Sub-national tailoring of malaria interventions in Mainland Tanzania: simulation of the impact of strata-specific intervention combinations using modelling

Abstract

Background To accelerate progress against malaria in high burden countries, a strategic reorientation of resources at the sub-national level is needed. This paper describes how mathematical modelling was used in mainland Tanzania to support the strategic revision that followed the mid-term review of the 2015–2020 national malaria strategic plan (NMSP) and the epidemiological risk stratification at the council level in 2018. Methods Intervention mixes, selected by the National Malaria Control Programme, were simulated for each malaria risk strata per council. Intervention mixes included combinations of insecticide-treated bed nets (ITN), indoor residual spraying, larval source management, and intermittent preventive therapies for school children (IPTsc). Effective case management was either based on estimates from the malaria indicator survey in 2016 or set to a hypothetical target of 85%. A previously calibrated mathematical model in OpenMalaria was used to compare intervention impact predictions for prevalence and incidence between 2016 and 2020, or 2022. Results For each malaria risk stratum four to ten intervention mixes were explored. In the low-risk and urban strata, the scenario without a ITN mass campaign in 2019, predicted high increase in prevalence by 2020 and 2022, while in the very-low strata the target prevalence of less than 1% was maintained at low pre-intervention transmission intensity and high case management. In the moderate and high strata, IPTsc in addition to existing vector control was predicted to reduce the incidence by an additional 15% and prevalence by 22%. In the high-risk strata, all interventions together reached a maximum reduction of 76%, with around 70% of that reduction attributable to high case management and ITNs. Overall, the simulated revised NMSP was predicted to achieve a slightly lower prevalence in 2020 compared to the 2015–2020 NMSP (5.3% vs 6.3%). Conclusion Modelling supported the choice of intervention per malaria risk strata by providing impact comparisons of various alternative intervention mixes to address specific questions relevant to the country. The use of a council-calibrated model, that reproduces local malaria trends, represents a useful tool for compiling available evidence into a single analytical platform, that complement other evidence, to aid national programmes with decision-making processes.

Performance of antigen detection for HRP2-based malaria rapid diagnostic tests in community surveys: Tanzania, July–November 2017

“…In this study, RDT prevalence rates ranged from 20.0% to 43.6%, and these results from this community population shows evidence for the high P. falciparum transmission in these northwestern and southeastern border regions of Tanzania. These high-transmission regions have been confirmed by recent reports [5,31,32], and similar findings by NIMR were the impetus for selection of villages within these border regions for this 2017 survey [17]. No correlation was seen between any RDT LOD estimates (at the 50, 75, 90, or 95% probability levels of RDT positivity) and the observed percentage of the study population infected with P. falciparum at the time of enrollment.…”

Modelling to support decisions about the geographic and demographic extensions of seasonal malaria chemoprevention in Benin

“…Modelling can account for multiple data sources (seasonality, burden, planned interventions, coverage) to determine which strategy is likely to save most lives. Previous works using mathematical modelling for strategic planning focused on showing the likely impact of already determined combinations of interventions to advocate for funding [14, 30]. This analysis shows that country-specific models can also be used to guide the development of strategies by answering to specific questions of interest for decision makers.…”

“…Modelling can account for multiple data sources (seasonality, burden, planned interventions, coverage) to determine which strategy is likely to save most lives. Previous works using mathematical modelling for strategic planning focused on showing the likely impact of already determined combinations of interventions to advocate for funding [14,30].…”

“…Such a quantification will intrinsically be context-specific and as such requires a method which can be adapted to the setting of interest. Mathematical modelling can mimic the past and current malaria burden in administrative units of a country and simulate the impact of various interventions, and has already been used to support decision-making [13, 14].…”

Disability inclusion in malaria services in western Tanzania: A rapid barrier analysis

“…Recent reports on malaria status in Tanzania showed that malaria is still the leading cause of morbidity and mortality, with notifications of infection transmission resurgence in some areas (Chacky et al 2018 ; Finda et al 2018 ; Insehngoma et al 2018 ; Mapua et al 2022 ; Mitchell et al 2022 ; WHO 2021 ). Kigoma Region is one area in Tanzania contributing to the observed high burden of malaria, as it has a very high transmission risk (Runge et al 2022 ).…”