{"title":"Big Data and Oral Health Disparities: A Critical Appraisal.","authors":"T Tiwari, J S Patel, G G Nascimento","doi":"10.1177/00220345241285847","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":94075,"journal":{"name":"Journal of dental research","volume":" ","pages":"119-130"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of dental research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/00220345241285847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/4 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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距的关键资产。大数据包括来自不同渠道的大量与医疗保健相关的生物医学信息,如索赔数据、患者登记和电子健康记录(EHRs)。这项研究是一项批判性的综述,综合了证据,确定了知识差距,并讨论了大数据分析和口腔健康差距的未来影响。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和口腔健康差异之间关系的报告,这些报告以英文发表,并可在电子数据库中获得。搜索引擎是MEDLINE via PubMed, b谷歌Scholar和Web of Science。本综述共纳入23项研究,均为回顾性数据分析。研究使用了各种大数据源,包括电子病历、索赔和国家或地区登记。本研究使用具有内在(数据属性)和上下文价值(数据提供的信息,在本例中为口腔健康差异)的数据质量维度框架来批判性地评估纳入的研究。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、获得护理和其他护理障碍的口腔健康结果和牙科保健利用的差异。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。这些研究清楚地提供了上下文数据维度。从增值的角度来看,一些研究提供了与种族口腔健康不平等有关的新颖和新的信息。一些研究使用了一个以上的口腔健康差异变量或一个复合变量。然而,一些研究的结论是基于关联分析的,很少有研究使用人工智能方法来了解人群的口腔健康不公平——在研究设计和因果分析中可以看到差距。