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Analysis of data from a PPS

This article will explain how we analyse PPS's.
An image of colourful chairs with the word ANALYSES underneath.
© “In the waiting room for “Analyses”” flickr photo by notfrancois, shared under a Creative Commons (BY) license

In Week 1 we learned what a PPS is, considered what is required to perform a PPS and discussed a range of useful tools to support data collection for PPS.

We will now consider what to do with PPS data once it has been collected.

Once you have collected your PPS data the next step is to analyse it to allow you to describe what the data shows and share key results with your colleagues.

We will now consider methods of analysing your data and how to share your data with various audiences to provide feedback on performance and clinical practice.

There are various methods to analyse the data collected from a PPS and the method you choose to use will depend on resources available and the quantity of data you need to analyse. The actual methodology is simple and does not require complex statistical tests so can be done by anyone with basic number skills. Large PPS systems with on-line data collection include data analysis tools which allow the user to generate reports automatically and compare with other centres. However, data collected via a small-scale PPS using a paper-based system can easily be analysed with paper forms and a calculator or using an Excel spreadsheet. Whichever method is used, the outputs are similar and provide useful data to share with colleagues and identify areas for improvement.

Ward level data

Ward level data is used to calculate the prevalence of antibiotic use. This can be done for individual wards and can also be aggregated to calculate prevalence for the whole hospital. You would expect variation in prevalence between wards and between hospitals depending on the types of patients they treat and whether they have specialties such as Intensive Care and Oncology Units. Prevalence data is most useful to monitor trends over time within a ward or hospital. Increasing prevalence may indicate unnecessary use of antibiotics but could also be due to a changing case mix. Similarly, reducing prevalence could suggest more prudent use of antibiotics but it would be important to ensure that patients who do require antibiotics are receiving them.

Patient level data

Data collected from prescriptions for individual patients can be analysed to provide qualitative data about prescribing.

The simplest method that would be suitable for analysing data from one or more wards or a small hospital is simply to aggregate the data using paper-based forms as illustrated below.

Example of data collection form for individual patient

Patient ID 552386
Name of drug Amoxicillin
Route PO
Unit dose 500mg
Dosage frequency TID
Indication Exacerbation COPD
Complies with (local) guidance Yes

If the indication being treated is not listed in the local policy, then compliance cannot be assessed.

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Challenges in Antibiotic Resistance: Point Prevalence Surveys

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