IDEAS home Printed from
   My bibliography  Save this article

Uno studio della non autosufficienza a partire dai dati dell’Indagine Multiscopo: il caso dell’Umbria


  • Giorgio E. Montanari

    () (University of Perugia)

  • M. Giovanna Ranalli

    () (University of Perugia)


This paper proposes a methodology for the estimation of the number of people that show a severe disability and are dependent, using data coming from the Italian National Survey on Health conditions and Appeal to Medicare. Dependency is treated as a latent trait hidden behind a set of items that survey difficulties in movements and in accomplishing everyday tasks (Activities of daily living). Latent class models are used to classify the population according to different levels of disability. The analysis provides a good classification using four classes. Looking at posterior probabilities, people belonging to each class may be labelled as being without disability, with light disability, with some dependence, with severe disability (dependent). The survey provides reliable estimates at regional – NUTS 2 – level. Estimating the amount of population within each latent class at sub-regional level, e.g. sanitary districts, requires small area estimation techniques. To this end, a multinomial unit level model is used with individual level covariates.

Suggested Citation

  • Giorgio E. Montanari & M. Giovanna Ranalli, 2010. "Uno studio della non autosufficienza a partire dai dati dell’Indagine Multiscopo: il caso dell’Umbria," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(1), pages 53-71, April.
  • Handle: RePEc:isa:journl:v:12:y:2010:i:1:p:53-71

    Download full text from publisher

    File URL:
    Download Restriction: no

    More about this item


    Latent variables; Latent Class Models; Small areas estimates;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I19 - Health, Education, and Welfare - - Health - - - Other


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:isa:journl:v:12:y:2010:i:1:p:53-71. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Stefania Rossetti). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.