Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

Abstract

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017–2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.

Comparison of Fine-Scale Malaria Strata Derived from Population Survey Data Collected Using mRDTs, Microscopy and qPCR in South-Eastern Tanzania

“…Our findings, benchmarked against qPCR, reveal limited detection capabilities of RDTs and microscopy in overall fine-scale stratifications, especially in low transmission settings. Previous studies have emphasized the usefulness of routine hospital data for micro-stratifications [8,10,78,79]. However, evidence indicates that both microscopy and RDTs are less effective in identifying stable febrile malaria hotspots, except for asymptomatic hotspots, which are reliably identified by microscopy [77], however still not stable when transmission is low [80].…”

“…These stratifications involve assessing risk levels in geographical areas at the subnational level (e.g. zones, regions, and districts) [2,4,5], and can include fine scale mapping (down to wards and villages levels) as countries progress towards elimination [6][7][8]. The data for such stratification may come from health facilities, active malaria screening during population surveys, or proxy data sources such as antenatal care clinic visits [6,9,10].…”

High prevalence and risk of malaria among asymptomatic individuals from villages with high prevalence of artemisinin partial resistance in Kyerwa district of Kagera region, north-western Tanzania

“…Based on recent reports, malaria remains a major public health problem in Tanzania, and the country still experiences persistent transmission in some regions [ 2 ], but the drivers of these patterns are not clearly known. Malaria transmission in Tanzania has become heterogeneous, with a high burden in the north-western, southern and western regions, while the central corridor, north-eastern and south-western parts of the country have low to very low transmission intensities [ 21 , 22 ]. The recent epidemiological transition and trends of malaria have raised critical questions that need to be addressed to allow the country to progress to its elimination targets by 2030.…”

High prevalence and risk of malaria among asymptomatic individuals from villages with high rates of artemisinin partial resistance in Kyerwa district, North-western Tanzania

“…Based on recent reports, malaria remains a major public health problem and the country still experiences persistent transmission in some regions [2], but the drivers of these patterns are not clearly known. Malaria transmission in Tanzania has become heterogenous with high burden in north-western, southern and western regions while the central corridor, north-eastern and south-western parts of the country have low to very low transmission intensities [21,22]. The recent epidemiological transition and trends of malaria have raised critical questions which need to be addressed to allow the country to progress to its elimination targets by 2030.…”