Abstract
BACKGROUND: 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, a high-resolution survey of Plasmodium falciparum prevalence rate (PfPR) was conducted in two Tanzanian districts using rapid diagnostic tests (RDTs), microscopy, and quantitative polymerase chain reaction (qPCR) assays, enabling the comparison of fine-scale strata derived from these different diagnostic methods. METHODS: A cross-sectional survey was conducted in 35 villages in Ulanga and Kilombero districts, south-eastern Tanzania between 2022 and 2023. A total of 7,628 individuals were screened using RDTs (SD-BIOLINE) and microscopy, with two thirds of the samples further analysed 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 mixed 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 and compared to qPCR as reference. RESULTS: Significant fine-scale variations in malaria risk were observed within and between the 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 as low as < 20% in very low transmission villages to > 80% in moderate and high transmission villages. Sensitivity was 62% for RDTs and 41% for microscopy; specificity was 93% and 96%, respectively. Kappa values were 0.7 for RDTs and 0.5 for microscopy. School-age children (5-15 years) 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 burden within and between the study districts and emphasizes the variable performance of the testing methods when stratifying risk at local scales. 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. The findings emphasize that, where precise mapping and effective targeting of malaria are required in localized settings, tests must be both operationally feasible and highly sensitive. Furthermore, when planning microstratification efforts to guide local control measures, it is crucial to carefully consider both the strengths and limitations of the available data and the testing methods employed.