7 Summary

In this module you have been introduced to many important aspects of data, including AMR data.

Congratulations on making it this far!

You have seen that in order for data to be useful, it needs to be transformed into information that can be used to support evidence-based decision making, policy formulation and program design. Information needs are best represented as a cycle – each stage of data collection, management and analysis to support decision-making can lead to more questions and gaps to address, prompting the cycle to continue. You have become familiar with the core concepts and properties of data, including how its classifications and measurements affect the way that it is processed and analysed. You have learned to identify the characteristics of a good AMR indicator, and how indicators can help to answer important questions on how to tackle AMR. You have reviewed common sources of AMR-related data and explored the flow of AMR data at different levels in surveillance systems. Finally, you have learned to identify common sources of error and bias that affect analyses of AMR-related data, as well as some strategies to reduce these errors and biases.

You should now be able to:

• explain why it is important to measure AMR, AMU and AMC data
• identify different types of AMR, AMU and AMC data
• explain how data become useful and lead to informed decision-making in the AMR response
• identify and explain potential sources of error and bias that affect the quality and reliability of AMR, AMU and AMC data.

Now that you have completed this module, consider the following questions:

• What is the single most important lesson that you have taken away from this module?
• How relevant is it to your work?
• Can you suggest ways in which this new knowledge can benefit your practice?

When you have reflected on these, go to your reflective blog [Tip: hold Ctrl and click a link to open it in a new tab. (Hide tip)]   and note down your thoughts.