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de Oliveira C, Matias MA, Jacobs R. Microsimulation Models on Mental Health: A Critical Review of the Literature. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024; 27:226-246. [PMID: 37949353 DOI: 10.1016/j.jval.2023.10.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/20/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVES To retrieve and synthesize the literature on existing mental health-specific microsimulation models or generic microsimulation models used to examine mental health, and to critically appraise them. METHODS All studies on microsimulation and mental health published in English in MEDLINE, Embase, PsycINFO, and EconLit between January 1, 2010, and September 30, 2022, were considered. Snowballing, Google searches, and searches on specific journal websites were also undertaken. Data extraction was done on all studies retrieved and the reporting quality of each model was assessed using the Quality Assessment Reporting for Microsimulation Models checklist, a checklist developed by the research team. A narrative synthesis approach was used to synthesize the evidence. RESULTS Among 227 potential hits, 19 studies were found to be relevant. Some studies covered existing economic-demographic models, which included a component on mental health and were used to answer mental-health-related research questions. Other studies were focused solely on mental health and included models that were developed to examine the impact of specific policies or interventions on specific mental disorders or both. Most models examined were of medium quality. The main limitations included the use of model inputs based on self-reported and/or cross-sectional data, small and/or nonrepresentative samples and simplifying assumptions, and lack of model validation. CONCLUSIONS This review found few high-quality microsimulation models on mental health. Microsimulation models developed specifically to examine mental health are important to guide healthcare delivery and service planning. Future research should focus on developing high-quality mental health-specific microsimulation models with wide applicability and multiple functionalities.
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Affiliation(s)
- Claire de Oliveira
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
| | - Maria Ana Matias
- Centre for Health Economics, University of York, York, England, UK
| | - Rowena Jacobs
- Centre for Health Economics, University of York, York, England, UK
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Seamon E, Megheib M, Williams CJ, Murphy CF, Brown HF. Estimating County Level Health Indicators Using Spatial Microsimulation. POPULATION, SPACE AND PLACE 2023; 29:e2647. [PMID: 37822803 PMCID: PMC10564386 DOI: 10.1002/psp.2647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 01/19/2023] [Indexed: 10/13/2023]
Abstract
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, diabetes) taken from regionalized 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 categorization, 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 optimized 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 .79 to .85, with p values all below .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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Affiliation(s)
- Erich Seamon
- Institute for Modeling, Collaboration, and Innovation (IMCI), University of Idaho, Moscow, Idaho, United States
| | - Mohamed Megheib
- Institute for Modeling, Collaboration, and Innovation (IMCI), University of Idaho, Moscow, Idaho, United States
| | - Christopher J. Williams
- Department of Mathematics and Statistical Sciences, University of Idaho, Moscow, Idaho, United States
| | - Christopher F. Murphy
- Department of Health and Welfare (IDHW), State of Idaho, Boise, Idaho, United States
| | - Helen F. Brown
- Department of Movement Sciences, University of Idaho, Moscow, Idaho, United States
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Li Y, Duan G, Xiong L. Research on the design of serious illness insurance scheme in Shanghai based on micro-simulation. BMC Health Serv Res 2022; 22:392. [PMID: 35337328 PMCID: PMC8953335 DOI: 10.1186/s12913-022-07783-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 03/07/2022] [Indexed: 11/10/2022] Open
Abstract
Background Urban and rural residents’ basic medical insurance (URRBMI) is an institutional arrangement for rural residents and unemployed urban residents in China. The serious illness medical insurance system (SIMIS) was established to provide additional medical cover. At present, the SIMIS payment method in China is based on large expenses, and only a few areas, such as Shanghai, pay according to the treatment of serious diseases. This study aims to simulate and analyse the effect of the two payment methods on SIMIS in Shanghai. Methods We developed a micro-simulation model to predict the number and characteristics of SIMIS participants among urban and rural residents in Shanghai and to simulate the process of medical treatment, medical consumption, and medical insurance payments for each insured person from 2020 to 2025. We then summarised and analysed the payment compensation effect, and compared it with Shanghai’s current policies. Results The payment of SIMIS according to high expenses, the total medical expenses of seriously ill patients show an increasing trend, with an average annual growth rate of 3.56%. The URRBMI fund payment covers 56%–58% of total medical expenses, and the SIMIS fund covers 5%–7% of the total medical expenses. Both cover 62%–63% of total medical expenses. Self-payment under SIMIS covers 22%–23% of the total medical expenses, total self-payment covers 14%–15% of the total medical expenses, and the medical expenses borne by individuals cover 36%–38% of the total medical expenses.The fund expenditure is 213 million yuan and average annual cost borne by individual patients ranges from 40 000 to 60 000 yuan. Conclusions The policy of designing SIMIS according to national guidelines does not meet the development needs of Shanghai. Shanghai should take the current policy of paying compensation according to the treatment of serious illness as the policy basis, consider the security needs of patients with large medical expenses outside the scope of protection, and adjust policies appropriately to prevent poverty caused by illness.
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Affiliation(s)
- Yang Li
- Department of Health Service, Naval Medical University, Shanghai, 200433, China
| | - Guangfeng Duan
- Department of Health Service, Naval Medical University, Shanghai, 200433, China
| | - Linping Xiong
- Department of Health Service, Naval Medical University, Shanghai, 200433, China.
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Picascia S, Mitchell R. Social integration as a determinant of inequalities in green space usage: Insights from a theoretical agent-based model. Health Place 2021; 73:102729. [PMID: 34902695 PMCID: PMC8826000 DOI: 10.1016/j.healthplace.2021.102729] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/09/2021] [Accepted: 11/29/2021] [Indexed: 11/25/2022]
Abstract
Visiting urban green spaces (UGS) benefits physical and mental health. However, socio-economic and geographical inequalities in visits persist and their causes are under-explored. Perceptions of, and attitudes to, other UGS users have been theorised as a determinant of visiting. In the absence of data on these factors, we created a spatial agent-based model (ABM) of four cities in Scotland to investigate intra- and inter-city inequalities in UGS visiting. The ABM focused on the plausibility of a 'social integration hypothesis' whereby the primary factor in decisions to visit UGS is an assessment of who else is likely to be using the space. The model identified the conditions under which this mechanism was sufficient to reproduce the observed inequalities. The addition of environmental factors, such as neighbourhood walkability and green space quality, increased the ability of the model to reproduce observed phenomena. The model identified the potential for unanticipated adverse effects on both overall visit numbers and inequalities of interventions targeting those in lower socio-economic groups.
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Affiliation(s)
- Stefano Picascia
- MRC/CSO, University of Glasgow, Social and Public Health Science Unit, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR, Scotland, United Kingdom.
| | - Richard Mitchell
- MRC/CSO, University of Glasgow, Social and Public Health Science Unit, Berkeley Square, 99 Berkeley Street, Glasgow, G3 7HR, Scotland, United Kingdom
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Smith DM, Vogel C, Campbell M, Alwan N, Moon G. Adult diet in England: Where is more support needed to achieve dietary recommendations? PLoS One 2021; 16:e0252877. [PMID: 34161358 PMCID: PMC8221484 DOI: 10.1371/journal.pone.0252877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 05/24/2021] [Indexed: 11/22/2022] Open
Abstract
Background Small-area estimation models are regularly commissioned by public health bodies to identify areas of greater inequality and target areas for intervention in a range of behaviours and outcomes. Such local modelling has not been completed for diet consumption in England despite diet being an important predictor of health status. The study sets out whether aspects of adult diet can be modelled from previously collected data to define and evaluate area-level interventions to address obesity and ill-health. Methods Adults aged 16 years and over living in England. Consumption of fruit, vegetables, and sugar-sweetened beverages (SSB) are modelled using small-area estimation methods in English neighbourhoods (Middle Super Output Areas [MSOA]) to identify areas where reported portions are significantly different from recommended levels of consumption. The selected aspects of diet are modelled from respondents in the National Diet and Nutrition Survey using pooled data from 2008–2016. Results Estimates indicate that the average prevalence of adults consuming less than one portion of fruit, vegetables or 100% juice each day by MSOA is 6.9% (range of 4.3 to 14.7%, SE 0.06) and the average prevalence of drinking more than 330ml/day of SSB is 11.5% (range of 5.7 to 30.5%, SE 0.03). Credible intervals around the estimates are wider for SSB consumption. The results identify areas including regions in London, urban areas in the North of England and the South coast which may be prioritised for targeted interventions to support reduced consumption of SSB and/or an increase in portions of fruit and vegetables. Conclusion These estimates provide valuable information at a finer spatial scale than is presently feasible, allowing for within-country and locality prioritisation of resources to improve diet. Local, targeted interventions to improve fruit and vegetable consumption such as subsidies or voucher schemes should be considered where consumption of these foods is predicted to be low.
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Affiliation(s)
- Dianna M. Smith
- Geography & Environmental Science, University of Southampton, Southampton, United Kingdom
- NIHR Applied Research Collaboration (ARC) Wessex, Southampton, United Kingdom
- * E-mail:
| | - Christina Vogel
- NIHR Applied Research Collaboration (ARC) Wessex, Southampton, United Kingdom
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Monique Campbell
- Geography & Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Nisreen Alwan
- NIHR Applied Research Collaboration (ARC) Wessex, Southampton, United Kingdom
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
- School of Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, United Kingdom
| | - Graham Moon
- Geography & Environmental Science, University of Southampton, Southampton, United Kingdom
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Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health. J 2021. [DOI: 10.3390/j4020015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
There is an ongoing demand for data on population health, for reasons of resource allocation, future planning and crucially to address inequalities in health between people and between populations. Although there are regular sources of data at coarse spatial scales, such as countries or large sub-national units such as states, there is often a lack of good quality health data at the local level. One method to develop reliable estimates of population health outcomes is spatial microsimulation, an approach that has its roots in economic studies. Here, we share a review of this method for estimating health in populations, explaining the different approaches available and examples where the method is applied successfully for creating both static and dynamic populations. Recent notable advances in the method that allow uncertainty to be represented are highlighted, along with the evolving approaches to validation that are an ongoing challenge in small-area estimation. The summary serves as a primer for academics new to the area of research as well as an overview for non-academic researchers who consider using these models for policy evaluations.
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Chung Y, Bagheri N, Salinas-Perez JA, Smurthwaite K, Walsh E, Furst M, Rosenberg S, Salvador-Carulla L. Role of visual analytics in supporting mental healthcare systems research and policy: A systematic scoping review. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020. [DOI: 10.1016/j.ijinfomgt.2019.04.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Olstad DL, McIntyre L. Reconceptualising precision public health. BMJ Open 2019; 9:e030279. [PMID: 31519678 PMCID: PMC6747655 DOI: 10.1136/bmjopen-2019-030279] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 07/22/2019] [Accepted: 07/30/2019] [Indexed: 01/11/2023] Open
Abstract
As currently conceived, precision public health is at risk of becoming precision medicine at a population level. This paper outlines a framework for precision public health that, in contrast to its current operationalisation, is consistent with public health principles because it integrates factors at all levels, while illuminating social position as a fundamental determinant of health and health inequities. We review conceptual foundations of public health, outline a proposed framework for precision public health and describe its operationalisation within research and practice. Social position shapes individuals' unequal experiences of the social determinants of health. Thus, in our formulation, precision public health investigates how multiple dimensions of social position interact to confer health risk differently for precisely defined population subgroups according to the social contexts in which they are embedded, while considering relevant biological and behavioural factors. It leverages this information to uncover the precise and intersecting social structures that pattern health outcomes, and to identify actionable interventions within the social contexts of affected groups. We contend that studies informed by this framework offer greater potential to improve health than current conceptualisations of precision public health that do not address root causes. Moreover, expanding beyond master categories of social position and operationalising these categories in more precise ways across time and place can enrich public health research through greater attention to the heterogeneity of social positions, their causes and health effects, leading to the identification of points of intervention that are specific enough to be useful in reducing health inequities. Failure to attend to this level of particularity may mask the true nature of health risk, the causal mechanisms at play and appropriate interventions. Conceptualised thus, precision public health is a research endeavour with much to offer by way of understanding and intervening on the causes of poor health and health inequities.As currently conceived, precision public health is at risk of becoming precision medicine at a population level. This paper outlines a framework for precision public health that, in contrast to its current operationalization, is consistent with public health principles because it integrates factors at all levels, while illuminating social position as a fundamental determinant of health and health inequities. We review conceptual foundations of public health, outline a proposed framework for precision public health and describe its operationalization within research and practice. Social position shapes individuals' unequal experiences of the social determinants of health. Thus, in our formulation, precision public health investigates how multiple dimensions of social position interact to confer health risk differently for precisely defined population subgroups according to the social contexts in which they are embedded, while considering relevant biological and behavioural factors. It leverages this information to uncover the precise and intersecting social structures that pattern health outcomes, and to identify actionable interventions within the social contexts of affected groups. We contend that studies informed by this framework offer greater potential to improve health than current conceptualizations of precision public health that do not address root causes. Moreover, expanding beyond master categories of social position and operationalizing these categories in more precise ways across time and place can enrich public health research through greater attention to the heterogeneity of social positions, their causes and health effects, leading to identification of points of intervention that are specific enough to be useful in reducing health inequities. Failure to attend to this level of particularity may mask the true nature of health risk, the causal mechanisms at play and appropriate interventions. Conceptualized thus, precision public health is a research endeavour with much to offer by way of understanding and intervening on the causes of poor health and health inequities.
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Affiliation(s)
- Dana Lee Olstad
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Lynn McIntyre
- Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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