Geography Geography of Disease
by
Marilyn O. Ruiz
  • LAST MODIFIED: 28 February 2017
  • DOI: 10.1093/obo/9780199874002-0153

Introduction

Research on the geography of disease seeks to determine how health is influenced by geographical factors. Geographical investigations of the spatial variation of diseases provide important insights into what contributes to disease outcomes and options for disease prevention. When diseases occur in some places but not in others, or when disease rates vary from place to place, the characteristics that differentiate those places provide clues to etiology. When the number of cases of a disease changes across time periods, spatiotemporal analysis can reveal the direction and speed that the disease has spread, and declining disease rates may indicate the effect of preventative actions or development of immunity. Preventative actions include vaccinations, early detection and treatment, quarantine, reduction in exposure or (in the case of animals) culling. Each of these can also vary by location—thus having an impact on the local patterns of health or illness. Diseases vary dramatically, so investigations of the causal factors that lead to them are likewise varied. Biologists and ecologists, for example, often work on the transmission of vector-borne illnesses. Social scientists have been more active in neighborhood health issues and social justice. Civil and environmental engineers often investigate water-borne illnesses and conditions related to toxic exposures. Spatial epidemiologists provide a methodological core for model-based work, but significant trends in methods and theoretical frameworks come from multiple disciplines. Besides those already listed, these include geography, public health and medical sciences, environmental science, anthropology, and biostatistics. Besides accounting for the diseases themselves, investigations into the geographical drivers of diseases must account for the characteristics and dynamics of pathogens; vectors and host populations; the presence of xenobiotic chemicals; and the movement of vectors, hosts, or toxic agents. These are related to the weather and geohydrology of a region, the built and natural environment, and population differences in age and cultural and social habits. Spatial patterns are often linked to temporal factors. Seasonal differences in rainfall and temperature, patterns of movement due to sporting or religious events, and agriculture can affect disease patterns and transmission. Sometimes disease occurs only after long exposure, even skipping generations, and prior immunity, nutritional health, and genetic differences can have significant impacts on the spatial patterns of diseases. Given this variety, cross-disciplinary research is critical for research on complex disease problems. The organization and content of this article reflects an attempt to represent methodological and theoretical threads across a variety of disease systems that are all related to the geography of diseases.

General Overviews

The study of the geography of diseases involves a varied set of theoretical frameworks, multiple sources of data, and many different methods and technological approaches. On top of this, disease transmission is complex and varies significantly depending on the specific nature of the disease system of interest. The research most central to study of the geography of disease includes the work of medical geographers, disease ecologists, and spatial epidemiologists. But many other disciplines contribute to geographically oriented analyses related to particular diseases. Analyses that combine sophisticated and appropriate methods with a deep biological understanding of the disease in question are often carried out by interdisciplinary groups. For geographers with an interest in health, Meade and Emch 2010 is a standard tome that spans disease systems. Infectious diseases have been less central to geographical research in the past fifty years or so, but that has changed more recently, and infectious diseases will be reflected more thoroughly in the next edition of Meade and Emch 2010, due for publication in 2017. A practical and application-oriented text, Cromley and McLafferty 2012 offers information on methods related to measuring disease clusters and provides a wealth of case studies geared mostly toward human illnesses of public health concern. Pfeiffer, et al. 2008 is one of the few that has examples on the geography of animal diseases and is especially helpful for veterinary scientists. An online book, Burton, et al. 2011 provides a social psychology view of places and their contribution to health. The contributing authors emphasize the theoretical aspects of place and how various options for operationalizing these concepts affect the analyses. Macintyre, et al. 2002 is a more succinct view of the role of places in health and disease from the social science perspective of public health. The contributors note especially the need to go beyond purely ecological studies of spatial associations, the need for an expansion of spatial statistical techniques, the growing practice of the inclusion of geographical location fields with health data, and the need to coalesce the theoretical views of place with those of a technical bent.

  • Burton, Erlinda L. M., Stephen A. Matthews, Mai-Chui Leung, eds. Communities, Neighborhoods, and Health Expanding the Boundaries of Place. Social Disparities in Health and Health Care 1. New York: Springer, 2011.

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    The thirteen chapters in this book were conceived originally in 2009, when a group met in Seattle, Washington, to discuss the topic of health and place. The book includes a good treatment of the theoretical frameworks related to the health of neighborhoods, and it is useful especially in providing a perspective on social justice and health inequalities.

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  • Cromley, Ellen K., and Sara L. McLafferty. GIS and Public Health. 2d ed. New York: Guilford, 2012.

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    This text provides comprehensive information about many of the same topics as are found in this article. It is a good survey of the geographical dimensions of public health topics, ranging from health-care access to disease mapping to models of infectious and vector-borne illness.

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  • Macintyre, Sally, Anne Ellaway, and Steven Cummins. “Place Effects on Health: How Can We Conceptualise, Operationalise and Measure Them?” Social Science & Medicine 55.1 (2002): 125–139.

    DOI: 10.1016/S0277-9536(01)00214-3Save Citation »Export Citation »E-mail Citation »

    This foundational and widely cited paper is especially important for readers who are interested in the social science perspectives on the geography of disease. It includes a breakdown of five factors that characterize a healthy neighborhood and provides a good rationale for the selection of variables to represent neighborhood characteristics.

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  • Meade, Melinda S., and Michael Emch. Medical Geography. 3d ed. New York: Guilford, 2010.

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    From the medical geography tradition started by Jacques May. A text for both undergraduate and graduate students covering the geographical aspects of both disease ecology and health-care services.

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  • Pfeiffer, Dirk, Timothy P. Robinson, Mark Stevenson, et al. Spatial Analysis in Epidemiology. Oxford: Oxford University Press, 2008.

    DOI: 10.1093/acprof:oso/9780198509882.001.0001Save Citation »Export Citation »E-mail Citation »

    This succinct text describes GIS data structures, digital mapping, spatial clustering metrics, methods to identify environmental factors related to disease, and the concept of risk. The dataset used for examples is on bovine TB in Great Britain, making this book especially useful for veterinary audiences.

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Journals

Papers on the geography of disease have increased across many fields, and those with an interest in geographical aspects of specific diseases will likely find many examples of articles and sources in the context of that particular field. Readers with an interest in the geography of cancer, for example, should search on this topic in publications devoted to the topic of cancer. For those interested in particular infectious, vector-borne, or veterinary-oriented diseases, spatially oriented papers are found increasingly in many publications. Methods-oriented publications focused on geographic information systems (GIS), digital technologies and data, and spatial statistical approaches are all important topics for any kind of spatial analysis, and geographical studies of health and illness can be found as examples across methods. Of the short list of interdisciplinary journals that span both the domain focus of a variety of health issues and use many methodological approaches, Health and Place has the longest record and carries a strong disciplinary geography focus; International Journal of Health Geographics provides a forum for research on geospatial methods specifically oriented toward health; Spatial and Spatio-temporal Epidemiology recognizes the particular need to address complex statistical and research approaches that are common to spatial epidemiology and risk assessment, and Geospatial Health has more of a focus on infectious diseases and animal health than the others.

Historical Trends

Maps and narratives have played important roles in preserving historic disease outbreaks and improving our understanding of these. Drake 1971, a narrative from the 1800s, illustrates both the limits and the potential of direct observation as the author reported on diseases in the region of the catchment of the Mississippi River in the United States and Canada. Drake was especially interested in the geology, hydrology, weather, and social conditions relative to diseases, with an important distinction drawn between diseases that were place specific and those that were ubiquitous. He thus reported on yellow fever and autumnal fevers (attributed to malaria), while cancers, cataracts, or injuries did not elicit as much interest. He traveled across Illinois in 1844, for example, where the case reports he received about “intermittent and remittent autumnal fever” led him to suggest the possibility that newly plowed prairie was a risk factor for this illness. In a more systematic manner, the Russian Evgenij Pavlovsky’s concept of the natural nidality of diseases is a precursor to current understandings of disease ecology. Pavlovsky 1966 focuses on determining the natural settings, or “nidi,” of diseases to identify how they promoted the transmission of vector-borne pathogens among reservoir hosts. Pavlovsky also noted how human illness comes about due to exposure to infected vectors. Pavlovsky’s examples included plague, tularemia, and tick-borne encephalitis. May 1960 provides a more human-centric view of disease ecology, and it is considered to be a key text to the development of medical geography as a discipline. May also led a systematic mapping of major human illnesses, illustrated by maps of helminthic illnesses (May 1952). Cliff and Haggett 1984 treats historical measles outbreaks in Iceland is an example of a modern analysis that used detailed historical records of contact networks and illness to re-create the general patterns of the spread of measles. Historical accounts can help to describe the timing of disease outbreaks, but those details may be lost when maps are separated from the text. Mengel 2011 emphasizes the impact of one often-reproduced map of plague in Europe on the common notions regarding the impact of the Black Death. The map showed Bohemia to have been spared from plague, but without the accompanying text, this omission took on more importance than intended by the original author. Koch 2005 deals with mapping of diseases in noting how selective reproductions of John Snow’s map of cholera (1855) illustrated how the message of maps can change in subsequent retellings.

  • Cliff, Andrew, and Peter Haggett. “Island Epidemics.” Scientific American 250 (1984): 138–147.

    DOI: 10.1038/scientificamerican0584-138Save Citation »Export Citation »E-mail Citation »

    This succinct, classic paper demonstrates how historical narratives and medical records can be brought to bear on general principles, such as the population size needed to maintain an infection. The authors use data on measles from islands of varying population sizes to demonstrate the temporal and spatial patterns of disease spread.

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  • Drake, Daniel. A Systematic Treatise, Historical, Etiological, and Practical, on the Principal Diseases of the Interior Valley of North America As They Appear in the Caucasian, African, Indian, and Esquimaux Varieties of Its Population. New York: B. Franklin, 1971.

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    Drake wrote this extensive volume in the 1840s and 1850s. It provides a detailed account of his observations of important diseases that varied by place, including cholera, yellow fever, and malaria, along with a detailed account of the hydrology, agricultural, and social and meteorological factors of a wide variety of places from New Orleans to the Hudson Bay.

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  • Koch, Tom. Cartographies of Disease: Maps, Mapping, and Medicine. Redlands, CA: ESRI, 2005.

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    Koch uses a series of maps showing disease outbreaks, with a special emphasis on the significance of differences among versions of John Snow’s map of cholera. The book has many examples that illustrate the difference between mapping, in which maps are part of an investigation, and mapmaking, in which maps are used to illustrate a conclusion.

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  • May, Jacques M. “Map of the World Distribution of Helminthiases.” Geographical Reviews 42.1 (1952): 98–101.

    DOI: 10.2307/211254Save Citation »Export Citation »E-mail Citation »

    The map in this publication shows the distribution of infections caused by parasitic worms along with a brief overview of the geographical patterns observed in them. The map is part of an ambitious Atlas of Diseases project initiated by May under the auspices of the American Geographical Society.

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  • May, Jacques M. “The Ecology of Human Disease.” Annals of the New York Academy of Sciences 84 (1960): 789–794.

    DOI: 10.1111/j.1749-6632.1960.tb39113.xSave Citation »Export Citation »E-mail Citation »

    This brief essay expresses May’s views on illness as a social and environmental construct. He called on physicians to look beyond a single-cause focus on diseases.

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  • Mengel, Daniel C. “A Plague on Bohemia? Mapping the Black Death.” Past & Present 211.1 (2011): 3–34.

    DOI: 10.1093/pastj/gtq069Save Citation »Export Citation »E-mail Citation »

    Elisabeth Carpentier’s map of the spread of plague in Europe from 1347 to 1350 is at the center of this essay on both the power and the weakness of maps. Bohemia was a prominent region on the map due to its lack of plague. The need for better spatial and temporal data on disease cases is as relevant for current disease distributions as they are for our efforts to understand disease outbreaks from the past.

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  • Pavlovsky, Evgenij N. Natural Nidality of Transmissible Diseases, with Special Reference to the Landscape Epidemiology of Zooanthroponoses. Translated by Norman D. Levine. Urbana: University of Illinois Press, 1966.

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    English translation published after the author’s death. Although some of the specific knowledge described about pathogens and transmission routes does not reflect today’s understanding, many of the same concepts are used today by disease ecologists.

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Cancer and the Environment

Concern over spatial patterns of cancer morbidity and mortality have led to analyses to identify the associations between environmental exposures and cancer. An early ecological analysis using digital mapping, Hanchette and Schwartz 1992 reveals a modest but significant negative correlation between county-level prostate cancer mortality and exposure to ultraviolet radiation (UV), with UV exposure as a surrogate for protective levels of vitamin D. Thirteen years later, Klassen and Platz 2006 reviews the geographical dimensions of prostate cancer, noting the ongoing lack of knowledge about why it develops more often to life-threatening stages in some places than in others. In China, rates of prostate cancer are 2.9 cases per 100,000 men, compared to more than 100 in the United States. The authors developed a helpful framework to assess prostate cancer risk using longitudinal data on exposures as well as variables related to the physical and social environment, lifestyle, diet, immunological responses, and genetic risk factors, and they recommended a multilevel statistical approach to address both individual-level and environmental-level factors. Mayer 1983 urges that geographical investigations would be better poised to determine epidemiological causation if geographers and epidemiologists worked together more closely to bridge the gap between appropriate spatial methods and the specific details of disease transmission. Mayer also highlights questions of how to account for the tendency of people to move and even the possibility that parents’ exposure has an impact on cancer risk. A long-time lag between negative exposures and the manifestation of cancer requires geographical estimates of past exposure. Sloan, et al. 2015 incorporates twenty years of residential histories in a case-control study of testicular cancer among Danish men, finding that there were no identifiable clusters of exposures that might have led to cancer after controlling for family history of cancer, changing locations of residences of men and mothers, and considering only a single form of the cancer. Jacquez and Greiling 2003 advocates for novel methods to measure spatial clusters to account for changes in residential locations. The systematic review of geographical trends in cancer data supported by public health agencies are an integral part of cancer surveillance. US Cancer Statistics Working Group 2015 is a dynamic online atlas of cancer mortality combining maps, graphs, and data tables showing cancer data broken down by cancer site, gender, race/ethnicity, and time period. Many resources to better address the geography of cancer are located online at a site hosted by the North American Association of Central Cancer Registries. An outline summary is found in Pickle, et al. 2005.

  • Hanchette, Carol L., and Gary G. Schwartz. “Geographic Patterns of Prostate Cancer Mortality. Evidence for a Protective Effect of Ultraviolet Radiation.” Cancer 70.12 (1992): 2861–2869.

    DOI: 10.1002/1097-0142(19921215)70:12<2861::AID-CNCR2820701224>3.0.CO;2-GSave Citation »Export Citation »E-mail Citation »

    The authors in this innovative early analysis used trend surface analysis, with results mapped in the SYMAP software. It also provides an example of how the examination of residuals from a regression can be used to determine why the model did not work in some areas.

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  • Jacquez, Geoffrey M., and Dunrie A. Greiling. “Geographic Boundaries in Breast, Lung and Colorectal Cancers in Relation to Exposure to Air Toxics in Long Island, New York.” International Journal of Health Geographics 2 (2003): 4.

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    The second of a two-part analysis, with Part 1 focused on the identification of spatial clusters. This part assessed the variability of cancer of the same type for men and women and the authors introduced boundary overlap analysis as a means to determine if two spatial patterns were similar.

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  • Klassen, Ann C., and Elizabeth A. Platz. “What Can Geography Tell Us about Prostate Cancer?” American Journal of Preventive Medicine 30 (2 Supplement) (2006): S7–S15.

    DOI: 10.1016/j.amepre.2005.09.004Save Citation »Export Citation »E-mail Citation »

    The framework presented in figure 1 can guide other analyses. The two examples of implementation of the framework include addressing the impact of racial inequalities and the effects of cultural changes with migration or development during “Westernization” of formerly more traditional groups.

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  • Mayer, Jonathan D. “The Role of Spatial-Analysis and Geographic Data in the Detection of Disease Causation.” Social Science & Medicine 17.16 (1983): 1213–1221.

    DOI: 10.1016/0277-9536(83)90014-XSave Citation »Export Citation »E-mail Citation »

    This thoughtful review is equally relevant today as it was in 1983. It includes a good discussion of the meaning of medical causality and then goes on to describe how data and geographical data and technologies can be used in the context of better understanding of causality.

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  • Pickle, Linda W., Lance A. Waller, and Andrew B. Lawson. “Current Practices in Cancer Spatial Data Analysis: A Call for Guidance.” International Journal of Health Geographics 4 (2005): 3.

    DOI: 10.1186/1476-072X-4-3Save Citation »Export Citation »E-mail Citation »

    This short paper is mostly descriptive, but it is important as a record of how the cancer registry community has taken steps to be proactive and well informed in using GIS, geocoding, and spatial statistics. The reader should go the NAACCR website on GIS resources for more detailed information on this topic.

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  • Sloan, Chantel D., Rike B. Nordsborg, Geoffrey M. Jacquez, Ole Raaschou-Nielsen, and Jaymie R. Meliker. “Space-Time Analysis of Testicular Cancer Clusters Using Residential Histories: A Case-Control Study in Denmark.” PLoS One 10.3 (2015): e0120285.

    DOI: 10.1371/journal.pone.0120285Save Citation »Export Citation »E-mail Citation »

    This cluster analysis was done with very robust data on residential histories on 3,297 testicular cancer cases from the Danish Cancer Registry matched to two separate control groups matched to date of birth drawn from the Danish Civil Registration system. A novel statistic (Q) measured clusters taking into account length of residence at different locations.

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  • US Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-Based Report. Atlanta: US Department of Health and Human Services, Center for Disease Control and Prevention and National Cancer Institute, 2015.

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    This exemplary website has a mapping application with excellent use of color choices and combines mapping, data, and graphs in a particularly intuitive manner. The visual effects are enhanced by the data that come from the NCI.

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Health Disparities and Neighborhoods

Research on the health disparities of people in different neighborhoods has focused on neighborhood economic conditions, racial composition, the “obesogenic” environment, and the availability of healthy foods. Chronic diseases related to the increasing problem of obesity are central to many studies, as illustrated in the many examples reviewed in Black and Macinko 2008. Diez Roux and Mair 2010 argues that inequalities of health indicators are related to residential segregation by race, ethnicity, and income, which result in uneven resource availability. Poor health may result from the increased stress and associated behaviors to deal with that stress when living in areas with higher crime, lower aesthetic attractiveness, and greater exposures to toxic substances. Early studies from the late 1980s onward tended to be based on aggregated secondary data, but the collection of individual-level data coded by geography has taken a more central role over time. Krieger, et al. 2003 emphasizes the importance of better definitions of the meaning of neighborhood effects—distinguishing between the effects of social interactions of people, the specific characteristics of the socioeconomic fabric, and the conditions of the built and biological environments. The effect of the size of the geographical unit of analysis on the outcome of ecological analyses has received attention, and Kreiger and colleagues have developed the Public Health Disparities Geocoding Project, which has resulted in an exceptionally rich literature available as an online resource. The Public Health Disparities Geocoding Project Monograph demonstrates how individual characteristics can be compared to neighborhood characteristics as well as to more individually nuanced spatial variables, such as the distance to fast-food restaurants and high quality supermarkets using multilevel modeling as an approach. In particular, this approach makes it possible to control for basic variables that affect health, including age, sex, educational level, and race, while also assessing the impact of the neighborhood of residence. Caspi, et al. 2012 finds in a review of thirty-eight papers on the food environment and health that purely geographical studies were the least consistent across locations and that they needed to be made more useful by incorporating more refined methods of measuring access.

Health Disparities, Environmental Exposures, and Toxic Substances

The exposure to toxic substances holds great potential for detrimental health effects. Much work in this arena uses environmental and engineering approaches and emphasizes the use of the terms risk, exposure, and vulnerability. Cutter 1996 treats the terminology and methods related to geographically-oriented definitions of vulnerability in describing the potential for places to be affected by a hazard as being related to both the degree to which a threat is likely to occur in a particular area as well as the ability of the population to respond to a threat. This variability in population characteristics is especially important in considering environmental justice issues. Vulnerability to climate change, for example, will be greater in places where food security is already unsteady. Vulnerability to lead in drinking water might be worse when people do not have the resources to purchase drinking water from a clean source. Hanna-Attisha, et al. 2016 finds that elevated blood lead levels in children in Flint, Michigan, stemmed both from the presence of lead in drinking water and from the lack of political power in the populations most at risk and the special vulnerability of younger people to the toxic effects of lead. Environmental exposures to xenobiotic chemicals can result in acute poisoning, increased risk for cancer, and birth-related disorders, but the timing of such exposures poses particular problems since effects can cross generations, as seen in epigenetic analyses, as described in Guthman and Mansfield 2013. Robinson and Vrijheid 2015 provides a way to look at this more deeply through application by the authors of the exposome concept to the period of pregnancy. Sellier, et al. 2014 notes that high temporal resolution of air quality data could be achieved more readily than refinement of spatial scale, and the authors demonstrate different ways to overcome this. Morello-Frosch, et al. 2001 describes the importance of possible differences between the locations of exposure relative to the location of the source of the toxic substance and the variable vulnerability of the population. The contributors develop the concept of a “riskscape,” which constitutes a useful construct in this regard. Oosterlee, et al. 1996 uses a spatial model of air quality based on traffic levels, variable weather conditions, and urban building structure to select subjects in high-quality versus low-quality urban regions, demonstrating that higher levels of self-reported respiratory events occurred in children but not in adults living in the lower quality areas.

  • Cutter, Susan L. “Vulnerability to Environmental Hazards.” Progress in Human Geography 20.4 (1996): 529–539.

    DOI: 10.1177/030913259602000407Save Citation »Export Citation »E-mail Citation »

    In this report, Cutter reviews eighteen different definitions of vulnerability relative to different geographical locations, with an emphasis on the variability of these. She then provides a framework to address vulnerability and suggests the need for better incorporation of the temporal changes within existing definitions.

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  • Guthman, Julie, and Becky Mansfield. “The Implications of Environmental Epigenetics: A New Direction for Geographic Inquiry on Health, Space, and Nature-Society Relations.” Progress in Human Geography 37.4 (2013): 486–504.

    DOI: 10.1177/0309132512463258Save Citation »Export Citation »E-mail Citation »

    Epigenetic research focuses on the phenotypic changes related to heritable modifications of gene expression. In plainer terms, it means that the stress, toxic exposure, or nutrition of a mother, especially at or near gestation, can have an effect on the development of a fetus. The geography of diseases that appears random may be related to exposure from prior generations.

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  • Hanna-Attisha, Mona, Jenny LaChance, Richard C. Sadler, and Allison Champney Schnepp. “Elevated Blood Lead Levels in Children Associated with the Flint Drinking Water Crisis: A Spatial Analysis of Risk and Public Health Response.” American Journal of Public Health 106.2 (2016): 283–290.

    DOI: 10.2105/AJPH.2015.303003Save Citation »Export Citation »E-mail Citation »

    The elevated blood lead levels in children in Flint, Michigan, in 2015 constituted a public health emergency. The exposure to lead came through drinking water and the prevalence was higher in economically disadvantaged neighborhoods. Corrosion of lead pipes was related to the increase in lead levels.

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  • Morello-Frosch, Rachel, Manuel Pastor, and James Sadd. “Environmental Justice and Southern California’s ‘Riskscape.’” Urban Affairs Review 36.4 (2001): 551–578.

    DOI: 10.1177/10780870122184993Save Citation »Export Citation »E-mail Citation »

    Modeled estimation of risk from inhalation of hazardous materials was compared to population ethnic and racial characteristics at the census-tract level. The paper illustrates how to overcome common weaknesses of environmental justice research that may not take into account air pollution from mobile and dispersed sources.

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  • Oosterlee, A., Marjon Drijver, Erik Lebret, and B. Brunekreef. “Chronic Respiratory Symptoms in Children and Adults Living Along Streets with High Traffic Density.” Occupational and Environmental Medicine 53.4 (1996): 241–247.

    DOI: 10.1136/oem.53.4.241Save Citation »Export Citation »E-mail Citation »

    A spatial model of air quality relative to an individual-level survey of respiratory illness. Exposure effect was assessed by grouping families in the study in high and low air quality regions and then using regression analysis to determine the differences among the health outcomes of the participants while controlling for age, sex, and smoking behaviors.

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  • Robinson, Oliver, and Martine Vrijheid. “The Pregnancy Exposome.” Current Environmental Health Reports 2 (2015): 204–213.

    DOI: 10.1007/s40572-015-0043-2Save Citation »Export Citation »E-mail Citation »

    The exposome concept has three interacting prongs offered as a framework for analysis of the variable effect of toxic substances on health: external environmental and social factors, individual-level differences in nutrition and specific exposures, and internal genetic and biological factors. All three of these can be spatially structured and can thus affect the geography of health and disease.

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  • Sellier, Yann, Julien Galineau, Agnes Hulin, et al. “Health Effects of Ambient Air Pollution: Do Different Methods for Estimating Exposure Lead to Different Results?” Environment International 66 (2014): 165–173.

    DOI: 10.1016/j.envint.2014.02.001Save Citation »Export Citation »E-mail Citation »

    This analysis compared the output of four types of spatial estimates of air quality using buffers around air monitoring stations; geostatistical estimates from data at monitor points; regression models based on traffic, land use, and industrial activities; and dispersion estimates from monitoring stations.

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Vector-Borne Diseases

Diseases caused by pathogens carried by arthropods, including mosquitoes, ticks, midges, fleas, and flies, are called vector-borne diseases (VBD). With VBD, the geographical factors of interest must consider the biology of the vector. Many mosquito-borne diseases are in tropical regions, while tick-borne illnesses are more widespread. The subtlety of transmission dynamics of VBD with multihost pathogens can confound geographical inquiry. The authors of Lambin, et al. 2010 and Ruiz, et al. 2004 both use spatial analysis to assess the degree to which landscape features support the transmission of VBDs that include nonhuman hosts. West Nile virus, for example, is transmitted among birds (primary hosts) by particular species of Culex mosquitoes. It can cause illness in people or horses (dead-end hosts), but neither people nor horses transmit the virus. Hanley, et al. 2013 demonstrates that vector and host presence are a necessary but not sufficient explanation for VBD geographical risk. The genetically similar yellow fever and dengue viruses have the same vector, but the distribution of the viruses is strikingly different. Asia, Africa, and the Americas provide geographical conditions that could support both, but dengue virus is found in all three regions, while yellow fever virus is absent from Asia. This has generated considerable discussion over time. The introduction of mosquito-borne illnesses into new areas is related to the movement of people and mosquitoes. The slave trade from Africa to the Americas was an important driver historically in bringing numerous infections to the Americas. Tatem, et al. 2012 makes a strong case for using air traffic to model the potential for spread of VBDs into new areas. Travelers to tropical locations can carry VBD home, as happened with chikungunya virus, which spread to Italy from the Indian Ocean region in 2007. Stoddard, et al. 2009 also gives examples of how local movement of people can spread the pathogen, once it is locally introduced. Tick-borne illnesses are increasing globally and are more common in temperate regions. Randolph 2001 focuses on Lyme borreliosis and tick-borne encephalitis virus, using historical records of disease case reports in different areas to try to explain the observed patterns. Of particular concern is the lack of knowledge about new tick-borne pathogens revealed by new genomic approaches. Parola, et al. 2005 provides a review of rickettsial pathogens carried by ticks and the geographical implications of the biological details on disease distribution.

  • Hanley, Kathryn A., Thomas P. Monath, Scott C. Weaver, Shannon L. Rossi, Rebecca L. Richman, and Nikas Vasilakis. “Fever Versus Fever: The Role of Host and Vector Susceptibility and Interspecific Competition in Shaping the Current and Future Distributions of the Sylvatic Cycles of Dengue Virus and Yellow Fever Virus.” Infection, Genetics and Evolution 19 (2013): 292–311.

    DOI: 10.1016/j.meegid.2013.03.008Save Citation »Export Citation »E-mail Citation »

    The spread and maintenance of yellow fever and dengue viruses and zones of spillover from sylvatic cycles into human populations have profound implications for control and future expectations of disease potential. The complexities of the evolution of hosts, vectors, and pathogens are interrelated.

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  • Lambin, Eric F., Annelise Tran, Sophie O. Vanwambeke, Catherine Linard, and Valerie Soti. “Pathogenic Landscapes: Interactions between Land, People, Disease Vectors, and Their Animal Hosts.” International Journal of Health Geographics 9.1 (2010): 54.

    DOI: 10.1186/1476-072X-9-54Save Citation »Export Citation »E-mail Citation »

    The authors find that landscapes support eight types of VBD, including pathogens carried by tick, mosquito, sand fly, and rodent vectors. Different spatial modeling techniques in the case studies provide examples of how to analyze the geographical niche of multihost vector-borne pathogens.

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  • Parola, Philippe, Christopher D. Paddock, and Didier Raoult. “Tick-Borne Rickettsioses around the World: Emerging Diseases Challenging Old Concepts.” Clinical Microbiology Reviews 18.4 (2005): 719–756.

    DOI: 10.1128/CMR.18.4.719-756.2005Save Citation »Export Citation »E-mail Citation »

    Surveillance and diagnostics are hampered by the need for a clear taxonomy of the various species of tick-borne pathogens and evidence of their potential to create illness. This is a rapidly changing field, and any new research on VBD caused by rickettsials needs to consider the latest molecular research.

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  • Randolph, Sarah E. “The Shifting Landscape of Tick-Borne Zoonoses: Tick-Borne Encephalitis and Lyme borreliosis in Europe.” Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 356.11 (2001): 1045–1056.

    DOI: 10.1098/rstb.2001.0893Save Citation »Export Citation »E-mail Citation »

    The spatial and temporal variability of tick-borne illnesses are influenced by factors related to contact between people and infected ticks. Human activity patterns and land-use changes both have important local impacts, while climate has broader implications for the range of ticks.

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  • Ruiz, Marilyn O., Carmen Tedesco, Thomas J. McTighe, Comie Austin, and Uriel Kitron. “Environmental and Social Determinants of Human Risk during a West Nile Virus Outbreak in the Greater Chicago Area, 2002.” International Journal of Health Geographics 3.1 (2004): 8.

    DOI: 10.1186/1476-072X-3-8Save Citation »Export Citation »E-mail Citation »

    The West Nile virus introduction into the United States offered a unique opportunity to determine the spatial factors that contributed to the locations where it was most strongly transmitted among hosts that had no immunity, as demonstrated in this analysis of West Nile virus in Chicago, Illinois.

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  • Stoddard, Steven T., Amy C. Morrison, Gonzalo M. Vazquez-Prokopec, et al. “The Role of Human Movement in the Transmission of Vector-Borne Pathogens.” PLoS Neglected Tropical Diseases 3.7 (2009): e481.

    DOI: 10.1371/journal.pntd.0000481Save Citation »Export Citation »E-mail Citation »

    This review covers the geographical literature on human movement and describes a framework to analyze the effect of human movement on exposure to vectors. It includes both general examples and specific data collection examples based on GPS trackers carried by residents of an area of sustained dengue virus transmission in Iquitos, Peru.

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  • Tatem, Andrew J., Zhen Huang, Anirrudha Das, Qiuyin Qi, J. Roth, and Yan. Qiu. “Air Travel and Vector-Borne Disease Movement.” Parasitology 139.14 (2012): 1816–1830.

    DOI: 10.1017/S0031182012000352Save Citation »Export Citation »E-mail Citation »

    Increased air travel has resulted in a greater possibility of movement of infectious people or mosquitoes across long distances in a short time. The authors have also developed a website to estimate the risk of the spread of vector-borne diseases via air travel. It is found at.

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Zoonoses and Wildlife

The diseases carried by and affecting animals have multiple effects on the locations of human illness. Zoonotic diseases are caused by multihost pathogens, where domestic or wild animals spread the disease to people. Many vector-borne pathogens are examples: see Vector-Borne Diseases. The authors of Pigott, et al. 2014 develop a spatial niche model of Ebola virus spillover potential based on an identification of a set of index cases recorded since 1976 in which humans became infected from animals. The subsequent secondary spread of the virus from person to person had different transmission dynamics than the spread from animals to people. Wood, et al. 2012 outlines an approach to assess the risk of zoonotic transmission in which both social and natural sciences are represented, including land-use change, policy implications, and conservation concerns. Responses to reduce illness will be more effective when the biological event is analyzed relative to the cultural and policy factors that affect contact between people and infectious animals. Urbanization is an especially powerful global process with potential for increased spread of pathogens. Bradley and Altizer 2007 shows how the changing relationship between wildlife and people in urban areas has implications for illness in both groups. Urban encroachment on previously natural lands and new pathogen and host interactions in complex, and often poorly managed, urban landscapes both have the potential for outbreaks of diseases in wildlife and raise the possibility of zoonotic transmission. Medical geographers are well poised to contribute to research that blends relevant spatial data and approaches in a manner that synthesizes the transmission pathways of pathogens among people and animals in the dynamic and complex urban environment. Ruiz, et al. 2013 analyzes the spatial patterns of chronic wasting disease (CWD), a rare but fatal disease of wildlife that both kills deer and elk and raises the concern that prions that cause the illness could be transferred to cattle to cause a problem similar to bovine spongiform encephalopathy (also known as mad cow disease). The spatial analysis of CWD in this paper illustrates the difficulty in mapping and analyzing a rare disease for which the host population (white-tailed deer) is not usually systematically counted, making risk mapping especially problematic.

  • Bradley, Catherine A., and Sonia Altizer. “Urbanization and the Ecology of Wildlife Diseases.” Trends in Ecology & Evolution 22.2 (2007): 95–102.

    DOI: 10.1016/j.tree.2006.11.001Save Citation »Export Citation »E-mail Citation »

    The effects of infectious diseases can reduce wildlife populations and limited or poor quality habitat found in and near urbanized regions can further increase the potential for loss of biodiversity. Infected wildlife near where people live can also serve as a reservoir for illness in people, such as seen with West Nile virus and Lyme borreliosis.

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  • Brearley, Grant, Jonathan Rhodes, A. Bradley, et al. “Wildlife Disease Prevalence in Human-Modified Landscapes.” Biological Reviews of the Cambridge Philosophical Society 88.2 (2013): 427–442.

    DOI: 10.1111/brv.12009Save Citation »Export Citation »E-mail Citation »

    This review of field studies or model-simulated experiments on the effect of the intensity of human modification of landscapes on disease prevalence in wildlife revealed that landscape differences can lead to both higher and lower prevalence. The heterogeneity of results is partly a result of the limited number of such studies and study sites.

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  • Hinchliffe, Steve “More Than One World, More Than One Health: Re-configuring Interspecies Health.” Social Science & Medicine 129 (2015): 28–35.

    DOI: 10.1016/j.socscimed.2014.07.007Save Citation »Export Citation »E-mail Citation »

    Using influenzas as examples, the author summarizes a social science view of “One Health.” This paper is from a special issue Social Science and Medicine. The author focuses especially on tensions between agricultural policies, economics, and public health.

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  • Pigott, David M., Nick Golding, Adrian Mylne, et al. “Mapping the Zoonotic Niche of Ebola Virus Disease in Africa.” eLife 3 (2014): e04395.

    DOI: 10.7554/eLife.04395Save Citation »Export Citation »E-mail Citation »

    This paper by the Spatial Ecology and Epidemiology Group at the University of Oxford demonstrates the expertise of the members in the investigation of disease outbreaks using maps in combination with statistical tools by starting with the development of cohesive and well-defined spatial data. The approach was used originally with malaria mapping and models and has since been extended to other pathogens.

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  • Ruiz, Marilyn O. H., Amy C. Kelly, William M. Brown, J. E. Novakofski, and Nohra E. Mateus-Pinilla. “Influence of Landscape Factors and Management Decisions on Spatial and Temporal Patterns of the Transmission of Chronic Wasting Disease in White-Tailed Deer.” Geospatial Health 8.1 (2013): 215–227.

    DOI: 10.4081/gh.2013.68Save Citation »Export Citation »E-mail Citation »

    Chronic wasting disease can be transmitted directly between animals through ingestion as well as by way of environmental contamination of prions in the soil. These interacting dynamics in combination with heterogeneous management strategies all affect the spatial analysis of risk that account for interactions and complexities.

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  • Wood, J. L., Melissa Leach, Linda Waldman, et al. “A Framework for the Study of Zoonotic Disease Emergence and Its Drivers: Spillover of Bat Pathogens as a Case Study.” Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences 367.1604 (2012): 2881–2892.

    DOI: 10.1098/rstb.2012.0228Save Citation »Export Citation »E-mail Citation »

    The authors use examples of infections from bat reservoirs to highlight the need for a systems approach to responding to zoonotic diseases. These include Ebola, Nipah, and SARs viruses. When domesticated animals are intermediate hosts, it is especially important to address the interaction of the bats with those hosts for effective response.

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Spatial Dimensions of Molecular Epidemiology

Major advances in molecular biology can be integrated with spatial epidemiology to better understand the patterns of disease. Foxman and Riley 2001 provides a brief review of definitions of molecular epidemiology and discusses how molecular techniques can help to identify common sources of infection and reveal clusters through meaningful classifications of biologically related events. Carrel and Emch 2013 is one of the few papers written about spatial analysis of the genetics of pathogens. This is important in an age of rapid increase in the availability of data on pathogen genetics, information on subtypes of pathogens that may be regionally important and regional differences in drug resistance, such as shown in Foxman and Riley 2001 with tuberculosis. Holmes 2004 gives a foundational overview of phylogeography and demonstrates how to exploit the evolution of pathogens to better understand their spatial distribution and spread. More rapidly transmitted viruses, for example, will disperse more quickly than those that remain longer in their host and require closer contact to spread. Three papers in this section are spatial analyses of pathogen genetics used to assess the dispersal and ecology of diseases. Raccoon rabies is the focus of Biek, et al. 2007. Raccoon rabies virus (RRV) moved from Florida to the northeastern United States with transported raccoons, with the first case in West Virginia reported in 1977. The estimated rate and direction of spread across thirty years based on the genetic relationships among the samples of RRV was very similar to the empirically measured observations based on case surveillance data. The Japanese encephalitis virus (JEV) is mosquito borne, multihost, and found throughout Asia. Le Flohic, et al. 2013 reviews worldwide literature on JEV epidemiology and notes that knowledge about the impact of genetics of the mosquito vectors and the bird and mammal reservoir hosts are essential to understanding the regional differences in JEV. Morelli, et al. 2010 reviews the phylogeography of Yersinia pestis and its relationship to the global spread of plague historically. The spread of plague has been the subject of many historical mapping projects. This genetic analysis of plague indicated strong evidence for the source origin in China, with an age of more than 2,200 years. Analyses of the historical and current geography of plague will be more complete if placed into a more nuanced understanding of the timing and likely origin of its spread over the past two thousand years.

  • Biek, Roman, J. Caroline Henderson, Lanie A. Waller, Charles E. Rupprecht, and Leslie A. Real. “A High-Resolution Genetic Signature of Demographic and Spatial Expansion in Epizootic Rabies Virus.” Proceedings of the National Academy of Sciences of the United States of America 104.19 (2007): 7993–7998.

    DOI: 10.1073/pnas.0700741104Save Citation »Export Citation »E-mail Citation »

    A well-defined interdisciplinary project that used empirical data from recorded cases of raccoon rabies placed on a map combined with analysis of samples from a subset of tissues from those cases that were sequenced genetically and analyzed using Bayesian skyline plots.

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  • Carrel, Margaret, and Michael Emch. “Genetics: A New Landscape for Medical Geography.” Annals of the Association of American Geographers 103.6 (2013): 1452–1467.

    DOI: 10.1080/00045608.2013.784102Save Citation »Export Citation »E-mail Citation »

    Written for an audience of professional geographers, this paper gives both an overview of infectious disease ecology and constitutes a call for medical geographers to more fully embrace the data about pathogens, vectors, and hosts available through data measured at a molecular level.

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  • Foxman, Betsy, and Lee Riley. “Molecular Epidemiology: Focus on Infection.” American Journal of Epidemiology 153.12 (2001): 1135–1141.

    DOI: 10.1093/aje/153.12.1135Save Citation »Export Citation »E-mail Citation »

    The authors draw on examples from cases of the spread of drug-resistant tuberculosis and the E. coli related to urinary tract infections to demonstrate how molecular changes provide insight into the transmission of the pathogens. The epidemiology of the problem is often more difficult to understand than the technical requirements of genetic testing.

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  • Holmes, Edward C. “The Phylogeography of Human Viruses.” Molecular Ecology 13 (2004): 745–756.

    DOI: 10.1046/j.1365-294X.2003.02051.xSave Citation »Export Citation »E-mail Citation »

    This framework for potential phylogeographical patterns describes the evolutionary processes that affect transmission dynamics. Some viruses are clearly organized by place, with strong regional characteristics. Others have a clear pattern over time. In more complex cases, hosts and pathogens evolve and interact in less clearly defined ways.

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  • Le Flohic, Guillaume, Vincent Porphyre, Philippe Barbazan, and Jeanpaul Gonzalez. “Review of Climate, Landscape, and Viral Genetics as Drivers of the Japanese Encephalitis Virus Ecology.” PLoS Neglected Tropical Diseases 7.9 (2013): e2208.

    DOI: 10.1371/journal.pntd.0002208Save Citation »Export Citation »E-mail Citation »

    This review of Japanese encephalitis virus transmission summarizes the processes that affect the potential for illness from this virus that are driven by the evolutionary ecology of the virus in combination with changing environmental and social characteristics.

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  • Morelli, Giovanni, Yajun Song, Camila J. Mazzoni, et al. “Yersinia pestis Genome Sequencing Identifies Patterns of Global Phylogenetic Diversity.” Nature Genetics 42 (2010): 1140–1143.

    DOI: 10.1038/ng.705Save Citation »Export Citation »E-mail Citation »

    The history of the geography of plague pandemics is interlinked with the phylogeography of the pathogen. An examination of the phylogenetic similarities and diversity among specimens from different parts of the world reveals the probable origins of these specimens, thus revealing the timing and direction of spread.

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Diseases Related to Water

Diseases related to water often result from fecal contamination of drinking water or when water use puts people into greater contact with water-borne disease agents. John Snow studied the connection between contaminated water supplies and cases of cholera, during a series of pandemics in the 1840s and 1850s in England (Snow 1936). Snow’s observations of the chain of contacts of people stricken with cholera convinced him that it was possible to be infected by caring for a sick patient, and also that direct contact was important. Snow showed that drinking water contaminated by runoff from fecal matter was the root of the problem, and his famous map of the 1854 cholera outbreak in the Golden Square and Broad Street area of London has become an icon of medical geography. Colwell 1996 provides a more recent view of the geographical patterns of cholera. The author notes that outbreaks have tended to be focused first in brackish coastal water. Considerably more is now known about the Vibrio cholerae and the various strains of these bacteria. Today, remote sensing can detect algal blooms that may carry the pathogen. Barcellos and Sabroza 2001 shows how fecal matter and trash both affect the geography of leptospirosis in urban Brazil. Waste piles near residential areas increased the rodent and dog populations, which are important reservoirs for the bacteria that cause leptospirosis. Halliday and Gast 2011 emphasizes that water quality on and near beaches is affected by many sources of fecal contamination, including storm water runoff contaminated by older, deteriorating sewage systems and tidal flows that cause dry/wet cycles. Exposure is related to the number of people and animals on beaches and the activities in which people engage. A review of the literature in Halliday and Gast 2011 reveals a paucity of information about the degree to which beach sands can harbor pathogens and affect disease potential. Hunter 2003 reports that water-related problems were accentuated in places with increased temperatures and rainfall, including harmful algae, such as cyanobacteria; helminthes, such as schistosomas; protozoa, such as Toxoplasma gondii; and viruses, including the hepatitis viruses and the one that causes polio. In less developed tropical countries these pathogens can be very common. Schur, et al. 2013 shows how spatial modeling of schistosomiasis can estimate risk from the disease while also estimating the relative certainty of the risk measures.

  • Barcellos, Christoram, and Paulo C. Sabroza. “The Place behind the Case: Leptospirosis Risks and Associated Environmental Conditions in a Flood-Related Outbreak in Rio de Janeiro.” Cadernos de Saúde Pública 17.Suppl. (2001): 59–67.

    DOI: 10.1590/S0102-311X2001000700014Save Citation »Export Citation »E-mail Citation »

    This paper illustrates how geographic information systems and basic statistical measures can be combined to determine the relative risk of an illness using cases of leptospirosis as the outcome of interest. This water-borne bacterium is causing increasing public health problems, especially in tropical areas that are prone to flooding.

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  • Colwell, Rita R. “Global Climate and Infectious Disease: The Cholera Paradigm.” Science 274.5295 (1996): 2025–2031.

    DOI: 10.1126/science.274.5295.2025Save Citation »Export Citation »E-mail Citation »

    This influential paper highlighted the role of remote sensing, spatial epidemiological investigations, and the use of advanced diagnostics to increase the ability to understand and predict the spread of cholera. It inspired much subsequent spatial investigations in the years since it was published.

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  • Halliday, Elizabeth, and Rebecca J. Gast. “Bacteria in Beach Sands: An Emerging Challenge in Protecting Coastal Water Quality and Bather Health.” Environmental Science & Technology 45.2 (2011): 370–379.

    DOI: 10.1021/es102747sSave Citation »Export Citation »E-mail Citation »

    This paper reviews epidemiological studies related to gastrointestinal symptoms in people relative to their exposure to water or sand during beach activities. It emphasizes the need to collect more data about activities related to both sand and water exposure to better assess a dose response between pathogens and disease outcomes.

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  • Hunter, P. R. “Climate Change and Waterborne and Vector-Borne Disease.” Journal of Applied Microbiology 94 (Suppl. S1) (2003): 37S–46S.

    DOI: 10.1046/j.1365-2672.94.s1.5.xSave Citation »Export Citation »E-mail Citation »

    This paper reviews the potential impacts of large rainfall events, flooding, and increased water temperatures on the prevalence of waterborne and vector-borne diseases in the United Kingdom. The emphasis is on the epidemiological outcomes that may result from these events, both in the United Kingdom and in other locations.

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  • Schur, Nadine, Eveline Hurlimann, Anna-Sofie S. Stensgaard, et al. “Spatially Explicit Schistosoma Infection Risk in Eastern Africa Using Bayesian Geostatistical Modelling.” Acta Tropica 128 (2013): 365–377.

    DOI: 10.1016/j.actatropica.2011.10.006Save Citation »Export Citation »E-mail Citation »

    This analysis used robust geospatial modeling tools and the data compiled through the Global Neglected Tropical Diseases project funded by the European Union project. These data made it possible to combine surveys of illness from multiple countries to develop a generalized mapping of risk for schistosomiasis over time.

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  • Snow, John. Snow on Cholera: Being a Reprint of Two Papers. London: Oxford University Press, 1936.

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    This reprint provides access to the detailed observations made by John Snow on the relationships among victims of cholera. He came to the conclusion that such activities as disposal of household sewage and the water used in washing linens soiled by a victim could contaminate the local drinking water.

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Diseases, Weather, and Climate Change

The climate in a region affects all aspects of daily life, including health. The densely populated tropical and subtropical nations face some of the worst global health threats and are especially vulnerable to climate change. The authors of Patz, et al. 1996 warn doctors that they need to consider the effects of climate change in disease distribution, with a special emphasis on emerging infectious diseases and vector-borne illnesses. But though agreement is general that increased temperatures will have some impact on mosquito vector populations, the analysis in Pascual, et al. 2006 of the patterns of temperature and rainfall and their likely effect on mosquitoes in highland African regions reveals a more ambiguous picture, suggesting that time-series analysis needs to be combined with spatial analysis to know how climate change caused increased incidence of malaria in different locations. McMichael 2015 describes how migration may occur due to climate change as a response to a rise in the sea level, extreme weather involving flooding and drought, and other ecological, economic, and agricultural issues also related to climate change. McMichael provides evidence that infectious disease rates in the areas receiving climate migrants may increase due to migrants carrying pathogens, through disruptions in social systems, and through reduction in economic conditions at the household level. More work is needed to establish the relationships between pathogen transmission and weather. Jacups, et al. 2008 showed how predictive models based on weather could be an early warning of illness from mosquito-borne pathogens. Epidemic cholera, which tends to occur inland along rivers, rather than in coastal areas, has been especially difficult to predict. Jutla, et al. 2013 notes that cholera outbreaks are often related to dry hot conditions, which create conditions conducive to pathogen growth, followed by widespread inundations, which can strain sewage systems and promote exposure. High and low temperatures can both increase mortality, especially among more vulnerable populations, such as older or poorer people. McMichael, et al. 2008 reviews mortality from heat or cold in twelve urban areas in low- to moderate-income nations. The statistical approach employed by the authors allowed them to use a comparable methodology across the cities, with identification of heat and cold thresholds above or below which mortality was more likely.

  • Jacups, Susan P., Peter I. Whelan, and Bart J. Currie. “Ross River Virus and Barmah Forest Virus Infections: A Review of History, Ecology, and Predictive Models, with Implications for Tropical Northern Australia.” Vector Borne and Zoonotic Diseases 8.2 (2008): 283–297.

    DOI: 10.1089/vbz.2007.0152Save Citation »Export Citation »E-mail Citation »

    Ross River virus and Barmah Forest virus are in the same virus family as Chikungunya. An increase in the number of cases prompted this review of what is known about these two viruses and the effect that global temperature changes might have on their incidences.

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  • Jutla, Antarpreet, Elizabeth Whitcombe, Nur Hasan, et al. “Environmental Factors Influencing Epidemic Cholera.” American Journal of Tropical Medicine and Hygiene 89.3 (2013): 597–607.

    DOI: 10.4269/ajtmh.12-0721Save Citation »Export Citation »E-mail Citation »

    The theoretical framework proposed in this paper includes four sections. These comprise above and below average air temperature, above and below average rainfall, condition of the sanitation system, and the relative risk of cholera. The use of historic data from the late 1880s in India to develop a cholera model was a novel contribution.

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  • McMichael, Celia. “Climate Change-Related Migration and Infectious Disease.” Virulence 6.6 (2015): 548–553.

    DOI: 10.1080/21505594.2015.1021539Save Citation »Export Citation »E-mail Citation »

    Climate change may affect infectious diseases through many mechanisms of migration. The framework considered the actual climate change-related effect (e.g., rise in sea level), the social and economic issues driving migration, the degree to which the migration was forced, and the mechanism related to infectious diseases.

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  • McMichael, Anthony J., Paul Wilkinson, R. Sari Kovats, et al. “International Study of Temperature, Heat and Urban Mortality: The ‘ISOTHURM’ Project.” International Journal of Epidemiology 37.5 (2008): 1121–1131.

    DOI: 10.1093/ije/dyn086Save Citation »Export Citation »E-mail Citation »

    The twelve cities investigated in this analysis were representative of low and moderate income cites around the world. They all have mortality data and temperature data across a similar time frame in the 1990s, which provided data to assess the mortality effects of air pollution in combination with temperature in different parts of the world.

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  • Pascual, Mercedes, J. A. Ahumada, Luis F. Chaves, X. Rodo, and Menno Bouma. “Malaria Resurgence in the East African Highlands: Temperature Trends Revisited.” Proceedings of the National Academy of Sciences of the United States of America 103.15 (2006): 5829–5834.

    DOI: 10.1073/pnas.0508929103Save Citation »Export Citation »E-mail Citation »

    Spectrum analysis and data from the Climate Research Unit and a model of mosquito populations were central to the sophisticated time-series analysis presented in this paper. Critical spatial variability could be missed in analyses that rely on weather data of large grid sizes.

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  • Patz, Jonathan A., Paul R. Epstein, Thomas A. Burke, and John M. Balbus. “Global Climate Change and Emerging Infectious Diseases.” JAMA: The Journal of the American Medical Association 275.3 (1996): 217–223.

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    An especially accessible overview of the many ways in which an increase in temperature and changes in rainfall will affect the risk for infectious diseases. The strongest emphasis is on vector-borne diseases, which are tied to both temperature and precipitation characteristics.

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Mapping and Surveillance

Disease surveillance requires both knowledge about where diseases are present along with the ability to detect where public health action is needed. Technology has helped, but challenges remain. Herrera, et al. 2016 illustrates the use of social networks to identify outbreak potential for a given location. Geographic information systems and related technologies are more efficient and accessible than ever, and they can address environmental issues related to the location of health-related events. Vine, et al. 1997 describes how spatial databases can be combined to develop or test hypotheses related to health and the environment. The case studies included several examples, including lead exposure, Lyme disease, effect of pesticides on immune functions, and cohort study development based on exposure to electromagnetic fields. Analyses of the geography of diseases need information on the distribution of disease or outcome of interest. Hay, et al. 2013 gives a summary of how large, complex, and dynamic data on disease distribution results in more refined infectious disease maps. The authors used unstructured data from many sources for improved surveillance. Stevens and Pfeiffer 2015 details similar efforts to acquire data on animal diseases. Once mapped, visualization can be aided through identification of disease clusters. The scan statistic in Kulldorff 2001 has been widely used to identify such situations and was designed to use data from systematic disease reporting systems to identify new or emerging clusters of illness. Mengel 2011 demonstrates the potential long-lasting influence of disease maps on perceptions. The influential map of plague published by French cartographer E. Carpentier in 1962, for example, showed the spread of plague across Europe and Central Asia. A dominant feature of the map was an area of Bohemia in which no plague was recorded. This “absence” was explained with more nuance and uncertainty in the text, but it has been taken as a fact based on the map by many history books. Mengel suggests that some maps should not be created, since they could be misleading due to missing data. Carroll, et al. 2014 challenges informatics and public health policymakers to accept these issues and find solutions. The authors provide a systematic review of how visualization of health data, including molecular findings, social network contact, and spatial data have been used for a variety of issues.

  • Carroll, Lauren N., Alan P. Au, Landon T. Detwiler, Tsungchieh C. Fu, Ian S. Painter, and Neil F. Abernethy. “Visualization and Analytics Tools for Infectious Disease Epidemiology: A Systematic Review.” Journal of Biomedical Informatics 51 (2014): 287–298.

    DOI: 10.1016/j.jbi.2014.04.006Save Citation »Export Citation »E-mail Citation »

    GIS, social network theory, and molecular epidemiology have all increased dramatically in use since the 1980s. To be useful for decision making, they need rich data sets with detailed and unbiased data on disease occurrence. Challenges remain of data sensitivity and incompleteness.

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  • Hay, Simon I., Dylan B. George, Catherine L. Moyes, and John. S. Brownstein. “Big Data Opportunities for Global Infectious Disease Surveillance.” PLoS Medicine 10.4 (2013): e1001413.

    DOI: 10.1371/journal.pmed.1001413Save Citation »Export Citation »E-mail Citation »

    The framework described here was developed first for the Malaria Atlas Project and now has been refined for the era of “big data.” Big data have the characteristics of high volume, a dynamic nature, and complexity. These data offer considerable opportunity for mapping infectious diseases.

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  • Herrera, José. L., Ravi Srinivasan, John S. Brownstein, Alison P. Galvani, and Lauren A. Meyers. “Disease Surveillance on Complex Social Networks.” PLoS Computational Biology 12.7 (2016): e1004928.

    DOI: 10.1371/journal.pcbi.1004928Save Citation »Export Citation »E-mail Citation »

    The degree to which information derived from social contact networks can inform public health surveillance for a particular location depends on the specific surveillance goals. In particular, the data on the population at large will yield patterns different from data focused on specific subpopulations.

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  • Kulldorff, Martin. “Prospective Time Periodic Geographical Disease Surveillance Using a Scan Statistic.” Journal of the Royal Statistical Society Series A 164 (2001): 61–72.

    DOI: 10.1111/1467-985X.00186Save Citation »Export Citation »E-mail Citation »

    Kulldorff has published extensively on the statistics behind the spatial scan statistics that he developed. Many examples, including this one, are about cancer clusters. The importance of the spatial and temporal scan is that it provides a flexible and robust system to identify places and times with greater than expected disease.

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  • Mengel, David C. “A Plague on Bohemia? Mapping the Black Death.” Past & Present 211.1 (2011): 3–34.

    DOI: 10.1093/pastj/gtq069Save Citation »Export Citation »E-mail Citation »

    Carpentier’s map of the spread of plague in Europe from 1347 to 1350 illustrated the limitations of showing uncertainty on maps and the effect this can have on the interpretation of the cause of disease patterns when taken out of the context of accompanying text.

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  • Stevens, Kim B., and Dirk U. Pfeiffer. “Sources of Spatial Animal and Human Health Data: Casting the Net Wide to Deal More Effectively with Increasingly Complex Disease Problems.” Spatial and Spatio-temporal Epidemiology 13 (2015): 15–29.

    DOI: 10.1016/j.sste.2015.04.003Save Citation »Export Citation »E-mail Citation »

    The authors summarize current sources of data dedicated to surveillance of animal diseases. Resource-poor settings are often not well represented by these systems. Effective systems need to work across administrative boundaries and must be sensitive to identification of new outbreaks.

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  • Vine, Marilyn F., Darrah Degnan, and Carol Hanchette. “Geographic Information Systems: Their Use in Environmental Epidemiologic Research.” Environmental Health Perspectives 105.6 (1997): 598–605.

    DOI: 10.1289/ehp.97105598Save Citation »Export Citation »E-mail Citation »

    This early paper gives a basic overview of how GIS can be used for health research. The general approaches and issues discussed, including spatial and temporal detail in the data, confidentiality concerns, and the difficulty in developing technical expertise are all still pertinent today.

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Spatial Models of Risk

Disease maps portray information on disease cases or vector occurrence, while spatial models provide insight into the reasons behind the observed patterns. Models use statistical and mathematical methods to decipher how a disease pattern is related to the environmental and other factors in particular locations. Jones and Duncan 1995 shows the need to identify differences in disease across space due to characteristics of the natural, social, or built environment and the effect of low access to health facilities or to similarities in the population at risk that live near each other. In a somewhat analogous but more biologically oriented approach, Kitron 1998 shows that transmission dynamics of vector-borne pathogen transmission are related to landscape features. The emphasis iss on spatial heterogeneity of vegetation, climate, soils, or other environmental features and how spatial data at several scales and statistical models can be exploited. The authors of Jones, et al. 2008 use methods similar to those described by Kitron to demonstrate how emerging global infectious disease events followed specific environmental pathways toward emergence. They created global risk maps with logistic regression and covariates related to climate, population density, and biodiversity. The authors of Blackburn, et al. 2007 use an extension of this approach to determine places in the United States where the environmental suitability of soils and other conditions are present relative to anthrax in cattle and deer, revealing that the old cattle driving corridor from west Texas to Canada is especially favorable for local spread of the pathogen through spores in soils. The authors of Messina, et al. 2016 use a boosted regression tree approach to determine the biological suitability of different regions for local transmission of the Zika virus. Many factors can lead to variability in spatial risk for disease, which is reflected by transmission potential depicted mathematically. This includes models of the variable efficacy of vaccines in different places. Anderson and May 1991 presents this from a theoretical perspective. Emch, et al. 2007 describes an empirical analysis of the variability of the efficacy of a cholera vaccine being tested across a region in Bangladesh. This analysis measured the efficacy of the vaccine, using neighborhoods of different sizes to show that when this factor is measured for the entire region, the local variability will be overlooked.

  • Anderson, Roy M., and Robert M. May. “Spatial and Other Kinds of Heterogeneity.” In Infectious Diseases of Humans: Dynamics and Control. Edited by Roy M. Anderson and Robert M. May, 304–318. Oxford: Oxford University Press, 1991.

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    In this classic book, Anderson and May use mathematics to model the transmission of infectious diseases. This chapter examines the effects of differences in transmission dynamics in different places and how these affect estimates of the percentage of population that needs to receive vaccinations to eliminate a disease.

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  • Blackburn, Jason K., Kristine M. McNyset, Andrew Curtis, and M. E. Hugh-Jones. “Modeling the Geographic Distribution of Bacillus anthracis, the Causative Agent of Anthrax Disease, for the Contiguous United States Using Predictive Ecological Niche Modeling.” The American Journal of Tropical Medicine and Hygiene 77.6 (2007): 1103–1110.

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    This analysis used ecological niche modeling to estimate the suitability for local transmission of anthrax using a set of environmental factors stored as digital maps. The same approach has continued to be employed for other infectious diseases, using various approaches in this family of statistical models.

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  • Emch, Michael, Mohammad Ali, Camilo Acosta, M. Yunus, David A. Sack, and J. D. Clemens. “Efficacy Calculation in Randomized Trials: Global or Local Measures?” Health & Place 13.1 (2007): 238–248.

    DOI: 10.1016/j.healthplace.2006.01.005Save Citation »Export Citation »E-mail Citation »

    The efficacy of vaccines can vary by location, and this can lead to local-level differences in results from trials of experimental treatments. The vaccination trials of an oral cholera vaccine in Bangladesh with highly detailed household-level data on one region revealed the scale and patterns of efficacy.

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  • Jones, Kelvyn, and Craig Duncan. “Individuals and Their Ecologies: Analysing the Geography of Chronic Illness within a Multilevel Modelling Framework.” Health & Place 1.1 (1995): 27–40.

    DOI: 10.1016/1353-8292(95)00004-6Save Citation »Export Citation »E-mail Citation »

    This foundational paper provides an argument for why and how multilevel models can improve the ability of quantitative social science to assess the importance of the geographical context in disease outcomes. This paper is related to many of the papers in the section Health Disparities and Neighborhoods.

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  • Jones, Kate E., Nikkita G. Patel, Marc A. Levy, et al. “Global Trends in Emerging Infectious Diseases.” Nature 451 (2008): 990–993.

    DOI: 10.1038/nature06536Save Citation »Export Citation »E-mail Citation »

    The authors identified 335 emerging infectious disease events first seen between 1940 and 2004. This influential paper used emergence locations and risk factors to reveal areas of greatest risk for diseases emerging from production of animals and from wildlife, those without animal hosts, and those related to drug-resistant pathogens.

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  • Kitron, Uriel. “Landscape Ecology and Epidemiology of Vector-Borne Diseases: Tools for Spatial Analysis.” Journal of Medical Entomology 35.4 (1998): 435–445.

    DOI: 10.1093/jmedent/35.4.435Save Citation »Export Citation »E-mail Citation »

    Examples are provided of how GIS, remotely sensed imagery, spatial data, and statistical models can provide insight into the reasons for heterogeneity of vector-borne disease. A suite of examples includes Lyme disease, malaria, trypanosomiasis, eastern equine encephalitis, and LaCrosse encephalitis.

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  • Messina, Jane P., M. U. Kraemer, Oliver J. Brady, et al. “Mapping Global Environmental Suitability for Zika Virus.” eLife 5 (2016): e15272.

    DOI: 10.7554/eLife.15272Save Citation »Export Citation »E-mail Citation »

    The spread of Zika virus into Brazil in 2015 and subsequent birth defect outcomes led to a global crisis related to this mosquito-borne virus. The mapping and modeling of Zika described in this paper is a blend of mapping from multiple sources and the use of a machine-learning boosted regression tree analysis to estimate risk across regions.

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