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      Prediction of Frailty and Dementia Using Oral Health Impact Profile from a Population-Based Survey

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          Abstract

          Oral health and dentition have been associated with cognitive ability and frailty, but an applicable screening tool has not yet been developed. This study aimed to establish risk prediction models for dementia and frailty. A sample of 2905 community-dwelling older adults aged ≥58 years using the Taiwan Longitudinal Study on Aging (TLSA) survey was adapted and analyzed for this study. Risk scores were estimated by stepwise logistic regression. In models adjusted for covariates, increased age, female sex, no dental prosthesis (adjusted Odds ratio [adjOR], 1.61; 95% confidence interval [CI], 1.11–2.35), diabetes mellitus, chronic kidney disease, and an increased Oral Health Impact Profile (OHIP)-7T Q3 score (adjOR, 1.33; 95% CI, 1.19–1.49) were all significantly associated with frailty. In addition to these factors, an inability to self-report height or weight (adjOR, 4.52; 95% CI, 3.52–5.81) and an increased OHIP-7T Q7 score (adjOR, 1.21; 95% CI, 1.06–1.37) were significantly associated with dementia. The cut-off points of the risk scores for frailty and dementia were 80 (sensitivity, 80.0%; specificity, 81.2%) and 77 (sensitivity, 83.4%; specificity, 71.5%), respectively. The findings highlighted a number of composite risk factors of frailty and dementia. Importantly, the developed prediction models were easily applicable to screen for frailty and dementia in communities or dental clinics.

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          Most cited references39

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          Development and evaluation of the Oral Health Impact Profile.

          The capacity of dental clinicians and researchers to assess oral health and to advocate for dental care has been hampered by limitations in measurements of the levels of dysfunction, discomfort and disability associated with oral disorders. The purpose of this research was to develop and test the Oral Health Impact Profile (OHIP), a scaled index of the social impact of oral disorders which draws on a theoretical hierarchy of oral health outcomes. Forty nine unique statements describing the consequences of oral disorders were initially derived from 535 statements obtained in interviews with 64 dental patients. The relative importance of statements within each of seven conceptual subscales was assessed by 328 persons using Thurstone's method of paired comparisons. The consistency of their judgements was confirmed (Kendall's mu, P < 0.05). The reliability of the instrument was evaluated in a cohort of 122 persons aged 60 years and over. Internal reliability of six subscales was high (Cronbach's alpha, 0.70-0.83) and test-retest reliability (intraclass correlation coefficient, 0.42-0.77) demonstrated stability. Validity was examined using longitudinal data from the 60 years and over cohort where the OHIP's capacity to detect previously observed associations with perceived need for a dental visit (ANOVA, p < 0.05 in five subscales) provided evidence of its construct validity. The Oral Health Impact Profile offers a reliable and valid instrument for detailed measurement of the social impact of oral disorders and has potential benefits for clinical decision-making and research.
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            Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection.

            The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability for classifying disease status. The Youden Index (J), the maximum potential effectiveness of a biomarker, is a common summary measure of the ROC curve. In biomarker development, levels may be unquantifiable below a limit of detection (LOD) and missing from the overall dataset. Disregarding these observations may negatively bias the ROC curve and thus J. Several correction methods have been suggested for mean estimation and testing; however, little has been written about the ROC curve or its summary measures. We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c *) for a biomarker affected by a LOD. We develop unbiased estimators of J and c * via ML for normally and gamma distributed biomarkers. Alpha level confidence intervals are proposed using delta and bootstrap methods for the ML, semi-parametric, and non-parametric approaches respectively. Simulation studies are conducted over a range of distributional scenarios and sample sizes evaluating estimators' bias, root-mean square error, and coverage probability; the average bias was less than one percent for ML and GLM methods across scenarios and decreases with increased sample size. An example using polychlorinated biphenyl levels to classify women with and without endometriosis illustrates the potential benefits of these methods. We address the limitations and usefulness of each method in order to give researchers guidance in constructing appropriate estimates of biomarkers' true discriminating capabilities. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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              Measuring oral health: a conceptual framework.

              D Locker (1988)
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                18 March 2020
                March 2020
                : 17
                : 6
                : 1997
                Affiliations
                [1 ]Department of Family Medicine, Pingtung Hospital, Ministry of Health and Welfare, Pingtung 90054, Taiwan; taichijung@ 123456gmail.com
                [2 ]Graduate Institute of Natural Products, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
                [3 ]School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; jehach@ 123456kmu.edu.tw
                [4 ]Department of Dentistry, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung 812, Taiwan
                [5 ]Department of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan; tzyyguey@ 123456gmail.com (T.-G.T.); emilyhei@ 123456gmail.com (Y.-T.L.)
                [6 ]Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
                [7 ]Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala SE-75185, Sweden
                [8 ]Department of Family Medicine, Taichung Hospital, Ministry of Health and Welfare, Taichung 40343, Taiwan; phoebe01026@ 123456gmail.com
                [9 ]Department of Public Health, Chung Shan Medical University, Taichung 40201, Taiwan
                [10 ]College of Management, Chaoyang University of Technology, Taichung 41331, Taiwan
                [11 ]Institute of Population Health Sciences, National Health Research Institutes, Miaoli 35053, Taiwan
                [12 ]School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
                [13 ]National Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan
                Author notes
                [* ]Correspondence: mengchihlee@ 123456gmail.com (M.-C.L.); yhyang@ 123456nhri.edu.tw (Y.-H.Y.); Tel.: +886-4-2229-4411 (M.-C.L.)
                Author information
                https://orcid.org/0000-0002-2988-8598
                https://orcid.org/0000-0002-5833-0040
                Article
                ijerph-17-01997
                10.3390/ijerph17061997
                7143751
                32197375
                721bfa20-3e83-43c6-b9d5-02dcd3724e54
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 23 February 2020
                : 16 March 2020
                Categories
                Article

                Public health
                oral frailty,dentition,gustatory system,cognitive impairment
                Public health
                oral frailty, dentition, gustatory system, cognitive impairment

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