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# A dental public health approach based on computational mathematics monte carlo simulation of childhood dental decayid j12003

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### A dental public health approach based on computational mathematics monte carlo simulation of childhood dental decayid j12003

1. 1. International Dental Journal 2013; 63: 39–42 ORIGINAL ARTICLE doi: 10.1111/idj.12003A dental public health approach based on computationalmathematics: Monte Carlo simulation of childhood dentaldecayMarc Tennant and Estie KrugerCentre for Rural and Remote Oral Health, The University of Western Australia, Nedlands, WA, Australia.This study developed a Monte Carlo simulation approach to examining the prevalence and incidence of dental decayusing Australian children as a test environment. Monte Carlo simulation has been used for a half a century in particlephysics (and elsewhere); put simply, it is the probability for various population-level outcomes seeded randomly to drivethe production of individual level data. A total of ﬁve runs of the simulation model for all 275,000 12-year-olds inAustralia were completed based on 2005–2006 data. Measured on average decayed/missing/ﬁlled teeth (DMFT) andDMFT of highest 10% of sample (Sic10) the runs did not differ from each other by more than 2% and the outcome waswithin 5% of the reported sampled population data. The simulations rested on the population probabilities that areknown to be strongly linked to dental decay, namely, socio-economic status and Indigenous heritage. Testing the simu-lated population found DMFT of all cases where DMFT<>0 was 2.3 (n = 128,609) and DMFT for Indigenous casesonly was 1.9 (n = 13,749). In the simulation population the Sic25 was 3.3 (n = 68,750). Monte Carlo simulations werecreated in particle physics as a computational mathematical approach to unknown individual-level effects by resting asimulation on known population-level probabilities. In this study a Monte Carlo simulation approach to childhood den-tal decay was built, tested and validated.Key words: Dental public health, computational mathematics, Monte CarloNational data on childhood decay is often difﬁcult to late every occurrence in a population. The results of allobtain except on occasional childhood dental sur- the individual applications of the population probabili-veys1,2. This is particularly the case in countries with ties are accumulated to provide the speciﬁc data forextremely distributed populations or in those still devel- testing.oping a large-scale public dental service. Often surveys Over the last 30 years the prevalence of dentalfocus on measuring the oral health of subsets of the decay in children in Australia has reduced signiﬁcantly.children and leave the reader to extrapolate the results Currently, 60–70% of all 12-year-olds suffer no decayto the wider community. However, this approach faces and only about 10% of children have more than twomany difﬁculties, including (in countries with popula- decayed teeth1. This quite exceptional outcome hastion ﬂuoride programmes) the problem of a small resulted fundamentally from the near-universal popu-cohort of disease spread in a large population, or more lation-level coverage of ﬂuoride exposure (be it waterimportantly, where economics prevents large-scale or toothpaste)2. Notwithstanding this outstandingsurvey research. A parallel problem was faced nearly achievement, a small but persistent level of decay stillhalf a century ago in particle physics. In their case, put exists within Australian children, causing them signiﬁ-simply, the probability for various population level cant pain and suffering. The challenge in dental publicoutcomes for neutron movement was known but the health now is to ﬁnd a way to target these childrenspeciﬁc data on the penetration of individual neutrons with additional preventive strategies. Historically,remained unknown. The solution came with the devel- school-based dental services with universal coverageopment of a computational mathematical approach have been the norm in Australia. However, it is clearcalled Monte Carlo simulations3,4. This is where that the massive resources required to continue suchgeneral population probabilities are applied to simu- services, against a population background of only a© 2013 FDI World Dental Federation 39