Alternative Analysis of Different Methods for Estimating Prevalence Rate

The estimation of disease prevalence is a crucial part of the national planning of health services since it is a necessary step in the calculation of resource requirements. There are a number of different types and sources of morbidity data which prevalence estimation models can use. These data can be categorized both by disease type (e.g., terminal degenerative disease, non-terminal disease, infectious disease and trauma), and by the source of the data, (e.g., in-patient data, out-patient data, screening records, temporary disability information). The main problem with much of the morbidity data that is available in disaggregated form is that it comes from one part of the service, such as in-patient data, and will therefore give an incorrect picture of the morbidity of the general population. It therefore needs to be augmented by data relating to the general population, e.g. data from general samples or interviews. Since this general population data is usually expensive to collect, the best strategy is to collect it in aggregate form and combine it with the disaggregated data from particular parts of the service, e.g., in-patient data (which is available in a very disaggregated form in many countries)

Health Delivery Systems in Developing Countries: A Committee Report to IIASA by the Participants in an Informal Meeting, Laxenburg, Austria, July 2-5, 1979

During the past two years, the HSS Area at IIASA has been engaged in research on urbanization and development in developing countries. An important future component of this research is to be the study of the demand for services, such as health. Although health care research has been a central part of HSS activities since the establishment of the Area, it has only addressed problems in the developed world. The possibility of extending that research to consider the problems of health delivery in low-income countries, experiencing rapid rates of urbanization, led to the informal meeting of experts described in this report. The meeting was organized by Professor Bruce Johnston of Stanford University, USA, with the assistance of Professor Evgenii Shigan of the Ministry of Health in the USSR. Dr. Philip Aspden of the UK was the rapporteur for the meeting and Professor R.M. Maru served as rapporteur for drafting groups chaired by Professor Carl Taylor and Dr. Ashish Bose

Modeling Health Care Systems

The biomedical task at IIASA is part of the Research Area, Human Settlements and Services. The aim of the task is to build a national health care system model and to arrange for it to be applied at national centers so as to assist decision makers there. The proceedings of this workshop represent part of the continuing efforts in international cooperation with research institutes involved in health care modelling. It relates the IIASA experience in this field, national efforts, and suggests future needs for health care modelling

Alternative Approaches to Modeling Health Care Demand and Supply

In many developed countries the problem of allocating resources within the health care system is the main aspect of health care planning. These resources are usually allocated within different groups of population according to age, sex, disease, income, etc. Taking into account the differences in health care systems, the availability of medical information, and the period of planning, this paper analyzes the alternative ways of modeling the flow of patients between categories and the related health care demand-supply problems

Aggregate Model for Estimating Health Care System Resource Requirements (AMER)

In the process of national health care system model elaboration, the model for estimating resource requirements plays an important role. Resource needs are determined on the basis of estimates of population trends and morbidity rates, and a set of desired health care standards. For the creation of the morbidity estimation model, data from comprehensive studies carried out in the UK, Japan, and the USSR were used. The desired standards were taken from the practice of central planning now existing in the USSR. Using this model in an interactive regime, it is possible to test alternative planning strategies. Preliminary results of testing and running this model in various countries show that this computer model could be used in different developed countries for estimating resource requirements

Problems of Modeling and Decision-Making in Health Care

The present process of health care development has been characterized by several features, one of which is the increase in the amount and variety of needed personnel, beds and other resources. Because of this, more and more people have become involved in the decision making process in the various levels of the health care system. In order to deal with this increasing number of decision makers, it is necessary to review their functions and problems, and to develop new technologies for the decision making process. Models are one of the prime means for aiding the decision maker in the management process and if properly applied can be of great benefit in the solution of many of the problems that arise today in health care systems

Health Care Systems Modeling at IIASA: A Status Report

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