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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/793 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/793 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/793 archive 351 disk0/00/00/07/75 2016-01-15 01:44:25 2021-08-27 17:08:42 2016-01-15 01:44:25 monograph show 2 Shigan E.N. AL1720 pub iis_bio iis_met iis_mod iis_urb prog_hss 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). 1977-08 published RM-77-040 RM-77-040 10 2761 public research_memorandum IIASA, Laxenburg, Austria 22 FALSE info:eu-repo/semantics/book