Disability prevalence-context matters: A descriptive community-based survey

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1 janvier 2019

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Soraya Maart et al., « Disability prevalence-context matters: A descriptive community-based survey », African Journal of Disability (Online), ID : 10670/1.fuhrs8


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BACKGROUND: There is increasing interest in the collection of globally comparable disability data. Context may influence not only the rates but also the nature of disability, thus locally collected data may be of greater use in service delivery planning than national surveys OBJECTIVES: The objective of this article was to explore the extent to which two areas, both under-resourced but geographically and socially distinct, differed in terms of the prevalence and patterns of disability. METHOD: A cross-sectional descriptive survey design was utilised, using stratified cluster sampling in two under-resourced communities in the Western Cape, South Africa. Nyanga is an informal urban settlement in Cape Town and Oudtshoorn is a semi-rural town. The Washington Group Short Set of questions was used to identify persons with disabilities (PWD), and a self-developed questionnaire obtained socio-demographic information. RESULTS: The overall prevalence of disability was 9.7% (confidence intervals [CIs] 9.7-9.8) and the proportion of PWD was significantly different between the two sites (Chi-Sq = 129.5, p < 0.001). In the urban area, the prevalence rate of any disability was 13.1% (CIs 12.0-14.3) with 0.3% (CIs 0.1-0.6) reporting inability to perform any function at all. In contrast, the semi-rural community had a lower overall prevalence rate of 6.8% (CIs 6.0% - 7.8%) but a higher rate of those unable to perform any function: 1% (CIs 0.07-1.4). Disability was associated with gender, age, unemployment and lower income status in both areas. CONCLUSION: Deprived areas tend to show higher disability prevalence rates than the National Census estimates. However, the discrepancy in prevalence and patterns of disability between the two under-resourced areas indicates the need for locally specific data when planning health interventions.

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