预测疟疾输入中国江苏省的风险：一项建模研究。

Predicting the risk of malaria importation into Jiangsu Province, China: a modeling study.

机构信息

Institute of Public Health, Medical School of Yangzhou University, Yangzhou University, Yangzhou, Hanjiang District, 225007, China.

National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory On Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, China.

出版信息

Global Health. 2024 Nov 26;20(1):84. doi: 10.1186/s12992-024-01090-4.

BACKGROUND

The World Health Organization certified China malaria-free in 2021. Consequently, preventing the risk of malaria re-introduction caused by imported malaria has now become a major challenge. This study aims to characterize the dynamics and predict the risk of malaria importation in Jiangsu Province, where the number of imported malaria cases ranks among the highest in China.

METHODS

The annual number of cases with imported malaria in Jiangsu Province, the annual number of travelers from sub-Saharan Africa (SSA) to Jiangsu Province (both Chinese and international travelers), and the annual number of Chinese migrant workers from Jiangsu Province who stayed abroad between 2013 and 2020 were assessed. The spatio-temporal dynamics of malaria importation was characterized with ArcGIS 10.8. A negative binomial model was applied to model malaria importation to Jiangsu Province, China.

RESULTS

A total of 2,221 of imported malaria cases were reported from January 1, 2013, until December 31, 2020. Imported malaria cases into China were mainly from SSA (98%) and P. falciparum (78%), the most common species. A seasonal pattern was observed, with the most cases occurring from December to February. The negative binomial model, which incorporates the number of Chinese migrant workers from Jiangsu Province who stayed abroad as an independent variable, demonstrated better performance (AIC: 96.495, BIC: 94.230) compared to the model based solely on travelers from SSA to Jiangsu Province. The model indicated an estimated 139% increase in imported cases for a 10% increase in Chinese migrant workers from Jiangsu Province who stayed abroad.

CONCLUSION

In conclusion, our study underscores the importance of incorporating data on Chinese migrant workers who have stayed abroad when predicting malaria importation risks. By integrating both international travel patterns and migrant worker data, our findings offer a more robust framework for assessing and managing malaria risk in Jiangsu Province. This approach provides valuable insights for public health officials, enabling more effective resource allocation and targeted interventions to prevent the re-introduction of malaria and improve overall disease management.

背景

世界卫生组织于 2021 年认证中国消除疟疾。因此，预防因输入性疟疾而导致疟疾再次传入的风险已成为一项主要挑战。本研究旨在描述江苏省输入性疟疾的动态特征，并预测疟疾输入风险，因为江苏省的输入性疟疾病例数量位居全国前列。

方法

评估了 2013 年至 2020 年期间江苏省每年的输入性疟疾病例数、来自撒哈拉以南非洲（SSA）到江苏省的年度旅行者人数（包括中国和国际旅行者）以及江苏省每年在国外逗留的外出务工人员人数。使用 ArcGIS 10.8 描述疟疾输入的时空动态。应用负二项模型对中国江苏省疟疾输入进行建模。

结果

从 2013 年 1 月 1 日至 2020 年 12 月 31 日，共报告了 2221 例输入性疟疾病例。输入中国的疟疾病例主要来自 SSA（98%）和间日疟原虫（78%），这是最常见的疟原虫种类。观察到季节性模式，发病最多的月份是 12 月至 2 月。将江苏省外出务工人员人数作为自变量纳入负二项模型，与仅基于 SSA 到江苏省旅行者的模型相比，具有更好的性能（AIC：96.495，BIC：94.230）。该模型表明，江苏省外出务工人员增加 10%，输入性疟疾病例预计增加 139%。

结论

综上所述，本研究强调了在预测疟疾输入风险时纳入中国外出务工人员数据的重要性。通过整合国际旅行模式和外出务工人员数据，我们的研究结果为评估和管理江苏省疟疾风险提供了更强大的框架。这种方法为公共卫生官员提供了有价值的见解，使他们能够更有效地分配资源并采取有针对性的干预措施，以防止疟疾再次传入并改善整体疾病管理。