Abstract
The objective of this retrospective cohort study is to examine the determinants of tooth loss in a Medicaid-enrolled population using claims data from 2016 to 2018. Deidentified administrative claims data for Medicaid-enrolled adults between the ages of 50 and 90 y in 2016 to 2018 were examined using the IBM Watson MarketScan Medicaid Database. The sample size was 91,468. The entire sample was divided into 2 cohorts: no tooth loss cohort (n = 54,786) and tooth loss cohort (n = 36,682). The tooth loss cohort was further divided into 2 groups: 1 to 5 teeth lost (n = 29,141) and 6 or more teeth lost (n = 7,541). Tooth loss was described by age, gender, race, number of commodities, and if periodontal treatment was performed. Logistic regression models were conducted to examine factors associated with tooth loss. Within the tooth loss cohort, the patients who had periodontal treatment had higher odds of losing at least 1 tooth (odds ratio [OR], 1.15; confidence interval [CI], 1.10-1.20) and lower odds of losing 6 or more teeth (OR, 0.25; CI, 0.22-0.29). In the regression analysis, the predictive margins of tooth loss for 1 tooth and 6 or more teeth follow a linear path. Compared to no comorbidities, the odds of losing 6 or more teeth increased with 1, 2, or 3+ comorbidities. This study provides significant information about the quantification of comorbidities and its direct correlation with the increased odds of tooth loss. This study also highlighted the protective effect of periodontal treatment on tooth loss. This knowledge can be useful to dental care providers to understand the risk of tooth loss in their patient population.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Similar Papers
More From: JDR Clinical & Translational Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.