Causal Effects of Social Vulnerability and Multimorbidity on Tooth Loss in Chile: A National Survey Analysis

Background/Objectives: Tooth loss reflects cumulative biological and social processes across the life course. However, population-level causal evidence on the influence of structural social vulnerability and multimorbidity on tooth-loss severity remains limited in middle-income contexts. This study evaluated the causal impacts of social vulnerability and multimorbidity on tooth-loss severity in Chilean adults under explicit potential-outcomes assumptions. Methods: We analyzed nationally representative data from the Chilean National Health Survey 2016–2017 (N=5165 adults aged ≥20 years with oral examination; analytic sample n=4521). Outcomes comprised ordinal severity (y1: functioning dentition, moderate loss, severe loss, edentulism) and continuous tooth count (y2). Exposures included a Social Vulnerability Index (SVI, 0–1) and Multimorbidity Score (MS, 0–1). We estimated confounder-adjusted proportional-odds and survey-weighted linear regression models. Population-averaged causal contrasts were obtained via g-computation comparing 75th and 25th exposure percentiles, with 95% confidence intervals from probability-proportional-to-size bootstrap (1000 replications). Age-dependent edentulism trajectories were generated using discrete-time Markov projections. Results: In the weighted population, 72.6% retained functional dentition, whereas 5.5% were edentulous. Increasing SVI from 0.091 to 0.345 was associated with a 0.110-point severity increase and 1.95 fewer teeth. Increasing MS from 0.00 to 0.20 was associated with a 0.062-point severity increase and 1.20 fewer teeth. SVI showed larger population-averaged effects than multimorbidity. Conclusions: Within a potential-outcomes framework and under the stated identifying assumptions, structural social vulnerability and multimorbidity each exerted independent effects on tooth-loss severity, with socioeconomic disadvantage showing the stronger distributional gradient across the life course. Because the data are cross-sectional, this causal interpretation is conditional on those assumptions rather than established by the design.