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.

牙周病（PD）仍然是美国主要的公共卫生负担。 PD 的发生和进展是多因素的，并且可能因性别而异，有/没有其他合并症，例如 例如 2 型糖尿病 (T2D)，其中合并症受试者口腔健康受损的风险较高。 总体上缺乏临床可解释且具有全国代表性的横截面总结 评估多种合并症方面的众多风险因素（及其复杂的相互作用）（此处，PD和 T2D），以及在基于实践的环境中精确估计 PD 的相关因果治疗方法，并考虑到 性别影响的相互作用。公开的全国性调查数据库（例如 NHANES）， 大型口腔健康数据库（例如 HealthPartners®、HP）很重要，但利用率较低 此类评估和实际解释的资源，主要是由于一些独特的统计和 流行病学的复杂性往往超出了现有标准分析工具的能力 此外，如何根据患者的性别确定口腔门诊就诊的优先顺序。 决定因素和多种合并症风险仍然悬而未决，在这个项目中，我们解决了这些问题。 挑战，并初步提出一个随机原则的、具有全国意义的总结风险指数（目标 1） 代表约 11,700 名成年齿状受试者的横截面 PD 协会，他们是 NHANES 2009-2014 年研究，针对 4 个目标群体：(a) 患有 T2D 的男性，(b) 未患 T2D 的男性，(c) 患有 T2D 的女性 T2D，以及 (d) 女性，无 T2D，然后我们完善并验证该导出的指数，并提出一个随时间变化的指标。 四个目标亚组的 PD 指数（目标 2），适应牙周治疗效果的因果关系，通过 在基于实践的过程中，将其应用到包含约 25,000 名受试者的丰富、纵向、观察性 HP 数据库 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 接下来，我们利用时变指数构建一个最优的 1,17,000 个具有相似特征的受试者。 优先考虑高风险患者以加快就诊速度的政策（目标 3） 最后，我们制作了一个免费的、互动的、 通过 R Shiny 的网络应用程序工具（目标 4），用于估计和计算个性化索引和召回率 我们有原则的、全面的、独特的 PD 整合指数。 来自两家大型 HMO 的电子病历将是同类中第一个产生以下方面新知识的电子病历 此外，所提出的方法很容易推广到其他方法。 不同性别选择的合并症，如心血管疾病、肾脏和肝脏疾病等。 就目前而言，在严格的模型验证之前，得出的指数有可能被整合到流行的 椅边软件，例如 Patterson 的 EagleSoft®，可促进高效的从工作台到床边的转换。