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Objectives: Biological products have contributed to extraordinary advances in disease treatments over the last decade. However, the cost-saving potential of imitator products, so-called biosimilars, is still under-researched in Switzerland. This study aims to assess biosimilars' prescriptions at treatment initiation and their determinants, as well as biological therapy switches., Design: The study included all patients who had at least one biosimilar available on the market at the time when they were prescribed a biological product. We analysed longitudinal data for biosimilar prescriptions in Switzerland using descriptive statistics and logistic regression to quantify the associations with individual, pharmaceutical and provider-related variables., Setting: The analysis is based on de-identified claims data of patients with mandatory health insurance at Helsana, one of the Swiss health insurance companies with a substantial enrollee base in mandatory health insurance., Participants: Overall, 18 953 patients receiving at least one biological product between 2016 and 2021 were identified., Outcome Measures: We differentiated between initial prescriptions and follow-up prescriptions. Our regression focused on initial prescriptions due to evidence indicating that patients tend to follow the medication prescribed at therapy initiation., Results: Although biosimilars' market share was low (28.6%), the number of prescriptions has increased (from 1016 in 2016 to 6976 in 2021). Few patients with medication switches (n=1492, 8.5%) were detected. Increased relative price difference (difference in the price of available biosimilars relative to price of corresponding reference product) was associated with decreased probability of biosimilar prescriptions, whereas male sex, an increase of available imitator drugs on the market, larger packaging sizes, and prescriptions from specialists or physicians in outpatient settings were associated with increased biosimilar use., Conclusion: The low number of biosimilar prescriptions, despite the proliferating biosimilar market, indicates a high potential for biosimilar diffusion. The findings indicate that patients typically adhere to the therapy options initially chosen and are less inclined to make changes following the initiation of treatment. Our research highlights the need for awareness initiatives to improve understanding among patients and physicians, enabling informed, shared decision-making about biosimilar prescriptions., Competing Interests: Competing interests: Helsana Group provided support in the form of salaries for authors (KW, MN, CH, EB)., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Aims: Atrial fibrillation (AF) costs are expected to be substantial, but cost comparisons with the general population are scarce. Using data from the prospective Swiss-AF cohort study and population-based controls, we estimated the impact of AF on direct healthcare costs from the Swiss statutory health insurance perspective., Methods: Swiss-AF patients, enrolled from 2014 to 2017, had documented, prevalent AF. We analysed 5 years of follow-up, where clinical data, and health insurance claims in 42% of the patients were collected on a yearly basis. Controls from a health insurance claims database were matched for demographics and region. The cost impact of AF was estimated using five different methods: (1) ordinary least square regression (OLS), (2) OLS-based two-part modelling, (3) generalised linear model-based two-part modelling, (4) 1:1 nearest neighbour propensity score matching and (5) a cost adjudication algorithm using Swiss-AF data non-comparatively and considering clinical data. Cost of illness at the Swiss national level was modelled using obtained cost estimates, prevalence from the Global Burden of Disease Project, and Swiss population data., Results: The 1024 Swiss-AF patients with available claims data were compared with 16 556 controls without known AF. AF patients accrued CHF5600 (EUR5091) of AF-related direct healthcare costs per year, in addition to non-AF-related healthcare costs of CHF11100 (EUR10 091) per year accrued by AF patients and controls. All five methods yielded comparable results. AF-related costs at the national level were estimated to amount to 1% of Swiss healthcare expenditure., Conclusions: We robustly found direct medical costs of AF patients were 50% higher than those of population-based controls. Such information on the incremental cost burden of AF may support healthcare capacity planning., Competing Interests: Competing interests: Professor Beer reports grant support from the Swiss National Foundation of Science, The Swiss Heart Foundation and the Stiftung Kardio; grant support, speakers- and consultation fees to the institution from Bayer, Sanofi and Daichii Sankyo. Dr Blozik reports grants from Swiss Cancer Research Foundation; institutional grants from Amgen, MSD, Novartis, Pfizer, all outside the submitted work. Dr Bonati reports personal fees and nonfinancial support from Amgen, grants from AstraZeneca, personal fees and nonfinancial support from Bayer, personal fees from Bristol-Myers Squibb, personal fees from Claret Medical, grants from Swiss National Science Foundation, grants from University of Basel, grants from Swiss Heart Foundation, outside the submitted work. Dr Conen received consulting fees from Roche Diagnostics, and speaker fees from Servier and BMS/Pfizer, all outside of the current work. Dr Kühne reports personal fees from Bayer, personal fees from Böhringer Ingelheim, personal fees from Pfizer BMS, personal fees from Daiichi Sankyo, personal fees from Medtronic, personal fees from Biotronik, personal fees from Boston Scientific, personal fees from Johnson&Johnson, personal fees from Roche, grants from Bayer, grants from Pfizer, grants from Boston Scientific, grants from BMS, grants from Biotronik, grants from Daiichi Sankyo. Dr Moschovitis has received consultant fees for taking part to advisory boards from Novartis, Boehringer Ingelheim, Bayer, Astra Zeneca and Daiichi Sankyo, all outside of the presented work. Dr Müller reports fellowship and training support from Biotronik, Boston Scientific, Medtronic, Abbott/St. Jude Medical, and Biosense Webster; speaker honoraria from Biosense Webster, Medtronic, Abbott/St. Jude Medical, AstraZeneca, Daiichi Sankyo, Biotronik, MicroPort, Novartis, and consultant honoraria for Biosense Webster, Medtronic, Abbott/St. Jude Medcal, and Biotronik. Dr Osswald received research grants from the Swiss National Science Foundation and from the Swiss Heart Foundation, research grants from Foundation for CardioVascular Research Basel, research grants from Roche, educational and speaker office grants from Roche, Bayer, Novartis, Sanofi AstraZeneca, Daiichi-Sankyo and Pfizer. Dr Reichlin has received research grants from the Swiss National Science Foundation, the Swiss Heart Foundation, and the sitem insel support fund, all for work outside the submitted study. Speaker/consulting honoraria or travel support from Abbott/SJM, Astra Zeneca, Brahms, Bayer, Biosense-Webster, Biotronik, Boston-Scientific, Daiichi Sankyo, Medtronic, Pfizer-BMS and Roche, all for work outside the submitted study. Support for his institution’s fellowship program from Abbott/SJM, Biosense-Webster, Biotronik, Boston-Scientific and Medtronic for work outside the submitted study. Dr Schwenkglenks reports grants from Swiss National Science Foundation, for the conduct of the study; grants and personal fees from Amgen, grants from MSD, grants from Novartis, grants from Pfizer, grants from The Medicines Company, all outside the submitted work. Dr Serra-Burriel reports grants from the European Commission outside of the present work. Dr Sticherling has received speaker honoraria from Biosense Webster and Medtronic and research grants from Biosense Webster, Daiichi-Sankyo, and Medtronic. The remaining authors have nothing to disclose., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Objectives: The optimal use of opioids after knee replacement (KR) remains to be determined, given the growing evidence that opioids are no more effective than other analgesics and that their adverse effects can impair quality of life. Therefore, the objective is to examine opioid prescriptions after KR., Design: In this retrospective study, we used descriptive statistics and estimated the association of prognostic factors using generalised negative binomial models., Setting: The study is based on anonymised claims data of patients with mandatory health insurance at Helsana, a leading Swiss health insurance., Participants: Overall, 9122 patients undergoing KR between 2015 and 2018 were identified., Primary and Secondary Outcome Measures: Based on reimbursed bills, we calculated the dosage (morphine equivalent dose, MED) and the episode length (acute: <90 days; subacute: ≥90 to <120 days or <10 claims; chronic: ≥90 days and ≥10 claims or ≥120 days). The incidence rate ratios (IRRs) for postoperative opioids were calculated., Results: Of all patients, 3445 (37.8%) received opioids in the postoperative year. A large majority had acute episodes (3067, 89.0%), 2211 (65.0%) had peak MED levels above 100 mg/day and most patients received opioids in the first 10 postoperative weeks (2881, 31.6%). Increasing age (66-75 and >75 vs 18-65) was associated with decreased IRR (0.776 (95% CI 0.7 to 0.859); 0.723 (95% CI 0.649 to 0.805)), whereas preoperative non-opioid analgesics and opioids were associated with higher IRR (1.271 (95% CI 1.155 to 1.399); 3.977 (95% CI 4.409 to 3.591))., Conclusion: The high opioid demand is unexpected given that current recommendations advise using opioids only when other pain therapies are ineffective. To ensure medication safety, it is important to consider alternative treatment options and ensure that benefits outweigh potential risks., Competing Interests: Competing interests: The authors declare that they have no conflicts of interest. Helsana Group provided support in the form of salaries for authors (KW, MN, SB and EB), without having any additional role in the study design, data collection and analysis, decision to publish or preparation of the manuscript., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: When research evidence is lacking, patient and provider preferences, expected to vary geographically, might have a stronger role in clinical decisions. We investigated whether the strength or the direction of recommendation is associated with the degree of geographic variation in utilisation., Design: In this cross-sectional study, we selected 24 services following a comprehensive approach. The strength and direction of recommendations were assessed in duplicate. Multilevel models were used to adjust for demographic and clinical characteristics and estimate unwarranted variation., Setting: Observational study of claims to mandatory health insurance in Switzerland in 2014., Participants: Enrolees eligible for the 24 healthcare services., Primary Outcome Measures: The variances of regional random effects, also expressed as median odds ratios (MOR). Services grouped by strength and direction of recommendations were compared with Welch's t-test., Results: The sizes of the eligible populations ranged from 1992 to 409 960 patients. MOR ranged between 1.13 for aspirin in secondary prevention of myocardial infarction to 1.68 for minor surgical procedures performed in inpatient instead of outpatient settings. Services with weak recommendations had a negligibly higher variance and MOR (difference in means (95% CI) 0.03 (-0.06 to 0.11) and 0.05 (-0.11 to 0.21), respectively) compared with strong recommendations. Services with negative recommendations had a slightly higher variance and MOR (difference in means (95% CI) 0.07 (-0.03 to 0.18) and 0.14 (-0.06 to 0.34), respectively) compared with positive recommendations., Conclusions: In this exploratory study, the geographical variation in the utilisation of services associated with strong vs weak and negative vs positive recommendations was not substantially different, although the difference was somewhat larger for negative vs positive recommendations. The relationships between the strength or direction of recommendations and the variation may be indirect or modified by other characteristics of services. As initiatives discouraging low-value care are gaining attention worldwide, these findings may inform future research in this area., Competing Interests: Competing interests: MS declares a grant from Helsana Insurance Group, outside the submitted work. Helsana Group provided support in the form of salaries for authors BB, EB and CB, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The other authors declare no competing interests., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objectives: The quality of ambulatory care in Switzerland is widely unknown. Therefore, this study aimed to evaluate the recently proposed quality indicators (QIs) based on a nationwide healthcare claims database and determine their association with the risk of subsequent hospitalisation at patient-level., Design: Retrospective cohort study., Setting: Inpatient and outpatient claims data of a large health insurance in Switzerland covering all regions and population strata., Participants: 520 693 patients continuously insured during 2015 and 2016., Measures: A total of 24 QIs were obtained by adapting the existing instruments to the Swiss national context and measuring at patient-level. The association between each QI and hospitalisation in the subsequent year was assessed using multiple logistic regression models., Results: The proportion of patients with good adherence to QIs was high for the secondary prevention of diabetes and myocardial infarction (glycated haemoglobin (HbA1c) control, 89%; aspirin use, 94%) but relatively low for polypharmacy (53%) or using potentially inappropriate medications (PIMs) in the elderly (PIM, 33%). Diabetes-related indicators such as the HbA1c control were significantly associated with a lower risk of hospitalisation (OR, 0.87; 95% CI, 0.80 to 0.95), whereas the occurrence of polypharmacy and PIM increased the risk of hospitalisation in the following year (OR, 1.57/1.08; 95% CI, 1.51 to 1.64/1.05 to 1.12)., Conclusions: This is the first study to evaluate the recently presented QIs in Switzerland using nationwide real-life data. Our study suggests that the quality of healthcare, as measured by these QIs, varied. The majority of QIs, in particular QIs reflecting chronic care and medication use, are considered beneficial markers of healthcare quality as they were associated with reduced risk of hospitalisation in the subsequent year. Results from this large practical test on real-life data show the feasibility of these QIs and are beneficial in selecting the appropriate QIs for healthcare implementation in general practice., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Evaluating whether future studies to develop prediction models for early readmissions based on health insurance claims data available at the time of a hospitalisation are worthwhile., Design: Retrospective cohort study of hospital admissions with discharge dates between 1 January 2014 and 31 December 2016., Setting: All-cause acute care hospital admissions in the general population of Switzerland, enrolled in the Helsana Group, a large provider of Swiss mandatory health insurance., Participants: The mean age of 138 222 hospitalised adults included in the study was 60.5 years. Patients were included only with their first index hospitalisation. Patients who deceased during the follow-up period were excluded, as well as patients admitted from and/or discharged to nursing homes or rehabilitation clinics., Measures: The primary outcome was 30-day readmission rate. Area under the receiver operating characteristic curve (AUC) was used to measure the discrimination of the developed logistic regression prediction model. Candidate variables were theory based and derived from a systematic literature search., Results: We observed a 30-day readmission rate of 7.5%. Fifty-five candidate variables were identified. The final model included pharmacy-based cost group (PCG) cancer, PCG cardiac disease, PCG pain, emergency index admission, number of emergency visits, costs specialists, costs hospital outpatient, costs laboratory, costs therapeutic devices, costs physiotherapy, number of outpatient visits, sex, age group and geographical region as predictors. The prediction model achieved an AUC of 0.60 (95% CI 0.60 to 0.61)., Conclusions: Based on the results of our study, it is not promising to invest resources in large-scale studies for the development of prediction tools for hospital readmissions based on health insurance claims data available at admission. The data proved appropriate to investigate the occurrence of hospitalisations and subsequent readmissions, but we did not find evidence for the potential of a clinically helpful prediction tool based on patient-sided variables alone., Competing Interests: Competing interests: Both authors were employed at the Helsana Group at the time of writing., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Indicators of guideline adherence are frequently used to examine the appropriateness of healthcare services. Only some potential indicators are actually usable for research with routine administrative claims data, potentially leading to a biased selection of research questions. This study aimed at developing a systematic approach to extract potential indicators from clinical practice guidelines (CPG), evaluate their feasibility for research with claims data and assess how the extracted set reflected different types of healthcare services. Diabetes mellitus (DM), Swiss national guidelines and health insurance claims data were analysed as a model case., Methods: CPG for diabetes patients were retrieved from the Swiss Endocrinology and Diabetes Society website. Recommendation statements involving a specific healthcare intervention for a defined patient population were translated into indicators of guideline adherence. Indicators were classified according to disease stage and healthcare service type. We assessed for all indicators whether they could be analysed with Swiss mandatory health insurance administrative claims data., Results: A total of 93 indicators were derived from 15 CPG, representing all sectors of diabetes care. For 63 indicators, the target population could not be identified using claims data only. For 67 indicators, the intervention could not be identified. Nine (10%) of all indicators were feasible for research with claims data (three addressed gestational diabetes and screening, five screening for complications and one glucose measurement). Some types of healthcare services, eg, management of risk factors, treatment of the disease and secondary prevention, lacked corresponding indicators feasible for research., Conclusions: Our systematic approach could identify a number of indicators of healthcare service utilisation, feasible for DM research with Swiss claims data. Some areas of healthcare were covered less well. The approach could be applied to other diseases and countries, helping to identify the potential bias in the selection of indicators and optimise research., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)