Malaria elimination in Zanzibar: where next?

In 2018, Zanzibar developed a national malaria strategic plan IV (2018-2023) to guide elimination of malaria by 2023. We assessed progress in the implementation of malaria activities as part of the end-term review of the strategic plan. The review was done between August and October 2022 following the WHO guideline to assess progress made towards malaria elimination, effectiveness of the health systems in delivering malaria case management; and malaria financing. A desk review examined available malaria data, annual work plans and implementation reports for evidence of implemented malaria activities. This was complemented by field visits to selected health facilities and communities by external experts, and interviews with health management teams and inhabitants to authenticate desk review findings. A steady increase in the annual parasite incidence (API) was observed in Zanzibar, from 2.7 (2017) to 3.6 (2021) cases per 1,000 population with marked heterogeneity between areas. However, about 68% of the detected malaria cases were imported into Zanzibar. Malaria case follow-up and investigation increased from <70% in 2017 to 94% and 96% respectively, in 2021. The review noted a 3.7-fold increase of the health allocation in the country's budget, from 31.7 million USD (2017/18) to 117.3 million USD (2022/23) but malaria allocation remained low (<1%). The varying transmission levels in the islands suggest a need for strategic re-orientation of the elimination attempts from a national-wide to a sub-national agenda. We recommend increasing malaria allocation from the health budget to ensure sustainability of malaria elimination interventions.

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

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.

Upgrading Supply Chain Management Systems to Improve Availability of Medicines in Tanzania: Evaluation of Performance and Cost Effects.

BACKGROUND: To address challenges in public health supply chain performance, Tanzania invested in a national logistics management unit (LMU) and a national electronic logistics management information system (eLMIS). This evaluation examined the impact of those 2 key management upgrades approximately 1 year after they were introduced. METHODS: We used a nonexperimental pre-post study design to compare the previous system with the upgraded management system. We collected baseline data from August to November 2013. We conducted round 1 of post-implementation data collection during April and May 2015, about 1 year after implementation of the upgrades. We evaluated key indicators of data use and reporting; supply chain management practices such as storage and supervision; supply chain performance including stock-out and expiry rates; and supply chain cost and savings. We analyzed the data using a range of techniques including statistical testing of baseline and round-1 results, and cost, cost-effectiveness, and return on investment analysis. RESULTS: The upgrades were associated with improvements in data use, accessibility, visibility, and transparency; planning, control, and monitoring; support for quantification; stock-out rates; stock-out duration; commodity expiry; and forecast error. The upgraded system was more costly, but it was also more efficient, particularly when adjusting for the performance improvements. The upgrades also generated substantial savings that defrayed some, but not all, of the investment costs. CONCLUSION: Upgrades to Tanzania's supply chain management systems created multiple and complex pathways to impact. One year after implementation, the LMU and eLMIS brought about performance improvements through better data use and through improvements in some, but not all, management practices. Furthermore, the upgrades-while not inexpensive-contributed to greater system efficiency and modest savings.