Waiting times and socioeconomic status: evidence from England

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

Location, quality and choice of hospital: Evidence from England 2002–2013

We investigate (a) how patient choice of hospital for elective hip replacement is influenced by distance, quality and waiting times, (b) differences in choices between patients in urban and rural locations, (c) the relationship between hospitals' elasticities of demand to quality and the number of local rivals, and how these changed after relaxation of constraints on hospital choice in England in 2006. Using a data set on over 500,000 elective hip replacement patients over the period 2002 to 2013 we find that patients became more likely to travel to a provider with higher quality or lower waiting times, the proportion of patients bypassing their nearest provider increased from 25% to almost 50%, and hospital elasticity of demand with respect to own quality increased. By 2013 average hospital demand elasticity with respect to readmission rates and waiting times were −0.2 and −0.04. Providers facing more rivals had demand that was more elastic with respect to quality and waiting times. Patients from rural areas have smaller disutility from distance

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

Class II treatment by palatal miniscrew-system appliance: A case report

This case shows that using a rapid palatal expander (RPE) and then a pendulum appliance anchored to palatal miniscrews is an option for improving treatment management in a noncompliant patient requiring maxillary expansion and molar distalization in the late mixed dentition. First, an RPE was used to expand the maxillary arch. Then, a modified pendulum appliance was used to distalize the maxillary first permanent molars. Optimal positioning of two palatal miniscrews enabled both appliances to be supported by skeletal anchorage. Treatment was finished using multibracket fixed appliances, and after 2 years, skeletal Class I as well as dental Class I canine and molar relationships were achieved

Ghigliottin-AI @ EVALITA2020: Evaluating artificial players for the language game “La Ghigliottina”

Evaluating Artificial Players for the Language Game “La Ghigliottina” (Ghigliottin-AI) task is one of the tasks organized in the context of the 2020 EVALITA edition, a periodic evaluation campaign of Natural Language Processing (NLP) and speech tools for the Italian language. Ghigliottin-AI participants are asked to build an artificial player able to solve “La Ghigliottina”, namely the final game of an Italian TV show called “L'Eredità”. The game involves a single player who is given a set of five words unrelated to each other, but related with a sixth word that represents the solution to the game. Fourteen teams registered to Ghigliottin-AI. Nevertheless, only two teams submitted their run. In order to evaluate the submitted systems, we rely on an API base methodology, via a Remote Evaluation Server (RES). In this report we describe the Ghigliottin-AI task, the data, the evaluation and we discuss results

Extracting relations from Italian wikipedia using self-training

In this paper, we describe a supervised approach for extracting relations from Wikipedia. In particular, we exploit a self-training strategy for enriching a small number of manually labeled triples with new self-labeled examples. We integrate the supervised stage in WikiOIE, an existing framework for unsupervised extraction of relations from Wikipedia. We rely on WikiOIE and its unsupervised pipeline for extracting the initial set of unlabelled triples. An evaluation involving different algorithms and parameters proves that self-training helps to improve performance. Finally, we provide a dataset of about three million triples extracted from the Italian version of Wikipedia and perform a preliminary evaluation conducted on a sample dataset, obtaining promising results