Spatial Distribution of Malaria Prevalence and Predictors Among Pregnant Women in Ondo State, Southwest, Nigeria
Bibliographic record
Abstract
Abstract Globally, malaria increase the rate maternal mortality. Nigeria has high malaria endemicity and the world's highest malaria burden. Nigeria reports about 51 million cases and 31.3% mortality annually, while 97% of the population (213.4m) is at risk. Malaria killed no fewer than 200,000 Nigerians, of which there were 61 million cases that were responsible for 11% of maternal mortality in Nigeria in 2021. This study examined the prevalence and patterns of malaria endemicity among pregnant women in Ondo State, and determined the malaria prevalence from 2013–2017. Geospatial technology was employed to examine the distribution of malaria cases among pregnant women and one-way Analysis of Variance (ANOVA) was used to determine the malaria prevalence in eight Local Government Areas. Temporal analysis revealed a gradual increase in malaria occurrence over the years. In 2013, 2015, and 2017, Akure South recorded the highest prevalence, with approximately 8 cases (40.11%), 5 cases (23.64%), and 9 cases (27.94%) per 1000 pregnant women, respectively. Ondo East had the highest prevalence of 9 cases (27.06%) in 2014, while in 2016, Akoko Southwest had the highest prevalence of 3 cases (19.04%) per 1000 pregnant women. The cumulative malaria patterns for the five years showed that Akure South had the highest prevalence of 18.76–31.42 per 1000 pregnant women. In contrast, the lowest prevalence occurred in Odigbo and Okitipupa having 5–9 cases per 1000 pregnant women. From 2013–2017, findings showed variations in the disease's prevalence in Ondo state.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".