How Fee and Insurance Changes Could Affect Dentistry: Results From a Microsimulation Model

Brown, L. Jackson; Caldwell, Steven; Eklund, Stephen A.

doi:10.14219/jada.archive.1995.0207

Cited by 8 publications

(7 citation statements)

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“…While there is evidence of improvements in oral health as reflected in changes in service patterns over time within a population of insured patients (44), simulation models show that reductions in coverage would have adverse effects on oral health status with increases in percentages of decayed teeth and untreated decay compared to baseline (45). New patients had less crown and bridge, endodontic, and restorative, but more diagnostic service.…”

Section: Dental Knowledge and Behaviormentioning

confidence: 99%

Influence of Patient, Visit, and Oral Health Factors on Dental Service Provision

Brennan¹,

Spencer²

2002

J Public Health Dent

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After controlling for oral health, visit characteristics persisted as significant predictors of services, with nonemergency visits, insurance, and capital city location associated with more favorable service mix patterns. Higher socioeconomic status areas and payment scale ratings also were associated with a better service pattern in particular service areas. These findings show that a wide range of factors, in addition to oral health, contribute to variation in service provision.

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Section: Dental Knowledge and Behaviormentioning

confidence: 99%

Influence of Patient, Visit, and Oral Health Factors on Dental Service Provision

Brennan¹,

Spencer²

2002

J Public Health Dent

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“…The method has its origins in aspatial microsimulation models, primarily developed by economists, and there is a long successful history of applications of national public policies that have not analysed geographical differences. A rare example of this application within dental public health was a model investigating the effects of fee and insurance changes on dental attendance in the United States . This remains one of the few uses of microsimulation in the oral health literature; however, due to its national‐level scale, as well as the lack of comparison of geographical areas, this would not necessarily be considered a “spatial” microsimulation.…”

Section: Where Are We Now?mentioning

confidence: 99%

“…A rare example of this application within dental public health was a model investigating the effects of fee and insurance changes on dental attendance in the United States. 38 This remains one of the few uses of microsimulation in the oral health literature; however, due to its national-level scale, as well as the lack of comparison of geographical areas, this would not necessarily be considered a "spatial" microsimulation. Guy Orcutt is considered the creator of the microsimulation technique, 39 which was used to generate large-scale synthetic populations on which to analyse the impacts of population changes on policies (and vice versa).…”

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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“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] Our group has used studies to understand provider behavior, 1-7 measure treatment outcomes, 8,9 measure cost impact of treatment changes, [11][12][13][14][15] and calculate the utility of risk assessment for disease management. 16 The data in our warehouse were validated using the patient chart as the gold standard for treatment actually rendered.…”

Section: Dental Data Warehousesmentioning

confidence: 99%

Data Warehouses and Evidence-Based Dental Insurance Benefits

Aguila

Felber

2004

Journal of Evidence Based Dental Practice

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How Fee and Insurance Changes Could Affect Dentistry: Results From a Microsimulation Model

Cited by 8 publications

References 1 publication

Influence of Patient, Visit, and Oral Health Factors on Dental Service Provision

Influence of Patient, Visit, and Oral Health Factors on Dental Service Provision

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

Data Warehouses and Evidence-Based Dental Insurance Benefits

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