
The
Public Health Disparities Geocoding Project Monograph 

Geocoding
and Monitoring US Socioeconomic Inequalities in Health: An introduction to using areabased socioeconomic measures 
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STEP
BY STEP COMPARISON 

Step
8: Calculate the population attributable fraction of all cause mortality due to CT poverty. 

SAS
PROGRAMMING click here to download SAS program 

a. Starting with the data from Step 4, sum up over AREAKEY into strata defined by AGECAT and CT poverty. b. Calculate (i) the total cases in each age stratum, over poverty; and (ii) the rate in the reference group of CT poverty. c. Calculate stratum specific rates, rate ratios, and case fractions. d. Calculate the agestratumspecific population attributable fractions. e. Calculate the grand total of all cases to use in calculating weights for all age strata. f. Finally, calculate the aggregated population attributable fraction, using the age specific weights based on proportion of cases in each age stratum. 
To calculate the agestandardized RIIst, we fit the following Poisson model for the expected cases: 
Using the agestratified numerators and denominators from Step 4, calculate the agestratum specific population attributable risk fractions and aggregate population attributable risk fraction over all age strata following the method of Hanley8. Assume that the
dataset provided has stratum specific numbers of Subscript i as age, j as covariate (in this case, CINDPOV)
2. Calculate the
quantities 3. Merge the quantities from (2) with the dataset and calculate (a) rate RATEij=NUMERij/DENOMij
5. Calculate the grand total of cases NUMER++ to use to calculate agespecific weights. 6. Determine agestratum specific weights from the case distribution: w_i = NUMERi+/NUMER++ and calculate the aggregated AFPagg = SUM(w_i * AFPi) ***********************************************************; ********************************** PROC SORT DATA=Step4
; DATA Step8a ; RETAIN NNN DDD ; IF FIRST.CINDPOV
THEN DO ; NNN=NNN+NUMER ; RUN ;
NUMERI+ **********************************; DATA Step8b ; NIPLUS=NIPLUS +
NNN ; KEEP AGECAT NIPLUS RATEIREF; ********************************** **********************************; DATA Step8c ; ******************************* RRij = RATEIJ/RATEIREF
; **********************************; DATA Step8d ; DUMMY=1 ; IF FIRST.AGECAT
THEN DO ; ************************* IF LAST.AGECAT THEN
DO ; KEEP AGECAT NIPLUS
AFPI DUMMY ; ********************************** **********************************; DATA Step8e ;
Determine agestratum specific weights from the case distribution: w_i = NUMERi+/NUMER++ and alculate the
aggregated AFPagg = SUM(w_i * AFPi) DATA Step8f ; RETAIN AFPAGG ; AFPAGG = AFPAGG + ((NIPLUS/NPLUSPLUS)*AFPi) ; IF LASTOBS THEN DO ; OUTPUT ; END ; RUN ; proc print ; 
Exponentiation of the  


yields the RII, which is interpretable as an incidence rate ratio comparing the rates in the bottom to the top of the socioeconomic hierarchy. A larger RII indicates a greater the degree of inequality across a socioeconomic hierarchy, which may be due to a steep socioeconomic gradient or large inequalities in the distribution of the ABSM itself.  
6.
Population Attributable Fraction The population attributable fraction (PAF) is a useful summary measure for characterizing the public health impact of an exposure on population patterns of health and disease. It is defined as “the fraction of all cases (exposed and unexposed) that would not have occurred if exposure had not occurred.”8 For a polytymous exposure, the population attributable fraction is a weighted sum of the attributable fractions for each level of the exposure, with the weights defined by the case fractions (number of exposed cases divided by overall number of cases): 



In order to aggregate multiple PAFs over several age strata i=1,…,I, note that  
that is, a weighted average of stratum specific PAFs, with the number of cases in each age stratum as weights.  

This
work was funded by the National Institutes of Health (1RO1HD3686501)
via the National Institute of Child Health & Human Development (NICHD)
and the Office of Behavioral & Social Science Research (OBSSR). 

Copyright
© 2004 by the President and Fellows of Harvard College  The Public
Health Disparities Geocoding Project. 