Waiting time distributions and national targets for elective surgery in UK: theoretical modelling and duration analysis

Waiting times for elective surgery constitute a key performance indicator for the NHS. The principal policy response has been to introduce maximum waiting time targets against which performance is measured and rewarded. The aim of this thesis is to shed light on the mechanism of patients’ admittance for elective surgery in UK by examining the whole distribution of their waiting times from an empirical and theoretical perspective. In Chapter 2, we empirically investigate the effect of government targets on the distribution of patients’ waiting times by applying duration analysis techniques to waiting time data from 2001/02 and 2002/03 for three specialties: general surgery, trauma & orthopaedics and ophthalmology. In Chapter 3, we examine further the variation in the way hospitals and surgeons manage their waiting lists by exposing detailed patterns regarding the shape of the survival and hazard curves of patients’ waits. We use an expanded dataset (1997/98 to 2005/06) both in a cross-sectional and across time framework controlling for factors such as size, type and performance rating for hospitals and activity level for doctors. We also address the issue of the evolution of waiting time distributions over time. Chapter 4 provides a theoretical supply model on how a hospital manages its stock of patients given its objective function and the constraints it is faced with. We derive the optimal waiting time distribution and identify important factors that could explain the differences between the observed empirical patterns

The asymmetric effects of waiting time targets in health care

Waiting time targets have been a key policy intervention in many OECD countries, aimed at reducing persistent waiting times for healthcare. What is the impact of targets on the distribution of patients' waiting time? Do they affect healthcare outcomes? We address the first question by developing a theoretical model of healthcare provision and empirically assessing the entire distribution of patients' durations at the hospital level. Our model and empirical evidence identify two distinct admission patterns. Hospitals respond by either treating all patients faster or by `substituting' among short and long waiters, indicating an asymmetric effect across patients. In order to address the impact of targets on healthcare outcomes (mortality, prolonged healthcare, delayed discharge at the patient level) we explore the identified heterogeneity of responses across hospitals. We find supportive evidence of a systematic difference in outcomes of patients treated in hospitals that exhibit asymmetric responses to targets

How consumption carbon emission intensity varies across Spanish households

La prominencia de las políticas de mitigación de emisiones exige una comprensión de su impacto distributivo potencial. Para evaluar la heterogeneidad distributiva, cuantificamos y analizamos la intensidad de emisión del consumo, definida como las emisiones de carbono por unidad de consumo, en los hogares de España. A excepción de los hogares más pobres, la intensidad de las emisiones disminuye con los ingresos y alcanza su punto máximo para los hogares cuya persona de referencia es de mediana edad (40 años). Además, los hogares cuya persona de referencia tiene menos educación y es hombre emiten más por unidad de gasto. Por lo tanto, las políticas de mitigación de emisiones pueden afectar de manera desproporcionada a los hogares de mediana edad cuyos ingresos rondan los 1.000 euros y cuyo cabeza de familia es hombre y tiene menos educación.The prominence of emission mitigation policies calls for an understanding of their potential distributional impact. To assess the distributional heterogeneity, we quantify and analyse the consumption emission intensity, defined as carbon emissions per unit of consumption, across households in Spain. With the exception of the poorest households, emission intensity decreases with income and peaks for households whose head is middle-aged (40 years old). Moreover, households whose main earner is less educated and male emit more per unit of expenditure. Thus, emission mitigation policies may disproportionately impact middle-aged households whose income is around €1,000, and whose head is male and less educated

How inflation varies across Spanish households

La inflación tiene efectos distributivos. Aprovechando los datos sobre el gasto de consumo de bienes en los hogares proporcionados por la Encuesta de Presupuestos Familiares de España, estimamos la inflación específica de los hogares de 2006 a 2021 en España y analizamos cómo varía según las características conocidas de los hogares. Mostramos que los hogares con menores ingresos, un número superior de miembros y un jefe de familia con menos estudios, mayor y varón experimentan una inflación más alta. Finalmente, también describimos los efectos de los aumentos de precios más recientes en los hogares. Las diferencias son sustanciales: en 2021, la inflación para los hogares de menores ingresos (cuartil inferior) fue 2 puntos porcentuales (pp) superior a la de los hogares de mayores ingresos (cuartil superior), mientras que para los hogares cuya persona de referencia es mayor de 60 años fue 1,5 pp mayor que para los hogares más jóvenes.Inflation has distributional effects. Leveraging the data on consumption expenditure on goods across households provided in the Spanish Household Budget Survey we estimate household-specific inflation from 2006 to 2021 in Spain and analyse how it varies according to households’ known characteristics. We show that households with lower income and more members and whose head is less educated, older and male experience higher inflation. Lastly, we also depict the effects of the most recent price increases across households. The differences are substantial: in 2021, inflation for lower-income households (bottom quartile) was 2 percentage points higher than for higher-income households (top quartile), while for households whose head is over the age of 60 it was 1.5 percentage points higher than for younger households

Waiting time distribution in public health care: empirics and theory

Excessive waiting times for elective surgery have been a long-standing concern in many national healthcare systems in the OECD. How do the hospital admission patterns that generate waiting lists affect different patients? What are the hospitals characteristics that determine waiting times? By developing a model of healthcare provision and analysing empirically the entire waiting time distribution we attempt to shed some light on those issues. We first build a theoretical model that describes the optimal waiting time distribution for capacity constraint hospitals. Secondly, employing duration analysis, we obtain empirical representations of that distribution across hospitals in the UK from 1997–2005. We observe important differences on the ‘scale’ and on the ‘shape’ of admission rates. Scale refers to how quickly patients are treated and shape represents trade-offs across duration-treatment profiles. By fitting the theoretical to the empirical distributions we estimate the main structural parameters of the model and are able to closely identify the main drivers of these empirical differences. We find that the level of resources allocated to elective surgery (budget and physical capacity), which determines how constrained the hospital is, explains differences in scale. Changes in benefits and costs structures of healthcare provision, which relate, respectively, to the desire to prioritise patients by duration and the reduction in costs due to delayed treatment, determine the shape, affecting short and long duration patients differently

Bronchiectasis in Europe:data on disease characteristics from the European Bronchiectasis registry (EMBARC)

Background: Bronchiectasis is a heterogeneous, neglected disease with few multicentre studies exploring the causes, severity, microbiology, and treatment of the disease across Europe. This aim of this study was to describe the clinical characteristics of bronchiectasis and compare between different European countries.Methods: EMBARC is an international clinical research network for bronchiectasis. We report on a multicentre, prospective, observational, non-interventional, cohort study (the EMBARC registry) conducted across 27 European countries and Israel. Comprehensive clinical data were collected from adult patients (aged ≥18 years) at baseline and annual follow-up visits using electronic case report form. Data from individual countries were grouped into four regions (the UK, northern and western Europe, southern Europe, and central and eastern Europe according to modified EU EuroVoc classification). Follow-up data were used to explore differences in exacerbation frequency between regions using a negative binomial regression model.Findings: Between Jan 12, 2015, and April 12, 2022, 16 963 individuals were enrolled. Median age was 67 years (IQR 57-74), 10 335 (60·9%) participants were female and 6628 (39·1%) were male. The most common cause of bronchiectasis in all 16 963 participants was post-infective disease in 3600 (21·2%); 6466 individuals (38·1%) were classified as idiopathic. Individuals with bronchiectasis experienced a median of two exacerbations (IQR 1-4) per year and 4483 (26·4%) patients had a hospitalisation for exacerbation in the previous year. When examining the percentage of all isolated bacteria, marked differences in microbiology were seen between countries, with a higher frequency of Pseudomonas aeruginosa and lower Haemophilus influenzae frequency in southern Europe, compared with higher H influenzae in the UK and northern and western Europe. Compared with other regions, patients in central and eastern Europe had more severe bronchiectasis measured by the Bronchiectasis Severity Index (51·3% vs 35·1% in the overall cohort) and more exacerbations leading to hospitalisations (57·9% vs 26·4% in the overall cohort). Overall, patients in central and eastern Europe had an increased frequency of exacerbations (adjusted rate ratio [RR] 1·12, 95% CI 1·01-1·25) and a higher frequency of exacerbations leading to hospitalisations (adjusted RR 1·71, 1·44-2·02) compared with patients in other regions. Treatment of bronchiectasis was highly heterogeneous between regions.Interpretation: Bronchiectasis shows important geographical variation in causes, microbiology, severity, and outcomes across Europe.</p

Multidimensional severity assessment in bronchiectasis:An analysis of 7 European cohorts.

INTRODUCTION: Bronchiectasis is a multidimensional disease associated with substantial morbidity and mortality. Two disease-specific clinical prediction tools have been developed, the Bronchiectasis Severity Index (BSI) and the FACED score, both of which stratify patients into severity risk categories to predict the probability of mortality. METHODS: We aimed to compare the predictive utility of BSI and FACED in assessing clinically relevant disease outcomes across seven European cohorts independent of their original validation studies. RESULTS: The combined cohorts totalled 1612. Pooled analysis showed that both scores had a good discriminatory predictive value for mortality (pooled area under the curve (AUC) 0.76, 95% CI 0.74 to 0.78 for both scores) with the BSI demonstrating a higher sensitivity (65% vs 28%) but lower specificity (70% vs 93%) compared with the FACED score. Calibration analysis suggested that the BSI performed consistently well across all cohorts, while FACED consistently overestimated mortality in 'severe' patients (pooled OR 0.33 (0.23 to 0.48), p<0.0001). The BSI accurately predicted hospitalisations (pooled AUC 0.82, 95% CI 0.78 to 0.84), exacerbations, quality of life (QoL) and respiratory symptoms across all risk categories. FACED had poor discrimination for hospital admissions (pooled AUC 0.65, 95% CI 0.63 to 0.67) with low sensitivity at 16% and did not consistently predict future risk of exacerbations, QoL or respiratory symptoms. No association was observed with FACED and 6 min walk distance (6MWD) or lung function decline. CONCLUSION: The BSI accurately predicts mortality, hospital admissions, exacerbations, QoL, respiratory symptoms, 6MWD and lung function decline in bronchiectasis, providing a clinically relevant evaluation of disease severity