Nagarajan Ramya, Ayyasamy Lavanya, Ganeshkumar Parasuraman, Velusamy Saravanakumar, Murhekar Manoj
Division of Non-Communicable Disease, Indian Council of Medical Research- National Institute of Epidemiology, Chennai, Tamil Nadu, India.
Division of Infectious Disease and Epidemiology, Indian Council of Medical Research- National Institute of Epidemiology, Chennai, Tamil Nadu, India.
Indian J Community Med. 2024 Nov-Dec;49(6):805-811. doi: 10.4103/ijcm.ijcm_63_24. Epub 2024 Oct 17.
Information about the chikungunya disease burden by age groups and geographic distribution is necessary to guide appropriate control measures. With this, we conducted a systematic review and meta-analysis to estimate the disease burden of chikungunya fever in India.
We conducted this systematic review according to the Cochrane Collaboration guidelines. We retrieved relevant articles from PubMed and a free online search. Two investigators screened titles and abstracts and extracted data from the relevant articles. Our primary outcome is the proportion of laboratory-confirmed Chikungunya fever among clinically suspected patients. We used a random effect model to estimate the pooled proportion of Chikungunya fever.
A total of 20 articles were included in the quantitative syntheses. The pooled proportion of laboratory-confirmed chikungunya fever from 20 studies estimated using the random effects model is 24% (95%CI: 15-34%). We found the pooled proportion in the southern region was 35% (95%CI: 4-66%), 28% (95%CI: 3-58%) in the western region, 24% (95%CI: 1-48%) in the eastern region, 20% (95%CI: 12-29%) in the northern region, and 4% (95%CI: 1-6%) in North-eastern region.
This review emphasizes the need to strengthen the surveillance of disease burden using multiple diagnostic tests and the need for an appropriate molecular diagnostic for early detection of the chikungunya virus.
了解基孔肯雅热按年龄组和地理分布的疾病负担对于指导采取适当的控制措施至关重要。为此，我们进行了一项系统评价和荟萃分析，以估计印度基孔肯雅热的疾病负担。
我们按照Cochrane协作网指南进行了这项系统评价。从PubMed和一次免费在线搜索中检索相关文章。两名研究人员筛选标题和摘要，并从相关文章中提取数据。我们的主要结局是临床疑似患者中实验室确诊的基孔肯雅热的比例。我们使用随机效应模型来估计基孔肯雅热的合并比例。
定量综合分析共纳入20篇文章。使用随机效应模型估计的20项研究中实验室确诊的基孔肯雅热的合并比例为24%（95%置信区间：15-34%）。我们发现南部地区的合并比例为35%（95%置信区间：4-66%），西部地区为28%（95%置信区间：3-58%），东部地区为24%（95%置信区间：1-48%），北部地区为20%（95%置信区间：12-29%），东北地区为4%（95%置信区间：1-6%）。
本综述强调了使用多种诊断测试加强疾病负担监测的必要性，以及需要一种适当的分子诊断方法用于早期检测基孔肯雅病毒。
