Untreated dental disease and lung transplant waitlist evaluation time for individuals with cystic fibrosis

Effects of Adult Medicaid Dental Benefits Elimination on Child Dental Care Use

OBJECTIVE

The objective of this study was to examine the effect of adult dental benefit cuts on child dental use in Washington state Medicaid and determine if cuts affect child demographic subgroups differentially.

RESEARCH DESIGN

The study used an interrupted time-series methodology to measure differences in child dental use after adult dental benefit elimination and reinstatement. Monthly data came from Washington state Medicaid enrollee and dental claim files from January 2008 to December 2015.

SUBJECTS

Medicaid-enrolled children with at least one Medicaid-enrolled adult in the same household were the intervention group, and Medicaid-enrolled children without a Medicaid-enrolled adult in the same household were the control group.

MEASURES

The outcome was the monthly proportion of Medicaid-enrolled children with a dental examination per 10,000 Medicaid-enrolled children.

RESULTS

After adult dental benefits elimination, dental examinations among children with Medicaid-enrolled adults in the same household gradually decreased, corresponding to 65 fewer dental examinations per 10,000 children per year (5.4 fewer dental examinations per 10,000 children per month; 95% confidence interval: -7.7, -3.1; P =0.006). Adult dental benefits had no effect on dental examination for children without a Medicaid-enrolled adult in the same household. Dental examinations for children with a Medicaid-enrolled adult in the same household continued to gradually decrease after adult benefits reinstatement. Children younger than age 6 were the most adversely impacted by changes to adult Medicaid dental benefits.

CONCLUSION

Policymakers should consider the spillover effects and ethical considerations of eliminating adult Medicaid dental benefits on children's access to dental care.

A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models

OBJECTIVES

We propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in cystic fibrosis offer a complex case study.

METHODS

We used longitudinal RWD for a cohort of adults (n = 4247) from the Cystic Fibrosis Foundation Patient Registry to compare outcomes of an LTx referral policy based on machine learning (ML) mortality risk predictions to referral based on (1) forced expiratory volume in 1 second (FEV₁) alone and (2) heterogenous usual care (UC). We then developed a patient-level simulation model to project number of patients referred for LTx and 5-year survival, accounting for transplant availability, organ allocation policy, and heterogenous treatment effects.

RESULTS

Only 12% of patients (95% confidence interval 11%-13%) were referred for LTx over 5 years under UC, compared with 19% (18%-20%) under FEV₁ and 20% (19%-22%) under ML. Of 309 patients who died before LTx referral under UC, 31% (27%-36%) would have been referred under FEV₁ and 40% (35%-45%) would have been referred under ML. Given a fixed supply of organs, differences in referral time did not lead to significant differences in transplants, pretransplant or post-transplant deaths, or overall survival in 5 years.

CONCLUSIONS

Health outcomes modeling with RWD may help to identify novel ML risk prediction models with high potential real-world clinical utility and rule out further investment in models that are unlikely to offer meaningful real-world benefits.

Effect of Low Amperage Electric Current on Staphylococcus Aureus-Strategy for Combating Bacterial Biofilms Formation on Dental Implants in Cystic Fibrosis Patients, In Vitro Study

Cystic fibrosis is an inherited disease that affects multiple organs and systems. The oral cavity can serve as a substantial source of bacteria, causing respiratory infections and diseases which continue to dictate the clinical course of the disease and prognosis in patients with CF. Low voltage and electric current could effectively kill bacteria and biofilms, and the activity of milliampere currents could be used as an effective method of fighting bacteria. This study evaluated the effect of low amperage electric current on the formation of Staphylococcus aureus biofilms on dental implants such as titanium and zirconium in patients with cystic fibrosis. Our studies suggest that a constant electric current at a low intensity of 1 mA and 10 mA is inhibiting bacterial adhesion, detaching biofilm-forming bacteria on biomaterials used in dental implants such as titanium and zirconium, and destroying bacterial cells of Staphylococcus aureus strains. In addition, we observed the selection of an appropriate biomaterial for implants in people affected by chronic diseases, such as CF, should be carefully planned.