Impact of Malaria Diagnostic Technologies on the Disease Burden in the Sub-Saharan Africa

Malaria infection among adults residing in a highly endemic region from the Democratic Republic of the Congo

BACKGROUND

Adults infected with Plasmodium spp. in endemic areas need to be re-evaluated in light of global malaria elimination goals. They potentially undermine malaria interventions but remain an overlooked aspect of public health strategies.

METHODS

This study aimed to estimate the prevalence of Plasmodium spp. infections, to identify underlying parasite species, and to assess predicting factors among adults residing in an endemic area from the Democratic Republic of Congo (DRC). A community-based cross-sectional survey in subjects aged 18 years and above was therefore carried out. Study participants were interviewed using a standard questionnaire and tested for Plasmodium spp. using a rapid diagnostic test and a nested polymerase chain reaction assay. Logistic regression models were fitted to assess the effect of potential predictive factors for infections with different Plasmodium spp.

RESULTS

Overall, 420 adults with an estimated prevalence of Plasmodium spp. infections of 60.2% [95% CI 55.5; 64.8] were included. Non-falciparum species infected 26.2% [95% CI 22.2; 30.5] of the study population. Among infected participants, three parasite species were identified, including Plasmodium falciparum (88.5%), Plasmodium malariae (39.9%), and Plasmodium ovale (7.5%) but no Plasmodium vivax. Mixed species accounted for 42.3% of infections while single-species infections predominated with P. falciparum (56.5%) among infected participants. All infected participants were asymptomatic at the time of the survey. Adults belonging to the "most economically disadvantaged" households had increased risks of infections with any Plasmodium spp. (adjusted odds ratio, aOR = 2.87 [95% CI 1.66, 20.07]; p < 0.001), compared to those from the "less economically disadvantaged" households. Conversely, each 1 year increase in age reduced the risk of infections with any Plasmodium spp. (aOR = 0.99 [95% CI 0.97, 0.99]; p = 0.048). Specifically for non-falciparum spp., males had increased risks of infection than females (aOR = 1.83 [95% CI 1.13, 2.96]; p = 0.014).

CONCLUSION

Adults infected with malaria constitute a potentially important latent reservoir for the transmission of the disease in the study setting. They should specifically be taken into account in public health measures and translational research.

The impact of home-based management of malaria on clinical outcomes in sub-Saharan African populations: a systematic review and meta-analysis

BACKGROUND

Malaria remains a significant cause of morbidity and mortality globally and continues to disproportionately afflict the African population. We aimed to evaluate the effect of home management of malaria intervention on health outcomes.

METHODS

In our systematic review and meta-analysis, six databases (Pubmed, Cochrane CENTRAL, EMBASE, CAB Abstracts and Global Health, CINAHL Complete, and BIOSIS) were searched for studies of home management of malaria from inception until November 15, 2023. We included before-after studies, observational studies, and randomised controlled trials of home management intervention delivered in community settings. The primary outcomes were malaria mortality and all-cause mortality. The risk of bias in individual observational studies was assessed using the ROBINS-I tool, whilst randomised controlled trials were judged using a revised Cochrane risk of bias tool and cluster-randomised controlled trials were evaluated using an adapted Cochrane risk of bias tool for cluster-randomised trials. We computed risk ratios with accompanying 95% confidence intervals for health-related outcomes reported in the studies and subsequently pooled the results by using a random-effects model (DerSimonian-Laird method).

RESULTS

We identified 1203 citations through database and hand searches, from which 56 articles from 47 studies encompassing 234,002 participants were included in the systematic review. All studies were conducted in people living in sub-Saharan Africa and were rated to have a low or moderate risk of bias. Pooled analyses showed that mortality rates due to malaria (RR = 0.40, 95% CI = 0.29-0.54, P = 0.00001, I2 = 0%) and all-cause mortality rates (RR = 0.62, 95% CI = 0.53-0.72, P = 0.00001, I2 = 0%) were significantly lower among participants receiving home management intervention compared to the control group. However, in children under 5 years of age, there was no significant difference in mortality rates before and after implementation of home management of malaria. In terms of secondary outcomes, home management of malaria was associated with a reduction in the risk of febrile episodes (RR = 1.27, 95% CI = 1.09-1.47, P = 0.002, I2 = 97%) and higher effective rates of antimalarial treatments (RR = 2.72, 95% CI = 1.90-3.88, P < 0.00001, I2 = 96%) compared to standard care. Home malaria management combined with intermittent preventive treatment showed a significantly lower incidence risk of malaria than home management intervention that exclusively provided treatment to individuals with febrile illness suggestive of malaria. The risks for adverse events were found to be similar for home management intervention using different antimalarial drugs. Cost-effectiveness findings depicted that home malaria management merited special preferential scale-up.

CONCLUSIONS

Home management of malaria intervention was associated with significant reductions in malaria mortality and all-cause mortality. The intervention could help decrease health and economic burden attributable to malaria. Further clinical studies are warranted to enable more meaningful interpretations with regard to wide-scale implementation of the intervention, settings of differing transmission intensity, and new antimalarial drugs.

Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease's impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) website and consisted of 27,558 photos, including 13,780 parasitized cell images and 13,778 uninfected cell images. In conclusion, the MobileNetV2 model outperformed by achieving an accuracy rate of 97.06% for better disease detection. Also, other metrics like training and testing loss, precision, recall, fi-score, and ROC curve were calculated to validate the considered models.