Conclusion:

In the initial Power BI model, we scrutinized the count of autism diagnoses among children aged 0-9 in England for the fiscal year 2019-20, dissecting the performance of Integrated Care Boards (ICBs) across the nation. The analysis highlighted significant variations in ICB performance, shedding light on the regional landscape of autism diagnoses. It is crucial to acknowledge that the data's novelty and experimental nature introduced variability in the coverage of General Practitioner (GP) Practices, prompting us to utilize a coverage report from NHS England for compensation. Nevertheless, the model's accuracy remains contingent on the level of coverage.

Our choice of the 2019-20 timeframe was deliberate, predating the onset of COVID, which had the potential to disrupt diagnosis programs. This disruption was evident in the second Power BI model, where we delved into autism diagnoses and associated conditions such as Profound and Multiple Learning Disabilities (PMLD), Moderate Learning Difficulties (MLD), and Severe Learning Difficulties (SLD) using Special Educational Needs statistics from GOV.UK. While autism diagnoses exhibited a consistent upward trend from 2016 to 2023, PMLD + SLD + MLD, ostensibly correlating with autism at 60% to 70%, demonstrated a decline over the same period.

Autism, or Autistic Spectrum Disorder, encompasses a spectrum from severe intellectual challenges to genius, often co-occurring with PMLD, SLD, MLD, or Asperger's. Identifying these diverse needs early is critical, yet a considerable challenge. The failure to do so is emerging as a pressing issue, with some Members of Parliament rightly highlighting the neglect of children with special educational needs by the government since the advent of COVID. Addressing this oversight will likely require a concerted effort and time.

In light of these findings, the subsequent sections offer advice and resources for concerned parents and children navigating the complexities of special educational needs.