Impact of the Covid-19 pandemic on inpatient health care in Switzerland 2020-2021-A descriptive retrospective study using admission data of all Swiss hospitals.

BACKGROUND: As part of the Covid-19-restrictions in Switzerland, a federal ban on non-urgent examinations and treatments was applied to all hospitals during six weeks in spring 2020 ("spring lockdown"). The aim of this study was to comprehensively investigate the consequences of the Covid-19 pandemic on Swiss inpatient admissions based on data of all hospitals, focusing on selected procedures of different medical urgency. METHODS: The study includes all acute care inpatient cases (including Covid-19 cases, excluding cases in psychiatry and rehabilitation) according to the Swiss Medical Statistics of Hospitals. Besides the total number of admissions, subdivided by regions, hospital types and age groups, we focused on selected procedures representing different medical urgency: elective surgeries, cancer surgeries, and emergencies. Procedures were selected based on expert interviews. We compared the number of admissions during spring lockdown and for the whole years 2020 and 2021 in absolute numbers and in percentage changes to the corresponding periods in 2019 (baseline year). RESULTS: During spring lockdown, the number of admissions decreased by 47,156 (32.2%) without catch-up effect by the end of 2020 (-72,817 admissions/-5.8%). With procedure-specific decreases of up to 86%, the decline in admissions was largest for elective surgery, a decline that was only fully reversed in the case of a few procedures, such as joint arthroplasty. Strikingly, admissions due to emergencies also substantially decreased during spring lockdown (stroke -14%; acute myocardial infarction STEMI: -9%, NSTEMI: -26%). Results for the selected procedures in cancer surgery showed no consistent pattern. In 2021, admission numbers for most procedures reached or even exceeded those in 2019. CONCLUSIONS: The substantial reduction in admissions, particularly in elective procedures, may reflect the impact of the triage in favor of anticipated Covid-19-cases during spring lockdown. By the end of 2020, admissions were still at lower levels relative to the previous, pre-pandemic year. The numbers in 2021 reached the same levels as those in 2019, which suggests that the Covid-19 pandemic only temporarily impacted inpatient health care in Switzerland. Long-term consequences of the observed reduction in admissions for emergencies and cancer surgery need to be investigated at the individual level.

Real-world treatment patterns and medical costs of prostate cancer patients in Switzerland - A claims data analysis.

BACKGROUND: Prostate cancer (PC) is the most prevalent cancer in men in Switzerland. However, evidence on the real-world health care use of PC patients is scarce. The aim of this study is to describe health care utilization, treatment patterns, and medical costs in PC patients over a period of five years (2014-2018). METHOD: We used routinely collected longitudinal individual-level claims data from a major provider of mandatory health insurance in Switzerland. Due to the lack of diagnostic coding in the claims data, we identified treated PC patients based on the treatments received. We described health care utilization and treatment pathways for patients with localized and metastatic PC. Costs were calculated from a health care system perspective. RESULTS: A total of 5591 PC patients met the inclusion criteria. Between 2014 and 2018, 1741 patients had outpatient radiotherapy for localized or metastatic PC and 1579 patients underwent radical prostatectomy. 3502 patients had an androgen deprivation therapy (ADT). 9.5% of these patients had a combination therapy with docetaxel, and 11.0% had a combination with abiraterone acetate. Docetaxel was the most commonly used chemotherapy (first-line; n = 413, 78.4% of all patients in chemotherapy). Total medical costs of PC in Switzerland were estimated at CHF 347 m (95% CI 323-372) in 2018. CONCLUSION: Most PC patients in this study were identified based on the use of ADT. Medical costs of PC in Switzerland amounted to 0.45% of total health care spending in 2018. Treatment of metastatic PC accounted for about two thirds of spending.

What drives health care spending in Switzerland? Findings from a decomposition by disease, health service, sex, and age.

BACKGROUND: High and increasing spending dominates the public discussion on healthcare in Switzerland. However, the drivers of the spending increase are poorly understood. This study decomposes health care spending by diseases and other perspectives and estimates the contribution of single cost drivers to overall healthcare spending growth in Switzerland between 2012 and 2017. METHODS: We decompose total healthcare spending according to National Health Accounts by 48 major diseases, injuries, and other conditions, 20 health services, 21 age groups, and sex of patients. This decomposition is based on micro-data from a multitude of data sources such as the hospital inpatient registry, health and accident insurance claims data, and population surveys. We identify the contribution of four main drivers of spending: population growth, change in population structure (age/sex distribution), changes in disease prevalence, and changes in spending per prevalent patient. RESULTS: Mental disorders were the most expensive major disease group in both 2012 and 2017, followed by musculoskeletal disorders and neurological disorders. Total health care spending increased by 19.7% between 2012 and 2017. An increase in spending per prevalent patient was the most important spending driver (43.5% of total increase), followed by changes in population size (29.8%), in population structure (14.5%), and in disease prevalence (12.2%). CONCLUSIONS: A large part of the recent health care spending growth in Switzerland was associated with increases in spending per patient. This may indicate an increase in the treatment intensity. Future research should show if the spending increases were cost-effective.

Factors associated with the choice of supplementary hospital insurance in Switzerland - an analysis of the Swiss Health Survey.

BACKGROUND: Switzerland has universal coverage via mandatory health insurance that covers a generous basket of health services. In addition to the basic coverage, the insured can buy supplementary insurance for the inpatient sector. Supplementary hospital insurance in Switzerland provides additional services during inpatient stays. Little is known about which factors are associated with the choice of semi-private and private hospital insurances. However, this is of importance to policy makers and the insured population, who might be concerned about a "two-class" inpatient care system. Therefore, the aim of the paper was to explore the factors associated with supplementary hospital insurance enrolment in Switzerland. METHODS: We used the five most recent waves of the representative Swiss Health Survey (1997, 2002, 2007, 2012, 2017) to explore which factors are associated with supplementary hospital insurance enrolment in adults aged 25 or older. We estimated the same probit model for all five surveys waves and computed average marginal effects. RESULTS: Our study shows that in all cross-sections the likelihood of enrolling in supplementary hospital insurance increased with higher age, education, household income and was higher for people with a strong preference for unrestricted choice of a specialist and with a higher-than-default deductible choice. The likelihood of supplementary hospital insurance enrolment was lower for the unemployed relative to their inactive counterparts and those living in rural areas relative to comparable urban residents. Ever-smoker status was not statistically significantly associated with supplementary hospital insurance choice. However, our findings indicated differences in estimates over the years regarding demographic as well as insurance-related variables. For example, women were more likely to choose supplementary hospital insurance than comparable men in earlier years. CONCLUSION: Most importantly, our results indicate that factors related to socioeconomic status - such as education, labour market status, and income - consistently show significant associations with the probability of having supplementary hospital insurance for the entire study period, as opposed to demographic variables - such as nationality and sex.

Sigmoid isostiffness-lines: An in-vitro model for the assessment of aortic stenosis severity.

Introduction: The aortic valve opening area (AVA), used to quantify aortic stenosis severity, depends on the transvalvular flow rate (Q). The currently accepted clinical echocardiographic method assumes a linear relation between AVA and Q. We studied whether a sigmoid model better describes this relation and determined "isostiffness-lines" across a wide flow spectrum, thus allowing building a nomogram for the non-invasive estimation of valve stiffness. Methods: Both AVA and instantaneous Q (Qinst) were measured at 10 different mean cardiac outputs of porcine aortic valves mounted in a pulsatile flow loop. The valves' cusps were chemically stiffened to obtain three stiffness grades and the procedure was repeated for each grade. The relative stiffness was defined as the ratio between LV work at grade with the added stiffness and at native stiffness grade. AVA peak ¯ corresponding to the selected Q peak ¯ of the highest 3 and 5 cardiac output values was predicted in K-fold cross-validation using sequentially a linear and a sigmoid model. The accuracy of each model was assessed with the Akaike information criterion (AIC). Results: The sigmoid model predicted more accurately AVA peak ¯ (AIC for prediction of AVA with Q peak ¯ of the 3 highest cardiac output values: -1,743 vs. -1,048; 5 highest cardiac output values: -1,471 vs. -878) than the linear model. Conclusion: This study suggests that the relation between AVA and Q can be better described by a sigmoid than a linear model. This construction of "isostiffness-lines" may be a useful method for the assessment of aortic stenosis in clinical echocardiography.

Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data.

BACKGROUND: Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases. METHODS: In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment. RESULTS: Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending. CONCLUSIONS: Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.

Systemic detectability of dexamethasone and prednisolone after eye drop application in horses.

BACKGROUND: Equine sport agencies list steroids as prohibited substances for competing horses. OBJECTIVES: The objective of this study was to investigate if the controlled substances dexamethasone and prednisolone are detectable in equine serum and urine samples during and after treatment with eye drops and if this can generate a positive doping test. STUDY DESIGN: Prospective cohort study. METHODS: The study cohort included 11 horses. One eye of the horses was treated with either dexamethasone (Maxitrol® 0.1%, n = 5 eyes) or prednisolone (Pred forte® 1%, n = 6 eyes) eye drops 3 times daily for 14 days. Dexamethasone and prednisolone concentrations were determined in serum and urine at day 0 (negative control), 1, 7, 14, 15, 17 and 21 using liquid chromatography-tandem mass spectrometry. Blood samples were collected within 2 hours post application. Urine samples were collected during spontaneous urination. RESULTS: All serum samples (range: 0.7-43 ng/mL, mean 2.1 ng/mL) and urine samples (range 1.2-5 ng/mL, mean 0.8 ng/mL) showed measurable amounts of dexamethasone during the course of treatment. Concentrations in both serum and urine samples were below limit of detection (LOD) 24 hours after the last dexamethasone treatment (day 15). All serum samples (range 1.1-32.5 ng/mL, mean 6.4 ng/mL) and urine samples (range 3.7-19 ng/mL, mean 4.6 ng/mL) were positive for prednisolone during treatment. Urine samples were below LOD on day 15; serum samples on day 21. CONCLUSIONS: Dexamethasone and prednisolone eye drops can induce detectable drug levels in serum and urine samples of horses after a 14-day treatment plan. This can lead to a positive doping result. All samples tested negative (below LOD of the analytical method) for dexamethasone one day and for prednisolone one week after treatment cessation.

Factors related to the change in Swiss inpatient costs by disease: a 6-factor decomposition.

There is currently little systematic knowledge about the contribution of different factors to the increase in health care spending in high-income countries such as Switzerland. The aim of this paper is to decompose inpatient care costs in the Swiss canton of Zurich by 100 diseases and 42 age/sex groups and to assess the contribution of six factors to the change in aggregate costs between 2013 and 2017. These six factors are population size, age and sex structure, inpatient treated prevalence, utilization in terms of stays per patient, length of stay per case, and costs per treatment day. Using detailed inpatient cost data at the case level, we find that the most important contributor to the change in disease-specific costs was a rise in costs per treatment day. For most conditions, this effect was partly offset by a reduction in the average length of stay. Changes in population size accounted for one third of the total increase, but population structure had only a small positive association with costs. The most expensive cases accounted for the largest part of the increase in costs, but the magnitude of this effect differed across diseases. A better understanding of the factors related to cost changes at the disease level over time is essential for the design of targeted health policies aiming at an affordable health care system.