PubMed Health⌕ Search

PubMed · 8680032

What does morbidity have to do with disability?

Abstract

This study compares and contrasts survey data on disablement from six countries according to their distributions and prevalence of causes and underlying medical conditions, using a framework proposed by the author that includes morbidity as one important determinant of disablement. The main thesis of this paper is that the study of disablement (that is impairments, disabilities and handicaps) should be conceptualized and classified independently of illness and poor health. Disablement, by definition, is not an illness. Using the World Health Organization global definitions of health it is possible to be both healthy and disabled. The assertion, however, that disability is not an illness does not preclude the joint study of morbidity and disablement; nor does it diminish the significance of disease and injury in the explanation of rates of disablement. Disablement is classified through the use of the WHO International Classification of Impairments, Disabilities, and Handicaps (ICIDH). The WHO International Statistical Classification of Diseases and Related Health Problems (ICD-10) is the primary classification used for the study of morbidity. This paper demonstrates that statistical guidelines and instructions on the use of the ICD and the ICIDH, including the development of appropriate short-listings of codes, are needed in order to increase data comparability and to further encourage sound research. National survey data examined are from the United Nations International Disability Statistics Data Base, DISTAT.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M Chamie. 1995. What does morbidity have to do with disability?. https://doi.org/10.3109/09638289509166718

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

KEEP EXPLORING

Related citations

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗