Economic assessment of the impact of telecare on the use of social care resources using a zero-inflated, hierarchical linear statistical model

Introduction: There is an ever-increasing demand for social care in the UK, with expenditure predicted to double to 56 pound billion by 2038/39. Many councils are under budget restrictions putting pressure on the number of services provided and their quality. Telecare complements social care and involves the implementation of technology to keep individuals more independent. Methods: This study utilised a retrospective time-series analysis of data provided by Lancashire County Council between the period January-2013 to March-2018. A generalised linear mixed model (GLMM) was used to control for potential confounders. Two groups were identified: those using telecare (telecare group, n= 699) and those who did not (control group, n= 839). Results: The fixed effects data showed that telecare group start 75 pound per week lower in cost and as time progressed this reduced further by 9p per service user per week. In contrast, control group costs rose 5p per week per user. This effect was independent of age but was affected by measure of dependency. Analysis was then utilised to make predictions based on weighted averages. The scenario showed a total difference of 4,949 pound per service user over the whole year. A second scenario pro-rata'd costs for the full year showed a difference of 6,214 pound, where telecare would avoid costs of 17 pound million per year. Discussion: This analysis demonstrates that there is evident potential for the use of telecare to reduce social care resource use and costs. This study also highlights the use of a GLMM as a novel method of analysing observed data by controlling confounders.

Automated summary

This study demonstrates the potential of telecare in reducing social care resource use and costs. The use of telecare was associated with a lower weekly cost compared to the control group. The study utilized a generalized linear mixed model (GLMM) to analyze the data and control for confounding factors.