Data-Driven Dental Public Health: Improving Community Oral Health through Analytics
Keywords:
Data-driven, Dental public health, Analytics, Population-level data, Disparities, Interventions, Resource allocation, Machine learning, Predictive modeling, Oral health equityAbstract
Data-driven approaches are revolutionizing public health interventions, and dental public health is no exception. This paper explores the pivotal role of data analytics in enhancing community oral health outcomes. By harnessing population-level data, disparities in oral health can be identified, interventions can be targeted effectively, and resource allocation can be optimized. Through the integration of advanced analytics techniques, including machine learning and predictive modeling, dental public health practitioners can gain deeper insights into the factors influencing oral health outcomes within communities. This abstract provides an overview of the key components of data-driven dental public health, including data collection methods, analysis techniques, intervention strategies, and challenges faced. By leveraging the power of data analytics, dental public health initiatives can achieve greater precision, efficiency, and effectiveness in promoting oral health equity and improving overall community well-being.
Downloads
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
