Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

Smith, Dianna; Heppenstall, Alison; Campbell, Monique

doi:10.3390/j4020015

Cited by 4 publications

(4 citation statements)

References 57 publications

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“…Techniques such as Iterative Proportional Fitting (Lomax & Norman, 2016 ) can be used to reweight sample data. Alternatively, Combinatorial Optimization (Smith et al., 2021 ), can be used to synthesize and replicate individuals. In both cases the resulting dataset is a combination of the attribute rich (micro) data and the sample representation of the administrative (macro) data.…”

Section: Section D: What Processes Are Important When Developing Csms?mentioning

confidence: 99%

Guidance on the use of complex systems models for economic evaluations of public health interventions

Breeze

Squires

Ennis³

et al. 2023

Health Economics

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To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models.A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

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Section: Section D: What Processes Are Important When Developing Csms?mentioning

confidence: 99%

Guidance on the use of complex systems models for economic evaluations of public health interventions

Breeze

Squires

Ennis³

et al. 2023

Health Economics

View full text Add to dashboard Cite

show abstract

“…Microsimulations are used when individuals have numerous characteristics that affect an outcome of interest and do not interact with one another. Unlike ABM, microsimulations focus on individuals, and while they can include spatial effects [67], they normally do not cover detailed interactions among individuals [52,68]. They use detailed datasets describing population characteristics, usually from national data sources such as the United States Census or Statistics Canada [65], to determine individual characteristics and identify which subpopulations are harmed or benefited by interventions.…”

Section: Microsimulation Network Simulation and Discrete Event Simula...mentioning

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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“…Research by Schofield et al (2018), as well as Campbell and Ballas (2016), have additionally developed IPF techniques that simulate health outcomes at refined spatial scales. From a meta‐analysis perspective, Smith et al (2021) reviewed 24 papers on the subject of microsimulation in health settings. Their assessment highlighted the value of dynamic microsimulation, noting the challenges around validation and sample size.…”

Section: Introductionmentioning

confidence: 99%

Estimating county level health indicators using spatial microsimulation

Seamon

Megheib

Williams

et al. 2023

Population Space and Place

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Given the importance of understanding health outcomes at fine spatial scales, iterative proportional fitting (IPF), a form of small area estimation, was applied to a fixed number of health-related variables (obesity, overweight, and diabetes) taken from regionalised 2019 survey responses (n = 5474) from the Idaho Behavioral Risk Factor Surveillance System (BRFSS). Using associated county-level American Community Survey (ACS) census data, a set of constraints, which included age categorisation, race, sex, and education level, were used to create county-level weighting matrices for each variable, for each of the seven (7) Idaho public health districts. Using an optimised modeling construction technique, we identified significant constraints and grouping splits for each variable/region, resulting in estimates that were internally and externally validated. Externally validated model results for the most populated counties showed correlations ranging from 0.79 to 0.85, with p-values all below 0.05. Estimates indicated higher levels of obesity and overweight individuals for midsouth and southwestern Idaho counties, with a cluster of higher diabetes estimates in the center of the state (Gooding, Lincoln, Minidoka, and Jerome counties). Alternative external sources for health outcomes aligned extremely well with our estimates, with wider confidence intervals in more rural counties with sparse populations.

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Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health

Cited by 4 publications

References 57 publications

Guidance on the use of complex systems models for economic evaluations of public health interventions

Guidance on the use of complex systems models for economic evaluations of public health interventions

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

Estimating county level health indicators using spatial microsimulation

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