Sex/Gender influences on periodontal disease and diabetes: A population science approach, with software

性别/性别对牙周病和糖尿病的影响：人口科学方法与软件

• 批准号: 10531704
• 负责人: Dipankar Bandyopadhyay
• 金额: $ 57.52万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10531704
• 关键词: Accounting; Address; Adult; Area; Behavior; Biological Markers; COVID-19; Cardiovascular Diseases; Characteristics; Clinic Visits; Clinical; Clinical Trials; Complex; Computer software; Computerized Medical Record; Data; Data Science; Data Set; Databases; Debridement; Dental Care; Diabetes Mellitus; Disease; Electronic Health Record; Epidemiologist; Epidemiology; Etiology; Evaluation; Evaluation Studies; Female; Funding; Future; Gender; Goals; Government; Health; Healthcare; High Prevalence; Incidence; Internet; Intervention; Kidney Diseases; Knowledge; Life Expectancy; Light; Literature; Liver diseases; Maintenance; Measures; Methodology; Methods; Modeling; National Health and Nutrition Examination Survey; National Institute of Dental and Craniofacial Research; Non-Insulin-Dependent Diabetes Mellitus; Operative Surgical Procedures; Oral; Oral health; Pathogenesis; Patients; Performance; Periodontal Diseases; Periodontal Index; Phase; Policies; Population; Population Sciences; Prognostic Factor; Public Health; Recommendation; Records; Research; Resources; Risk; Risk Assessment; Risk Estimate; Risk Factors; Risk Reduction; Services; Site; Strategic Planning; Subgroup; Surveys; Techniques; Testing; Time; Tooth Loss; Tooth structure; Uncertainty; United States; United States National Institutes of Health; Validation; Variant; Visit; Widespread Disease; Woman; Women' s Health; analytical tool; base; bench-to-bedside translation; care costs; comorbidity; cost; experience; flexibility; high risk; immunosuppressed; improved; indexing; interest; male; men; nonlinear regression; novel; population based; precision medicine; response; sex; tool; treatment effect; user-friendly; validation studies; web app

Periodontal Disease (PD) continues to remain a major public health burden in the United States. Manifestation and progression of PD are multifactorial, and may vary across gender, with/without additional comorbidities, such as Type-2 Diabetes (T2D), where comorbid subjects are at an elevated risk of compromised oral health. There is an overall paucity of clinically interpretable and nationally representative cross-sectional summaries of numerous risk factors (and their complex interactions) in assessing multi-comorbidity aspects (here, PD and T2D), and precise estimation of associated causal treatments for PD in practice-based settings, factoring in the interactions of sex/gender influences. Publicly available nationwide survey databases (such as the NHANES), and large oral health databases (such as the HealthPartners®, HP) are important, but somewhat under-utilized resources for such evaluations and practical interpretations, mainly due to several unique statistical and epidemiological complexities, which are often beyond the capabilities of existing standard analytical tools and software packages. Furthermore, how to prioritize patients for oral clinic visits based on their sex/gender determinants, and multi-comorbidity risks continues to remain unresolved. In this project, we address these challenges, and initially propose a stochastically-principled, nationally meaningful, summary risk index (Aim 1) representing cross-sectional PD association from about 11,700 adult dentate subjects, who are part of the NHANES 2009-2014 study, for the 4 target groups: (a) Males with T2D, (b) Males without T2D, (c) Females with T2D, and (d) Females, without T2D. We then refine and validate this derived index, and propose a time-varying PD index (Aim 2) for the four target subgroups, accommodating causality of periodontal treatment effects, via application to the rich, longitudinal, observational HP database of about 25,000 subjects in a practice-based setting, with further model fitting and cross-validation using the Kaiser Permanente Northwest database of about 1,17,000 subjects with similar characteristics. Next, we utilize the time-varying index to construct an optimal policy (Aim 3) for prioritizing high-risk patients for quicker clinic visits. Finally, we produce a free, interactive, web-application tool (Aim 4) via R Shiny, for estimation and computation of the personalized index and recall decisions for any future patient. Our statistically principled, comprehensive, unique index for PD integrating electronic medical records from two large HMOs will be the first of its kind to generate new knowledge in regards to assessing sex/gender influences. Furthermore, the proposed methodology is readily generalizable to other comorbidities across gender choices, such as cardiovascular disease, kidney and liver disease, etc. In the longer term, pending rigorous model validation, the derived index has the potential to be integrated into popular chairside software, such as Patterson’s EagleSoft®, thereby facilitating efficient bench to bedside translation.

牙周疾病（PD）仍然是美国的主要公共卫生伯恩。表现 PD的进展是多因素的，并且可能因性别而异，并且没有其他合并症，例如 作为2型糖尿病（T2D），合并症受试者的口腔健康风险较高。那里 是对临床上易于解释和全国代表性的横截面摘要的总体缺乏 在评估多种症状方面（此处，PD和 T2D），以及基于实践环境中PD的相关灾难性处理的精确估计 性别/性别影响的相互作用。公开可用的国家范围调查数据库（例如NHANES）， 大型口腔健康数据库（例如HealthPartners®，HP）很重要，但充分利用了 进行此类评估和实际解释的资源，主要是由于几个独特的统计和 流行病学复杂性，通常超出现有标准分析工具的能力和 软件包。此外，如何根据性别/性别优先考虑患者口腔诊所就诊 决定因素和多种疾病的风险继续尚未解决。在这个项目中，我们解决这些问题 挑战，最初提出一个随机原理，全国意义，简易风险指数（AIM 1） 代表大约11,700名成人牙齿受试者的横截面PD关联，这些受试者属于 NHANES 2009-2014研究，针对4个目标组：（a）t2d的男性，（b）没有T2D的男性，（c）女性 T2D和（d）女性，没有T2D。然后，我们完善并验证该派生的索引，并提出时间变化 通过利用牙周治疗效果的四个目标亚组的PD指数（AIM 2） 在基于实践的富裕，纵向，观察性HP数据库中的应用 设置，使用Kaiser Permanente Northwest数据库进行进一步的模型拟合和交叉验证 1,17,000名具有相似特征的受试者。接下来，我们利用随时间变化的索引来构建最佳 政策（AIM 3）优先考虑高危患者进行更快的诊所就诊。最后，我们产生一个免费的互动性， Web应用工具（AIM 4）通过R Shiny，以估算和计算个性化索引和召回 对任何未来患者的决定。我们的统计校长，全面，独特的索引用于PD集成 来自两个大型HMO的电子病历将是同类产品中的第一个产生新知识 评估性别/性别影响。此外，提出的方法很容易被推广到其他 性别选择的合并症，例如心血管疾病，肾脏和肝病等。 术语，即将进行严格的模型验证，派生指数有可能集成到流行 椅子方软件（例如Patterson的Eaglesoft®），从而支持有效的长凳到床边翻译。