The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania

Abstract

Background Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2). This paper describes the process undertaken in mainland Tanzania using routine data to develop a high-resolution, micro-stratification risk map to guide future malaria control efforts. Methods Combinations of various routine malariometric indicators collected from 7098 HFs were assembled across 3065 wards of mainland Tanzania for the period 2017–2019. The reported council-level prevalence classification in school children aged 5–16 years (PfPR5–16) was used as a benchmark to define four malaria risk groups. These groups were subsequently used to derive cut-offs for the routine indicators by minimizing misclassifications and maximizing overall agreement. The derived-cutoffs were converted into numbered scores and summed across the three indicators to allocate wards into their overall risk stratum. Results Of 3065 wards, 353 were assigned to the very low strata (10.5% of the total ward population), 717 to the low strata (28.6% of the population), 525 to the moderate strata (16.2% of the population), and 1470 to the high strata (39.8% of the population). The resulting micro-stratification revealed malaria risk heterogeneity within 80 councils and identified wards that would benefit from community-level focal interventions, such as community-case management, indoor residual spraying and larviciding. Conclusion The micro-stratification approach employed is simple and pragmatic, with potential to be easily adopted by the malaria programme in Tanzania. It makes use of available routine data that are rich in spatial resolution and that can be readily accessed allowing for a stratification of malaria risk below the council level. Such a framework is optimal for supporting evidence-based, decentralized malaria control planning, thereby improving the effectiveness and allocation efficiency of malaria control interventions.

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

“…There was very high heterogeneity across the five villages which are located close to each other, suggesting that there are critical factors associated with the micro-geographic pattern of malaria in this community. The findings are similar to what was reported elsewhere [47], where high level of heterogeneity at micro-geographic levels was reported within the wards from 80 councils of Mainland Tanzania. Although few studies have assessed the prevalence of malaria in asymptomatic individuals of all age groups in Tanzania, the prevalence reported in this study is likely the highest in the country in recent years.…”

“…This suggests that efforts to eliminate malaria need to target groups at high risk, and to consider and concurrently implement strategies for poverty reduction. Although previous studies showed high heterogeneity of malaria transmission and risk at macro-geographic levels [47,55], there is paucity of studies of micro-geographic variations of malaria burden and associated risk factors [47]. Thus, more studies are required to further assess malaria burden and risk factors associated with its transmission at different geographic levels.…”

“…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.…”

Prevalence and risk factors associated with malaria infections at a micro-geographic level in three villages of Muheza district, north-eastern Tanzania

“…In the current study, the prevalence of malaria infections ranged from 15% to 25% by microscopy and 24% and 35% for RDTs, and these variations were not significantly associated with the key features studied, the location of the dispensary, and a big pond. These findings are in contrast to what was reported by NMCP in 2020 and others in 2022 that Muheza district council including the study villages had an overall high prevalence which was 30% and above [25,26,28]. Among the study villages, Mamboleo had a slightly higher prevalence of malaria infections than the other two villages, possibly due to the presence of the pond within the village.…”

“…In the 2020 malaria stratification, the country was divided into five epidemiological strata with councils and regions of high, moderate, low, and very low, and the urban stratum with 116/184 (63.1%) of the councils in high/moderate while the low/very low strata had 68/184 (36.9%) of all the councils [16]. The next round of stratification was done in 2022 and showed a slight increase of councils (70/184 (38.0%) located in low and very low malaria transmission [26]. Based on stratification, NMCP designed and has been implementing area-specific interventions with the goal of burden reduction in areas with high and moderate transmission and eliminating malaria in low/very transmission areas [25].…”