ObjectiveThis study aimed to examine the prevalence and determinants of benzodiazepine prescription among older adults in Switzerland, and analyse association with hospitalisation and costs.DesignRetrospective analysis of claims data.SettingThe study was conducted in nine cantons in Switzerland.ParticipantsOlder adults aged 65 years and older enrolled with a large Swiss health insurance company participated in the study.Primary and secondary outcome measuresThe primary outcome was prevalence of benzodiazepine prescription. The secondary outcomes were (1) determinants of any benzodiazepine prescription; (2) the association between any prescription and the probability of hospitalisation for trauma and (3) the association between any prescription and total healthcare expenditures.ResultsOverall, 69 005 individuals were included in the study. Approximately 20% of participants had at least one benzodiazepine prescription in 2017. Prescription prevalence increased with age (65–69: 15.9%; 70–74: 18.4%; 75–80: 22.5%; >80: 25.8%) and was higher in women (25.1%) compared with men (14.6%). Enrollees with the highest deductible of Swiss Francs (CHF) 2500 were 70% less likely to receive a prescription than enrollees with the lowest deductible of CHF 300 (adjusted OR=0.29, 95% CI 0.24 to 0.35).Individuals with at least one prescription had a higher probability of hospitalisation for trauma (OR=1.31, 95% CI 1. 20 to 1.1.44), and 70% higher health care expenditures (β=0.72, 95% CI 0. 67 to 0.77). Enrollees in canton Valais were three times more likely to receive a prescription compared to enrollees from canton Aargau (OR=2.84, 95% 2.51 to 3.21).ConclusionsThe proportion of older adults with at least one benzodiazepine prescription is high, as found in the data of one large Swiss health insurance company. These enrollees are more likely to be hospitalised for trauma and have higher healthcare expenditures. Important differences in prescription prevalence across cantons were observed, suggesting potential overuse. Further research is needed to understand the drivers of variation, prescription patterns across providers, and trends over time.

Background Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed. Objective This study aims to investigate the patient multimorbidity and complexity of Swiss residents aged ≥50 years using clustering methodology in claims data. Methods We adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters. Results Based on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients. Conclusions Our study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs. This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery.

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Low back pain (LBP) is one of the most common musculoskeletal disorders worldwide and a frequent cause for health care utilization with a high economic burden. A large proportion of diagnostic imaging in patients with LBP is inappropriate and can cause more harm than good, which in turn can lead to higher health care costs. The aim of this study was to determine characteristics and health care costs for patients with a diagnostic imaging for LBP in Switzerland. Groupe Mutuel, one of the biggest health care insurance companies in Switzerland and covering approximately 12% of the population, provided data for this analysis. Patients were identified by diagnostic imaging for the lumbar spine in 2016 or 2017. The study period was 2015–2019, that is one year before and two years after the year of imaging. Regression analysis models were used to identify patient variables associated with higher health care costs. A total of 75,296 patients (57% female, mean age: 54.5 years) were included into the study. Magnetic resonance imaging was the most commonly used diagnostic method (44.3%). Patients generated annual mean health care costs of 518,488,470 CHF (466,639,621 Euro) in the whole observation period; 640 million CHF (576 million Euro) in the index year. Overall, costs for LBP patients were 72% higher compared with the costs of no LBP patients. Our findings confirm the economic burden of LBP and highlight the importance of ongoing efforts to improve prevention, diagnostics and patient care in patients with LBP.

BACKGROUND Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed. OBJECTIVE This study aims to investigate the patient multimorbidity and complexity of Swiss residents aged ≥50 years using clustering methodology in claims data. METHODS We adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters. RESULTS Based on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients. CONCLUSIONS Our study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs. This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery.