Title: Information and value based commissioning
Funder: Department of Health (England)

Abstract: The growth in planned care post-2002 The challenge to identifying the source of activity growth is that both supply and demand-side factors have been changing in the NHS in England over the period that we will investigate. Our approach is to exploit both the variation within England in the adoption of various supply-side and demand-side factors and variation between Scotland and England where Scotland often acts as a natural control not having been subject to either Patient Choice or Payment by Results policies. The generic form of the econometric model that we adopt can be expressed as follows. The dependent variable h is the level of elective activity in PCT or provider i in HRG j in year t. Two vectors Xijt and Ui are observable covariates at PCT or Provider level such as population characteristics financial status (FT or non-FT surplus or deficit) provider or PCT specific shifts (Fixed Effects) and capacity. Two vectors Wjt and Qj are observable covariates at procedure level such as for example average cost Length of Stay HRG specific shifts (Fixed Effects) and policies affecting that procedure. Particular parameterisations of the model will permit the impact of demand-side and supply-side factors on activity to be estimated. For example consider the impact of ‘patient choice’. ‘Choice’ came into effect in England in 2006 thus a simple modelling of the impact of this factor is to suppose that it affected all HRGs in England after 2006. The relevant variable of Qj takes a value 1 if the activity occurs in 2006 or later and zero otherwise. Herein lies an immediate problem; every activity indicator in England is potentially affected by the adoption of the Choice Agenda from 2006 on. However this policy was not adopted in Scotland and so for each hijt relating to Scotland the relevant value of the Choice variable is zero. For this policy and this particular formulation of the inception of the policy activity in Scotland acts as the control to the policy treatment in England. The equation will be estimated by standard Fixed-Effects and Fixed-effects-dummy-variable regression methods. Model development will be undertaken to consider refinements in terms of modelling specific HRGs or developing alternative measures of activity. The potential for demand and supply-side factors to be interdependent in affecting activity will be allowed for by including interaction terms in the regression specifications and analysed where they are statistically significant.To implement our approach we will construct a data set that comprises for the dependent variable activity measures (number of procedures or procedures per head of population or relative to non-elective activity) for each elective HRG in each year aggregated over either each provider or each PCT (equivalently in Scotland – Health Board) area. These measures can be derived from HES and SMR (Scottish Morbidity Record) data for England and Scotland respectively. Similarly covariates will be constructed either from HES and SMR or from other publicly accessible health care data sources. Learning from PROMs HES and other data We seek to describe and explain the difference in health gain performance across CCGs using both simple approaches and also by estimating patient gain models similar to the previous equation. CCGs may perform well/badly due to both the demography of the area and also because the mean performance across all demographic groups is higher in a CCG area which is captured by a fixed effect. This might reflect patient selection or provider quality. We will extend the model to include pre-operative conditions to establish their relationship to health gain using a flexible parametric form with and without control for patient characteristics. This framework will also allow us to establish variation in patient QALY gain between demographic groups conditional upon pre-operative conditions.There exists considerable variation in activity between CCGs and we shall explore the impact of activity variation on patient health gain controlling for demographic characteristics that influence patient reported gain. There is informal evidence that patient gain is on average as great in high activity areas as in those with little activity. This will be explored to examine the implications for the capacity to refer and treat patients most able to achieve health gain.Good patient selection depends on finding those who benefit most. By considering CCGs with few treated patients we can identify the characteristics of patients who are not treated when an area offers low activity and estimate the health loss for restricted patient groups in low activity CCGs. A combination of high health gain with high volume is clearly a good performance and it will be useful to examine these commissioners in order to infer pointers to good performance. Another dimension of performance is consistency. Using similar methods we shall examine commissioners consistency across the four procedures in terms of adjusted volume average health gain pre-op condition and so on. If demand is to be reduced the obvious first step is to withhold treatment from patients who gain little from it. Using the individual PROMS data it is possible to uncover the correlates of patients in terms of pre-op condition who experience health gain below some selected level obviously negative health gain and who therefore benefit less from treatment. It may then be possible to offer guidance as to those who should not be treated.Reports of activities and outcomes will be sent to CCGs outlining evidence for their own population and appropriately compared to other CCG populations using forms of presentation such as funnel plots for comparative data. In order to maximise accessibility and possible impact the group will encourage dialogue and opportunity for clarification of data provided as part of the proposed study and work with the NHS Alliance to build a dialogue with commissioners and patients.


Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE