Application of Spatial Methods in identifying Geographic variations in populations with Chronic Diseases: A Critical Review

Background Advances in geographic information systems, statistical methodology, and availability of geographically referenced health and environmental quality data have created unprecedented opportunities for investigation of environmental and other contributing factors in the explanation of local geographic variation in diseases. Objective The objective of this review is to describe different spatial methods that were used principally on chronic, non-infectious diseases so that investigators may reach a better understanding of the complex relationship of environment with public health Methods Search was performed for studies published in during of Jan 2003 to Dec 2008 using key words “spatial analysis” AND “disease mapping or public health outcomes”. Electronic databases such as Pub Med and CINAHL, journals focusing on spatial analysis and public health, references of the relevant articles were also reviewed. Search criteria comprised case control, cross sectional, ecological and cohort studies published in English. Study population included children and adults, both gender and all races. Results Using above defined search criteria we found 352 articles in PubMed, 97 in CINAHL and 256 in International Journal of Health Geographics. After excluding the articles that did not match the inclusion criteria or were duplicates were excluded. Total articles reviewed were 34, of which 16 were included in final review comprising 6 studies on respiratory conditions, 5 on cancer, 4 on cardiovascular diseases and 1 on neurology. 2 of the 16 studies focused on children while remaining focused on adults. 7 of the 16 studies were done in United States. Geographic analysis performed at spatial grid, census area unit and zip code levels used spatial interpolation and disease clustering methods in majority of studies. Discussion Understanding spatial variation of chronic diseases may lead to better knowledge of disease etiology, more appropriate of treatment resource allocation, and implement targeted intervention programs to at-risk subpopulations in identified regions.