The minimum wage has gone up by less for those under 20 than for older workers since 2016.
The youngest workers have seen the minimum wage rise by less than older colleagues - as the latest employment figures reveal average wages are not keeping up with the rising cost of living.I've had part-time jobs since I was 15, often earning the minimum wage. As a younger worker that meant I was paid less than older colleagues - even when I was doing the same work.
Data from the Office for National Statistics shows that minimum rates of pay have gone up less for those under 20 than for older workers since 2016. That older cohort have since 2017 been paid what is known as the National Living Wage. Originally for over 25s, it has been extended to all workers over 23, and there are plans to lower the threshold further to 21 by 2024.
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Early detection of autism spectrum disorder in young children with machine learning using medical claims dataObjectives Early diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict the risk of ASD in young children between 18 months and 30 months based on their medical histories using real-world health claims data. Methods Using the MarketScan Health Claims Database 2005–2016, we identified 12 743 children with ASD and a random sample of 25 833 children without ASD as our study cohort. We developed logistic regression (LR) with least absolute shrinkage and selection operator and random forest (RF) models for predicting ASD diagnosis at ages of 18–30 months, using demographics, medical diagnoses and healthcare service procedures extracted from individual’s medical claims during early years postbirth as predictor variables. Results For predicting ASD diagnosis at age of 24 months, the LR and RF models achieved the area under the receiver operating characteristic curve (AUROC) of 0.758 and 0.775, respectively. Prediction accuracy further increased with age. With predictor variables separated by outpatient and inpatient visits, the RF model for prediction at age of 24 months achieved an AUROC of 0.834, with 96.4% specificity and 20.5% positive predictive value at 40% sensitivity, representing a promising improvement over the existing screening tool in practice. Conclusions Our study demonstrates the feasibility of using machine learning models and health claims data to identify children with ASD at a very young age. It is deemed a promising approach for monitoring ASD risk in the general children population and early detection of high-risk children for targeted screening. Data may be obtained from a third party and are not publicly available.
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