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23. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin e. V.

Deutsches Netzwerk Evidenzbasierte Medizin e. V.

01. - 03.09.2022, Lübeck

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Using directed acyclic graphs (DAGs) to guide trial protocol design investigating the effect of nature-based prescribing on physical and mental health

Meeting Abstract

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  • Felicitas Kuehne - UMIT – University for Health Sciences, Medical Informatics and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich
  • Marjan Arvandi - UMIT – University for Health Sciences, Medical Informatics and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich
  • Igor Stojkov - UMIT – University for Health Sciences, Medical Informatics and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich
  • Jill Litt - ISGlobal-The Barcelona Institute of Global Health, Barcelona, Spanien
  • Ashby Sachs - ISGlobal-The Barcelona Institute of Global Health, Barcelona, Spanien
  • Sergi Blancafort - Fundació Salut i Envelliment UAB, Spanien
  • Richard Kimberlee - University of the West of England, Großbritannien
  • Kaisu Pitkala - University of Helsinki, Department of General Practice and Helsinki University Hospital, Helsinki, Finnland
  • Anu Jansson - University of Helsinki, Department of General Practice and Helsinki University Hospital, Helsinki, Finnland
  • Joan Colom Farran - Government of Catalonia, Public Health Agency, Program on Substance Abuse, Spanien
  • Iva Holmerova - Charles University, Faculty of Humanities, Tschechien
  • Stephanie Gentile - Aix Marseille Univ, School of medicine – La Timone Medical Campus, Marseille, Frankreich
  • Sarah Bekessy - RMIT University, School of Global, Urban and Social Studies, Australien
  • Katherine Johnson - RMIT University, School of Global, Urban and Social Studies, Australien
  • Ursula Rochau - UMIT – University for Health Sciences, Medical Informatics and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich
  • Uwe Siebert - UMIT – University for Health Sciences, Medical Informatics and Technology, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich; Harvard T.H. Chan School of Public Health, Departments of Health Policy & Management and Epidemiology, USA

Evidenzbasierte Medizin für eine bedarfsgerechte Gesundheitsversorgung. 23. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin. Lübeck, 01.-03.09.2022. Düsseldorf: German Medical Science GMS Publishing House; 2022. Doc22ebmPOS-2_5-07

doi: 10.3205/22ebm155, urn:nbn:de:0183-22ebm1553

Published: August 30, 2022

© 2022 Kuehne et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Background/research question: Loneliness is a common condition impacting productivity and wellbeing. Nature-based social prescribing (NBSP) may promote nature contact, activate and strengthen social structures, and improve longer term mental and physical health.

In six studies (three observational and three randomized controlled trials), the RECETAS consortium aims at evaluating how NBSP can reduce feelings of loneliness and improve quality of life (QoL) in urban contexts. Observational studies bear the risk of confounding-by-indication, and trials investigating sustained intervention effects have the risk of post-randomization bias.

Indicators for NBSP (e.g., low socioeconomic status, low education, recent immigration, isolation, old age, and unlikely users of nature and outdoor spaces) are also risk factors for the outcomes (loneliness, poor health status, and decreased QoL) and simultaneously are affected by the NBSP interventions. This bears the risk of time-dependent confounding. Therefore, proper adjustment for time-dependent confounding using causal inference methods such as g-methods, are needed to draw causal conclusions from such study.

In this study, we aim at identifying potential baseline and time-dependent confounding, structure based as well as other biases using causal diagrams, and proposing analytic pathways to account for the biases.

Methods: Our framework is based on directed acyclic graphs (DAGs), which are a visual and structured approach to identify variables directly or indirectly influencing the action and outcome of interest and their relation. This approach may help to identify biases, such as confounding and selection bias, and draw conclusions on the required statistical analytic framework essential for a causal interpretation of the results.

Experts in social science, epidemiology, and causal inference discuss and create DAGs for several nature-based interventions and their effect on loneliness, quality of life, physical, and mental health. Each potential intervention and corresponding outcome of interest will be discussed separately

Preliminary/expected results, outlook: One DAG per intervention and outcome will be presented. These DAGs will be used to identify open backdoor paths and to identify causal analytic frameworks that allow for drawing causal conclusions from the studies. The findings will guide and improve the design of planned studies and assure sufficient data collection to enable the application of adequate analytic methods.

Competing interests: We have no conflicts of interests