Estimating Diabetes Estimating Diabetes Prevalence for US Zip Code Prevalence for US Zip Code Areas using the Behavioral Areas using the Behavioral

Risk Factor Surveillance Risk Factor Surveillance System.System.

Peter Congdon, Geography Peter Congdon, Geography QMUL (in collaboration with QMUL (in collaboration with

National Minority Quality National Minority Quality Forum)Forum)

BackgroundBackground

► Information regarding small area prevalence Information regarding small area prevalence of diabetes is important for ensuring that of diabetes is important for ensuring that resources for diabetes care match need and resources for diabetes care match need and for effective targeting of diabetes-prevention for effective targeting of diabetes-prevention services. services.

► In US there is evidence of rising diabetes In US there is evidence of rising diabetes levels (Mokdad et al, 2001), considerable levels (Mokdad et al, 2001), considerable differences in relative risk between ethnic differences in relative risk between ethnic groups (Kenny et al 1995; Davidson, 2001), groups (Kenny et al 1995; Davidson, 2001), and wide geographic contrasts in prevalence. and wide geographic contrasts in prevalence.

Source: Mokdad et al., Diabetes Care 2000;23:1278-83; J Am Med Assoc 2001;286:10.

Diabetes Trends Among Adults in US (State Diabetes Trends Among Adults in US (State Level)Level)

BRFSS, 1990,1995 and 2001BRFSS, 1990,1995 and 20011990 1995

2001

Differentiation by EthnicityDifferentiation by Ethnicity

► In 2007 age standardised rate of In 2007 age standardised rate of diagnosed diabetes was highest diagnosed diabetes was highest among Native Americans and Alaska among Native Americans and Alaska Natives (16.5%), followed by blacks Natives (16.5%), followed by blacks (11.8%) and Hispanics (10.4%), with (11.8%) and Hispanics (10.4%), with whites at 6.6 % (CDC, 2008).whites at 6.6 % (CDC, 2008).

► Source: CDC Press Release - June 24, 2008. Number of People with Diabetes Source: CDC Press Release - June 24, 2008. Number of People with Diabetes

Increases to 24 Million. Estimates of Diagnosed Diabetes Now Available for all Increases to 24 Million. Estimates of Diagnosed Diabetes Now Available for all U.S. CountiesU.S. Counties

Age Standardised Rates 2005-07 Age Standardised Rates 2005-07 for Statesfor States

Current Geographic Variations Current Geographic Variations (2005 Estimated Crude Rates for (2005 Estimated Crude Rates for

Counties)Counties)

County EstimatesCounty Estimates

►Derived from Behavioral Risk Factor Derived from Behavioral Risk Factor Surveillance Survey (BRFSS) and census Surveillance Survey (BRFSS) and census data, the estimates provide a clearer picture data, the estimates provide a clearer picture of areas within states that have higher of areas within states that have higher diabetes rates. diabetes rates.

►Higher estimated diabetes rates in areas of Higher estimated diabetes rates in areas of Southeast & Appalachia that have Southeast & Appalachia that have traditionally been recognized as being at traditionally been recognized as being at higher risk for many chronic diseases, higher risk for many chronic diseases, including heart disease & stroke. including heart disease & stroke.

ZCTA Prevalence ModellingZCTA Prevalence Modelling► This paper develops a binary regression model This paper develops a binary regression model

based on 2005 BRFSS survey data, and 2000 US based on 2005 BRFSS survey data, and 2000 US census data, to derive micro area prevalence census data, to derive micro area prevalence estimates for doctor diagnosed diabetesestimates for doctor diagnosed diabetes

► Over 30,000 Zip Code Tabulation Areas in US (Over 30,000 Zip Code Tabulation Areas in US (http://www.census.gov/geo/ZCTA/zcta.htmlhttp://www.census.gov/geo/ZCTA/zcta.html) which ) which are “generalized area representations of U.S. Postal are “generalized area representations of U.S. Postal Service ZIP Code service areas”Service ZIP Code service areas”

► Model for ZCTA estimates based on around 360,000 Model for ZCTA estimates based on around 360,000 survey responses to the 2005 BRFSSsurvey responses to the 2005 BRFSS

► Extends CDC's Division of Diabetes Translation Extends CDC's Division of Diabetes Translation county-level estimates for adults living with county-level estimates for adults living with diagnosed diabetes. diagnosed diabetes.

Survey Regression Model Survey Regression Model

► Regression model includes individual level risk Regression model includes individual level risk factors (age, gender, ethnic group, education)factors (age, gender, ethnic group, education)

► Regression model also adjusts for US state Regression model also adjusts for US state level impacts on diabetes of levels of poverty level impacts on diabetes of levels of poverty and urban-rural mix (% of state population in and urban-rural mix (% of state population in poverty, % of state population in rural areas)poverty, % of state population in rural areas)

► Further element are unmeasured state level Further element are unmeasured state level influences - use spatial random effects influences - use spatial random effects differentiated by state and four ethnic groupsdifferentiated by state and four ethnic groups

► Now describe rationale for model elementsNow describe rationale for model elements

Matching Risk Factors – Survey Matching Risk Factors – Survey & ZCTA & ZCTA

► Ultimate goal is small area prevalence Ultimate goal is small area prevalence estimationestimation

► So inclusion of risk factors (and interactions) So inclusion of risk factors (and interactions) in survey regression model is on assumption in survey regression model is on assumption that included risks also available in that included risks also available in tabulations for ZCTA populations. tabulations for ZCTA populations.

► Any interaction between risk factors in Any interaction between risk factors in regression model (e.g. age gradients that regression model (e.g. age gradients that differ by ethnic group) requires a matching differ by ethnic group) requires a matching cross-tabulation in the ZCTA population. cross-tabulation in the ZCTA population.

Information of ZCTA Demographic Information of ZCTA Demographic CompositionComposition

►Census 2000 provides ZCTA level Census 2000 provides ZCTA level tabulation which cross-tabulates adult tabulation which cross-tabulates adult populations by ethnicity, five year age populations by ethnicity, five year age group, and gender. group, and gender.

►So for comparably defined demographic So for comparably defined demographic risk groups (e.g. age-ethnic-gender risk groups (e.g. age-ethnic-gender subgroups), parameters from the survey subgroups), parameters from the survey model (e.g. for Hispanic males aged 45-model (e.g. for Hispanic males aged 45-49) can be transferred to the ZCTA sub-49) can be transferred to the ZCTA sub-populationpopulation

Geographic Modifiers and Micro-Geographic Modifiers and Micro-Area SESArea SES

►One could stop there , only taking One could stop there , only taking account of age, ethnicity, & gender mix account of age, ethnicity, & gender mix in ZCTAsin ZCTAs

►““Demography only” synthetic area Demography only” synthetic area estimates by are often made, and estimates by are often made, and assume that only person level assume that only person level demographic risk factors are relevant.demographic risk factors are relevant.

►Such estimates do not take account of Such estimates do not take account of the modifying impact of geographic the modifying impact of geographic context, or of the SES of the small area.context, or of the SES of the small area.

Geographic Context Effects: Geographic Context Effects: Health ExamplesHealth Examples

► Evidence of direct effects on health of area Evidence of direct effects on health of area variables after controlling for person level risk variables after controlling for person level risk factors, or of interactions between place & factors, or of interactions between place & person variables.person variables.

► For example, Cubbin et al (2001) report higher For example, Cubbin et al (2001) report higher levels of hypertension & diabetes among levels of hypertension & diabetes among African American women living in African American women living in socioeconomically deprived neighborhoods as socioeconomically deprived neighborhoods as against African American women in affluent against African American women in affluent neighborhoods, after allowing for individual-neighborhoods, after allowing for individual-level SESlevel SES

► Example of “deprivation amplification”Example of “deprivation amplification”

Place-person interactionsPlace-person interactions

► As for place-person interactions, Barnett et al As for place-person interactions, Barnett et al (2001) and Casper et al (2000) report that (2001) and Casper et al (2000) report that ethnic disparities in CHD mortality vary by area ethnic disparities in CHD mortality vary by area of residence. of residence.

► Regarding diabetes, CDC (2004) report that Regarding diabetes, CDC (2004) report that "Hispanics continued to have a higher "Hispanics continued to have a higher prevalence of diabetes than non-Hispanic prevalence of diabetes than non-Hispanic whites and that disparities in diabetes between whites and that disparities in diabetes between these two populations varied by area of these two populations varied by area of residence“residence“

► [Center for Disease Control and Prevention (CDC) (2004) Prevalence of Diabetes Among Hispanics --- Selected [Center for Disease Control and Prevention (CDC) (2004) Prevalence of Diabetes Among Hispanics --- Selected Areas, 1998--2002. MMWR, 53(40), 941-944]Areas, 1998--2002. MMWR, 53(40), 941-944]

► So ethnic risk gradient varies spatially (justifies So ethnic risk gradient varies spatially (justifies ethnic specific state level random effects)ethnic specific state level random effects)

Taking Account of Taking Account of Socioeconomic Composition of Socioeconomic Composition of

ZCTA Populations ZCTA Populations ► Strong SES impacts on many chronic Strong SES impacts on many chronic

diseases.diseases.►Maty et al (2005) report that socioeconomic Maty et al (2005) report that socioeconomic

disadvantage, esp. low educational disadvantage, esp. low educational attainment, is significant predictor of attainment, is significant predictor of incident Type 2 diabetes.incident Type 2 diabetes.

► So advisable to modify prevalence rates for So advisable to modify prevalence rates for age/sex/ethnicity according to status age/sex/ethnicity according to status composition (education/occupation/income) composition (education/occupation/income) in each ZCTA. in each ZCTA.

Education Gradient in Education Gradient in DiabetesDiabetes

►SES indicators tend to be correlated, so for SES indicators tend to be correlated, so for initial diabetes work focused on education initial diabetes work focused on education gradient (4 groups: 1=never attended, gradient (4 groups: 1=never attended, elementary only, or some high school; elementary only, or some high school; 2=high school graduate; 3=some college 2=high school graduate; 3=some college or technical school; 4=college graduate )or technical school; 4=college graduate )

►ZCTA matching data: Census data for ZCTA matching data: Census data for ZCTAs includes data on adult education ZCTAs includes data on adult education mixmix

Existing Existing Evidence Evidence

for for EducatioEducatio

n n GradientGradient

Other possibilities for SES in Other possibilities for SES in ZCTAsZCTAs

►Other approaches to representing impact of Other approaches to representing impact of SES on health outcomes in ZCTAsSES on health outcomes in ZCTAs

►De Fede et al in Int. J of Tuberculosis and De Fede et al in Int. J of Tuberculosis and Lung Disease define ZCTA SES according to Lung Disease define ZCTA SES according to (1) % of population in poverty 2) Townsend (1) % of population in poverty 2) Townsend Deprivation Index. ZCTAs were grouped into Deprivation Index. ZCTAs were grouped into SES quartiles. SES quartiles.

► Similar to quintile grouping of 30,000+ Similar to quintile grouping of 30,000+ Super Output Areas in England according to Super Output Areas in England according to Index of Multiple Deprivation. Health Survey Index of Multiple Deprivation. Health Survey for England includes SOA deprivation quintilefor England includes SOA deprivation quintile

Technical Aspects of ZCTA Technical Aspects of ZCTA ModelModel

► Use binary regression, with log link so coefficients Use binary regression, with log link so coefficients measure relative riskmeasure relative risk

► Use weighted likelihood to reflect differential Use weighted likelihood to reflect differential sampling weightssampling weights

► Use Bayesian estimation via WINBUGSUse Bayesian estimation via WINBUGS► Use spatially correlated state effects, specific to 4 Use spatially correlated state effects, specific to 4

ethnic groups – reflects evidence that ethnic risks ethnic groups – reflects evidence that ethnic risks vary geographically; also unstructured random vary geographically; also unstructured random effects (needed for spatial isolates)effects (needed for spatial isolates)

► Use age gradients, differentiated by (and Use age gradients, differentiated by (and correlated over) ethnic groupscorrelated over) ethnic groups

► Separate regressions for males and females Separate regressions for males and females (allows for gender effect modification over a range (allows for gender effect modification over a range of risk variables)of risk variables)

County EffectsCounty Effects

►Over 3000 US counties. Many counties are Over 3000 US counties. Many counties are sparsely represented in survey (though may sparsely represented in survey (though may pool over span of years to improve matters), so pool over span of years to improve matters), so random effects at this level are not adopted. random effects at this level are not adopted. Risk of over-smoothing, lack of precision in Risk of over-smoothing, lack of precision in estimatesestimates

► Potentially, county level variables can be used Potentially, county level variables can be used as risk indices – not done for diabetes but was as risk indices – not done for diabetes but was done for CVD prevalence estimates for ZCTAsdone for CVD prevalence estimates for ZCTAs

► In CVD work, used county level % of population In CVD work, used county level % of population in poverty (2005) and a category variable, in poverty (2005) and a category variable, namely the 9 category urban-rural continuumnamely the 9 category urban-rural continuum

Selected regression effects, Selected regression effects, Diabetes BRFSS Regression Diabetes BRFSS Regression

(Relative Risks)(Relative Risks)  Males     Females    

Ethnicity Mean 2.5% 97.5% Mean 2.5% 97.5%

Whites 1     1    

Blacks 1.56 1.51 1.61 1.93 1.84 2.05

Hispanic 1.14 1.09 1.2 1.36 1.26 1.49

Other 1.06 1.01 1.11 1.33 1.27 1.4

Education        

Less than High 1     1    

High School 0.92 0.87 0.96 0.74 0.72 0.77

Some College 0.96 0.91 1.01 0.68 0.65 0.7

College Graduate 0.68 0.64 0.71 0.42 0.4 0.44

Important FindingsImportant Findings►Relative risk more elevated for Relative risk more elevated for

black ethnicity than for “other” black ethnicity than for “other” ethnicity (conflates Asian ethnicity (conflates Asian Americans and Native Americans and Native Americans)Americans)

►Decline in relative risk over Decline in relative risk over education groups steeper for education groups steeper for femalesfemales

Education Gradient by Education Gradient by GenderGender

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

State EffectsState Effects

► State effects in the model are residuals after State effects in the model are residuals after controlling for the age, ethnic & educational controlling for the age, ethnic & educational composition of state populations, and also composition of state populations, and also for state levels of poverty and rurality. for state levels of poverty and rurality.

►Despite this there are consistent patterns, Despite this there are consistent patterns, such as lesser diabetes risk (even after such as lesser diabetes risk (even after controlling for individual level demography controlling for individual level demography & SES) in Colorado, Iowa, Louisiana, Nevada, & SES) in Colorado, Iowa, Louisiana, Nevada, North Carolina, Utah, Wisconsin and North Carolina, Utah, Wisconsin and Wyoming.Wyoming.

OutputsOutputs

►From the model we get distinct From the model we get distinct age/sex/ethnic prevalence schedules age/sex/ethnic prevalence schedules for each of the 53 states (which for each of the 53 states (which include DC, PR, VI)include DC, PR, VI)

►To derive prevalence estimates at To derive prevalence estimates at ZCTA level within states, we need to ZCTA level within states, we need to take account of varying SES level of take account of varying SES level of ZCTAs, using relative risk gradient for ZCTAs, using relative risk gradient for education from the BRFSS binary education from the BRFSS binary regressionregression

ZCTA EstimatesZCTA Estimates

► For each age/sex/ethnic rate in a specific state, For each age/sex/ethnic rate in a specific state, adjust according to education mix in each adjust according to education mix in each ZCTA. ZCTA.

► ZCTA with US average education mix ZCTA with US average education mix unchanged, ZCTAs with more than average unchanged, ZCTAs with more than average college graduates have rates scaled down college graduates have rates scaled down (according to modelled RR pattern), ZCTAs with (according to modelled RR pattern), ZCTAs with lower than average college graduates have lower than average college graduates have rates scaled up.rates scaled up.

► Apply scaled rates to 2000 Census ZCTA Apply scaled rates to 2000 Census ZCTA age/sex/ethnic populationsage/sex/ethnic populations

Apply model to other survey Apply model to other survey yearsyears

►Apply same model as initially Apply same model as initially developed for 2005 BRFSS to BRFSS developed for 2005 BRFSS to BRFSS surveys for earlier and later yearssurveys for earlier and later years

►ZCTA maps for 2000, 2003, 2007 show ZCTA maps for 2000, 2003, 2007 show evolving diabetes prevalence over evolving diabetes prevalence over small areas and timesmall areas and time

Diabetes Prevalence Zip Code Areas Diabetes Prevalence Zip Code Areas 20002000

Diabetes Prevalence Zip Code Areas Diabetes Prevalence Zip Code Areas 20032003

Diabetes Prevalence Zip Code Areas Diabetes Prevalence Zip Code Areas 20072007

Improvements/DevelopmentsImprovements/Developments► Apply model to more recent survey data.Apply model to more recent survey data.► Take account of county poverty & rural-urban Take account of county poverty & rural-urban

mix rather than state poverty & rurality (as mix rather than state poverty & rurality (as already done for CVD estimates)already done for CVD estimates)

► Take account of SES inequality within states Take account of SES inequality within states or counties (Income inequality linked to worse or counties (Income inequality linked to worse self-rated health (Kennedy et al. 1998) & self-rated health (Kennedy et al. 1998) & higher obesity at US state level (Kahn et al. higher obesity at US state level (Kahn et al. 1998).1998).

► Consider % ZCTA populations in poverty as Consider % ZCTA populations in poverty as opposed to education mix – policy importance opposed to education mix – policy importance of poverty. So could use BRFSS variable of poverty. So could use BRFSS variable “below poverty level” as diabetes risk factor.“below poverty level” as diabetes risk factor.