Antillón Marina, Warren Joshua L, Crawford Forrest W, Weinberger Daniel M, Kürüm Esra, Pak Gi Deok, Marks Florian, Pitzer Virginia E
Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America.
Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America.
PLoS Negl Trop Dis. 2017 Feb 27;11(2):e0005376. doi: 10.1371/journal.pntd.0005376. eCollection 2017 Feb.
Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data.
We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model.
We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9-48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2-4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models.
Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases.
即将开展的伤寒疫苗接种工作需要评估风险国家的疾病基线负担。大多数低收入和中等收入国家(LMICs)没有伤寒发病率数据,因此在缺乏现成数据的情况下,基于模型的估计为决策者提供了见解。
我们开发了一个混合效应模型,该模型适合来自14个国家22个地点的32项基于人群的伤寒发病率研究的数据。我们使用随机搜索变量选择算法测试了经济和环境指数对预测伤寒发病率的贡献。我们进行了样本外验证,以评估模型的预测性能。
我们估计低收入和中等收入国家每年发生1780万例伤寒(95%可信区间:690 - 4840万例)。预计中非的伤寒发病率最高,其次是中亚、南亚和东南亚的部分国家。发病率通常在2 - 4岁年龄组达到峰值。发现纳入广泛可用的经济和环境指标的模型比空模型能更好地描述发病率。
近期对伤寒负担的估计可能低估了病例数和伤寒发病率的不确定性程度。我们的分析允许预测低收入和中等收入国家伤寒热的总体发病率以及特定年龄组的发病率,并纳入模型结构和预测指标估计中的不确定性。未来需要进一步研究以进一步验证和完善模型预测,并更好地理解病例的逐年变化。