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Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning

Author

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  • Man Hung

    (College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA
    Department of Orthopaedic Surgery Operations, University of Utah, Salt Lake City, UT 84108, USA)

  • Eric S. Hon

    (Department of Economics, University of Chicago, Chicago, IL 60637, USA)

  • Bianca Ruiz-Negron

    (Department of Orthopaedic Surgery Operations, University of Utah, Salt Lake City, UT 84108, USA)

  • Evelyn Lauren

    (Department of Biostatistics, Boston University, Boston, MA 02115, USA)

  • Ryan Moffat

    (College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA)

  • Weicong Su

    (Department of Mathematics, University of Utah, Salt Lake City, UT 84112, USA)

  • Julie Xu

    (College of Nursing, University of Utah, Salt Lake City, UT 84112, USA)

  • Jungweon Park

    (College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA)

  • David Prince

    (College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA)

  • Joseph Cheever

    (College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA)

  • Frank W. Licari

    (College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, USA)

Abstract

The goals of this study were to develop a risk prediction model in unmet dental care needs and to explore the intersection between social determinants of health and unmet dental care needs in the United States. Data from the 2016 Medical Expenditure Panel Survey were used for this study. A chi-squared test was used to examine the difference in social determinants of health between those with and without unmet dental needs. Machine learning was used to determine top predictors of unmet dental care needs and to build a risk prediction model to identify those with unmet dental care needs. Age was the most important predictor of unmet dental care needs. Other important predictors included income, family size, educational level, unmet medical needs, and emergency room visit charges. The risk prediction model of unmet dental care needs attained an accuracy of 82.6%, sensitivity of 77.8%, specificity of 87.4%, precision of 82.9%, and area under the curve of 0.918. Social determinants of health have a strong relationship with unmet dental care needs. The application of deep learning in artificial intelligence represents a significant innovation in dentistry and enables a major advancement in our understanding of unmet dental care needs on an individual level that has never been done before. This study presents promising findings and the results are expected to be useful in risk assessment of unmet dental care needs and can guide targeted intervention in the general population of the United States.

Suggested Citation

  • Man Hung & Eric S. Hon & Bianca Ruiz-Negron & Evelyn Lauren & Ryan Moffat & Weicong Su & Julie Xu & Jungweon Park & David Prince & Joseph Cheever & Frank W. Licari, 2020. "Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning," IJERPH, MDPI, vol. 17(19), pages 1-13, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7286-:d:424124
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    References listed on IDEAS

    as
    1. Lee, Sang-Yi & Kim, Chul-Woung & Kang, Jeong-Hee & Seo, Nam-Kyu, 2015. "Unmet healthcare needs depending on employment status," Health Policy, Elsevier, vol. 119(7), pages 899-906.
    2. Guarnizo-Herreño, C.C. & Wehby, G.L., 2012. "Explaining racial/ethnic disparities in children's dental health: A decomposition analysis," American Journal of Public Health, American Public Health Association, vol. 102(5), pages 859-866.
    3. Malecki, K. & Wisk, L.E. & Walsh, M. & McWilliams, C. & Eggers, S. & Olson, M., 2015. "Oral health equity and unmet dental care needs in a population-based sample: Findings from the survey of the health of Wisconsin," American Journal of Public Health, American Public Health Association, vol. 105, pages 466-474.
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    Cited by:

    1. Michelle Johnson-Motoyama & Deborah Moon & Nancy Rolock & David Crampton & C. Bailey Nichols & Hanna Haran & Yiran Zhang & Yasuyuki Motoyama & Eric Gonzalez & Nicole Sillaman, 2022. "Social Determinants of Health and Child Maltreatment Prevention: The Family Success Network Pilot," IJERPH, MDPI, vol. 19(22), pages 1-8, November.

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