Big Data and Oral Health Disparities: A Critical Appraisal

大数据 卫生公平 医学 民族 医疗保健 梅德林 一致性(知识库) 社会经济地位 数据科学 环境卫生 公共卫生 计算机科学 政治学 数据挖掘 人口 护理部 人工智能 法学
作者
Tamanna Tiwari,Jay S. Patel,Gustavo G. Nascimento
出处
期刊:Journal of Dental Research [SAGE Publishing]
标识
DOI:10.1177/00220345241285847
摘要

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.
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