The assumption of no long reporting delays may result in underestimates of US AIDS incidence.
The assumption of no long reporting delays may result in underestimates of US AIDS incidence.
复制标题
假设没有长期报告延迟可能会导致美国艾滋病发病率的低估。
DOI：
10.1097/00002030-199310000-00012
复制
发表时间：
1993
期刊：
影响因子：
--
通讯作者：
Liner,EC
中科院分区：
文献类型：
--
作者：
Cooley,PC;Hamill,DN;Myers,LE;Liner,EC
目的：为了评估没有长期报告延迟的假设对估计艾滋病发病率的影响，设计：报告的艾滋病病例必须对报告延迟进行调整，以估计艾滋病发病率。我们将疾病控制和预防中心（CDC）艾滋病公共信息数据集提供的调整与CDC假设的一组调整进行了比较。这两种方法都假设延迟没有发生变化，在整个reportingperiod.Methods：报告延迟的概率分布计算由CDC提供的延迟调整权重，并从另一种方法，估计从监测数据的长时间延迟的概率。艾滋病发病率估计从这两种方法进行了计算和compared.Results：CDC的调整假设，将不会有超过61个月的报告延迟，而另一种方法估计，5.9%的病例报告将延迟超过61个月。CDC的调整结果在较低的估计艾滋病发病率和一个平坦的epidemiology.Conclusions：除了减少6.2%的估计艾滋病发病率，CDC的假设改变了估计的流行病的形状。这些可能导致艾滋病毒发病率和流行率的模型估计值减少多达4-21%。
Objective: To evaluate the effect of the assumption of no long reporting delays on estimates of AIDS incidence.Design: Reported AIDS cases must be adjusted for reporting delays to estimate AIDS incidence. We compared the adjustments supplied with the Centers for Disease Control and Prevention (CDC) AIDS Public Information Data Set with a set of adjustments that differ with respect to CDC assumption of no long delays. Both methods assume that no changes in delay have occurred throughout the reporting period.Methods: Probability distributions of reporting delays were calculated from the delay adjustment weights supplied by CDC, and from an alternative method that estimates the probability of long delays from the surveillance data. AIDS incidence estimates from these two approaches were calculated and compared.Results: The CDC adjustments assume that there will be no reporting delays longer than 61 months, whereas the alternative method estimates that 5.9% of case reports will be delayed longer than 61 months. The CDC adjustments result in lower estimates of AIDS incidence and a flattening of the epidemic.Conclusions: In addition to a 6.2% reduction in total estimated AIDS incidence, the CDC assumption changes the shape of the estimated epidemic. These may result in as much as 4-21% reductions in model estimates of HIV incidence and prevalence.