Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo.
Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo.
复制标题
DOI：
10.1371/journal.pcbi.1008532
复制
发表时间：
2021-01
影响因子：
4.3
通讯作者：
Rock KS
中科院分区：
文献类型：
--
作者：
Crump RE;Huang CI;Knock ES;Spencer SEF;Brown PE;Mwamba Miaka E;Shampa C;Keeling MJ;Rock KS
冈比亚人非洲锥虫病（gHAT）是一种致命的疾病，负担下降，但仍然在西非和中非流行。虽然它的目标是到2030年消除传播，但关于感染的驱动因素以及这些因素在地理上如何变化仍然存在许多问题。在这项研究中，我们重点关注刚果民主共和国（DRC），该国家占2016年全球病例负担的84%，以探索全国传播的变化，并阐明可能导致疾病持续存在或不同地区干预措施成功的因素。我们提出了一种贝叶斯拟合方法，适用于168个地方病健康区（100，000人口规模），它允许在自适应和自动化框架中将机械gHAT模型校准到病例数据（来自世界卫生组织HAT Atlas）。结果发现，该模型需要捕捉被动检测的改进，以匹配在前班顿杜和下刚果省内观察到的数据趋势，表明这些地区大大减少了检测时间。由于额外的模型参数，这些省份的卫生区在拟合期间通常有较长的老化期。在每个健康区的一系列拟合参数中发现了后验概率分布;这些分布包括1998年以前的基本生殖数估计值（R0），推断为1至1.14之间，与先前的gHAT估计值一致，2000年代病例报告较多的健康区通常具有较高的中值。此前，尚不清楚这一时期活跃病例发现的下降是否导致了病例数的下降。这里的模型解释了可变筛查，并表明潜在的传播也大大减少了-前赤道省平均减少了96%，前下刚果省减少了93%，前班顿杜-赤道省和班顿杜省减少了89%，2000年的病例负担最高。该分析还提出了一个框架，以便对该国的未来进行预测。冈比亚人非洲锥虫病（gHAT;昏睡病）是一种致命的疾病，目标是到2030年消除传播，但仍有一些未知因素影响持续传播以及这种传播如何随地理位置而变化。在这项研究中，我们重点关注刚果民主共和国（DRC），该国报告了2016年全球84%的病例，试图解释为什么该国某些地区在降低病例负担方面比其他地区更成功。为了实现这一目标，我们使用了最先进的统计框架，将数学gHAT模型与2000 - 2016年期间168个地区报告的病例数据进行了匹配。分析表明，班顿杜省和下刚果省这两个以前的省在固定卫生设施的病例发现方面有了实质性的改善。总体而言，估计所有省份的（不可观察的）传播都有所减少，包括前赤道省的96%。这是令人欣慰的，因为该地区的病例发现工作已经减少。本文提出的模型拟合将允许在未来的研究中进行替代干预策略下的gHAT预测。
Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones (∼100,000 population size), which allows for calibration of a mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.14, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly—on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu—Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country. Gambiense human African trypanosomiasis (gHAT; sleeping sickness) is a deadly disease targeted for elimination of transmission by 2030, however there are still several unknowns about what factors influence continued transmission and how this changes with geographic location. In this study we focus on the Democratic Republic of Congo (DRC), which reported 84% of the global cases in 2016 to try and explain why some regions of the country have had more success than others in bringing down case burden. To achieve this we used a state-of-the-art statistical framework to match a mathematical gHAT model to reported case data for 168 regions with some case reporting during 2000–2016. The analysis indicates that two former provinces, Bandundu and Bas Congo had substantial improvements to case detection in fixed health facilities in the time period. Overall, all provinces were estimated to have reductions in (unobservable) transmission including ∼96% in former Equateur. This is reassuring as case finding effort has decreased in that region. The model fitting presented here will allow predictions of gHAT under alternative intervention strategies to be performed in future studies.
登录
查看更多内容
影响因子：
1.8
作者：
Checchi F;Funk S;Chandramohan D;Haydon DT;Chappuis F
通讯作者：
Chappuis F
影响因子：
4.9
作者：
Lumbala, Crispin;Simarro, Pere P.;Jannin, Jean G.
通讯作者：
Jannin, Jean G.
影响因子：
3.8
作者：
Behrend, Matthew R.;Basanez, Maria-Gloria;de Vlas, Sake J.
通讯作者：
de Vlas, Sake J.
影响因子：
3.8
作者：
Castano, Maria Soledad;Ndeffo-Mbah, Martial L.;Chitnis, Nakul
通讯作者：
Chitnis, Nakul
DOI：
10.1093/cid/ciy018
发表时间：
2018-06-01
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
Rock KS;Ndeffo-Mbah ML;Castaño S;Palmer C;Pandey A;Atkins KE;Ndung'u JM;Hollingsworth TD;Galvani A;Bever C;Chitnis N;Keeling MJ
通讯作者：
Keeling MJ