Impact of the COVID-19 pandemic on the uptake of intermittent sulphadoxine-pyrimethamine for prevention of malaria amongst pregnant women in Sierra Leone
Bibliographic record
Abstract
Abstract Background Malaria in pregnancy contributes to high maternal mortality, warranting the adoption of intermittent preventive treatment (IPTp) of malaria in pregnancy in Sierra Leone in 2017. Since then, the uptake of IPTp has increased but it is unclear whether COVID-19 has any impact on these services. We aimed to assess the impact of COVID-19 on the uptake of IPTp among pregnant women in Sierra leone at district, regional, and national levels. Methods A retrospective cross-sectional study design was used to assess the monthly aggregated data from DHIS2 for all 5 regions of Sierra Leone. We describe the trends of IPTp1, IPTp2, and IPTp3+ uptake at national, regional, and district levels before, during and after COVID-19. Data was analyses using STATA. The median and interquartile range for each outcome in each period were calculated. Results The median monthly uptake of total IPTp was the lowest before COVID (53,244) but increased during COVID-19 (55,257) and after COVID-19 (59,439). IPTp1 uptake was lowest during COVID-19 (20,338) but highest after COVID-19 (20,914). All three variables (IPTp1, IPTp2 and IPTp3) showed a clear decline in uptake in early 2020 and recovered within 3 months in a similar pattern of magnitude, except in Eastern Sierra Leone. Both IPTp2 and IPTp3 showed more pronounced increases in uptake after recovery than IPTp1 and they both experienced sustained increases in the post-COVID-19 period. Conclusion The negative impact of the COVID-19 pandemic in reducing IPTp uptake was followed by rapid recovery, underscoring the resilience of the Sierra Leone’s health system in response to public health emergencies. However, the delay in recovery in the IPTp uptake in the Eastern Sierra Leone highlight the need for strong differentiated regional strategies in protecting essential health services during public health emergencies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".