Room
1er étage - F130
Theme
Preparations and forecast for a pandemic
Chair
Kaspar Wyss
Title
Is Early Detection of Pandemics Cost-Effective? Economic Study on Pathogen Surveillance in Switzerland
Name
Laurent Bächler
Affiliation
Abstract
Objectives:This study aims to assess the cost-benefit of institutionalizing a pandemic early warning system in Switzerland. The proposed system comprises three components: wastewater monitoring, genomic sequencing, and data processing of five potentially pandemic pathogens, which would operate both during and outside of pandemics, with different capacities tailored to the different scenarios (pandemic situations similar to COVID-19, severe and extreme pandemics; normal situation without any ongoing pandemic).
Methods:
An economic impact model was developed to calculate avoided human and economic costs based on a quantity and value structure and the Flaxman et al. 2020 stochastic renewal process. This process models the daily infections over time based on different functions such as the generation time, the reproduction number R and the effectiveness of the lockdown as a proxy for effective non-pharmaceutical measures in reducing R to <1 within a given time. The study also conducted qualitative interviews with experts from academia, government, and the health sector, along with in-depth desk research.
Results:
Implementing a pandemic early warning system in Switzerland is economically beneficial. Regular monitoring and sequencing costs CHF 5 million annually outside pandemics. Based on conservative assumptions, the benefit ranges from CHF 1 billion to CHF 30 billion, depending on pandemic severity. Each franc invested yields around four francs in a pandemic similar to COVID-19 and up to 129 francs in an extreme pandemic. Increased pathogen surveillance also correlates with heightened health protection outside pandemics, highlighting the need for institutionalizing these monitoring practices and ensuring their long-term sustainability.
Title
Change in diet, physical activity, and body composition since the end of pandemic restrictions: cohort study
Name
Nicole Bender
Affiliation
Institute of Evolutionary Medicine, University of Zurich
Abstract
The Swiss nationwide restrictions due to the COVID-19 pandemic in the years 2000-2021 lead to changes in diet and physical activity habits in large parts of the population. Related to these changes many people reported an increase in body weight during the pandemic. BMI has already before reached high levels in most population subgroups in Switzerland, increasing the risk for cardiometabolic diseases. An additional BMI increase due to the pandemic could aggravate such health risks. It is therefore important to know if diet related behaviours, physical activity, and health outcome measures changed again since the end of the COVID-19 restrictions. We assessed if diet and physical activity changed since the end of the COVID-19 restrictions using a standardized questionnaire in a general population cohort (2021-2023). We measured BMI, anthropometry, and body composition using a 3D body scanner and bioimpedance analysis. We investigated potential modifying factors such as socioeconomic indicators, age, and sex. We further assessed potential changes in diet related behaviours such as dieting, as well as body perception measures such as body appreciation and body dissatisfaction since the end of the pandemic restrictions. To compare our results with the situation prior to the pandemic, we asked about changes in diet behaviours and weight gain during the pandemic, and for a subgroup of our cohort we have questionnaire data as well as measured weight and body composition from 2019 (before the pandemic). Our Results shall inform future obesity preventive measures as they show population reactions to specific situations, allowing for a more subgroup specific preventive approach (personalized public health).Title
Impfbezogene Gesundheitskompetenz der Schweizer Bevölkerung nach COVID-19
Name
Rebecca Jaks
Affiliation
Careum Zentrum für Gesundheitskompetenz
Abstract
Hintergrund:Impfungen gehören zu den wirksamsten und kostengünstigsten Massnahmen zur Bekämpfung von Infektionskrankheiten und leisten einen wichtigen Beitrag zum Schutz der Bevölkerung. Aufgrund der COVID-19-Pandemie und den in diesem Zusammenhang entwickelten neuartigen Impfstoffen sind das Thema Impfen und die damit verbundenen Diskussionen verstärkt in den gesellschaftlichen Fokus gerückt. Die durch die digitale Transformation vorangetriebenen Infodemie scheint die kontroversen Diskussionen zu Gesundheitsthemen und im Speziellen zum Thema Impfen angeheizt und zu einer zunehmenden Verunsicherung von Gesundheitsfachpersonen, als auch der Bevölkerung geführt zu haben. Vor diesem Hintergrund gewinnt die Gesundheitskompetenz (GK) zunehmend an Bedeutung. Denn die GK und insbesondere die impfbezogene GK sind zentral für das Impfverhalten bzw. gut informierte Entscheidungen bezüglich Impfungen. Im Auftrag des Bundesamts für Gesundheit BAG wurde das Careum Zentrum für Gesundheitskompetenz deshalb beauftragt, die Einstellung, das Wissen bzw. Verständnis zum Thema Impfen, das Impf- und Informationsverhalten sowie die Schwierigkeiten in diesem Bereich der Bevölkerung zu untersuchen und mit früheren Untersuchungen zu diesem Thema zu vergleichen.
Methode:
Für die Beantwortung der unterschiedlichen Fragestellungen wird ein Mixed-Method Design angewandt. In der quantitativen Studie werden in einer repräsentativen Stichprobe in der Schweiz wohnhafte Erwachsene mittels online Erhebung zu ihrer impfbezogenen GK befragt. Die Erhebung findet zwischen Juni und August 2024 statt. Der dafür entwickelte Fragebogen umfasst Fragen zu Wissen, Einstellungen, Verhalten, Entscheidungen im Bereich Impfen, Informationsverhalten sowie soziodemografische Aspekte. Dieser Fragebogen baut auf Vorgängerstudien auf und berücksichtigt gleichzeitig die neusten Entwicklungen im Bereich Impfungen sowie die COVID-19-Pandemie. Für die qualitative Studie, die nach der online Erhebung stattfinden wird, werden rund 30 Interviews mit Vertreter:innen der Bevölkerung aus allen drei Sprachregionen durchgeführt, um die quantitativen Resultate zu vertiefen.
Ergebnisse/Fazit:
An der Tagung werden erste vorläufige quantitative Ergebnisse präsentiert und diskutiert. Die Erkenntnisse dieser Studie bilden die Grundlage für die Erarbeitung von Empfehlungen zur Reduzierung von Unsicherheiten, zur Erhöhung des Kenntnisstandes zu Impfungen und folglich zur Erleichterung der Entscheidungsfindung der Bevölkerung.
Title
Who would take part in a pandemic preparedness cohort study? Cross-sectional survey
Name
Aziz Mert Ipekci
Affiliation
ISPM
Abstract
Authors: Aziz Mert Ipekci1, Eva Maria Hodel1, Gilles Wandeler2, Selina Wegmüller1, Nicola Low11 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
2 Department of Infectious Diseases, Bern University Hospital, University of Bern, Bern, Switzerland
Background:
Active cohort studies can play an important role in research about emerging infections and pandemics, as they can collect data about new public health risks. Studying whole households, including pets, is important since infectious agents can spread during extended close contact and between humans and animals. The aim of this study was to investigate factors associated with willingness to participate in the BEready (“Bern, get ready”) project, a Swiss cohort study focusing on pandemic preparedness.
Methods:
We did a cross-sectional online survey. We randomly selected 15,000 private households in the canton of Bern (3,000 each with 1, 2, 3, 4, or 5+ members). One randomly selected person aged 18+ years per household was invited. Data were collected about demographic, social and household characteristics, as well as willingness to take part in a cohort study. We did descriptive analyses and built a multivariable logistic regression model, including: participant age, gender, education, current work situation and nationality; and household income, size, location, and language, as explanatory variables. Regression analyses were weighted by household size.
Results:
The response rate was 22.6% (n=3,394), including participants from 273 of 306 municipalities with at least one household selected. Among 3,394 households, 1,083 had at least one child and 1,231 were pet owners. Among responders, 48.9% (n=1,660) were willing to take part in a cohort study. In multivariable analysis, the factors most strongly associated with willingness to participate were higher educational level (adjusted OR 2.6, 95% CI 1.9-3.4 vs. compulsory or below) and monthly income SFr.>11k (adjusted OR 2.9, 1.9-3.5 vs. <3k). Younger age, not being in full-time employment and smaller household size were also associated with willingness to participate. The main reasons given by those who wanted to participate were to contribute to the health of fellow human beings (n=1,559) and to contribute to better preparation for the next pandemic (n=1,134).
Conclusion:
In the population of Bern, participation in the BEready cohort study is likely to be low. Community engagement to raise awareness and oversampling of underrepresented groups could improve participation.
Title
Short-term forecasting of COVID-19 hospital admissions using routine hospital data
Name
Martin Wohlfender
Affiliation
Institute of Social and Preventive Medicine (University of Bern)
Abstract
Background and aims of study:
The COVID-19 pandemic has highlighted the need for real-time infectious disease surveillance and forecasting systems to detect outbreaks early and identify trends in transmission. This can be crucial to take measures in time to prevent hospitals, especially intensive care units, from being overwhelmed. Current monitoring systems, typically based on surveillance data such as the number of laboratory confirmed cases, can be improved by including routinely collected data sources such as electronic health records from hospital patients. In this study, we present an innovative prediction model integrating various types of routinely collected hospital data from the canton of Bern to provide short-term forecasts of hospital admissions due to COVID-19.
Methods and Results:
We systematically compare the ability of various machine learning algorithms to produce forecasts for COVID-19 hospital admissions 1 to 4 weeks ahead based on retrospective electronic health record data from the Insel Group hospitals between 25 February 2020 and 30 June 2023. We apply last-observation carried forward (baseline), linear regression, tree-based models (XGBoost, BART) and neural networks (CNN and LSTM) to leave-future-out training time series with multiple cutting points. We evaluate the performance of the different algorithms using the root mean square error of the prediction and the true observation. For each model we also assess the predictive power of a large set of variables such as the number of hospital admissions with various specific ICD-10 codes, number of admissions to certain hospital wards, e.g., intensive care unit, as well as measurements such as body temperature, blood pressure and laboratory results. We combine the best performing machine learning algorithm and the set of variables with the highest predictive power to a final prediction model.
Implications:
This study presents an innovative approach to use routinely collected hospital data to forecast increases in hospital demand related to epidemic waves. With the ongoing digitalisation of the healthcare system and the increasing availability and volume of electronic health records with decreasing delays, tools such as ours will increasingly provide more precise and timely information, which may improve evidence-based public health decision-making during future disease outbreaks and epidemics.