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)在美国仍然是一个主要的公共卫生负担。表现形式 帕金森病的进展是多因素的，可能因性别而异，有/没有额外的共病，如 如2型糖尿病(T2D)，其并存的受试者口腔健康受损的风险增加。那里 是临床上可解释的和具有全国代表性的横断面总结的总体缺乏 评估多个共病方面的众多风险因素(及其复杂的相互作用)(在这里，PD和 T2D)，并在以实践为基础的环境中精确估计帕金森病的相关因果治疗，考虑到 性/性别影响的交互作用。可公开获得的全国调查数据库(如NHANES)， 大型口腔健康数据库(如HealthPartners®、HP)很重要，但有些未得到充分利用 用于这种评价和实际解释的资源，主要是由于几个独特的统计和 流行病学的复杂性，这往往超出现有标准分析工具和 软件包。此外，如何根据患者的性别/性别确定患者口腔就诊的优先顺序 决定因素和多种共病风险仍然悬而未决。在这个项目中，我们解决了以下问题 挑战，并初步提出一个具有随机原则、对国家有意义的综合风险指数(目标1) 代表了来自大约11,700名成年齿状受试者的横断面PD关联，这些受试者是 NHANES 2009-2014年研究，针对4个目标群体：(A)患有T2D的男性，(B)没有T2D的男性，(C)有T2D的女性 T2D，和(D)雌性，没有T2D。然后，我们提炼和验证了这个派生索引，并提出了一个时变的 四个目标亚组的PD指数(目标2)，反映牙周治疗效果的因果关系 在以实践为基础的约25,000名受试者的丰富的、纵向的、观察性的HP数据库中的应用 设置，使用Kaiser Permanente西北数据库进行进一步的模型拟合和交叉验证 11.7万名具有相似特征的受试者。接下来，我们利用时变索引来构造一个最优的 政策(目标3)对高危患者进行优先排序，以便更快地就诊。最后，我们制作了一个免费的、互动的、 WEB应用工具(AIM 4)，通过R SHINY，用于评估和计算个性化指数和召回 为任何未来的病人做决定。我们的统计原则性、综合性、唯一性的PD集成指标 来自两个大型医疗保健组织的电子医疗记录将是第一个在以下方面产生新知识的同类组织 评估性/性别影响。此外，所提出的方法很容易推广到其他 跨性别选择的共病，如心血管疾病、肾脏和肝脏疾病等。 长期而言，在严格的模型验证之前，派生的索引有可能整合到Popular 座椅端软件，如Patterson的EagleSoft®，从而促进高效的从长凳到床边的翻译。