Grand challenges and future oral epidemiology research

statistical analytic methods, and an ability to understand and integrate strengths and limitations of the approach in interpreting the results-can be applied to study a wide range of factors, from climate change to social factors to molecular mechanisms.Oral epidemiology is a subspecialty of epidemiology focusing on conditions and disease in the mouth, their distribution, and related factors and conditions. This article highlights opportunities and challenges in the field of oral epidemiology and defines the scope of this journal section. Among the many epidemiologic sub-disciplines, we concentrate here on five broad groupings: oral health disparities, social epidemiology, clinical epidemiology, molecular epidemiology, and epidemiologic methods.Oral health disparities. Amidst the many improvements in oral health for some populations or groups, oral health disparities persist (Peres et al, 2019) and are among the largest across various health indicators. These include unequal access to oral healthcare services and unequal oral health outcomes (Patrick et al, 2006) attributable to socioeconomic backgrounds (Locker, 2000;Sanders and Spencer, 2004;Celeste et al, 2009;Borrell and Baquero, 2011) (Northridge, Kumar and Kaur, 2020), geography-including urban versus rural areas (Ogunbodede et al, 2015), race or ethnicity-with specific barriers to accessing culturally appropriate dental care (Butani, Weintraub, Barker, 2008), and age (Northridge, Kumar and Kaur, 2020), to name a few dimensions of oral health inequality.Oral health disparities and inequalities need to be addressed because they are unfair, unjust, and avoidable (Whitehead, 1992;Braveman and Gruskin, 2003). An equity-based and datainformed approach to health investment decision-making will provide a constructive framework for addressing service delivery disparities (Nemser et al, 2018). First, more granular data are needed to identify inequalities across various dimensions, for example, race, ethnicity, gender or sexual identity, and geographic location, among others. Considering populations and communities to be uniform precludes the targeting of intervention strategies to meet the needs of specific sub-populations. Thus, data collection and analytic approaches that avoid aggregation are a necessary precursor to measure and address heterogeneity of health care access and oral health outcomes. Also critical is to enhance proficiency in data representation for diverse populations.Second, oral health surveillance systems are needed to monitor oral health, inequities, and their temporal trends. These systems play a vital role in tracking indicators of equality, such as healthcare accessibility, educational achievement, income distribution, and representation in decision-making processes. Such data are also needed to implement effective oral health interventions (El Tantawi et al, 2018). Unfortunately, the countries and populations that experience the greatest burden of disease and greatest inequity also have some of the weakest oral health surveillance systems globally (Petersen, Baez, Ogawa, 2020).Third, the incorporation of oral health research and oral disease control programs into wellestablished general health initiatives, can greatly enhance their effectiveness (Kumar and a Shweta Somasundara, 2017). Integration enables the pooling of resources, expertise, and data, leading to a more coordinated and holistic approach to overall health (World Health Organizations, 2015; O' Daniel and Rosenstein, 2008;Rosen et al, 2018). There is lack of evidence regarding the cost-effectiveness of integrated oral and systemic health programs, and on sustainable models of integrated oral care in different contexts. There is also a lack of consistency regarding associations between oral and systemic health conditions, associations that could be more effectively identified through integrated oral and general health records and surveillance.Fourth, established implementation frameworks and models, such as the Consolidated Framework for Implementation Research (Damschroder et al, 2009) or the RE-AIM framework (Glasgow, Vogt and Boles, 1999), offer a systematic approach to understanding and addressing implementation challenges (King et al, 2020). A multidisciplinary approach will enable the dissemination of research findings to relevant stakeholders and facilitate the uptake of evidence-based practices through tailored messaging, stakeholder engagement, and effective knowledge exchange platforms. biospecimens. Such assays may target a single biomarker-for example, a single inflammatory cytokine or a specific bacterial pathogen. Or, as is increasingly the case, highthroughput techniques may be used to interrogate an entire complement of biomarkers, such the microbiome, the transcriptome, or the exposome. Not limited to dentistry or oral health, the molecular epidemiology literature is rife with unreproducible results. One reason for this is likely to be the multidisciplinary expertise required to conduct this work. For example, in oral microbiome research, microbiologists without methodologic training may lack expertise on study design and statistical analysis, and epidemiologists without microbiology training may lack the knowledge to ground their questions and interpretation of results. Under time pressure, multidisciplinary collaborations often involve transactional exchanges of data or information. To enhance rigor and reproducibility of research, and to go beyond studies of association to validation and experimentation, the field requires better-integrated, transdisciplinary collaborations.Epidemiologic methods. Research in any of the above areas is only as strong as the methods on which they are based, and each raises its own methodologic challenges. We especially welcome articles describing application or development of novel techniques to address such challenges, comparing different methodological approaches, highlighting concerns about commonly used methods, and discussing application of sound epidemiologic principles. We will also give high priority to research invoking modern causal inference frameworks, developing, and assessing the validity and reliability of innovative digital or ML/AI technologies, and applying multilevel modelling as appropriate, for example, for observations taken repeatedly from the same individuals (e.g., for different teeth or over time) or for data clustered geographically or socially.Regardless of a manuscript's focus, we encourage all authors to explain why and how their results are meaningful despite potential limitations, for example, small sample size, sources of bias, potential confounding, measurement error or misclassification, among others.Inclusion of sensitivity analyses can enhance the clarity of explanations and often helps strengthen conclusions. Consistent with the journal's editorial principles, we also encourage the submission of papers irrespective of whether they yield statistically significant results.Results that can advance the field and are interpreted appropriately will be valued contributions.We provide below a list of areas where we encourage manuscripts to help meet this grand challenge.1. Investigations regarding oral health disparities or interventions to reduce disparities and their impact.2. Implementation research. This could include an examination of barriers, facilitators, contextual factors, and strategies to promote evidence-based practices and to identify interventions that are effective in real-world practice. 3. Integration of oral health data collection into National Demographic Health Surveys in developing countries. 4. Reliability and validity of new imaging modalities, including those incorporating machine learning and/or artificial intelligence (ML/AI). 5. Molecular epidemiologic studies going beyond identification of associations to validation and experimentation regarding molecular mechanisms or biomarker applications. 6. Research on other potential sources of administrative data for oral diseases, with a particular emphasis on understanding the status of oral health policies and surveillance systems globally. 7. Generation of evidence that can help achieve the United Nations Sustainable Development Goals, which encompass a wide range of aims towards addressing social, economic, and environmental challenges. 8. Research embracing a "One health" approach, recognizing the interconnectedness of human, animal, and environmental health. Possible topics include zoonotic disease, environmental factors, and disease common to humans and animals. 9. Elucidation of associations between oral and systemic diseases, particularly through research designs and analyses aiming to eliminate confounding as a possible explanation of such relationships. 10. Work focusing on epidemiologic methods with special relevance to oral epidemiology or application to oral health factors or outcomes. 11. Explanations of how to apply state-of-the-art methods to oral epidemiology topics with a view towards improving the quality of oral epidemiology research and its communication. Examples include guidance on cost-effectiveness research, metaanalytic or synthetic reviews, or effective communication of research through tables and figures. 12. Establishment of standardized measurements of oral health or for specific oral diseases, or the incorporation of oral health into quality-adjusted life-year metrics. 13. Work involving collaborations with communities and advocates.

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