Processing and analysing AMR data

Introduction

This module looks at how antimicrobial resistance (AMR) data is transformed into information, locally, nationally and globally. It provides an overview of the stages from data collection, to data management and data analysis. It introduces the core concepts, approaches and methods for analysing data, including descriptive and inferential statistics, and how they can be used to answer important questions about AMR. Sources of error and bias will also be reviewed.

This module is part of a series of modules related to AMR data. You should complete Fundamentals of data for AMR and Sampling (either the animal health or human health version) in order to understand the basics of AMR data before starting this module. By the end of the module, you should be able to:

• describe components of the information cycle
• list and explain principles of best practice for data collection
• list and explain principles of best practice for data management
• explain the difference between descriptive and inferential statistics
• calculate measures of central tendency
• understand concepts related to hypothesis testing
• interpret reported findings from a hypothesis test, including strength of statistical evidence, and potential sources of error and bias.