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Introduction to Hospital Episode Statistics - 2018

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Course Information

ADRC

Course Summary

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.

Target Audience

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.

Pre-requisites

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.

Further course details can be found here.

More information regarding our courses can be found here.

Podcast for some of our previous courses can be found here.

Course Code

ADRCE-Training051 Hardelid

Course Dates

5th March 2018 – 6th March 2018

Places Available

Course Leader

Dr Pia Hardelid
Course Description

Course Contents:

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
  • learn how to carry out basic cleaning, management and analysis tasks using HES data
  • learn how to ensure anonymity and confidentiality when using HES

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