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Background: Half of Connecticut’s population resides in Hartford and New Haven counties but these counties account for 71% of gonorrhea cases and 63% of chlamydia cases reported in Connecticut.
Objectives: Determine risk factors associated with gonorrhea and chlamydia co-infection.
Methods: As part of the Centers for Disease Control and Prevention’s Sexually Transmitted Disease (STD) Surveillance Network (SSuN), a random sample of newly diagnosed gonorrhea case-patients from Hartford and New Haven counties are interviewed each month to determine demographic information of sex partners and behavioral risk factors. Data for this analysis were obtained from interviews and the Connecticut STD Control Program database. A chi-square test was used to measure differences between case-patients with gonorrhea infection only and gonorrhea and chlamydia co-infection. Variables with significant associations (P<.05) were included in a logistic regression model.
Results: Between July 1, 2009–June 30, 2011, 28.5% (110/386) of gonorrhea case-patients interviewed were co-infected with chlamydia. In the model, younger age, (15–29 years old) (OR, 1.92 [95%CI, 1.00–3.69]), black race (OR, 5.34 [95%CI, 1.56–18.21]), Hispanic ethnicity (OR, 4.57 [95%CI, 1.25–16.63]), and residence in Hartford County (OR, 1.92 [95%CI, 1.17–3.17]) were associated with co-infection. Not using a condom at last sexual encounter (OR, 1.75 [95%CI, 1.06-2.92]) was the only behavioral risk factor associated with a co-infection diagnosis.
Conclusions: Almost one-third of gonorrhea cases interviewed from these two counties are co-infected with chlamydia with a significant proportion of these infections occurring among the black and Hispanic populations residing in Hartford County. Not using a condom was associated with an increased risk of chlamydia co-infection.
Implications for Programs, Policy, and Research: STD Programs should use these data to better target partner notification and screening activities on populations and geographic areas that would have the greatest impact on infection rates.