Abstract Introduction
Demand on adult social care is increasing. Public Health could be beneficial to the local authority by improving the disability-free life expectancy and producing evidence to prioritise interventions. A public health consultant in Enfield led the analysis of adult social care data to find opportunities for prevention by producing a best-fit cost prediction model related to long-term conditions.
Method
“Care First” adult social care data for 5 years to November 2014 were included to produce descriptive epidemiology and inferential statistics using regression techniques.
Results
Although around 30% of the clients were 20-49 years, they have a higher unit cost to social care than older age groups. Residential based patients constitute three-quarters of the clients whereas the average weekly cost per person (£632) was two times that of community-based clients (£310). Learning disabilities and physical disabilities were the most common causes in younger ages. In both residential and community settings, congenital conditions showed the highest cost per client whereas musculoskeletal conditions resulted in the highest total cost. A predictive model with only significant predictors had these parameters: age, deprivation quintile, placement setting, care group, dementia, mental condition, nervous system, stroke, vision impairment, hearing impairment and learning disability.Data quality could be a critical issue especially around the recording of the medical conditions. Linking with NHS data can improve the data quality and the care of the clients.
Conclusion
Analysis should be repeated multiple times with bigger and improved dataset to produce more reliable results. External funding details