Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.
Malar J. 2010 May 17;9:132. doi: 10.1186/1475-2875-9-132.
Individuals in a malaria endemic community differ from one another. Many of these differences, such as heterogeneities in transmission or treatment-seeking behaviour, affect malaria epidemiology. The different kinds of heterogeneity are likely to be correlated. Little is known about their impact on the shape of age-prevalence and incidence curves. In this study, the effects of heterogeneity in transmission, treatment-seeking and risk of co-morbidity were simulated.
Simple patterns of heterogeneity were incorporated into a comprehensive individual-based model of Plasmodium falciparum malaria epidemiology. The different types of heterogeneity were systematically simulated individually, and in independent and co-varying pairs. The effects on age-curves for parasite prevalence, uncomplicated and severe episodes, direct and indirect mortality and first-line treatments and hospital admissions were examined.
Different heterogeneities affected different outcomes with large effects reserved for outcomes which are directly affected by the action of the heterogeneity rather than via feedback on acquired immunity or fever thresholds. Transmission heterogeneity affected the age-curves for all outcomes. The peak parasite prevalence was reduced and all age-incidence curves crossed those of the reference scenario with a lower incidence in younger children and higher in older age-groups. Heterogeneity in the probability of seeking treatment reduced the peak incidence of first-line treatment and hospital admissions. Heterogeneity in co-morbidity risk showed little overall effect, but high and low values cancelled out for outcomes directly affected by its action. Independently varying pairs of heterogeneities produced additive effects. More variable results were produced for co-varying heterogeneities, with striking differences compared to independent pairs for some outcomes which were affected by both heterogeneities individually.
Different kinds of heterogeneity both have different effects and affect different outcomes. Patterns of co-variation are also important. Alongside the absolute levels of different factors affecting age-curves, patterns of heterogeneity should be considered when parameterizing or validating models, interpreting data and inferring from one outcome to another.
疟疾流行社区中的个体彼此不同。这些差异中的许多，例如传播或寻求治疗行为的异质性，都会影响疟疾的流行病学。不同类型的异质性可能相互关联。人们对它们对年龄流行率和发病率曲线形状的影响知之甚少。在这项研究中，模拟了传播、寻求治疗和合并症风险异质性的影响。
将简单的异质性模式纳入到恶性疟原虫疟疾流行病学的综合个体模型中。不同类型的异质性被系统地单独模拟，并在独立和共同变化的对中进行模拟。研究了寄生虫流行率、单纯和严重发作、直接和间接死亡率以及一线治疗和住院治疗的年龄曲线的影响。
不同的异质性影响不同的结果，对那些直接受异质性作用影响的结果，而不是通过对获得性免疫或发热阈值的反馈影响的结果，保留了较大的影响。传播异质性影响所有结果的年龄曲线。峰值寄生虫流行率降低，所有年龄发病率曲线都与参考情景的曲线相交，年龄较小的儿童发病率较低，年龄较大的年龄组发病率较高。寻求治疗的可能性的异质性降低了一线治疗和住院治疗的峰值发病率。合并症风险的异质性总体影响较小，但高值和低值会相互抵消其作用直接影响的结果。独立变化的异质性对产生加性效应。对于共同变化的异质性，产生了更多可变的结果，对于一些受两种异质性单独影响的结果，与独立对相比，结果存在显著差异。
不同类型的异质性具有不同的影响，影响不同的结果。协同变化的模式也很重要。在考虑年龄曲线的不同因素的绝对水平时，应该考虑异质性的模式，例如在参数化或验证模型、解释数据和从一个结果推断到另一个结果时。
