Accounting for racial bias and social determinants of health in a model of hypertension control
Abstract Background 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 demograph...
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2025-02-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02873-4 |
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| author | Yang Hu Nicholas Cordella Rebecca G. Mishuris Ioannis Ch. Paschalidis |
| author_facet | Yang Hu Nicholas Cordella Rebecca G. Mishuris Ioannis Ch. Paschalidis |
| author_sort | Yang Hu |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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). Conclusions 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. |
| format | Article |
| id | doaj-art-78076af6670f4964a46cd7a59102a3ee |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-78076af6670f4964a46cd7a59102a3ee2025-02-09T12:40:14ZengBMCBMC Medical Informatics and Decision Making1472-69472025-02-0125111210.1186/s12911-025-02873-4Accounting for racial bias and social determinants of health in a model of hypertension controlYang Hu0Nicholas Cordella1Rebecca G. Mishuris2Ioannis Ch. Paschalidis3Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Division of Systems Engineering, and Faculty of Computing & Data Sciences, Boston UniversityDepartment of Medicine, Boston Medical Center and Boston University School of MedicineMass General Brigham and Harvard Medical SchoolDepartment of Electrical and Computer Engineering, Department of Biomedical Engineering, Division of Systems Engineering, and Faculty of Computing & Data Sciences, Boston UniversityAbstract Background 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. Methods 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. Results 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). Conclusions 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.https://doi.org/10.1186/s12911-025-02873-4HypertensionSocial determinants of healthRacial biasMachine learning |
| spellingShingle | Yang Hu Nicholas Cordella Rebecca G. Mishuris Ioannis Ch. Paschalidis Accounting for racial bias and social determinants of health in a model of hypertension control BMC Medical Informatics and Decision Making Hypertension Social determinants of health Racial bias Machine learning |
| title | Accounting for racial bias and social determinants of health in a model of hypertension control |
| title_full | Accounting for racial bias and social determinants of health in a model of hypertension control |
| title_fullStr | Accounting for racial bias and social determinants of health in a model of hypertension control |
| title_full_unstemmed | Accounting for racial bias and social determinants of health in a model of hypertension control |
| title_short | Accounting for racial bias and social determinants of health in a model of hypertension control |
| title_sort | accounting for racial bias and social determinants of health in a model of hypertension control |
| topic | Hypertension Social determinants of health Racial bias Machine learning |
| url | https://doi.org/10.1186/s12911-025-02873-4 |
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