County-level Characteristics Associated with Gonorrhea Rates – United States, 2002

M Greenberg, M Sternberg, E Swint, R Kerani, E Koumans

mgreenberg1@cdc.gov

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