Modeling the impact of social determinants on breast cancer screening: a data-driven approach
BackgroundThis study addresses the critical science challenge of operationalizing social determinants of health (SDoH) in clinical practice. We develop and validate models demonstrating how SDoH predicts mammogram screening behavior within a rural population. Our work provides healthcare systems wit...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1644287/full |
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| author | Guofang Ma Miranda G. Scully Jiahui Luo Jiazuo H. Feng Jiazuo H. Feng Christine M. Gunn Christine M. Gunn Roberta M. diFlorio-Alexander Anna N. A. Tosteson Anna N. A. Tosteson Anna N. A. Tosteson Sally A. Kraft Sally A. Kraft Wesley J. Marrero Wesley J. Marrero |
| author_facet | Guofang Ma Miranda G. Scully Jiahui Luo Jiazuo H. Feng Jiazuo H. Feng Christine M. Gunn Christine M. Gunn Roberta M. diFlorio-Alexander Anna N. A. Tosteson Anna N. A. Tosteson Anna N. A. Tosteson Sally A. Kraft Sally A. Kraft Wesley J. Marrero Wesley J. Marrero |
| author_sort | Guofang Ma |
| collection | DOAJ |
| description | BackgroundThis study addresses the critical science challenge of operationalizing social determinants of health (SDoH) in clinical practice. We develop and validate models demonstrating how SDoH predicts mammogram screening behavior within a rural population. Our work provides healthcare systems with an evidence-based framework for translating SDoH data into effective interventions.MethodsWe model the relationship between SDoH and breast cancer screening adherence using data from over 63,000 patients with established primary care relationships within the Dartmouth Health System, an academic health system serving northern New England through seven hospitals and affiliated ambulatory clinics. Our analytical framework integrates multiple machine learning techniques including light gradient boosting machine, random forest, elastic-net logistic regression, Bayesian regression, and decision tree classifier with SDoH questionnaire responses, demographic information, geographic indicators, insurance status, and clinical measures to quantify and characterize the influence of SDoH on mammogram scheduling and attendance.ResultsOur models achieve moderate discriminative performance in predicting screening behaviors, with an average Area Under the Receiver Operating Characteristic Curve (ROC AUC) of 71% for scheduling and 70% for attendance in validation datasets. Key social factors influencing screening behaviors include geographic accessibility measured by the Rural–Urban Commuting Area, neighborhood socioeconomic status captured by the Area Deprivation Index, and healthcare access factors related to clinical sites. Additional influential variables include months since the last mammogram, current age, and the Charlson Comorbidity Score, which intersect with social factors influencing healthcare utilization. By systematically modeling these SDoH and related factors, we identify opportunities for healthcare organizations to transform SDoH data into targeted, facility-level intervention strategies while adapting to payer incentives and addressing screening disparities.ConclusionOur model provides healthcare systems with a data-driven approach to understanding and addressing how SDoH shape mammogram screening behaviors, particularly among rural populations. This framework offers valuable guidance for healthcare providers to better understand and improve patients’ screening behaviors through targeted, evidence-based interventions. |
| format | Article |
| id | doaj-art-96aaee3518e344439b4dc8c1da2ac6f2 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-96aaee3518e344439b4dc8c1da2ac6f22025-08-20T05:32:54ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-08-011210.3389/fmed.2025.16442871644287Modeling the impact of social determinants on breast cancer screening: a data-driven approachGuofang Ma0Miranda G. Scully1Jiahui Luo2Jiazuo H. Feng3Jiazuo H. Feng4Christine M. Gunn5Christine M. Gunn6Roberta M. diFlorio-Alexander7Anna N. A. Tosteson8Anna N. A. Tosteson9Anna N. A. Tosteson10Sally A. Kraft11Sally A. Kraft12Wesley J. Marrero13Wesley J. Marrero14Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, United StatesDepartment of Computer Science, Dartmouth College, Hanover, NH, United StatesThayer School of Engineering, Dartmouth College, Hanover, NH, United StatesDartmouth Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, NH, United StatesDepartment of Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, United StatesDartmouth Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, NH, United StatesThe Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, United StatesDartmouth Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, NH, United StatesDartmouth Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, NH, United StatesDepartment of Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, United StatesThe Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, United StatesDepartment of Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, United StatesPopulation Health, Dartmouth Health, Lebanon, NH, United StatesThayer School of Engineering, Dartmouth College, Hanover, NH, United StatesDartmouth Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, NH, United StatesBackgroundThis study addresses the critical science challenge of operationalizing social determinants of health (SDoH) in clinical practice. We develop and validate models demonstrating how SDoH predicts mammogram screening behavior within a rural population. Our work provides healthcare systems with an evidence-based framework for translating SDoH data into effective interventions.MethodsWe model the relationship between SDoH and breast cancer screening adherence using data from over 63,000 patients with established primary care relationships within the Dartmouth Health System, an academic health system serving northern New England through seven hospitals and affiliated ambulatory clinics. Our analytical framework integrates multiple machine learning techniques including light gradient boosting machine, random forest, elastic-net logistic regression, Bayesian regression, and decision tree classifier with SDoH questionnaire responses, demographic information, geographic indicators, insurance status, and clinical measures to quantify and characterize the influence of SDoH on mammogram scheduling and attendance.ResultsOur models achieve moderate discriminative performance in predicting screening behaviors, with an average Area Under the Receiver Operating Characteristic Curve (ROC AUC) of 71% for scheduling and 70% for attendance in validation datasets. Key social factors influencing screening behaviors include geographic accessibility measured by the Rural–Urban Commuting Area, neighborhood socioeconomic status captured by the Area Deprivation Index, and healthcare access factors related to clinical sites. Additional influential variables include months since the last mammogram, current age, and the Charlson Comorbidity Score, which intersect with social factors influencing healthcare utilization. By systematically modeling these SDoH and related factors, we identify opportunities for healthcare organizations to transform SDoH data into targeted, facility-level intervention strategies while adapting to payer incentives and addressing screening disparities.ConclusionOur model provides healthcare systems with a data-driven approach to understanding and addressing how SDoH shape mammogram screening behaviors, particularly among rural populations. This framework offers valuable guidance for healthcare providers to better understand and improve patients’ screening behaviors through targeted, evidence-based interventions.https://www.frontiersin.org/articles/10.3389/fmed.2025.1644287/fullpredictive modelingmachine learningcancer screeningimplementation sciencebreast cancer |
| spellingShingle | Guofang Ma Miranda G. Scully Jiahui Luo Jiazuo H. Feng Jiazuo H. Feng Christine M. Gunn Christine M. Gunn Roberta M. diFlorio-Alexander Anna N. A. Tosteson Anna N. A. Tosteson Anna N. A. Tosteson Sally A. Kraft Sally A. Kraft Wesley J. Marrero Wesley J. Marrero Modeling the impact of social determinants on breast cancer screening: a data-driven approach Frontiers in Medicine predictive modeling machine learning cancer screening implementation science breast cancer |
| title | Modeling the impact of social determinants on breast cancer screening: a data-driven approach |
| title_full | Modeling the impact of social determinants on breast cancer screening: a data-driven approach |
| title_fullStr | Modeling the impact of social determinants on breast cancer screening: a data-driven approach |
| title_full_unstemmed | Modeling the impact of social determinants on breast cancer screening: a data-driven approach |
| title_short | Modeling the impact of social determinants on breast cancer screening: a data-driven approach |
| title_sort | modeling the impact of social determinants on breast cancer screening a data driven approach |
| topic | predictive modeling machine learning cancer screening implementation science breast cancer |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1644287/full |
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