Identifying risk factors for clinical Lassa fever in Sierra Leone, 2019–2021
Bibliographic record
Abstract
Abstract Lassa fever (LF) virus (LASV) is endemic in Sierra Leone (SL) and poses a significant public health threat to the region; however, no risk factors for clinical LF have been reported in SL. The objective of this study was to identify the risk factors for clinical LF in an endemic community in SL. We conducted a case–control study by enrolling 37 laboratory-confirmed LF cases identified through the national LF surveillance system in SL and 140 controls resided within a one-kilometre radius of the case household. We performed a conditional multiple logistic regression analysis to identify the risk factors for clinical LF. Of the 37 cases enrolled, 23 died (62% case fatality rate). Cases were younger than controls (19.5 years vs 28.9 years, p < 0.05) and more frequently female (64.8% vs 52.8%). Compared to the controls, clinical LF cases had higher contact with rodents (rats or mice) in their households in the preceding three weeks (83.8% vs 47.8%). Households with a cat reported a lower presence of rodents (73% vs 38%, p < 0.01) and contributed to a lower rate of clinical LF (48.6% vs 55.7%) although not statistically significant (p = 0.56). The presence of rodents in the households (matched adjusted odds ratio (mAOR): 11.1) and younger age (mAOR: 0.99) were independently associated with clinical LF. Rodent access to households and younger age were independently associated with clinical LF. Rodent access to households is likely a key risk factor for clinical LF in rural SL and potentially in other countries within the West African region. Implementing measures to control rodents and their access to households could potentially decrease the number of clinical LF cases in rural SL and West Africa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".