ESSAYS ON THE INFLUENCE OF DOCTORS’ SOCIO-DEMOGRAPHIC CHARACTERISTICS ON MEDICAL SPECIALTY ALLOCATION

Medical workforce planning is a key element of any health care system, however factors influencing medical career choice are poorly understood. This thesis contains three essays on the influence of doctors’ socio-demographic characteristics on their medical specialty in both the UK and Spain. This thesis aims at understanding the drivers of the occupational segregation between socio-demographic groups, with the objective of helping regulators and policy makers in the design of interventions aimed at reducing the undesired consequences associated with the occupational segregation. Chapter 2 constitutes a descriptive exercise of the socio-demographic composition of the new cohorts of junior doctors in the UK by analysing their distribution across specialties. The findings show large disparities in that distribution. This chapter provides a discussion of the possible sources of the observed disparities and relates the occupational segregation with the literature on statistical discrimination. Chapter 3 seeks to disentangle the origins of the outcomes observed in Chapter 2. It develops a conceptual framework that acknowledges the sequential, two-sided nature of the process and that serves as a base for the empirical analysis. The focus of the latter is the estimation of how doctors’ socio-demographic characteristics affect their application strategies and specialty choices and selectors’ valuations of candidates. Chapter 4 focuses on the Spanish resident market and explores two of the possible causes leading to the persistent gender gap in surgical specialties. The first focus is on the role of social interactions in shaping doctors’ decisions to specialize, more specifically whether female role models constitute an attractor factor for female doctors. The second analyses the functioning of the specialty allocation system and tests whether a policy change has had the unintended consequence of reducing the probability of female doctors accessing highly demanded specialties, including surgical specialties

Productivity of the English National Health Service: 2015/16 update

This report updates the Centre for Health Economics’ time series of National Health Service (NHS) productivity growth for the period 2014/15 to 2015/16. It also reports trends in output, input and productivity since 2004/05. NHS productivity growth is measured by comparing growth in the outputs produced by the NHS to growth in the inputs used to produce them. NHS outputs include all the activities undertaken for NHS patients wherever they are treated in England and 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

Productivity of the English National Health Service : 2016/17 update

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

Productivity of the English NHS : 2014/15 Update

This report updates the Centre for Health Economics’ time-series of National Health Service (NHS) productivity growth. The full productivity series runs from 1998/99, but this report updates the series to account for growth between 2013/14 and 2014/15, as well as looking at 10 year growth trends since 2004/05. NHS productivity is measured by comparing growth in the outputs produced by the NHS to growth in the inputs used to produce them. NHS outputs include the amount and quality of care provided to patients. Inputs include the number of doctors, nurses and support staff providing care, the equipment and clinical supplies used, and the hospitals and other premises where care is provided. The measure of NHS output captures all the activities undertaken for all NHS patients wherever they are treated in England. NHS output has increased between 2004/05 and 2014/15 primarily because ever more patients are receiving treatment. Compared to 2004/05, hospitals are treating 4.6 million (27%) more patients, while the number of outpatient attendances has increased by 19%. The output measure also accounts for changes in quality. On the upside, there have been year-on year improvements in hospital survival rates. On the downside, waiting times have been getting longer since 2009/10, although they remain shorter than they were in 2004/05. Taking account of the amount and quality of care, overall NHS output increased by 51% between 2004/05 and 2014/15. Output growth between 2013/14 and 2014/15 was 2.67%. Increased NHS output has come about in response to pronounced increases in NHS expenditure. This has funded both higher wages and more staff and resources. Wages rose by 19% between 2004/05 and 2014/15, while there was a 10% increase in the number of NHS staff. There has been increased use of agency staff, but there have been periods of retrenchment, notably whenever the hospital sector has been struggling to reduce deficits. The use of non-staff resources, such as equipment and supplies, has increased by virtually the same annual proportion (11%) year-on-year. Taken together, NHS inputs increased by 33% between 2004/05 and 2014/15. Input growth between 2013/14 and 2014/15 amounted to 1.78%. We calculate productivity growth by comparing output growth with input growth. Over the last decade NHS productivity has increased by 13.83% in total. Productivity growth has been especially strong since 2009/10, year-on-year growth averaging 1.75%. Growth between 2013/14 and 2014/15, as these latest figures show, amounted to 0.87%. This rate of NHS productivity growth since 2004/05 compares favourably with that achieved by the economy as a whole. Annual NHS productivity growth kept pace with that of the economy up to the recession in 2008/09. Since then NHS productivity growth has consistently outpaced that of the economy, which has stagnated

Drivers of Health Care Expenditure

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