Small area estimation of obesity prevalence and dietary patterns: A model applied to Rio de Janeiro city, Brazil

Cataife, Guido

doi:10.1016/j.healthplace.2013.12.004

Cited by 14 publications

(14 citation statements)

References 22 publications

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“…42 Examples from beyond the UK include studies of depression rates in the Republic of Ireland, 43 small area estimations of angina and diabetes prevalence for New South Wales, Australia, 44 and a study of dietary patterns (including soda consumption) and obesity rates in high and low-income areas of Rio de Janeiro, Brazil. 45 Within the field, there are a number of different types of spatial microsimulation models, with a number of papers providing an overview of these. 12,46 A visual demonstration of the spatial microsimulation method is provided in Figure 1.…”

Section: Spatial Microsimulation Modellingmentioning

confidence: 99%

Application of geographic information systems and simulation modelling to dental public health: Where next?

Broomhead

Ballas

Baker

2018

Comm Dent Oral Epid

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Public health research in dentistry has used geographic information systems since the 1960s. Since then, the methods used in the field have matured, moving beyond simple spatial associations to the use of complex spatial statistics and, on occasions, simulation modelling. Many analyses are often descriptive in nature; however, and the use of more advanced spatial simulation methods within dental public health remains rare, despite the potential they offer the field. This review introduces a new approach to geographical analysis of oral health outcomes in neighbourhoods and small area geographies through two novel simulation methods—spatial microsimulation and agent‐based modelling. Spatial microsimulation is a population synthesis technique, used to combine survey data with Census population totals to create representative individual‐level population datasets, allowing for the use of individual‐level data previously unavailable at small spatial scales. Agent‐based models are computer simulations capable of capturing interactions and feedback mechanisms, both of which are key to understanding health outcomes. Due to these dynamic and interactive processes, the method has an advantage over traditional statistical techniques such as regression analysis, which often isolate elements from each other when testing for statistical significance. This article discusses the current state of spatial analysis within the dental public health field, before reviewing each of the methods, their applications, as well as their advantages and limitations. Directions and topics for future research are also discussed, before addressing the potential to combine the two methods in order to further utilize their advantages. Overall, this review highlights the promise these methods offer, not just for making methodological advances, but also for adding to our ability to test and better understand theoretical concepts and pathways.

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Section: Spatial Microsimulation Modellingmentioning

confidence: 99%

Application of geographic information systems and simulation modelling to dental public health: Where next?

Broomhead

Ballas

Baker

2018

Comm Dent Oral Epid

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“…The legal, social, and economic framework for this decision-making in Argentina and Brazil was recently reviewed by Arbex et al (87), once again supporting the hypothesis that cultural factors are part of the LA obesity problem. A potential tool to untangle the intricacies of obesity care in LA uses Census Tract data, as in the case for Rio de Janeiro city, Brazil, and a combinatorial spatial microsimulation model (88). Here, obesity ranged from 8 to 25% without socio-economic or gender differences at small local scales, with significant correlations with sugared-soda consumption (88).…”

Section: Weight Loss In Overweight and Obesity (Fig 3)mentioning

confidence: 99%

Transculturalization Recommendations For Developing Latin American Clinical Practice Algorithms In Endocrinology Proceedings Of The 2015 Pan-American Workshop By The American Association Of Clinical Endocrinologists And American College Of Endocrinology

Mechanick

Harrell²,

Allende-Vigo

et al. 2016

Endocrine Practice

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“…We recognise that microsimulation methodologies have been used extensively in infectious disease epidemiology however these models typically dynamic and conducted at much coarser geographical levels and therefore lacking the small areal level spatial component (Habbema et al, 1996;Morris and Kretzschmar, 2000). Also, available published literature on spatial models used to simulate health conditions like smoking, obesity and osteoarthritis, these studies have found little use outside the microsimulation community (Edwards and Clarke, 2009;Smith et al, 2011;Cataife, 2014). We believe that this may be due to an inherent weakness in static, deterministic spatial microsimulation methodologies -Data limitations (Tanton and Edwards, 2012).…”

Section: Introductionmentioning

confidence: 99%

Spatial microsimulation of osteoarthritis prevalence at the small area level in England – Constraint selection for a 2-stage microsimulation process

Ifesemen¹,

Bestwick-Stevenson²,

Edwards³

2018

IJM

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The presence of identical benchmark/constraint variables in both geographic and survey datasets is a principal requirement for static spatial microsimulation models, particularly in the field of medicine and health sciences. This is also a key limitation of static spatial models because geographical datasets rarely contain all variables required to realistically simulate an outcome. We believe this challenge can be overcome by a multilevel approach to spatial microsimulation using a case study of estimating the small area level prevalence of knee osteoarthritis in England.In the paper, we describe constraint selection and demonstrate a novel two-stage spatial microsimulation procedure using SimObesity, a static deterministic combinatorial spatial microsimulation model. We also present the validation parameters of our synthetic data, important areas for consideration and avenues for future research.Our findings demonstrate that important benchmark variables absent from the geographical dataset can be incorporated into spatial microsimulation models without compromising model robustness. JEL classification: I100

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Small area estimation of obesity prevalence and dietary patterns: A model applied to Rio de Janeiro city, Brazil

Cited by 14 publications

References 22 publications

Application of geographic information systems and simulation modelling to dental public health: Where next?

Application of geographic information systems and simulation modelling to dental public health: Where next?

Transculturalization Recommendations For Developing Latin American Clinical Practice Algorithms In Endocrinology Proceedings Of The 2015 Pan-American Workshop By The American Association Of Clinical Endocrinologists And American College Of Endocrinology

Spatial microsimulation of osteoarthritis prevalence at the small area level in England – Constraint selection for a 2-stage microsimulation process

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