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Course

Processing and analysing AMR data

Processing and analysing AMR data

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  • 6 hours study
  • Level 2: Intermediate
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    • Antimicrobial resistance (AMR) is a major global concern because drug-resistant infections can kill, spread to others, and impose huge costs on individuals and society. In order to tackle this problem we first need to understand it; we can do this by collecting and sharing high-quality data that is relevant to AMR.

      Data relating to AMR means that trends in resistance and antimicrobial use can be monitored and used to make better decisions to help tackle the AMR crisis. As an AMR surveillance professional, you have a key role to play in this surveillance process, whether it is in collecting, analysing or interpreting data, or in using the data to develop new policies and practices.

      This Fleming Fund course, Tackling antimicrobial resistance, comprises a series of online modules. The aim of this course is to help you to identify, develop and apply skills and knowledge relevant to your role in order to change and improve your working practice and the practice of people that you work with.

      This module explains 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. It introduces descriptive and inferential statistics, and shows how they can be used to answer important questions about AMR. It also identifies sources of error and bias and discusses how to avoid them.

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    • This course is part of a collection

      This course is part of a collection

      This course is part of a collection of courses called Tackling antimicrobial resistance. There are 25 courses in this collection so you may find other courses here that maybe of interest to you.

      See this collection

    • Course learning outcomes

      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.

    • Course dates:

      First Published 29/03/2021.

      Updated 18/06/2021

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      About this course

      • 6 hours study
      • Level 2: Intermediate
      • Gain a digital badge

      Ratings

      0 out of 5 stars

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