Comparison of different strategies to measure medication adherence via claims data in patients with chronic heart failure

Medication adherence correlates with morbidity and mortality in patients with chronic heart failure (CHF), but is difficult to assess. We conducted a retrospective methodological cohort study in 3,808 CHF patients, calculating adherence as proportion of days covered (PDC) utilizing claims data from 2010 to 2015. We aimed to compare different parameters’ influence on the PDC of elderly CHF patients exemplifying a complex chronic disease. Investigated parameters were the assumed prescribed daily dose (PDD), stockpiling, and periods of hospital stay. Thereby, we investigated a new approach using the PDD assigned to different percentiles. The different dose assumptions had the biggest influence on the PDC, with variations from 41.9% to 83.7%. Stockpiling and hospital stays increased the values slightly. These results queries that a reliable PDC can be calculated with an assumed PDD. Hence, results based on an assumed PDD have to be interpreted carefully and should be presented with sensitivity analyses to show the PDC's possible range

Heterogeneous Effects of a Nonlinear Price Schedule for Outpatient Care

Theoretical considerations suggest that nonlinear health care price schedules have heterogeneous effects on health care demand. In this paper, we develop and apply a finite mixture bivariate probit model to analyze whether there are heterogeneous reactions to the introduction of a nonlinear price schedule in the German statutory health insurance system. In administrative insurance claims data from the largest German health insurance plan, we find that some individuals strongly react to the new price schedule while a second group of individuals does not react. Post-estimation analyses reveal that the group of the individuals who do not react to the reform includes the relatively sick. These results are in line with forward-looking behavior: Individuals who are already sick expect that they will hit the kink in the price schedule and thus are less sensitive to the co-payment

Impact of Record-Linkage Errors in Covid-19 Vaccine-Safety Analyses using German Health-Care Data: A Simulation Study

With unprecedented speed, 192,248,678 doses of Covid-19 vaccines were administered in Germany by July 11, 2023 to combat the pandemic. Limitations of clinical trials imply that the safety profile of these vaccines is not fully known before marketing. However, routine health-care data can help address these issues. Despite the high proportion of insured people, the analysis of vaccination-related data is challenging in Germany. Generally, the Covid-19 vaccination status and other health-care data are stored in separate databases, without persistent and database-independent person identifiers. Error-prone record-linkage techniques must be used to merge these databases. Our aim was to quantify the impact of record-linkage errors on the power and bias of different analysis methods designed to assess Covid-19 vaccine safety when using German health-care data with a Monte-Carlo simulation study. We used a discrete-time simulation and empirical data to generate realistic data with varying amounts of record-linkage errors. Afterwards, we analysed this data using a Cox model and the self-controlled case series (SCCS) method. Realistic proportions of random linkage errors only had little effect on the power of either method. The SCCS method produced unbiased results even with a high percentage of linkage errors, while the Cox model underestimated the true effect

Expression of cell cycle regulators and frequency of TP53 mutations in high risk gastrointestinal stromal tumors prior to adjuvant imatinib treatment

Despite of multitude investigations no reliable prognostic immunohistochemical biomarkers in GIST have been established so far with added value to predict the recurrence risk of high risk GIST besides mitotic count, primary location and size. In this study, we analyzed the prognostic relevance of eight cell cycle and apoptosis modulators and of TP53 mutations for prognosis in GIST with high risk of recurrence prior to adjuvant treatment with imatinib. In total, 400 patients with high risk for GIST recurrence were randomly assigned for adjuvant imatinib either for one or for three years following laparotomy. 320 primary tumor samples with available tumor tissue were immunohistochemically analyzed prior to treatment for the expression of cell cycle regulators and apoptosis modulators cyclin D1, p21, p16, CDK4, E2F1, MDM2, p53 and p-RB1. TP53 mutational analysis was possible in 245 cases. A high expression of CDK4 was observed in 32.8% of all cases and was associated with a favorable recurrence free survival (RFS), whereas high expression of MDM2 (12.2%) or p53 (35.3%) was associated with a shorter RFS. These results were independent from the primary KIT or PDGFRA mutation. In GISTs with higher mitotic counts was a significantly increased expression of cyclin D1, p53 and E2F1. The expression of p16 and E2F1 significantly correlated to a non-gastric localization. Furthermore, we observed a significant higher expression of p21 and E2F1 in KIT mutant GISTs compared to PDGFRA mutant and wt GISTs. The overall frequency of TP53 mutations was low (n = 8; 3.5%) and could not be predicted by the immunohistochemical expression of p53. In summary, mutation analysis in TP53 plays a minor role in the subgroup of high-risk GIST before adjuvant treatment with imatinib. Strong expression of MDM2 and p53 correlated with a shorter recurrence free survival, whereas a strong expression of CDK4 correlated to a better recurrence free survival.Peer reviewe

Development and internal validation of prognostic models to predict negative health outcomes in older patients with multimorbidity and polypharmacy in general practice

Background Polypharmacy interventions are resource-intensive and should be targeted to those at risk of negative health outcomes. Our aim was to develop and internally validate prognostic models to predict health-related quality of life (HRQoL) and the combined outcome of falls, hospitalisation, institutionalisation and nursing care needs, in older patients with multimorbidity and polypharmacy in general practices. Methods Design: two independent data sets, one comprising health insurance claims data (n=592 456), the other data from the PRIoritising MUltimedication in Multimorbidity (PRIMUM) cluster randomised controlled trial (n=502). Population: >= 60 years, >= 5 drugs, >= 3 chronic diseases, excluding dementia. Outcomes: combined outcome of falls, hospitalisation, institutionalisation and nursing care needs (after 6, 9 and 24 months) (claims data); and HRQoL (after 6 and 9 months) (trial data). Predictor variables in both data sets: age, sex, morbidity-related variables (disease count), medication-related variables (European Union-Potentially Inappropriate Medication list (EU-PIM list)) and health service utilisation. Predictor variables exclusively in trial data: additional socio-demographics, morbidity-related variables (Cumulative Illness Rating Scale, depression), Medication Appropriateness Index (MAI), lifestyle, functional status and HRQoL (EuroQol EQ-5D-3L). Analysis: mixed regression models, combined with stepwise variable selection, 10-fold cross validation and sensitivity analyses. Results Most important predictors of EQ-5D-3L at 6 months in best model (Nagelkerke's R-2 0.507) were depressive symptoms (-2.73 (95% CI: -3.56 to -1.91)), MAI (-0.39 (95% CI: -0.7 to -0.08)), baseline EQ-5D-3L (0.55 (95% CI: 0.47 to 0.64)). Models based on claims data and those predicting long-term outcomes based on both data sets produced low R-2 values. In claims data-based model with highest explanatory power (R-2=0.16), previous falls/fall-related injuries, previous hospitalisations, age, number of involved physicians and disease count were most important predictor variables. Conclusions Best trial data-based model predicted HRQoL after 6 months well and included parameters of well-being not found in claims. Performance of claims data-based models and models predicting long-term outcomes was relatively weak. For generalisability, future studies should refit models by considering parameters representing well-being and functional status

Gute Praxis Datenlinkage (GPD) : Good Practice Data Linkage

Das personenbezogene Verknüpfen verschiedener Datenquellen (Datenlinkage) für Forschungszwecke findet in den letzten Jahren in Deutschland zunehmend Anwendung. Jedoch fehlen hierfür konsentierte methodische Standards. Ziel dieses Beitrages ist es, solche Standards für Forschungsvorhaben zu definieren. Eine weitere Intention ist es, dem Lesenden eine Checkliste zur Bewertung geplanter Forschungsvorhaben und Artikel bereitzustellen. Zu diesem Zweck hat eine aus Mitgliedern verschiedener Fachgesellschaften zusammengesetzte Expertengruppe seit 2016 insgesamt 7 Leitlinien mit 27 konkreten Empfehlungen erstellt. Die Gute Praxis Datenlinkage beinhaltet die folgenden Leitlinien: (1) Forschungsziele, Fragestellung, Datenquellen und Ressourcen, (2) Dateninfrastruktur und Datenfluss, (3) Datenschutz, (4) Ethik, (5) Schlüsselvariablen und Linkageverfahren, (6) Datenprüfung/Qualitätssicherung sowie (7) Langfristige Datennutzung für noch festzulegende Fragestellungen. Jede Leitlinie wird ausführlich diskutiert. Zukünftige Aktualisierungen werden wissenschaftliche und datenschutzrechtliche Entwicklungen berücksichtigen