The Swiss neighbourhood index of socioeconomic position: update and re-validation

Associations of exposure to transportation noise with sleep and cardiometabolic health: exploration of pathways

Transportation noise is known to increase the risk for multiple cardiometabolic diseases. However, the complex relationship between different noise sources, sleep, metabolic and cardiovascular risk factors is still not well understood. To study how chronic noise exposure contributes to the development of cardiometabolic disease, data from the cross-sectional COmPLETE-Health Study were used, which includes a comprehensive cardiometabolic assessment and accelerometer-based behavioral monitoring from 527 healthy Swiss adults aged 20-89 years. Road traffic, aircraft and railway noise were modelled at the participants' home addresses, followed by a measurement based model validation. For each noise source, the following conceptual model was tested using Structural Equation Modelling (SEM): Noise was assumed to affect the two latent constructs metabolic risk (defined by waist-to-hip ratio and HDL-cholesterol) and cardiovascular risk (defined by systolic blood pressure and pulse wave velocity) directly, as well as indirectly via sleep efficiency. The SEM showed very good fit of the conceptual models. Road traffic noise had a significant, small-medium sized direct link with cardiovascular risk (β = 0.16, 95 %CI: 0.01, 0.31), while negligible associations with sleep efficiency or metabolic risk were observed. Conversely, railway noise was mainly associated with lower sleep efficiency (β = -0.15, 95 %CI: -0.23, -0.06) and increased metabolic risk (β = 0.14, 95 %CI: -0.05, 0.32), with only a negligible association with cardiovascular risk. The inclusion of physical activity did not change results, suggesting that associations of noise with cardiometabolic health are robust to the additional consideration of physical activity. These insights are important to inform effective measures to protect populations from harmful transportation noise exposure.

Paediatric Personalized Research Network Switzerland (SwissPedHealth): a joint paediatric national data stream

INTRODUCTION

Children represent a large and vulnerable patient group. However, the evidence base for most paediatric diagnostic and therapeutic procedures remains limited or is often inferred from adults. There is an urgency to improve paediatric healthcare provision based on real-world evidence generation. Digital transformation is a unique opportunity to shape a data-driven, agile, learning healthcare system and deliver more efficient and personalised care to children and their families. The goal of Paediatric Personalized Research Network Switzerland (SwissPedHealth) is to build a sustainable and scalable infrastructure to make routine clinical data from paediatric hospitals in Switzerland interoperable, standardised, quality-controlled, and ready for observational research, quality assurance, trials and health-policy creation. This study describes the design, aims and current achievements of SwissPedHealth.

METHODS AND ANALYSIS

SwissPedHealth was started in September 2022 as one of four national data streams co-funded by the Swiss Personalized Health Network (SPHN) and the Personalized Health and Related Technologies (PHRT). SwissPedHealth develops modular governance and regulatory strategies and harnesses SPHN automatisation procedures in collaboration with clinical data warehouses, the Data Coordination Center, Biomedical Information Technology Network, and other SPHN institutions and funded projects. The SwissPedHealth consortium is led by a multisite, multidisciplinary Steering Committee, incorporating patient and family representatives. The data stream contains work packages focusing on (1) governance and implementation of standardised data collection, (2) nested projects to test the feasibility of the data stream, (3) a lighthouse project that enriches the data stream by integrating multi-omics data, aiming to improve diagnoses of rare diseases and 4) engagement with families through patient and public involvement activities and bioethics interviews.

ETHICS AND DISSEMINATION

The health database regulation of SwissPedHealth was approved by the ethics committee (AO_2022-00018). Research findings will be disseminated through national and international conferences and publications in peer-reviewed journals, and in lay language via online media and podcasts.

Sociodemographic and regional differences in neonatal and infant mortality in Switzerland in 2011-2018: the Swiss National Cohort

BACKGROUND AND AIMS

Despite a well-funded healthcare system with universal insurance coverage, Switzerland has one of the highest neonatal and infant mortality rates among high-income countries. Identifying avoidable risk factors targeted by evidence-based policies is a public health priority. We describe neonatal and infant mortality in Switzerland from 2011 to 2018 and explore associations with neonatal- and pregnancy-related variables, parental sociodemographic information, regional factors and socioeconomic position (SEP) using data from a long-term nationwide cohort study.

METHODS

We included 680,077 live births, representing 99.3% of all infants born in Switzerland between January 2011 and December 2018. We deterministically linked the national live birth register with the mortality register and with census and survey data to create a longitudinal dataset of neonatal- and pregnancy-related variables; parental sociodemographic information, such as civil status, age, religion, education, nationality; regional factors, such as urbanity, language region; and the Swiss neighbourhood index of socioeconomic position (Swiss-SEP index). Information on maternal education was available for a random subset of 242,949 infants. We investigated associations with neonatal and infant mortality by fitting multivariable Poisson regression models with robust standard errors. Several sensitivity analyses assessed the robustness of our findings.

RESULTS

Overall, neonatal mortality rates between 2011 and 2018 were 3.0 per 1000 live births, with regional variations: 3.2 in German-speaking, 2.4 in French-speaking and 2.1 in Italian-speaking Switzerland. For infant mortality, the rates were 3.7 per 1000 live births overall, and 3.9 in the German-speaking, 3.3 in the French-speaking and 2.9 in the Italian-speaking region. After adjusting for sex, maternal age, multiple birth and birth rank, neonatal mortality remained significantly associated with language region (adjusted rate ratio [aRR] 0.72, 95% confidence interval [CI]: 0.64-0.80 for the French-speaking region and aRR 0.66, 95% CI: 0.51-0.87 for the Italian-speaking region vs German-speaking region), with marital status (aRR 1.55, 95% CI: 1.40-1.71 for unmarried vs married), nationality (aRR 1.40, 95% CI: 1.21-1.62 for non-European Economic Area vs Swiss) and the Swiss-SEP index (aRR 1.17, 95% CI: 1.00-1.36 for lowest vs highest SEP quintile). In the subset, we showed a possible association of neonatal mortality with maternal education (aRR 1.24, 95% CI: 0.95-1.61 for compulsory vs tertiary education).

CONCLUSION

We provide detailed evidence about the social patterning of neonatal and infant mortality in Switzerland and reveal important regional differences with about 30% lower risks in French- and Italian-speaking compared with German-speaking regions. Underlying causes for such regional differences, such as cultural, lifestyle or healthcare-related factors, warrant further exploration to inform and provide an evidence base for public health policies.

Individualized pretest risk estimates to guide treatment decisions in patients with clinical high risk for psychotic disorders

INTRODUCTION

Clinical high risk for psychosis (CHR) states are associated with an increased risk of transition to psychosis. However, the predictive value of CHR screening interviews is dependent on pretest risk enrichment in referred patients. This poses a major obstacle to CHR outreach campaigns since they invariably lead to risk dilution through enhanced awareness. A potential compensatory strategy is to use estimates of individual pretest risk as a 'gatekeeper' for specialized assessment. We aimed to test a risk stratification model previously developed in London, UK (OASIS) and to train a new predictive model for the Swiss population.

METHOD

The sample was composed of 513 individuals referred for CHR assessment from six Swiss early psychosis detection services. Sociodemographic variables available at referral were used as predictors whereas the outcome variable was transition to psychosis.

RESULTS

Replication of the risk stratification model developed in OASIS resulted in poor performance (Harrel's c=0.51). Retraining resulted in moderate discrimination (Harrel's c=0.67) which significantly differentiated between different risk groups. The lowest risk group had a cumulative transition incidence of 6.4% (CI: 0-23.1%) over two years.

CONCLUSION

Failure to replicate the OASIS risk stratification model might reflect differences in the public health care systems and referral structures between Switzerland and London. Retraining resulted in a model with adequate discrimination performance. The developed model in combination with CHR assessment result, might be useful for identifying individuals with high pretest risk, who might benefit most from specialized intervention.

Gender/Sex Disparities in the COVID-19 Cascade From Testing to Mortality: An Intersectional Analysis of Swiss Surveillance Data

Objectives

This study investigates gender and sex disparities in COVID-19 epidemiology in the Canton of Vaud, Switzerland, focusing on the interplay with socioeconomic position (SEP) and age.

Methods

We analyzed COVID-19 surveillance data from March 2020 to June 2021, using an intersectional approach. Negative binomial regression models assessed disparities between women and men, across SEP quintiles and age groups, in testing, positivity, hospitalizations, ICU admissions, and mortality (Incidence Rate Ratios [IRR], with 95% Confidence Intervals [CI]).

Results

Women had higher testing and positivity rates than men, while men experienced more hospitalizations, ICU admissions, and deaths. The higher positivity in women under 50 was mitigated when accounting for their higher testing rates. Within SEP quintiles, gender/sex differences in testing and positivity were not significant. In the lowest quintile, women's mortality risk was 68% lower (Q1: IRR 0.32, CI 0.20-0.52), with decreasing disparities with increasing SEP quintiles (Q5: IRR 0.66, CI 0.41-1.06).

Conclusion

Our findings underscore the complex epidemiological patterns of COVID-19, shaped by the interactions of gender/sex, SEP, and age, highlighting the need for intersectional perspectives in both epidemiological research and public health strategy development.

Area-level excess mortality in times of COVID-19 in Switzerland: geographical, socioeconomic and political determinants

The coronavirus disease 2019 (COVID-19)-related excess mortality in Switzerland is well documented, but no study examined mortality at the small-area level. We analysed excess mortality in 2020 for 2141 Swiss municipalities using a Bayesian spatiotemporal model fitted to 2011-19 data. Areas most affected included the Ticino, the Romandie and the Northeast. Rural areas, municipalities within cross-border labour markets, of lower socioeconomic position and with less support for control measures in the popular vote on the COVID-19 Act had greater excess mortality. Particularly vulnerable municipalities require special efforts to mitigate the impact of pandemics.

Health of singleton neonates in Switzerland through time and crises: a cross-sectional study at the population level, 2007-2022

BACKGROUND

Being exposed to crises during pregnancy can affect maternal health through stress exposure, which can in return impact neonatal health. We investigated temporal trends in neonatal outcomes in Switzerland between 2007 and 2022 and their variations depending on exposure to the economic crisis of 2008, the flu pandemic of 2009, heatwaves (2015 and 2018) and the COVID-19 pandemic.

METHODS

Using individual cross-sectional data encompassing all births occurring in Switzerland at the monthly level (2007-2022), we analysed changes in birth weight and in the rates of preterm birth (PTB) and stillbirth through time with generalized additive models. We assessed whether the intensity or length of crisis exposure was associated with variations in these outcomes. Furthermore, we explored effects of exposure depending on trimesters of pregnancy.

RESULTS

Over 1.2 million singleton births were included in our analyses. While birth weight and the rate of stillbirth have remained stable since 2007, the rate of PTB has declined by one percentage point. Exposure to the crises led to different results, but effect sizes were overall small. Exposure to COVID-19, irrespective of the pregnancy trimester, was associated with a higher birth weight (+12 grams [95% confidence interval (CI) 5.5 to 17.9 grams]). Being exposed to COVID-19 during the last trimester was associated with an increased risk of stillbirth (odds ratio 1.24 [95%CI 1.02 to 1.50]). Exposure to the 2008 economic crisis during pregnancy was not associated with any changes in neonatal health outcomes, while heatwave effect was difficult to interpret.

CONCLUSION

Overall, maternal and neonatal health demonstrated resilience to the economic crisis and to the COVID-19 pandemic in a high-income country like Switzerland. However, the effect of exposure to the COVID-19 pandemic is dual, and the negative impact of maternal infection on pregnancy is well-documented. Stress exposure and economic constraint may also have had adverse effects among the most vulnerable subgroups of Switzerland. To investigate better the impact of heatwave exposure on neonatal health, weekly or daily-level data is needed, instead of monthly-level data.

Development and internal validation of a prediction model for long-term opioid use-an analysis of insurance claims data

ABSTRACT

In the United States, a public-health crisis of opioid overuse has been observed, and in Europe, prescriptions of opioids are strongly increasing over time. The objective was to develop and validate a multivariable prognostic model to be used at the beginning of an opioid prescription episode, aiming to identify individual patients at high risk for long-term opioid use based on routinely collected data. Predictors including demographics, comorbid diseases, comedication, morphine dose at episode initiation, and prescription practice were collected. The primary outcome was long-term opioid use, defined as opioid use of either >90 days duration and ≥10 claims or >120 days, independent of the number of claims. Traditional generalized linear statistical regression models and machine learning approaches were applied. The area under the curve, calibration plots, and the scaled Brier score assessed model performance. More than four hundred thousand opioid episodes were included. The final risk prediction model had an area under the curve of 0.927 (95% confidence interval 0.924-0.931) in the validation set, and this model had a scaled Brier score of 48.5%. Using a threshold of 10% predicted probability to identify patients at high risk, the overall accuracy of this risk prediction model was 81.6% (95% confidence interval 81.2% to 82.0%). Our study demonstrated that long-term opioid use can be predicted at the initiation of an opioid prescription episode, with satisfactory accuracy using data routinely collected at a large health insurance company. Traditional statistical methods resulted in higher discriminative ability and similarly good calibration as compared with machine learning approaches.