Causal analyses in longitudinal observational studies in oral health: A scoping review.

Abstract

Objective: Causal analysis including causal inference and causal mediation is pivotal to inform effective interventions. In modern epidemilogy, causal analysis involves four key steps: formulating causal questions, employing directed acyclic graphs (DAGs), conducting data management and selecting statistical strategies. Our objective was to conduct a scoping review to assess how longitudinal observational studies (LOSs) in dental field have integrated these four steps to contribute leverage evidence that inform oral public health interventions.

Methods: LOSs focusing on determinants of dental caries published from 2012 to 2024 were systematically retrieved from five major databases. The Joanna Briggs Institute-scoping review guidance and the Covidence application were employed to identify eligible LOSs for being reviewed.

Results: Out of the 85 eligible LOSs, none formulated causal hypothesis by applying 'what if' question or investigated mediation across three levels of the determinants of oral health. A minority (18 studies, ~21.2%) employed DAGs to visualise relationships among study variables, while only one third (33 studies, ~39%) clearly defined confounders. The majority (64 studies, ~75%) incorporated a time-varying feature of their data, yet only a few (11 studies) fully leveraged this advanced aspect. Among these studies that fully utilised time-varying data, more than half encountered challenges in employing robust statistics to address confounders arising from such data dynamics.

Conclusions: Dental LOSs have, to date, mostly focused on investigating associations over causality, often neglecting the four-step causal analysis and not fully utilising time-varying data. Researchers necessitate to shift their focus to causal inference and prioritise building capacity in causal analysis with a consistent four-step approach to advance the field. Studies exploring mechanisms linking determinants of dental caries across levels and leveraging time-varying data are strongly encouraged.