Association between County-Level Racial Composition and Reported Cases of Chlamydia and Gonorrhea: Application of Spatial Econometric Models

Background:
STD rates in a given county can depend in part on STD rates in neighboring counties, requiring spatial regression models that can control for the impact of rates in neighboring counties.

Objective:
To explore the application of spatial econometric techniques to investigate the association between county level racial composition and STD rates.

Method:
We obtained chlamydia and gonorrhea data from the National Electronic Telecommunications System for Surveillance (NETSS) for 2000 for all 254 counties in Texas. Covariates included county-level factors as reported by the Census Bureau. The dependent variables were the logs of temporally-smoothed chlamydia and gonorrhea incidence rates. Independent variables included percent Black, percent White (omitted), percent Hispanic, percent American Indian, percent Asian, percent married, percent aged 18 to 24 years, male-female ratio, log of crime rate, log of population density, infant deaths per 1,000 live births, deaths per 1,000 residents and a commuter index. We fit ordinary least square (OLS) and spatial econometric models.

Result:
The spatial regression models were superior to OLS models (using goodness-of-fit measures). Percent Black, percent aged 18 to 24 years, log of crime rate, log of population density, deaths per 1,000 residents and the commuter index were all positively (p<0.05) associated with chlamydia and gonorrhea incidence rates. However, male-female ratio was negatively (p<0.05) associated with Chlamydia and gonorrhea incidence rate. Percent Hispanic was significant in the chlamydia regression only.

Conclusion:
Numerous previous studies have documented higher rates of reported STDs among some minority racial or ethnic groups. Our results suggest that these disparities persist at the county level after controlling for STD rates in neighboring counties, although the association between county-level STD rates and racial composition is likely dependent on the STD in question.

Implications:
Spatial analysis can be a useful tool in analyzing STD surveillance data.