ABSTRACT
The objective of this investigation was to describe maternal psychosocial, behavioral, and acculturation factors associated with early childhood caries in a sample of urban Latino mother-child dyads. A cross-sectional survey was conducted with 100 mothers whose children (under the age of 6 y) were patients at the Dental Center at Children's Hospital Colorado in Aurora, Colorado. All children participating in the study received an oral examination to measure decayed, missing, filled, surfaces (dmfs). Participating mothers were given the option to sign the consent form and complete the survey in English or Spanish, according to their preferred language. The survey used demographic, behavioral, knowledge, and several psychosocial variables. Bivariate analysis was conducted with dmfs as a dependent variable. The associations between independent variables and dmfs were modeled using negative binomial regression. Mean ± SD dmfs for the entire sample was about 11 ± 16.85. The mothers who spoke Spanish had children with significantly (P = 0.046) higher dmfs scores (15.2) compared to mothers who spoke English (7.56). Preference of Spanish language was significantly associated with self-efficacy (P = 0.0043), oral health knowledge (P = 0.0024), and 3 subscales of the health belief model: perceived severity (P = 0.057), perceived barriers (P = 0.0002), and perceived susceptibility (P = 0.008). Both in the univariate and the multivariate models, oral health behavior and preferential use of Spanish remained significantly associated with higher dmfs scores. Results of this study demonstrate that maternal oral health behaviors and preferred language are significant factors associated with early childhood caries in urban Latino children. Knowledge Transfer Statement: Results of this study indicate that maternal oral health behaviors and the level of acculturation are significantly associated with caries in urban Latino children. Caries prevention efforts in this population could use this information to tailor oral health messaging according to the level of acculturation in mothers.
ABSTRACT
Studies of reproductive physiology involve rapid sampling protocols that result in time series of hormone concentrations. The signature pattern in these times series is pulses of hormone release. Various statistical models for quantifying the pulsatile release features exist. Currently these models are fitted separately to each individual and the resulting estimates averaged to arrive at post hoc population-level estimates. When the signal-to-noise ratio is small or the time of observation is short (e.g., 6 h), this two-stage estimation approach can fail. This work extends the single-subject modelling framework to a population framework similar to what exists for complex pharamacokinetics data. The goal is to leverage information across subjects to more clearly identify pulse locations and improve estimation of other model parameters. This modelling extension has proven difficult because the pulse number and locations are unknown. Here, we show that simultaneously modelling a group of subjects is computationally feasible in a Bayesian framework using a birth-death Markov chain Monte Carlo estimation algorithm. Via simulation, we show that this population-based approach reduces the false positive and negative pulse detection rates and results in less biased estimates of population-level parameters of frequency, pulse size, and hormone elimination. We then apply the approach to a reproductive study in healthy women where approximately one-third of the 21 subjects in the study did not have appropriate fits using the single-subject fitting approach. Using the population model produced more precise, biologically plausible estimates of all model parameters. Copyright © 2017 John Wiley & Sons, Ltd.
