Evidence map›Paper›PMID 33587221›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2021

Personalized diabetes management: what do patients with diabetes mellitus prefer? A discrete choice experiment.

Axel C Mühlbacher, Andrew Sadler, Christin Juhnke

Open access · hybridAbstract read
In one paragraph

Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% 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, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
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  7. Assessing patient information needs for new antidiabetic medications to inform shared decision-making: A best-worst scaling experiment in China.Health expectations : an international journal of public participation in health care and health policy · 2024
    Article
  8. Outcomes and Attributes Patients Value When Choosing Glucose-Lowering Medications: A Mixed-Methods Study.Clinical diabetes : a publication of the American Diabetes Association · 2024
    Article
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  10. Review
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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

3 authors at 2 institutions in 2 countries.

Axel C MühlbacherGesundheitsökonomie und Medizinmanagement, Hochschule Neubrandenburg, Brodaer Straße 2, 17033, Neubrandenburg, Germany. muehlbacher@hs-nb.de.ORCID http://orcid.org/0000-0003-4402-9211
Andrew SadlerGesundheitsökonomie und Medizinmanagement, Hochschule Neubrandenburg, Brodaer Straße 2, 17033, Neubrandenburg, Germany.
Christin JuhnkeGesundheitsökonomie und Medizinmanagement, Hochschule Neubrandenburg, Brodaer Straße 2, 17033, Neubrandenburg, Germany.
Neubrandenburg University of Applied Sciences · DEDuke University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere are unresolved procedural and medical problems in the care of diabetes, which cause high costs for health systems. These include the inadequate glycemic adjustment, care gaps, therapeutic inertia, and a lack of motivation. Personalized diabetes management can be seen as a kind of "standard process" that provides both physicians and patients with a framework. The aim of this empirical survey is the evaluation of patient preferences regarding personalized diabetes management. The purpose of this experiment is to demonstrate the properties of the programs that are relevant for the choice of insulin-based therapy regimens for patients with type II diabetes mellitus.

methodsA discrete choice experiment (DCE) was applied to identify preferences for a personalized diabetes management in patients with type II diabetes mellitus. Six attributes were included. The DCE was conducted in June 2017 using a fractional factorial design, and the statistical data analysis used random effect logit models.

resultsN = 227 patients (66.1% male) were included. The preference analysis showed dominance for the attribute "occurrence of severe hypoglycemias per year" [level difference (LD) 2765]. Preference analysis also showed that participants weight the "risk of myocardial infarction (over 10 years)" (LD 1.854) highest among the side effects. Within the effectiveness criterion of "change in the long-term blood glucose level (HbA1c)" a change at an initial value of 9.5% (LD 1.146) is weighted slightly higher than changes at 7.5% (LD 1.141). Within the random parameter logit estimation, all coefficients proved to be significantly different from zero at the level p ≤ 0.01. The latent class analysis shows three heterogeneous classes, each showing clearly different weights of the therapeutic properties. This results in a clear three-folding: for 1/3 of the respondents the change of the long-term blood sugar (HbA1c value) is the top objective. Another third is solely interested in the short-term effectiveness of the therapy in the sense of the occurrence of severe hypoglycemias per year. The last third of the interviewees finally focuses on the follow-up regarding cardiovascular events. Overall, there were five structural and personality traits which have an influence on the respective probability of the class membership. DISCUSSION/

conclusionThis study identifies and weights the key decision-making criteria for optimal management of diabetes from the perspective of patients. It was shown that the effectiveness of a care program is the most important from the perspective of the patient and avoiding severe a hypoglycemia has the greatest influence on the choice. The risk of myocardial infarction as a follow-up disease and the long-term adjustment of the blood glucose follow the importance. In the analysis of possible subgroup differences by means of latent class analysis, it was found that three preference patterns exist within the sample. The generated preference data can be used for the design of personalized management approaches. It remains open to the extent to which expert opinions and patient preferences diverge.

Indexed as

Diabetes Mellitus, Type 2HypoglycemiaBlood GlucoseChoice BehaviorFemaleHumansMalePatient PreferenceBlood GlucoseDiscrete choice experimentLatent class analysisPersonalized diabetes managementType II diabetes mellitus

Identifiers

PMID33587221
PMCPMC7954752
OpenAlexW3133148962

What OpenQuestion holds

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LicenceCC BY
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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.