SummaryObjectives: The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health. Method: The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin. Results: A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia. Conclusion: Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health. KeywordsEpidemiology, geographic information system, public health, cluster analysis, spatial analysis Yearb Med Inform 2014:110-24 http://dx

Neighbourhoods are frequently used as a measure for individuals’ exposure to the food environment. However, the definitions of neighbourhoods fluctuate and have not been applied consistently in previous studies. Neighbourhoods defined from a single fixed location fail to capture people’s complete exposure in multiple locations, but measuring behaviour using traditional methods can be challenging. This study compares the traditional methods of measuring exposure to the food environment to methods that use data from GPS tracking. For each of the 187 participants, 11 different neighbourhoods were created in which the exposure to supermarkets and fast food outlets were measured. ANOVA, Tukey’s Honestly Significant Difference (HSD) test and t-tests were performed to compare the neighbourhoods. Significant differences were found between area sizes and the exposure to supermarkets and fast food outlets for different neighbourhood types. Second, significant differences in exposure to food outlets were found between the urban and rural neighbourhoods. Neighbourhoods are clearly a diffused and blurred concept that varies in meaning depending on each person’s perception and the conducted study. Complexity and heterogeneity of human mobility no longer appear to correspond to the use of residential neighbourhoods but rather emphasise the need for methods, concepts and measures of individual activity and exposure.

Governmental and commercial lists of food retailers are often used to measure food environments and foodscapes for health and nutritional research. Information about the validity of such secondary food source data is relevant to understanding the potential and limitations of its application. This study assesses the validity of two government lists of food retailer locations and types by comparing them to direct field observations, including an assessment of whether pre-classification of the directories can reduce the need for field observation. Lists of food retailers were obtained from the Central Business Register (CVR) and the Smiley directory. For each directory, the positive prediction value (PPV) and sensitivity were calculated as measures of completeness and thematic accuracy, respectively. Standard deviation was calculated as a measure of geographic accuracy. The effect of the pre-classification was measured through the calculation of PPV, sensitivity and negative prediction value (NPV). The application of either CVR or Smiley as a measure of the food environment would result in a misrepresentation. The pre-classification based on the food retailer names was found to be a valid method for identifying approximately 80% of the food retailers and limiting the need for field observation.

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