New prescription of antihyperglycemic agents among patients with diabetes in Germany: Moderate concordance between health insurance data and self-reports.

PURPOSE: To analyze the concordance of new prescription of antihyperglycemic agents between two data sources: patients' self-reports and statutory health insurance (SHI) data among patients with diabetes. METHODS: Within a cross-sectional study, 494 patients with diabetes were interviewed if and which new prescriptions of diabetes medication they received within the last 3 or 6 months. SHI data for 12 months were linked to cover these periods. For the agreement measurement, SHI data was set as reference, and kappa, positive predictive value (PPV), and sensitivity were calculated for single Anatomical Therapeutic Chemical (ATC) codes and cumulated code groups. RESULTS: The number of new prescriptions within 3 or 6 months was low, with 5.5% (n = 27) for Metformin/self-report being the highest. Contingency tables were unbalanced and showed large numbers in the no/no-cells. Regarding non-agreement, we found new prescriptions slightly more often in SHI data only than in self-reports only, with insulin and metformin representing an exception. Agreement results were moderate with large confidence intervals (CI). The values for cumulated "all drugs in diabetes" were: kappa = 0.58 (95% CI: 0.51-0.65), PPV = 62.0 (53.4-70.2), sensitivity = 55.6 (47.3-63.6). CONCLUSIONS: Patients reported a low number of new prescriptions within the last 3 or 6 months. In general we found moderate agreement and in case of non-agreement that self-report no/SHI yes was slightly more frequent than vice versa. These results were based on small case numbers, but could nevertheless be considered when collecting self-reported information on the prescription of antihyperglycemic agents.

Self-reported medication intake vs information from other data sources such as pharmacy records or medical records: Identification and description of existing publications, and comparison of agreement results for publications focusing on patients with cancer - a systematic review.

PURPOSE: To identify and describe publications addressing the agreement between self-reported medication and other data sources among adults and, in a subgroup of studies dealing with cancer patients, seek to identify parameters which are associated with agreement. METHODS: A systematic review including a systematic search within five biomedical databases up to February 28, 2019 was conducted as per the PRISMA Statement. Studies and agreement results were described. For a subgroup of studies dealing with cancer, we searched for associations between agreement and patients' characteristics, study design, comparison data source, and self-report modality. RESULTS: The literature search retrieved 3392 publications. Included articles (n = 120) show heterogeneous agreement. Eighteen publications focused on cancer populations, with relatively good agreement identified in those which analyzed hormone therapy, estrogen, and chemotherapy (n = 11). Agreement was especially good for chemotherapy (proportion correct ≥93.6%, kappa ≥0.88). No distinct associations between agreement and age, education or marital status were identified in the results. There was little evaluation of associations between agreement and study design, self-report modality and comparison data source, thus not allowing for any conclusions to be drawn. CONCLUSION: An overview of the evidence available from validation studies with a description of several characteristics is provided. Studies with experimental design which evaluate factors that might affect agreement between self-report and other data sources are lacking.

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.

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.

Comprehensive support for families with parental cancer (Family-SCOUT), evaluation of a complex intervention: study protocol for a non-randomized controlled trial.

BACKGROUND: Families with minor children affected by parental cancer are at risk of considerable emotional and organizational stress that can severely burden all family members. So far, there has been a lack of comprehensive support services for affected families. The aim of this project is to implement and evaluate a complex psychosocial intervention for these families by providing advice, information, and care on an emotional, psycho-social, and communicative level during and after the cancer experience and across healthcare sectors. METHODS: Family-SCOUT is a project supported by the German Innovation Fund ( https://innovationsfonds.g-ba.de/ ). The evaluation is based on a mixed-methods quasi-experimental design with the intervention and control groups. A standardized postal survey at three measurement points (T0: study enrollment; T1: 3 months of follow-up; T2: 9 months of follow-up), secondary data from the participating health insurance funds, and semi-structured qualitative interviews are used for summative and formative evaluation. The study aim is to include n=560 families. Data will be analyzed according to the intention-to-treat principle. The primary analysis is the comparison of the Hospital Anxiety and Depression Scale (HADS) response rates (minimal important difference (MID) ≥ 1.6 in at least one of the two parents) at T2 between the intervention and control group using Fisher's exact test. The conduct of the study as well as the development and implementation of the intervention will be accompanied by comprehensive study monitoring following the principles of an effectiveness-implementation hybrid study. DISCUSSION: The results will allow to test the effectiveness and efficiency of the intervention for the target group. The first experience with the implementation of the intervention in model regions will be available. The evaluation results will serve as the basis to assess the need of including the intervention in the catalog of services of the statutory health insurance funds in Germany. TRIAL REGISTRATION: ClinicalTrials.gov , NCT04186923. Retrospectively registered on 4 December 2019.

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.

How do patients with diabetes report their comorbidities? Comparison with administrative data.

AIMS: Patients with diabetes are probably often unaware of their comorbidities. We estimated agreement between self-reported comorbidities and administrative data. METHODS: In a random sample of 464 diabetes patients, data from a questionnaire asking about the presence of 14 comorbidities closely related to diabetes were individually linked with statutory health insurance data. RESULTS: Specificities were >97%, except cardiac insufficiency (94.5%), eye diseases (93.8%), peripheral arterial disease (92.6%), hypertension (90.9%), and peripheral neuropathy (85.8%). Sensitivities were <60%, except amputation (100%), hypertension (83.1%), and myocardial infarction (67.2%). A few positive predictive values were >90% (hypertension, myocardial infarction, and eye disease), and six were below 70%. Six negative predictive values were >90%, and two <70% (hypertension and eye disease). Total agreement was between 42.7% (eye disease) and 100% (dialysis and amputation). Overall, substantial agreement was observed for three morbidities (kappa 0.61-0.80: hypertension, myocardial infarction, and amputation). Moderate agreement (kappa 0.41-0.60) was estimated for angina pectoris, heart failure, stroke, peripheral neuropathy, and kidney disease. Factors associated with agreement were the number of comorbidities, diabetes duration, age, sex, and education. CONCLUSIONS: Myocardial infarction and amputation were well reported by patients as comorbidities; eye diseases and foot ulceration rather poorly, particularly in older, male, or less educated patients. Patient information needs improving.

Agreement found between self-reported and health insurance data on physician visits comparing different recall lengths.

OBJECTIVE: To analyze the impact of different recall lengths on agreement between self-reported physician visits and those documented in health insurance data applying an experimental design. STUDY DESIGN AND SETTING: We randomly assigned 432 patients with diabetes to one of two versions of a written survey, each asking about the number of physician visits over a 3- or 6-month recall period. Health insurance data were linked individually. RESULTS: In both groups, the mean number of self-reported physician visits per month was lower than in the insurance data, with a larger difference in the 6-month group (-0.9; 95% CI -1.0, -0.7) than in the 3-month group (-0.5; -0.7; -0.2), difference between the two groups: 0.4 (0.1-0.7; P = 0.009). The percentage of participants with correct reporting was small and did not differ largely between the two groups (6.5% and 9.3%). However, there was more overreporting in the 3-month group (25.6% vs. 11.1%). CONCLUSIONS: Shorter recall periods may produce more accurate results when estimating the mean number of physician visits. However, this may be driven not by a more accurate reporting, but by a higher proportion of respondents that overreported and a lower proportion of respondents that underreported, when compared to the longer reporting period.