Translating Observational Data Into Public Health Action: Reducing Early Childhood Caries Burden Related to Socioeconomic Disparities

Résumé fourni par la source

OBJECTIVES: The "valley of death" between research and practice remains a major challenge. The five-stage Translational Research framework (TR1-TR5) provides a pathway, moving evidence into real-world impact. Traditionally, Randomised Controlled Trials (RCTs) dominate TR stages, yet they are often impractical in public health. Longitudinal observational studies (LOSs), combined with advanced causal statistics, can emulate RCTs. Using the relationship between socioeconomic status (SES) and early childhood caries (ECC) as a case study, where SES is a complex, upstream determinant lacking trial evidence, this study illustrates how LOSs paired with causal statistics, within the TR framework, can generate policy-relevant action, highlighting a promising yet underused strategy. METHODS: This paper first reviewed causal inference gaps identified in a published scoping review of 85 LOSs (TR1), focusing on a subset of 20 studies presented in this paper that examined the relationship between SES and dental caries. The review analysed SES construction, gaps in SES-oral health behaviour-caries pathways, and the use of advanced causal statistics. Findings from TR2 (published) were then summarised to highlight key modifiable mediators linking SES and ECC, followed by TR3-4 (published), which examined interventions targeting these mediators across populations. Finally, this paper presented TR5 for the first time, extending the analysis to estimate population-level impact metrics. RESULTS: TR1 highlighted that most LOSs remained focused on associations rather than causal inference. SES construction was inconsistent and the mechanisms linking SES to dental caries have not been comprehensively examined. TR2 identified free sugar intake (FSI) as a key modifiable mediator. TR3-4 emphasised on reducing FSI to < 10% lowered ECC risk in the general population and suggested < 5% for the high-risk groups. TR5 highlighted that stricter FSI thresholds produced substantial population-level gains. CONCLUSION: The TR framework supported by advanced causal statistics enables translating LOSs into effective public health evidence in the absence of RCTs.

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