Pooled prevalence of malaria and associated factors among vulnerable populations in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Malaria is a serious, fatal disease, and a high-risk determinant for human health globally. Children, pregnant women, and migrants are vulnerable groups for malaria infection in African regions. Recently, malaria is an endemic disease in Ethiopia. This study aimed to determine the pooled prevalence of malaria and its determinant factors among the most vulnerable populations in Ethiopia. Electronic databases, including PubMed, Google Scholar, Web of Science, Semantic Scholar, and Scopus were used for searching articles published since the 2020 Gregorian calendar and onwards. All peer-reviewed Ethiopian journals, health institutions, and Universities were considered for article searching. A PRISMA flow chart and Endnote software were used for article screening, and to remove duplications, respectively. The modified version of the Newcastle–Ottawa Scale was used for potential risk of bias assessments. The heterogeneity among the included studies was evaluated using the indicator of heterogeneity (I 2 ). Egger’s test and funnel plot were used to examine the possible publication bias. A random-effects analysis was used to assess the pooled prevalence of malaria, and its determinant factors with a 95% CI. The screening process, data extraction, and quality assessment were done independently, and any disagreements were resolved through discussions. A total of twelve studies were included in this study. The pooled malaria prevalence was 11.10% (95% CI: 6.10, 16.11). Stagnant water (AOR: 4.19, 95% CI: 2.47, 7.11), no insecticide-treated net utilization (AOR: 3.15, 95% CI: 1.73, 5.73), and staying outdoors at night (AOR: 5.19, 95% CI: 2.08, 12.94) were the pooled estimated statistically risk factors for malaria prevalence. Whereas, insecticide-treated bed net utilization (AOR: 1.59, 95% CI: 0.23, 10.95) reduces the risk of malaria infection. The pooled prevalence of malaria is high among vulnerable populations. Creating awareness regarding utilization of insecticide-treated bed nets, and draining stagnant water from the environment are possible interventions to reduce the prevalence of 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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.050 |
| Bibliometrics | 0.012 | 0.010 |
| 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".