Evidence map›Paper›PMID 24950770›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2015

Chronic pain patients' treatment preferences: a discrete-choice experiment.

Axel C Mühlbacher, Uwe Junker, Christin Juhnke, Edgar Stemmler, Thomas Kohlmann, Friedhelm Leverkus, Matthias Nübling

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Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 5 pooled it
0.9field-weighted citation impact, top 26% 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

16 citing papers in PubMed, 5 syntheses or guidelines pooled it, 36 citations in OpenAlex.

  1. Pooled it
  2. CDC Clinical Practice Guideline for Prescribing Opioids for Pain - United States, 2022.MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports · 2022
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  11. Concern about addiction is associated with lower quality of life in patients with osteoarthritis: an exploratory, real-world data analysis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022
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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

7 authors at 4 institutions in 1 country.

Axel C MühlbacherIGM Institute Health Economics and Healthcare Management, Hochschule Neubrandenburg, Brodaer Straße 2, 17033, Neubrandenburg, Germany, muehlbacher@hs-nb.de.
Uwe Junker
Christin Juhnke
Edgar Stemmler
Thomas Kohlmann
Friedhelm Leverkus
Matthias Nübling
Neubrandenburg University of Applied Sciences · DEPfizer (Germany) · DESana Klinikum · DEUniversität Greifswald · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe objective of this study was to identify, document, and weight attributes of a pain medication that are relevant from the perspective of patients with chronic pain. Within the sub-population of patients suffering from "chronic neuropathic pain", three groups were analyzed in depth: patients with neuropathic back pain, patients with painful diabetic polyneuropathy, and patients suffering from pain due to post-herpetic neuralgia. The central question was: "On which features do patients base their assessment of pain medications and which features are most useful in the process of evaluating and selecting possible therapies?"

methodsA detailed literature review, focus groups with patients, and face-to-face interviews with widely recognized experts for pain treatment were conducted to identify relevant treatment attributes of a pain medication. A pre-test was conducted to verify the structure of relevant and dominant attributes using factor analyses by evaluating the most frequently mentioned representatives of each factor. The Discrete-Choice Experiment (DCE) used a survey based on self-reported patient data including socio-demographics and specific parameters concerning pain treatment. Furthermore, the neuropathic pain component was determined in all patients based on their scoring in the painDETECT(®) questionnaire. For statistical data analysis of the DCE, a random effect logit model was used and coefficients were presented.

resultsA total of 1,324 German patients participated in the survey, of whom 44 % suffered from neuropathic back pain (including mixed pain syndrome), 10 % complained about diabetic polyneuropathy, and 4 % reported pain due to post-herpetic neuralgia. A total of 36 single quality aspects of pain treatment, detected in the qualitative survey, were grouped in 7 dimensions by factor analysis. These 7 dimensions were used as attributes for the DCE. The DCE model resulted in the following ranking of relevant attributes for treatment decision: "no character change", "less nausea and vomiting", "pain reduction" (coefficient: >0.9 for all attributes, "high impact"), "rapid effect", "low risk of addiction" (coefficient ~0.5, "middle impact"), "applicability with comorbidity" (coefficient ~0.3), and "improvement of quality of sleep" (coefficient ~0.25). All attributes were highly significant (p < 0.001).

conclusionsThe results were intended to enable early selection of an individualized pain medication. The results of the study showed that DCE is an appropriate means for the identification of patient preferences when being treated with specific pain medications. Due to the fact that pain perception is subjective in nature, the identification of patients´ preferences will enable therapists to better develop and implement patient-oriented treatment of chronic pain. It is therefore essential to improve the therapists´ understanding of patient preferences in order to make decisions concerning pain treatment. DCE and direct assessment should become valid instruments to elicit treatment preferences in chronic pain.

Indexed as

Patient PreferenceAdolescentAdultAgedAnalgesicsBack PainChoice BehaviorChronic DiseaseDiabetic NeuropathiesFactor Analysis, StatisticalFemaleGermanyHumansInterpersonal RelationsLogistic ModelsMaleAnalgesics

Identifiers

PMID24950770
OpenAlexW2040163808

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Registered trials

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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.