Planning Health and Social Care Services for the Elderly

The Department of Health’s strategy, Shaping a Healthier Future – A Strategy for Effective Healthcare in the 1990s stated that “to provide the firmest possible basis for the planning of services in the longer-term, the Department of Health will commission a study on the implications for the health services of the projected increase in the elderly population over the next ten years Download the Report here

Planning for Dementia Care in Ireland: Conference Proceedings

The Conference, which took place on 4th June 1999 in the Royal Marine Hotel, Dun Laoghaire, marked the publication of the Councilâ?Ts latest report â?" An Action Plan for Dementia.  The Action Plan takes as its guiding principle the recognition of the individuality of the person with dementia and of his or her needs. It outlines an approach to developing available, accessible and high quality services in the context of existing resources and public expenditure constraints. Its aim is to describe a best practice model of dementia care in Ireland â?" a model which may inform and guide policy makers and others involved in planning service provision, and which may give support and assistance to those who endeavour to provide flexible services at the local level. Download the Report here

Planning for an Ageing Population: Strategic Considerations

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Temporal and spatial distribution of Leishmania mexicana infections in a population of Neotoma micropus

A 19-month mark-release-recapture study of Neotoma micropus with sequential screening for Leishmania mexicana was conducted in Bexar County, Texas, USA. The overall prevalence rate was 14.7% and the seasonal prevalence rates ranged from 3.8 to 26.7%. Nine incident cases were detected, giving an incidence rate of 15.5/100 rats/year. Follow-up of 101 individuals captured two or more times ranged from 14 to 462 days. Persistence of L. mexicana infections averaged 190 days and ranged from 104 to 379 days. Data on dispersal, density, dispersion, and weight are presented, and the role of N. micropus as a reservoir host for L. mexicana is discussed.

An Integrated Work Force Planning Strategy For The Health Services 2009 - 2012

An Integrated Work Force Planning Strategy For The Health Services 2009 – 2012 Click here to download PDF 1.6mb

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Planning Practice Guidance

The Department for Communities and Local Government has published National Planning Practice Guidance which recognises the importance of local infrastructure planning in the development of healthy communities. The guidance supports the National Planning Policy Framework and now includes a section on health and wellbeing. This guidance sets out the government’s planning policies for England and how these are expected to be applied by local authorities. A significant development in the guidance is the recognition of the important role the planning system can play in facilitating social interaction and creating healthy, inclusive communities. Local planning authorities should ensure that health and wellbeing, and health infrastructure are considered in local and neighbourhood plans and in planning decision making.

Spatial analysis of the distribution of leprosy in the State of Ceará, Northeast Brazil

The aim of this study was to describe spatial patterns of the distribution of leprosy and to investigate spatial clustering of incidence rates in the state of Ceará, Northeast Brazil. The average incidence rate of leprosy for the period of 1991 to 1999 was calculated for each municipality of Ceará. Maps were used to describe the spatial distribution of the disease, and spatial statistics were applied to explore large- and small-scale variations of incidence rates. Three regions were identified in which the incidence of leprosy was particularly high. A spatial gradient in the incidence rates was identified, with a tendency of high rates to be concentrated on the North-South axis in the middle region of the state. Moran's I statistic indicated that a significant spatial autocorrelation also existed. The spatial distribution of leprosy in Ceará is heterogeneous. The reasons for spatial clustering of disease rates are not known, but might be related to an heterogeneous distribution of other factors such as crowding, social inequality, and environmental characteristics which by themselves determine the transmission of Mycobacterium leprae.