OBJECTIVES: In response to the increased threat of the chikungunya virus (CHIKV), a mosquito-borne viral disease, a systematic literature review (SLR) and meta-analysis were conducted to consolidate and augment the existing information on this re-emerging disease. The objective of the meta-analyses was to consolidate data on symptom rates and hospitalization.

METHODS: The SLR aimed to comprehensively identify all available clinical data on chikungunya disease. After identifying all relevant studies, an additional refinement was carried out by excluding studies solely focusing on pregnant women or populations with comorbidities, enabling an upfront reduction of heterogeneity in the studies used in the meta- analyses. The pooling of effects was conducted using inverse-variance weighting methods and generalized linear mixed effects models, with measures of heterogeneity being reported.

RESULTS: The SLR resulted in a total of 313 articles to be included in a meta-analysis. These studies provided clinical data for a combined total of 751 population (sub)groups, with 70 of them matching our target population inclusion criteria. The most commonly observed symptoms during the acute phase of symptomatic disease were joint pain (95%, 95% CI; 83- 98%) arthralgia (89%, 95% CI; 81-94%), and fever (88%, 95% CI; 85-90%). The average hospitalization rate during the acute phase was estimated to be 13% (95% CI 8-20%). The heterogeneity of these (sub)groups was >92% for most outcomes.

CONCLUSIONS: Chikungunya disease is predominantly marked by joint pain, arthralgia, and fever, sometimes resulting in hospitalization. Due to limited research and inconsistent reporting of data, synthesizing evidence and pooling studies is challenging and results in significant heterogeneity. Despite heterogeneity these findings create a better understanding of the natural history of the disease and show a substantial burden of symptomatic disease. These findings help to inform future research and healthcare decision makers to evaluate the burden of disease more accurately.