Spatiotemporal models for periodontal disease monitoring and recall frequencies Tooth loss from periodontal disease (PD) remains a major public health burden in the US. With the rising cost of dental insurance premiums, future professional dental treatment plans will seek to prioritize patients based on their risk of disease and spend more resources monitoring and treating high-risk patients. Hence, there is a need to develop appropriate statistical models and tools for efficient risk assessment of PD, short-term prognosis, and periodontal recall intervals leading to cost-effectiveness of dental treatment plans. Dental datasets present many interesting statistical challenges (non-stationarity, non-normality, spatial dependence, non-random missingness, confounding by indication, huge cluster size, etc), which severely limit the potential of currently-available software (such as Patterson's EagleSoft(r), etc) loaded into the chair- side computer of a periodontist. Currently available statistical software might handle some, but not all of these challenges within a unified paradigm. Goals: The proposed study will develop statistical tools to (a) characterize risk factors for PD progression, (b) rapidly and efficiently indentify changes in a patient's PD status, (c) use short-term predictions to guide periodontal recall decisions, and (d) develop user-friendly software to implement these methods. Subjects: The statistical methods will be developed using a rich 8-year longitudinal database from the HealthPartners HMO, consisting of about 15,000 patients with follow-ups. Available data and study design: A clustered- longitudinal (CL) study design comprises the databases that recorded data for age, gender, race, complete restorative and periodontal records with follow-up, smoking status, diabetes status, oral hygiene, and other essential parameters. Significance: The potential translation to dental clinical practice for this project is strong because it will provide dental practitioners with evidence-based criteria to guid 'personalized' periodontal recalls and treatment decisions. The impact generated is expected to be far- reaching, and the long-term goal would incorporate these new methods into existing chair-side dental software leading to development of cost-effective treatment dental plans with prudent expectations.
This proposed research has the potential to exert tremendous impact from a dental public health perspective because it strives to develop an efficient stochastic framework for quantifying periodontal risk assessment leading to effective recall strategies. The long term goal is to bridge the gap between academic dental basic science researchers and periodontal practitioners by integrating these methods into available chair-side dental software packages which would facilitate development of cost-effective (dental) treatment plans.
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