Longitudinal analysis of the prevalence of minor <i>Plasmodium</i> spp. in the reservoir of asymptomatic infections through sequential interventions in Northern Sahelian Ghana
Bibliographic record
Abstract
ABSTRACT Current interventions targeting malaria control in sub-Saharan Africa (SSA) are focused on Plasmodium falciparum , the most prevalent species infecting humans. Despite renewed efforts for malaria elimination in SSA, little attention has been paid to the neglected parasites P. malariae and P. ovale spp. and the impact of interventions like long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS) with non-pyrethroid insecticides, and/or seasonal malaria chemoprevention (SMC) on these minor Plasmodium spp. To address this research gap, this study was undertaken to assess the efficacy of two sequential interventions, IRS and SMC combined with LLINs, on minor- Plasmodium spp. infections in Bongo District, an area characterized by high seasonal transmission in the Northern Sahelian belt of Ghana. Using an interrupted time-series study, five age-stratified surveys, each of ∼2,000 participants, were undertaken at the end of the wet seasons between 2012 and 2022. Across this 10-year study period, infections with P. malariae and P. ovale spp. were detected using a species-specific PCR targeting the 18S rRNA gene. In 2015, following IRS, the prevalence of the minor Plasmodium spp. declined in all ages from baseline in 2012, with participants being significantly less likely to be infected with P. malariae (13.7% vs 1.4%) and P. ovale spp. (5.7% vs 0.4%). Despite this decline, in 2017, 32 months after IRS was discontinued and SMC was introduced, the prevalence of P. malariae (2.9%) and P. ovale spp. (4.0%) rebounded 2- and 10-fold, respectively. This rebound in the minor species was observed in all age groups, except for the younger children (< 5 years) targeted by SMC. Finally, when we examined this population in 2020 and 2022 after sustained deployment of SMC, the prevalence of P. malariae continued to increase (7.4% and 5.8%), while the prevalence of P. ovale spp. declined (2.6% and 1.3%). Results show that both IRS and SMC were effective not only against P. falciparum but also reduced the prevalence of P. malariae and P. ovale spp. in Bongo District. Going forward, molecular diagnostics will be critical to identify changes in the submicroscopic reservoir of the minor Plasmodium spp. found in adolescents and adults and to achieve malaria elimination in this region of Ghana. AUTHOR SUMMARY To achieve malaria elimination, the use of molecular diagnostics for surveillance of the Plasmodium spp. reservoir is essential. Such diagnostics are critical to detect hidden reservoirs of submicroscopic minor-species infections with P. malariae and P. ovale spp. Here we examine the impact of two sequential malaria control interventions, indoor residual spraying (IRS) with non-pyrethroid insecticides and seasonal malaria chemoprevention (SMC) with sulfadoxine-pyrimethamine plus amodiaquine administered to all children < 5 years, combined with long-lasting insecticidal nets (LLINs) impregnated with pyrethroids to reduce the reservoir of Plasmodium spp. infections where P. falciparum is dominant and the minor species, P. malariae and P. ovale spp., were also found. This interrupted time-series study, undertaken over 10 years (2012-2022) in Bongo District, located in the Northern Sahelian belt of Ghana, identified that IRS and SMC reduced the prevalence of all Plasmodium spp. IRS significantly reduced the prevalence of single- and mixed- Plasmodium spp. infections across all age groups. However, following its discontinuation a rebound was observed for P. falciparum and minor-species infections, especially in adolescents and adults. Although SMC maintained a near-zero prevalence of all Plasmodium spp. infections, its impact was limited to the target age group (< 5 years). Furthermore, this study showed that following the discontinuation of IRS, the older children, adolescents, and adults, not eligible for SMC, continued to harbour Plasmodium spp. infections, including minor species, thus sustaining the reservoir. These results show that by combining vector control inventions as well as targeting all age groups with chemoprevention, high-burden countries in sub-Saharan Africa, including Ghana, could bring the Plasmodium spp. reservoir closer to elimination more rapidly.
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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 | 0.002 |
| 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 | 0.000 |
| 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".