Tiwari T, Patel J S, Nascimento G G
School of Dental Medicine, University of Colorado, Aurora, CO, USA.
Temple University Kornberg School of Dentistry, Philadelphia, PA, USA.
J Dent Res. 2025 Feb;104(2):119-130. doi: 10.1177/00220345241285847. Epub 2024 Dec 4.
Big data has emerged as a pivotal asset in addressing oral health disparities in recent years. Big data encompasses the vast pool of health care-related biomedical information sourced from diverse channels, such as claims data, patient registries, and electronic health records (EHRs). This study is a critical review that synthesizes the evidence, identifies gaps in knowledge, and discusses future implications regarding big data analytics and oral health disparities. Published reports from 2014 to 2023 that studied associations between big data, social determinants of oral health, and oral health disparities, published in English and available in electronic databases, were included. Search engines were MEDLINE via PubMed, Google Scholar, and Web of Science. A total of 23 studies were included in the review, and all were retrospective data analytics. Studies have used a variety of big data sources, including EHRs, claims, and national or regional registries. This study used a framework of data quality dimensions with intrinsic (data attributes) and contextual values (information provided by the data, in this case, oral health disparities) to critically appraise the included studies. Big data revealed disparities in oral health outcomes and dental care utilization based on race, ethnicity, socioeconomic status, geographical location, insurance category, access to care, and other barriers to care. For the intrinsic data dimension, none of the studies addressed or reported data missingness or consistency of the data. The studies clearly provided contextual data dimensions. From a value-added perspective, several studies provided novel and new information related to racial oral health inequities. Several studies used more than one oral health disparities variable or a composite variable. However, the conclusions from several studies were based on association-based analytics, and few studies used artificial intelligence approaches to understand the population's oral health inequities-gaps were seen in the study designs and causal analytics.
近年来，大数据已成为解决口腔健康差异问题的关键资产。大数据涵盖了从各种渠道获取的大量与医疗保健相关的生物医学信息，如理赔数据、患者登记册和电子健康记录（EHR）。本研究是一项批判性综述，综合了证据，识别了知识空白，并讨论了大数据分析与口腔健康差异的未来影响。纳入了2014年至2023年发表的、研究大数据、口腔健康的社会决定因素与口腔健康差异之间关联的英文报告，这些报告可在电子数据库中获取。搜索引擎包括通过PubMed的MEDLINE、谷歌学术和科学网。该综述共纳入23项研究，均为回顾性数据分析。研究使用了多种大数据来源，包括电子健康记录、理赔数据以及国家或地区登记册。本研究使用了一个数据质量维度框架，包括内在（数据属性）和背景值（数据提供的信息，在本案例中为口腔健康差异），对纳入的研究进行批判性评估。大数据揭示了基于种族、民族、社会经济地位、地理位置、保险类别、就医机会以及其他就医障碍的口腔健康结果和牙科护理利用方面的差异。对于内在数据维度，没有一项研究涉及或报告数据缺失或数据一致性。这些研究明确提供了背景数据维度。从增值角度来看，几项研究提供了与种族口腔健康不平等相关的新颖信息。几项研究使用了不止一个口腔健康差异变量或复合变量。然而，几项研究的结论基于关联分析，很少有研究使用人工智能方法来理解人群的口腔健康不平等——在研究设计和因果分析方面存在差距。
