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Association between County Level Racial Composition and Reported Cases of Chlamydia and Gonorrhea: Application of Spatial Econometric Models Kwame Owusu-Edusei Jr. Harrell W. Chesson Health Services Research & Evaluation Branch Division of STD Prevention Centers for Disease Control and Prevention Atlanta, GA Disclaimer: The findings and conclusions in this presentation are those of the authors and do not necessarily represent the views of the CDC.
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Background Spatial dependence: incidence rates in a county depend on incidence rates within its neighboring counties Previous works on STDs Semaan et al., 2007 – State level Syphilis and Gonorrhea Greenberg, 2004 – County level Gonorrhea Koumans et al., 2000 – County level HIV and Syphilis
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Objectives Explore the application of spatial econometric
techniques to investigate the association between county level racial composition and two STDs – chlamydia and gonorrhea for the state of Texas
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Why Texas? Largest number of counties - 254 Relatively high STD rates
Racial composition - fairly high for the two major minority groups 32% Hispanics 11.5% African-Americans Reduce spatial heterogeneity
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Criteria for Spatial Relationships
Rook – counties that share a common side Queen – counties that share a common side or corner (vertex)
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Rook and Queen Summary Relationships:
Texas Counties
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Summary of Spatial Relationships
Pecos Brewster Webb Hudspeth Presidio Terrell Reeves Val Verde Duval Harris Frio Bell Hill Starr Polk Edwards Bee Clay Jeff Davis Irion Uvalde Sutton Gaines Leon Bexar Hale Hall Jack Cass Ellis Erath Dallam Zavala Hartley Gray King Kent Oldham Wise Coke Lynn Jones Dimmit Travis Terry Andrews Ector Llano Ward Knox Real Bowie Foard Culberson Kerr Hidalgo Upton Kinney Rusk Tyler La Salle Kenedy Medina Hunt Lee Kimble Lamb Brazoria Floyd Liberty Falls Smith Zapata Jasper Potter Milam Reagan Garza Burnet Nolan Houston Cottle Young Mills Lamar Taylor Fisher Coleman Dallas Collin Castro Parker Brown Coryell Tom Green Atascosa Motley Maverick Cooke Crane Moore Mason Lavaca Brooks Scurry Navarro Archer Bosque Martin Carson Hardin Fannin Baylor Goliad Denton Fayette Runnels Hays Concho Schleicher Donley Crosby Deaf Smith Wharton Tarrant San Saba Bailey Parmer Panola Gillespie Borden Newton Haskell Shelby Wood Briscoe De Witt Roberts Grayson Sterling Wilson Randall Bastrop Menard Live Oak Gonzales Jim Hogg Victoria Swisher Dickens Nueces Howard Anderson Eastland Cherokee Grimes Wheeler Walker Hockley Dawson Midland Karnes McMullen Matagorda Jackson Colorado Jefferson Harrison Trinity Mitchell Winkler Sherman Kleberg Red River Lubbock Bandera Blanco Stephens Callahan Williamson Austin Cameron McLennan Wilbarger Hemphill Ochiltree Refugio Hansford McCulloch Montague Palo Pinto Angelina Yoakum Cochran Stonewall Freestone Hopkins Lipscomb Loving Johnson Fort Bend Comanche Jim Wells Sabine Kaufman Glasscock Henderson Brazos Comal Montgomery Robertson Armstrong Van Zandt Titus Upshur Wichita Waller Hutchinson Childress Burleson Hood Shackelford Nacogdoches Willacy Hardeman Guadalupe Collingsworth Throckmorton Chambers Marion San Patricio San Jacinto Delta Orange Calhoun Rains San Augustine Morris Camp Galveston Aransas Somervell Rockwall Summary of Spatial Relationships Average number of Queen neighbors ≈ 6 (5.74) Average number of Rook neighbors ≈ 5 (5.14) Crockett El Paso
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Empirical Models Ordinary Least Squares (OLS)
Spatial Autoregressive Model (SAM) Spatial Error Model (SEM) Spatial Durbin Model (SDM)
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Data Texas county level data: spatial unit of analysis is county
1999, 2000 and 2001 chlamydia and gonorrhea incidence rates (all race, ages and gender) We computed a three-year temporally smoothed rates to reduce variance instability County level data from US census bureau and ERS-USDA website Percent Black Percent White Percent Hispanic Percent American Indian Percent American Asian Percent aged Percent married couple Male-female ratio Crime rate Population density Infant death rate Death rate Suburban commute index
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Results
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Dependent variable: log of temporally smoothed chlamydia rate
Empirical Estimation Results: Chlamydia (n=254) Variables Dependent variable: log of temporally smoothed chlamydia rate OLS SAM SEM SDM Percent Black Percent Hispanic Percent Am. Indian Percent Am. Asian Spatial lags WQ*Log of Rate (ρ) WQ*Error (λ) WQ*Percent Hispanic WQ*Log of pop. Dens. 0.02 *** 0.01 *** 0.11 -0.01 0.01*** 0.13 -0.003 0.21 * 0.10 0.53 *** 0.06 -0.02 0.62 *** -0.01 *** -0.16 ** Adjusted R-squared BIC AIC 0.57 118.93 72.94 0.58 119.27 69.74 0.61 101.39 51.86 0.64 148.62 56.65 Mean VIFa Highest VIFa 2.11 4.12 2.07 4.13 2.04 4.16 19.43 Note: Significance levels * = 0.1, ** = 0.05, *** = 0.01. a Variance inflation factor . WQ represents row-standardized Queen contiguity matrix. BIC - Schwartz-Bayesian Information Criteria AIC - Akaike Bayesian Information Criteria
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Empirical Estimation Results: Gonorrhea (n=254)
Variables Dependent variable: log of temporally smoothed gonorrhea rate OLS SAM SEM SDM Percent Black Percent Hispanic Percent Am. Indian Percent Am. Asian Spatial lags WR*Log of rate (ρ) WR*Error (λ) 0.03*** 0.001 0.17 -0.02 0.02*** 0.002 0.18 -0.01 0.18** 0.15 0.35 *** 0.01 0.11 Adjusted R-squared BIC AIC 0.62 279.97 233.98 0.63 281.00 231.48 0.64 276.94 227.42 0.65 335.01 243.04 Mean VIFa Highest VIFa 2.11 4.12 2.16 4.13 2.04 4.15 4.09 17.28 Note: Significance levels * = 0.1, ** = 0.05, *** = 0.01. a Variance inflation factor . WR represents row-standardized Rook contiguity matrix.
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Combined Equation Results
Variable Chlamydia Gonorrhea Percent Black Percent Hispanic Percent Am. Indian Percent Am. Asian Percent aged 18-24 Percent married couple Male-female ratio Log of crime rate Log of pop. density Infant death rate Death rate Commute index W*Error (λ) R-squared Breusch-Pagan test of independence: chi-square 0.02 *** 0.01 *** 0.10 -0.01 0.04 *** 0.005 *** 0.07 ** 0.18 *** 0.0003 0.05 *** 0.004 * 0.44 *** 0.61 0.03 *** 0.001 0.15 * -0.02 -0.013 * 0.20 *** 0.21 *** 0.0004 0.003 * 0.35 *** 0.64 (p < ) Note: Significance levels * = 0.1, ** = 0.05, *** = 0.01.
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Summary Results were not different from the OLS model
SEMs were superior (in terms of model specification) The association between chlamydia and gonorrhea rates and percent African Americans was significantly higher than percent White The association between chlamydia rates and percent Hispanic was significantly higher than percent White, but NOT significantly different for gonorrhea
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Limitations Data Different sources and different identification methods Focus on different subpopulations Variance instability – temporal smoothing method is not a perfect fix Spatial heterogeneity – unstable coefficients Ignored contiguous counties in bordering states
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Conclusion Assortative mixing by location warrants using spatial regression techniques The criteria used to define spatial proximity is important The association between county-level STD rates and racial composition is likely dependent on the STD in question
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Acknowledgements Amy Pulver HSREB
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Thank you!
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Empirical Spatial Models
Spatial Autoregressive Model (SAM) Spatial Error Model (SEM) Spatial Durbin Model (SDM)
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Estimation Procedure & Diagnostics
Multicollinearity concerns Variance Inflation Factors (VIFs) - Belsley et al., (1980) suggest a critical value of 10 Close relationship between gonorrhea and chlamydia; Data from the same population Zellner (1962) recommended Seemingly Unrelated Regression Estimation (SURE) to improve efficiency
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