A person based formula for allocating commissioning funds to general practices in England: development of a statistical model

Abstract

Objectives To develop a formula for allocating resources for commissioning hospital care to all general practices in England based on the health needs of the people registered in each practice Design Multivariate prospective statistical models were developed in which routinely collected electronic information from 2005-6 and 2006-7 on individuals and the areas in which they lived was used to predict their costs of hospital care in the next year, 2007-8. Data on individuals included all diagnoses recorded at any inpatient admission. Models were developed on a random sample of 5 million people and validated on a second random sample of 5 million people and a third sample of 5 million people drawn from a random sample of practices. Results Models including person level information (age, sex, and ICD-10 codes diagnostic recorded) and a range of area level information (such as socioeconomic deprivation and supply of health facilities) were most predictive of costs. After accounting for person level variables, area level variables added little explanatory power. The best models for resource allocation could predict upwards of 77% of the variation in costs at practice level, and about 12% at the person level. With these models, the predicted costs of about a third of practices would exceed or undershoot the actual costs by 10% or more. Smaller practices were more likely to be in these groups. SettingConclusions A model was developed that performed well by international standards, and could be used for allocations to practices for commissioning. The best formulas, however, could predict only about 12% of the variation in next year's costs of most inpatient and outpatient NHS care for each individual. Person-based diagnostic data significantly added to the predictive power of the models.

Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding

“…Cost variables were examined, with secondary care activity costed according to the method used in development of the person-based formula for allocating commissioning funds to general practices in England 14. Ultimately, these were not included in the predictive models because of concerns about difficulties in constructing these variables by possible future users; however, costs are included in descriptive findings to help in the design of intervention strategies.…”

Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold

“…To illustrate the magnitude of the potential health gain ignored by this restriction, note that in a recent study of costs associated with all inpatient and outpatient activity (excluding mental health), those aged over 75 years accounted for 25% of all costs in 2007/8. 86 The results presented in this study are all from the estimation of the relationship between expenditure and mortality using data for a single time period. With the availability of several years of data for both expenditure and mortality, we wanted to estimate a panel data model because a panel can offer advantages over a one period model (e.g.…”

“…76 Subsequent refinements and updates to this model employed the implementation of the CARAN model 74 and the initial findings of a person-based resource allocation (PBRA) study. 86 The use of these alternative models for the need for health care was explored.…”

Does better disease management in primary care reduce hospital costs? Evidence from English primary care

“…4 We include expenditure on both outpatient attendances and all inpatient 2 Some patients of English practices are treated in hospitals located in Wales and Scotland. Previous work has shown that dropping the small number of patients in practices near to the borders with Wales and Scotland makes no difference to estimation results (Dixon et al, 2009). 3 Sutton et al (2007) report that a sample size of about 5 million individuals is sufficient to generate stable parameter estimates.…”

“…Buntin and Zaslavsky, 2004;Manning et a;, 2005). For the dataset used in this study the performance of alternative estimators was investigated in depth and it was found that, because of the very large sample sizes available, OLS models of untransformed expenditure outperformed alternatives in terms of predictive power (Dixon et al, 2009). This is in line with findings from other researchers using large samples (Dunn et al, 2003, Ellis andMcGuire, 2007).…”