Growth Patterns Between Ages 0 and 36 Months Among US 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 ages 0 to 36 months in US children with an orofacial cleft. DESIGN: A retrospective cohort study. PARTICIPANTS/SETTING: Children with cleft lip, cleft lip and palate, or cleft palate who were younger than age 36 months at a hospital encounter between 2010 and 2019 (N = 1334) were included. The setting was a US 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 age 0 to 36 months. RESULTS: Growth in infants with cleft slowed dramatically during the first 3 to 4 months of life, rebounded with catch-up growth until age 12 months for cleft lip and cleft palate and until age 36 months for cleft lip and palate. When comparing populations, children with any type of cleft demonstrated subpar growth compared with World Health Organization standards. Growth deficits were more common in those with cleft lip and palate and cleft palate compared with those with cleft lip. The intraclass coefficient showed that most of the variability in the WAZ (65%) was between individuals, whereas 35% was within an individual. The intraclass coefficient for LAZ showed that most of the variability in the LAZ (74%) was between individuals, whereas 26% was within an individual. The proportion of variance attributable to cleft type and/or comorbidities accounted for <5% of the variance for WAZ and LAZ. WAZ and LAZ were lower in children with comorbidities than those without comorbidities with cleft and World Health Organization standards. CONCLUSIONS: Infants with cleft lip and palate, cleft palate, and a cleft with comorbidities have higher rates of poor growth than peers with cleft lip and a cleft with 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.