{"doi":"10.1093/jamia/ocae256","title":"Health disparities in the risk of severe acidosis: real-world evidence from the <i>All of Us</i> cohort","abstract":"OBJECTIVE: To assess the health disparities across social determinants of health (SDoH) domains for the risk of severe acidosis independent of demographical and clinical factors. MATERIALS AND METHODS: A retrospective case-control study (n = 13 310, 1:4 matching) is performed using electronic health records (EHRs), SDoH surveys, and genomics data from the All of Us participants. The propensity score matching controls confounding effects due to EHR data availability. Conditional logistic regressions are used to estimate odds ratios describing associations between SDoHs and the risk of acidosis events, adjusted for demographic features, and clinical conditions. RESULTS: Those with employer-provided insurance and those with Medicaid plans show dramatically different risks [adjusted odds ratio (AOR): 0.761 vs 1.41]. Low-income groups demonstrate higher risk (household income less than $25k, AOR: 1.3-1.57) than high-income groups ($100-$200k, AOR: 0.597-0.867). Other high-risk factors include impaired mobility (AOR: 1.32), unemployment (AOR: 1.32), renters (AOR: 1.41), other non-house-owners (AOR: 1.7), and house instability (AOR: 1.25). Education was negatively associated with acidosis risk. DISCUSSION: Our work provides real-world evidence of the comprehensive health disparities due to socioeconomic and behavioral contributors in a cohort enriched in minority groups or underrepresented populations. CONCLUSIONS: SDoHs are strongly associated with systematic health disparities in the risk of severe metabolic acidosis. Types of health insurance, household income levels, housing status and stability, employment status, educational level, and mobility disability play significant roles after being adjusted for demographic features and clinical conditions. Comprehensive solutions are needed to improve equity in healthcare and reduce the risk of severe acidosis.","journal":"Journal of the American Medical Informatics Association","year":2024,"id":483770,"datarank":0.13654403483563432,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.032571957751642504,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.032571957751642504,"corpus_percentile":29.419045408834222,"corpus_rank":9124,"citation_count":1,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.6514,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":18.75,"fair_percentile":35.67716294711097,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1271911,"name":"Chenxi Xiong","orcid":null,"position":1,"is_corresponding":false},{"id":1201182,"name":"Yao Chen","orcid":"0000-0002-4998-5088","position":2,"is_corresponding":false},{"id":1325250,"name":"Shihui Jiang","orcid":null,"position":3,"is_corresponding":false},{"id":1324774,"name":"Chi M. Nguyen","orcid":"0000-0002-1582-1253","position":4,"is_corresponding":false},{"id":288146,"name":"Qianqian Song","orcid":"0000-0002-4455-5302","position":5,"is_corresponding":false},{"id":430677,"name":"Xiaochun Li","orcid":"0000-0002-1547-3041","position":6,"is_corresponding":false},{"id":12547,"name":"Pengyue Zhang","orcid":"0000-0002-7381-9303","position":7,"is_corresponding":false},{"id":307583,"name":"Michael T. Eadon","orcid":"0000-0003-3066-2876","position":8,"is_corresponding":false},{"id":458975,"name":"Jing Su","orcid":"0000-0003-4917-6173","position":9,"is_corresponding":false},{"id":367310,"name":"Allison E. Gatz","orcid":"0009-0002-2636-4729","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T02:07:33.718973Z","pmid":"39401251","pmcid":"PMC11631078","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":0.0,"fair_a":62.5,"fair_i":40.0,"fair_r":29.1667,"fair_zscore":-0.6214,"fair_rationale":{"fair_score":18.75,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":0.0,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"All data and codes are available at All of Us Research Workbench under the workspace 'Acidosis Risk Health Disparities' for users who are authorized to access All of Us Controlled Tier Data v7. 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A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No identifier (accession, DOI, RRID, version) for a resource other than the study's own dataset is provided; the All of Us data version string is not a standard identifier.","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No statement about when the data become available or how long they persist is present in the text.","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T13:37:54.417956Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}