At the crossroads of oral health inequities and precision public health

Abstract

Objectives: This paper reviews the precision public health literature pertaining to oral health, identifies possible threats that could inadvertently increase health inequities, and proposes potential opportunities that precision public health could utilize to reduce oral health inequities.

Methods: The health sciences literature was reviewed and supplemented with new data to identify important issues relating to precision medicine, precision oral health, precision public health, and health equity.

Results: Examples from general health and oral health were provided to illustrate salient concepts.

Conclusions: Future precision public health should utilize multifactorial, multi-level conceptual frameworks and conceptual causal models with upstream social determinants and downstream health effects, as well as a proportionate universalism perspective; and proper analytic methods, including sufficient sample sizes, appropriate statistical competitors, health disparity indices, causal modeling, and internal and external validation.

Keywords: health disparities; health equity; precision medicine dental research; public health.

Figure 1

An information commons might use a GIS-type structure (1). The proposed, individual-centric information commons (right panel) is somewhat analogous to a layered GIS (left panel). In both cases, the bottom layer defines the organization of all the overlays. However, in a GIS, any vertical line through the layers connects related snippets of information since all the layers are organized by geographical position. In contrast, data in each of the higher layers of the information commons will overlay on the patient layer in complex ways (e.g., patients with similar microbiomes and symptoms may have very different genome sequences). SOURCE: FPA 2011 (left panel), NRC 2011 (right panel). (Reprinted with permission from Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease, 2011, the National Academy of Sciences, Courtesy of the National Academies Press, Washington, DC.)

Figure 2

A knowledge network of disease would enable a new taxonomy (1). An individual-centric information commons, in combination with all extant biological knowledge, will inform a Knowledge Network of Disease, which will capture the exceedingly complex causal influences and pathogenic mechanisms that determine an individual’s health. The Knowledge Network of Disease would allow researchers to hypothesize new intralayer cluster and interlayer connections. Validated findings that emerge from the Knowledge Network, such as those which define new diseases or subtypes of diseases that are clinically relevant (e.g., which have implications for patient prognosis or therapy) would be incorporated into the New Taxonomy to improve diagnosis and treatment. SOURCE: Committee on A Framework for Developing a New Taxonomy of Disease. (Reprinted with permission from Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease, 2011, the National Academy of Sciences, Courtesy of the National Academies Press, Washington, DC.)

Figure 3

Simple example of overfitting in only 2-dimensions (N = 7).

Figure 4

Absolute difference (95% CI) in rampant caries (dft or DFT ≥7) prevalence by race/ethnicity and socioeconomic position, California Oral health needs assessment of children, 2004–5 (N = 21,399). CI = confidence interval. dft = number of decayed or filled primary teeth. DFT = number of decayed or filled permanent teeth. FRL = free/reduced cost lunch program. ref = reference group.