Abstract
Social determinants of health (SDoH) significantly influence health outcomes, accounting for nearly 40% of such outcomes globally. These determinants, pivotal in understanding health disparities, are insufficiently documented in clinical settings and academic clinical narratives. To address this gap, we examined clinical case reports from PubMed (1975–2022) to identify mentions of six specific SDoH, employing a pre-trained named-entity recognition (NER) model from Spark natural language processing (NLP). Multivariate logistic regression was utilized to investigate associations between article characteristics and the documentation of SDoH. From 463,546 reports, 4.4% mentioned SDoH, with race/ethnicity being the most dominant mention. Race/ethnicity was often cited by sub-Saharan African authors (adjusted odds ratio [AOR]: 4.47) and in general medicine (AOR: 2.18). Marital status mentions appeared predominantly in psychiatry (AOR: 2.60) and gynecology (AOR: 2.47). Sexual orientation mentions were correlated with infectious diseases (AOR: 25.00) and varied by authorship regions, with stronger associations observed in South America (AOR: 4.04) and North America (AOR: 2.15), and comparatively weaker associations noted in the Indian subcontinent and the Middle East (AOR: 0.16). Immigrant status mentions were closely related to infectious diseases (AOR: 4.51), gynecology (AOR: 4.25), and certain geographies. Homelessness mentions were more prominent in forensic medicine (AOR: 14.92) and in both infections (AOR: 6.36) and mental disorders (AOR: 5.80). Spiritual belief mentions were more prominent with sub-Saharan authors (AOR: 9.17) and psychiatry (AOR: 7.61). SDoH mentions in medical literature were also determined by the diagnosis, cultural background, and journal type. The limited SDoH registration emphasized their overlooked significance. Disproportionate emphasis on specific relationships, such as sexual orientation with infectious diseases, can perpetuate biases and stereotypes. Innovative tools such as Spark NLP offer promise in advancing research using electronic health records (EHRs), but a standardized approach to SDoH reporting and vigilant AI training is crucial for unbiased health-care analysis.
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