title: A dental public health approach based on computational mathematics: Monte Carlo simulation of childhood dental decay.
abstract: This study developed a Monte Carlo simulation approach to examining the prevalence and incidence of dental decay using Australian children as a test environment. Monte Carlo simulation has been used for a half a century in particle physics (and elsewhere); put simply, it is the probability for various population-level outcomes seeded randomly to drive the production of individual level data. A total of five runs of the simulation model for all 275,000 12-year-olds in Australia were completed based on 2005-2006 data. Measured on average decayed/missing/filled teeth (DMFT) and DMFT of highest 10% of sample (Sic10) the runs did not differ from each other by more than 2% and the outcome was within 5% of the reported sampled population data. The simulations rested on the population probabilities that are known to be strongly linked to dental decay, namely, socio-economic status and Indigenous heritage. Testing the simulated population found DMFT of all cases where DMFT<>0 was 2.3 (n = 128,609) and DMFT for Indigenous cases only was 1.9 (n = 13,749). In the simulation population the Sic25 was 3.3 (n = 68,750). Monte Carlo simulations were created in particle physics as a computational mathematical approach to unknown individual-level effects by resting a simulation on known population-level probabilities. In this study a Monte Carlo simulation approach to childhood dental decay was built, tested and validated.
authors: Centre for Rural and Remote Oral Health, The University of Western Australia, Nedlands, WA, Australia.
authors: Marc
authors: M
authors: Tennant
authors: Estie
authors: E
authors: Kruger
mesh_terms: D001315:Australia / Q000453:epidemiology; D002648:Child; D004245:DMF Index; D003731:Dental Caries / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D009010:Monte Carlo Method; D015995:Prevalence; D011636:Public Health Dentistry / Q000379:methods*; D012307:Risk Factors; D012494:Sampling Studies; D012923:Social Class