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Mapping Geographical Data: public health questions that can be answered by maps

Mapping Geographical Data: public health questions that can be answered by maps. Gerard Rushton, PhD Professor of Geography and Adjunct Professor of Health Management and Policy The University of Iowa Iowa City, Iowa, U.S.A. Gerard-rushton@uiowa.edu.

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Mapping Geographical Data: public health questions that can be answered by maps

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  1. Mapping Geographical Data: public health questions that can be answered by maps Gerard Rushton, PhD Professor of Geography and Adjunct Professor of Health Management and Policy The University of Iowa Iowa City, Iowa, U.S.A. Gerard-rushton@uiowa.edu

  2. Some important developments in map production and use • GIS for automated map production • Static and interactive maps • In producing maps • In viewing maps • Audiences are becoming more differentiated—therefore maps become more specialized and ephemeral • Maps can mislead: “How to lie with maps” by M. Monmonier

  3. Purpose of Maps • For comparing rates of disease in different areas; where is the mortality rate highest? • Pattern recognition: are there geographic trends in the data, or regions of unusually high or low rates? • Is the mortality pattern similar to the pattern of a “risk factor” shown in some other map? • For making resource allocation decisions; where should new resources be allocated? See Bell et al. 2006.

  4. The modifiable areal unit is a problem in all choropleth maps • What is the MAUP: the pattern shown on all choropleth maps reflects the size and shapes of the areas mapped: for the same data, changes in shapes and sizes will lead to a different map. • This sensitivity to shape and size can be separated into zonal and aggregation effects. Some references: Openshaw, S., 1984. The Modifiable Areal Unit Problem. Geobooks, Norwich, England. Openshaw, S. and P. taylor. 1979. “A million or so correlation coefficients,” in Statistical Methods in the Spatial Sciences, N. Wrigley, ed. Pion, London, pp. 127-144. Wang, D.W.S. 1996. “Aggregation effects in geo-referenced data,” in Advanced Spatial Statistics, D.A. Griffith, ed. CRC Press, Boca Raton, Florida, pp. 83-106.

  5. Aggregation and zoning issues in the MAUP. Adapted from Wang. 1996 Source: Carol A. Gotway Crawford and Linda J. Young. 2003. A spatial view of the ecological inference problem. p.3.

  6. Location of Infant Births and Deaths: 1996-98

  7. Des Moines, Iowa: Infant Mortality Rate ZipCode Areas, 1989-1993

  8. Des Moines, Iowa: Infant Mortality Rate Census Tracts, 1989-1993

  9. Des Moines, Iowa: Infant Mortality Rate Census Block Groups, 1989-1993

  10. The variability of any disease rate depends on the size of the areas mapped. • For this example of infant mortality in Des Moines, Iowa, 1989-1992: • for zip codes the imr rate is from 0 to 20 deaths per thousand • for census tracts the rate is from 0 to 36 • for block groups the rate is from 0 to 72

  11. What kind of rates should be mapped? What method of age adjustment? • Direct Adjustment: • Age-sex rates observed in local populations applied to a national or statewide age-sex weights. • Indirect Adjustment: • Number incidences or deaths expected in the local area when statewide age-adjusted rates are applied to local age-sex weights of local populations.

  12. Argument for Directly Adjusted Rates • “Comparisons of rates across different populations require standardization of the age-specific rates to account for differences in population age structures.” • “The indirect standardization method, or equivalently the standardized mortality (SMR), has been recommended for small areas where age-specific rates can be quite variable.” • “Indirect summary measures are not comparable across areas” …when the “assumption of independent age and area effects is violated.” • “We recommend the direct age-adjustment method for rates maps.” • Pickle and White, 1995, p. 615.

  13. Arguments for and against indirect rates For: • Are much more stable for small, local areas than directly adjusted rates. • Measure the cancer burden on local populations on account of their demographic characteristics as well as their differential age-adjusted rates. Against: • Areas with similar cancer rates by age groups may have substantially different indirect rates because of their different demographic compositions

  14. Indirectly adjusted rates can be mapped for smaller areal units than directly adjusted rates • the indirect rate involves multiplying national or statewide rates by local age-sex demographics; • direct rates involve multiplying local age-sex rates by national or statewide demographics. • the indirect rate involves multiplying stable disease rates by stable population totals; • the direct rate involves multiplying unstable disease rates by stable population totals.

  15. Which of these three measures of geographic information is needed to guide the spatial allocation of resources for disease control and prevention? • Directly age-sex adjusted disease rates—i.e. observed age-sex rates weighted by a national or regional set of age-sex weights. • number of excess incidences—i.e. observed incidences compared with the incidences expected by applying national disease rates to local population characteristics. • Indirectly age-sex adjusted disease rates—i.e. divide actual numbers locally with the disease by the expected numbers. Compute the expected number by applying national age-sex specific disease rates to local population characteristics.

  16. “Men and Heart Disease indicates where those programs are most needed and can have the greatest benefit. It is my hope that Men and Heart Disease: An Atlas of Racial and Ethnic Disparitiesin Mortality will be used to guide the distribution of funds and resources to those communities of men experiencing excess mortality from heart disease and will promote the development of culturally sensitive prevention strategies.”

  17. The issue is how we should measure the local “burden of disease” for the purpose of allocating resources to alleviate this burden. Does a “disease rate” standardized to a common age distribution do this, or a disease rate of actual to expected number of deaths, given the demographic composition of the local population?

  18. Case Study: Heart Disease Mortality for Women in Iowa Source:

  19. Age-adjusted Heart Disease Mortality Rates for Women - Ages 35 to 85+(Iowa, 1991 – 1995) Smoothed Direct Age-adjusted Rates Indirect Age-adjusted Rates Sivagnanam, IMGS, 2005

  20. Excess Mortality for Women - Ages 35 to 85+(Iowa, 1991 – 1995) Sivagnanam, IMGS, 2005

  21. Age-adjusted Mortality Rates and observed – expected deathsfor Women - Ages 35 to 85+ (Iowa, 1991 – 1995) Direct Age-adjusted Mortality Rates Indirect Age-adjusted Mortality Rates Sivagnanam, IMGS, 2005

  22. New York State Department of Health Cancer Surveillance Improvement Initiative (CSII)-Cancer Mapping and Related Information

  23. http://www.health.state.ny.us/statistics/cancer/registry/cntymaps/cntymaps.pdfhttp://www.health.state.ny.us/statistics/cancer/registry/cntymaps/cntymaps.pdf

  24. http://www.health.state.ny.us/statistics/cancer/registry/cntymaps/cntymapsci.pdfhttp://www.health.state.ny.us/statistics/cancer/registry/cntymaps/cntymapsci.pdf

  25. Indicating Unreliable Rates Source: MacEachren et al., Environment & Planning A, 1998.

  26. Indicating Unreliable Rates by Hatching

  27. What is the best design for rate maps? Choropleth Isopleth Proportional Symbol National Park Service: http://www.aqd.nps.gov/ard/figure3.html

  28. What do you expect?Do color conventions matter? Source: Carswell M, in Pickle & Herrmann, eds., Cognitive Aspects of Statistical Mapping,1995

  29. Evaluating color schemes - Sample test maps Source: Brewer et al., Annals of the Assoc of Amer Geographers, 1997.

  30. Color schemes tested Source: Brewer et al., Annals of the Assoc of Amer Geographers, 1997.

  31. Source: Brewer et al. 1997

  32. The Problem of Class Interval Selection: Breast Cancer Mortality Rates, 1988-92

  33. The problem of class interval selection Equal Width Tests of six data classification schemes for selecting “cutpoints” between legend colors. Maps are for U.S. white female stroke deaths Source: Brewer and Pickle 2002 Natural Breaks (Jenks) Quintile Box Plot Minimum Boundary Standard Deviation Shared Area Source: Brewer & Pickle, Annals of the AAG, 2002

  34. Source: Brewer and Pickle 2002, p.670.

  35. Issues in Choice of Areal Units to Map • Population size • Suitability for measuring numerator and denominator data • Flexibility for spatial aggregation • Can be related to potential pollution exposures • Familiarity to users of the map • Protects confidentiality of individuals • Availability of quality Registry geocodes

  36. Population Size: rates based on small numbers are unreliable • Rates based on fewer than 20 cases are generally unreliable; • Based on national rates, the typical population size required for an expected 20 cases, adjusted for variations in age and sex distributions, can be computed; • This is the population criterion that determines the minimum feasible areal unit for mapping cancer incidence and mortality.

  37. Suitability for measuring numerator and denominator data • Registry data generally provides numerator data. Registry policies and decisions on geocoding its records determines the units that are suitable. • Geographic information systems can place appropriately geocoded records in ANY areal units for which shapes have been encoded in compatible geographic codes. • Denominator data are often only available for a few kinds of areal units.

  38. Constraints on smallest areal unit • Since demographic data is often needed for denominator measures, the smallest areal unit is often the smallest area for which the required demographic data is available. • In urban areas this is the census block or census block group.

  39. Familiarity to Users of the Map • Can the user know where any given area of the map is? • Can the user find on the map a given place of their interest? • Can the user make verbal identifying statements about areas of the map? • Can users interrogate the map?

  40. Now We Can See the Underlying Geography Much Better--USGS 1:24,000 TIF Image 23

  41. The Ideal Areal Unit • Be very small but capable of being aggregated to any population size or shape chosen • Have both numerator and denominator data • Be capable of adaptive spatial aggregation—adaptive with respect to population size; attributes such as population characteristics; location of health hazards, etc.

  42. Source: Pickle et al., Atlas of United States Mortality, NCHS, 1996.

  43. Lung Cancer Mortality Rates, 1950-69, White Males Source: Mason et al., Atlas of Cancer Mortality for U.S. Counties, 1950-1969. NIH, 1975.

  44. Susan S. Devesa1 Dan J. Grauman1 William J. Blot2 Gene A. Pennello3 Robert N. Hoover1 Joseph F. Fraumeni, Jr.1 1Division of Cancer Epidemiology and Genetics National Cancer Institute 2International Epidemiology Institute, Ltd. 3Center for Devices and Radiological Health Food and Drug Administration Published December 1999

  45. Lung cancer mortality, 1950-945-year rates, white males

  46. Cervix uteri cancer, white females, 1950-94, state economic area Time trends 1950-54 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 Source: www.nic.nih.gov/atlasplus

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