Analyses of case-based surveillance data on malaria in pregnancy in Plateau State, Nigeria 2013–2017
Bibliographic record
Abstract
Background: Malaria in pregnancy accounts for 11% of maternal death in Nigeria. Plateau State has a low uptake of intermittent preventive treatment of malaria among women attending antenatal care. Objectives: This study examined the trend and made projections of reported cases of malaria in pregnancy in Plateau State. Methods: Data were extracted from the state disease surveillance system from January 2013 to December 2017. Reported cases of malaria in pregnancy within the 5 years under investigation were retrieved, merged and sorted by month of reporting and Local Government Area (LGA). Prevalence was calculated yearly for each LGA in Plateau State using Geographic Information System. Seasonal variation and projection were based on a multiplicative time series model. Results: In total, 62,997 cases of malaria in pregnancy were retrieved. Prevalence was 6.9% in 2013 and increased to 15.1% in 2017. Higher prevalence was observed in Wase, Kanam and Shendam LGAs. A cyclical trend with highest number of malaria in pregnancy cases was found within the third quarter of all the years. Within the 5 years, there was higher seasonal variation for quarters three (1.209834) and one (1.099711). The highest number of cases of malaria in pregnancy was likely to occur in the third quarter, while the least was found in the second quarter. The projected numbers of malaria in pregnancy cases are 20,121, 22,593 and 25,064 for year 2018, 2019 and 2020, respectively, and the highest number of cases occurs in the third quarter. Conclusion: Malaria in pregnancy follows an increasing trend in Plateau State, with greatest severity in the third quarter of the year. An effective intervention strategy against malaria among pregnant women is advocated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".