Critical Assessment on Unmet Oral Health Needs and Oral Health-related Quality of Life Among Old Age Home Inhabitants in Karnataka, India

Roma, M; Sen, Madhura; Kundabala, M; Sujir, Nanditha; Poojary, Dharnappa; Shetty, Neetha J.; Shenoy, Ramya; Saldanha, Sharon

doi:10.2147/ccide.s302824

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…A number of oral health factors have been linked with poor OHRQoL and this has been reported by several studies internationally [33,4,13,15,26] and a few from India [31,20]. However empirical evidence as to which factors are truly affecting OHRQoL is lacking.…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning model for data driven classification of Oral Health Related Quality of Life in Patients with Type 2 Diabetes Mellitus

Srivastava,

Murali,

Jain

et al. 2024

Preprint

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Type 2 Diabetes Mellitus(T2DM) is a debilitating condition with a number of complications including those of the oral cavity which can further deteriorate patients general and oral health related quality of life (OHRQoL). Machine Learning (ML) can help assign an individuals propensity to develop poor OHRQoL, given a set of variables, and at the same time identify the most important features contributing to this outcome. Previously inferential statistical methods have attempted to explain this, albeit with limited success. The aim of this cross sectional study is to determine the impact on OHRQoL in T2DM patients, and identify features most likely to be associated with this outcome and to compare ML and DL analytical methods with inferential statistics. Twelve-hundred T2DM patients were subjected to OHRQoL and demographic data questionnaires and WHO Oral Health Assessment form. K-means Clustering was performed to label individuals as having or not having an impact on OHRQoL. Class imbalance was addressed by undersampling of the majority class using informed subset selection. Further, using the collected data as input features we developed ML algorithms (Naive Bayes(NB), Random Forest(RF), Logistic Regression(LR), Kernel Support Vector Machine(SVM) and Artificial Neural Network(ANN)), to accurately classify individuals with or without poor oral health related quality of life (OHRQoL) and utilized SHapley Additive exPlanations (SHAP) analysis for feature importance. The best performing model was SVM (AUC=0.983; Sensitivity=1) for classifying the patients into into poor OHRQoL. SHAP values were highest for Age, Prosthetic Need, Tobacco use and years since onset of diabetes. Features closely related to diabetes, that is, periodontal pockets and loss of attachment were not identified as relevant by inferential statistics, but were deemed as important features associated with poor OHRQoL by SHAP analysis.

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Section: Introductionmentioning

confidence: 99%

Interpretable machine learning model for data driven classification of Oral Health Related Quality of Life in Patients with Type 2 Diabetes Mellitus

Srivastava,

Murali,

Jain

et al. 2024

Preprint

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Inclusion of gerodontology in dental curriculum: An urgent case for India

Shigli

Nayak²,

Lagali-Jirge

et al. 2022

Gerontology & Geriatrics Education

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Prosthetic status and Treatment need in Indian population:A Systematic Review

Jogi,

Deshpande,

Pande

et al. 2024

jida

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Introduction To examine the prevalence of partial or complete edentulousness in different geographic areas of the Indian population. Materials and Methods An electronic search in PubMed/Medline, Cochrane Library, ScienceDirect, Wiley Online Library, and Google Scholar was conducted to find relevant papers that had been written in English exclusively and published before May 2022. Based on the PICO, the primary research question for this study was ‘What are the prosthetic status and treatment needs of the adult population in India?’ By evaluating the title, abstract, and full text of the articles, if they met the inclusion criteria, the relevance of the articles was verified. The quality of the included studies was assessed using the Joanna Briggs Institute (JBI) tool for analytical cross-sectional studies. Result The database search yielded 278 items, of which 123 were discarded due to being irrelevant, duplicates, or lacking data. The remaining 155 full-text articles were assessed for eligibility 20 articles were yielded by manual search and 9 articles were added through a search of a reference list of relevant articles. A total of 151 full-text papers were evaluated for eligibility, with 5 articles being excluded. The current systematic review includes a total of 35 studies. Conclusion The current systematic review states the prevalence of complete edentulousness is comparatively less than partial edentulism, ranging between 0.8% to 14%, except for the tribal population where it is around 42-43%. On the other hand, figures depicting partial edentulism are between 15 to 98% distributed in the different geographical regions. The prosthetic status reported in the studies involved ranges between 3% to 100% with a mean of approximately 35%. Prosthetic treatment need reported overall was between 14% to 100%, in which requirement was found to be more in the maxilla in maximum of the studies included while only a few studies predicted more treatment need in the mandible. The requirement and correlation with gender cannot be established as the figures vary in all the studies included. This research establishes a baseline reference and highlights the lack of attention given to oral health. Key Words: Geographic areas, Population, Prosthetic treatment

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Critical Assessment on Unmet Oral Health Needs and Oral Health-related Quality of Life Among Old Age Home Inhabitants in Karnataka, India

Cited by 3 publications

References 12 publications

Interpretable machine learning model for data driven classification of Oral Health Related Quality of Life in Patients with Type 2 Diabetes Mellitus

Interpretable machine learning model for data driven classification of Oral Health Related Quality of Life in Patients with Type 2 Diabetes Mellitus

Inclusion of gerodontology in dental curriculum: An urgent case for India

Prosthetic status and Treatment need in Indian population:A Systematic Review

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