The problem of mapping mortality rate: geocoding complexity

• Developing smoothed maps of Prostate mortality rate, in Iowa, based on the combinations of different geographic reference level and methods.

Geographic references Methods

Downscaling Upscaling

County Level (99 locations) * *City and “rest of county” level (1053

Locations)N/A *

•Computing the observed and expected numbers of deaths

for county level geographic reference (99 locations) for city and “the rest of county” level geographic reference (1053 locations)

Downscaling and Upscaling methodsDownscaling

Upscaling

Inverse Distance Weight

Kernel Filter Method

Spatial Model

Measurement Model

Mortality DataIndividual prostate cancer mortality data (year 1999-2003)

associated with city codes and county codes in Iowa was provided by Iowa Department of Public Health. An made up example shows as following:

Date_Death sex Age Date_Birth County_Resid City_Resid

1/13/2010 M 83 12/20/1900 029 BUR

1/14/2010 M 67 12/21/1900 029 SPI

1/15/2010 M 82 12/22/1900 030 SPI

1/16/2010 M 71 12/24/1900 041 KEO

1/16/2010 M 74 12/23/1900 041 XXX

• Some death records are outside of city limits but within the corresponding county

•Some cities across different counties

Figure 1: Example of cities crossing different counties

  Male Population

Area Number

WEST BRANCH 1039

Cedar (Part) 997

Johnson (Part) 42

Compute the observed and expected numbers of deaths for county level, 99 locations

• Creating the geographic location files– County centroid file

• Aggregating mortality records based on corresponding county code combination

• Assigning the observed aggregated mortality records of rest counties to the county centroid file

• The expected number of deaths was calculated by indirectly standardization for age for these counties centroids

•Calculating statewide standard mortality rate for different age categories•Obtaining population of each age category for county1

1: available from State Date Center of Iowa http://data.iowadatacenter.org/browse/places.html#PopulationbyCounty

• Multiplying the population of each age category with corresponding statewide standard mortality rate to get expected number of deaths for each age category in each county

• Summing all the expected numbers of deaths in each age category together to obtain the total expected number of deaths for each county

• Assigning those values to corresponding county centroids

Compute the observed and expected numbers of deaths for county level, 99 locations (continued)

Assigning observed mortality records to city and “rest of county” locations

• Creating another geographic location file– City point location file (only one

point location for boundary crossed city) based on populated places point file2

• Aggregating mortality records based on corresponding unique city/county code combination

2: available from NRGIS library http://www.igsb.uiowa.edu/nrgislibx/gishome.htm

• Assigning the observed aggregated mortality records of cities to the city point location file

• Assigning the observed aggregated mortality records of rest counties to the county centroid file

Process to compute expected numbers of deaths for city and “rest of county” locations

• The expected number of deaths was calculated after the adjustment of age for these cities and counties centriods– Statewide standard mortality rate for different

age categories– Population of each age category for cities3

– Population of each age category for the rest of county

3: available from State Date Center of Iowa http://data.iowadatacenter.org/browse/places.html#PopulationbyCounty

• Compute the population of each age category for the rest of countyCounty City M0_39 M40_44 … M85ACedar 111546 14741 … 1370

City A (fully located within County) Pop_A_1 Pop_A_2 … Pop_A_11City B (fully located within County) Pop_B_1 Pop_B_2 … Pop_B_11

… … … … …

West Branch (Partly located in County) Partial_Pop_1 Partial_Pop_2 … Partial_Pop_11

Pop of each age category for the rest of Pork county Result_1 Result_2 … Result_11

County City M0_39 M40_44 … M85AJohnson 11470 1618 … 203

City A (fully located within County) Pop_A_1 Pop_A_2 … Pop_A_11City B (fully located within County) Pop_B_1 Pop_B_2 … Pop_B_11

… … … … …

West Branch (Partly located in County) Partial_Pop_1 Partial_Pop_2 … Partial_Pop_11

Pop of each age category for the rest of Pork county Result_1 Result_2 … Result_11

Process to compute expected numbers of deaths for city and “rest of county” locations (continued)

• Multiplying the population of each age category with corresponding statewide standard mortality rate to get expected number of deaths for each age category

• Summing all the expected numbers of deaths in each age category together to obtain the total expected number of deaths for each city and rest of county

• Assigning those values to corresponding city and county centroids

• Combining city point file and county centroid file together. We obtained a point file which contains 1053 point locations and corresponding observed and expected numbers of deaths.

Process to compute expected numbers of deaths for city and “rest of county” locations (continued)

Create smoothed Prostate mortality map

Figure 2: Indirect age standardized prostate cancer mortality in Iowa (1999-2003) using Inverse Distance Weight (IDW) and county centroids for geocodes (99 areas):

Create smoothed Prostate mortality map (continued)

Figure 3: Indirect age standardized prostate cancer mortality in Iowa (1999-2003) using a fixed distance filters and county centroids for geocodes (99 areas):a – 30 mile fixed distance filter b – 40 mile fixed distance filter

Correlation coefficient=0.65

Create smoothed Prostate mortality map (continued)

Figure 4: Indirect age standardized prostate cancer mortality in Iowa (1999-2003) using a fixed distance filters and city and “rest of county” geocodes (1053 areas)

a – 30 mile fixed distance filter b – 40 mile fixed distance filter

Correlation coefficient=0.73