title: Multidimensional profiles of health status: an application of the grade of membership model to the world health survey.
abstract: BACKGROUND
  The World Health Organization (WHO) conducted the World Health Survey (WHS) between 2002 and 2004 in 70 countries to provide cross-population comparable data on health, health-related outcomes and risk factors. The aim of this study was to apply Grade of Membership (GoM) modelling as a means to condense extensive health information from the WHS into a set of easily understandable health profiles and to assign the degree to which an individual belongs to each profile.
  
  
  RESULTS
  This paper described the application of the GoM models to summarize population health status using World Health Survey data. Grade of Membership analysis is a flexible, non-parametric, multivariate method, used to calculate health profiles from WHS self-reported health state and health conditions. The WHS dataset was divided into four country economic categories based on the World Bank economic groupings (high, upper-middle, lower-middle and low income economies) for separate GoM analysis. Three main health profiles were produced for each of the four areas: I. Robust; II. Intermediate; III. Frail; moreover population health, wealth and inequalities are defined for countries in each economic area as a means to put the health results into perspective.
  
  
  CONCLUSIONS
  These analyses have provided a robust method to better understand health profiles and the components which can help to identify healthy and non-healthy individuals. The obtained profiles have described concrete levels of health and have clearly delineated characteristics of healthy and non-healthy respondents. The GoM results provided both a useable way of summarising complex individual health information and a selection of intermediate determinants which can be targeted for interventions to improve health. As populations' age, and with limited budgets for additional costs for health care and social services, applying the GoM methods may assist with identifying higher risk profiles for decision-making and resource allocations.
authors.affiliation: CNR- Institute of Neuroscience, Padova Section, Padova, Italy. alessandra.andreotti@in.cnr.it
authors.forename: Alessandra
authors.initials: A
authors.lastname: Andreotti
authors.forename: Nadia
authors.initials: N
authors.lastname: Minicuci
authors.forename: Paul
authors.initials: P
authors.lastname: Kowal
authors.forename: Somnath
authors.initials: S
authors.lastname: Chatterji
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D014943:Global Health; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D008297:Male; D008603:Mental Health / Q000706:statistics & numerical data; D008875:Middle Aged; D015233:Models, Statistical; D012959:Socioeconomic Factors; D014480:United Nations; D014944:World Health Organization