Evidence map›Paper›PMID 27284242›Full record

ArticlePatient preference and adherence2016

Accurate reporting of adherence to inhaled therapies in adults with cystic fibrosis: methods to calculate "normative adherence".

Zhe Hui Hoo, Rachael Curley, Michael J Campbell, Stephen J Walters, Daniel Hind, Martin J Wildman

Open access · goldAbstract read
In one paragraph

Article in Patient preference and adherence, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 27 citations in OpenAlex.

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  8. Pragmatic criteria to define chronic pseudomonas aeruginosa infection among adults with cystic fibrosis.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2018
    Article
  9. Understanding Pseudomonas status among adults with cystic fibrosis: a real-world comparison of the Leeds criteria against clinicians' decision.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2018
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Zhe Hui HooSchool of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK; Sheffield Adult Cystic Fibrosis Centre, Northern General Hospital, University of Sheffield, Sheffield, UK.
Rachael CurleySchool of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK; Sheffield Adult Cystic Fibrosis Centre, Northern General Hospital, University of Sheffield, Sheffield, UK.
Michael J CampbellSchool of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
Stephen J WaltersSchool of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
Daniel HindSheffield Clinical Trials Research Unit, University of Sheffield, Sheffield, UK.
Martin J WildmanSchool of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK; Sheffield Adult Cystic Fibrosis Centre, Northern General Hospital, University of Sheffield, Sheffield, UK.
Northern General Hospital · GBUniversity of Sheffield · GB

Funding

Department of Health DRF-2014-07-092Department of Health RP-PG-1212-20015
6 · The paper itself

Abstract

backgroundPreventative inhaled treatments in cystic fibrosis will only be effective in maintaining lung health if used appropriately. An accurate adherence index should therefore reflect treatment effectiveness, but the standard method of reporting adherence, that is, as a percentage of the agreed regimen between clinicians and people with cystic fibrosis, does not account for the appropriateness of the treatment regimen. We describe two different indices of inhaled therapy adherence for adults with cystic fibrosis which take into account effectiveness, that is, "simple" and "sophisticated" normative adherence. METHODS TO CALCULATE NORMATIVE ADHERENCE: Denominator adjustment involves fixing a minimum appropriate value based on the recommended therapy given a person's characteristics. For simple normative adherence, the denominator is determined by the person's Pseudomonas status. For sophisticated normative adherence, the denominator is determined by the person's Pseudomonas status and history of pulmonary exacerbations over the previous year. Numerator adjustment involves capping the daily maximum inhaled therapy use at 100% so that medication overuse does not artificially inflate the adherence level. THREE ILLUSTRATIVE CASES: Case A is an example of inhaled therapy under prescription based on Pseudomonas status resulting in lower simple normative adherence compared to unadjusted adherence. Case B is an example of inhaled therapy under-prescription based on previous exacerbation history resulting in lower sophisticated normative adherence compared to unadjusted adherence and simple normative adherence. Case C is an example of nebulizer overuse exaggerating the magnitude of unadjusted adherence.

conclusionDifferent methods of reporting adherence can result in different magnitudes of adherence. We have proposed two methods of standardizing the calculation of adherence which should better reflect treatment effectiveness. The value of these indices can be tested empirically in clinical trials in which there is careful definition of treatment regimens related to key patient characteristics, alongside accurate measurement of health outcomes.

Indexed as

cystic fibrosisepidemiologic methodsmedication adherencenebulizers and vaporizers

Identifiers

PMID27284242
PMCPMC4883819
OpenAlexW2402934670

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.