Demographics and socioeconomic determinants of health predict continued participation in a CT lung cancer screening program
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning models identified key demographic and socioeconomic factors influencing participation in lung cancer screening. These models can predict individuals at risk for missed or delayed follow-up screening appointments.
Area Of Science
- Oncology
- Public Health
- Data Science
Background
- Lung cancer screening (LCS) aims to reduce mortality through early detection.
- Continued participation in LCS programs is crucial for maximizing screening benefits.
- Identifying barriers to adherence is essential for improving LCS program effectiveness.
Purpose Of The Study
- To develop machine learning (ML) models to predict continued participation in low-dose CT lung cancer screening (LCS).
- To assess the value of demographic and socioeconomic status (SES) variables in predicting LCS adherence.
- To identify predictors of no or delayed follow-up in LCS programs.
Main Methods
- Retrospective analysis of 480 LCS subjects.
- Evaluation of 14 socioeconomic, demographic, and clinical predictors for LCS adherence.
- Comparison of multivariate logistic regression (MLR), support vector machine (SVM), and shallow neural network (NN) models for prediction.
Main Results
- Age, sex, race, insurance status, personal cancer history, and median household income predicted follow-up visits (Outcome #1).
- Age, sex, race, and insurance status predicted absent or delayed follow-up (Outcome #2).
- MLR demonstrated the best predictive performance with an AUC of 0.732 for Outcome #1 and 0.633 for Outcome #2.
Conclusions
- Significant predictors of LCS adherence were identified.
- ML models can predict subjects at higher risk for no or delayed LCS follow-ups.
- Findings can inform interventions to engage vulnerable populations and improve LCS benefits.
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