1. Poverty Gradients and Racial/Ethnic Analysis of Gonorrhea in California
Michael C. Samuel, DrPH; Yuri Springer, PhD; Denise Gilson; Gail Bolan, MD
STD Control Branch
California Department of Public Health
2008 National STD Prevention Conference
Confronting Challenges, Applying Solutions
Chicago, Illinois, March 10-13, 2008

2. Background
Dramatic variation in STD rates among racial/ethnic groups
STD rates inversely related to socioeconomic status
Surveillance data generally do not include socioeconomic status data
Differentials in STD rates by race/ethnic group not statistically “accounted for” by differentials in socioeconomic status

3. Background
Project Questions:
How does the relationship between STD rates and socioeconomic status vary among different racial/ethnic groups in California?
How do these relationships vary geographically?

4. “Area Based Socioeconomic Measures” (ABSM)”
Methods - Data
STD data
2004 to 2006 gonorrhea cases in California
Provider/laboratory case reports
Generally includes address of case
“Area Based Socioeconomic Measures” (ABSM)”
Determine case census tract based on case address
Multi-tiered process of cleaning and geocoding case address
Associate 2000 US Census tract “percent living below federal poverty level” with each case in that census tract
Group poverty into four levels: <10%, 10-20%, 20-30%, >30%

5. Methods - Analyses Rate calculation Results visualization
GC rates in each census tract for each racial/ethnic groups (Asians, African Americans, Hispanics, Whites)
Numerator:
# of cases (weighted to account missing race/ethnicity data)
Denominator:
Corresponding 2000 US Census Bureau population counts for each census tract
Results visualization
Plot relationships between poverty and infection by race/ethnicity
Map spatial distribution of cases
Rates aggregated across full state and for individual counties

6. Results Address Category and Geocode Status
California Gonorrhea Cases –
Address Category . Cases Percent
Total 98, %
Blank 9, %
Incomplete 2, %
Non-resident address 1, %
Other %
P.O. Box 1, %
Potentially Geocodable 83, %
Successfully Geocoded 79, %

7. Results Geocode Status by Race/Ethnic Category Total % of Total
California Gonorrhea Cases –
Total % of Total
Race/Ethnicity cases cases geocoded
Asian 2,
African American 25,
Hispanic 20,
White 17,
Unknown 31,

8. Results Socioeconomic Gradients in Infection Rates by Race/Ethnicity
California Gonorrhea Cases,

9. Results Socioeconomic Gradients in Infection Rates by Race/Ethnicity
Selected Counties - California Gonorrhea Cases,
Los Angeles
San Diego
San Francisco
Sacramento

10. Results Census Tract Poverty Levels California 2000 US Census Data
3,259 (46%)
1,975 (28%)
986 (14%)
721 (10%)
108 (2%) .
7,049
Percent Below Poverty . # Tracts .

11. Results Spatial Clustering of Cases by Poverty Level Poverty <10%
N=19,135 cases
24% of all cases
(46% of all tracts)
Poverty 10-20%
N=23,784 cases
30% of all cases
(28% of all tracts)
Poverty 20-30%
N=16,876 cases
21% of all cases
(14% of all tracts)
Poverty >30%
N=18,968 cases
24% of all cases
(10% of all tracts)

12. Results Spatial Clustering of African American Cases by Poverty Level
13% of Af. Am. cases
(46% of all tracts)
Poverty 10-20%
N=5,771 cases
25% of Af. Am. Cases
(28% of all tracts)
Poverty 20-30%
N=5,729
25% of Af. Am. Cases
(14% of all tracts)
Poverty >30%
N=8,471 cases
37% of Af. Am. Cases
(10% of all tracts)
11% of all cases

13. Results
Case Density by and Poverty and Race/Ethnicity

14. Conclusions - Findings
Racial/Ethnic Trends
Pronounced variation in GC infection rates among racial/ethnic groups
Rates among African Americans 9-14 times higher than Whites
Poverty Gradients
Infection rates increase with increasing levels of neighborhood poverty
Rates for African Americans in even the wealthiest neighborhoods are higher than rates for any other race/ethnic group in any poverty level, including the poorest neighborhoods
Spatial Clustering of Cases
Poorest census tract represent only 10% of the state’s total tracts but contain ~25% of GC cases and ~37% of African American GC cases

15. Conclusions - Limitations
Use of ABSMs to estimate case-specific information
Use of year-2000 census data to calculate rates
Method of weighting for missing race/ethnicity data
Over 15% of cases could not be geocoded and were not included in our analyses

16. Conclusions – Implications/Recommendations
Targeted prevention efforts in neighborhood “hot spots” could reach large numbers of cases, particularly among African Americans
Screening programs?
Prevention outreach?
Social marketing?
Network interventions should be explored, since these findings suggest transmission in dense networks
Any/all efforts in this area need to be led at local level and incorporate input of affected populations

17. Contact Information:
Michael C. Samuel, DrPH
STD Control Branch
California Dept. of Public Health
850 Marina Bay Parkway,
Richmond, CA 94804
Acknowledgements
Alameda County Dept. of Public Health
Matt Beyers
Jane Martin
Los Angeles County Dept. of Public Health
Kai-Jen Cheng
San Francisco County Dept. of Public Health
Robert Kohn