Last data update: May 13, 2024. (Total: 46773 publications since 2009)
Records 1-3 (of 3 Records) |
Query Trace: Chang BA[original query] |
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Investigating multiple reported bacterial sexually transmitted infection hot spot counties in the United States: Ordered spatial logistic regression
Owusu-Edusei KJr , Chang BA . Sex Transm Dis 2019 46 (12) 771-776 PURPOSE: To identify and examine the correlates of multiple bacterial sexually transmitted infection (STI) hot spot counties in the United States. METHODS: We assembled and analyzed five years (2008-2012) of cross-sectional STI morbidity data to identify multiple bacterial STI (chlamydia, gonorrhea and syphilis) hot spot counties using hot spot analysis. Then, we examined the association between the multi-STI-hotspots and select multi-year (2008-2012) sociodemographic factors (data obtained from the American Community Survey) using ordered spatial logistic regression analyses. RESULTS: Of the 2,935 counties, the results indicated that 85 counties were hot spots for all three STIs [three-STI-hotspot counties], 177 were hot spots for two STIs [two-STI-hotspot counties], and 145 were hot spots for only one STI [one-STI-hotspot counties]. Approximately 93% (79/85) of the counties determined to be three-STI-hotspots were found in four Southern states--Mississippi (n=25), Arkansas (n=22), Louisiana (n=19), and Alabama (n=13). Counties determined to be two-STI-hotspots were found in seven Southern states--Arkansas, Louisiana, Mississippi, Alabama, Georgia, North and South Carolina had at least 10 two-STI-hotspot counties each. The multi-STI-hotspot classes were significantly (p<0.05) associated with percent Black (non-Hispanic), percent Hispanics, percent American Indians, population density, male-female sex ratio, percent aged 25-44 and violent crime rate. CONCLUSION: This study provides information on multiple STI hot spot counties in the United States and the associated sociodemographic factors. Such information can be used to assist planning, designing and implementing effective integrated bacterial STI prevention and control programs/interventions. |
Does including violent crime rates in ecological regression models of sexually transmissible infection rates improve model quality Insights from spatial regression analyses
Owusu-Edusei K , Chang BA , Aslam MV , Johnson RA , Pearson WS , Chesson HW . Sex Health 2019 16 (2) 148-157 Background:Violent crime rates are often correlated with the hard-to-measure social determinants of sexually transmissible infections (STIs). In this study, we examined whether including violent crime rate as an independent variable can improve the quality of ecological regression models of STIs. Methods: We obtained multiyear (2008-12) cross-sectional county-level data on violent crime and three STIs (chlamydia, gonorrhoea, and primary and secondary (P&S) syphilis) from counties in all the contiguous states in the US (except Illinois and Florida, due to lack of data). We used two measures of STI morbidity (one categorical and one continuous) and applied spatial regression with the spatial error model for each STI, with and without violent crime rate as an independent variable. We computed the associated Akaike's information criterion (AIC) and Bayesian information criterion (BIC) as our measure of the relative goodness of fit of the models. Results: Including the violent crime rate as an independent variable improved the quality of the regression models after controlling for several sociodemographic factors. We found that the lower calculated AICs and BICs indicated more favourable goodness of fit in all the models that included violent crime rates, except for the categorical P&S syphilis model, in which the violent crime variable was not statistically significant. Conclusion: Because violent crime rates can account for the hard-to-measure social determinants of STIs, including violent crime rate as an independent variable can improve ecological regression models of STIs. |
Correlates of county-level nonviral sexually transmitted infection hot spots in the US: application of hot spot analysis and spatial logistic regression
Chang BA , Pearson WS , Owusu-Edusei K Jr . Ann Epidemiol 2017 27 (4) 231-237 PURPOSE: We used a combination of hot spot analysis (HSA) and spatial regression to examine county-level hot spot correlates for the most commonly reported nonviral sexually transmitted infections (STIs) in the 48 contiguous states in the United States (US). METHODS: We obtained reported county-level total case rates of chlamydia, gonorrhea, and primary and secondary (P&S) syphilis in all counties in the 48 contiguous states from national surveillance data and computed temporally smoothed rates using 2008-2012 data. Covariates were obtained from county-level multiyear (2008-2012) American Community Surveys from the US census. We conducted HSA to identify hot spot counties for all three STIs. We then applied spatial logistic regression with the spatial error model to determine the association between the identified hot spots and the covariates. RESULTS: HSA indicated that ≥84% of hot spots for each STI were in the South. Spatial regression results indicated that, a 10-unit increase in the percentage of Black non-Hispanics was associated with approximately 42% (P < 0.01) [ approximately 22% (P < 0.01), for Hispanics] increase in the odds of being a hot spot county for chlamydia and gonorrhea, and approximately 27% (P < 0.01) [ approximately 11% (P < 0.01) for Hispanics] for P&S syphilis. Compared with the other regions (West, Midwest, and Northeast), counties in the South were 6.5 (P < 0.01; chlamydia), 9.6 (P < 0.01; gonorrhea), and 4.7 (P < 0.01; P&S syphilis) times more likely to be hot spots. CONCLUSION: Our study provides important information on hot spot clusters of nonviral STIs in the entire United States, including associations between hot spot counties and sociodemographic factors. |
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