A dental public health approach based on computational mathematics: Monte Carlo simulation of childhood dental decay. (7th January 2013) - Tennant, Marc; Kruger, Estie
<x xml:space="preserve">Abstract</x> 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.