Environmental epidemiology studies the effects of environmental exposures on health and wellbeing in the human population. The unit has a strong focus on epidemiological and statistical methods and applications including time-series and spatial analysis. Taking an eco-social approach, we broadly define the environment as anything external to the person including the physical, social, psychological and aesthetic environment and their interactions. This unit will provide students with a practical understanding of the research methods used to assess the exposure-response relationship between environmental hazards and health outcomes. Building on students knowledge of the environment - for example air, water, soil, climate, the built environment, the unit will cover study designs and methods of exposure assessment and statistical analysis used in assessing environmental health risks. Students will gain technical skills in data analysis and visualisation including spatial data involving Geographic Information Systems (GIS) for mapping and statistical analysis of exposures and health outcomes using the R Statistical Software. The unit will also explore future directions in the field of environmental epidemiology. Students will also learn about its importance in the Planetary Health framework when assessing global health risks and impacts in the context of climate change.

Throughout our lives, information about our health and the care we receive is recorded and stored across various health-related databases, e.g., hospital admissions, ambulance service, cancer registry. Data linkage is a process that brings together information from different databases about the same individual, family, place or event. This process creates a chronological sequence of health events or individual 'health story' that can be combined into a much larger story about the health of people. This information can be used for research or to improve health services. This unit is suitable for health services researchers, policy makers, clinical practitioners, biostatisticians and data managers. We explain how data linkage is conducted, illustrate how data linkage can be used for research, highlighting the advantages, and the dangers and pitfalls. We describe how to design linked data studies, outline the data management steps required before analysing the data, and discuss some of the methods and issues of analysing linked data. Students will have access to data from a real data linkage and will gain hands-on experience to develop their programming skills for handling large complex dataset

This unit introduces the principles underpinning the design and conduct of high quality controlled clinical trials to generate good evidence for health care decision making. The topics include clinical trial design, randomisation, sample size, measures of treatment effect, methodological issues, trial protocols, and ethical principles.

In this unit, you will learn how to analyse health data using statistical models. In particular, how to fit and interpret the results of different statistical models which are commonly used in medicine and health research: linear models, logistic models, and survival models. This unit is ideal for those who wish to further develop their research skills and/or improve their literacy in reading and critiquing journal articles in medicine and health.
The focus of the unit is very applied and not mathematical. Students gain hands on experience in fitting statistical models in real data. You will learn how to clean data, build an appropriate model, and interpret results. This unit serves as a prerequisite for PUBH5218 Advanced Statistical Modelling.

All models are wrong, but some are useful. Developing a useful statistical model from the available data can be challenging! For example, what should you do if a model assumption is violated, or if data are missing? Your statistical toolkit will be expanded to include modern techniques for tackling challenging issues that often exist in health research data, e.g. missing observations, non-linear effects, confounding and correlation between observations in a dataset. The methods for correlated data are relevant for analysing some epidemiological observational study designs (e.g., matched case-control studies, longitudinal studies with repeated measurements), and clinical trial designs (e.g. cluster RCTs, cross-over RCTs). Techniques to help assess the usefulness of a model will also be covered. This unit of study focuses on the application of statistical methods using the statistical software R. Topics: fractional polynomials for non-linear effects; mixed or random effects and marginal models (e.g. GEE) for correlated data; multiple imputation for missing data; propensity score for confounding; tools to assess model performance and classification.

This unit of study is intended for students who have completed Epidemiology Methods and Uses (or an equivalent unit of study) at a credit or higher level. It is designed to extend students' practical and theoretical knowledge of epidemiology beyond basic principles and in particular to give them a practical understanding of how epidemiological principles and practices are used in real world settings. Students are given an opportunity to acquire some of the practical knowledge and skills needed to undertake epidemiological research and also to consolidate their critical appraisal skills.

Comprehensive evaluation of public health and disease prevention programs is critical to developing an evidence base for public health practice as well as for accountability to stakeholders. Evaluations demonstrate the efficacy, effectiveness and/or efficiency of the program and provide models of good practice. This course builds skills in planning, conducting and using formative, process, impact and outcome evaluations of public health programs, with an emphasis on those which address public health approaches to chronic disease prevention. Using three highly interactive workshops (Workshops 1 and 2 on a consecutive Friday and Saturday) supplemented by online resources and four weeks of online discussions, students will participate in readings, group work, lectures and discussions, to develop skills in defining the purpose of an evaluation, defining the evaluation questions, selecting evaluation designs and measures for evaluation (and understand the process of measurement development). A specific focus will be on skills to critically appraise evaluations and to use results in practice. Workshop 3 will be devoted to methods for scaling up interventions to the population level, and to the design and evaluation of multi-faceted complex public health programs, including presentations by currently practicing public program managers.

The overall aim of the course is to develop students' knowledge and skills of economic evaluation as an aid to priority setting in health care. Students will be introduced to the principles of economic evaluation and develop skills in the application of those principles to resource allocation choices. Emphasis will be placed on learning by case study analysis and problem solving in small groups. This unit covers: principles and different types of economic evaluation; critical appraisal guidelines; measuring and valuing benefits; methods of costing; modeling in economic evaluation, the role of the PBAC, introduction to advanced methods including use of patient-level data and data linkage. The workshops consist of interactive lectures, class exercises and quizzes.

This unit combines decision theory and more advanced health economic concepts to provide students with hands-on skills in specialised analysis methods, and modelling techniques, for evaluating healthcare options and reaching recommendations in the face of uncertainty. Students will calculate and analyse data from clinical studies, extrapolate clinical study results to other settings, and construct models that synthesise evidence (and expert opinion) from multiple sources. Specific topics of study include: decision trees; expected utility theory; sensitivity and threshold analysis; the value of information (including screening and diagnostic tests); the calculation and analysis of costs and quality-adjusted survival using individual patient data (including bootstrapping techniques); Markov processes and micro-simulation; and presenting and interpreting the results of (health economic) evaluations. Lectures are accompanied by practical exercises and readings. Students gain experience applying the methods presented in lectures via computer practicals using Excel and decision analysis software (TreeAge).

This advanced unit of study extends students' practical and theoretical knowledge of qualitative research to provide advanced concepts and skills in qualitative data analysis and writing. You should have a basic understanding of qualitative research. We will explore the principles of qualitative analysis, and learn about different analytic strategies and key analytic tools. You will learn how to develop codes and themes, use memos and analytic maps, and interpret data through the process of writing. You will learn about starting writing, structuring articles, making analytic arguments, and editing your own work. Most importantly, we will consider what it means to think and write 'qualitatively'. You will analyse a portfolio of qualitative data, and produce a results and discussion section for a journal article. After completing this unit you will have increased your experience, skills and confidence in qualitative data analysis and writing.

This unit is intended for students nearing the end of their MPH/GradDipPH. The Special Project is a self-directed unit focussed on a specific MPH-related topic of interest to the student. The project is supervised by an academic within the School. An external person can act as the main supervisor but a School academic would also be required. This project may be developed by the student, or the student could develop a project in consultation with an intended supervisor. The student needs to meet with the supervisor during the semester. Preferably this would be at least three times but the frequency will depend on the project and the preference of the supervisor. As the project is self-directed, there is not an expectation that the supervisor would have close involvement, although they can have if they want to. The student would be expected to undertake approximately 120 to 150 hours of work for this unit. The format of the final report or other output can be whatever is appropriate, as agreed with the supervisor(s).

Students will do an independent research project in an area of interest relevant to public health. The student will work with a supervisor, who will help the student select a topic and then guide the student through the process of conducting a research project. The project may be a systematic review of the literature, analysis of an existing dataset, a quantitative or qualitative research study, a policy analysis or some other project acceptable to the dissertation supervisor.

Students will do an independent research project in an area of interest relevant to public health. The student will work with a supervisor, who will help the student select a topic and then guide the student through the process of conducting a research project. The project may be a systematic review of the literature, analysis of an existing dataset, a quantitative or qualitative research study, a policy analysis or some other project acceptable to the dissertation supervisor.

Students will do an independent research project in an area of interest relevant to public health. The student will work with a supervisor, who will help the student select a topic and then guide the student through the process of conducting a research project. The project may be a systematic review of the literature, analysis of an existing dataset, a quantitative or qualitative research study, a policy analysis or some other project acceptable to the dissertation supervisor.

This project unit provides an opportunity to apply skills learnt in other units and further develop knowledge and skills by undertaking a systematic review (ideally including a meta analysis) in a topic area nominated by the student. The unit is student-driven with no direct supervision, but the student will have three chances to interact with the unit coordinator, to define the research questions, to discuss the selected methodology, and prior to submission. The assessment task is to undertake a systematic review of randomised trials and present the review in the form of a paper suitable for submission to a peer reviewed scientific, academic or professional journal.

This unit of study will appeal to anyone wanting to write medical papers for conferences or journals, or to improve their paper writing skills. Students will work at their own pace through 9 modules covering research integrity, medical style, abstracts, presentations and posters, constructing a paper, data visualisation, manuscript submission, responding to reviewers' comments, post-publication research dissemination, and peer- reviewing a paper. This unit aims to teach students the principles of research integrity in writing for medical journals, typical issues they may face, and link to resources to help them maintain integrity through their publishing careers. It will guide them to reliable evidence-based resources to improve their conference abstract, presentation and poster design, and manuscript style and writing. Students will learn about reporting guidelines, common pitfalls in writing and presenting research, choosing a journal, keywords, improving tables and figures for manuscripts through open source software, copyright, writing cover letters and response letters to reviewers. Students will learn about measuring research impact and ways to improve research reach, dealing with the media and press releases, using social media in dissemination, digital archiving and basic skills needed to act as a peer-reviewer. This is an online unit, but those needing to study in block mode will do online study as well as a workshop.

In this unit of study, the student will develop his/her own research proposal, to a standard suitable for a peer-reviewed granting body. Each section of a grant proposal (Abstract, Aims, Background, Significance, Methods) will be discussed, with the student presenting and refining the corresponding section of his/her own proposal in a synchronous online workshop setting. This will be complemented by online presentations from experienced researchers on the practical aspects of clinical research. Topics include: observational studies, randomised controlled trials, diagnostic test evaluation, qualitative studies, economic evaluation, and process evaluation. The unit will conclude with a one-day, face-to-face, mandatory workshop where students will learn about budgeting, qualitative research, strategies and grant administration, research ethics and peer review of research grants.

This unit of study introduces the student to basic concepts behind diagnostic and screening tests, including: test accuracy, sources of bias in test evaluation, critical appraisal of test evaluation studies, application of test results to individual patients, place of tests in diagnostic pathways, impact of tests on patient outcomes, principles and use of evidence in population screening, overdiagnosis, systematic review of diagnostic tests and meta-analysis. After completing this unit of study, the student should have a comprehensive understanding of contemporary issues and the methodology underlying, diagnostic and screening test evaluation and application.

In this unit of study, we aim to introduce you to systematic reviews and meta-analyses of relevance to healthcare with a particular focus on systematic reviews of randomized controlled trials. Students can choose to learn in online or normal day (on-campus) mode. All students will work through four online modules, delivered over twelve weeks, addressing the following topics at an introductory level: What and why systematic reviews (and meta-analysis); How to formulate answerable healthcare questions and searching for systematic reviews; How a systematic review is conducted and understanding the principles of meta-analysis; and How to appraise, interpret and apply the results of systematic reviews (and meta-analyses). Students will have the opportunity to discuss unit of study learning materials in online tutorials or via weekly (on-campus) tutorials. Readings and other learning materials will be available via eLearning.