From Local Data toward Global Mapping of Disability Care: Prescriptive Trends for Bedridden Paediatric Patients in Sicily, Italy

Background: Prolonged immobilization in children with severe disabling conditions carries a high risk of complications, including pressure ulcers and musculoskeletal deterioration. In line with the Italian Essential Levels of Care (LEA), the Local Health Authority (ASP) of the province of Catania provides specialized beds and mattresses free of charge to eligible paediatric patients with severe disorders. This study aimed to stratify all prescriptions of bedridden aids for individuals under 18 years, collected over 18 months (January 2024–July 2025), analyzing patterns by age, sex, diagnosis, healthcare district, and cost, within the ASP’s broader digitalization strategy (Oslo Project) and Internationalization Digit-aids plan.

Methods: An observational analysis was conducted on 27 paediatric prescriptions across the healthcare districts of Catania. Chi-squared tests (χ²) and contingency coefficients (C) were used to assess associations between demographic, clinical, and economic variables.

Results Most prescriptions involved males (70.4%), clustered in the 7–16 years age range, and concentrated in the Catania district (37%). Tetraparesis was the leading diagnosis (40.7%), followed by muscular dystrophy and cerebral palsy (both 11%). Statistically significant associations emerged between age and diagnosis (χ²=182.5, p<0.0001, C=0.933), with tetraparesis peaking at 14–16 years and cerebral palsy and muscular dystrophy at age 7. District and age were also associated (χ²=59.3, df=40, p=0.025), with younger cases concentrated in Adrano and older ones in the Catania district. District-level analysis (χ²=104.5, p=0.0001, C=0.891) revealed Adrano and Catania as hotspots for tetraparesis, while Paternò showed a distinct profile with cases of muscular dystrophy, pressure ulcers, and tetraparesis. No significant correlation was found between diagnosis and cost (p=0.472), reflecting that device reimbursement is standardized under LEA criteria rather than being disease-specific.

Conclusions: Prescription data revealed a non-uniform distribution, with strong diagnostic and age clustering. Digital stratification enables mapping of paediatric disability needs, supporting equitable allocation of resources and scalable international models for long-term care.