Burden of Chikungunya Fever and Its Economic and Social Impacts Worldwide: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate the social and economic impacts and disease burden of Chikungunya Fever globally through a systematic literature review. METHODS: We performed a comprehensive literature search through MEDLINE (via PubMed), LILACS, and Embase databases, and grey literature, including studies of populations diagnosed with Chikungunya Fever or at risk of infection published in English, Spanish, French, or Portuguese, without date restrictions. Two reviewers independently performed study selection, data extraction, and quality assessment. Methodological quality was assessed using different tools. RESULTS: Forty-three publications were included. Until 2013, publications originated solely from the Asian and African continents. From 2015 onwards, South America emerged as the predominant source. Publications were classified as cost studies (25), including cost-of-illness (18) and program cost (6); burden of disease studies (10); cost-outcome studies (4), including cost-effectiveness (3) and cost-utility (1); and quality-of-life studies (15). Reported total direct costs associated with Chikungunya Fever ranged from US$ 3.5 million (US Virgin Islands, 2014-2015) to US$ 83.6 billion (Region of the Americas, 2013-2015). Direct medical costs varied from US$ 308.94 (Tamil Nadu, India, 2006) to US$ 33.7 million (Réunion Island, 2005-2006). Vector control program costs ranged from US$ 888,000 annually (Greece, 2013-2017) to US$ 466 million (Brazil, 2016). Estimated disability-adjusted life years per 100,000 population ranged from 4.53 (India, 2006) to 2432 (Region of the Americas, 2013-2015). Quality-of-life studies demonstrated substantial declines across multiple domains, indicating significant functional impairment due to Chikungunya Fever. CONCLUSION: Chikungunya Fever imposes a considerable economic and social burden, surpassing that of other endemic arboviral diseases such as dengue and yellow fever. These findings underscore the need for further research to accurately quantify the full scope of Chikungunya Fever-related costs and impacts on affected populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".