NDLI: Accounting for racial bias and social determinants of health in a model of hypertension control

Content Provider	Springer Nature : BioMed Central
Author	Hu, Yang Cordella, Nicholas Mishuris, Rebecca G. Paschalidis, Ioannis Ch.
Abstract	Hypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the role of sociodemographic factors. This study aims to identify patients with not well-controlled hypertension using readily available demographic and socioeconomic features and elucidate important predictive variables. In this retrospective cohort study, records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used. Patients with either a hypertension diagnosis or related records (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 164,041) were selected. Models were developed to predict which patients had uncontrolled hypertension defined as systolic blood pressure (SBP) records exceeding 160 mmHg. The predictive model of high SBP reached an Area Under the Receiver Operating Characteristic Curve of 74.49% ± 0.23%. Age, race, Social Determinants of Health (SDoH), mental health, and cigarette use were predictive of high SBP. Being Black or having critical social needs led to higher probability of uncontrolled SBP. To mitigate model bias and elucidate differences in predictive variables, two separate models were trained for Black and White patients. Black patients face a 4.7 $\times$ higher False Positive Rate (FPR) and a 0.58 $\times$ lower False Negative Rate (FNR) compared to White patients. Decision threshold differentiation was implemented to equalize FNR. Race-specific models revealed different sets of social variables predicting high SBP, with Black patients being affected by structural barriers (e.g., food and transportation) and White patients by personal and demographic factors (e.g., marital status). Models using non-clinical factors can predict which patients exhibit poorly controlled hypertension. Racial and SDoH variables are significant predictors but lead to biased predictive models. Race-specific models are not sufficient to resolve such biases and require further decision threshold tuning. A host of structural socioeconomic factors are identified to be targeted to reduce disparities in hypertension control.
Related Links	https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-025-02873-4.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	14726947
DOI	10.1186/s12911-025-02873-4
Journal	BMC Medical Informatics and Decision Making
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2025-02-03
Access Restriction	Open
Subject Keyword	Health Informatics Information Systems and Communication Service Management of Computing and Information Systems Hypertension Social determinants of health Racial bias Machine learning
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications Health Policy
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.9/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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