Determinants and disparities: A simulation approach to the case of child health care

Lay‐Yee, Roy; Milne, Barry; Davis, Peter; Pearson, Janet; McLay, Jessica

doi:10.1016/j.socscimed.2015.01.025

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…In their dynamic form, microsimulation models allow individuals to change their characteristics due to endogenous factors within the model [ 72 ]. In this sense, they are more suitable for modeling processes and large population dynamics, like the model Lay-Yee et al [ 54 ] uses for estimating child health utilization. The authors modeled a child with a set of attributes as a starting point.…”

Section: Resultsmentioning

confidence: 99%

“…ABM can program each agent with different characteristics [ 82 ]. Individualization is notable in the model by Lay-yee et al [ 54 ], where data is granular at patient level, with variables such as gender, ethnicity and housing status. Each of these variables affects the subject’s number of doctor visits, reading ability and conduct problems.…”

Section: Resultsmentioning

confidence: 99%

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Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection

Larrain¹,

Groene²

2021

PLoS ONE

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Background The guiding principle of many health care reforms is to overcome fragmentation of service delivery and work towards integrated healthcare systems. Even though the value of integration is well recognized, capturing its drivers and its impact as part of health system performance assessment is challenging. The main reason is that current assessment tools only insufficiently capture the complexity of integrated systems, resulting in poor impact estimations of the actions taken towards the ‘Triple Aim’. We describe the unique nature of simulation modeling to consider key health reform aspects: system complexity, optimization of actions, and long-term assessments. Research question How can the use and uptake of simulation models be characterized in the field of performance assessment of integrated healthcare systems? Methods A systematic search was conducted between 2000 and 2018, in 5 academic databases (ACM D. Library, CINAHL, IEEE Xplore, PubMed, Web of Science) complemented with grey literature from Google Scholar. Studies using simulation models with system thinking to assess system performance in topics relevant to integrated healthcare were selected for revision. Results After screening 2274 articles, 30 were selected for analysis. Five modeling techniques were characterized, across four application areas in healthcare. Complexity was defined in nine aspects, embedded distinctively in each modeling technique. ‘What if?’ & ‘How to?’ scenarios were identified as methods for system optimization. The mean time frame for performance assessments was 18 years. Conclusions Simulation models can evaluate system performance emphasizing the complex relations between components, understanding the system’s adaptability to change in short or long-term assessments. These advantages position them as a useful tool for complementing performance assessment of integrated healthcare systems in their pursuit of the ‘Triple Aim’. Besides literacy in modeling techniques, accurate model selection is facilitated after identification and prioritization of the complexities that rule system performance. For this purpose, a tool for selecting the most appropriate simulation modeling techniques was developed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection

Larrain¹,

Groene²

2021

PLoS ONE

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“…Post-implementation assessment is a positive step in recognizing disparities, but CDS should be guaranteed to not disproportionately affect certain subgroups prior to implementation. A number of methods are available to simulate outcomes, and we should begin to employ these in CDS development prior to implementation, especially when we have access to EHR data in the formal care setting [35,36].…”

Section: Bridging Evidence To Address Disparitiesmentioning

confidence: 99%

The Impact of Clinical Decision Support on Health Disparities and the Digital Divide

2023

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Objectives: This literature review summarizes relevant studies from the last three years (2020-2022) related to clinical decision support (CDS) and CDS impact on health disparities and the digital divide. This survey identifies current trends and synthesizes evidence-based recommendations and considerations for future development and implementation of CDS tools. Methods: We conducted a search in PubMed for literature published between 2020 and 2022. Our search strategy was constructed as a combination of the MEDLINE®/PubMed® Health Disparities and Minority Health Search Strategy and relevant CDS MeSH terms and phrases. We then extracted relevant data from the studies, including priority population when applicable, domain of influence on the disparity being addressed, and the type of CDS being used. We also made note of when a study discussed the digital divide in some capacity and organized the comments into general themes through group discussion. Results: Our search yielded 520 studies, with 45 included at the conclusion of screening. The most frequent CDS type in this review was point-of-care alerts/reminders (33.3%). Health Care System was the most frequent domain of influence (71.1%), and Blacks/African Americans were the most frequently included priority population (42.2%). Throughout the literature, we found four general themes related to the technology divide: inaccessibility of technology, access to care, trust of technology, and technology literacy.This survey revealed the diversity of CDS being used to address health disparities and several barriers which may make CDS less effective or potentially harmful to certain populations. Regular examinations of literature that feature CDS and address health disparities can help to reveal new strategies and patterns for improving healthcare.

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“…To enable modelling with maximal data, imputation of missing data was undertaken prior to modelling, following the methods adopted by the Social Genome Project (Winship & Owen, 2013). The CHDS was used as the base for the conceptual framework for the initial MELC model (Lay-Yee et al, 2015) and as such contains all the variables for all the years that need to be modelled. Thus, the only imputation that was needed for the CHDS data was to complete missing cases for some variables.…”

Section: Modelling Associationsmentioning

confidence: 99%

Modelling the Early life-course (MELC): A Microsimulation Model of Child Development in New Zealand

Milne¹,

Yee²,

McLay³

et al. 2014

IJM

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To understand the factors upon which policies can be devised to improve the lives of children and young people, we have developed a dynamic discrete-time micro-simulation model called 'Modelling the early life-course' (MELC). MELC models child development from birth through to age 13, encompassing changes in material and family circumstances, family functioning and early education. MELC focusses on three main outcomes: health service use, early literacy, and conduct problems. A synthetic base population (n=10,000) derived from the 2006 New Zealand Census is used, and transitions through the life-course are determined from estimates derived by analysing three New Zealand child cohort studies: the Christchurch Health and Development Study, the Dunedin Multidisciplinary Health and Development Study, and the Pacific Islands Families Study. The model has been validated against New Zealand norms for reading, general practitioner visits and hospital admissions. Three scenarios were demonstrated. First, we tested the impact of a number of putatively important factors on early literacy, and found small effects. Second, we found that halving the prevalence of single parenting reduced the prevalence of conduct problems. Third, we changed a number of material and family factors for Māori, Pacific and low-socioeconomic groups to be equal to those for the general population, and found that this produced small improvements in reading, but large reductions in conduct problems for these groups. MELC has been deployed as a user friendly desktop application at a number of New Zealand government agencies, where it can be used to test policy-relevant scenarios.

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Determinants and disparities: A simulation approach to the case of child health care

Cited by 11 publications

References 40 publications

Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection

Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection

The Impact of Clinical Decision Support on Health Disparities and the Digital Divide

Modelling the Early life-course (MELC): A Microsimulation Model of Child Development in New Zealand

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