The challenge pathway: a mixed methods evaluation of an innovative care model for the palliative and end-of-life care of people with dementia (innovative practice)

An innovative service for the palliative and end-of-life care of people with dementia was introduced at a UK hospice. This evaluation involved analysis of audit data, semi-structured interviews with project staff (n=3) and surveys of family carers (n=15) and professionals (n=20). The service has increased access to palliative, end-of-life care and other services. Improvements were reported in the knowledge, confidence and care skills of family carers and professionals. Carers felt better supported and it was perceived that the service enabled more patients to be cared for at home or in their usual place of care

Respiratory health and immunological profile of poultry workers

No Abstrac

Genotyping of 73 UM-SCC head and neck squamous cell carcinoma cell lines

Background. We established multiple University of Michigan Squamous Cell Carcinoma (UM-SCC) cell lines. With time, these have been distributed to other labs all over the world. Recent scientific discussions have noted the need to confirm the origin and identity of cell lines in grant proposals and journal articles. We genotyped the UM-SCC cell lines in our collection to confirm their unique identity. Method. Early-passage UM-SCC cell lines were genotyped and photographed. Results . Thus far, 73 unique head and neck UM-SCC cell lines (from 65 donors, including 21 lines from 17 females) were genotyped. In 7 cases, separate cell lines were established from the same donor. Conclusions. These results will be posted on the UM Head and Neck SPORE Tissue Core website for other investigators to confirm that the UM-SCC cells used in their laboratories have the correct features. Publications using UM-SCC cell lines should confirm the genotype. © 2009 Wiley Periodicals, Inc. Head Neck, 2010Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69163/1/21198_ftp.pd

Enrolment in clinical research at UCLH and geographically distributed indices of deprivation [version 1; peer review: awaiting peer review]

Healthcare should be judged by its equity as well as its quality. Both aspects depend not only on the characteristics of service delivery but also on the research and innovation that ultimately shape them. Conducting a fully-inclusive evaluation of the relationship between enrolment in primary research studies at University College London Hospitals NHS Trust and indices of deprivation, here we demonstrate a quantitative approach to evaluating equity in healthcare research and innovation. We surveyed the geographical locations, aggregated into Lower Layer Super Output Areas (LSOAs), of all England-resident UCLH patients registered as enrolled in primary clinical research studies. We compared the distributions of ten established indices of deprivation across enrolled and non-enrolled areas within Greater London and within a distance-matched subset across England. Bayesian Poisson regression models were used to examine the relation between deprivation and the volume of enrolment standardized by population density and local disease prevalence. A total of 54593 enrolments covered 4401 LSOAs in Greater London and 10150 in England, revealing wide geographical reach. The distributions of deprivation indices were similar between enrolled and non-enrolled areas, exhibiting median differences from 0.26% to 8.73%. Across Greater London, enrolled areas were significantly more deprived on most indices, including the Index of Multiple Deprivation; across England, a more balanced relationship to deprivation emerged. Regression analyses of enrolment volumes yielded weak biases, in favour of greater deprivation for most indices, with little modulation by local disease prevalence. Primary clinical research at UCLH has wide geographical reach. Areas with enrolled patients show similar distributions of established indices of deprivation to those without, both within Greater London, and across distance-matched areas of England. We illustrate a robust approach to quantifying an important aspect of equity in clinical research and provide a flexible set of tools for replicating it across other institutions

Quasars: What turns them off?

(Abridged) We explore the idea that the anti-hierarchical turn-off observed in the quasar population arises from self-regulating feedback, via an outflow mechanism. Using a detailed hydrodynamic simulation we calculate the luminosity function of quasars down to a redshift of z=1 in a large, cosmologically representative volume. Outflows are included explicitly by tracking halo mergers and driving shocks into the surrounding intergalactic medium. Our results are in excellent agreement with measurements of the spatial distribution of quasars, and we detect an intriguing excess of galaxy-quasar pairs at very short separations. We also reproduce the anti-hierarchical turnoff in the quasar luminosity function, however, the magnitude of the turn-off falls short of that observed as well as that predicted by analogous semi-analytic models. The difference can be traced to the treatment of gas heating within galaxies. The simulated galaxy cluster L_X-T relationship is close to that observed for z~1 clusters, but the simulated galaxy groups at z=1 are significantly perturbed by quasar outflows, suggesting that measurements of X-ray emission in high-redshift groups could well be a "smoking gun" for the AGN heating hypothesis.Comment: 16 pages, 11 figures, submitted to ApJ, comments welcome

Predicting scheduled hospital attendance with artificial intelligence

Failure to attend scheduled hospital appointments disrupts clinical management and consumes resource estimated at £1 billion annually in the United Kingdom National Health Service alone. Accurate stratification of absence risk can maximize the yield of preventative interventions. The wide multiplicity of potential causes, and the poor performance of systems based on simple, linear, low-dimensional models, suggests complex predictive models of attendance are needed. Here, we quantify the effect of using complex, non-linear, high-dimensional models enabled by machine learning. Models systematically varying in complexity based on logistic regression, support vector machines, random forests, AdaBoost, or gradient boosting machines were trained and evaluated on an unselected set of 22,318 consecutive scheduled magnetic resonance imaging appointments at two UCL hospitals. Highdimensional Gradient Boosting Machine-based models achieved the best performance reported in the literature, exhibiting an area under the receiver operating characteristic curve of 0.852 and average precision of 0.511. Optimal predictive performance required 81 variables. Simulations showed net potential benefit across a wide range of attendance characteristics, peaking at £3.15 per appointment at current prevalence and call efficiency. Optimal attendance prediction requires more complex models than have hitherto been applied in the field, reflecting the complex interplay of patient, environmental, and operational causal factors. Far from an exotic luxury, high-dimensional models based on machine learning are likely essential to optimal scheduling amongst other operational aspects of hospital care. High predictive performance is achievable with data from a single institution, obviating the need for aggregating large-scale sensitive data across governance boundaries

The impact of constructive operating lease capitalisation on key accounting ratios

Current UK lease accounting regulation does not require operating leases to be capitalised in the accounts of lessees, although this is likely to change with the publication of FRS 5. This study conducts a prospective analysis of the effects of such a change. The potential magnitude of the impact of lease capitalisation upon individual users' decisions, market valuations, company cash flows, and managers' behaviour can be indicated by the effect on key accounting ratios, which are employed in decision-making and in financial contracts. The capitalised value of operating leases is estimated using a method similar to that suggested by Imhoff, Lipe and Wright (1991), adapted for the UK accounting and tax environment, and developed to incorporate company-specific assumptions. Results for 1994 for a random sample of 300 listed UK companies show that, on average, the unrecorded long-term liability represented 39% of reported long-term debt, while the unrecorded asset represented 6% of total assets. Capitalisation had a significant impact (at the 1% level) on six of the nine selected ratios (profit margin, return on assets, asset turnover, and three measures of gearing). Moreover, the Spearman rank correlation between each ratio before and after capitalisation revealed that the ranking of companies changed markedly for gearing measures in particular. There were significant inter-industry variations, with the services sector experiencing the greatest impact. An analysis of the impact of capitalisation over the five-year period from 1990 to 1994 showed that capitalisation had the greatest impact during the trough of the recession. Results were shown to be robust with respect to key assumptions of the capitalisation method. These findings contribute to the assessment of the economic consequences of a policy change requiring operating lease capitalisation. Significant changes in the magnitude of key accounting ratios and a major shift in company performance rankings suggest that interested parties' decisions and company cash flows are likely to be affected

GeoSPM: Geostatistical parametric mapping for medicine

The characteristics and determinants of health and disease are often organised in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Though a mature discipline, spatial analysis is comparatively rare in medicine, arguably a consequence of the complexity of the domain and the inclemency of the data regimes that govern it. Drawing on statistical parametric mapping, a framework for topological inference well-established in the realm of neuroimaging, we propose and validate a novel approach to the spatial analysis of diverse clinical data - GeoSPM - based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is transparently interpretable, can be implemented with ease by non-specialists, enables flexible modelling of complex spatial relations, exhibits robustness to noise and under-sampling, offers well-founded criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation of GeoSPM, and suggest that its adoption could catalyse the wider use of spatial analysis across the many aspects of medicine that urgently demand it

GeoSPM: Geostatistical parametric mapping for medicine

The characteristics and determinants of health and disease are often organised in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Though a mature discipline, spatial analysis is comparatively rare in medicine, arguably a consequence of the complexity of the domain and the inclemency of the data regimes that govern it. Drawing on statistical parametric mapping, a framework for topological inference well-established in the realm of neuroimaging, we propose and validate a novel approach to the spatial analysis of diverse clinical data - GeoSPM - based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is transparently interpretable, can be implemented with ease by non-specialists, enables flexible modelling of complex spatial relations, exhibits robustness to noise and under-sampling, offers well-founded criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation of GeoSPM, and suggest that its adoption could catalyse the wider use of spatial analysis across the many aspects of medicine that urgently demand it.Comment: 29 pages, 22 figure