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IntroductionElectronic health record (EHR) systems provide investigators with rich data from which to examine actual impacts of care delivery in real-world settings. However, confounding is a major concern when comparison groups are not randomized.ObjectivesThis article introduced a step-by-step strategy to construct comparable matched groups in a dental study based on the EHR of the Willamette Dental Group. This strategy was employed in preparation for a longitudinal study evaluating the impact of a standardized risk-based caries prevention and management program across patients with public versus private dental insurance in Oregon.MethodsThis study constructed comparable dental patient groups through a process of 1) evaluating the need for and feasibility of matching, 2) considering different matching methods, and 3) evaluating matching quality. The matched groups were then compared for their average ratio in the number of decayed, missing, and filled tooth surfaces (DMFS + dmfs) at baseline.ResultsThis systematic process resulted in comparably matched groups in baseline covariates but with a clear baseline disparity in caries experience between them. The weighted average ratio in our study showed that, at baseline, publicly insured patients had 1.21-times (95% CI: 1.08 to 1.32) and 1.21-times (95% CI: 1.08 to 1.37) greater number of DMFS + dmfs and number of decayed tooth surfaces (DS + ds) than privately insured patients, respectively.ConclusionMatching is a useful tool to create comparable groups with EHR data to resemble randomized studies, as demonstrated by our study where even with similar demographics, neighborhood and clinic characteristics, publicly insured pediatric patients had greater numbers of DMFS + dmfs and DS + ds than privately insured pediatric patients.Knowledge transfer statementThis article provides a systematic, step-by-step strategy for investigators to follow when matching groups in a study-in this case, a study based on electronic health record data. The results from this study will provide patients, clinicians, and policy makers with information to better understand the disparities in oral health between comparable publicly and privately insured pediatric patients who have similar values in individual, clinic, and community covariates. Such understanding will help clinicians and policy makers modify oral health care and relevant policies to improve oral health and reduce disparities between publicly and privately insured patients.

ObjectivesTo ascertain differences across states in children's oral health care access and oral health status and the factors that contribute to those differences.Study designObservational study using cross-sectional surveys.MethodsUsing the 2007 National Survey of Children's Health, we examined state variation in parents' report of children's oral health care access (absence of a preventive dental visit) and oral health status. We assessed the unadjusted prevalences of these outcomes, then adjusted with child-, family-, and neighbourhood-level variables using logistic regression; these results are presented directly and graphically. Using multilevel analysis, we then calculated the degree to which child-, family-, and community-level variables explained state variation. Finally, we quantified the influence of state-level variables on state variation.ResultsUnadjusted rates of no preventive dental care ranged 9.0-26.8% (mean 17.5%), with little impact of adjusting (10.3-26.7%). Almost 9% of the population had fair/poor oral health; unadjusted range 4.1-14.5%. Adjusting analyses affected fair/poor oral health more than access (5.7-10.7%). Child, family and community factors explained ∼¼ of the state variation in no preventive visit and ∼½ of fair/poor oral health. State-level factors further contributed to explaining up to a third of residual state variation.ConclusionGeography matters: where a child lives has a large impact on his or her access to oral health care and oral health status, even after adjusting for child, family, community, and state variables. As state-level variation persists, other factors and richer data are needed to clarify the variation and drive changes for more egalitarian and overall improved oral health.