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

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.

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.

Sub-national stratification of malaria risk in mainland Tanzania: a simplified assembly of survey and routine data.

BACKGROUND: Recent malaria control efforts in mainland Tanzania have led to progressive changes in the prevalence of malaria infection in children, from 18.1% (2008) to 7.3% (2017). As the landscape of malaria transmission changes, a sub-national stratification becomes crucial for optimized cost-effective implementation of interventions. This paper describes the processes, data and outputs of the approach used to produce a simplified, pragmatic malaria risk stratification of 184 councils in mainland Tanzania. METHODS: Assemblies of annual parasite incidence and fever test positivity rate for the period 2016-2017 as well as confirmed malaria incidence and malaria positivity in pregnant women for the period 2015-2017 were obtained from routine district health information software. In addition, parasite prevalence in school children (PfPR5to16) were obtained from the two latest biennial council representative school malaria parasitaemia surveys, 2014-2015 and 2017. The PfPR5to16 served as a guide to set appropriate cut-offs for the other indicators. For each indicator, the maximum value from the past 3 years was used to allocate councils to one of four risk groups: very low (< 1%PfPR5to16), low (1- < 5%PfPR5to16), moderate (5- < 30%PfPR5to16) and high (≥ 30%PfPR5to16). Scores were assigned to each risk group per indicator per council and the total score was used to determine the overall risk strata of all councils. RESULTS: Out of 184 councils, 28 were in the very low stratum (12% of the population), 34 in the low stratum (28% of population), 49 in the moderate stratum (23% of population) and 73 in the high stratum (37% of population). Geographically, most of the councils in the low and very low strata were situated in the central corridor running from the north-east to south-west parts of the country, whilst the areas in the moderate to high strata were situated in the north-west and south-east regions. CONCLUSION: A stratification approach based on multiple routine and survey malaria information was developed. This pragmatic approach can be rapidly reproduced without the use of sophisticated statistical methods, hence, lies within the scope of national malaria programmes across Africa.

The potential of pregnant women as a sentinel population for malaria surveillance.

BACKGROUND: With increasing spatial heterogeneity of malaria transmission and a shift of the disease burden towards older children and adults, pregnant women attending antenatal care (ANC) have been proposed as a pragmatic sentinel population for malaria surveillance. However, the representativeness of routine ANC malaria test-positivity and its relationship with prevalence in other population subgroups are yet to be investigated. METHODS: Monthly ANC malaria test-positivity data from all Tanzanian health facilities for January 2014 to May 2016 was compared to prevalence data from the School Malaria Parasitaemia Survey 2015, the Malaria Indicator Survey (MIS) 2015/16, the Malaria Atlas Project 2015, and a Bayesian model fitted to MIS data. Linear regression was used to describe the difference between malaria test-positivity in pregnant women and respective comparison groups as a function of ANC test-positivity and potential covariates. RESULTS: The relationship between ANC test-positivity and survey prevalence in children follows spatially and biologically meaningful patterns. However, the uncertainty of the relationship was substantial, particularly in areas with high or perennial transmission. In comparison, modelled data estimated higher prevalence in children at low transmission intensities and lower prevalence at higher transmission intensities. CONCLUSIONS: Pregnant women attending ANC are a pragmatic sentinel population to assess heterogeneity and trends in malaria prevalence in Tanzania. Yet, since ANC malaria test-positivity cannot be used to directly predict the prevalence in other population subgroups, complementary community-level measurements remain highly relevant.

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

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.

Risk of imported malaria infections in Zanzibar: a cross-sectional study.

BACKGROUND: Zanzibar has made substantial progress in malaria control with vector control, improved diagnosis, and artemisinin-based combination therapy. Parasite prevalence in the population has remained around 1% but imported infections from mainland Tanzania contribute to sustained local transmission. Understanding travel patterns between mainland Tanzania and Zanzibar, and the risk of malaria infection, may help to control malaria importation to Zanzibar. METHODS: A rolling cross-sectional survey linked to routine reactive case detection of malaria was carried out in Zanzibar between May 2017 and October 2018. Households of patients diagnosed with malaria at health facilities were surveyed and household members were tested for malaria using rapid diagnostic tests and a sub-sample by quantitative PCR (qPCR). Interviews elicited a detailed travel history of all household members who had travelled within the past two months, including trips within and outside of Zanzibar. We estimated the association of malaria infection with travel destinations in pre-defined malaria endemicity categories, trip duration, and other co-variates using logistic regression. RESULTS: Of 17,891 survey participants, 1177 (7%) reported a recent trip, of which 769 (65%) visited mainland Tanzania. Among travellers to mainland Tanzania with travel destination details and a qPCR result available, 241/378 (64%) reported traveling to districts with a 'high' malaria endemicity and for 12% the highest endemicity category was 'moderate'. Travelers to the mainland were more likely to be infected with malaria parasites (29%, 108/378) than those traveling within Zanzibar (8%, 16/206) or to other countries (6%, 2/17). Among travellers to mainland Tanzania, those visiting highly endemic districts had a higher odds of being qPCR-positive than those who travelled only to districts where malaria-endemicity was classified as low or very low (adjusted odd ratio = 7.0, 95% confidence interval: 1.9-25.5). Among travellers to the mainland, 110/378 (29%) never or only sometimes used a mosquito net during their travel. CONCLUSIONS: Strategies to reduce malaria importation to Zanzibar may benefit from identifying population groups traveling to highly endemic areas in mainland Tanzania. Targeted interventions to prevent and clear infections in these groups may be more feasible than attempting to screen and treat all travellers upon arrival in Zanzibar.