A community-based assessment of learning disabilities using environmental and contextual risk factors
AbstractChildhood placement in learning disability (LD) programs in the USA has tripled over the last few decades to 6% of all children enrolled in the public schools today. The revision of educational laws to improve LD testing and reporting guidelines has been credited for these trends. However, some researchers also believe that the increase in LD incidence may be due, in part, to chronic low level exposure to toxicants such as lead, heavy metals, solvents and others chemicals in the physical environment. This study employs the use of geo-statistical methods to explore the potential linkages between these pollution sources and the prevalence rates of LD within an urbanized environment, in the USA. The role of contextual factors such as housing quality, poverty, low parental educational achievement, and other disadvantages are also examined. Using primary data on childhood disabilities for 1997, the LD cases were queried and analyzed to identify the spatial clusters within the community. The neighborhoods within the LD clusters were then compared to other areas in the community on the basis of the environmental and contextual risk factors. The results confirmed that areas of high risk for LD were strongly associated with historically significant sources of lead toxicity and air pollution facilities. Among the socio-economic indicators, the high-risk neighborhoods were characterized by multiple/subdivided housing units, poverty, higher percentage of residents on public assistance and lower adult educational attainment. Taken together, these results suggest the need for a more inclusive multi-disciplinary research on LD that extends beyond the classroom context to the neighborhoods and communities in which these children reside.
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Bibliographic InfoArticle provided by Elsevier in its journal Social Science & Medicine.
Volume (Year): 56 (2003)
Issue (Month): 5 (March)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description
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- Vladimir Pozdnyakov & Joseph Glaz & Martin Kulldorff & J. Steele, 2005. "A martingale approach to scan statistics," Annals of the Institute of Statistical Mathematics, Springer, vol. 57(1), pages 21-37, March.
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