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

牙周病（PD）仍然是美国主要的公共卫生负担。表现 PD的进展是多因素的，可能因性别而异，有/无其他合并症，如 2型糖尿病（T2 D），其中共病受试者口腔健康受损的风险升高。那里 总体上缺乏临床可解释性和全国代表性的横断面总结， 在评估多种合并症方面（此处，PD和 T2 D），并在基于实践的环境中精确估计PD的相关因果治疗，考虑 性/性别影响的相互作用。公开提供的全国性调查数据库（如全国家庭健康和营养状况调查）， 和大型口腔健康数据库（如HealthPartners®，HP）是重要的，但有些利用不足 这种评价和实际解释的资源，主要是由于一些独特的统计和 流行病学的复杂性，这往往超出了现有标准分析工具的能力， 软件包。此外，如何根据患者的性别/性别优先考虑口腔诊所就诊 决定因素和多种共患病风险仍然没有得到解决。在这个项目中，我们解决这些问题。 挑战，并初步提出一个随机原则，全国有意义的，总结风险指数（目标1） 代表了来自约11，700名成年齿状受试者的横断面PD相关性，这些受试者是 NHANES 2009-2014研究，针对4个目标群体：（a）T2 D男性，（B）无T2 D男性，（c）T2 D女性 T2 D，和（d）女性，无T2 D。然后，我们完善和验证这个衍生指数，并提出了一个随时间变化的 四个目标亚组的PD指数（目标2），包括牙周治疗效果的因果关系，通过 应用于丰富的，纵向的，观察性的HP数据库，约25,000名受试者，在一个基于实践的 设置，进一步的模型拟合和交叉验证使用凯萨永久西北数据库约 1，17，000例具有相似特征的受试者。接下来，我们利用时变指数来构造一个最优的 优先考虑高风险患者以加快诊所就诊的政策（目标3）。最后，我们制作了一个免费的，互动的， 网络应用工具（Aim 4）通过R Shiny，用于估计和计算个性化索引和召回率 为未来的病人做决定。我们的统计原则，全面，独特的PD积分指数 来自两个大型HMO的电子医疗记录将是第一个产生新知识的电子医疗记录， 评估性/性别影响。此外，所提出的方法很容易推广到其他 跨性别选择的合并症，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，衍生指数有可能被纳入流行的 椅旁软件，如Patterson的ZuleSoft ®，从而促进有效的工作台到床边的转换。

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.