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 Glossary
 CASE EXAMPLE (ANSWERS): Analysis of all cause mortality rates in Suffolk County, Massachusetts, 1989-1991, by CT poverty strata. Step 5 Step 6 Step 7 Step 8
 • Step 5: For each category of CT poverty, calculate the age-standardized incidence rate, using the year 2000 standard million. d. Calculate the age-standardized incidence rate, standardized to the year 2000 standard million, and the corresponding “gamma” confidence intervals for the direct standardized rates. CT poverty IRst (age standardized rate per 100,000) 95% confidence intervals ("gamma" intervals) 0-4.9% 730 (680, 783) 5-9.9% 966 (941, 992) 10-19.9% 1014 (987, 1041) 20-100% 1019 (993, 1046)
 SAS Output Obs Cindpov IRW LGAM2 UGAM2 1 1 0.007297 .00679633 0.007825 2 2 0.009662 .00940757 0.009922 3 3 0.010140 .00987470 0.010411 4 4 0.010193 .00993398 0.010457
 View Methods & SAS Programming for Step 5
 •Step 6: Estimate the age-standardized incidence rate difference and the age-standardized incidence rate ratio [see Analytic Methods section 4] comparing the age standardized rates in each poverty stratum to the rate in the least impoverished poverty stratum (0-4.9%). Calculate the 95% confidence limits on the incidence rate difference and incidence rate ratio. Fill out the table below. CT poverty IRDrst (age standardized incidence rate difference) 95% confidence intervals IRrst (age standardized incidence rate ratio) 95% confidence intervals 0-4.9% 0 (reference) 1.00 (reference) 5-9.9% 236 (115, 358) 1.32 (1.23, 1.43) 10-19.9% 284 (161, 407) 1.39 (1.29, 1.50) 20-100% 290 (167, 412) 1.40 1.30, 1.50)
 SAS Output Obs Cindpov IRD L_IRD U_IRD IRR L_IRR U_IRR 1 1 0 -.001538594 .001538594 1.00000 0.90593 1.10383 2 2 .002365223 0.001147229 .003583217 1.32413 1.22878 1.42687 3 3 .002843004 0.001614570 .004071438 1.38960 1.28963 1.49732 4 4 .002895946 0.001673817 .004118075 1.39686 1.29671 1.50474 View Methods & SAS Programming for Step 6
 • Step 7: Estimate the relative index of inequality (RII) [see Analytic Methods section 5] for CT level poverty in relation to all cause mortality. a. Estimate the approximate cumulative distribution function for CT poverty, based on the population denominator for each poverty stratum (summed up over age). b. Calculate the expected cases in each CT poverty stratum, based on the age-standardized incidence rate. c. Fit a Poisson log linear model, modeling the expected number of cases as a function of the approximate cumulative distribution of CT poverty, using the population denominator as an offset. d. Exponentiate the beta term from this model to get the relative index of inequality. RII (relative index of inequality) 95% confidence intervals Estimate 1.15 (1.09, 1.21)
 SAS Output Obs Parameter riiest riilo95 riihi95 1 dxpct 1.15024 1.09242 1.21113 View Methods & SAS Programming for Step 7
 • Step 8: Calculate the population attributable fraction [see Analytic Methods section 6] of all cause mortality due to CT poverty. 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 age-stratum-specific 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. Aggregated population attributable fraction Estimate 25.5% SAS Output Obs AFPAGG 1 0.25479