STD coinfections in The Netherlands: Specific sexual networks at highest risk
Maaike G van Veen1, Femke D H Koedijk, Marianne A B van der Sande
1National Institute for Public Health and the Environment, RIVM, Department Epidemiology and Surveillance, Centre for Infectious Disease Control, Bilthoven, The Netherlands. maaike.van.veen@rivm.nl <maaike.van.veen@rivm.nl>
Background:
Specific subpopulations infected with multiple bacterial sexually transmitted diseases (STDs) may facilitate ongoing STD transmission. To identify these subpopulations we determined the extent of concurrent incident STD infections and their risk factors among the high-risk population seen at Dutch STD clinics.
Methods:
STD surveillance data submitted routinely by STD clinics to the National Institute for Public Health on demographics, sexual behavior, STD testing, and diagnoses for the period 2004-2007 were analyzed.
Results:
Bacterial STD coinfections were diagnosed concurrently in 2120 (7%) of the 31,754 incident bacterial STD diagnoses (chlamydia, gonorrhea, infectious syphilis). In univariate logistic regression analyses, coinfections were significantly more often diagnosed in men who have sex with men (MSM, OR = 5.4) than in heterosexuals. Multivariate analyses showed a significant interaction between age and sexual preference. Subsequent stratified analyses by sexual preference showed a linear rise in coinfections with age in MSM. In heterosexuals, by contrast, bacterial coinfections peaked in those aged 19 or less; they had 27% of coinfections, while having only 14% of monodiagnoses and 10% of consultations. Heterosexual STD clinic attendees of Surinamese or Antillean origin were significantly at higher risk for coinfection (OR = 6.5) than all other ethnicities.
Conclusions:
Attendees belonging to specific sexual networks, such as MSM, ethnic groups, and young heterosexuals were at increased risk for STD coinfections. The different trend with age in MSM versus heterosexuals suggests that these 2 high-risk networks have different determinants of higher risk, such as age-related sexual risk-taking, biologic susceptibility, and insufficient knowledge or compliance with prevention measures. Prevention should therefore be targeted differently towards specific sexual networks.
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