Training and Events
Introduction to Hospital Episode Statistics (few places remaining)
|University of Southampton/ADRC-E|
Dr Pia Hardelid
27/03/2017 - 28/03/2017
Southampton Statistical Sciences Research Institute, Building 39, University of Southampton,Highfield,Southampton
View in Google Maps (SO17 1BJ)
Course number: ADRCE-training033 Hardelid
Course places are limited and registration by 20th March 2017 is strongly recommended.
Summary of Course:
This course will provide participants with an understanding of how Hospital Episode Statistics (HES) data are collected and coded, their structure, and how to clean and analyse HES data. A key focus will be on developing an understanding of the strengths and weaknesses of HES data, how inconsistencies arise, and approaches to deal with these. Participants will also learn how to ensure individuals’ anonymity and confidentiality when analysing and publishing using HES. The course consists of a mixture of lectures and practicals for which participants will use Stata software to clean and analyse HES data.
The course covers:
By the end of the course participants will:
Computer Software and Computer workshops:
This is a computer lab based course.
Dr Pia Hardelid is senior research associate and NIHR research fellow at the Institute of Child Health and in the Department of Primary Care and Population Health at University College London. Her research interests are in the use of administrative databases, including hospital, intensive and primary care databases for child health research and infectious disease surveillance. Her particular research focus is on the epidemiology of respiratory tract infections, chronic conditions and mortality in childhood. She has many years’ experience in using HES data for research. She holds a PhD in infectious disease epidemiology from University College London.
Other speakers from UCL and LSHTM TBC
Researchers at all levels in academia, government and private sector at all levels who are using/planning to use Hospital Episode Statistics in their work.
Participants will write and execute programmes in Stata during the practical sessions. Previous experience of programming in Stata, R or SAS will therefore be helpful, but Stata code and instructions will be provided to all participants. There are no pre-requisites for the lectures.
Programme (subject to change):
Course schedule: Day 1
9.30-10.00 Registration and computer set up (with coffee)
10.00-10.15 Welcome and introductions, learning objectives
10.15-11.30 Lecture 1: What is HES? What can HES data be used for?
11.45-12.45 Lecture 2: Structure of HES and planning analyses
12.45 -13.30 Lunch
13.30- 15.30 Practical 1: HES data structure, linking episodes and admissions
(including coffee at 14.30)
15.30-16.15 Lecture 3: Introduction to coding in HES
10.00-11.45 Practical 2: Working with HES coding (including coffee)
11.45-12.30: Lecture 4: Applying for access to HES data, ethical considerations and disclosure control
13.30-14.00 Analysing HES Accident and Emergency and Outpatient datasets
14.00-14.30 Lecture 5: Comparing hospital performance using HES
14.45-15.15 Lecture 6: Using clinical data for validation of hospital administrative data
15.15-15.45 Lecture 7: Linking patients within HES and the HESID
15.45-16.00 Concluding remarks
Participants will receive written course notes.
Podcasts for some of our previous courses can be found at https://adrn.ac.uk/about/network/england/training-podcasts/
Our courses are very popular and are often oversubscribed. If you cannot attend a course you have registered for, it is essential to kindly notify us a minimum of 30 days in advance so that your place can be released for another attendee. Details of our cancellation policy are here: http://store.southampton.ac.uk/help/?HelpID=1 . Please see our full course list here: http://store.southampton.ac.uk/browse/product.asp?compid=1&modid=5&catid=113.
Intermediate (some prior knowledge)
The fee per day is:
Website and registration
Secondary Analysis, Analysis of official statistics, Analysis of administrative data, Data Quality and Data Management (other), Longitudinal Data Analysis
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