PHPS41110 Advanced Epidemiology

Academic Year 2023/2024

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 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.

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Curricular information is subject to change

Learning Outcomes:

At the end of this module students will know how 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 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 also 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 analysis
Reasons for, impact of, and techniques for imputation of missing data
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

16
Small Group

8
Specified Learning Activities

16
Autonomous Student Learning

60
Total

100
Approaches to Teaching and Learning:
Face-to-face classes
Critical review of relevant published work
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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Group Project: In-class Presentation: Development, applications and assessment of an assigned PROM or QOL measure Throughout the Trimester n/a Standard conversion grade scale 40% Yes

25
Assignment: Written report: Analysis of a dataset provided using one of the techniques covered in the module Coursework (End of Trimester) n/a Standard conversion grade scale 40% Yes

50
Group Project: Written report: Application of a data reduction technique to a dataset provided, with interpretation of results Throughout the Trimester n/a Standard conversion grade scale 40% No

25

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 available in Brightspace
Name Role
Mr John Loughrey Tutor
Dr Guerrino Macori Tutor
Assoc Professor Conor McAloon Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 13:00 - 14:50