Department of Parasitology, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka.
PLoS Negl Trop Dis. 2010 Apr 13;4(4):e655. doi: 10.1371/journal.pntd.0000655.
To assess if a probabilistic model could be used to estimate the combined prevalence of infection with any species of intestinal nematode worm when only the separate prevalence of each species is reported, and to estimate the extent to which simply taking the highest individual species prevalence underestimates the combined prevalence.
Data were extracted from community surveys that reported both the proportion infected with individual species and the combined proportion infected, for a minimum sample of 100 individuals. The predicted combined proportion infected was calculated based on the assumption that the probability of infection with one species was independent of infection with another species, so the probability of combined infections was multiplicative.
Thirty-three reports describing 63 data sets from surveys conducted in 20 countries were identified. A strong correlation was found between the observed and predicted combined proportion infected (r = 0.996, P<0.001). When the observed and predicted values were plotted against each other, a small correction of the predicted combined prevalence by dividing by a factor of 1.06 achieved a near perfect correlation between the two sets of values. The difference between the single highest species prevalence and the observed combined prevalence was on average 7% or smaller at a prevalence of <or=40%, but at prevalences of 40-80%, the difference was about 12%.
A simple probabilistic model of combined infection with a small correction factor is proposed as a novel method to estimate the number of individuals that would benefit from mass deworming when data are reported only for separate species.
评估当仅报告每种寄生虫的单独流行率时，是否可以使用概率模型来估计任何肠道线虫感染的综合流行率，并估计仅采用最高个体物种流行率来低估综合流行率的程度。
从报告了单个物种感染率和综合感染率的社区调查中提取数据，样本量至少为 100 人。根据感染一种物种的概率与感染另一种物种的概率独立的假设，预测综合感染率。因此，合并感染的概率是相乘的。
确定了 33 份报告，描述了来自 20 个国家的 63 个数据集。观察到的和预测的综合感染率之间存在很强的相关性（r = 0.996，P<0.001）。当将观察值和预测值相互比较时，通过除以 1.06 的因子对预测的综合流行率进行小的校正，可以实现两组值之间的近乎完美的相关性。在流行率<或=40%时，单一最高物种流行率与观察到的综合流行率之间的差异平均为 7%或更小，但在流行率为 40-80%时，差异约为 12%。
提出了一种简单的合并感染概率模型，并采用小的校正因子作为一种新方法，当仅报告单独的物种数据时，可以估计需要进行大规模驱虫治疗的受益人群数量。
