To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions

Mass Testing and Treatment to Accelerate Malaria Elimination: A Systematic Review and Meta-Analysis

In regions where malaria transmission persists, the implementation of approaches aimed at eliminating parasites from the population can effectively decrease both burden of disease and transmission of infection. Thus, mass strategies that target symptomatic and asymptomatic infections at the same time may help countries to reduce transmission. This systematic review assessed the potential benefits and harms of mass testing and treatment (MTaT) to reduce malaria transmission. Searches were conducted in March 2021 and updated in April 2022 and included cluster-randomized controlled trials (cRCTs) as well as nonrandomized studies (NRSs) using malaria infection incidence, clinical malaria incidence, or prevalence as outcomes. The risk of bias was assessed with Cochrane's risk of bias (RoB2) tool and Risk of Bias Tool in Nonrandomized Studies - of Interventions (ROBINS-I), and the certainty of evidence (CoE) was graded for each outcome. Of 4,462 citations identified, seven studies (four cRCTs and three NRSs) contributed outcome data. The analysis revealed that MTaT did not reduce the incidence (risk ratio [RR]: 0.95, 95% CI: 0.87-1.04; 1,181 participants; moderate CoE) or prevalence (RR: 0.83, 95% CI: 0.67-1.01; 7,522 participants; moderate CoE) of malaria infection but resulted in a small reduction in clinical malaria (RR: 0.82; 95% CI: 0.70-0.95; 334,944 participants; moderate CoE). Three studies contributing data on contextual factors concluded that MTaT is an acceptable, feasible, and cost-effective intervention. Mathematical modeling analyses (n = 10) suggested that MTaT effectiveness depends on the baseline transmission level, diagnostic test performance, number of rounds, and other co-interventions. Based on the limited evidence available, MTaT has little to no impact on reducing malaria transmission.

A systematic review and meta-analysis on sub-microscopic Plasmodium infections in India: Different perspectives and global challenges

Background

The long-term maintenance of parasite biomass below the detection threshold of microscopy may stymie malaria elimination. Variation in microscopists' competencies to detect and correctly identify parasite species reflect in microscopy sensitivity, resulting in incorrect species-specific burden.

Methods

The study estimated Plasmodium SMI pooled burden from published reports using a random effect model & identifies their hotspots in India. The study applied a prediction model for the first time on Indian data, emphasizing the importance of such models that can predict PCR-prevalence from slide- prevalence.

Findings

A total of 17,449 samples from 39 districts were examined for Plasmodium by microscopy and PCR. The overall heterogeneity in clinic-based and community-based studies was 91% and 96%, respectively, with the pooled prevalence of 3.63%. The SMI prevalence in individual studies ranged from 38.4% to 0.4%. Sensitivity of microscopy for mono-P. vivax (91%) was found to be better than mono-P. falciparum (82 %). But surprisingly, it was much lower for mixed PfPv (45%).

Interpretation

Primary regional data in the form of SMIs hot spots should be generated from countries on the verge of malaria elimination, and genetic monitoring should be integrated into national programs, particularly in key areas for successful malaria elimination.

Funding

Not applicable.

Guiding placement of health facilities using multiple malaria criteria and an interactive tool

BACKGROUND

Access to healthcare is important in controlling malaria burden and, as a result, distance or travel time to health facilities is often a significant predictor in modelling malaria prevalence. Adding new health facilities may reduce overall travel time to health facilities and may decrease malaria transmission. To help guide local decision-makers as they scale up community-based accessibility, the influence of the spatial allocation of new health facilities on malaria prevalence is evaluated in Bunkpurugu-Yunyoo district in northern Ghana. A location-allocation analysis is performed to find optimal locations of new health facilities by separately minimizing three district-wide objectives: malaria prevalence, malaria incidence, and average travel time to health facilities.

METHODS

Generalized additive models was used to estimate the relationship between malaria prevalence and travel time to the nearest health facility and other geospatial covariates. The model predictions are then used to calculate the optimisation criteria for the location-allocation analysis. This analysis was performed for two scenarios: adding new health facilities to the existing ones, and a hypothetical scenario in which the community-based healthcare facilities would be allocated anew. An interactive web application was created to facilitate efficient presentation of this analysis and allow users to experiment with their choice of health facility location and optimisation criteria.

RESULTS

Using malaria prevalence and travel time as optimisation criteria, two locations that would benefit from new health facilities were identified, regardless of scenarios. Due to the non-linear relationship between malaria incidence and prevalence, the optimal locations chosen based on the incidence criterion tended to be inequitable and was different from those based on the other optimisation criteria.

CONCLUSIONS

This study findings underscore the importance of using multiple optimisation criteria in the decision-making process. This analysis and the interactive application can be repurposed for other regions and criteria, bridging the gap between science, models and decisions.