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This paper constitutes the first and foundational output of the ESHCRU2 project 3 - Analysis of purcher-provider contracts: modelling risk sharing and incentive implications. In this project, we have focused on the implications of payment reform of what is called blended payment for emergency care. This paper sets out the theoretical model developed to understand how behavioural choices could be influenced by this payment reform. We construct a framework in which two organisations - a hospital and a purchaser - influence respectively admissions from, and attendance at, emergency departments. These decisions are each influenced by the payment system and interact to determine an equilibrium. We show how the equilibrium is affected by the characteristics of the hospital and the purchaser and how it will be changed by shifting towards a greater reliance on fixed payment. We further establish what outcomes (in terms of admissions and attendances) can be sustained as an equilibrium under different parameters of the payment system.

This report updates the Centre for Health Economics’ time-series of National Health Service (NHS) productivity growth for the period 2018/19 to 2019/20. NHS productivity growth is measured by comparing the growth in outputs produced by the NHS to the growth in inputs used to produce them. NHS outputs include all the activities undertaken for NHS patients wherever they are treated in England. It also accounts for changes in the quality of care provided to those patients. NHS inputs include the number of doctors, nurses, and support staff providing care, the equipment, and clinical supplies used, and the facilities of hospitals and other premises where care is provided.

Payments to healthcare providers are often based on the number of patients they treat according to their particular health condition with well-known limitations. Payment based on health outcomes, a form of pay-for-performance, has long been advocated as a possible solution. This study adopts a contract theory approach and illustrates how it can inform practical implementation of pay-for-performance schemes that reward health outcomes. We first provide a simple but general model on the design of an incentive scheme that rewards providers for improved health, as a function of key parameters related to patient health benefits and provider costs. We then calibrate the model using data from two elective procedures, hip and knee replacement, using patient reported outcome measures. The pricing rule suggests that the bonus should be set to reflect the difference between the provider’s marginal cost of a health improvement before the policy intervention and the provider’s marginal cost evaluated at the target health set by the purchaser. We provide estimates of the optimal bonus for hip and knee replacement under a range of assumptions about provider cost functions and the value of health improvements.

Reimbursement of English mental health hospitals is moving away from block contracts and towards activity and outcome‐based payments. Under the new model, patients are categorised into 20 groups with similar levels of need, called clusters, to which prices may be assigned prospectively. Clinicians, who make clustering decisions, have substantial discretion and can, in principle, directly influence the level of reimbursement the hospital receives. This may create incentives for upcoding. Clinicians are supported in their allocation decision by a clinical clustering algorithm, the Mental Health Clustering Tool, which provides an external reference against which clustering behaviour can be benchmarked. The aims of this study are to investigate the degree of mismatch between predicted and actual clustering and to test whether there are systematic differences amongst providers in their clustering behaviour. We use administrative data for all mental health patients in England who were clustered for the first time during the financial year 2014/15 and estimate multinomial multilevel models of over, under, or matching clustering. Results suggest that hospitals vary systematically in their probability of mismatch but this variation is not consistently associated with observed hospital characteristics.

This report updates the Centre for Health Economics’ time-series of National Health Service (NHS) productivity growth for the period 2017/18 to 2018/19. This update has not been affected by the COVID-19 pandemic. NHS productivity growth is measured by comparing the growth in outputs produced by the NHS to the growth in inputs used to produce them. NHS outputs include all the activities undertaken for NHS patients wherever they are treated in England. It also accounts for changes in the quality of care provided to those patients. NHS inputs include the number of doctors, nurses and support staff providing care, the equipment and clinical supplies used, and the facilities of hospitals and other premises where care is provided.

This report updates the Centre for Health Economics’ time-series of National Health Service (NHS) productivity growth for the period 2016/17 to 2017/18. NHS productivity growth is measured by comparing the growth in outputs produced by the NHS to the growth in inputs used to produce them. NHS outputs include all the activities undertaken for NHS patients wherever they are treated in England. It also accounts for changes in the quality of care provided to those patients. NHS inputs include the number of doctors, nurses and support staff providing care, the equipment and clinical supplies used, and the facilities of hospitals and other premises where care is provided.

and slides from oral presentation at the 5th EuHEA PhD Student-Supervisor and Early Career Researcher Conference in Catania, Italy, between 5th – 7th September 2018.

Since the NHS was established in 1948, growth in health care expenditure (HCE) has outpaced the rise in both GDP and in total public expenditure. Known drivers of HCE growth include demographic factors, income and wealth effects, technology and cost pressures. To identify the challenges and opportunities for developing a model of healthcare demand, this report addressed two research questions: 1.What are the drivers of past trends in health care expenditure and how much has each of the drivers contributed to past increases in expenditure? 2.How much has each type of service contributed to past trends in health care expenditure and why have there been different trends for different types of care? We set out a conceptual framework for understanding drivers of HCE, placing it in the broader context of underlying drivers of demand and macroeconomic trends. We reviewed studies from higher-income countries published over the last decade, and analysed datasets compiled in-house of cost and volume of care by different settings. We linked data on HCE trends to relevant, setting-specific evidence from the literature review. We identified 52 studies using aggregate data and 54 individual-level studies. The relative contribution of different drivers could not be quantified due to heterogeneity in study methodologies. Aggregate studies using longer panels of data show that the relationship between HCE and its drivers is non-linear, varies over time and varies cross countries. These studies mostly find a strong, positive relationship between HCE and technological progress. Individual-level studies usually rely on observational, non-experimental data from administrative databases, such as claims data or registers, or on survey data or cohort studies. Trends in HCE from 2008/9 to 2016/17 reveal that the largest rises were in high cost drugs (231%), chemotherapy (113%) and attendances at A&E (60%) or outpatient departments (57%). Most evidence on the drivers of HCE related to hospital care, but we found no studies explaining the factors behind the rise in expenditure on chemotherapy or high cost drugs. We conclude by presenting four lessons that could inform decisions on building a projections model of health care expenditure.

We study the impact of contracting-out of maternal health care by the government of Malawi to providers from the Christian Health Association of Malawi (CHAM) in the form of Service Level Agreements (SLAs). Under a SLA, a CHAM facility provides agreed maternal and newborn services free-of-charge to patients, and is reimbursed on a fixed price per service. We merge data on health facilities in Malawi with pregnancy histories from the 2010 Malawi Demographic and Health Survey, and exploit the staggered implementation of SLAs across facilities. Using difference-in-differences, we estimate the differential effects on pregnancy- related health care utilisation to mothers residing near and far from facilities with a SLA over time. Our findings show that SLAs reduced home births and increased skilled deliveries at CHAM hospitals. We observe greater provision of prenatal care services at CHAM health centres but no overall increase in the number of prenatal care visits. We find evidence of a reduction in certain components of prenatal care.

This report updates the Centre for Health Economics’ time-series of National Health Service (NHS) productivity growth for the period 2015/16 to 2016/17 and reports trends in output, input and productivity since 2004/05. NHS productivity growth is measured by comparing the growth in outputs produced by the NHS to the growth in inputs used to produce them. NHS outputs include all the activities undertaken for NHS patients wherever they are treated in England, and also accounts for changes in the quality of care provided to those patients. NHS inputs include the number of doctors, nurses and support staff providing care, the equipment and clinical supplies used, and the facilities of hospitals and other premises where care is provided.