Introduction: Understanding patient preferences for attributes of type 2 diabetes mellitus (T2DM) medications may help explain how the attributes differentially affect patient perceptions and behaviors. In this survey, we quantified the relative preferences among patients in Germany and Spain in separate analyses.
Methods: A stated-preference, discrete-choice experiment (DCE) survey was designed to elicit preferences for T2DM treatment attributes among patients with self-reported T2DM and who reported being prescribed T2DM medication for > 2 years. Patients recruited from an online national consumer panel completed an online survey. The survey presented choices between eight pairs of hypothetical T2DM treatments defined by seven attributes: chance of reaching target hemoglobin A1c (HbA1c) level; reduced risk of serious heart attack or stroke; frequency of hypoglycemia; risk of gastrointestinal (GI) problems; weight change; mode of administration (oral or injectable); dosing frequency. Data were analyzed using random-parameters logit. Minimum acceptable benefit (MAB) was defined as the minimum increase in the probability of reaching target HbA1c for which respondents would accept less desirable levels of other attributes.
Results: In Germany and Spain, 474 and 401 respondents completed the survey, respectively. DCE analysis showed that risk of GI problems was most important to German respondents. MAB analysis found that respondents would require a 56 percentage point increase in the probability of reaching their HbA1c target to offset a change from 0% to 30% risk of GI problems. For Spanish respondents, mode of administration was the most important attribute. These respondents would require a 59 percentage point increase in the probability of reaching their HbA1c target to offset moving from oral to injectable medications.
Conclusions: Respondents in Germany and Spain were willing to trade efficacy for improvements in side effects and mode of administration. Given the variety of T2DM medications currently available, the results suggest that careful discussion about patient preferences could help improve patient satisfaction with T2DM treatment.
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http://dx.doi.org/10.1007/s13300-017-0326-8 | DOI Listing |
Nat Med
January 2025
Environment & Health Modelling (EHM) Lab, Department of Public Health Environment & Society, London School of Hygiene & Tropical Medicine, London, UK.
Previous health impact assessments of temperature-related mortality in Europe indicated that the mortality burden attributable to cold is much larger than for heat. Questions remain as to whether climate change can result in a net decrease in temperature-related mortality. In this study, we estimated how climate change could affect future heat-related and cold-related mortality in 854 European urban areas, under several climate, demographic and adaptation scenarios.
View Article and Find Full Text PDFSci Data
January 2025
Department of Engineering Technology, University of Houston, Houston, TX, USA.
Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique that measures cortical hemodynamic activity in a non-invasive and portable fashion. Although the fNIRS community has been successful in disseminating open-source processing tools and a standard file format (SNIRF), reproducible research and sharing of fNIRS data amongst researchers has been hindered by a lack of standards and clarity over how study data should be organized and stored. This problem is not new in neuroimaging, and it became evident years ago with the proliferation of publicly available neuroimaging datasets.
View Article and Find Full Text PDFNat Commun
January 2025
Group Genome Instability in Tumors, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.
Clin Microbiol Infect
January 2025
Infectious Diseases and Microbiology Division, Hospital Universitario Virgen Macarena; Department of Medicine, University of Seville; Instituto de Biomedicina de Sevilla (IBiS)/Consejo Superior de Investigaciones Científicas (CSIC), Seville, Spain; CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain.
Background: Data sharing accelerates scientific progress and improves evidence quality. Even though journals and funding institutions require investigators to share data, only a small part of studies made their data publicly available upon publication. The procedures necessary to share retrospective data for re-use in secondary data analysis projects can be cumbersome.
View Article and Find Full Text PDFAnn Intern Med
January 2025
Clinical Epidemiology and Research Center (CERC), Department of Biomedical Sciences, Humanitas University, and IRCCS Humanitas Research Hospital, Milan, Italy, and Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany (H.J.S.).
Description: Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise.
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