Growth patterns between 0 to 36 months of age among U.S. children with orofacial cleft: A retrospective cohort study.

BACKGROUND: Little is known about how young children with orofacial cleft grow over time. OBJECTIVE: To characterize longitudinal growth patterns from 0-36 months of age in U.S. children with an orofacial cleft. DESIGN: A retrospective cohort study. PARTICIPANTS/SETTING: Children with cleft lip (CL), cleft lip and palate (CLP), or cleft palate (CP) who were <36 months of age at a hospital encounter between 2010-2019 (N=1334) were included. The setting was a U.S. tertiary care children's hospital with a cleft center that serves a 5-state region. MAIN OUTCOME MEASURE: Weight-for-age z-scores (WAZ) and length-for-age z-scores (LAZ). STATISTICAL ANALYSES PERFORMED: Longitudinal growth patterns were characterized using generalized linear mixed models to estimate mean WAZ and LAZ from 0 to 36 months of age. RESULTS: Growth in infants with cleft slowed dramatically in the first 3 to 4 months of life, rebounded with catch-up growth until 12 months of age for CL and CP and until 36 months for CLP. When comparing populations, children with any type of cleft demonstrated subpar growth compared to WHO standards. Growth deficits were more common in those with CLP and CP compared to those with CL. The intraclass coefficient (ICC) showed that most of the variability in the WAZ (65%) was between individuals while 35% was within an individual. The ICC for LAZ showed that most of the variability in the LAZ (74%) was between individuals while 26% was within an individual. The proportion of variance attributable to cleft type and/or comorbidities accounted for less than 5% of the variance for WAZ and LAZ. WAZ and LAZ were lower in children with comorbidities than those without comorbidities with cleft and WHO standards. CONCLUSION: Infants with CLP and CP, and cleft plus comorbidities have higher rates of poor growth than their peers with CL and no comorbidities, respectively.

Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method.

In this extant paper, a multivariate time series model using the seemingly unrelated times series equation (SUTSE) framework is proposed to forecast the peak and short-term electricity demand using time series data from February 2, 2014, to August 2, 2018. Further the Markov Chain Monte Carlo (MCMC) method, Gibbs Sampler, together with the Kalman Filter were applied to the SUTSE model to simulate the variances to predict the next day's peak and electricity demand. Relying on the study results, the running ergodic mean showed the convergence of the MCMC process. Before forecasting the peak and short-term electricity demand, a week's prediction from the 28th to the 2nd of August of 2018 was analyzed and it found that there is a possible decrease in the daily energy over time. Further, the forecast for the next day (August 3, 2018) was about 2187 MW and 44090 MWh for the peak and electricity demands respectively. Finally, the robustness of the SUTSE model was assessed in comparison to the SUTSE model without MCMC. Evidently, SUTSE with the MCMC method had recorded an accuracy of about 96% and 95.8% for Peak demand and daily energy respectively.