Spatial epidemiology

Abstract: Spatial epidemiology is the study of spatial variation in disease risk or incidence. Several ecological processes can result in strong spatial patterns of such risk or incidence: for example, pathogen dispersal might be highly localized, vectors or reservoirs for pathogens might be spatially restricted, or susceptible hosts might be clumped. Here, we briefly describe approaches to spatial epidemiology that are spatially implicit, such as metapopulation models of disease transmission, and then focus on research in spatial epidemiology that is spatially explicit, such as the creation of risk maps for particular geographical areas. Although the spatial dynamics of infectious diseases are the subject of intensive study, the impacts of landscape structure on epidemiological processes have so far been neglected. The few studies that demonstrate how landscape composition (types of elements) and configuration (spatial positions of those elements) influence disease risk or incidence suggest that a true integration of landscape ecology with epidemiology will be fruitful.

Abstract: Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health.

Abstract: This book provides an overview of the use of spatial statistics in epidemiology - the study of the incidence and distribution of diseases. Used appropriately, spatial analytical methods in conjunction with GIS and remotely sensed data can provide significant insights into the biological patterns and processes that underlie disease transmission. In turn, these can be used to understand and predict disease prevalence. This book brings together the specialised and widely-dispersed literature on spatial analysis to make these methodological tools accessible to epidemiologists for the first time. With its focus on application rather than theory, this book includes examples taken from both medical (human) and veterinary (animal) disciplines, and describes both infectious diseases and non-infectious conditions. It also provides worked examples of methodologies using a single data set from the same disease example throughout, and is structured to follow the logical sequence of description of spatial data, visualisation, exploration, modelling, and decision support.

Abstract: Person, place, time: these are the basic elements of outbreak investigations and epidemiology. Historically, however, the focus in epidemiologic research has been on person and time, with little regard for the implications of place or space even though disease mapping has been done for over a hundred years. The development of geographic information systems (GISs) over the last 20 years has provided a more powerful and rapid ability to examine spatial patterns and processes. This, in turn, has fostered the discussion of such policyrelevant issues as health services and planning (1), as well as the use of GISs for epidemiologic investigations and disease surveillance. Methods of spatial analysis are given little, if any, introduction in modern epidemiology texts, and few epidemiologists have ventured further than making dot or choropleth maps, or listing geographic units along with rates of disease in tabular form (2-6). Spatial patterns are frequently intricate and complex, while most spatial methods used by epidemiologists can capture or identify only gross, simplistic patterns (2). Epidemiologists understand that disease processes have an historical (time) component, and formal methods of time series and hazard analyses are well-developed to study them. Few, however, recognize that every epidemic also has a geography (space). Some epidemiologists may not be aware that evaluation of the spatial distribution of measures of disease risk may provide etiologic insight. The logic of using geography to study disease or health care is derived from appreciation of factors causing non-uniformity of disease distribution (2). These fac-