Platt Lucy, Hickman Matthew, Rhodes Tim, Mikhailova Larissa, Karavashkin Victor, Vlasov Alexander, Tilling Kate, Hope Vivian, Khutorksoy Mikhail, Renton Adrian
Department of Primary Care and Social Medicine, Imperial College Faculty of Medicine, University of London, London, UK.
Addiction. 2004 Nov;99(11):1430-8. doi: 10.1111/j.1360-0443.2004.00848.x.
This study sought to estimate the prevalence of injecting drug users (IDU) in Togliatti city and to examine the implications of these estimates for HIV prevalence and harm reduction.
Routine data sources of IDUs were identified. Covariate capture-recapture techniques were used on the individuals identified on the three data sources and used to estimate the number of IDU 'not observed' by the data sources, and thereby estimate the prevalence of IDU.
Togliatti City, Samara Oblast, Russian Federation.
IDUs recorded on three data sources (narcology records, HIV positive test results and police arrest data) during 2001.
Poisson regression models were fitted to the observed data, with interactions between data sources fitted to replicate 'dependencies' between those data sources. To select the best model the goodness of fit was approximated by chi2 distribution and the best-fitting model was selected on the basis of standard information criteria and log likelihood ratio tests.
The total estimated population of IDUs is 20 226 [95% confidence interval (CI): 16 971-24 749] giving a population prevalence of 5.4% (95% CI: 4.5-6.6%) of the registered population and 2.7% (95% CI: 2.4-3.5%) of the population (including migrants) aged 15-44 years. For every one IDU in contact with a service there were three out of contact.
There is a high prevalence of IDU which, in the context of a fast-emerging IDU-associated HIV epidemic, will have serious public health implications.
本研究旨在估算陶里亚蒂市注射吸毒者(IDU)的患病率,并探讨这些估算结果对艾滋病毒患病率及减少伤害的影响。
确定了IDU的常规数据来源。对在三个数据来源中识别出的个体使用协变量捕获-再捕获技术,以估算数据来源“未观察到”的IDU数量,从而估算IDU的患病率。
俄罗斯联邦萨马拉州陶里亚蒂市。
2001年期间记录在三个数据来源(戒毒记录、艾滋病毒检测呈阳性结果和警方逮捕数据)上的IDU。
对观察到的数据拟合泊松回归模型,并拟合数据来源之间的相互作用以复制这些数据来源之间的“依赖性”。为了选择最佳模型,通过卡方分布近似拟合优度,并根据标准信息标准和对数似然比检验选择最佳拟合模型。
IDU的总估计人数为20226人[95%置信区间(CI):16971 - 24749],在登记人口中的患病率为5.4%(95%CI:4.5 - 6.6%),在15 - 44岁人口(包括移民)中的患病率为2.7%(95%CI:2.4 - 3.5%)。每有一名与服务机构接触的IDU,就有三名未接触。
IDU的患病率很高,在与IDU相关的艾滋病毒疫情迅速蔓延的背景下,将产生严重的公共卫生影响。