The Public Health Disparities Geocoding Project Monograph Geocoding and Monitoring US Socioeconomic Inequalities in Health: An introduction to using area-based socioeconomic measures
 WHY? READ MORE HOW TO TRY IT OUT! TOOLS Executive Summary Introduction Publications Geocoding Generating ABSMs Analytic Methods Multi-level Modeling Visual Display Case Example U.S. Census Tract Poverty Data
 STEP BY STEP COMPARISON A step by step comparison of each task of the Case Example, the relevant section of Analytic Methods, and sample SAS code (click here for a pdf version of all 8 steps) Step by Step 1 Step by Step 2 Step by Step 3 Step by Step 4 Step by Step 5 Step by Step 6 Step by Step 7 Step by Step 8
 Step 2: Aggregate the denominator data. CASE EXAMPLE ANALYTIC METHODS SAS PROGRAMMING click here to download SAS program The file rawdenom.csv is a comma-delimited file containing the estimated population count in 31 age categories for the 189 census tracts in Suffolk County, from the 1990 U.S. Census. Each census tract is represented by one line in the data file, with the 31 age categories arrayed horizontally. a. Aggregate the population counts into the five broad age categories listed above. b. Transpose the structure of the data, so that is one record for each age stratum within a census tract, with a corresponding categorical age variable and population count. You should end up with 5 records for each census tract, with each record represented by one line of your output dataset. c. Multiply the population count by 3, to yield a person-time denominator for three years worth of death data. Denominator data at the census tract level typically come from the decennial census, which gives population counts in 31 age categories (<1, 1-2, 3-4, 5, 6, 7-9, 10-11, 12-13, 14, 15, 16, 17, 18, 19, 20, 21, 22-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-61, 62-64, 65-69, 70-74, 75-79, 80-84, 85+). For the purposes of age standardization, these age categories need to be re-aggregated to match the age categories used for categorizing case data (numerators, above) and the age categories from the standard million reference population. Additionally, when using case data from multiple years, in order to calculate an average annual incidence rate, one needs to use a person-time denominator (population count multiplied by number of years of case data). PROC IMPORT OUT= rawdenom DATAFILE= "G:\monograph\example\rawdenom.csv" DBMS=CSV REPLACE; GETNAMES=YES; DATAROW=2; RUN; DATA Step2a ; SET rawdenom ; AGECAT1= SUM(OF P0130001-P0130009) ; AGECAT2= SUM(OF P0130010-P0130017) ; AGECAT3= SUM(OF P0130018-P0130021) ; AGECAT4= SUM(OF P0130022-P0130026) ; AGECAT5= SUM(OF P0130027-P0130031) ; LENGTH AGECAT 3 ; ARRAY AGES [5] AGECAT1-AGECAT5 ; DO I=1 TO 5 ; AGECAT=I ; DENOM=3*AGES[I] ; OUTPUT ; END ; DROP I AGECAT1-AGECAT5 P0130001-P0130031 ; RUN ;