PMC:1247195 / 2754-3800 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1247195","sourcedb":"PMC","sourceid":"1247195","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1247195","text":"Recently, the availability of local geographically indexed health and population data, together with advances in computing and geographic information systems, has encouraged the analysis of health data on a small geographic scale (Elliott et al. 2000). The motivation is the increased interpretability of small-scale studies, as they are in principle less susceptible to the component of ecologic bias created by the within-area heterogeneity of exposure or other determinants. They are also better able to detect highly localized effects such as those related to industrial pollution in the vicinity. Conversely, small-scale studies require more sophisticated statistical analysis techniques than, for example, an analysis between countries, because the data are typically sparse with low (even zero) counts of events in many of the small areas. Further, frequently there is evidence of overdispersion of the counts with respect to the Poisson model as well as spatial patterns indicating some dependence between the counts in neighboring areas.","tracks":[]}