SynthesisPreventing chronic disease2021
Applications of System Dynamics Models in Chronic Disease Prevention: A Systematic Review.
Synthesis in Preventing chronic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Pooled it
- Impact of Salt Reduction on Medical Expenditure for Hypertension in Japan: National and Subnational Simulation Models.Nutrients · 2026Article
- Enhancing social participation in older adults with noncommunicable diseases: insights from system dynamics modeling.BMC public health · 2025Article
- Enhancing noncommunicable public health programs: A system dynamics approach to understanding reach, maintenance, and implementation costs.Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Article
- Integrating Multi-Cancer Early Detection (MCED) Tests with Standard Cancer Screening: System Dynamics Model Development and Feasibility Testing.PharmacoEconomics - open · 2025Article
- Assessing biomarker trajectories for mortality risk in peritoneal dialysis: A focus on multivariate joint modeling.PloS one · 2025Article
- Mapping the Dynamic Complexity of Hypertension Management in São Paulo, Brazil.International journal of public health · 2025Article
- Bystanders Saving Lives with Naloxone: A Scoping Review on Methods to Estimate Overdose Reversals.The western journal of emergency medicine · 2024Article
- Unveiling the Comorbidities of Chronic Diseases in Serbia Using ML Algorithms and Kohonen Self-Organizing Maps for Personalized Healthcare Frameworks.Journal of personalized medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionChronic disease is a serious health problem worldwide. Given that health care resources are limited, a comprehensive, effective, and affordable way is needed to provide insights to prevent chronic diseases. System dynamics models provide a comprehensive and systematic method that can predict results over time. These models can simulate and predict appropriate prevention measures for chronic diseases to determine the best practice.
methodsTwo researchers (Y.W., B.H.) independently searched databases (PubMed, Web of Science, Scopus, and Embase) for full-text articles published from January 2000 through February 2021. A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020-compliant search was carried out to review system dynamics models of chronic disease prevention. A total of 34 articles were included in our study.
resultsWe divided the prevention measures of system dynamics models into 2 main categories: upstream prevention and downstream prevention. Upstream prevention measures include lifestyle (eg, tobacco control, balanced diet, mental health, moderate exercise), obesity prevention, and social factors. Downstream prevention measures include clinical treatment of chronic diseases. Results showed that effective upstream prevention measures could reduce the prevalence of chronic diseases, and downstream prevention measures could reduce the incidence of complications, improve quality of life, prolong life, save medical costs, and reduce mortality.
conclusionTo our knowledge, our systematic review is the first to evaluate the application of system dynamics models in preventing chronic diseases. Such models can provide effective simulations. Hence, we can use system dynamics models to design and implement effective prevention measures for people with chronic diseases.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.