Attributes and weights in health care priority setting: a systematic review of what counts and to what extent

In most societies resources are insufficient to provide everyone with all the health care they want. In practice, this means that some people are given priority over others. On what basis should priority be given? In this paper we are interested in the general public's views on this question. We set out to synthesis what the literature has found as a whole regarding which attributes or factors the general public think should count in priority setting and what weight they should receive. A systematic review was undertaken (in August 2014) to address these questions based on empirical studies that elicited stated preferences from the general public. Sixty four studies, applying eight methods, spanning five continents met the inclusion criteria. Discrete Choice Experiment (DCE) and Person Trade-off (PTO) were the most popular standard methods for preference elicitation, but only 34% of all studies calculated distributional weights, mainly using PTO. While there is heterogeneity, results suggest the young are favoured over the old, the more severely ill are favoured over the less severely ill, and people with self-induced illness or high socioeconomic status tend to receive lower priority. In those studies that considered health gain, larger gain is universally preferred, but at a diminishing rate. Evidence from the small number of studies that explored preferences over different components of health gain suggests life extension is favoured over quality of life enhancement; however this may be reversed at the end of life. The majority of studies that investigated end of life care found weak/no support for providing a premium for such care. The review highlights considerable heterogeneity in both methods and results. Further methodological work is needed to achieve the goal of deriving robust distributional weights for use in health care priority setting.12 page(s

Emigración y recaudo tributario

Según el World Migration Report de 2020, el número de migrantes internacionales aumentó de 84 millones en 1970 a 272 millones en 2019, lo que representa el 3,5 % de la población mundial. Este documento investiga el efecto agregado de la emigración en los ingresos fiscales de los países de origen haciendo énfasis en países en vía de desarrollo. Usando un modelo de gravedad, construimos un instrumento exógeno que varía en el tiempo a partir de características diádicas geográficas invariantes en el tiempo, los cuales nos permiten estimar la tasa de emigración predicha para cada país. Luego utilizamos la tasa de emigración predicha como un instrumento de la tasa de migración observada. Los resultados muestran que la tasa de emigración pronosticada es un buen instrumento de la tasa de emigración de los países en desarrollo, y que existe un efecto agregado positivo de la emigración sobre los ingresos fiscales de los países de origen. Los resultados varían según el tipo de impuesto: la emigración aumenta los ingresos fiscales por impuestos sobre bienes y servicios, pero disminuye los ingresos por impuestos sobre la renta, las ganancias y las ganancias de capital.According to the World Migration Report 2020, the number of international migrants increased from 84 million in 1970 to 272 million in 2019, accounting for 3.5% of the world’s population. This paper investigates the aggregated effect of emigration on the tax revenue of sending countries with a focus on developing nations. Using a gravity approach, we construct a time-varying exogenous instrument out of geographic time-invariant dyadic characteristics that allow us to estimate the predicted emigration rate for every country. Then, we follow an instrumental variable approach where we use our predicted emigration rate as an instrument of the observed migration rate. The results show that the predicted emigration rate is a good instrument of the current emigration rate for developing countries, and that there is a positive aggregated effect of emigration on tax revenue of sending countries. The results vary depending on the type of tax: emigration increases goods and services tax revenue, but it decreases income, profit, and capital gains tax revenue.Enfoque Existe una estrecha relación entre la capacidad de generar ingresos fiscales y el desarrollo económico. Mientras que los países desarrollados recaudan en promedio el 40 por ciento de su Producto Interno Bruto (PIB), los países en desarrollo recaudan entre el 10 y el 20 por ciento. Esta diferencia permite que las naciones desarrolladas proporcionen más y mejores bienes y servicios públicos a sus habitantes. Como resultado, es imperativo explorar los factores que afectan el recaudo tributario para aumentar nuestra comprensión de sus determinantes. Se ha encontrado que variables como la capacidad institucional, el PIB per cápita, la composición de la producción y el grado de apertura comercial son determinantes cruciales de los ingresos fiscales. Por otro lado, según el Informe sobre las Migraciones en el Mundo del 2020, el número de migrantes internacionales ha aumentado en las últimas cinco décadas, de 84 millones en 1970 a 272 millones en 2019, lo que representa el 3,5 % de la población mundial. Entre todos los migrantes en 2019, aproximadamente 141 millones de personas viven en Europa y América del Norte. Este artículo intenta contribuir a esta literatura estudiando el papel de la emigración sobre los ingresos fiscales en los países en desarrollo durante el período 1990-2015. Contribución Existen diferentes mecanismos a través de los cuales la emigración puede afectar las finanzas públicas en el país de origen. En primer lugar, los emigrantes representan una proporción de la fuerza laboral que suele tener un alto nivel educativo, y el 74 % de ellos se encuentra en el rango de edad laboral de 20 a 64 años, lo que representa una pérdida de capital humano y productividad para los países de origen. En segundo lugar, una menor fuerza laboral en el país de origen puede reducir la recaudación del impuesto sobre la renta dado que la población de mayores ingresos está emigrando. Finalmente, los emigrantes envían dinero a los países de origen en forma de remesas, lo que aumenta el consumo y los ingresos fiscales pagados en bienes y servicios. Este artículo sigue un enfoque de dos etapas donde, en la primera etapa, aprovechamos un instrumento exógeno variable en el tiempo para la tasa de emigración. Este instrumento variable en el tiempo se construyó con un modelo de pseudo-gravedad donde la emigración total depende de las características geográficas diádicas y las interacciones temporales que capturan el cambio tecnológico en la industria del transporte aéreo. Resultados Nuestros resultados indican que la emigración aumenta la recaudación fiscal total en los países en desarrollo, con efectos heterogéneos según el tipo de impuesto. Por ejemplo, encontramos que los ingresos por impuestos sobre la renta y corporativos disminuyen con una tasa de emigración más alta, mientras que los impuestos sobre bienes y servicios y el IVA aumentan con una tasa de emigración más alta. Estos resultados no cambian bajo diferentes controles de robustez. Un emigrante más por cada 1000 nativos puede aumentar la recaudación de impuestos sobre bienes y servicios per cápita en USD 4,6 mientras reduce la renta per cápita la recaudación de impuestos per cápita en USD 0,9. El efecto positivo en la recaudación del impuesto sobre bienes y servicios per cápita compensa el impacto negativo en la recaudación del impuesto sobre la renta per cápita, lo que permite que la recaudación total del impuesto sobre la renta per cápita aumente en USD 2,5. En general, los resultados parecen sólidos en diferentes especificaciones. Nuestros hallazgos indican que el impacto positivo en los impuestos al consumo compensa el efecto negativo en los ingresos por impuestos a la renta y corporativos

Essays on Bayesian choice modelling with applications in health economics

The thesis develops flexible Bayesian choice models and these models are often highly-parameterised. All inference is obtained from the posterior density and evaluated using computationally intensive Markov chain Monte Carlo (MCMC) estimation. The models are applied to health economics studies and the empirical results of this thesis represent major contributions to the applied literature in their own right. The thesis consists of three separate but broadly related essays. The first essay is concerned with a heteroscedastic probit model with random effects. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross-validation of the predictive density, the deviance information criterion, and ROC (Receiver Operating Characteristic) curves for model comparison. This research has been published in The Econometrics Journal. The second essay contributes to the development of Bayesian methods in the context of valuing informal carers’ needs. The random effects heteroscedastic probit model estimated in the first essay is extended to accommodate the new data structure and account for more potential sources of heterogeneity, e.g., the individual scale effect which implies that choice behaviour is simply more random for some people than others. The other contributions of this study include examining the entire predictive distributions of Willingness to Accept (WTA) and using the exponential transformation to allow the distribution to be skewed. The third essay follows the work of Leslie et al. (2009) on using a Dirichlet process normal mixture prior to flexibly estimate the link function of binary choice models. This essay provides a unified framework for applying the prior to different types of contingent valuation models. We illustrate the approach through a multiple-bounded contingent valuation study on eliciting the Willingness to Pay (WTP) for cataract surgery in rural India. In particular we demonstrate how to estimate an individual’s WTP distribution and its percentiles as well as the predictive probability of an affirmative answer to a given bid price conditional on a particular set of covariates. In this essay we also conduct an experiment to examine Leslie et al.’s (2009) identification approach and find that by appropriately adjusting the magnitude of the variables, Leslie et al.’s (2009) identification approach can produce stable estimates

The relative value of different QALY types

The oft-applied assumption in the use of Quality Adjusted Life Years (QALYs) in economic evaluation, that all QALYs are valued equally, has been questioned from the outset. The literature has focused on differential values of a QALY based on equity considerations such as the characteristics of the beneficiaries of the QALYs. However, a key characteristic which may affect the value of a QALY is the type of QALY itself. QALY gains can be generated purely by gains in survival, purely by improvements in quality of life, or by changes in both. Using a discrete choice experiment and a new methodological approach to the derivation of relative weights, we undertake the first direct and systematic exploration of the relative weight accorded different QALY types and do so in the presence of equity considerations; age and severity. Results provide new evidence against the normative starting point that all QALYs are valued equally.This study was funded by an Australian National Health and Medical Research Council project grant APP1047788

Content-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolution

In content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents. In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates. Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions. Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression. Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts. As a result, our proposed method reduces the SAM by 42.8% compared to the recently SOTA method to achieve the same low bpp (0.25) on the KAIST dataset

Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children

Purpose Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. Methods We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were  0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study. Results SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation. Conclusions The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems