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System dynamics models of depression at the population level: a scoping review

AIMS: Depression is a disease driven by dynamic processes both at the individual- and system-level. System dynamics (SD) models are a useful tool to capture this complexity, project the future prevalence of depression and understand the potential impact of interventions and policies. SD models have...

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Autores principales: Graham, Eva, Gariépy, Geneviève, Orpana, Heather
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262132/
https://www.ncbi.nlm.nih.gov/pubmed/37312087
http://dx.doi.org/10.1186/s12961-023-00995-7
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author Graham, Eva
Gariépy, Geneviève
Orpana, Heather
author_facet Graham, Eva
Gariépy, Geneviève
Orpana, Heather
author_sort Graham, Eva
collection PubMed
description AIMS: Depression is a disease driven by dynamic processes both at the individual- and system-level. System dynamics (SD) models are a useful tool to capture this complexity, project the future prevalence of depression and understand the potential impact of interventions and policies. SD models have been used to model infectious and chronic disease, but rarely applied to mental health. This scoping review aimed to identify population-based SD models of depression and report on their modelling strategies and applications to policy and decision-making to inform research in this emergent field. METHODS: We searched articles in MEDLINE, Embase, PsychInfo, Scopus, MedXriv, and abstracts from the System Dynamics Society from inception to October 20, 2021 for studies of population-level SD models of depression. We extracted data on model purpose, elements of SD models, results, and interventions, and assessed the quality of reporting. RESULTS: We identified 1899 records and found four studies that met the inclusion criteria. Studies used SD models to assess various system-level processes and interventions, including the impact of antidepressant use on population-level depression in Canada; the impact of recall error on lifetime estimates of depression in the USA; smoking-related outcomes among adults with and without depression in the USA; and the impact of increasing depression incidence and counselling rates on depression in Zimbabwe. Studies included diverse stocks and flows for depression severity, recurrence, and remittance, but all models included flows for incidence and recurrence of depression. Feedback loops were also present in all models. Three studies provided sufficient information for replicability. CONCLUSIONS: The review highlights the usefulness of SD models to model the dynamics of population-level depression and inform policy and decision-making. These results can help guide future applications of SD models to depression at the population-level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12961-023-00995-7.
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spelling pubmed-102621322023-06-14 System dynamics models of depression at the population level: a scoping review Graham, Eva Gariépy, Geneviève Orpana, Heather Health Res Policy Syst Review AIMS: Depression is a disease driven by dynamic processes both at the individual- and system-level. System dynamics (SD) models are a useful tool to capture this complexity, project the future prevalence of depression and understand the potential impact of interventions and policies. SD models have been used to model infectious and chronic disease, but rarely applied to mental health. This scoping review aimed to identify population-based SD models of depression and report on their modelling strategies and applications to policy and decision-making to inform research in this emergent field. METHODS: We searched articles in MEDLINE, Embase, PsychInfo, Scopus, MedXriv, and abstracts from the System Dynamics Society from inception to October 20, 2021 for studies of population-level SD models of depression. We extracted data on model purpose, elements of SD models, results, and interventions, and assessed the quality of reporting. RESULTS: We identified 1899 records and found four studies that met the inclusion criteria. Studies used SD models to assess various system-level processes and interventions, including the impact of antidepressant use on population-level depression in Canada; the impact of recall error on lifetime estimates of depression in the USA; smoking-related outcomes among adults with and without depression in the USA; and the impact of increasing depression incidence and counselling rates on depression in Zimbabwe. Studies included diverse stocks and flows for depression severity, recurrence, and remittance, but all models included flows for incidence and recurrence of depression. Feedback loops were also present in all models. Three studies provided sufficient information for replicability. CONCLUSIONS: The review highlights the usefulness of SD models to model the dynamics of population-level depression and inform policy and decision-making. These results can help guide future applications of SD models to depression at the population-level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12961-023-00995-7. BioMed Central 2023-06-13 /pmc/articles/PMC10262132/ /pubmed/37312087 http://dx.doi.org/10.1186/s12961-023-00995-7 Text en © Crown 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Review
Graham, Eva
Gariépy, Geneviève
Orpana, Heather
System dynamics models of depression at the population level: a scoping review
title System dynamics models of depression at the population level: a scoping review
title_full System dynamics models of depression at the population level: a scoping review
title_fullStr System dynamics models of depression at the population level: a scoping review
title_full_unstemmed System dynamics models of depression at the population level: a scoping review
title_short System dynamics models of depression at the population level: a scoping review
title_sort system dynamics models of depression at the population level: a scoping review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10262132/
https://www.ncbi.nlm.nih.gov/pubmed/37312087
http://dx.doi.org/10.1186/s12961-023-00995-7
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