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, how inconsistencies arise, and approaches to deal with these. Participants will also learn how to ensure individuals’ anonymity and confidentiality when carrying out analyses and publishing results based on 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:
• HES data collection and coding
• HES data structure
• How to clean and manage HES data
• How to ensure anonymity and confidentiality
• How to carry out basic analyses using HES data
• Sources of variation in HES data
• How to apply for HES data
By the end of the course participants will:
• understand how and why HES data are collected
• become aware of the strength and weaknesses of using HES data for research
• know how to carry out basic cleaning, management and analysis tasks using HES data
• know how to ensure anonymity and confidentiality when using HES
This course is aimed at researchers and analysts at any level working in universities, local authorities, civil service, the NHS, private sector or third sector organisations.
Pre-requisites
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.
Cost
The fee per teaching day is:
• £30 per day for UK/EU registered students
• £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff and staff at UK/EU registered charity organisations and recognised UK/EU research institutions.
• £220 per day for all other participants
All fees include event materials, lunch, morning and afternoon tea. They do not include travel and accommodation costs.