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PHPS41110

Academic Year 2024/2025

Advanced Epidemiology (PHPS41110)

Subject:
Public Health & Population Sci
College:
Health & Agricultural Sciences
School:
Public Hlth, Phys & Sports Sci
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Mary Codd
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module builds on epidemiological knowledge and skills acquired in a basic epidemiology course such as PHPS40010 (Principles of Epidemiology). The emphasis in the module is on a thorough understanding of different types of data incorporated into epidemiological and clinical research, with specific focus on:

a) Patent Reported Outcomes data, including the development and uses of Patient Reported Outcome Measures (PROMs) and Quality of Life (QOL) measures for the subjective reporting and objective assessment of a broad range of health, disease and disability states. These include assessments of: General Health Status; Psychological Wellbeing; Social health and Social support;
Physical handicap and disability; Mental health states; Mental status; and Pain and illness assessments;
b) Quality of Care (QOC) data, incorporating assessments of the Donabedian components of Quality of Care;
c) Aggregate data used to identify geographic distribution and spatial clustering of disease;
d) The data requirements for, and applications of, disease modelling and disease projections; and
e) The integration of data from basic science and epidemiology to identify causal associations and transmission patterns.

Analytical techniques to be used will include:
a) Assessment of the quality of data generated using PROMs and QOL instruments, specifically the validity, reliability and internal consistency reliability of instruments using measures of validity, Kappa statistics and Cronbach's alpha;
b) Establishment of population normative data for endpoints measured using PROMs and QOL instruments;
c) Data reduction techniques e.g. Principal Component Analysis (PCA) and Factor Analysis (FA);
d) Methods for identification and construction of latent variables in datasets;
e) Use of clustering techniques for the analysis of PROMs, QOL and QOC data;
f) Impact of missing data on analysis and interpretation of results, and methods used to adjust for missing data;
g) Orientation to geographical and spatial software.

About this Module

Learning Outcomes:

At the end of this module students will be able to:
a) critically appraise the quality of data collected using PROMs and QOL instruments, specifically the validity, reliability and internal consistency reliability of instruments using measures of validity, Kappa statistics and Cronbach's alpha;
b) assess normative data for endpoints measured using PROMs and QOL instruments;
c) apply data reduction techniques e.g. Principal Component Analysis (PCA) and Factor Analysis (FA) to suitable datasets;
d) identify and construct latent variables in datasets;
e) use clustering techniques in SPSS for the analysis of PROMs, QOL and QOC data;
f) assess the impact of missing data on analysis and interpretation of results, and methods used to adjust for missing data;
g) apply geographical and spatial software to suitable data

They will understand the purpose, assumptions and implementation of disease modelling, and be aware of the contributions of basic sciences and genomic analyses in unravelling transmission patterns and dynamics.

Indicative Module Content:

Design and assessment of PROMs, measures of QOL and QOC
Techniques for data reduction and extraction
Identification and construction of latent variables
Analysis of data using cluster techniques
Geographic and spatial distribution of disease occurrence
Disease modelling and projections
The contribution of basic science to analysis of population health

Student Effort Hours:
Student Effort Type Hours
Lectures

14

Small Group

8

Specified Learning Activities

18

Autonomous Student Learning

60

Total

100


Approaches to Teaching and Learning:
In-person classes
Critical review of relevant published work
Class participation
Group work and discussion
Practical assignments using appropriate software

Requirements, Exclusions and Recommendations
Learning Requirements:

PHPS40010 or equivalent


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Assessment of an assigned PROM or QOL measure wrt development, purpose, properties and analysis. In-class Presentation Week 4 Standard conversion grade scale 40% No
25
No
Group Work Assignment: Review and presentation of a disease modelling study with critique of assumptions, data used and predictions. Week 7 Standard conversion grade scale 40% No
25
No
Individual Project: Analysis of an assigned dataset using cluster analysis. Written report on the data, analytical technique and results Week 14 Standard conversion grade scale 40% No
50
No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Required and recommended readings are made available each week in Brightspace

Name Role
Dr Guerrino Macori Lecturer / Co-Lecturer
Professor Conor McAloon Lecturer / Co-Lecturer
Parnian Jalili Tutor