Epidemiology is, broadly, the investigation of health or disease conditions, causes and distributions. It is the study of health and population. There are a number of mechanisms by which GIS analysts can aid this branch of medicine:
Spatial epidemiology is about describing and analysing disease risk in relation to space. It has been developed in recent years as a function of geo-referenced health data availability, in addition to advancement of the capabilities of GIS and statistical technology. Small area analysis is preferred because of particular correlating relationships between social and environmental variables and health outcomes (i.e. Geographical correlation studies). Important internal details would be lost using a large-scale analysis framework. An issue with small-scale studies is spatial misalignment whereby data might be available in two different spatial units (i.e. polling districts vs dissemination areas), in addition to uncertainty resulting from changing qualities of population datasets, and metrics which are difficult to measure such as population movement. Best practises in spatial epidemiology include allowing for spatial heterogeneity, using well defined population groups, use consistent data (i.e. from surveys) and allow for both latency times and population movement.
Environmental hazards can also be analysed using GIS. This involves investigating the influence of environmental variances (such as the spraying of DDT) on health risk in particular areas, considering the differential exposures of communities to harmful substances or diseases. Historical data can be incorporated to build on our understanding of the spatial distribution of environmental hazards.
The modelling of health services can take a number of forms. For example, models of public accessibility to health services can be constructed to calculate the extent to which individual communities can easily seek out medical care.
Identifying health inequalities is a particularly valuable outcome of health GIS analysis. Many studies have outlined higher levels of risk of smoking-related disease to individuals in lower socio-economic groups. Furthermore, communities often exhibit inequity in accessibility of healthcare services.
Disease classifications are challenging because they may be the result of multiple causal agents and can have different manifestations among different population subgroups.