No abstract

Children with congenital anomalies have poorer intellectual and cognitive development compared to their peers, but evidence for academic achievement using objective measures is lacking. We aimed to summarize and synthesize evidence on academic outcomes and special education needs (SEN) of school-aged children born with selected major structural congenital anomalies. Electronic databases (MEDLINE, EMBASE, Scopus, PsycINFO, CINAHL, ProQuest Natural Science and Education Collections), reference lists and citations for 1990-2020 were systematically searched. We included original-research articles on academic achievement in children with non-syndromic congenital anomalies that involved school test results, standardized tests and/or SEN data. Randomeffects meta-analyses were performed to estimate pooled mean test scores in mathematics and/or reading where possible and pooled odds ratios (ORs) for SEN in children with severe congenital heart defects (CHDs) and children with orofacial clefts (OFCs). Thirty-nine eligible studies (n = 21,066 children) were synthesized narratively. Sixteen studies were included in meta-analyses. Children with non-syndromic congenital anomalies were at a higher risk of academic underachievement than controls across school levels. Children with severe CHD (pooled OR = 2.32, 95% CI: 1.90, 2.82), and children with OFC (OR = 1.38 (95% CI: 1.20, 1.57), OR = 3.07 (95% CI: 2.65, 3.56), and OR = 3.96 (95% CI: 3.31, 4.72) for children with cleft lip, cleft palate and cleft lip/palate, respectively) had significantly higher ORs for SEN than controls. Children with non-syndromic congenital anomalies underperform academically and have higher SEN rates compared to their peers. Early monitoring and development of differential SEN are important to promote academic progress in these children.

We perform fully Bayesian inference for stochastic differential equation mixed-effects models (SDEMEMs) using data at discrete times that may be incomplete and subject to measurement error. SDEMEMs are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dynamics, as well as the variability between experimental units and, optionally, account for measurement error. We consider inference for state-space SDEMEMs, however the inference problem is complicated by the typical intractability of the observed data likelihood which motivates the use of sampling-based approaches such as Markov chain Monte Carlo. Our proposed approach is the use of a Gibbs sampler to target the marginal posterior of all parameter values of interest. Our algorithm is made computationally efficient through careful use of blocking strategies and correlated pseudo-marginal Metropolis-Hastings steps within the Gibbs scheme. The resulting methodology is flexible and is able to deal with a large class of SDEMEMs. We demonstrate the methodology on state-space models describing two applications of increasing complexity and compare with alternative approaches. For these two applications, we found that our algorithm is about ten to forty times more efficient, depending on the considered application, than similar algorithms not using correlated particle filters.