Freitas Luzia T, Khan Mashroor Ahmad, Uddin Azhar, Halder Julia B, Singh-Phulgenda Sauman, Raja Jeyapal Dinesh, Balakrishnan Vijayakumar, Harriss Eli, Rahi Manju, Brack Matthew, Guérin Philippe J, Basáñez Maria-Gloria, Kumar Ashwani, Walker Martin, Srividya Adinarayanan
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
PLoS Negl Trop Dis. 2024 Jan 16;18(1):e0011882. doi: 10.1371/journal.pntd.0011882. eCollection 2024 Jan.
Lymphatic filariasis (LF) is a neglected tropical disease (NTD) targeted by the World Health Organization for elimination as a public health problem (EPHP). Since 2000, more than 9 billion treatments of antifilarial medicines have been distributed through mass drug administration (MDA) programmes in 72 endemic countries and 17 countries have reached EPHP. Yet in 2021, nearly 900 million people still required MDA with combinations of albendazole, diethylcarbamazine and/or ivermectin. Despite the reliance on these drugs, there remain gaps in understanding of variation in responses to treatment. As demonstrated for other infectious diseases, some urgent questions could be addressed by conducting individual participant data (IPD) meta-analyses. Here, we present the results of a systematic literature review to estimate the abundance of IPD on pre- and post-intervention indicators of infection and/or morbidity and assess the feasibility of building a global data repository.
We searched literature published between 1st January 2000 and 5th May 2023 in 15 databases to identify prospective studies assessing LF treatment and/or morbidity management and disease prevention (MMDP) approaches. We considered only studies where individual participants were diagnosed with LF infection or disease and were followed up on at least one occasion after receiving an intervention/treatment.
We identified 138 eligible studies from 23 countries, having followed up an estimated 29,842 participants after intervention. We estimate 14,800 (49.6%) IPD on pre- and post-intervention infection indicators including microfilaraemia, circulating filarial antigen and/or ultrasound indicators measured before and after intervention using 8 drugs administered in various combinations. We identified 33 studies on MMDP, estimating 6,102 (20.4%) IPD on pre- and post-intervention clinical morbidity indicators only. A further 8,940 IPD cover a mixture of infection and morbidity outcomes measured with other diagnostics, from participants followed for adverse event outcomes only or recruited after initial intervention.
The LF treatment study landscape is heterogeneous, but the abundance of studies and related IPD suggest that establishing a global data repository to facilitate IPD meta-analyses would be feasible and useful to address unresolved questions on variation in treatment outcomes across geographies, demographics and in underrepresented groups. New studies using more standardized approaches should be initiated to address the scarcity and inconsistency of data on morbidity management.
淋巴丝虫病(LF)是一种被世界卫生组织列为目标以消除作为公共卫生问题(EPHP)的被忽视热带病(NTD)。自2000年以来,通过大规模药物给药(MDA)计划已在72个流行国家分发了超过90亿次抗丝虫药物治疗,并且17个国家已实现消除公共卫生问题目标。然而在2021年,仍有近9亿人需要接受阿苯达唑、乙胺嗪和/或伊维菌素联合的MDA。尽管依赖这些药物,但在理解治疗反应的差异方面仍存在差距。正如其他传染病所表明的那样,一些紧迫问题可以通过开展个体参与者数据(IPD)荟萃分析来解决。在此,我们展示一项系统文献综述的结果,以估计关于感染和/或发病率的干预前和干预后指标的IPD数量,并评估建立全球数据存储库的可行性。
我们检索了2000年1月1日至2023年5月5日期间在15个数据库中发表的文献,以识别评估LF治疗和/或发病率管理及疾病预防(MMDP)方法的前瞻性研究。我们仅考虑个体参与者被诊断为LF感染或疾病且在接受干预/治疗后至少随访一次的研究。
我们从23个国家识别出138项符合条件的研究,在干预后对估计29,842名参与者进行了随访。我们估计有14,800(49.6%)个IPD涉及干预前和干预后感染指标,包括微丝蚴血症、循环丝虫抗原和/或使用8种以各种组合给药的药物在干预前后测量的超声指标。我们识别出33项关于MMDP的研究,仅估计有6,102(20.4%)个IPD涉及干预前和干预后临床发病率指标。另外8,940个IPD涵盖了通过其他诊断方法测量的感染和发病率结果的混合情况,这些参与者仅因不良事件结果而被随访或在初始干预后招募。
LF治疗研究情况各异,但研究数量和相关IPD表明,建立一个全球数据存储库以促进IPD荟萃分析对于解决关于不同地理区域、人口统计学和代表性不足群体中治疗结果差异的未解决问题是可行且有用的。应启动使用更标准化方法的新研究,以解决发病率管理数据的稀缺和不一致问题。