Decision tree for the children without learning difficulty.

Decision tree for the children without learning difficulty.</p

Confusion matrix/two by two table of the DT model.

Confusion matrix/two by two table of the DT model.</p

Characteristics of the study population.

BackgroundSchool readiness is a measure of a child’s cognitive, social, and emotional readiness to begin formal schooling. Children with low school readiness need additional support from schools for learning, developing required social and academic skills, and catching-up with their school-ready peers. This study aims to identify the most significant risk factors associated with low school readiness using linked routine data for children in Wales.MethodThis was a longitudinal cohort study using linked data. The cohort comprises of children who completed the Foundation Phase assessment between 2012 and 2018. Individuals were identified by linking Welsh Demographic Service and Pre16 Education Attainment datasets. School readiness was assessed via the binary outcome of the Foundation Phase assessment (achieved/not achieved). This study used multivariable logistic regression model and a decision tree to identify and weight the most important risk factors associated with low school readiness.ResultsIn order of importance, logistic regression identified maternal learning difficulties (adjusted odds ratio 5.35(95% confidence interval 3.97–7.22)), childhood epilepsy (2.95(2.39–3.66)), very low birth weight (2.24(1.86–2.70), being a boy (2.11(2.04–2.19)), being on free school meals (1.85(1.78–1.93)), living in the most deprived areas (1.67(1.57–1.77)), maternal death (1.47(1.09–1.98)), and maternal diabetes (1.46(1.23–1.78)) as factors associated with low school readiness. Using a decision tree, eligibility for free school meals, being a boy, absence/low attendance at school, being born late in the academic year, being a low birthweight child, and not being breastfed were factors which were associated with low school readiness.ConclusionThis work suggests that public health interventions focusing on children who are: boys, living in deprived areas, have poor early years attendance, have parents with learning difficulties, have parents with an illness or have illnesses themselves, would make the most difference to school readiness in the population.</div

Flow diagram of the study population.

BackgroundSchool readiness is a measure of a child’s cognitive, social, and emotional readiness to begin formal schooling. Children with low school readiness need additional support from schools for learning, developing required social and academic skills, and catching-up with their school-ready peers. This study aims to identify the most significant risk factors associated with low school readiness using linked routine data for children in Wales.MethodThis was a longitudinal cohort study using linked data. The cohort comprises of children who completed the Foundation Phase assessment between 2012 and 2018. Individuals were identified by linking Welsh Demographic Service and Pre16 Education Attainment datasets. School readiness was assessed via the binary outcome of the Foundation Phase assessment (achieved/not achieved). This study used multivariable logistic regression model and a decision tree to identify and weight the most important risk factors associated with low school readiness.ResultsIn order of importance, logistic regression identified maternal learning difficulties (adjusted odds ratio 5.35(95% confidence interval 3.97–7.22)), childhood epilepsy (2.95(2.39–3.66)), very low birth weight (2.24(1.86–2.70), being a boy (2.11(2.04–2.19)), being on free school meals (1.85(1.78–1.93)), living in the most deprived areas (1.67(1.57–1.77)), maternal death (1.47(1.09–1.98)), and maternal diabetes (1.46(1.23–1.78)) as factors associated with low school readiness. Using a decision tree, eligibility for free school meals, being a boy, absence/low attendance at school, being born late in the academic year, being a low birthweight child, and not being breastfed were factors which were associated with low school readiness.ConclusionThis work suggests that public health interventions focusing on children who are: boys, living in deprived areas, have poor early years attendance, have parents with learning difficulties, have parents with an illness or have illnesses themselves, would make the most difference to school readiness in the population.</div

Prediction model performance (n = 14,575 children from Rhondda Cynon Taff).

Prediction model performance (n = 14,575 children from Rhondda Cynon Taff).</p

Logistic regression model to identify the risk factors associated with low school readiness.

Logistic regression model to identify the risk factors associated with low school readiness.</p

S1 File -

BackgroundSchool readiness is a measure of a child’s cognitive, social, and emotional readiness to begin formal schooling. Children with low school readiness need additional support from schools for learning, developing required social and academic skills, and catching-up with their school-ready peers. This study aims to identify the most significant risk factors associated with low school readiness using linked routine data for children in Wales.MethodThis was a longitudinal cohort study using linked data. The cohort comprises of children who completed the Foundation Phase assessment between 2012 and 2018. Individuals were identified by linking Welsh Demographic Service and Pre16 Education Attainment datasets. School readiness was assessed via the binary outcome of the Foundation Phase assessment (achieved/not achieved). This study used multivariable logistic regression model and a decision tree to identify and weight the most important risk factors associated with low school readiness.ResultsIn order of importance, logistic regression identified maternal learning difficulties (adjusted odds ratio 5.35(95% confidence interval 3.97–7.22)), childhood epilepsy (2.95(2.39–3.66)), very low birth weight (2.24(1.86–2.70), being a boy (2.11(2.04–2.19)), being on free school meals (1.85(1.78–1.93)), living in the most deprived areas (1.67(1.57–1.77)), maternal death (1.47(1.09–1.98)), and maternal diabetes (1.46(1.23–1.78)) as factors associated with low school readiness. Using a decision tree, eligibility for free school meals, being a boy, absence/low attendance at school, being born late in the academic year, being a low birthweight child, and not being breastfed were factors which were associated with low school readiness.ConclusionThis work suggests that public health interventions focusing on children who are: boys, living in deprived areas, have poor early years attendance, have parents with learning difficulties, have parents with an illness or have illnesses themselves, would make the most difference to school readiness in the population.</div

Normalized expression data for the NASC Arabidopsis biotic stress series (Additional file ) were extracted and plotted as shown

The legends indicate the correspondence between the plots and the respective Arabidopsis gene identification designation. The numerical key for each array experiment is given along the X-axis. While the full list of the agents can be found in Additional file , here is a brief list: 1–16, control and infection; 17–22, control and infection; 23–36, control and elicitors treatment; 37–52, dark and different light treatment.<p><b>Copyright information:</b></p><p>Taken from "Arabidopsis mRNA polyadenylation machinery: comprehensive analysis of protein-protein interactions and gene expression profiling"</p><p>http://www.biomedcentral.com/1471-2164/9/220</p><p>BMC Genomics 2008;9():220-220.</p><p>Published online 14 May 2008</p><p>PMCID:PMC2391170.</p><p></p

Normalized expression data for the NASC Arabidopsis chemical/hormone series (Additional file ) were extracted and plotted as shown

The legends indicate the correspondence between the plots and the respective Arabidopsis gene identification designation. The numerical key for each array experiment is given along the X-axis, and the detail can be found in Additional file . The single arrows indicate the position for cycloheximide; double arrows for GA mutants; empty arrows for imbibition and ABA treatment.<p><b>Copyright information:</b></p><p>Taken from "Arabidopsis mRNA polyadenylation machinery: comprehensive analysis of protein-protein interactions and gene expression profiling"</p><p>http://www.biomedcentral.com/1471-2164/9/220</p><p>BMC Genomics 2008;9():220-220.</p><p>Published online 14 May 2008</p><p>PMCID:PMC2391170.</p><p></p

The values for each gene in the array analysis of mature pollen were plotted as shown

<p><b>Copyright information:</b></p><p>Taken from "Arabidopsis mRNA polyadenylation machinery: comprehensive analysis of protein-protein interactions and gene expression profiling"</p><p>http://www.biomedcentral.com/1471-2164/9/220</p><p>BMC Genomics 2008;9():220-220.</p><p>Published online 14 May 2008</p><p>PMCID:PMC2391170.</p><p></p