work at the pace that suits them and whole-group discussion will be minimal. For some sessions notes will be provided in advance. All sessions will focus on developing practical skills.**Assessment:**

Take-home assignment involving importing and manipulation of multiple data files in order to generate summary figures and tables; and associated interpretation.

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## Principles of Public Health

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**Module Length** 5 days over 3 weeks

**Module Aims**

This core module aims to provide students with an understanding of some key principles in public health relevant to students working towards all themes and none. It provides brief introductory sessions on a number of issues covered in more depth in student selected modules in term 2 (e.g. Changing Behaviour, Health Economics, Policy & Public Health etc). This will provide those students who do not take relevant student selected modules with some knowledge of those topics, whilst acting as ‘tasters’ for those who are considering taking these modules. The module will explore the scope of public health, wider social and environmental influences on health and illness, approaches to prevention, assessing the population impact of influences on health, economic aspects of healthcare and public health, ethical aspects of public health approaches, and how we can use population health research findings to make an impact on society.

**Module Learning Outcomes**

By the end of the module, students should be able to:

- Critically discuss the nature and scope of the public health approach
- Describe the influence that social and environmental factors can have on health and disease
- Distinguish a number of different approaches to management of public health problems and determine when each might be most appropriate
- Calculate measures of population impact and understand the strengths and limitations of these measures
- Assess a proposed early detection or screening programme against appropriate criteria
- Understand the principles of health care, public health financing and healthcare organisation
- Demonstrate an understanding of the core influences on public health and health care policy
- Discuss the ethical implications of different public health approaches

**Pre-requisites**

Principles of Epidemiology

**Teaching Strategy**

The module will be delivered using a combination of lectures, workshops, small-group exercises, and class discussions. Some reading may be required prior to some sessions.

**Assessment**

The module will be assessed with a 1500w collaborative essay, conducted in course supervision groups; plus a 250w individual reflection on the process of working on the collaborative essay.

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## Research Skills

**Module Length:** 7 days

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**Module Aims: **

This core module aims to provide all students with an understanding of research as a process from research question construction and systematic examination and synthesis of existing knowledge, through research design, data collection and analysis, to reporting and dissemination. It will equip students with skills to develop the research protocol for their dissertation and project management strategies that they can apply during the course, and beyond.

**Module Learning Outcomes: **

By the end of the module students should be able to:

- Describe the philosophical underpinnings of their research projects
- Plan a research project, including development of a research question, implementation of a systematic search strategy and production of a research protocol and analysis plan
- Critically appraise and synthesise published research literature using appropriate methods
- Communicate research plans and findings to different audiences, and design an appropriate strategy for achieving impact from population health science research

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**Pre-requisites: **Principles of Epidemiology

**Teaching Strategy:**

The module will be delivered using a combination of lectures, workshops, group discussion and experiential learning. Some reading and preparatory work will be required prior to most sessions.

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**Assessment:**

This module will be assessed by a written research protocol or analysis plan for the dissertation, to be handed in one week after the end of the module.

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