Place, Space, and Health: GIS and Epidemiology
From the Department of Health and Social Behavior, Harvard School of Public Health, Boston, MA.
Address correspondence to: Nancy Krieger, Department of Health and Social Behavior, Harvard School of Public Health, 677 Huntington Avenue, Boson, MA 02115. E-mail: email@example.com.
Place. Area. Neighborhood. Latitude. Longitude. Distance. These geographic terms are increasingly finding their way into the epidemiologic literature, as advances in geographic information system (GIS) technology make it ever easier to connect spatially referenced physical and social phenomena to population patterns of health, disease, and well-being. 1–3
Indeed, links between location and health have long captured the imagination of perceptive observers. Consider the Hippocratic treatise, “Airs, Waters, and Places,” written about 2,400 years ago, which roundly (and rather deterministically) declared: “You will find, as a general rule, that the constitutions and habits of a people follows the nature of the land where they live.”4, p. 168 Early 19th century research decisive to epidemiology’s development as a discipline 5 likewise looked to geography to discern etiologic clues.
For example, neighborhood mortality rates were linked to poverty rates, 6,7 and the risk of outbreaks of yellow fever was analyzed by distance from docks. 8,9 A celebrated late 19th century text went so far as to call epidemiology the science of “geographical and historical pathology,”10, p. 2 a definition embraced by Wade Hampton Frost 11, p. 494 and other prominent epidemiologists well into the early 20th century.
Yet, despite epidemiology’s longstanding concern with “time, place, and person”12 (or, perhaps more accurately, “time, place, and population”13), “place” had receded into the background by the mid-20th century, conceptually unmoored from increasingly influential etiologic frameworks based on characteristics of the individual. 5,13 Fortunately, GIS has contributed in recent years to a reviving awareness that any epidemiologic explanation worth its salt must encompass geographic—and temporal—variations in population health. Discussions are being enlivened by new research drawing on multilevel frameworks and methods exploring the public health salience of “place.”5,13–18
GIS unquestionably offers epidemiology a wonderful new tool. If poorly used (or poorly made), however, a well-intended tool can do more damage than good. Underscoring this concern are two issues raised by four papers in this issue on aspects of GIS. 19–22
First, “completeness” in geocoding does not equal “success”. Accuracy—and choice of geographic level–matters as much if not more. Research from our Public Health Disparities Geocoding Project, 23 for example, has found not only significant variability in geocoding accuracy by diverse commercial firms 24 but also introduction of major bias by spatiotemporal mismatches between census-defined areas and zip codes. 25 Extending this work, Hurley et al 19 powerfully demonstrate that serious misclassification can occur if post office boxes are geocoded to their zip code centroid and then analyzed as if the centroid were where people actually live. Their study also found that persons with post office boxes differed from those with residential addresses on such key characteristics as age, race/ethnicity, and whether data on tumor stage at diagnosis were missing. McElroy and colleagues spell out the costs of attempting to geocode every single address, using multiple methods of unknown accuracy (a time-consuming strategy that cost $12,500 to geocode approximately 15,000 records 20). A likely preferable, albeit less complete, alternative would be to use a single cost-effective method with verified high accuracy (eg, one costing approximately $550 for 15,000 addresses 24), and then thoughtfully consider how selection bias could potentially affect results.
“If poorly used (or poorly made), a well-intentioned tool can do more damage than good.”
Second, requirements for geocoding accuracy and types of spatially linked data will vary depending on study needs. Investigations such as Floret’s 21 on cancer incidence and incinerator emissions require precise distance between a given address and a specified location. Such studies can benefit from methodologic research like Bonner’s 22 on positional accuracy, which reassuringly found generally good agreement between latitudes and longitudes assigned by a widely used software package and by a global positioning system receiver. Positional accuracy, however, is only one piece of the picture. Studies investigating links between an area’s characteristics and health additionally face the challenge of defining relevant areas, choosing apt area-based measures, and delimiting appropriate exposure periods. 15–18,23–26 These choices depend on the study’s objective, eg, enhancing public health surveillance, or delineating and testing particular etiologic pathways. 23
In summary, and as usual, both methodological and conceptual precision matter, as does attention to practical details of cost and time. There are no ready-made answers. If GIS is to generate valid data for testing hypotheses about population health, epidemiologists will need to document the validity of our GIS methods and provide conceptual justification for the geographic levels we choose to study, as well as for the measures we employ.
ABOUT THE AUTHOR
Nancy Krieger is a social epidemiologist and Associate Professor in the Department of Health and Social Behavior at the Harvard School of Public Health. She has been working with geocoding and census data since 1985 to document and analyze health disparities involving race/ethnicity, class and gender. Current projects include using GIS to improve monitoring of United States socioeconomic inequalities in health.
1. Richards TB, Croner CM, Rushton G, Brown CK, Fowler L. Geographic information systems and public health: mapping the future. Public Health Rep. 1999; 114: 359–373.
2. Moore DA, Carpenter TE. Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiol Rev. 1999; 21: 143–161.
3. Elliott P, Wakefield J, Best N, Briggs D (eds). Spatial Epidemiology: Methods and Applications. Oxford: Oxford University Press, 2000; 51–67.
4. Airs, Waters, Places. In: Lloyd GER (ed). Hippocratic Writings. London: Penguin Books, 1983; 148–169.
5. Krieger N. Epidemiology and social sciences: towards a critical reengagement in the 21st century. Epidemiol Rev. 2000; 11: 155–163.
6. Villermé LR. Rapport fait par M. Villermé, et lu àa l’Académie royale de Médicine, au nom de la Commission de statistique, sur une série de tableaux relatifs au movement de la population dans les doúze arrondisements municipaux de la ville de Paris, pendant les cinq années 1817, 1817, 1819, 1820 et 1821. Archives Générales de Médicine. 1826; 10: 216–247.
7. Coleman W. Death is a Social Disease: Public Health and Political Economy in Early Industrial France. Madison, WI: University of Wisconsin Press, 1982.
8. Rush B. Medical Inquiries and Observations (Vol 3): Containing an Account of the Bilious and Remitting and Intermitting Yellow Fever, as it Appeared in Philadelphia in the Year 1794: Together with an Inquiry into the Proximate Cause of Fever; and a Defense of Blood-Letting as a Remedy for Certain Diseases. 3rd Ed. Philadelphia, PA: Johnson & Warner, 1809. (1st Ed: Philadelphia, PA: Thomas Dobson, 1796).
9. Coleman W. Yellow Fever in the North: The Methods of Early Epidemiology. Madison, WI: University of Wisconsin Press, 1987.
10. Hirsch A. Handbook of Geographic and Historical Pathology, Vol I, Acute Infective Disease (transl. from the second German edition by Charles Creighton)
. London: The New Sydenham Society, 1886.
11. Frost WH. Epidemiology. In: Maxcy K (ed). Papers of Wade Hampton Frost, MD. New York: Commonwealth Fund, 1941; 493–542.
12. Lilienfeld AM (ed). Times, Places, and Persons: Aspects of the History of Epidemiology. Baltimore, MD: The Johns Hopkins University Press, 1980.
13. Krieger N. Epidemiology and the web of causation: has anyone seen the spider? Soc Sci Med. 1994; 39: 887–903.
14. Krieger N. Theories for social epidemiology in the 21st century: an ecosocial perspective. Int J Epidemiol. 2001; 30: 668–677.
15. O’Campo P. Invited commentary: advancing theory and methods for multilevel models of residential neighborhoods and health. Am J Epidemiol. 2003; 157: 9–13.
16. Macintyre S, Ellaway A, Cummins S. Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med. 2002; 55: 125–139.
17. Diez-Roux AV. Multilevel analysis in public health research. Annu Rev Public Health. 2000; 21: 171–192.
18. Subramanian SV, Jones K, Duncan C. Multilevel methods for public health research. In: Kawachi I, Berkman L (eds). Neighborhoods and Health. Oxford: Oxford University Press; 2003: 65–111.
19. Hurley S, Saunders T, Nivas R, Hertz A, Reynolds P. Post office box addresses: a challenge for geographic information system-based studies. Epidemiology. 2003; 14: 386–391.
20. McElroy J, Remington P, Trentham-Dietz A, Robert S, Newcomb P. Geocoding addresses from a large population-based study: lessons learned. Epidemiology. 2003; 14: 399–407.
21. Floret N, Mauny F, Challier B, Arveux P, Arveux J-Y, Viel J-F. Dioxin emissions from a solid waste incinerator and risk of non-Hodgkin’s lymphoma. Epidemiology. 2003; 14: 392–398.
22. Bonner M, Han D, Nie J, Vena J, Rogerson P, Freudenheim J. Positional accuracy of geocoded addresses in epidemiologic research. Epidemiology. 2003; 14: 408–412.
23. Krieger N, Chen JT, Waterman PD, Soobader M-J, Subramanian SV, Carson R. Geocoding and monitoring US socioeconomic inequalities in mortality and cancer incidence: does choice of area-based measure and geographic level matter? The Public Health Disparities Geocoding Project. Am J Epidemiol. 2002; 156: 471–482.
24. Krieger N, Waterman P, Lemieux K, Zierler S, Hogan JW. On the wrong side of the tracts? Evaluating accuracy of geocoding for public health research. Am J Public Health. 2001; 91: 1114–1116.
25. Krieger N, Waterman P, Chen JT, Soobader M-J, Subramanian SV, Carson R. Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined areas. The Public Health Disparities Geocoding Project. Am J Public Health. 2002; 92: 1100–1102.
26. Oppenshaw S, Taylor PJ. The modifiable areal unit problem. In: Wrigley N, Bennett RJ (eds). Quantitative Geography. London: Routledge & Kegan Paul, 1981; 60–69.
This article has been cited 16 time(s).
Health Information Management Journal
Geocoding coronial data: tools and techniques to improve data quality
Health Information Management Journal, 41(3):
Environmental ResearchComparison of residential geocoding methods in population-based study of air quality and birth defectsEnvironmental Research
Using GIS to model and forecast HIV/AIDS rates in Africa, 1986-2010
Professional Geographer, 60(1):
Smart Homes and Health Telematics
Observing outdoor activity using global positioning system-enabled cell phones
Smart Homes and Health Telematics, 5120():
Ciencia & Saude Coletiva
Georeferenced data in epidemiologic research
Ciencia & Saude Coletiva, 13(6):
Health & PlaceInequalities in mortality in small areas of eleven Spanish cities (the multicenter MEDEA project)Health & Place
Social Science & MedicineConceptualizing the healthscape: Contributions of time geography, location technologies and spatial ecology to place and health researchSocial Science & Medicine
Plos OneDecreases in Community Viral Load Are Accompanied by Reductions in New HIV Infections in San FranciscoPlos One
American Journal of EpidemiologyAccuracy and repeatability of commercial geocodingAmerican Journal of Epidemiology
Expert Review of VaccinesEcological aspects in vaccine trialsExpert Review of Vaccines
Health & PlacePoverty and childhood overweight in California Assembly districtsHealth & Place
American Journal of EpidemiologyInvited Commentary: Built Environment and Obesity Among Older Adults-025EFCan Neighborhood-level Policy Interventions Make a Difference?American Journal of Epidemiology
International Journal of Health GeographicsA study of spatial resolution in pollution exposure modellingInternational Journal of Health Geographics
Epidemiologic ReviewsThe social epidemiology of human immunodeficiency virus/acquired immunodeficiency syndromeEpidemiologic Reviews
Ethnicity & HealthDoes outdoor alcohol advertising around elementary schools vary by the ethnicity of students in the school?Ethnicity & Health
Journal of PediatricsInequalities in Neighborhood Child Asthma Admission Rates and Underlying Community Characteristics in One US CountyJournal of Pediatrics
© 2003 Lippincott Williams & Wilkins, Inc.
What does "Remember me" mean?
By checking this box, you'll stay logged in until you logout. You'll get easier access to your articles, collections,
media, and all your other content, even if you close your browser or shut down your
To protect your most sensitive data and activities (like changing your password),
we'll ask you to re-enter your password when you access these services.
What if I'm on a computer that I share with others?
If you're using a public computer or you share this computer with others, we recommend
that you uncheck the "Remember me" box.
Data is temporarily unavailable. Please try again soon.
Readers Of this Article Also Read