Module manager: Innocent Anioke
Email: I.anioke@leeds.ac.uk
Taught: Semester 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr) View Timetable
Year running 2025/26
Students are required to meet the programme entry requirements prior to studying the module.
N/A
This module is not approved as an Elective
This module seeks to equip students with fundamental statistical skills to identify patterns and trends in population health data. Students will use data analysis tools to uncover inequalities in health status and health service provision, as well as evaluate intervention effectiveness, to inform evidence-based decision-making.
In this module you will have the opportunity to develop keys skills to interrogate statistical test results, interpret epidemiological indicators, and critically evaluate and appraise the epidemiological evidence and health system research findings and discuss challenges/opportunities in implementation of the evidence. You will also be engaged in developing skills in health surveillance system.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Explain statistical tools and methods used to carry out quantitative analysis of population health data.
2. Apply basic statistical measures (e.g descriptive analyses) to analyse population health data and interpret result from quantitative research findings.
3. Interpret epidemiological indicators such as life expectancy, infant mortality, morbidity rates, DALYs (Disability-Adjusted Life Years), and QALYs (Quality-Adjusted Life Years) and evaluate their implications on health systems.
4. Apply techniques [e.g., Geographic Information Systems (GIS) tools] for mapping health data and visualising the pattern of global distribution of disease, health service coverage, and environmental risk factors.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Informed decision-making skill: Use data-driven evidence to make recommendations.
2. Statistical skills: Conduct basic statistical tests of public health data.
3. Digital tools: Use digital tools to interrogate health data.
4. Effective communication skills: Communicate results of health data analysis to diverse stakeholders.
Indicative content for this module includes:
1. Introduction to health data analysis.
2. Descriptive Statistics in Population Health; Inferential Statistics and Hypothesis Testing.
3. Epidemiological indicators and their implications on health systems.
4. Interpretation of Global quantitative research.
5. Introduction to health surveillance using GIS technique.
6. Apply health surveillance.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 6 | 1 | 6 |
| WEBINAR | 1 | 1.5 | 1.5 |
| WEBINAR | 5 | 1 | 5 |
| Independent online learning hours | 42 | ||
| Private study hours | 95.5 | ||
| Total Contact hours | 12.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Formative assessment- sample questions on:
- Statistical tests/calculations – then for students to make recommendations based on calculations. These will be provided in a workbook consisting of exercises to conduct epidemiological and statistical calculations. Answers will be provided for students to review their own work. This exercise will support students to do the summative assessment.
- Analysis of research of quantitative research findings. Students will be given research papers with quantitative findings and asked to interpret the findings and make recommendations related to the finding. Sample answers will be provided. This exercise will help students prepare for the summative assessment.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | Short Answer Questions | 100 |
| Assignment | Workbook for Statistical Analysis (formative) | 0 |
| Assignment | Review Research Paper (formative) | 0 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team