science

Spatial epidemiology

Spatial epidemiology is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Spatial epidemiology rather than just read about it. In short: Spatial epidemiology is a subfield of epidemiology focused on the study of the spatial distribution of health outcomes; it is closely related to health geography. Specifically, spatial epidemiology is concerned with the description and examination of disease and its geographic variations.

Key takeaways

Spatial epidemiology belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Spatial epidemiology to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Spatial epidemiology from memory before moving on to harder problems.

Reference excerpt

Spatial epidemiology is a subfield of epidemiology focused on the study of the spatial distribution of health outcomes; it is closely related to health geography. 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) Complete spatial randomness Geographic information system Geographic information science GIS and public health Modifiable areal unit problem Mutual standardisation Spatial analysis Spatial autocorrelation Time geography Specific applications French paradox Stroke Belt

References

Further reading Linda Beale, Abellan; Hodgson, S.; Jarup, L. (2008). "Methodologic issues and approaches to spatial epidemiology". Environmental Health Perspectives. 116 (8): 1105–1110. doi:10.1289/ehp.10816. PMC 2516558. PMID 18709139. Paul Elliott, J. C. Wakefield, Nicola G. Best, and David J. Briggs, editors (2000). Spatial Epidemiology: Methods and Applications. Oxford University Press, ISBN 978-0-19-851532-6 Gruebner, Oliver; Khan, Md Mobarak H; Lautenbach, Sven; Müller, Daniel; Kraemer, Alexander; Lakes, Tobia; Hostert, Patrick (2011). "A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka". International Journal of Health Geographics. 10: 36. doi:10.1186/1476-072X-10-36. PMC 3123168. PMID 21599932. Gruebner, Oliver; Khan, Mobarak Hossain; Hostert, Patrick (2011). "Spatial Epidemiological Applications in Public Health Research: Examples from the Megacity of Dhaka". In Krämer, Alexander; Khan, Mobarak Hossain; Kraas, Frauke (eds.). Health in Megacities and Urban Areas. Contributions to Statistics. pp. 243–61. doi:10.1007/978-3-7908-2733-0. ISBN 978-3-7908-2733-0. 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 Wilschut, L.I.; Laudisoit, A.; Hughes, N.K.; Addink, E.A.; de Jong, S.M.; Heesterbeek, J.A.P.; Reijniers, J.; Eagle, S.; Dubyanskiy, V.M.; Begon, M. (2015). "Spatial distribution patterns of plague hosts: point pattern analysis of the burrows of great gerbils in Kazakhstan". Journal of Biogeography. 42 (7): 1281–1292. Bibcode:2015JBiog..42.1281W. 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 ebpi.uzh.ch/en/aboutus/activities/spatial_digital_epidemiology - Spatial and digital epidemiology: Annual International Summer School at the University of Zürich, Switzerland.*

Worked examples

Example 1 — a first encounter with Spatial epidemiology

Start with the simplest possible case. Write down what Spatial epidemiology claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Spatial epidemiology before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Spatial epidemiology ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Spatial epidemiology

In research: Spatial epidemiology appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Spatial epidemiology in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Spatial epidemiology is common in secondary-school and first-year university syllabi. It links to neighbouring topics Spatial epidemiology, so understanding it makes those chapters shorter.
In everyday life: Look for Spatial epidemiology outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Spatial epidemiology in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Spatial epidemiology means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Spatial epidemiology out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Spatial epidemiology in simple terms?

Why does Spatial epidemiology matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Spatial epidemiology?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Spatial epidemiology.