BEYOND MOSQUITO BITES: ANALYZING MALARIA RISK FACTORS IN SOUTHERN NIGERIA
Bibliographic record
Abstract
This study investigates how various risk factors affect the prevalence of febrile diseases, with a focus on malaria in the southern states of Nigeria. The study employed Pearson correlation and multilinear regression analyses to examine the relationships between risk factors and disease prevalence. Pearson correlation analysis revealed that genetic conditions, high blood pressure, and direct contact with infected individuals were negatively correlated with malaria, while poor personal hygiene, substandard living conditions, and exposure to endemic areas had weak positive correlations. The strongest association was found with mosquito bites, which also had the highest unstandardized beta coefficient among the factors studied. Nevertheless, the study also highlights secondary risk factors, such as poor living conditions and hygiene, which are often overlooked in malaria intervention programs. These factors, although not as strong as mosquito exposure, can exacerbate the risk of infection, particularly in vulnerable populations living in impoverished areas. These results highlight the critical role of mosquito exposure in malaria and emphasize the need for targeted interventions in such areas. The findings can be beneficial to clinicians, general public, and all stakeholders involved in the fight against malaria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".