What is it about?

Taking prescribed asthma medicine regularly is important to stop symptoms and attacks happening, and yet we know that many people with asthma don’t do this. Often when researchers measure how well the people studied stick to their medicine regime, the researchers just reduce complicated patterns down to a single number, like 70% of doses. This means interesting information is missed - such as how many people mostly miss doses at the weekend, only take their medicine in summer (say, for hayfever) or miss doses mostly at random. But how do we get more information in a manageable way? - we can’t look at every single detail. In this study, we wanted to see if we could let the numbers speak for themselves (known as data-driven research) to find patterns of children not sticking to their regime - instead of guessing patterns first. We used data from a study in New Zealand with 211 children, giving us over 35,000 days of data. All children were on twice a day treatment (morning and evening). We compared inhaler use five ways. First, we looked at the percentage of all the doses that a child should have taken that were taken, the percentage of days on which no doses were taken, and the percentage of days on which both doses were taken. Next, we looked at treatment gaps: 5 or more days in a row where no doses were taken, possibly because a child forgot to take their inhaler on holiday, or because they only need it for hay fever. We counted the number of treatment gaps per 100 study days, and how long they lasted altogether. We used a method called cluster analysis to find groups that had similar children in them and were as different as possible to the other groups. We then tried to find a simpler way for working out which group a child should be in that was easier to understand and to explain. In the end, we found three groups of children - those who stuck to their inhaler regimes poorly, moderately well, and very well. We found the percentage of doses that were taken during the study was the best and simplest way to put children into these groups. In the future, we want to repeat the study in more children and also in adults to see if the same groupings are useful, or if we can see other patterns. We’ll also want to work out how these, and other, patterns of taking medicine affect symptoms and risk of asthma attacks. Using this information, researchers, healthcare workers, patients and families can find the best ways of sticking to asthma medicine regimes.

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Why is it important?

Often researchers describe people as being good or bad at taking their medication, using some criteria that haven't been tested to see if they really identify all people who are missing too much medicine. Being able to identify people who aren't taking their medicine well is important so we can help them understand why it is so important, and show them ways to make it easier to remember, but we need to know that we are helping the people who need it most.


This was a study I was really excited to do, because I hope now we can more easily identify children that need the most help and guide them to get their asthma back under control.

Holly Tibble
University of Edinburgh

Read the Original

This page is a summary of: A data-driven typology of asthma medication adherence using cluster analysis, Scientific Reports, September 2020, Springer Science + Business Media,
DOI: 10.1038/s41598-020-72060-0.
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