Human Factors in Leveraging Systems Science to Shape Public Policy for Obesity: A Usability Study

Giabbanelli, Philippe J.; Vesuvala, Chirag X.

doi:10.3390/info14030196

Cited by 6 publications

(3 citation statements)

References 69 publications

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“…What are the obstacles preventing model application? The transition from simulation findings to the design and evaluation of interventions is often a difficult step [48][49][50]. Thus, identifying obstacles (particularly as they are shared across models) is an important way to ease this transition.…”

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confidence: 99%

Simulation Models for Suicide Prevention: A Survey of the State-of-the-Art

Schuerkamp,

Liang,

Rice

et al. 2023

Computers

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Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions.

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confidence: 99%

Simulation Models for Suicide Prevention: A Survey of the State-of-the-Art

Schuerkamp,

Liang,

Rice

et al. 2023

Computers

Self Cite

View full text Add to dashboard Cite

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“…The final article also takes a policy perspective on an important population health issue, that of obesity. Giabbanelli and Vesuvala [6] examine the ways in which a specific software tool supports and constrains the capacity of policy makers to think about complex health interventions.…”

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confidence: 99%

“…In many ways, some data science applications are natural extensions of systematic case collection and pattern analysis established as good practice centuries ago, albeit with a much larger sample of cases [2,5]. But data science also enhances the effectiveness of health systems and services by supporting more precise surgery [3], monitoring of health promotion programs [4], and even the way in which we think about the complexity of health systems [6].…”

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confidence: 99%