Prospective health care costs and lost work days associated with diabetes-related distress and depression symptoms among 1488 individuals with diabetes.

The aim of this study was to investigating the impact of major depression symptoms and diabetes-related distress on future health care costs and lost workdays in individuals with diabetes. We linked survey data from a random sample of a German statutory health insurance (SHI) with diabetes (n = 1488, 63.0% male, mean age 66.9 years) with their SHI data one year after the survey. Within the survey data we identified major depression symptoms (Patient Health Questionnaire-9) and diabetes-related distress (Problem Areas in Diabetes Scale). We retrieved health care costs and lost workdays from SHI data. To assess the impact of major depression symptoms and diabetes-related distress on health care costs and lost workdays, we adjusted regression models for age, sex, education, employment status, and diabetes duration, type, and severity. Major depression symptoms were associated with significantly higher costs (by a factor of 1.49; 95% CI: 1.18-1.88). Lost workdays were also more likely for respondents with depression symptoms (RR1.34; 0.97-1.86). Health care costs (by a factor of 0.81; 0.66-1.01) and the risk of lost workdays (RR 0.86; 0.62-1.18) may be lower among respondents with high diabetes-related distress. While major depression and diabetes-related distress have overlapping indicators, our results indicate different impacts on health care costs.

PHQ-9, CES-D, health insurance data-who is identified with depression? A Population-based study in persons with diabetes.

AIMS: Several instruments are used to identify depression among patients with diabetes and have been compared for their test criteria, but, not for the overlaps and differences, for example, in the sociodemographic and clinical characteristics of the individuals identified with different instruments. METHODS: We conducted a cross-sectional survey among a random sample of a statutory health insurance (SHI) (n = 1,579) with diabetes and linked it with longitudinal SHI data. Depression symptoms were identified using either the Centre for Epidemiological Studies Depression (CES-D) scale or the Patient Health Questionnaire-9 (PHQ-9), and a depressive disorder was identified with a diagnosis in SHI data, resulting in 8 possible groups. Groups were compared using a multinomial logistic model. RESULTS: In total 33·0% of our analysis sample were identified with depression by at least one method. 5·0% were identified with depression by all methods. Multinomial logistic analysis showed that identification through SHI data only compared to the group with no depression was associated with gender (women). Identification through at least SHI data was associated with taking antidepressants and previous depression. Health related quality of life, especially the mental summary score was associated with depression but not when identified through SHI data only. CONCLUSION: The methods overlapped less than expected. We did not find a clear pattern between methods used and characteristics of individuals identified. However, we found first indications that the choice of method is related to specific underlying characteristics in the identified population. These findings need to be confirmed by further studies with larger study samples.

Study protocol for a mixed methods exploratory investigation of aftercare services for gestational diabetes in women to develop a new patient-centred model in Germany: the GestDiNa_basic study.

INTRODUCTION: Women with gestational diabetes mellitus (GDM) have a higher risk of developing type 2 diabetes mellitus compared with women who never had GDM. Consequently, the question of structured aftercare for GDM has emerged. In all probability, many women do not receive care according to the guidelines. In particular, the process and interaction between obstetrical, diabetic, gynaecological, paediatric and general practitioner care lacks clear definitions. Thus, our first goal is to analyse the current aftercare situation for women with GDM in Germany, for example, the participation rate in aftercare diabetes screening, as well as reasons and attitudes stated by healthcare providers to offer these services and by patients to participate (or not). Second, we want to develop an appropriate, effective and patient-centred care model. METHODS AND ANALYSIS: This is a population-based mixed methods study using both quantitative and qualitative research approaches. In various working packages, we evaluate data of the GestDiab register, of the Association of Statutory Health Insurance Physicians of North Rhine and the participating insurance companies (AOK Rheinland/Hamburg, BARMER, DAK Gesundheit, IKK classic, pronova BKK). In addition, quantitative (postal surveys) and qualitative (interviews) surveys will be conducted with randomly selected healthcare providers (diabetologists, gynaecologists, paediatricians and midwives) and affected women, to be subsequently analysed. All results will then be jointly examined and evaluated. ETHICS AND DISSEMINATION: The study was approved by the ethics committee of the Faculty of Medicine, Heinrich-Heine-University Düsseldorf (Ethics Committee No.: 2019-738). Participants of the postal surveys and interviews will be informed in detail about the study and the use of data as well as the underlying data protection regulations before voluntarily participating. The study results will be disseminated through peer-reviewed journals, conferences and public information. TRIAL REGISTRATION NUMBER: DRKS00020283.

Health Care Use and Costs in Individuals With Diabetes With and Without Comorbid Depression in Germany: Results of the Cross-sectional DiaDec Study.

OBJECTIVE: Increased health care use and costs have been reported in individuals with diabetes with comorbid depression. Knowledge regarding cost differences between individuals with diabetes alone and those with diabetes and diagnosed/undiagnosed depression is, however, scarce. We therefore compared use and costs for patients with diabetes and no depression and patients with diabetes and documented depression diagnosis or self-reported depression symptoms for several cost components, including mental health care costs. RESEARCH DESIGN AND METHODS: Data from a 2013 cross-sectional survey of randomly sampled members of a nationwide German statutory health insurance (SHI) provider with diabetes (n = 1,634) were linked individually with SHI data covering four quarters before and after the survey. Self-reported depression symptoms were assessed with the Patient Health Questionnaire-9, with depression diagnosis taken from SHI data. We analyzed health care use and costs, using regression analysis to calculate cost ratios (CRs) with adjustment for sociodemographic/socioeconomic factors and comorbidities for two groups: 1) those with no symptoms and no diagnosis and 2) those with symptoms or diagnosis. In our explorative subanalysis we analyzed subgroups with either symptoms or diagnosis separately. RESULTS: Annual mean total health care costs were higher for patients with comorbid depression (EUR 5,629 [95% CI 4,987-6,407]) than without (EUR 3,252 [2,976-3,675], the CR being 1.25 [1.14-1.36]). Regression analysis showed that excess costs were highly associated with comorbidities. Mental health care costs were very low for patients without depression (psychotherapy EUR 2; antidepressants EUR 4) and still relatively low for those with depression (psychotherapy EUR 111; antidepressants EUR 76). CONCLUSIONS: Costs were significantly higher when comorbid depression was present either as symptoms or diagnosed. Excess costs for mental health services were rather low.

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 65.7 years), 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.