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Gaceta Sanitaria

Print version ISSN 0213-9111

Abstract

SANCHEZ-NIUBO, Albert et al. Two methods to analyze trends in the incidence of heroin and cocaine use in Barcelona [Spain]. Gac Sanit [online]. 2007, vol.21, n.5, pp. 397-403. ISSN 0213-9111.  http://dx.doi.org/10.1590/S0213-91112007000500006.

Objectives: To describe 2 statistical methods for estimating trends in the incidence of heroin and cocaine use in Barcelona. Methods: Admissions for treatment of heroin and cocaine consumption recorded by the Barcelona Drug Information System between 1991 and 2003 were used. We selected 4,367 subjects initiating treatment for the first time for heroin use, and 2,147 for cocaine use. Two statistical techniques were employed: Reporting Delay Adjustment (RDA) and the Log-linear Model (LLM). RDA was used in subjects who initiated drug consumption between 1991 and 2003, and LLM for those who began heroin use between 1967 and 2003 and cocaine use between 1971 and 2003. In addition, for each drug and method the latency period (LP) was determined (years between first consumption and first treatment). Results: Comparison of the distributions of the LP for each drug revealed that heroin users initiated treatment for the first time sooner than cocaine users, regardless of the method employed. In general, the estimated incidence of heroin use in Barcelona fell progressively after 1982. In contrast, the incidence of cocaine use rose rapidly until 1998, and has been irregular since. The incidence of cocaine use began to be substantial in the early 1990s, but took several years to manifest itself as problematic. Conclusion: The estimated incidence was underestimated by RDA compared with LLM, but the incidence of heroin use could be biased before 1991 due to changes in treatment provisions. Although the estimated incidence is relative to individuals who are admitted for treatment at some time in their life, trends in incidence can be used to plan future actions.

Keywords : Incidence; Latency period; Heroin; Cocaine; Reporting Delay Adjustment; Log-Linear Model.

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