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

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

Preferences of women in difficult life situations for a physical activity programme: protocol of a discrete choice experiment in the German NU-BIG project

Introduction The BIG project (‘Bewegung als Investition in die Gesundheit’, ie, ‘Movement as Investment in Health’) was developed in 2005 as a community-based participatory research programme to offer accessible opportunities for physical activity to women in difficult life situations. Since then, the programme has been expanded to eight sites in Germany. A systematic evaluation of BIG is currently being conducted. As part of this effort, we strive to understand the preferences of participating women for different aspects of the programme, and to analyse their willingness to pay.Methods and analysis In this protocol, we describe the development and analysis plan of a discrete choice experiment (DCE) to investigate participants’ preferences for a physical activity programme for women in difficult life situations. The experiment will be embedded in a questionnaire covering several aspects of participation in the programme (eg, reach, efficacy and further effects) and the socioeconomic characteristics of all active participants. After a thorough search of the literature, BIG documents review and expert interviews, we identified five important attributes of the programme: course times, travel time to the course venue, additional social activities organised by BIG, consideration of wishes and interests for the further planning of courses and costs per course unit. Thereafter, we piloted the experiment with a sample of participants from the target group. After data collection, the experiment will be analysed using a conditional logit model and a latent class analysis to assess eventual heterogeneity in preferences.Ethics and dissemination Understanding women’s preferences will provide useful insights for the further development of the programme and ultimately increase participation and retention. The questionnaire, the included DCE and the pretest on participants received ethical approval (application no. 20-247_1-B). We plan to disseminate the results of the DCE in peer-reviewed journals, national conferences and among participants and programme coordinators and organisers

Using statutory health insurance data to evaluate non-response in a cross-sectional study on depression among patients with diabetes in Germany

Background: Low response rates do not indicate poor representativeness of study populations if non-response occurs completely at random. A non-response analysis can help to investigate whether non-response is a potential source for bias within a study. Methods: A cross-sectional survey among a random sample of a health insurance population with diabetes (n=3642, 58.9% male, mean age 65.7years), assessing depression in diabetes, was conducted in 2013 in Germany. Health insurance data were available for responders and non-responders to assess non-response bias. The response rate was 51.1%. Odds ratios (ORs) for responses to the survey were calculated using logistic regression taking into consideration the depression diagnosis as well as age, sex, antihyperglycaemic medication, medication utilization, hospital admission and other comorbidities (from health insurance data). Results: Responders and non-responders did not differ in the depression diagnosis [OR 0.99, confidence interval (CI) 0.82-1.2]. Regardless of age and sex, treatment with insulin only (OR 1.73, CI 1.36-2.21), treatment with oral antihyperglycaemic drugs (OAD) only (OR 1.77, CI 1.49-2.09), treatment with both insulin and OAD (OR 1.91, CI 1.51-2.43) and higher general medication utilization (1.29, 1.10-1.51) were associated with responding to the survey. Conclusion: We found differences in age, sex, diabetes treatment and medication utilization between responders and non-responders, which might bias the results. However, responders and non-responders did not differ in their depression status, which is the focus of the DiaDec study. Our analysis may serve as an example for conducting non-response analyses using health insurance data

Using statutory health insurance data to evaluate non-response in a cross-sectional study on depression among patients with diabetes in Germany

Background: Low response rates do not indicate poor representativeness of study populations if non-response occurs completely at random. A non-response analysis can help to investigate whether non-response is a potential source for bias within a study. Methods: A cross-sectional survey among a random sample of a health insurance population with diabetes (n=3642, 58.9% male, mean age 65.7years), assessing depression in diabetes, was conducted in 2013 in Germany. Health insurance data were available for responders and non-responders to assess non-response bias. The response rate was 51.1%. Odds ratios (ORs) for responses to the survey were calculated using logistic regression taking into consideration the depression diagnosis as well as age, sex, antihyperglycaemic medication, medication utilization, hospital admission and other comorbidities (from health insurance data). Results: Responders and non-responders did not differ in the depression diagnosis [OR 0.99, confidence interval (CI) 0.82-1.2]. Regardless of age and sex, treatment with insulin only (OR 1.73, CI 1.36-2.21), treatment with oral antihyperglycaemic drugs (OAD) only (OR 1.77, CI 1.49-2.09), treatment with both insulin and OAD (OR 1.91, CI 1.51-2.43) and higher general medication utilization (1.29, 1.10-1.51) were associated with responding to the survey. Conclusion: We found differences in age, sex, diabetes treatment and medication utilization between responders and non-responders, which might bias the results. However, responders and non-responders did not differ in their depression status, which is the focus of the DiaDec study. Our analysis may serve as an example for conducting non-response analyses using health insurance data