DOI: 10.5061/dryad.vhhmgqp10
title: Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles

description: Without comprehensive examination of available literature on health
 disparities and minority health (HDMH), the field is left vulnerable to
 disproportionately focus on specific populations or conditions, curtailing
 our ability to fully advance health equity. Using scalable open-source
 methods, we conducted a computational scoping review of more than 200,000
 articles to investigate major populations, conditions, and themes in the
 literature as well as notable gaps. We also compared trends in studied
 conditions to their relative prevalence in the general population using
 insurance claims (42 million Americans). HDMH publications represent 1% of
 articles in MEDLINE. Most studies are observational in nature, though
 randomized trial reporting has increased five-fold in the last twenty
 years. Half of all HDMH articles concentrate on only three disease groups
 (cancer, mental health, endocrine/metabolic disorders), while hearing,
 vision, and skin-related conditions are among the least well-represented
 despite substantial prevalence. To support further investigation, we also
 present HDMH Monitor, an interactive dashboard and repository generated
 from the HDMH bibliome.
description: This is a repository to accompany the publication, Mining the Health
 Disparities and Minority Health Bibliome: A Computational Scoping Review
 and Gap Analysis of 200,000+ Articles, in Science Advances (doi:
 10.1126/sciadv.adf9033) and its associated website, HDMH Monitor. ##
 General Information **Dataset:** Mining the health disparities and
 minority health bibliome: A computational scoping review and gap analysis
 of 200,000+ articles [Dataset]. Dryad.
 **Corresponding Author
 Information** Name: Harry Reyes Nieva Institution: Columbia University
 Address: New York, NY, USA Email: **Date
 of data collection:** 25 August 2021 **Funding sources:** National Library
 of Medicine (NLM) under Award Numbers T15LM007079 and R01LM013043 in
 addition to a Computational and Data Science Fellowship to Harry Reyes
 Nieva from the Association for Computing Machinery Special Interest Group
 in High Performance Computing (ACM SIGHPC). ## Description of the data and
 file structure **DATA & FILE OVERVIEW** File List: *
 hdmh_abstracts.csv * text.out **DATA-SPECIFIC INFORMATION FOR:
 hdmh_abstracts.csv** The dataset was collected by querying the MEDLINE
 application programming interface (API). For each article, we extracted
 the PubMed reference number (PMID) and associated metadata using the
 open-source Entrez package of the Biopython Python library. We then used
 latent Dirichlet allocation (LDA) to model 50 topics from concatenated
 article title and abstract text. Variable List: * Index * PMID: PubMed ID
 (PMID) * Year: Year of publication * Article_Type: Publication type (e.g.,
 journal article) * Language: Language of article * Title: Article title *
 Abstract: Article abstract * MeSH: Medical Subject Headings of article *
 Dominant_Topic: Dominant topic number among 50 possible topics generated
 by the topic model * Topic_Perc_Contrib: Percent contribution of the
 dominant topic of the article among 50 possible topics * Topic_Name_No:
 Dominant topic name (i.e., assigned label) **DATA-SPECIFIC INFORMATION
 FOR: text.out** The text.out dataset is a file that was produced by [Batch
 MetaMap](https://lhncbc.nlm.nih.gov/ii/tools/MetaMap.html), an open-source
 biomedical literature tool designed and maintained by the National Library
 of Medicine for recognizing Unified Medical Language System (UMLS)
 concepts via natural language processing, when we used the tool to process
 concatenated article title and abstract text from the articles in
 hdmh_abstracts.csv. Detailed explanation of fielded MetaMap Indexing (MMI)
 output is available at:
 . Last accessed: 20 Jan 2024. Fields: * PubMed ID (PMID) * MetaMap Indexing (MMI) * MMI Score * UMLS Concept Preferred Name * UMLS Concept Unique Identifier (CUI) * Semantic Type List * Trigger Information * Location * Positional information * Treecode(s) ## Sharing/Access information Links to other publicly accessible locations of the data: * Data was derived from the following sources: * MEDLINE