Evaluating Public Health Efforts to Prevent and Control Chronic Disease: A Systems Modeling Approach

The growing burden of chronic disease represents a complex challenge to public health. Innovative approaches, such as system dynamics simulation modeling, can aid public health professionals in understanding such complex issues and identifying effective solutions. This paper describes a system dynam...

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Published inSystems (Basel) Vol. 10; no. 4; p. 89
Main Authors Clennin, Morgan, Homer, Jack, Erkenbeck, Alex, Kelly, Cheryl
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 28.06.2022
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ISSN2079-8954
2079-8954
DOI10.3390/systems10040089

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Abstract The growing burden of chronic disease represents a complex challenge to public health. Innovative approaches, such as system dynamics simulation modeling, can aid public health professionals in understanding such complex issues and identifying effective solutions. This paper describes a system dynamics model and its application in projecting the impacts of evidence-based interventions on chronic disease for the state of Colorado. The development of the model was guided by data and input from subject matter expertise, peer-reviewed literature, and surveillance data. The model includes 28 intervention levers for chronic disease prevention, screening, and management. Interventions were simulated from 2020 to 2050 to project their impact on ten preventable causes of death. The simulations indicated the 6 most impactful interventions by 2050 to be adult smoking prevention, diabetes prevention, smoking cessation, blood pressure management, adult physical activity promotion, and colorectal cancer screening. Together, these 6 interventions could reduce preventable deaths by 7.1%, or 74% of the 9.6% reduction from all 28 interventions combined. This system dynamics model is a flexible tool that could be adapted or extended to include other populations or preventable chronic diseases. Prioritization and wide-scale implementation of the most impactful interventions could significantly reduce preventable deaths resulting from chronic disease.
AbstractList The growing burden of chronic disease represents a complex challenge to public health. Innovative approaches, such as system dynamics simulation modeling, can aid public health professionals in understanding such complex issues and identifying effective solutions. This paper describes a system dynamics model and its application in projecting the impacts of evidence-based interventions on chronic disease for the state of Colorado. The development of the model was guided by data and input from subject matter expertise, peer-reviewed literature, and surveillance data. The model includes 28 intervention levers for chronic disease prevention, screening, and management. Interventions were simulated from 2020 to 2050 to project their impact on ten preventable causes of death. The simulations indicated the 6 most impactful interventions by 2050 to be adult smoking prevention, diabetes prevention, smoking cessation, blood pressure management, adult physical activity promotion, and colorectal cancer screening. Together, these 6 interventions could reduce preventable deaths by 7.1%, or 74% of the 9.6% reduction from all 28 interventions combined. This system dynamics model is a flexible tool that could be adapted or extended to include other populations or preventable chronic diseases. Prioritization and wide-scale implementation of the most impactful interventions could significantly reduce preventable deaths resulting from chronic disease.
Audience Academic
Author Kelly, Cheryl
Homer, Jack
Clennin, Morgan
Erkenbeck, Alex
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SubjectTerms Age groups
Asthma
Blood pressure
Business metrics
Cancer
Cardiovascular disease
cardiovascular diseases
chronic disease
Chronic diseases
Chronic illnesses
Chronic obstructive pulmonary disease
Diabetes
Disease control
Disease prevention
Evidence-based medicine
Exercise
Fatalities
Health aspects
Health care
Hypertension
Intervention
Methods
Modelling
Mortality
Prevention
Public health
Public health administration
public health practice
Risk factors
Screening
Simulation
simulation model
Smoking
System dynamics
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