1. County-level Characteristics Associated with Gonorrhea Rates – United States, 2002 M Greenberg, M Sternberg, E Swint, R Kerani, E Koumans mgreenberg1@cdc.gov

2. Background Gonorrhea is the 2 nd most frequently reported communicable disease in the US 351,852 cases reported to CDC in 2002 Rates have declined dramatically in the past two decades Significant disparities remain geographically and by race

3. Gonorrhea — rates by race and ethnicity: United States, 1981–2002 and the Healthy People 2010 objective

4. Gonorrhea — rates by region: United States, 1981–2002 and the Healthy People 2010 objective

5. Objectives Identify county-level characteristics independently associated with the gonorrhea rate Evaluate effects of race and geographic clustering on county-level predictors

6. Sources of data National Electronic Telecommunications System for Surveillance (NETSS) US Census 2000 Summary File 3 3,141 United States Counties SAS USA Counties Map file (SAS Institute Inc., Cary, NC)

7. Methods - univariate analysis Census variables chosen socio-demographic relevance comparison with other studies Grouped into categories felt to measure different effects Density, race/ethnicity, family structure, poverty/income, fertility/health, crime, education, housing Correlated variables independently with the gonorrhea rate

8. Most strongly correlated variable from each category included in the regression model Ordinary least squares (OLS) residual showed spatial autocorrelation Demonstrated need for a multivariate spatial model Spatial model explains all variables controlling for spatial autocorrelation Methods: multivariate analysis

9. Measuring spatial effects One county and its “neighbors” “Neighbors” related by: Distance Contiguity

12. Spatial autocorrelation Pairs of adjacent counties where the gonorrhea rate is significantly correlated and may in some way influence one another High-high Low-low Not significant

13. Univariate analysis: Correlation between county-level characteristics and gonorrhea rate* Variable Pearson correlation coefficient (r)‡ % population black*0.71 % female head of household0.63 % people <18 below poverty0.37 Serious crimes/100,000 population0.29 % renter occupied*0.27 % ≥ 25 high school graduates-0.26 Persons/square mile*0.19 *log-transformed ‡p<0.0001

15. Spatial autocorrelation High-high Low-low Low-high High-low Not significant

16. Multivariate models Standardized Coefficient VariableOLSSpatial % population Black*0.620.58 % female-headed households 0.250.23 Persons/sq mile*-0.25-0.22 Serious crimes/100,0000.070.10 % ≥25 HS graduate0.140.10 ‡ % renter occupied*0.180.18 ‡ % age 18-44-0.17-0.18 *log-transformed ‡ p<0.05; all others p<0.001

17. Change in gonorrhea rate for one standard deviation increase in each variable % Pop. Black* % Female HH % Rent occupied* Serious crimes/100,000 % ≥25 HS grad % Pop 18-44 years Persons/sq mile* *log scale Change in Gonorrhea Rate (persons/100,000 population)

18. Limitations Ecologic study Effects of differential reporting by states not included in the model Spatial autocorrelation may be a surrogate for an unmeasured county-level variable or an artifact of county partitioning County may not be most appropriate area level for analysis Model does not include measures of health resource utilization

19. Summary The gonorrhea rate varied significantly across US counties There was significant geographic clustering Clustering was not explained by measured population characteristics alone Relative contribution of certain county-level characteristics changed when accounting for geographic clustering

20. Recommendations Consider geographic clustering as an important effect in analyses of STD surveillance data County-level characteristics and geographic context may help direct screening and prevention efforts.

21. Acknowledgements CDC Emily Koumans Maya Sternberg Emmitt Swint Hillard Weinstock James Heffelfinger Stuart Berman University of Washington Roxanne Kerani

23. Gonorrhea — Rates: United States, 1970–2002 and the Healthy People 2010 objective Note: The Healthy People 2010 objective for gonorrhea is 19.0 cases per 100,000 population.

25. Gonorrhea Rate – US 2002 by race/ethnicity Rate (per 100,000 population) Overall125.0 African-American742.3 Am. Indian/Alaska Native126.8 Hispanic76.0 White31.1 Asian/Pacific Islanders24.1

26. Methods - Overview Create a baseline OLS model; residuals still spatially correlated Choose a spatial model to account for spatial correlation Model results using baseline OLS and spatial term Assess geographic clustering of GC rates 3 Bivariate analysis of county-level characteristics 1 24 5

27. Methods: Spatial Analysis Assess clustering of GC rate Establish a weighting matrix – based on Distance Contiguity Other measures of interaction Global clustering Moran’s I statistic

28. Methods: Spatial Analysis Choose a spatial model spatial lag model or spatial error model Model results using baseline OLS model with spatial term

29. Geographic Clustering of the Gonorrhea Rate Global Moran’s I = 0.552 (p<0.0001) Indicates significant clustering of GC rate Chose spatial error model

30. Multivariate model with Spatial Term VariableCoefficientProbability (p) Constant2.681630.0000000 Midwest-0.026353080.6810362 North-0.53740320.0000002 West-0.28007380.0008845 % All ages < poverty*-0.076467290.4747951 % Female HH0.067094980.0000000 % Black*0.29746840.0000000 % Renter occupied0.12359810.0317941 % >25 HS Grad0.00002510.9923115 Serious crimes0.00005030.0000000 % <18 below poverty0.008747150.1167759 Births/1000 pop-0.02554740.0000247 Lambda0.49897580.0000000

31. Ordinary Least Squares Model VariableCoefficientProbability (p) Constant1.807340.0000000 Midwest-0.053859230.2367081 North-0.63510990.0000000 West-0.2609480.0000065 % All ages < poverty*0.17906120.0781330 % Female HH0.083370910.0000000 % Black*0.31302890.0000000 % Renter occupied0.16321460.0020724 % >25 HS Grad0.0039930790.0995493 Serious crimes0.000010910.1379701 % <18 below poverty0.008747150.1167759 Births/1000 pop-0.02554740.0000247

32. Contiguity Second-order contiguity

33. Comparison of spatial to baseline model *ratio = spatial model coefficient OLS model coefficient VariableRatio* % population Black*0.93 % female households no spouse0.93 Persons/sq mile*0.85 Serious crimes/100,000 pop1.44 % ≥25 HS graduate0.73 % renter occupied*0.97 % age 18-441.08

34. Categories of county-level characteristics Density Race/ethnicity Family structure Poverty/income Fertility/health Crime Education Housing Correlated variables independently with the gonorrhea rate