The course of study for the MPhil consists of eleven taught modules and a dissertation. All students take five core modules, attending up to 24 hours of class over 4 days each week in term 1. Candidates then follow a pathway of six additional modules in pursuit of one of the named specialisation themes (Epidemiology, Global Health, Health Data Science, Infectious Diseases, Public Health, Primary Care Research), or select freely from the full list of additional modules (although their choice may be constrained by dependencies, pre-requisites and timetabling). Student-selected modules typically involve 24 hours of class time on 4 days spread over 2 to 4 weeks. Students also begin work on their dissertations during term 2, increasing focus on the dissertations in term 3. An indicative timetable is available here.
The following sections include details of likely modules, but please be aware that the list of core and additional modules is not announced by the Degree Committee until nearer the start of the course. Not all of the modules below will necessarily be offered in any one year, although this list is indicative of what students can expect. Detailed outlines of all modules offered each year will be published in the course handbook. Any module selected by fewer than three students in any particular cohort may be withdrawn for that year.
Candidates choose either Principles of Biostatistics or Statistics for Health Data Science, and also take Principles of Epidemiology, Applied Data Analysis, Principles of Public Health, and Research Skills. These core modules provide an essential foundation for the study of population health sciences.
Principles of Biostatistics
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. Students may choose to take this, or Statistics for HDS
Statistics for Health Data Science
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. Students following the Health Data Science theme are required to take this core module instead of Principles of Biostatistics. Other students with sufficient pre-requisite knowledge may also choose to take this module.
Principles of Epidemiology
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.
Applied Data Analysis
This core module aims to equip students with the skills to manage both bespoke and routinely collected datasets of all sizes, and to prepare an analysis-ready dataset using an R script. The module covers identification of the data required to answer a question, combination of multiple datasets, data collection (design, file formats, data types, data entry, avoidance of errors, etc.), file organisation, data cleaning, data manipulation, data preparation, and data presentation.
The module revises and applies epidemiological and statistical concepts covered earlier in the course, including measures of risk, chance, bias, confounding, standard deviation, standard error, χ2, t-tests and their non-parametric equivalent, p-values and confidence intervals.
Principles of Public Health
This core module aims to provide students with an understanding of some core principles in public health relevant to students working towards all specialisations and none. It provides brief introductory sessions to a number of issues covered in more depth in term 2 modules. This will provide those students who do not take these term 2 modules with some knowledge of those topics, whilst acting as ‘tasters’ for those who are considering taking these modules in term 2. 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.
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. In addition, this module will introduce students to the regulatory and ethical frameworks relevant to population health research and the involvement of patients and the public in research.
In addition to five core modules, each candidate pursues a pathway of at least six student-selected modules in pursuit of one of the named themes, or selects freely from the full list of modules (although choices may be constrained by dependencies, pre-requisites and timetabling). Each theme has its own aims and learning outcomes, in addition to the overall course objectives, and offers a selection of specialised modules relevant to advanced practice in that discipline.
Epidemiology is the study of the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. The Epidemiology theme aims to provide students with advanced knowledge and understanding of epidemiological principles and procedures, and their application to health and human population-based research, including training in critical appraisal, study design, protocol development and statistical analysis of epidemiological data.
Global health is characterised by placing health as an intrinsic social goal, and by aiming to justly and equitably generate health within and across populations. It consists of the international, transdisciplinary, and inter-sectoral research, knowledge, and policies for understanding health determinants and improving population health from a local to a planetary scale. It is an endeavour that requires the fostering of cross-country learning networks and communities of practice, including through building supportive institutions. The Global Health theme will provide students with the knowledge and analytical skills to understand global drivers of ill-health and inequity, and will enable them to contribute to sustainably improving health and reducing inequality worldwide. Students will be equipped to participate actively in the development of global health itself, while encouraging boundary-spanning practices.
Health Data Science (HDS)
Health Data Science is a discipline that combines mathematics, statistics and computing to answer questions in the biomedical sciences by the analysis of data. The Health Data Science theme aims to provide training in biostatistics, epidemiology, machine learning and health informatics, to equip students with the quantitative knowledge and skills for a career in health data science. The course offers a strong academic grounding in current and emerging knowledge and methods, and practical experience of analysing biomedical datasets.
Infectious Diseases are essential to a full understanding of population health. The Infectious Disease theme aims to explore infectious disease epidemiology, infectious disease dynamics, models to approximate these dynamics, the use of genomic sequencing to investigate outbreaks and trends in both local and global diseases, and the use of appropriate tools to design and evaluate key control interventions. Through practical examples of endemic and epidemic infections, students will gain the knowledge and skills to understand how and why microbes might spread, and how best to control them, and will thus be prepared for a wide variety of careers in communicable disease control and prevention.
Primary Care Research
Primary Care Research is a discipline that provides evidence to support the essential components of primary care services, which provide the first point of contact in the healthcare system. The Primary Care Research theme aims to provide students with theoretical knowledge and skills as well as practical research experience to launch an academic career in primary care research.
Public Health is the art and science of preventing disease, prolonging life and promoting health through the organized efforts of society. The Public Health theme aims to provide students with the theoretical knowledge and applied skills to launch a career in public health service, research, policy or advocacy.
A variety of teaching methods are employed, including lectures and extensive practical work. Some teachers opt for a flipped or blended learning approach, in which students are expected to engage with material ahead of class, in preparation for practical activities and discussions in class. We use a variety of individual and group activities, including problem solving activities, group discussions, presentations, critical appraisal, role play, and exercises with various data sets and computational tools.
Students are also expected to engage in a substantial amount of additional study time per module, to be spent on activities that include reading and assignments. Students are assigned to a small course supervision group, under the guidance of a Course Supervisor. Dissertation Supervisors support student work on dissertations. These supervisions combine to form an integral part of student learning throughout the year.
In order to pass the degree overall, a candidate must pass eleven modules, including five core modules, and the dissertation. Each component is graded as fail, pass or distinction. To gain a distinction for the degree, a candidate must gain distinction in at least five of those modules and in the dissertation The module assessment deadlines are spread throughout the academic year, from November to July, with deadlines typically around one week after completion of related module teaching.
Each module is assessed by an assignment of 1,500 words, or assignments deemed their equivalent. Some modules also include formative assessments. Assessment modalities include quizzes, presentations, reports, short projects, essays, data analyses, and a variety of tasks that reflect authentic practice in the field. While most assessments are based on individual work, some include group work.
A dissertation not exceeding 15,000 words in length is required and is completed by the end of July. This is usually on a topic of the candidate’s choice, selected from a list of topics provided by (or negotiated with) available supervisors. Dissertations are normally related to the specialisation theme the candidate has chosen to follow. You do not need to decide on a dissertation topic or find a supervisor prior to starting the course. There is a dissertation fair in November. At the dissertation fair, potential Dissertation Supervisors give brief summaries of the projects they are offering and there will be plenty of opportunity to discuss your dissertation choice during the first term of the course.
A candidate who achieves a ‘marginal fail’ grade in the dissertation may be examined by oral examination, at the discretion of the examiners. These oral examinations will take place in September.