Background: The healthcare system in Scotland is facing increased demands as a consequence of the aging population and budgetary constraints. There has also been a shift in government policy towards a more preventative and anticipatory approach to the management of long term conditions. The value of this is maximised when it is targeted at patients who are most likely to benefit. To achieve this, it is essential to have valid and robust techniques, which enable stratification of the population by risk. SPARRA is a stratification tool, developed for the National Health Service in Scotland. Launched in 2006, SPARRA is a predictive tool to evaluate an individual’s risk of being admitted to hospital as an emergency inpatient within the next year. In 2012, it’s cohort was widened to allow risk scores to be derived for more patients and for patients from lower risk strata, who may benefit from early preventative interventions. Risk scores are calculated for approximately 4.2 million patients and details of patients whose score indicates that they may be at increased risk are distributed to a range of health care professionals
Description: SPARRA requires a patient-level history comprising information about each patient during a pre-prediction period and information on whether or not the patient experienced an emergency admission in the twelve months following this period. In the 2012 SPARRA (Version 3), national patient-level data on hospitalisations, prescribing, emergency department, outpatients and psychiatric admissions were linked and a range of variables were derived from this combined dataset. The SPARRA cohort was then divided into three sub-cohorts: ‘Frail Elderly’, ‘Long Term Conditions’ and ‘Younger Emergency Department’. Following the creation of these three sub-cohorts, logistic regression was used to test the variables’ predictive power in evaluating the risk of emergency admission for each sub-cohort. Performance of the resulting model was then tested by considering some relevant statistical measures, such as sensitivity and positive predicted value.
Results: The modelling process identified a number of risk factors which improved the predictive power of the SPARRA algorithm. Some of these variables are relevant to each sub-cohort, some to only one or two. Widening of the cohort has meant that more than 95% of patients experiencing an emergency hospital admission within the year are captured. The remaining 5% that do not appear in the SPARRA cohort are largely unpredictable admissions, for example, trauma and orthopaedics patients. When we compared the performance of SPARRA against the Devon Prediction Model (a combined model based on a systematic review of around 26 models) at thresholds 50%, SPARRA performed better and reported a positive predicted value of 59.8% compared to Devon’s 54.6%.SPARRA also reported a sensitivity of 10.5% while Devon reported 8.4%. SPARRA risk scores can range from 1 to 99%; patients with a score of 40+% are generally regarded to be at increased risk.
Highlights: SPARRA plays a key role in the shift to preventative, anticipatory approaches to healthcare, as a case finding tool and as a service planning tool. Details of patients at risk of emergency admission are distributed to the relevant NHS Health Boards, Health & Social Care Partnerships, GP Practices and other health agencies. This allows anticipatory interventions at the individual patient level and, at a higher level, assists in service planning. For example, SPARRA data can define lists of polypharmacy patients that GPs and pharmacists can use to select the individuals most in need of a medicine review.
Conclusion: SPARRA has proven very effective both as a case finding tool and as a service planning tool. For SPARRA to fully support health and social care integration, the linked datasets used in SPARRA will be widened to include data from GP systems, out-of-hours, NHS 24, care home and social care.