Accounting for racial bias and social determinants of health in a model of hypertension control
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 so...
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| Vydáno v: | BMC medical informatics and decision making Ročník 25; číslo 1; s. 53 - 12 |
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| Médium: | Journal Article |
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BioMed Central
03.02.2025
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1472-6947, 1472-6947 |
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| 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
×
higher False Positive Rate (FPR) and a 0.58
×
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. |
|---|---|
| AbstractList | 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. 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.BACKGROUNDHypertension 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.METHODSIn 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 × higher False Positive Rate (FPR) and a 0.58 × 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).RESULTSThe 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 × higher False Positive Rate (FPR) and a 0.58 × 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.CONCLUSIONSModels 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. BackgroundHypertension 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.MethodsIn 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.ResultsThe 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).ConclusionsModels 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. 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 higher False Positive Rate (FPR) and a 0.58 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. 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 ([greater than or equal to] 130 mmHg systolic or [greater than or equal to] 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 [formula omitted] higher False Positive Rate (FPR) and a 0.58 [formula omitted] 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. 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 ([greater than or equal to] 130 mmHg systolic or [greater than or equal to] 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 [formula omitted] higher False Positive Rate (FPR) and a 0.58 [formula omitted] 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. Keywords: Hypertension, Social determinants of health, Racial bias, Machine learning 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 × higher False Positive Rate (FPR) and a 0.58 × 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. |
| ArticleNumber | 53 |
| Audience | Academic |
| Author | Cordella, Nicholas Paschalidis, Ioannis Ch Mishuris, Rebecca G. Hu, Yang |
| Author_xml | – sequence: 1 givenname: Yang surname: Hu fullname: Hu, Yang organization: Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Division of Systems Engineering, and Faculty of Computing & Data Sciences, Boston University – sequence: 2 givenname: Nicholas surname: Cordella fullname: Cordella, Nicholas organization: Department of Medicine, Boston Medical Center and Boston University School of Medicine – sequence: 3 givenname: Rebecca G. surname: Mishuris fullname: Mishuris, Rebecca G. organization: Mass General Brigham and Harvard Medical School – sequence: 4 givenname: Ioannis Ch surname: Paschalidis fullname: Paschalidis, Ioannis Ch email: yannisp@bu.edu organization: Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Division of Systems Engineering, and Faculty of Computing & Data Sciences, Boston University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39901187$$D View this record in MEDLINE/PubMed |
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| Keywords | Hypertension Social determinants of health Racial bias Machine learning |
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Hypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the role of... Hypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the role of... Background Hypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the role of... BackgroundHypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the role of... Abstract Background Hypertension control remains a critical problem and most of the existing literature views it from a clinical perspective, overlooking the... |
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| Title | Accounting for racial bias and social determinants of health in a model of hypertension control |
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