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

Flexible modelling of spatial variation in agricultural field trials with the R package INLA

The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ( AR1⊗AR1 ) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the AR1⊗AR1 and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA

Association of HLA Class I and Class II genes with bcr-abl transcripts in leukemia patients with t(9;22) (q34;q11)

BACKGROUND: Based on the site of breakpoint in t(9;22) (q34;q11), bcr-abl fusion in leukemia patients is associated with different types of transcript proteins. In this study we have seen the association of HLA genes with different types of bcr-abl transcripts. The association could predict the bcr-abl peptide presentation by particular HLA molecules. METHODS: The study included a total of 189 patients of mixed ethnicity with chronic myelogenous leukemia and acute lymphocytic leukemia who were being considered for bone marrow transplantation. Typing of bcr-abl transcripts was done by reverse transcriptase PCR method. HLA typing was performed by molecular methods. The bcr-abl and HLA association was studied by calculating the relative risks and chi-square test. RESULTS: Significant negative associations (p < 0.05) were observed with HLA-A*02 (b2a2, e1a2), -A*68 (b2a2, b3a2, e1a2), -B*14 (b2a2, b3a2, e1a2), -B*15 (b2a2, b3a2), -B*40 (b2a2), -DQB1*0303 (b2a2, b3a2), -DQB1*0603 (b2a2), -DRB1*0401 (e1a2), -DRB1*0701 (b3a2), and -DRB1*1101 (b2a2). CONCLUSIONS: The negative associations of a particular bcr-abl transcript with specific HLA alleles suggests that these alleles play a critical role in presenting peptides derived from the chimeric proteins and eliciting a successful T-cell cytotoxic response. Knowledge of differential associations between HLA phenotypes and bcr-abl fusion transcript types would help in developing better strategies for immunization with the bcr-abl peptides against t(9;22) (q34;q11)-positive leukemia