Does including violent crime rates in ecological regression models of sexually transmissible infection rates improve model quality? Insights from spatial regression analyses

Abstract

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.