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. 2013 May 3;71(1):10.
doi: 10.1186/0778-7367-71-10.

Improved benchmark-multiplier method to estimate the prevalence of ever-injecting drug use in Belgium, 2000-10

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Improved benchmark-multiplier method to estimate the prevalence of ever-injecting drug use in Belgium, 2000-10

Kaatje Bollaerts et al. Arch Public Health. .

Abstract

Background: Accurate estimates of the size of the drug-using populations are essential for evidence-based policy making. However, drug users form a 'hidden' population, necessitating the use of indirect methods to estimate population sizes.

Methods: The benchmark-multiplier method was applied to estimate the population size of ever injecting drug users (ever-IDUs), aged 18-64 years, in Belgium using data from the national HIV/AIDS register and from a sero-behavioral study among injecting drug users. However, missing risk factor information and absence of follow-up of the HIV+/AIDS- cases, limits the usefulness of the Belgian HIV/AIDS register as benchmark. To overcome these limitations, statistical corrections were required. In particular, Imputation by Chained Equations was used to correct for the missing risk factor information whereas stochastic mortality modelling was applied to account for the mortality among the HIV+/AIDS- cases. Monte Carlo simulation was used to obtain confidence intervals, properly reflecting the uncertainty due to random error as well as the uncertainty associated with the two statistical corrections mentioned above.

Results: In 2010, the prevalence (/1000) of ever-IDUs was estimated to be 3.5 with 95% confidence interval [2.5;4.8]. No significant time trends were observed for the period 2000-2010.

Conclusions: To be able to estimate the ever-IDU population size using the Belgian HIV/AIDS register as benchmark, statistical corrections were required without which seriously biased estimates would result. By developing the improved methodology, Belgium is again able to provide ever-IDU population estimates, which are essential to assess the coverage of treatment and to forecast health care needs and costs.

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