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Spatial epidemiology is a subfield of
epidemiology Epidemiology is the study and analysis of the distribution (who, when, and where), patterns and determinants of health and disease conditions in a defined population. It is a cornerstone of public health, and shapes policy decisions and evidenc ...
focused on the study of the spatial distribution of health outcomes; it is closely related to
health geography Health geography is the application of geographical information, perspectives, and methods to the study of health, disease, and health care. Medical geography, a sub-discipline of or sister field of health geography, Oxford Bibliographies entry of ...
. Specifically, spatial epidemiology is concerned with the description and examination of disease and its geographic variations. This is done in consideration of “demographic, environmental, behavioral, socioeconomic, genetic, and infections risk factors."


Types of studies

;Disease Mapping: * Disease maps are visual representations of intricate geographic data that provide a quick overview of said information. Mainly used for explanatory purposes, disease maps can be presented to survey high-risk areas and to help policy and resource allocation in said areas. ;Geographic correlation studies * Geographic correlation studies attempt to study the geographical factors and their effects on geographically differentiated health outcomes. Measured on an ecologic scale, these factors include environmental variables (quality of surrounding space), socioeconomic and demographic statistics (income and race), or even lifestyle choices (nutrition or diet) of the population groups under study. This approach has the convenience of being able to employ already available data from various surveying sources. ;Clustering, disease clusters, and surveillance. * Disease clusters, or spatial groupings of proximity and characteristically related epidemics. While the term itself is relatively poorly defined, it generally “implies an excess of cases above some background rate bounded in time and space.” Although clustering is not the most precise method for spatial analysis, it can and has proved useful for health-related surveillance and monitoring. Because the statistical models used to draw up such research are complex, the data analysis and the interpretation of results should be carried out by qualified statisticians. Sometimes, the proliferation of errors in disease mapping has led to inefficient decision-making, implementation of inappropriate health policies and negative impact on the advancement of scientific knowledge.


Challenges

;Data availability and quality * Since spatial epidemiology is almost entirely based on the analysis of data and its various visual representations, data collection methods must be routine, accurate, and publicly available. With the advent of specialized and accurate health equipment and global information networks, these methods can be relatively and easily improved. Compiling and standardizing data can also be done efficiently and usefully given the right tools and processes. ;Data protection and confidentiality * In our current time, legislation in the United States regarding individual human rights are gaining increasing support, especially in regards to the confidentiality of personal health data and consent over its use in medical investigations. Safe and secure data is a crucial aspect of successful epidemiologic research. ;Exposure assessment and mapping * Typically always seen as an analytical weakness, the quality of exposure data, or reported accuracy of the spatial reach of epidemics, is especially important in spatial epidemiology. With the more mainstream use of geographic information systems, the capabilities of spatial interpolation and mapping have been tremendously improved, yet these still greatly depend on the precision and legitimacy of the source data commissioned.


See also

*
Cluster (epidemiology) A disease cluster is an unusually large aggregation of a relatively uncommon medical condition or event within a particular geographical location or period. Recognition of a cluster depends on its size being seen as greater than would be expected ...
*
Complete spatial randomness Complete spatial randomness (CSR) describes a point process whereby point events occur within a given study area in a completely random fashion. It is synonymous with a ''homogeneous spatial Poisson process''.O. Maimon, L. Rokach, ''Data Mining and ...
*
Geographic information system A geographic information system (GIS) is a type of database containing Geographic data and information, geographic data (that is, descriptions of phenomena for which location is relevant), combined with Geographic information system software, sof ...
* Geographic information science ** GIS and public health *
Modifiable areal unit problem __NOTOC__ The modifiable areal unit problem (MAUP) is a source of statistical bias that can significantly impact the results of statistical hypothesis tests. MAUP affects results when point-based measures of spatial phenomena are aggregated into ...
* Mutual standardisation *
Spatial analysis Spatial analysis or spatial statistics includes any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques, many still in their early deve ...
*
Spatial autocorrelation Spatial analysis or spatial statistics includes any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques, many still in their early dev ...
*
Time geography Time geography or time-space geography is an evolving transdisciplinary perspective on spatial and temporal processes and events such as social interaction, ecological interaction, social and environmental change, and biographies of individuals. T ...
;Specific applications *
French paradox The French paradox is an apparently paradoxical epidemiological observation that French people have a relatively low incidence of coronary heart disease (CHD), while having a diet relatively rich in saturated fats, in apparent contradicti ...
* Stroke Belt


References


Further reading

* * Paul Elliott, J. C. Wakefield, Nicola G. Best, and David J. Briggs, editors (2000). ''Spatial Epidemiology: Methods and Applications''. Oxford University Press, * * * Andrew B. Lawson (2018). ''Bayesian disease mapping: hierarchical modeling in spatial epidemiology'' CRC Press 3rd Ed. * Andrew B. Lawson (2006) ''Statistical Methods in Spatial Epidemiology''. 2nd Ed, Wiley, New York * Andrew B. Lawson, D. Boehning, E. Lessafre, A. Biggeri, J.-F. Viel and R. Bertollini editors (1999) Disease Mapping and Risk Assessment for Public Health. Wiley/WHO New York * {{cite journal, last1=Wilschut, first1=L.I., last2=Laudisoit, first2=A., last3=Hughes, first3=N.K., last4=Addink, first4=E.A., last5=de Jong, first5=S.M., last6=Heesterbeek, first6=J.A.P., last7=Reijniers, first7=J., last8=Eagle, first8=S., last9=Dubyanskiy, first9=V.M., last10=Begon, first10=M., title=Spatial distribution patterns of plague hosts: point pattern analysis of the burrows of great gerbils in Kazakhstan, journal=Journal of Biogeography, date=2015, volume=42, issue=7, pages=1281-1292, doi=10.1111/jbi.12534, pmc=4737218, pmid=26877580 *Andrew B. Lawson, Sudipto Banerjee, Robert Haining, Maria Dolores Ugarte (eds) (2016) Handbook of Spatial Epidemiology. CRC Press, New York


External links


Spatialepidemiology.net
- Provides a map-based interface for the display and analysis of infectious disease epidemiological data

- Spatial and digital epidemiology: Annual International Summer School at the University of Zürich, Switzerland.* *