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Spatiotemporal models for periodontal disease monitoring and recall frequencies

牙周病监测和召回频率的时空模型

基本信息

批准号：
8983525
负责人：
Dipankar Bandyopadhyay
金额：
$ 40.07万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-25 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8983525
关键词：
Accounting Address Age Algorithms American Basic Science Clinical Collaborations Computer software Computers Consensus Data Data Analyses Data Set Databases Dental Dental Care Dental Hygiene Dental Insurance Dependence Deterioration Development Diabetes Mellitus Disease Progression Education Evaluation Exhibits Frequencies Future Gender Goals Individual Institutes Institution Linear Models Location Longitudinal Studies Measures Medical Methodology Methods Minnesota Modeling Monitor National Institute of Dental and Craniofacial Research Normalcy Oral Oral cavity Oral health Patients Performance Periodontal Diseases Policies Prognostic Marker Public Health Race Recommendation Records Research Research Design Research Personnel Resources Risk Assessment Risk Factors Schedule Services Side Site Smoking Status Statistical Methods Statistical Models Tooth Diseases Tooth Loss Tooth structure Translations Uncertainty Visit base checkup examination clinical practice cost cost effective cost effectiveness disorder prevention disorder risk effective therapy evidence base expectation follow-up high risk insight interest longitudinal database novel novel strategies oral care outcome forecast personalized medicine public health relevance sound spatiotemporal time interval tool treatment effect treatment planning user friendly software

项目摘要

DESCRIPTION (provided by applicant): 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.