2,449 research outputs found

    The IIASA Health Care Resource Allocation Sub-Model: Mark 2 - the Allocation of Many Different Resources

    Get PDF
    The function of the resource allocation sub-model within the IIASA Health Care System model is to simulate how the HCS allocates limited supplies of resources between competing demands. The principal outputs of the sub-model are the numbers of patients treated, in different categories, and the modes and quotas of treatment they receive. The Mark 2 version of the sub-model described in this paper simulates the allocation of many resources within one mode of treatment. It uses the same main assumption as used in the Mark 1 version previously reported; namely that in allocating its resources the HCS attempts to optimise a utility function whose parameters can be inferred from data on past allocations. Depending upon the type of data that is availabledifferent procedures for parameter estimation are required. This paper analyses estimation procedures which use historical allocation data directly. Both these procedures and the solution algorithm have been realized in a small computer program which can be readily installed on most scientific computer installations. The use of the sub-model is illustrated by three hypothetical applications using hospital data

    A Model of the Equilibrium between Different Levels of Treatment in the Health Care System: Pilot Version

    Get PDF
    Health Care Systems manage to balance competing demands for care with limited supplies of resources. They achieve an equilibrium. This paper describes a resource allocation model that represents this equilibrium as the equalizing of pressures between different levels of treatment. A pilot version of the model is formulated, solved, and programmed; and an illustrative example is given. Work towards a more sophisticated model is proposed

    DRAM: A Model of Health Care Resource Allocation

    Get PDF
    The principal aim of health care research at IIASA has been to develop a family of submodels of national health care systems for use by health service planners. The modeling work is proceeding along the lines proposed in the Institute's current Research Plan. It involves the construction of linked submodels dealing with population, disease prevalence, resource need, resource allocation, and resource supply. This is the second research report on the disaggregated resource allocation sub-model called DRAM. It describes the extension of the Mark 1 version (RR-78-8) to include the distribution of many resources across different modes of care. The earlier assumption that all available resources must be used has been relaxed, and an extensive analytic treatment suggests various methods for estimating the submodel's parameters. Several case studies that use the model are in progress and reports on these applications will be forthcoming. This paper is an output of a collaboration between two Areas at IIASA. It describes how a health resource allocation model, developed in the Health Care Systems Task of the Human Settlements and Services Area, may be solved by using optimization techniques studied in the Optimization Task of the Systems and Decision Sciences Area

    Health Care Systems Modeling at IIASA: A Status Report

    Get PDF
    Governmental policies in all countries strongly influence the medical services available to society. It is therefore essential that decision makers be aware of changing demands and needs for health resources and services. In light of this, the Health Care Systems (HCS) Modeling Task of the Human Settlement and Services (HSS) Area has set a goal of creating a model that will assist national decision makers in formulating policy. This model consists of a number of linked submodels dealing with various related topics from population growth to resource allocation. Some of these submodels have already been tested, and collaborating national research centers have started to implement them with their own data. The resulting experience of the past several years is described in this review, which has been prepared by members of the HCS Modeling group. By sharing our aims and achievements with a wider audience, we hope to facilitate future international collaborative work on this research

    Nondifferentiable Optimization Promotes Health Care

    Get PDF
    An example of a health resource allocation model, solved previously by piecewise linear approximation with data from Devon, U.K., is solved using nondifferentiable optimization (NDO). The example illustrates a new application for NDO, and the novel approach makes clearer the workings of the model

    Health Care Systems Modelling at IIASA: A Status Report

    Get PDF
    The focus of the Human Settlements and Services Area at. IIASA is people -- their number and geographical distribution, their needs and demands for resources and services, and their impact on the environment. Research in the Area is divided into three themes: urban systems management, human resources and services, and human settlement systems. This report describes work that has been carried out up to the Fall of 1978 by the Health Care Systems , Modeling Task, representing the human resources and services theme. It focuses in particular on the submodels that have been developed and tested, and on the collaboration that has been established with similar research teams in a number of countries around the world. Governmental policies in all countries strongly influence the medical services available to society. It is therefore essential that decisionmakers be aware of changing demands and needs for health resources and services. In the light of this, the HCS Modeling Task has set a goal of creating a model that will assist national decisionmakers in their policy formation. This model consists of a number of linked submodels dealing with various related topics from population growth to resource allocation. . Some of these submodels have already been tested, and collaborating national research centers have started to implement them with their own data. The resulting experience of the past several years is described in this review prepared by members of the HCS Modeling group. By sharing our aims and achievements with a wider audience, we hope to facilitate future international collaborative work on this research

    Genetic differentiation, reproductive mode, and gene flow in the brooding coral Pocillopora damicornis along the Great Barrier Reef, Australia

    Get PDF
    The widespread and morphologically variable coral Pocillopora damicornis has been reported to exhibit huge variation in life-history traits (e.g. mode of reproduction, growth rate, longevity and dispersal) both locally and regionally throughout the Indo-Pacific Ocean. Dispersal may be achieved by the settlement of sexually or asexually generated brooded planula larvae, by broadcast spawning or more locally through asexual fragmentation of large colonies. In the present study, we conducted a hierarchical survey of allozyme variation within and among reef-crest sites on 3 mid-shelf reefs separated by up to 1200 km on the Great Barrier Reef (GBR), Australia. Our objective was to use allozyme data (1) to quantify local and regional patterns of variation in P. damicornis (along the northeastern coast of Australia), (2) to determine the relative contribution of sexual versus asexual production of planulae in P. damicornis, and (3) to estimate levels of gene flow among adjacent sites (>5 km apart) and among reefs separated by 500 to 1200 km. High levels of genotypic diversity in our samples of P. damicornis imply that dispersive propagules in this species are produced sexually rather than asexually along the length of the GBR. Corals at all sites displayed the same level of multi-locus genotypic diversity expected for randomly mating, sexually derived populations, and the majority of individual colonies possessed unique 7-locus genotypes. We also detected consistent deficits of heterozygotes within each collection (from 3 local sites on each of the 3 widely spaced reefs). This pattern is consistent with the predicted effects of sexual reproduction associated with some localised dispersal of gametes or larvae (although other explanations cannot be excluded). Furthermore, each reef was genetically subdivided, suggesting that larval recruitment was localised and that these populations are slightly inbred: hierarchical analysis of the standardised genetic variances (FST)(estimated as Weir & Cockerham's Θ) revealed that, although there was only moderate variation among all 9 sites (FST = 0.055 ± 0.029), more variation was found among sites within reefs (FSL= 0.035 ± 0.04 to 0.088 ± 0.033) than among distant reefs (FLT= 0.008 ± 0.014). This homogeneity of gene frequencies across widely separated reefs implies that reefs are connected by high levels of gene flow (Nem = ca 31) and that local populations of P. damicornis separated by >1000 km can interbreed sufficiently to maintain a consistent suite of life-history characters

    A Simple Sick-Leave Model used for International Comparison

    Get PDF
    This paper describes a simple sick-leave model and its application to data from Austria, the German Democratic Republic and the U.K. With this model, not only present resource requirements can be estimated, but also forecasts for future requirements can be predicted from knowledge of the country's demographic structure and change. Also included in the paper are possible extensions of the model

    The IIASA Health Care Resource Allocation Submodel: Estimation of Parameters

    Get PDF
    The function of the resource allocation submodel within the IIASA Health Care System model is to simulate how the HCS allocates limited supplies of resources between competing demands. The principal outputs of the submodel are the numbers of patients treated, in different categories, and the modes and quotas of treatment they receive. This paper reviews the data which are available for estimating the parameters of the model, and develops methods which made direct use of historical allocation data. Separate procedures are developed for estimating elasticities, ideal levels of care, and resource costs. These procedures have been realized as computer programs, and their use is illustrated by three examples using hospital data
    • …
    corecore