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Background: A Pay-for-Performance (P4P) programme, known as Prescribed Specialised Services Commissioning for Quality and Innovation (PSS CQUIN), was introduced for specialised services in the English NHS in 2013/2014. These services treat patients with rare and complex conditions. We evaluate the implementation of PSS CQUIN contracts between 2016/2017 and 2018/2019. Methods: We used a mixed methods evaluative approach. In the quantitative analysis, we used a difference-in-differences design to evaluate the effectiveness of ten PSS CQUIN schemes across a range of targeted outcomes. Potential selection bias was addressed using propensity score matching. We also estimated impacts on costs by scheme and financial year. In the qualitative analysis, we conducted semi-structured interviews and focus group discussions to gain insights into the complexities of contract design and programme implementation. Qualitative data analysis was based on the constant comparative method, inductively generating themes. Results: The ten PSS CQUIN schemes had limited impact on the targeted outcomes. A statistically significant improvement was found for only one scheme: in the clinical area of trauma, the incentive scheme increased the probability of being discharged from Adult Critical Care within four hours of being clinically ready by 7%. The limited impact may be due to the size of the incentive payments, the complexity of the schemes' design, and issues around ownership, contracting and flexibility. Conclusion: The PSS CQUIN schemes had little or no impact on quality improvements in specialised services. Future P4P programmes in healthcare could benefit from lessons learnt from this study on incentive design and programme implementation. [ABSTRACT FROM AUTHOR]

We study whether hospitals that exhibit systematically higher bed occupancy rates are associated with lower quality in England over 2010/11–2017/18. We develop an economic conceptual framework to guide our empirical analysis and run regressions to inform possible policy interventions. First, we run a pooled OLS regression to test if high bed occupancy is associated with, and therefore acts as a signal of, lower quality, which could trigger additional regulation. Second, we test whether this association is explained by exogenous demand–supply factors such as potential demand, and unavoidable costs. Third, we include determinants of bed occupancy (beds, length of stay, and volume) that might be associated with quality directly, rather than indirectly through bed occupancy. Last, we use a within-between random-effects specification to decompose these associations into those due to variations in characteristics between hospitals and variations within hospitals. We find that bed occupancy rates are positively associated with overall and surgical mortality, negatively associated with patient-reported health gains, but not associated with other indicators. These results are robust to controlling for demand–supply shifters, beds, and volume. The associations reduce by 12%-25% after controlling for length of stay in most cases and are explained by variations in bed occupancy between hospitals. [ABSTRACT FROM AUTHOR]

A number of OECD countries have introduced waiting time prioritisation policies which give explicit priority to severely ill patients with high marginal disutility of waiting. There is however little empirical evidence on how patients are actually prioritised. We exploit a unique opportunity to investigate this issue using a large national dataset with accurate measures of severity on nearly 400,000 patients. We link data from a national patient-reported outcome measures survey to administrative data on all patients waiting for a publicly funded hip and knee replacement in England during the years 2009–14. We find that patients suffering the most severe pain and immobility have shorter waits than those suffering the least, by about 24% for hip replacement and 11% for knee replacement, and that the association is approximately linear. These differentials are more closely associated with pain than immobility, and are larger in hospitals with longer average waiting times. These result suggests that doctors prioritise patients according to severity even when no formal prioritisation policy is in place and average waiting times are short. [ABSTRACT FROM AUTHOR]

Abstract: Waiting times for elective surgery, like hip replacement, are often referred to as an equitable rationing mechanism in publicly-funded healthcare systems because access to care is not based on socioeconomic status. Previous work has established that that this may not be the case and there is evidence of inequality in NHS waiting times favouring patients living in the least deprived neighbourhoods in England. We advance the literature by explaining variations of inequalities in waiting times in England in four different ways. First, we ask whether inequalities are driven by education rather than income. Our analysis shows that education and income deprivation have distinct effects on waiting time. Patients in the first quintile with least deprivation in education wait 9% less than patients in the second quintile and 14% less than patients in the third-to-fifth quintile. Patients in the fourth and fifth most income-deprived quintile wait about 7% longer than patients in the least deprived quintile. Second, we investigate whether inequalities arise “across” hospitals or “within” the hospital. The analysis provides evidence that most inequalities occur within hospitals rather than across hospitals. Moreover, failure to control for hospital fixed effects results in underestimation of the income gradient. Third, we explore whether inequalities arise across the entire waiting time distribution. Inequalities between better educated patients and other patients occur over large part of the waiting time distribution. Moreover we find that the education gradient becomes smaller for very long waiting. Fourth, we investigate whether the gradient may reflect the fact that patients with higher socioeconomic status have a different severity as proxied through a range of types and the number of diagnoses (in addition to age and gender) compared to those with lower socioeconomic status. We find no evidence that differences in severity explain the social gradient in waiting times. [Copyright &y& Elsevier]

We investigate the extent to which small hospitals are associated with lower quality. We first take a patient perspective, and test if, controlling for casemix, patients admitted to small hospitals receive lower quality than those admitted to larger hospitals. We then investigate if differences in quality between large and small hospitals can be explained by hospital characteristics such as hospital type and staffing. We use a range of quality measures including hospital mortality rates (overall and for specific conditions), hospital acquired infection rates, waiting times for emergency patients, and patient perceptions of the care they receive. We find that small hospitals, with fewer than 400 beds, are generally not associated with lower quality before or after controlling for hospital characteristics. The only exception is heart attack mortality, which is generally higher in small hospitals. • We investigate whether small hospitals are associated with lower quality indicators. • We use patient mortality, infections, A&E waiting times, care perception as quality. • Small hospitals (<400 beds) are not generally associated with lower quality. • However, AMI mortality is generally higher in small hospitals. • Our findings do not support closures of small hospitals to achieve quality gains. [ABSTRACT FROM AUTHOR]

• We investigate the effect of hospital volume on health for hip replacement surgery. • Exogenous hospital volumes are derived using patient distance to the hospital. • Hospital volumes have no effect on health after accounting for volume endogeneity. • Volume-outcome associations may be driven by patient demand for hospital quality. This study investigates the causal effect of hospital volume on health gains from hip replacement surgery in the English National Health Service. We exploit a unique dataset, which links routine hospital records and patient-reported outcome measures (PROMs) for all public hospitals in England between 2011 and 2015. PROMs assess patients' health along key dimensions of pain and mobility shortly before and six months after the surgery. We investigate whether higher hospital volume increases patient health six months post-surgery, conditioning on severity through an accurate measure of pre-surgery health, other patient medical and socioeconomic indicators and a rich set of hospital characteristics. We address possible reverse-causality bias due to hospital demand being responsive to quality by constructing a measure of predicted hospital volumes based on a patient choice model, in line with approaches adopted in the hospital competition literature. Results from a pooled OLS model show that the observed effect of volume on health outcomes in hip replacement surgery is positive and clinically small, but no longer statistically significant once we account for the endogeneity of volume. Results from an alternative specification with hospital fixed effects further confirm that hospital volume does not have a causal effect on health outcomes. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the most common cancers with 36,016 registered cases in 2020 in the United Kingdom. Access to cancer care remains a policy priority across OECD countries, but there are concerns that socioeconomic inequalities have been amplified following the COVID-19 pandemic due to accumulated backlogs. Using administrative data from the Hospital Episodes Statistics, this study examines socioeconomic inequalities in different dimensions of access to prostate cancer surgery in England in 2015–22, and whether such inequalities increased after COVID-19. Access is a multifaceted concept encompassing several dimensions along the patient pathway. We investigate socioeconomic inequalities in the volume of surgeries, waiting time of patients on the list (patient responsiveness), hospital length of stay (intensity of care), 30-day emergency readmissions and 3-year mortality (health outcomes). We find that, after controlling for a range of factors (e.g. age composition, ethnicity, rurality, primary care availability), the number of prostate surgeries for the most income-deprived quintile is 41.0 % lower than the least income-deprived quintile in the pre-COVID period. The gradient appears similar in the post-COVID period in proportionate terms when volume dropped. We generally find no evidence of inequalities in other dimensions of access, such as waiting times (from patient addition to treatment list) and length of stay (from hospital admission to discharge). We also find no evidence of inequalities in the probability of being readmitted as an emergency within 30 days from discharge. Instead, patients in the most income-deprived quintile have a higher mortality rate by at least one percentage point relative to the least-deprived quintile. This difference in mortality does not appear to be related to differences in the quality of care across hospitals. The analysis highlights the need to take a multidimensional approach when assessing socioeconomic inequalities in access to hospital care along the patient pathway. • Evidence of socioeconomic inequalities in the number of prostate surgeries. • Inequalities did not amplify following COVID-19. • No evidence of inequalities in other dimensions of access, such as waiting times. [ABSTRACT FROM AUTHOR]

We study a pay-for-efficiency scheme that encourages hospitals to admit and discharge patients on the same calendar day when clinically appropriate. Since 2010, hospitals in the English NHS are incentivised by a higher price for patients treated as same-day discharge than for overnight stays, despite the former being less costly. We analyse administrative data for patients treated during 2006-2014 for 191 conditions for which same-day discharge is clinically appropriate - of which 32 are incentivised. Using difference-in-difference and synthetic control methods, we find that the policy had generally a positive impact with a statistically significant effect in 14 out of the 32 conditions. The median elasticity is 0.24 for planned and 0.01 for emergency conditions. Condition-specific design features explain some, but not all, of the differential responses. [ABSTRACT FROM AUTHOR]