County-level Characteristics Associated with Gonorrhea Rates – United States, 2002
M Greenberg, M Sternberg, E Swint, R Kerani, E Koumans
Background
Gonorrhea is the 2nd most frequently reported communicable disease in the US
351,852 cases reported to CDC in 2002Rates have declined dramatically in the past
two decadesSignificant disparities remain
geographically and by race
Gonorrhea — rates by race and ethnicity: United States, 1981–2002
and the Healthy People 2010 objective
Rate (per 100,000 population)
WhiteBlackHispanicAsian/Pac IslAm Ind/AK Nat2010 Objective
0
500
1,000
1,500
2,000
2,500
1981 83 85 87 89 91 93 95 97 99 2001
Gonorrhea — rates by region: United States, 1981–2002 and the
Healthy People 2010 objective
Rate (per 100,000 population)
WestMidwestNortheastSouth2010 Objective
0
120
240
360
480
600
1981 83 85 87 89 91 93 95 97 99 2001
Objectives
Identify county-level characteristics independently associated with the gonorrhea rate
Evaluate effects of race and geographic clustering on county-level predictors
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)
Methods - univariate analysisCensus 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
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
Measuring spatial effects
One county and its “neighbors”“Neighbors” related by:
• Distance• Contiguity
Contiguity
Contiguity
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
Univariate analysis:Correlation between county-level
characteristics and gonorrhea rate*
Variable
Pearson
correlation
coefficient (r)‡
% population black* 0.71
% female head of household 0.63
% people <18 below poverty 0.37
Serious crimes/100,000 population 0.29
% renter occupied* 0.27
% ≥ 25 high school graduates -0.26
Persons/square mile* 0.19
*log-transformed‡p<0.0001
Spatial autocorrelation
High-high
Low-low
Low-high
High-low
Not significant
Multivariate modelsStandardized Coefficient
Variable OLS Spatial % population Black* 0.62 0.58
% female-headedhouseholds
0.25 0.23
Persons/sq mile* -0.25 -0.22
Serious crimes/100,000 0.07 0.10
% ≥25 HS graduate 0.14 0.10‡
% renter occupied* 0.18 0.18‡
% age 18-44 -0.17 -0.18
*log-transformed
‡ p<0.05; all others p<0.001
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 scaleChange in Gonorrhea Rate
(persons/100,000 population)
-8.9
-7.6
5.0
5.2
9.2
12.6
39.4
Limitations
Ecologic studyEffects of differential reporting by states not
included in the modelSpatial 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
Summary
The gonorrhea rate varied significantly across US counties
There was significant geographic clusteringClustering was not explained by measured
population characteristics aloneRelative contribution of certain county-level
characteristics changed when accounting for geographic clustering
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.
Acknowledgements
CDC• Emily Koumans• Maya Sternberg• Emmitt Swint• Hillard Weinstock• James Heffelfinger• Stuart Berman
University of Washington• Roxanne Kerani