Spatiotemporal models for periodontal disease monitoring and recall frequencies

牙周病监测和召回频率的时空模型

• 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
• 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

 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.

 描述（由申请人提供）：牙周病监测和召回频率的时空模型 牙周病（PD）造成的牙齿脱落仍然是美国的主要公共卫生负担，随着牙科保险费的上涨，未来的专业牙科治疗计划将寻求。根据疾病风险对患者进行优先排序 因此，需要开发适当的统计模型和工具来有效评估PD、短期预后和牙周回忆间隔，从而提高牙科治疗计划的成本效益。牙科数据集提出了许多有趣的统计挑战（非平稳性、非正态性、空间依赖性、非随机缺失、指示混淆、巨大的簇大小等），这严重限制了当前可用软件（例如Patterson 的 EagleSoft(r) 等）加载到牙周病医生的椅旁计算机中，可以在统一的范式中处理一些但不是所有这些挑战。 ) 描述风险因素 对于 PD 进展，(b) 快速有效地识别患者 PD 状态的变化，(c) 使用 短期预测来指导牙周召回决策，以及 (d) 开发用户友好的软件来实施这些方法 受试者：统计方法将使用 HealthPartners HMO 的丰富的 8 年纵向数据库开发，该数据库由约 15,000 名患者组成。可用数据和研究设计：集群纵向（CL）研究设计包括记录年龄、性别、种族、完整恢复和牙周记录以及随访、吸烟状况等数据的数据库。意义：该项目对牙科临床实践的潜在转化很大，因为它将为牙科治疗师提供基于证据的标准，以指导“个性化”牙周回顾和治疗决策。预计将影响深远，长期目标是将这些新方法纳入现有的椅旁牙科软件中，从而以审慎的期望制定具有成本效益的治疗牙科计划。