MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands

In many countries, HIV infections among MSM (MSMHIV) are closely monitored, and updated epidemiological reports are made available annually, yet the true prevalence of MSMHIV can be masked for areas with small population density or lack of data. Therefore, this study aimed to investigate the feasibility of small area estimation with a Bayesian approach to improve HIV surveillance. Data from the European MSM Internet Survey 2017 (EMIS-2017, Dutch subsample, n=3,459) and the Dutch survey ‘Men & Sexuality-2018’ (SMS-2018, n=5,653) were utilized in this study. We first applied a frequentist calculation to compare the observed relative risk of MSMHIV per Public Health Services (GGD) region in the Netherlands. We then applied a Bayesian spatial analysis and ecological regression to account for variance due to space and determinants associated with HIV among MSM to obtain more robust estimates. Results of the prevalence and risk estimations from EMIS-2017 and SMS-2018 converged with minor differences. Both estimations confirmed that the risk of MSMHIV is heterogenous across the Netherlands with some GGD regions, such as GGD Amsterdam [RR=1.21 (95% credible interval 1.05-1.38) by EMIS-2017; RR=1.39 (1.14-1.68) by SMS-2018], having a higher-than-average risk. Results from our ecological regression modelling revealed significant regional determinants which can impact on the risk for MSMHIV. In sum, our Bayesian approach to assess the risk of HIV among MSM was able to close data gaps and provide more robust prevalence and risk estimations. It is feasible and directly applicable for future HIV surveillance as a statistical adjustment tool.