GIS and Health Geography, Part III — Epidemiology, continued, 4 March 2015
There are several ways to understand disease. One way is through manifestational criteria: observing manifestations of the condition. This requires that each disease has a distinct set of symptoms, and this is how the disease is defined. Another way to define disease is by causal criteria, which relies upon an understanding of the etiology of the disease. One challenge in defining disease is the issue of equifinality: there are only so many ways that the body can react, so the same symptoms may be related to multiple diseases. As well, one causal agent may have multiple manifestations.
In order to study disease, researchers must study the occurrence; two factors that affect this are prevalence (incidents at a given point in time) and incidence (rate of occurrence within a population). These two proportions help to determine the occurrence of a disease over a landscape.
Understanding demography is crucial to understanding epidemiology. When considering data for studies, researchers must take into account the size of the study, multi-level models, and adjacent geographical areas. Often they need to strike a balance between “statistical stability of the estimates and geographic precision.” There can also be many statistical challenges in dealing with epidemiological data and models. One such challenge is the use of the standardized mortality ratio which may not work in all scenarios. For example, in the case of Pellagra in the US, spatial smoothing (an interpolation process) was applied so there could be more confidence in the data. Another option for small areas is shrinkage estimation (head-bang interpolation), which uses the information from nearby areas to give confidence to the area being studied.