Back

Overdispersion in Malaria-Schistosoma Co-infection: Insights from a Meta-analytical Approach and Systematic Review

Koellsch-Amet, C.; Boissier, J.; AUGUSTO, R. D. C.

2025-11-02 infectious diseases
10.1101/2025.10.31.25339164 medRxiv
Show abstract

Malaria and schistosomiasis are two major parasitic diseases that are co-endemic in many regions of sub-Saharan Africa. Despite their frequent overlap, the potential epidemiological interactions in cases of co-infection remain poorly understood. We conducted a systematic review and meta-analysis in accordance with the PRISMA-ScR guidelines. A total of 192 studies published between 1996 and 2023 were reviewed, of which 59 studies involving 73,383 individuals were included in the meta-analysis. Pooled analysis showed that Plasmodium- Schistosoma co-infection occurs more often than expected by chance (overall odds ratio [OR] = 1.20, 95% CI: 1.02-1.40), despite substantial heterogeneity (I2 = 89%). Co-infection prevalence ranged from 1.1% (Benin) to 36.6% (Mali), with school-age children and pregnant women disproportionately affected. Subgroup analyses revealed no consistent differences by sex or by Schistosoma or Plasmodium species. Observational and experimental evidence suggests that co-infection may exacerbate anemia and modulate host immunity, but mechanistic pathways remain poorly defined. The high co-endemicity of these parasites underscores the need for integrated surveillance and control programs and highlights the potential interactions between the two parasites. Understanding these mechanisms is essential for designing integrated control strategies and highlights the need to take polyparasitism into account in public health policies, particularly in endemic regions.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.