Heterogeneous population effects of an alcohol excise tax increase on sexually transmitted infections morbidity

Abstract

Background and aims: Alcohol taxes reduce population-level excessive alcohol use and alcohol-related morbidity and mortality, yet little is known about the distribution of the effects of alcohol taxation across race/ethnicity and age subgroups. We examined the race/ethnicity- and age group-specific effects of an excise alcohol tax increase on a common and routinely collected alcohol-related morbidity indicator, sexually transmitted infections.

Methods: We used an interrupted time series design to examine the effect of a 2009 alcohol tax increase in Illinois, USA on new cases of two common sexually transmitted infections (chlamydia and gonorrhea) reported to the US National Notifiable Disease Surveillance System from January 2003 to December 2011 (n = 108 repeated monthly observations). We estimated the effects of the tax increase on infection rates in the general population and within specific race/ethnicity and age subgroups using mixed models accounting for temporal trends and median income.

Results: Following the Illinois alcohol tax increase, state-wide rates of gonorrhea decreased 21% [95% confidence Interval (CI) = -25.7, -16.7] and chlamydia decreased 11% [95% CI = -17.8, -4.4], resulting in an estimated 3506 fewer gonorrhea infections and 5844 fewer chlamydia infections annually. The null hypothesis of homogenous effects by race/ethnicity and age was rejected (P < 0.0001). Significant reductions were observed among non-Hispanic blacks: gonorrhea rates decreased 25.6% (95% CI = -30.0, -21.0) and chlamydia rates decreased 14.7% (95% CI = -20.9, -8.0). Among non-Hispanics, point estimates suggest decreases were highest among 25-29-year-olds.

Conclusions: Increased alcohol taxes appear to reduce sexually transmitted infections, especially among subpopulations with high disease burdens, such as non-Hispanic blacks.

Keywords: Alcohol; alcohol-related morbidity; excise tax; race/ethnicity; sexually transmitted infections; time series analysis.