Interdental cleaning aids are beneficial for oral health at 7-year follow-up: Results from the Study of Health in Pomerania (SHIP-TREND)

AIM

To estimate association between the use of interdental cleaning aids (IDAs) and type on 7-year follow-up levels of interdental plaque, interdental gingival inflammation, interdental periodontitis severity, the number of interdental sound surfaces and the number of missing teeth in a population-based cohort study.

MATERIALS AND METHODS

We used 7-year follow-up data of 2224 participants from the Study of Health in Pomerania (SHIP-TREND). We applied generalized linear and ordinal logistic models, adjusting for confounding and selection bias using inverse probability treatment weighting and multiple imputation.

RESULTS

Flossers were 32% less likely to have higher interdental plaque (iPlaque) levels than non-users of IDAs (odds ratio [OR] = 0.68; 95% confidence interval [CI]: 0.50-0.94); flossing resulted in 5% lower means of iPlaque. Effects on interdental bleeding on probing (iBOP), mean interdental probing depths and mean interdental clinical attachment levels were direction-consistent but statistically non-significant. Interdental brushing was associated with lower follow-up levels for interdental plaque (OR = 0.73; 95% CI: 0.57-0.93) and iBOP (OR = 0.69; 95% CI: 0.53-0.89). IDAs were more effective in reducing iPlaque in participants with periodontitis, whereas iBOP reduction was more pronounced in participants with no or mild periodontitis. The analyses did not suggest that the use of IDAs affected caries. Finally, applying change score analyses, flossing reduced tooth loss incidence (incidence rate ratio [IRR] = 0.71) compared with non-users of IDAs.

CONCLUSIONS

Recommending flossing and interdental brushing in dental practices represents an approach to the prevention of gingivitis and consequently periodontitis.

Cortisol and periodontitis: Prospective observational and Mendelian randomization studies

PURPOSE

Cortisol has obesogenic, hyperglycemic and immunomodulating effects. Preclinical and observational research suggested that it is associated with periodontitis but the evidence for potential causality in humans is sparse. We triangulated results from prospective observational and Mendelian randomization (MR) analyses to further explore this.

METHODS

Using pooled data from 3,388 participants of two population cohort studies embedded in the Study of Health in Pomerania (SHIP) project, we associated serum cortisol levels with periodontal outcomes measured after a median follow-up time of 6.9 years, adjusting for confounding and selection bias using propensity score weighting and multiple imputation. We further examined the effect of genetically proxied plasma morning cortisol levels on periodontitis using two-sample MR of 17,353 cases and 28,210 controls.

RESULTS

In SHIP, we found that cortisol levels were positively associated with follow-up levels of mean clinical attachment level (CAL), deep interdental CAL and bleeding on probing but were unrelated to mean probing pocket depth and deep periodontal pockets. In MR analysis, cortisol was not associated with periodontitis.

CONCLUSION

The observational study revealed a prospective association of spot cortisol with makers of periodontitis. Contrary to observational studies, genetically instrumented, long-term cortisol was unrelated to periodontitis. Our results find no univocal evidence that cortisol plays a role in periodontitis pathology, casting doubt on cortisol-related pathways.

A two-stage super learner for healthcare expenditures

Objective

To improve the estimation of healthcare expenditures by introducing a novel method that is well-suited to situations where data exhibit strong skewness and zero-inflation.

Data Sources

Simulations, and two real-world datasets: the 2016-2017 Medical Expenditure Panel Survey (MEPS); the Back Pain Outcomes using Longitudinal Data (BOLD).

Study Design

Super learner is an ensemble machine learning approach that can combine several algorithms to improve estimation. We propose a two-stage super learner that is well suited for healthcare expenditure data by separately estimating the probability of any healthcare expenditure and the mean amount of healthcare expenditure conditional on having healthcare expenditures. These estimates can then be combined to yield a single estimate of expenditures for each observation. The analytical strategy can flexibly incorporate a range of individual estimation approaches for each stage of estimation, including both regression-based approaches and machine learning algorithms such as random forests. We compare the performance of the two-stage super learner with a one-stage super learner, and with multiple individual algorithms for estimation of healthcare cost under a broad range of data settings in simulated and real data. The predictive performance was compared using Mean Squared Error and R².

Conclusions

Our results indicate that the two-stage super learner has better performance compared with a one-stage super learner and individual algorithms, for healthcare cost estimation under a wide variety of settings in simulations and in empirical analyses. The improvement of the two-stage super learner over the one-stage super learner was particularly evident in settings when zero-inflation is high.