Knight Gwenan M, Colijn Caroline, Shrestha Sourya, Fofana Mariam, Cobelens Frank, White Richard G, Dowdy David W, Cohen Ted
Tuberculosis Modelling Group, Centre for the Mathematical Modelling of Infectious Diseases, Tuberculosis Centre, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine.
Department of Mathematics, Imperial College London, United Kingdom.
Clin Infect Dis. 2015 Oct 15;61Suppl 3(Suppl 3):S147-54. doi: 10.1093/cid/civ579.
Drug resistance poses a serious challenge for the control of tuberculosis in many settings. It is well established that the expected future trend in resistance depends on the reproductive fitness of drug-resistant Mycobacterium tuberculosis. However, the variability in fitness between strains with different resistance-conferring mutations has been largely ignored when making these predictions.
We developed a novel approach for incorporating the variable fitness costs of drug resistance-conferring mutations and for tracking this distribution of fitness costs over time within a transmission model. We used this approach to describe the effects of realistic fitness cost distributions on the future prevalence of drug-resistant tuberculosis.
The shape of the distribution of fitness costs was a strong predictor of the long-term prevalence of resistance. While, as expected, lower average fitness costs of drug resistance-conferring mutations were associated with more severe epidemics of drug-resistant tuberculosis, fitness distributions with greater variance also led to higher levels of drug resistance. For example, compared to simulations in which the fitness cost of resistance was fixed, introducing a realistic amount of variance resulted in a 40% increase in prevalence of drug-resistant tuberculosis after 20 years.
The differences in the fitness costs associated with drug resistance-conferring mutations are a key determinant of the future burden of drug-resistant tuberculosis. Future studies that can better establish the range of fitness costs associated with drug resistance-conferring mutations will improve projections and thus facilitate better public health planning efforts.
在许多情况下，耐药性对结核病控制构成了严峻挑战。众所周知，未来耐药性的预期趋势取决于耐药结核分枝杆菌的繁殖适应性。然而，在进行这些预测时，具有不同耐药性赋予突变的菌株之间适应性的变异性在很大程度上被忽视了。
我们开发了一种新方法，用于纳入赋予耐药性突变的可变适应度成本，并在传播模型中跟踪这种适应度成本随时间的分布。我们使用这种方法来描述实际适应度成本分布对耐药结核病未来流行率的影响。
适应度成本分布的形状是耐药性长期流行率的有力预测指标。正如预期的那样，赋予耐药性突变的平均适应度成本较低与耐药结核病更严重的流行相关，但具有更大方差的适应度分布也会导致更高水平的耐药性。例如，与耐药性适应度成本固定的模拟相比，引入实际数量的方差会导致20年后耐药结核病的流行率增加40%。
与赋予耐药性突变相关的适应度成本差异是耐药结核病未来负担的关键决定因素。能够更好地确定与赋予耐药性突变相关的适应度成本范围的未来研究将改善预测，从而有助于更好地开展公共卫生规划工作。
