Defining the relationship between Plasmodium falciparum parasite rate and clinical disease: statistical models for disease burden estimation
Defining the relationship between Plasmodium falciparum parasite rate and clinical disease: statistical models for disease burden estimation
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DOI：
10.1186/1475-2875-8-186
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发表时间：
2009-08-05
期刊：
影响因子：
3
通讯作者：
Hay, Simon I.
中科院分区：
文献类型：
--
作者：
Patil, Anand P.;Okiro, Emelda A.;Hay, Simon I.
背景：临床疟疾已被证明是一个难以计数的负担。许多病例未被常规疾病记录系统发现。因此，流行病学家经常默认主动测量人群队列中的疟疾。纵向测量疟疾的临床发病率是一项劳动密集型工作，不可能普遍进行。因此，有必要确定临床发病率与更容易和更普遍测量的感染流行指数“寄生虫率”之间的关系。这种关系可以帮助提供一个知情的基础上，以确定疟疾负担的地区，卫生统计资料不足。方法：进行正式的文献检索恶性疟原虫疟疾发病率调查进行前瞻性的，通过积极的情况下，至少每14天检测。对数据进行了提取、标准化和地理参照。发病率调查的时间-空间匹配的感染流行率的模型估计来自一个更大的数据库的寄生虫流行率调查和建模程序开发的全球疟疾流行地图。临床发病率和感染流行率之间的几个潜在的关系，然后指定在一个非参数高斯过程模型与最小的，生物信息，事先约束。贝叶斯推理，然后使用候选人models.Results之间进行选择：所建议的关系与可信区间显示为非洲和美国和中亚和东南亚地区的组合。在这两个地区，临床发病率随着感染流行率的增加而缓慢平稳地增加。在非洲，当感染流行率超过40%时，临床发病率达到每年每千人500例的稳定水平。在美洲和中亚及东南亚地区，这一高峰期达到每年每千人250例。一个时间波动模型也被纳入，以方便更密切的描述中观察到的data.Conclusion的方差：这是可能的模型之间的关系，临床发病率和恶性疟原虫感染率，但最适合的模型是非常嘈杂的，反映了观察到的机会主义的数据样本内的大方差。这种连续定量允许从任何可获得恶性疟原虫流行率估计值的地方，以已知置信度估计恶性疟原虫的临床负担。
Background: Clinical malaria has proven an elusive burden to enumerate. Many cases go undetected by routine disease recording systems. Epidemiologists have, therefore, frequently defaulted to actively measuring malaria in population cohorts through time. Measuring the clinical incidence of malaria longitudinally is labour-intensive and impossible to undertake universally. There is a need, therefore, to define a relationship between clinical incidence and the easier and more commonly measured index of infection prevalence: the "parasite rate". This relationship can help provide an informed basis to define malaria burdens in areas where health statistics are inadequate.Methods: Formal literature searches were conducted for Plasmodium falciparum malaria incidence surveys undertaken prospectively through active case detection at least every 14 days. The data were abstracted, standardized and geo-referenced. Incidence surveys were time-space matched with modelled estimates of infection prevalence derived from a larger database of parasite prevalence surveys and modelling procedures developed for a global malaria endemicity map. Several potential relationships between clinical incidence and infection prevalence were then specified in a non-parametric Gaussian process model with minimal, biologically informed, prior constraints. Bayesian inference was then used to choose between the candidate models.Results: The suggested relationships with credible intervals are shown for the Africa and a combined America and Central and South East Asia regions. In both regions clinical incidence increased slowly and smoothly as a function of infection prevalence. In Africa, when infection prevalence exceeded 40%, clinical incidence reached a plateau of 500 cases per thousand of the population per annum. In the combined America and Central and South East Asia regions, this plateau was reached at 250 cases per thousand of the population per annum. A temporal volatility model was also incorporated to facilitate a closer description of the variance in the observed data.Conclusion: It was possible to model a relationship between clinical incidence and P. falciparum infection prevalence but the best-fit models were very noisy reflecting the large variance within the observed opportunistic data sample. This continuous quantification allows for estimates of the clinical burden of P. falciparum of known confidence from wherever an estimate of P. falciparum prevalence is available.