Early detection and treatment of STI/HIV are public health
priorities. Our objective was to compare characteristics of men who have sex
with men (MSM) in Dutch data available in 2010 from EMIS, an international
internet survey, Schorer Monitor, a Dutch internet survey, and data from STI-
clinic visits, since these might be subject to different and unknown biases.
Data from Dutch MSM Internet Surveys (EMIS NLN = 3,787; Schorer Monitor, SMON N = 3,602),
and 3,800 STI clinic visits (SOAP) were combined into one dataset. We included
factors that were measured in all three databases. The socio-demographics
included were age (at the time of the survey), zip code, and ethnicity.
Behavioural variables included were the number of sexual partners, condom use
with last sexual partner, drug use, being diagnosed with STI, being diagnosed
with HIV, and HIV testing. Outcomes we investigated were being diagnosed with
STI, HIV, and never been tested for HIV.
Logistic regressions showed that determinants for being
diagnosed with STI were having more sexual partners, drug use, and having had
an HIV test (aORs 1.3 to 17.1) in EMIS and SMON. Determinants for being
diagnosed with HIV in all three databases were older age, living in Amsterdam,
and having more partners (aORs 1.8 to 4.4). In EMIS and SMON, drug use,
non-condom use, and having STI were additional determinants (aORs 1.6 to 8.9).
Finally, determinants associated with never been tested for HIV were being
younger (only SOAP), living outside of Amsterdam, having fewer partners, no
drug use, and no STI (aORs 0.2 to 0.8).
Risk factors from internet surveys were largely similar, but
differed from STI clinics, possibly because it involves self-reports rather
than diagnoses or because of differences in timing. The difference between the
internet surveys and STI clinic data is much less pronounced for having never
been tested, suggesting both are appropriate for this outcome. These findings
shed light on conclusions drawn from different data sources, as well as the
comparability of recruitment strategies, the robustness of risk factors,
consequences of phrasing questions differently, and on (policy) implications
based on different data sources.
By: Chantal den Daas1*, Maaike Goenee2, Bouko H. W. Bakker2, Hanneke de Graaf2 and Eline L. M. Op de Coul1
1Centre for Infectious Disease Control,
National Institute for Public Health and the Environment, P.O. Box 1,
Bilthoven, The Netherlands
2Rutgers, Utrecht, The Netherlands