Comparison of Fine-Scale Malaria Strata Derived from Population Survey Data Collected Using mRDTs, Microscopy and qPCR in South-Eastern Tanzania
Authors: Mshani Issa H. , Jackson Frank M. , Minja Elihaika G , Abbas Said , Lilolime Nasoro S. , Makala Faraji E. , Lazaro Alfred B. , Mchola Idrisa S. , Mukabana Linda N. , Kahamba Najat , Limwagu Alex , Njalambaha Rukia. M. , Ngowo Halfan S. , Bisanzio Donal , Baldini Francesco , Babayan Simon A. , Okumu Fredros 
Published: June 2024

Introduction

Malaria-endemic countries are increasingly adopting data-driven risk stratification, often at district or higher regional levels, to guide their intervention strategies. The data typically comes from population-level surveys collected by rapid diagnostic tests (RDTs), which unfortunately perform poorly in low transmission settings. Here, we conducted a high-resolution survey ofPlasmodium falciparumprevalence rate (PfPR) in two Tanzanian districts and compared the fine-scale strata obtained using data from RDTs, microscopy and quantitative polymerase chain reaction (qPCR) assays.

Methods

A cross-sectional survey was conducted in 35 villages in Ulanga and Kilombero districts, south-eastern Tanzania between 2022 and 2023. We screened 7,628 individuals using RDTs (SD-BIOLINE) and microscopy, with two thirds of the samples further analyzed by qPCR. The data was used to categorize each district and village as having very low (PfPR<1%), low (1%≤PfPR<5%), moderate (5%≤PfPR<30%), or high (PfPR≥30%) parasite prevalence. A generalized linear model was used to analyse infection risk factors. Other metrics, including positive predictive value (PPV), sensitivity, specificity, parasite densities, and Kappa statistics were computed for RDTs or microscopy using qPCR as reference.

Results

Significant fine-scale variations in malaria risk were observed within and between districts, with village prevalence ranging from 0% to >50%. Prevalence varied by testing method: Kilombero was low risk by RDTs (PfPR=3%) and microscopy (PfPR=2%) but moderate by qPCR (PfPR=9%); Ulanga was high risk by RDTs (PfPR=39%) and qPCR (PfPR=54%) but moderate by microscopy (PfPR=26%). RDTs and microscopy classified majority of the 35 villages as very low to low risk (18 - 21 villages). In contrast, qPCR classified most villages as moderate to high risk (29 villages). Using qPCR as the reference, PPV for RDTs and microscopy ranged from <20% in very low transmission villages to >80% in moderate to high transmission villages. Sensitivity was 62% for RDTs and 41% for microscopy; specificity was 93% and 96%, respectively. Kappa values were 0.58 for RDTs and 0.42 for microscopy. School-age children (5-15years) had higher malaria prevalence and parasite densities than adults (P<0.001). High-prevalence villages also had higher parasite densities (Spearman r=0.77, P<0.001 for qPCR; r=0.55, P=0.003 for microscopy).

Conclusion

This study highlights significant fine-scale variability in malaria risk within and between districts and emphasizes the variable performance of the testing methods when stratifying risk. While RDTs and microscopy were effective in high-transmission areas, they performed poorly in low-transmission settings; and classified most villages as very low or low risk. In contrast, qPCR classified most villages as moderate or high risk. While we cannot conclude on which public health decisions would be subject to change because of these differences, the findings suggest the need for improved testing approaches that are operationally feasible and sufficiently sensitive, to enable precise mapping and effective targeting of malaria in such local contexts. Moreover, public health authorities should recognize the strengths and limitations of their available data when planning local stratification or making decisions.