Lassa fever in West Africa: a systematic review and meta-analysis of attack rates, case fatality rates and risk factors
Bibliographic record
Abstract
BACKGROUND: Lassa fever is an acute viral haemorrhagic disease endemic to West Africa, with Nigeria, Sierra Leone, and Liberia bearing the greatest burden. Despite repeated outbreaks and rising incidence, a regional synthesis of epidemiologic indicators remains lacking. This systematic review and meta-analysis aimed to estimate pooled attack rates (proportion of a population affected during an outbreak), case fatality rates (CFRs), and identify consistent risk factors associated with Lassa fever in West Africa. METHODS: Following PRISMA 2020 guidelines, we systematically searched PubMed, Scopus, AJOL, and Web of Science for observational studies published between 1969 and 2025. Eligible studies reported attack rates, CFRs, or risk factors for Lassa fever among human populations. Only English-language, peer-reviewed studies were included and included study quality was assessed using the Newcastle-Ottawa Scale. Data were analyzed using random-effects meta-analysis. Heterogeneity was assessed using the I² statistic, and subgroup analyses were conducted by country, diagnostic method, study design, and setting. RESULTS: Out of 333 identified studies, thirty-five studies were included. The pooled attack rate was 21.0% (95% CI: 18.0%-24.0%, I² = 98.6%) and CFR was 27.0% (95% CI: 20.0%-35.0%, I² = 99.4%), with significant heterogeneity. Subgroup analysis showed higher CFRs in Sierra Leone (48%) and Liberia (40%) compared to Nigeria (16%). Cohort studies and hospital-based settings reported markedly higher attack rates and CFRs. RT-PCR-based diagnostics were associated with lower CFRs than serological or mixed-method studies. The pooled odds ratio for risk factors was 1.29 (95%CI: 1.10-1.49), with environmental exposures (rodent infestation, poor housing) having the strongest association (OR = 2.18). Evidence also pointed to a shift in disease transmission from rural to peri-urban settings, driven by urbanization, habitat loss, and climate change. CONCLUSION: Lassa fever continues to pose a severe public health threat in West Africa, characterized by high transmission, elevated mortality, and evolving ecological dynamics. To reduce morbidity and mortality, integrated strategies are needed such as early diagnosis via decentralized RT-PCR, environmental hygiene, risk communication, and vaccine preparedness within a One Health framework.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".