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020 ▼a 9780438128095
035 ▼a (MiAaPQ)AAI10825156
035 ▼a (MiAaPQ)indiana:15252
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 614
1001 ▼a Zautra, Nicole Thurlow. ▼0 (orcid)0000-0002-0374-9282.
24510 ▼a Family and System Influences on Dental Healthcare Utilization: A Dynamic Framework of Dental Health Disparities.
260 ▼a [S.l.]: ▼b Indiana University., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 128 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 ▼a Adviser: David K. Lohrmann.
5021 ▼a Thesis (Ph.D.)--Indiana University, 2018.
520 ▼a Objectives: The purpose of this study was (1) to examine predictors of dental care utilization among young children and mothers of young children, and (2) to explore latent factors contributing to the dynamic system of dental health disparities.
520 ▼a Methods: A retrospective multi-year cross-sectional design was used to analyze Medical Expenditure Panel Survey data pooled from 2010--2015. Statistical procedures included logistic regression, discriminant function, and classification tree analyses.
520 ▼a Results: Weighted logistic regression captured child dental visit conditional on mother's dental visit and revealed children whose mothers reported a dental visit were more likely to also visit the dentist, compared to children whose mothers had no dental visit (OR = 10.65; 95% CI 9.41--12.04). Mother's dental visit conditional on child's dental visit revealed mothers whose young child reported a dental visit were also more likely to have visited the dentist, compared to mothers whose children had no dental visits (OR = 13.12; 95% CI 11.57--14.87). Four predictors of dental treatment need group membership (income, age, # medical visits/year, and # ED visits/year) were tested by calculating two discriminant functions, resulting in a combined x2(8) = 1284.93, p < .001. After removal of the first function, significant association were still found between dental treatment need groups and predictors x2(3) = 116.47, p < .001. The two DFs accounted for 91% and 9%, respectively, of the between-group variability. The classification function led to 45.17% of correct group classification, compared to 33.85% based on chance alone. Classification tree analysis identified cut points and probabilities of group membership for each terminal node corresponding to dental treatment need. Across four classification models, factors determined to significantly contribute to differences in dental treatment need included age, income, annual medical office and ED visits, health insurance type, family size, and Hispanic ethnicity.
520 ▼a Conclusion: A strong positive association between mothers reporting a dental visit and children experiencing at least one dental visit was detected. Results suggest that increased dental care utilization among mothers of young children may have a secondary effect on their children's utilization. Significant differences across dental treatment need groups were also detected. Future research testing and validating assessment criteria for unmet dental treatment needs may enhance intervention opportunities.
590 ▼a School code: 0093.
650 4 ▼a Public health.
650 4 ▼a Health sciences.
650 4 ▼a Dentistry.
650 4 ▼a Individual & family studies.
690 ▼a 0573
690 ▼a 0566
690 ▼a 0567
690 ▼a 0628
71020 ▼a Indiana University. ▼b Public Health.
7730 ▼t Dissertation Abstracts International ▼g 79-11B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0093
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15013674 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033