1. Estimating Diabetes Prevalence for US Zip Code Areas using the Behavioral Risk Factor Surveillance System. Peter Congdon, Geography QMUL (in collaboration with National Minority Quality Forum)

2. Background ► Information regarding small area prevalence of diabetes is important for ensuring that resources for diabetes care match need and for effective targeting of diabetes-prevention services. ► In US there is evidence of rising diabetes levels (Mokdad et al, 2001), considerable differences in relative risk between ethnic groups (Kenny et al 1995; Davidson, 2001), and wide geographic contrasts in prevalence.

3. Source: Mokdad et al., Diabetes Care 2000;23:1278-83; J Am Med Assoc 2001;286:10. Diabetes Trends Among Adults in US (State Level) BRFSS, 1990,1995 and 2001 19901995 2001

4. Differentiation by Ethnicity ► In 2007 age standardised rate of diagnosed diabetes was highest among Native Americans and Alaska Natives (16.5%), followed by blacks (11.8%) and Hispanics (10.4%), with whites at 6.6 % (CDC, 2008). ► Source: CDC Press Release - June 24, 2008. Number of People with Diabetes Increases to 24 Million. Estimates of Diagnosed Diabetes Now Available for all U.S. Counties

5. Age Standardised Rates 2005-07 for States

6. Current Geographic Variations (2005 Estimated Crude Rates for Counties)

7. County Estimates ► Derived from Behavioral Risk Factor Surveillance Survey (BRFSS) and census data, the estimates provide a clearer picture of areas within states that have higher diabetes rates. ► Higher estimated diabetes rates in areas of Southeast & Appalachia that have traditionally been recognized as being at higher risk for many chronic diseases, including heart disease & stroke.

8. ZCTA Prevalence Modelling ► This paper develops a binary regression model based on 2005 BRFSS survey data, and 2000 US census data, to derive micro area prevalence estimates for doctor diagnosed diabetes ► Over 30,000 Zip Code Tabulation Areas in US (http://www.census.gov/geo/ZCTA/zcta.html) which are “generalized area representations of U.S. Postal Service ZIP Code service areas” http://www.census.gov/geo/ZCTA/zcta.html ► Model for ZCTA estimates based on around 360,000 survey responses to the 2005 BRFSS ► Extends CDC's Division of Diabetes Translation county-level estimates for adults living with diagnosed diabetes.

9. Survey Regression Model ► Regression model includes individual level risk factors (age, gender, ethnic group, education) ► Regression model also adjusts for US state level impacts on diabetes of levels of poverty and urban-rural mix (% of state population in poverty, % of state population in rural areas) ► Further element are unmeasured state level influences - use spatial random effects differentiated by state and four ethnic groups ► Now describe rationale for model elements

10. Matching Risk Factors – Survey & ZCTA ► Ultimate goal is small area prevalence estimation ► So inclusion of risk factors (and interactions) in survey regression model is on assumption that included risks also available in tabulations for ZCTA populations. ► Any interaction between risk factors in regression model (e.g. age gradients that differ by ethnic group) requires a matching cross-tabulation in the ZCTA population.

11. Information of ZCTA Demographic Composition ► Census 2000 provides ZCTA level tabulation which cross-tabulates adult populations by ethnicity, five year age group, and gender. ► So for comparably defined demographic risk groups (e.g. age-ethnic-gender subgroups), parameters from the survey model (e.g. for Hispanic males aged 45-49) can be transferred to the ZCTA sub-population

12. Geographic Modifiers and Micro-Area SES ► One could stop there, only taking account of age, ethnicity, & gender mix in ZCTAs ► “Demography only” synthetic area estimates by are often made, and assume that only person level demographic risk factors are relevant. ► Such estimates do not take account of the modifying impact of geographic context, or of the SES of the small area.

13. Geographic Context Effects: Health Examples ► Evidence of direct effects on health of area variables after controlling for person level risk factors, or of interactions between place & person variables. ► For example, Cubbin et al (2001) report higher levels of hypertension & diabetes among African American women living in socioeconomically deprived neighborhoods as against African American women in affluent neighborhoods, after allowing for individual-level SES ► Example of “deprivation amplification”

14. Place-person interactions ► As for place-person interactions, Barnett et al (2001) and Casper et al (2000) report that ethnic disparities in CHD mortality vary by area of residence. ► Regarding diabetes, CDC (2004) report that "Hispanics continued to have a higher prevalence of diabetes than non-Hispanic whites and that disparities in diabetes between these two populations varied by area of residence“ ► [Center for Disease Control and Prevention (CDC) (2004) Prevalence of Diabetes Among Hispanics --- Selected Areas, 1998--2002. MMWR, 53(40), 941-944] ► So ethnic risk gradient varies spatially (justifies ethnic specific state level random effects)

15. Taking Account of Socioeconomic Composition of ZCTA Populations ► Strong SES impacts on many chronic diseases. ► Maty et al (2005) report that socioeconomic disadvantage, esp. low educational attainment, is significant predictor of incident Type 2 diabetes. ► So advisable to modify prevalence rates for age/sex/ethnicity according to status composition (education/occupation/income) in each ZCTA.

16. Education Gradient in Diabetes ► SES indicators tend to be correlated, so for initial diabetes work focused on education gradient (4 groups: 1=never attended, elementary only, or some high school; 2=high school graduate; 3=some college or technical school; 4=college graduate ) ► ZCTA matching data: Census data for ZCTAs includes data on adult education mix

17. Existing Evidence for Education Gradient

18. Other possibilities for SES in ZCTAs ► Other approaches to representing impact of SES on health outcomes in ZCTAs ► De Fede et al in Int. J of Tuberculosis and Lung Disease define ZCTA SES according to (1) % of population in poverty 2) Townsend Deprivation Index. ZCTAs were grouped into SES quartiles. ► Similar to quintile grouping of 30,000+ Super Output Areas in England according to Index of Multiple Deprivation. Health Survey for England includes SOA deprivation quintile

19. Technical Aspects of ZCTA Model ► Use binary regression, with log link so coefficients measure relative risk ► Use weighted likelihood to reflect differential sampling weights ► Use Bayesian estimation via WINBUGS ► Use spatially correlated state effects, specific to 4 ethnic groups – reflects evidence that ethnic risks vary geographically; also unstructured random effects (needed for spatial isolates) ► Use age gradients, differentiated by (and correlated over) ethnic groups ► Separate regressions for males and females (allows for gender effect modification over a range of risk variables)

20. County Effects ► Over 3000 US counties. Many counties are sparsely represented in survey (though may pool over span of years to improve matters), so random effects at this level are not adopted. Risk of over-smoothing, lack of precision in estimates ► Potentially, county level variables can be used as risk indices – not done for diabetes but was done for CVD prevalence estimates for ZCTAs ► In CVD work, used county level % of population in poverty (2005) and a category variable, namely the 9 category urban-rural continuum

21. Selected regression effects, Diabetes BRFSS Regression (Relative Risks) Males Females EthnicityMean2.5%97.5%Mean2.5%97.5% Whites1 1 Blacks1.561.511.611.931.842.05 Hispanic1.141.091.21.361.261.49 Other1.061.011.111.331.271.4 Education Less than High1 1 High School0.920.870.960.740.720.77 Some College0.960.911.010.680.650.7 College Graduate0.680.640.710.420.40.44

22. Important Findings ► Relative risk more elevated for black ethnicity than for “other” ethnicity (conflates Asian Americans and Native Americans) ► Decline in relative risk over education groups steeper for females

23. Education Gradient by Gender

24. Age Adjusted Diabetes Prevalence (%) 2005, Combined Effect of Ethnicity & Education

25. State Effects ► State effects in the model are residuals after controlling for the age, ethnic & educational composition of state populations, and also for state levels of poverty and rurality. ► Despite this there are consistent patterns, such as lesser diabetes risk (even after controlling for individual level demography & SES) in Colorado, Iowa, Louisiana, Nevada, North Carolina, Utah, Wisconsin and Wyoming.

26. Outputs ► From the model we get distinct age/sex/ethnic prevalence schedules for each of the 53 states (which include DC, PR, VI) ► To derive prevalence estimates at ZCTA level within states, we need to take account of varying SES level of ZCTAs, using relative risk gradient for education from the BRFSS binary regression

27. ZCTA Estimates ► For each age/sex/ethnic rate in a specific state, adjust according to education mix in each ZCTA. ► ZCTA with US average education mix unchanged, ZCTAs with more than average college graduates have rates scaled down (according to modelled RR pattern), ZCTAs with lower than average college graduates have rates scaled up. ► Apply scaled rates to 2000 Census ZCTA age/sex/ethnic populations

28. Apply model to other survey years ► Apply same model as initially developed for 2005 BRFSS to BRFSS surveys for earlier and later years ► ZCTA maps for 2000, 2003, 2007 show evolving diabetes prevalence over small areas and time

29. Diabetes Prevalence Zip Code Areas 2000

30. Diabetes Prevalence Zip Code Areas 2003

31. Diabetes Prevalence Zip Code Areas 2007

32. Improvements/Developments ► Apply model to more recent survey data. ► Take account of county poverty & rural-urban mix rather than state poverty & rurality (as already done for CVD estimates) ► Take account of SES inequality within states or counties (Income inequality linked to worse self- rated health (Kennedy et al. 1998) & higher obesity at US state level (Kahn et al. 1998). ► Consider % ZCTA populations in poverty as opposed to education mix – policy importance of poverty. So could use BRFSS variable “below poverty level” as diabetes risk factor.