SUMMARY: The uptake and acceptance of Geographic Information Systems (GIS) technology has increased since the early 1990s and public health applications are rapidly expanding. In this paper, we summarize the common uses of GIS technology in the public health sector, emphasizing applications related to mapping and understanding of parasitic diseases. We also present some of the success stories, and discuss the challenges that still prevent a full scope application of GIS technology in the public health context. Geographical analysis has allowed researchers to interlink health, population and environmental data, thus enabling them to evaluate and quantify relationships between health-related variables and environmental risk factors at different geographical scales. The ability to access, share and utilize satellite and remote-sensing data has made possible even wider understanding of disease processes and of their links to the environment, an important consideration in the study of parasitic diseases. For example, disease prevention and control strategies resulting from investigations conducted in a GIS environment have been applied in many areas, particularly in Africa. However, there remain several challenges to a more widespread use of GIS technology, such as: limited access to GIS infrastructure, inadequate technical and analytical skills, and uneven data availability. Opportunities exist for international collaboration to address these limitations through knowledge sharing and governance.

SUMMARY: Fast response and decision making about containment, management, eradication and prevention of diseases, are increasingly important aspects of the work of public health officers and medical providers. Diseases and the agents causing them are spatially and temporally distributed, and effective countermeasures rely on methods that can timely locate the foci of infection, predict the distribution of illnesses and their causes, and evaluate the likelihood of epidemics. These methods require the use of large datasets from ecology, microbiology, health and environmental geography. Geodatabases integrating data from multiple sets of information are managed within the frame of geographic information systems (GIS). Many GIS software packages can be used with minimal training to query, map, analyse and interpret the data. In combination with other statistical or modelling software, predictive and spatio-temporal modelling can be carried out. This paper reviews some of the concepts and tools used in epidemiology and parasitology. The purpose of this review is to provide public health officers with the critical tools to decide about spatial analysis resources and the architecture for the prevention and surveillance systems best suited to their situations.

Background: Enteric protozoa are associated with diarrhoeal illnesses in humans; however there are no recent studies on their epidemiology and geographical distribution in Australia. This study describes the epidemiology of enteric protozoa in the state of New South Wales and incorporates spatial analysis to describe their distribution. Design and methods: Laboratory and clinical records from four public hospitals in Sydney for 910 patients, who tested positive for enteric protozoa over the period January 2007 - December 2010, were identified, examined and analysed. We selected 580 cases which had residence post code data available, enabling us to examine the geographic distribution of patients, and reviewed the clinical data of 252 patients to examine possible links between protozoa, demographic and clinical features. Results: Frequently detected protozoa were Blastocystis spp. (57%), Giardia intestinalis (27%) and Dientamoeba fragilis (12%). The age distribution showed that the prevalence of protozoa decreased with age up to 24 years but increasing with age from 25 years onwards. The geographic provenance of the patients indicates that the majority of cases of Blastocystis (53.1%) are clustered in and around the Sydney City Business District, while pockets of giardiasis were identified in regional/rural areas. The distribution of cases suggests higher risk of protozoan infection may exist for some communities. Conclusions: These findings provide useful information for policy makers to design and tailor interventions to target high risk communities. Follow-up investigation into the risk factors for giardiasis in regional/rural areas is needed.