Constructing Matched Groups in Dental Observational Health Disparity Studies for Causal Effects
Constructing Matched Groups in Dental Observational Health Disparity Studies for Causal Effects
复制标题
DOI：
10.1177/2380084419830655
复制
发表时间：
2020-01-01
影响因子：
--
通讯作者：
Mertz, E. A.
中科院分区：
文献类型：
--
作者：
Cheng, J.;Gregorich, S. E.;Mertz, E. A.
电子健康记录（EHR）系统为调查人员提供了丰富的数据，从中可以检查现实世界环境中医疗服务的实际影响。然而，当对照组不是随机的时候，混淆是一个主要的问题。目的：本文介绍了一个循序渐进的策略，以威拉米特牙科集团的电子病历为基础，在牙科研究中构建可比较的匹配组。该策略被用于一项纵向研究的准备工作，该研究评估了俄勒冈州公立与私立牙科保险患者之间标准化的基于风险的龋齿预防和管理计划的影响。方法：本研究通过1)评估匹配的必要性和可行性，2)考虑不同的匹配方法，3)评估匹配质量，构建可比较的牙科患者群体。然后比较匹配组在基线时蛀牙、缺牙和补牙面数量的平均比率（DMFS + DMFS）。结果：这一系统过程导致基线协变量相当匹配的组，但他们之间的龋病经验有明显的基线差异。本研究的加权平均比率显示，在基线时，公共保险患者的DMFS + DMFS数（95% CI: 1.08 ~ 1.32）和蛀牙面数（DS + DS）分别是私人保险患者的1.21倍（95% CI: 1.08 ~ 1.37）和1.21倍（95% CI: 1.08 ~ 1.37）。结论：匹配是一种有用的工具，可以用电子病历数据创建类似随机研究的可比组，正如我们的研究所证明的那样，即使人口统计学、社区和诊所特征相似，公共保险的儿科患者的DMFS + DMFS和DS + DS的数量也高于私人保险的儿科患者。知识转移声明：本文提供了一个系统的、逐步的策略，供研究人员在匹配研究中的组时遵循——在这种情况下，是基于电子健康记录数据的研究。本研究的结果将为患者、临床医生和政策制定者提供信息，以更好地了解在个人、诊所和社区协变量中具有相似价值的可比较的公共保险和私人保险儿科患者之间的口腔健康差异。这样的理解将有助于临床医生和政策制定者修改口腔卫生保健和相关政策，以改善口腔健康，减少公共和私人保险患者之间的差距。
Introduction: Electronic health record (EHR) systems provide investigators with rich data from which to examine actual impacts of care delivery in real-world settings. However, confounding is a major concern when comparison groups are not randomized. Objectives: This article introduced a step-by-step strategy to construct comparable matched groups in a dental study based on the EHR of the Willamette Dental Group. This strategy was employed in preparation for a longitudinal study evaluating the impact of a standardized risk-based caries prevention and management program across patients with public versus private dental insurance in Oregon. Methods: This study constructed comparable dental patient groups through a process of 1) evaluating the need for and feasibility of matching, 2) considering different matching methods, and 3) evaluating matching quality. The matched groups were then compared for their average ratio in the number of decayed, missing, and filled tooth surfaces (DMFS + dmfs) at baseline. Results: This systematic process resulted in comparably matched groups in baseline covariates but with a clear baseline disparity in caries experience between them. The weighted average ratio in our study showed that, at baseline, publicly insured patients had 1.21-times (95% CI: 1.08 to 1.32) and 1.21-times (95% CI: 1.08 to 1.37) greater number of DMFS + dmfs and number of decayed tooth surfaces (DS + ds) than privately insured patients, respectively. Conclusion: Matching is a useful tool to create comparable groups with EHR data to resemble randomized studies, as demonstrated by our study where even with similar demographics, neighborhood and clinic characteristics, publicly insured pediatric patients had greater numbers of DMFS + dmfs and DS + ds than privately insured pediatric patients. Knowledge Transfer Statement: This article provides a systematic, step-by-step strategy for investigators to follow when matching groups in a study-in this case, a study based on electronic health record data. The results from this study will provide patients, clinicians, and policy makers with information to better understand the disparities in oral health between comparable publicly and privately insured pediatric patients who have similar values in individual, clinic, and community covariates. Such understanding will help clinicians and policy makers modify oral health care and relevant policies to improve oral health and reduce disparities between publicly and privately insured patients.