Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two datasets, one consisting of fictional dmft counts in two groups and the other on DMFS among schoolchildren from a randomized clinical trial (RCT) comparing three toothpaste formulations to prevent incident dental caries, are analysed with negative binomial hurdle (NBH), zero-inflated negative binomial (ZINB), and marginalized zero-inflated negative binomial (MZINB) models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the RCT were similar despite their distinctive interpretations. Choice of statistical model class should match the study’s purpose, while accounting for the broad decline in children’s caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts

Path-specific causal decomposition analysis with multiple correlated mediator variables

A causal decomposition analysis allows researchers to determine whether the difference in a health outcome between two groups can be attributed to a difference in each group's distribution of one or more modifiable mediator variables. With this knowledge, researchers and policymakers can focus on designing interventions that target these mediator variables. Existing methods for causal decomposition analysis either focus on one mediator variable or assume that each mediator variable is conditionally independent given the group label and the mediator-outcome confounders. In this paper, we propose a flexible causal decomposition analysis method that can accommodate multiple correlated and interacting mediator variables, which are frequently seen in studies of health behaviors and studies of environmental pollutants. We extend a Monte Carlo-based causal decomposition analysis method to this setting by using a multivariate mediator model that can accommodate any combination of binary and continuous mediator variables. Furthermore, we state the causal assumptions needed to identify both joint and path-specific decomposition effects through each mediator variable. To illustrate the reduction in bias and confidence interval width of the decomposition effects under our proposed method, we perform a simulation study. We also apply our approach to examine whether differences in smoking status and dietary inflammation score explain any of the Black-White differences in incident diabetes using data from a national cohort study

The Effect Of Education On Oral Health Students’ Attitudes In Australia And New Zealand

Objective: The aim of this study was to evaluate the oral health attitudes and behavior of students in the oral health curriculum in Australia and New Zealand. Materials and Methods: The Hiroshima University ‑ Dental Behavioral Inventory was administered to students in the first (year 1) and final years (year 3) of the oral health curriculum at Charles Sturt University in Australia and the University of Otago in New Zealand. A total of fifty‑two year 1 students and forty‑five year 3 students completed English version of the questionnaire in 2013. The responses were statistically analyzed by Fisher’s exact tests and exact logistic regression models. Results: The responses of students in years 1 and 3 differed significantly for “I worry about the color of my teeth” at Charles Sturt University and at the University Otago, for “I think my teeth are getting worse despite my daily brushing,” “I put off going to the dentist until I have a toothache,” and “I don’t feel I’ve brushed well unless I brush with strong strokes.” The estimated odds ratios from the exact logistic regression models showed that year 1 students were more likely to agree with above‑mentioned four questions. Conclusions: Oral Health students who had been educated in a 3‑year oral health curriculum in Australia and New Zealand had more positive attitudes and behaviors related oral health than did students at the beginning of their curriculum

Population-average mediation analysis for zero-inflated count outcomes

Mediation analysis is an increasingly popular statistical method for explaining causal pathways to inform intervention. While methods have increased, there is still a dearth of robust mediation methods for count outcomes with excess zeroes. Current mediation methods addressing this issue are computationally intensive, biased, or challenging to interpret. To overcome these limitations, we propose a new mediation methodology for zero-inflated count outcomes using the marginalized zero-inflated Poisson (MZIP) model and the counterfactual approach to mediation. This novel work gives population-average mediation effects whose variance can be estimated rapidly via delta method. This methodology is extended to cases with exposure-mediator interactions. We apply this novel methodology to explore if diabetes diagnosis can explain BMI differences in healthcare utilization and test model performance via simulations comparing the proposed MZIP method to existing zero-inflated and Poisson methods. We find that our proposed method minimizes bias and computation time compared to alternative approaches while allowing for straight-forward interpretations.Comment: 34 pages, 2 figures, 4 tables, 49 pages of Supplemental material, 2 supplemental figure

Effect of Vaginal Lubricants on Natural Fertility

Over-the-counter vaginal lubricants have been shown to negatively affect in vitro sperm motility. The objective of this study was to estimate the effect of vaginal lubricant use during procreative intercourse on natural fertility

A marginalized zero-inflated Poisson regression model with overall exposure effects

The zero-inflated Poisson (ZIP) regression model is often employed in public health research to examine the relationships between exposures of interest and a count outcome exhibiting many zeros, in excess of the amount expected under sampling from a Poisson distribution. The regression coefficients of the ZIP model have latent class interpretations, which correspond to a susceptible subpopulation at risk for the condition with counts generated from a Poisson distribution and a non-susceptible subpopulation that provide the extra or excess zeros. The ZIP model parameters, however, are not well suited for inference targeted at marginal means, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. We develop a marginalized ZIP model approach for independent responses to model the population mean count directly, allowing straightforward inference for overall exposure effects and empirical robust variance estimation for overall log incidence density ratios. Through simulation studies, the performance of maximum likelihood estimation of the marginalized ZIP model is assessed and compared to other methods of estimating overall exposure effects. The marginalized ZIP model is applied to a recent study of a motivational interviewing-based safer sex counseling intervention, designed to reduce unprotected sexual act counts

Center Variation in the Delivery of Indicated Late Preterm Births

Evidence for optimal timing of delivery for some pregnancy complications at late preterm gestation is limited. The purpose of this study was to identify center variation of indicated late preterm births

Urinary Follicle-Stimulating Hormone as a Measure of Natural Fertility in a Community Cohort

High serum follicle-stimulating hormone (FSH) levels have been associated with diminished ovarian reserve; however, the association between high urinary FSH and reduced natural fertility has yet to be established. We sought to characterize the relationship between a single or multiple measurements of early follicular phase urinary FSH and fertility. Women (n = 209), 30 to 44 years old with no history of infertility, who had been trying to conceive for less than 3 months, provided early follicular phase urine. Participants subsequently kept a diary to record bleeding and intercourse and conducted standardized pregnancy testing for up to 6 months. A subset of women (N = 95) collected urine on cycle day 3 for up to 6 cycles. Urine was analyzed for FSH and creatinine (cr) corrected. Proportional hazard models were used to calculate fecundability ratios (FRs). Urinary FSH levels across cycles from the same woman were highly correlated (adjusted intraclass correlation = .77); within-woman variance was 3-fold lower than variance among women. Women with an initial urinary FSH level <7 mIU/mg cr exhibited a nonsignificant reduction in the probability of pregnancy (adjusted FR 0.71, 95% confidence interval [CI]: 0.45-1.13), as did women with elevated urinary FSH (≥12 mIU/mg cr; adjusted FR 0.78, 95% CI: 0.46-1.32). Using the most recent or maximum urinary FSH value did not strengthen the association. In the general population, urinary FSH levels appear to be nonlinearly associated with fertility; however, broad CIs indicate a lack of statistical significance. Repetitive testing appears to be of little benefit

Cervical mucus monitoring prevalence and associated fecundability in women trying to conceive

To assess the use of cervical mucus monitoring (CMM) in women trying to conceive and determine whether monitoring is associated with increased cycle-specific probability of conception (fecundability)