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Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning
Man Hung1,2, Eric S Hon3, Bianca Ruiz-Negron2
1College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA.
International Journal of Environmental Research and Public Health
|October 10, 2020
Summary
A new risk model accurately predicts unmet dental care needs, identifying key social factors like age and income. This advancement aids targeted interventions for better public oral health.
Area of Science:
- Public Health
- Health Services Research
- Dental Public Health
Background:
- Access to dental care is a significant public health issue in the United States.
- Unmet dental care needs disproportionately affect vulnerable populations.
- Understanding the interplay between social determinants of health and dental access is crucial.
Purpose of the Study:
- To develop a predictive risk model for unmet dental care needs.
- To investigate the relationship between social determinants of health and unmet dental care needs.
- To leverage machine learning for identifying individuals at risk of unmet dental care needs.
Main Methods:
- Utilized data from the 2016 Medical Expenditure Panel Survey.
- Employed chi-squared tests to analyze social determinants of health differences.
- Applied machine learning algorithms to build a risk prediction model.
Main Results:
- Age, income, family size, education, and unmet medical needs were key predictors.
- The risk prediction model achieved 82.6% accuracy, with an AUC of 0.918.
- A strong association was confirmed between social determinants of health and unmet dental care needs.
Conclusions:
- Social determinants of health significantly impact unmet dental care needs.
- The developed risk model offers a novel approach for identifying at-risk individuals.
- Findings support targeted interventions to improve dental care access in the US population.
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