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不同防治策略下血吸虫病分布的动态演变:中国贵池 1991-2014 年监测结果。

Dynamic evolution of schistosomiasis distribution under different control strategies: Results from surveillance covering 1991-2014 in Guichi, China.

机构信息

Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China.

Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.

出版信息

PLoS Negl Trop Dis. 2021 Jan 6;15(1):e0008976. doi: 10.1371/journal.pntd.0008976. eCollection 2021 Jan.

DOI:10.1371/journal.pntd.0008976
PMID:33406136
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7787434/
Abstract

BACKGROUND

Since the founding of the China, the Chinese government, depending on the changing epidemiological situations over time, adopted different strategies to continue the progress towards elimination of schistosomiasis in the country. Although the changing pattern of schistosomiasis distribution in both time and space is well known and has been confirmed by numerous studies, the problem of how these patterns evolve under different control strategies is far from being understood. The purpose of this study is, therefore, to investigate the spatio-temporal change of the distribution of schistosomiasis with special reference to how these patterns evolve under different control strategies.

METHODOLOGY / PRINCIPAL FINDINGS: Parasitological data at the village level were obtained through access to repeated cross-sectional surveys carried out during 1991-2014 in Guichi, a rural district along the Yangtze River in Anhui Province, China. A hierarchical dynamic spatio-temporal model was used to evaluate the evolving pattern of schistosomiasis prevalence, which accounted for mechanism of dynamics of the disease. Descriptive analysis indicates that schistosomiasis prevalence displayed fluctuating high-risk foci during implementation of the chemotherapy-based strategy (1991-2005), while it took on a homogenous pattern of decreasing magnitude in the following period when the integrated strategy was implemented (2006-2014). The dynamic model analysis showed that regularly global propagation of the disease was not present after the effect of proximity to river was taken into account but local pattern transition existed. Maps of predicted prevalence shows that relatively high prevalence (>4%) occasionally occurred before 2006 and prevalence presents a homogenous and decreasing trend over the study area afterwards.

CONCLUSIONS

Proximity to river is still an important determinant for schistosomiasis infection regardless of different types of implemented prevention and control strategies. Between the transition from the chemotherapy-based strategy to the integrated one, we noticed a decreased prevalence. However, schistosomiasis would remain an endemic challenge in these study areas. Further prevention and control countermeasures are warranted.

摘要

背景

自中国建国以来,政府根据不同时期的流行情况,采取了不同的策略来继续推进全国消除血吸虫病的工作。虽然血吸虫病在时间和空间上的分布模式变化已经广为人知,并得到了大量研究的证实,但这些模式在不同控制策略下的演变方式还远未被理解。因此,本研究旨在调查血吸虫病的时空分布变化,并特别关注这些模式在不同控制策略下的演变方式。

方法/主要发现:通过获取安徽省长江沿岸贵池县 1991-2014 年期间进行的多次横断面调查的寄生虫学数据,利用分层动态时空模型来评估血吸虫病流行率的演变模式,该模型考虑了疾病动态的机制。描述性分析表明,在实施基于化疗的策略(1991-2005 年)期间,血吸虫病流行率呈现出波动的高风险焦点,而在实施综合策略(2006-2014 年)的后续时期,流行率呈现出逐渐减少的均匀模式。动态模型分析表明,在考虑到靠近河流的影响后,疾病并没有呈现出定期的全球传播,但存在局部模式的转变。预测流行率的地图显示,在 2006 年之前,相对较高的流行率(>4%)偶尔发生,之后整个研究区域的流行率呈现出均匀下降的趋势。

结论

无论实施何种类型的预防和控制策略,靠近河流仍然是血吸虫病感染的一个重要决定因素。从基于化疗的策略向综合策略过渡期间,我们注意到流行率有所下降。然而,这些研究区域仍将面临血吸虫病的地方性挑战。需要进一步采取预防和控制对策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/694d1cae4822/pntd.0008976.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/9f8d403932cf/pntd.0008976.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/0de165515fc2/pntd.0008976.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/c5a12e7b4d59/pntd.0008976.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/4128e4bbcbf7/pntd.0008976.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/5f6c62c01b6a/pntd.0008976.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/694d1cae4822/pntd.0008976.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/9f8d403932cf/pntd.0008976.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/0de165515fc2/pntd.0008976.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/c5a12e7b4d59/pntd.0008976.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/4128e4bbcbf7/pntd.0008976.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/5f6c62c01b6a/pntd.0008976.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf5b/7787434/694d1cae4822/pntd.0008976.g006.jpg

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