PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 933574

16. Recent studies using a global approach to measuring illness.

R M Rosser
View original source ↗Export metadata ↓

Abstract

Global measures of illness are designed to measure the performance of a health service as a whole and the contribution of its various components. Such measures convert data about the outcome of medical care into information for use in planning at all administrative levels including the highest levels of government. This is achieved by means of a classification of states of illness applicable to all patients, whatever their diagnosis and symptoms, and of a scale which places valuations as perceived by society on the defined states. The valuations of changes in the states of individuals during treatment or over a period of time can then be summed for heterogeneous populations. By adjusting the valuations of future changes for time preferences it may be possible to take into account prognosis, and by placing a valuation on death, mortality and morbidity data can be combined. The properties appropriate to the classification and scale are defined and problems encountered in their derivation are discussed. Examples are presented of the application of one global measure to a group of medical specialists and to population data for the United Kingdom.

Explore related subjects

ClassificationDeathDiseaseHealth FacilitiesHealth PlanningHumansMorbidityMortalityProbabilityPrognosisResidence CharacteristicsUnited KingdomJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R M Rosser. 1976. 16. Recent studies using a global approach to measuring illness.. https://pubmed.ncbi.nlm.nih.gov/933574/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Development of an ecologic marine classification in the new zealand region.

We describe here the development of an ecosystem classification designed to underpin the conservation management of marine environments in the New Zealand region. The classification was defined using multivariate classification using explicit environmental layers chosen for their role in driving spatial variation in biologic patterns: depth, mean annual solar radiation, winter sea surface temperature, annual amplitude of sea surface temperature, spatial gradient of sea surface temperature, summer sea surface temperature anomaly, mean wave-induced orbital velocity at the seabed, tidal current velocity, and seabed slope. All variables were derived as gridded data layers at a resolution of 1 km. Variables were selected by assessing their degree of correlation with biologic distributions using separate data sets for demersal fish, benthic invertebrates, and chlorophyll-a. We developed a tuning procedure based on the Mantel test to refine the classification's discrimination of variation in biologic character. This was achieved by increasing the weighting of variables that play a dominant role and/or by transforming variables where this increased their correlation with biologic differences. We assessed the classification's ability to discriminate biologic variation using analysis of similarity. This indicated that the discrimination of biologic differences generally increased with increasing classification detail and varied for different taxonomic groups. Advantages of using a numeric approach compared with geographic-based (regionalisation) approaches include better representation of spatial patterns of variation and the ability to apply the classification at widely varying levels of detail. We expect this classification to provide a useful framework for a range of management applications, including providing frameworks for environmental monitoring and reporting and identifying representative areas for conservation.

Classification↗
PubMed2007

SINEs of progress: Mobile element applications to molecular ecology.

Mobile elements represent a unique and under-utilized set of tools for molecular ecologists. They are essentially homoplasy-free characters with the ability to be genotyped in a simple and efficient manner. Interpretation of the data generated using mobile elements can be simple compared to other genetic markers. They exist in a wide variety of taxa and are useful over a wide selection of temporal ranges within those taxa. Furthermore, their mode of evolution instills them with another advantage over other types of multilocus genotype data: the ability to determine loci applicable to a range of time spans in the history of a taxon. In this review, I discuss the application of mobile element markers, especially short interspersed elements (SINEs), to phylogenetic and population data, with an emphasis on potential applications to molecular ecology.

Classification↗