Isabella Locatelli

AIMS: To examine all-cause and cause-specific mortality by age group in Switzerland during and after the COVID-19 pandemic, in order to determine whether pre-pandemic trends recovered in 2023–24. METHODS: Standardised mortality rates since COVID-19 were compared with those expected by continuing trends estimated over the pre-pandemic years 2000–19 by log-linear Poisson models, resulting in estimates of all-age and age-specific excess death and mortality for 2020–24. Mortality trends for the leading causes of death were analysed by age group using Joinpoint regression models. RESULTS: In 2024, all-cause mortality in Switzerland showed a complete return to pre-pandemic trends. However, marked differences between age groups were observed. While mortality among people aged 85 and over was significantly lower than trend in 2024 (–5.4% for men and –3.5% for women), people aged 65 to 84 were still struggling to recover the pre-pandemic trend this year (+4.2% for men and +3.5% for women). Mortality in the 15–44 age group began to stagnate a few years before the pandemic and remained significantly above trend in recent years, with excess mortality being systematically outside the 95% prediction intervals, reaching +33.6% for men and +20.4% for women in 2024. At the same time, accidents and suicides, among the leading causes of death in this age group, appear to have stopped declining, with a stabilisation in the suicide trend since 2014 among men (p = 0.005) and 2013 among women (p = 0.001), and in the accident trend since 2019 among men (p = 0.031). A slowdown in the decline of cardiovascular and respiratory mortality has been observed in the 65–84 age group. CONCLUSIONS: The long-standing decline in mortality has stalled in recent years in Switzerland among young adults and, to a lesser extent, among young retirees, due to stagnation or slowdown of several major causes of death in these age groups. These phenomena, in addition to their importance for considering appropriate preventive interventions, also raise questions about the future of mortality and longevity in Switzerland and, more generally, in Western societies, and must be taken into account when developing realistic life expectancy and demographic scenarios. These are essential for decision-making in the areas of insurance, retirement planning and healthcare cost management.

BACKGROUND: Long-term control of cardiovascular risk factors after acute coronary syndrome (ACS) is the cornerstone for preventing recurrence. We investigated the extent of cardiovascular risk factor management in males and females with and without familial hypercholesterolemia (FH) 5 years after ACS. METHODS: We studied patients hospitalized for ACS between 2009 and 2017 in a Swiss multicenter prospective cohort study. FH was defined based on clinical criteria from the Dutch Lipid Clinic Network and Simon Broome definitions. Five years post-ACS, we assessed low-density lipoprotein-cholesterol (LDL-c) levels, lipid-lowering therapy (LLT), and other cardiovascular risk factors, comparing males to females with and without FH using generalized estimating equations. RESULTS: A total of 3139 patients were included; mean age was 61.4 years (SD, 12.1), 620 (19.8%) were female, and 747 (23.5%) had possible FH. Compared with males at 5-years post-ACS, females were more likely to not use statins (odds ratio, 1.61 [95% CI, 1.28–2.03]) and less likely to have combination LLT (odds ratio, 0.72 [95% CI, 0.55–0.93]), without difference between patients with FH and without FH. Females in both FH and non-FH groups less frequently reached LDL-c values ≤1.8 mmol/L (odds ratio, 0.78 [95% CI, 0.78–0.93]). Overall, patients with FH were more frequently on high-dose statins compared with patients without FH (51.0% versus 42.9%; P =0.001) and presented more frequently with a combination of 2 or more LLT compared with patients without FH (33.8% versus 17.7%; P P P =0.001). CONCLUSIONS: Five years after ACS, females had less intensive LLT and were less likely to reach target LDL-c levels than males, regardless of FH status. Males and females with FH had less optimal control of LDL-c despite more frequently taking high-dose statins or combination LLT compared with patients without FH. Long-term management of patients with ACS and FH, especially females, warrants optimization.

The concept of (potential) years of life lost is a measure of premature mortality that can be used to compare the impacts of different specific causes of death. However, interpreting a given number of years of life lost at face value is more problematic because of the lack of a sensible reference value. In this paper, we propose three denominators to divide an excess years of life lost, thus obtaining three indicators, called average life lost , increase of life lost , and proportion of life lost , which should facilitate interpretation and comparisons. We study the links between these three indicators and classical mortality indicators, such as life expectancy and standardized mortality rate, introduce the concept of weighted standardized mortality rate , and calculate them in 30 countries to assess the impact of COVID‐19 on mortality in the year 2020. Using any of the three indicators, a significant excess loss is found for both genders in 18 of the 30 countries.

Background: Patient adherence to medications can be assessed using interactive digital health technologies such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterized to establish the actual level of medication adherence. Objective: We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users. Methods: In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients’ demographic data. The script formats the data longitudinally and calculates each day’s medication implementation. Results: We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (Δ+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence. Conclusions: The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies.

Objective: During the COVID-19 pandemic, excess mortality has generally been estimated comparing overall mortality in a given year with either past mortality levels or past mortality trends, with different results. Our objective was to illustrate and compare the two approaches using mortality data for Switzerland in 2022, the third year of the COVID-19 pandemic.Methods: Using data from the Swiss Federal Statistical Office, standardized mortality rates and life expectancies in 2022 were compared with those of the last pre-pandemic year 2019 (first approach), as well as with those that would be expected if the pre-pandemic downward trend in mortality had continued during the pandemic (second approach). The pre-pandemic trend was estimated via a Poisson log-linear model on age-specific mortality over the period 2010–19.Results: Using the first approach, we estimated in Switzerland in 2022 an excess mortality of 2.6% (95%CI: 1.0%-4.1%) for men and 2.5% (95%CI: 1.0%-4.0%) for women, while the excess mortality rose to 8.4% (95%CI: 6.9%-9.9%) for men and 6.0% (95%CI: 4.6%-7.5%) for women using the second approach. Age classes over 80 were the main responsible for the excess mortality in 2022 for both sexes using the first approach, although a significant excess mortality was also found in most age classes above 30 using the second approach. Life expectancy in 2022 has been reduced by 2.7 months for men and 2.4 months for women according to the first approach, whereas it was reduced by respectively 8.8 and 6.0 months according to the second approach.Conclusions: The excess mortality and loss of life expectancy in Switzerland in 2022 are around three times greater if the pre-pandemic trend is taken into account than if we simply compare 2022 with 2019. These two different approaches, one being more speculative and the other more factual, can also be applied simultaneously and provide complementary results. In Switzerland, such a dual-approach strategy has shown that the pre-pandemic downward trend in mortality is currently halted, while pre-pandemic mortality levels have largely been recovered by 2022.

BACKGROUND Patient adherence to medications can be assessed using interactive digital health technologies such as electronic monitors (EMs). Changes in treatment regimens and deviations from EM use over time must be characterized to establish the actual level of medication adherence. OBJECTIVE We developed the computer script CleanADHdata.R to clean raw EM adherence data, and this tutorial is a guide for users. METHODS In addition to raw EM data, we collected adherence start and stop monitoring dates and identified the prescribed regimens, the expected number of EM openings per day based on the prescribed regimen, EM use deviations, and patients’ demographic data. The script formats the data longitudinally and calculates each day’s medication implementation. RESULTS We provided a simulated data set for 10 patients, for which 15 EMs were used over a median period of 187 (IQR 135-342) days. The median patient implementation before and after EM raw data cleaning was 83.3% (IQR 71.5%-93.9%) and 97.3% (IQR 95.8%-97.6%), respectively (Δ+14%). This difference is substantial enough to consider EM data cleaning to be capable of avoiding data misinterpretation and providing a cleaned data set for the adherence analysis in terms of implementation and persistence. CONCLUSIONS The CleanADHdata.R script is a semiautomated procedure that increases standardization and reproducibility. This script has broader applicability within the realm of digital health, as it can be used to clean adherence data collected with diverse digital technologies.

Guidelines recommend chest X-rays (CXRs) to diagnose pneumonia and guide antibiotic treatment. This study aimed to identify clinical predictors of pneumonia that are visible on a chest X-ray (CXR+) which could support ruling out pneumonia and avoiding unnecessary CXRs, including oxygen saturation. A secondary analysis was performed in a clinical trial that included patients with suspected pneumonia in Swiss primary care. CXRs were reviewed by two radiologists. We evaluated the association between clinical signs (heart rate > 100/min, respiratory rate ≥ 24/min, temperature ≥ 37.8 °C, abnormal auscultation, and oxygen saturation < 95%) and CXR+ using multivariate analysis. We also calculated the diagnostic performance of the associated clinical signs combined in a clinical decision rule (CDR), as well as a CDR derived from a large meta-analysis (at least one of the following: heart rate > 100/min, respiratory rate ≥ 24/min, temperature ≥ 37.8 °C, or abnormal auscultation). Out of 469 patients from the initial trial, 107 had a CXR and were included in this study. Of these, 26 (24%) had a CXR+. We found that temperature and oxygen saturation were associated with CXR+. A CDR based on the presence of either temperature ≥ 37.8 °C and/or an oxygen saturation level < 95% had a sensitivity of 69% and a negative likelihood ratio (LR−) of 0.45. The CDR from the meta-analysis had a sensitivity of 92% and an LR− of 0.37. The addition of saturation < 95% to this CDR increased the sensitivity (96%) and decreased the LR− (0.21). In conclusion, this study suggests that pulse oximetry could be added to a simple CDR to decrease the probability of pneumonia to an acceptable level and avoid unnecessary CXRs.

Background: Lower respiratory tract infections (LRTIs) in primary care are a promising target for antibiotic stewardship. A clinical trial in Switzerland showed a large decrease in antibiotic prescriptions with procalcitonin guidance (cut-off < 0.25 µg/L) compared with usual care. However, one-third of patients with low procalcitonin at baseline received antibiotics by day 28. Aim: To explore the factors associated with the overruling of initial procalcitonin guidance. Design and Setting: Secondary analysis of a cluster randomized trial in which patients with an LRTI were included. Method: Using the characteristics of patients, their disease, and general practitioners (GPs), we conducted a multivariate logistic regression, adjusted for clustering. Results: Ninety-five out of 301 (32%) patients with low procalcitonin received antibiotics by day 28. Factors associated with an overruling of procalcitonin guidance were: a history of chest pain (adjusted OR [aOR] 1.81, 95% confidence interval 1.03–3.17); a prescription of chest X-ray by the GP (aOR 4.65, 2.32–9.34); a C-reactive protein measured retrospectively above 100 mg/L (aOR 7.48, 2.34–23.93, reference ≤ 20 mg/L); the location of the GP practice in an urban setting (aOR 2.27, 1.18–4.37); and the GP’s number of years of experience (aOR per year 1.05, 1.01–1.09). Conclusions: Overruling of procalcitonin guidance was associated with GPs’ socio-demographic characteristics, pointing to the general behavioral problem of overprescription by physicians. Continuous medical education and communication training might support the successful implementation of procalcitonin point-of-care tests aimed at antibiotic stewardship.

The cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) palbociclib is administered orally and cyclically, causing medication adherence challenges. We evaluated components of adherence to palbociclib, its relationship with pharmacokinetics (PK), and drug-induced neutropenia. Patients with metastatic breast cancer (MBC) receiving palbociclib, delivered in electronic monitors (EM), were randomized 1:1 to an intervention and a control group. The intervention was a 12-month interprofessional medication adherence program (IMAP) along with monthly motivational interviews by a pharmacist. Implementation adherence was compared between groups using generalized estimating equation models, in which covariates were included. Model-based palbociclib PK and neutrophil profiles were simulated under real-life implementation scenarios: (1) optimal, (2) 2 doses omitted and caught up at cycle end. At 6 months, implementation was slightly higher and more stable in the intervention (n = 19) than in the control (n = 19) group, 99.2% and 97.3% (Δ1.95%, 95% CI 1.1–2.9%), respectively. The impact of the intervention was larger in patients diagnosed with MBC for >2 years (Δ3.6%, 95% CI 2.1–5.4%), patients who received >4 cycles before inclusion (Δ3.1%, 95% CI 1.7–4.8%) and patients >65 (Δ2.3%, 95% CI 0.8–3.6%). Simulations showed that 25% of patients had neutropenia grade ≥3 during the next cycle in scenario 1 versus 30% in scenario 2. Education and monitoring of patient CDK4/6i cycle management and adherence along with therapeutic drug monitoring can help clinicians improve prescription and decrease toxicity.

Introduction/Purpose: High-intensity statin after acute coronary syndrome (ACS) may cause reduced physical activity because of statin-associated muscle symptoms. We aimed to assess the association between statin intensity and physical activity one year after ACS. Methods: We studied patients from the Special Program University Medicine-Acute Coronary Syndromes (SPUM-ACS) study, a large multicenter prospective Swiss cohort. In a cross-sectional assessment one year after the index ACS, we identified high-intensity, low/moderate intensity, and no statin users. Physical activity was assessed with the International Physical Activity Questionnaire (IPAQ). Using a multivariable adjusted negative binomial hurdle model, metabolic equivalent-minutes per week (MET-min/week) were first stratified into sedentary and physically active categories, and then analyzed continuously among physically active patients. Models were adjusted for age, sex, education, body mass index, depression, type of ACS, pre-existing chronic disease, premature coronary heart disease, LDL-cholesterol, anti-hypertensive treatment, cardiac rehabilitation, and follow-up medical visits after hospital discharge. Results: Among the 2274 patients included in the SPUM-ACS cohort, 1222 (53.7%) were on high intensity statin, 890 (39.1%) on low/moderate intensity statin, and 162 (7.1%) were not on statin. Compared to no statin users, low/moderate intensity statin users were more likely to be physically active than sedentary, with a fully adjusted odds ratio (OR) of 2.78, 95% confidence interval (CI) 1.11–6.93. A similar association was found when comparing non-users with high-intensity statin users, with a fully adjusted OR of 4.18, 95% CI 1.65–10.60. Among physically active patients, physical activity level median scores were 2792.5 MET-min/week in no statin category, 2712.0 and 2839.5 in moderate/low statin category and high statin category respectively (p=0.307), showing no statistically significant difference with median ratios of 1.02, 95% CI 0.84–1.22 and 1.06, 95% CI of 0.88–1.27 for low/moderate intensity and high-intensity statin use, respectively. Conclusion: One year after an ACS, neither low/moderate nor high-intensity statin was associated with reduced physical activity compared to no statin use. These findings go against the belief that statin therapy may lead to reduced physical activity among ACS patients because of associated muscular symptoms. Funding Acknowledgement: Type of funding sources: Public Institution(s). Main funding source(s): Center for primary care and public health (Unisanté)