Dataset Information

Different information needs in subgroups of people with diabetes mellitus: a latent class analysis.

ABSTRACT: Background: Current evidence suggests that the information needs of people with diabetes mellitus differ across patient groups. With a view to being able to provide individualized information, this study aims to identify (i) the diabetes-related information needs of people with diabetes mellitus; (ii) different subgroups of people with specific information needs; and (iii) associated characteristics of the identified subgroups, such as sociodemographic characteristics, diabetes-related comorbidities, and well-being.

Methods: This cross-sectional study was based on data from 837 respondents with diabetes mellitus who participated in the population-based KORA (Cooperative Health Research in the Augsburg Region) Health Survey 2016 in Southern Germany (KORA GEFU 4 study) (45.6% female, mean age 71.1?years, 92.8% Type 2 diabetes). Diabetes-related information needs were assessed with a questionnaire asking about patients' information needs concerning 11 diabetes-related topics, e.g. 'long-term complications' and 'treatment/therapy'. Subgroups of people with different information needs and associated characteristics were identified using latent class analysis.

Results: We identified the following four classes of people with different information needs: 'high needs on all topics', 'low needs on all topics', 'moderate needs with a focus on complications and diabetes in everyday life', and 'advanced needs with a focus on social and legal aspects and diabetes research'. The classes differed significantly in age, years of education, type of diabetes, diabetes duration, diabetes-related comorbidities, smoking behaviour, diabetes education, current level of information, and time preference.

Conclusions: Knowledge about different patient subgroups can be useful for tailored information campaigns or physician-patient interactions. Further research is needed to analyse health care needs in these groups, changes in information needs over the course of the disease, and prospective health outcomes.

SUBMITTER: Borgmann SO

PROVIDER: S-EPMC7730786 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Different information needs in subgroups of people with diabetes mellitus: a latent class analysis.

Borgmann Sandra O SO Gontscharuk Veronika V Sommer Jana J Laxy Michael M Ernstmann Nicole N Karl Florian M FM Rückert-Eheberg Ina-Maria IM Schwettmann Lars L Ladwig Karl-Heinz KH Peters Annette A Icks Andrea A

BMC public health 20201210 1

<h4>Background</h4>Current evidence suggests that the information needs of people with diabetes mellitus differ across patient groups. With a view to being able to provide individualized information, this study aims to identify (i) the diabetes-related information needs of people with diabetes mellitus; (ii) different subgroups of people with specific information needs; and (iii) associated characteristics of the identified subgroups, such as sociodemographic characteristics, diabetes-related co ...[more]

PMID: 33302924

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Project description:Background The identification of reproducible subtypes within autistic populations is a priority research area in the context of neurodevelopment, to pave the way for identification of biomarkers and targeted treatment recommendations. Few previous studies have considered medical comorbidity alongside behavioural, cognitive, and psychiatric data in subgrouping analyses. This study sought to determine whether differing behavioural, cognitive, medical, and psychiatric profiles could be used to distinguish subgroups of children on the autism spectrum in the Australian Autism Biobank (AAB). Methods Latent profile analysis was used to identify subgroups of children on the autism spectrum within the AAB (n = 1151), utilising data on social communication profiles and restricted, repetitive, and stereotyped behaviours (RRBs), in addition to their cognitive, medical, and psychiatric profiles. Results Our study identified four subgroups of children on the autism spectrum with differing profiles of autism traits and associated comorbidities. Two subgroups had more severe clinical and cognitive phenotype, suggesting higher support needs. For the ‘Higher Support Needs with Prominent Language and Cognitive Challenges’ subgroup, social communication, language and cognitive challenges were prominent, with prominent sensory seeking behaviours. The ‘Higher Support Needs with Prominent Medical and Psychiatric and Comorbidity’ subgroup had the highest mean scores of challenges relating to social communication and RRBs, with the highest probability of medical and psychiatric comorbidity, and cognitive scores similar to the overall group mean. Individuals within the ‘Moderate Support Needs with Emotional Challenges’ subgroup, had moderate mean scores of core traits of autism, and the highest probability of depression and/or suicidality. A fourth subgroup contained individuals with fewer challenges across domains (the ‘Fewer Support Needs Group’). Limitations Data utilised to identify subgroups within this study was cross-sectional as longitudinal data was not available. Conclusions Our findings support the holistic appraisal of support needs for children on the autism spectrum, with assessment of the impact of co-occurring medical and psychiatric conditions in addition to core autism traits, adaptive functioning, and cognitive functioning. Replication of our analysis in other cohorts of children on the autism spectrum is warranted, to assess whether the subgroup structure we identified is applicable in a broader context beyond our specific dataset. Supplementary Information The online version contains supplementary material available at 10.1186/s13034-023-00565-3.

Project description:ObjectivesThis study aimed to identify: (1) information needs of people with recently diagnosed type 1 or type 2 diabetes mellitus (DM); (2) information needs within different subgroups; and (3) factors associated with information needs concerning DM such as current level of information, health-related quality of life or participation preferences.DesignA mixed-method approach combining quantitative and qualitative methods was used. Information needs for different topics and estimated associated factors were described using logistic regression models. Additionally, a qualitative content analysis was performed.SettingMonocentre study.ParticipantsInformation needs were assessed and analysed in 138 consecutive participants with DM who took part in the German Diabetes Study (54% type 2 diabetes, 64% male, mean age 46.3±12.3 years, known diabetes duration <1 year).ResultsMost participants displayed a need for information in all topics provided, especially in diabetes research (86%) and treatment/therapy (80%). Regarding those topics, participants wished for information regarding new treatments that simplify their everyday life. In general, participants preferred topics that focus on the management or handling of DM over topics related to clinical factors of DM, such as causes and complications. A low current level of information and treatment with antihyperglycaemic medication were significantly associated with higher information needs, and diabetes-related comorbidity and higher mental component summary score in the 36-Item Short-Form Health Survey (SF-36) with lower information needs.ConclusionPeople with recently diagnosed DM display high information needs, which differ according to the current level of information, mode of diabetes treatment, diabetes-related comorbidity and mental component summary score in the SF-36. There appears to be a preference for information, which can help to simplify life with diabetes and for information that corresponds to their level of knowledge. This should be considered in patient information activities.Trial registration numberNCT01055093.

Dataset Information

Different information needs in subgroups of people with diabetes mellitus: a latent class analysis.

Publications

Different information needs in subgroups of people with diabetes mellitus: a latent class analysis.

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