A Bayesian spatio-temporal framework to assess the effect of seasonal malaria chemoprevention on children under 5 years in Cameroon from 2016 to 2021 using routine data
Bibliographic record
Abstract
BACKGROUND: Malaria affects millions of Cameroonian children under 5 years of age living in the North and Far North regions. These regions bear the greatest burden, particularly for children under 5 years of age. To reduce the burden of disease in these regions, Cameroon adopted the Seasonal Malaria Chemoprevention (SMC) in 2016 and has implemented it each year since its adoption. However, no previous studies have systematically assessed the effects of this intervention in Cameroon. It is important to understand its effect and whether its implementation could be improved. This study aimed to assess the effect of SMC in Cameroon during the period 2016-2021 on malaria morbidity in children under 5 years of age using routine data. METHODS: Data on malaria cases were extracted from the Cameroon Health Monitoring Information System (HMIS) from January 1, 2011, to December 31, 2021. Health facilities report these data monthly on a single platform, the District Health Information System version 2 (DHIS2). Thus, a controlled interrupted time-series model in a Bayesian framework was used to evaluate the effects of the SMC on malaria morbidity. RESULTS: SMC implementation was associated with a reduction in the incidence of uncomplicated malaria cases during the high-transmission periods from 2016 to 2021. Regarding the incidence of severe malaria during the high-transmission period, a reduction was found over the period 2016-2019. The highest reduction was registered during the second year of implementation in 2017:15% (95% Credible Interval, 10-19) of uncomplicated malaria cases and 51% (47-54) of confirmed severe malaria cases. CONCLUSION: The addition of SMC to the malaria intervention package in Cameroon decreased the incidence of uncomplicated and severe malaria among children under 5 years of age. Based on these findings, this study supports the wide implementation of SMC to reduce the malaria burden in Cameroon as well as the use of routine malaria data to monitor the efficiency of the strategy in a timely manner.
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 | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".