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What can we infer about incarceration and sexually transmitted diseases?

A handful of recent ecologic and cross-sectional studies have found evidence of a positive association between incarceration rates and sexually transmitted disease (STD) rates. In this issue of STDs, Dauria et al. publish the first ecologic study of the association between incarceration and STD rates over time. As expected, Dauria et al. found evidence supporting a positive association between higher baseline incarceration rates and higher STD incidence rates. Intuitively, this makes sense, and other studies have observed a similar association. One simple explanation of the association is that high incarceration rates indicate neighborhoods with high crime and illicit activity, such as drugs and commercial sex, all of which favor STD transmission. Another more complex theory is that when a large number of men in a community are removed, for example, because of incarceration, the ratio of men to women becomes unbalanced. This imbalance destabilizes the community and shifts power dynamics, giving the remaining men in the community more power, and women less power, to negotiate sexual partnerships, relationship dynamics, and sexual behaviors, creating to a high-risk sexual environment. There is also evidence that correctional populations have higher rates of infectious diseases than do the general population, including HIV and other STDs. It is unclear when infections are acquired, but presumably, many men are infected before they are incarcerated, and many become infected while incarcerated. Incarcerated men who become infected while in the system may spread infection when they return to their home community at the end of their sentence, or while on probation or parole. By extension, the in-community sexual partners of incarcerated men may have other sexual partners during the incarceration period, complicating the sexual network and increasing risk of STD acquisition and transmission. In addition, men with a history of incarceration and unstable housing situations have more sex partners and more unprotected sex, increasing their risk for STD acquisition and transmission. Unexpectedly, Dauria et al. also observed decreases in STD rates with increases in incarceration rates over time. Although they cannot explain the mechanism(s) underlying this trend given their data, they hypothesized that removing men from the community who are infected with an STD before they are incarcerated may reduce STD prevalence. Sexually transmitted disease rates could remain lower in the longer term if these men receive STD screening and treatment while incarcerated, suggesting that these results could have meaningful policy and practice implications. Before getting too excited and making grand interpretations and recommendations, we need to examine the results in more depth for their correctness. Incarceration and STD rates were calculated using administrative data, which have the advantage of being collected from entire populations and thus theoretically have zero sampling variance. Administrative data also have the advantage of being routinely collected over time, allowing for trend analysis. Administrative data definitions can change, especially over time, as seen with definitions of HIV/AIDS and incarceration (prison or prison and jail sentences), and data quality and completeness can also vary, as seen with STD risk factor data collected on reportable disease reports. Dauria et al. limited STDs to include chlamydial infection, gonorrhea, and syphilis, the definitions, detection, and reportability of which remained consistent over the study period. They also limited their definition of incarceration to prison sentences. Dauria et al. compare longitudinal incarceration and STD data by census tract, and this design creates conditions for variables in the analysis to be both temporally and spatially correlated (i.e., between variables) and auto-correlated (i.e., within variable); in other words, they are spatially and temporally dependent. Sexually transmitted diseases are inherently temporally auto-correlated because current STD rates are dependent on past STD rates, and future STD rates are dependent on current STD rates.