Developing an expanded vector control toolbox for malaria elimination.

Vector control using long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) accounts for most of the malaria burden reductions achieved recently in low and middle-income countries (LMICs). LLINs and IRS are highly effective, but are insufficient to eliminate malaria transmission in many settings because of operational constraints, growing resistance to available insecticides and mosquitoes that behaviourally avoid contact with these interventions. However, a number of substantive opportunities now exist for rapidly developing and implementing more diverse, effective and sustainable malaria vector control strategies for LMICs. For example, mosquito control in high-income countries is predominantly achieved with a combination of mosquito-proofed housing and environmental management, supplemented with large-scale insecticide applications to larval habitats and outdoor spaces that kill off vector populations en masse, but all these interventions remain underused in LMICs. Programmatic development and evaluation of decentralised, locally managed systems for delivering these proactive mosquito population abatement practices in LMICs could therefore enable broader scale-up. Furthermore, a diverse range of emerging or repurposed technologies are becoming available for targeting mosquitoes when they enter houses, feed outdoors, attack livestock, feed on sugar or aggregate into mating swarms. Global policy must now be realigned to mobilise the political and financial support necessary to exploit these opportunities over the decade ahead, so that national malaria control and elimination programmes can access a much broader, more effective set of vector control interventions.

The central role of national programme management for the achievement of malaria elimination: a cross case-study analysis of nine malaria programmes.

BACKGROUND: A malaria eradication goal has been proposed, at the same time as a new global strategy and implementation framework. Countries are considering the strategies and tools that will enable progress towards malaria goals. The eliminating malaria case-study series reports were reviewed to identify successful programme management components using a cross-case study analytic approach. METHODS: Nine out of ten case-study reports were included in the analysis (Bhutan, Cape Verde, Malaysia, Mauritius, Namibia, Philippines, Sri Lanka, Turkey, Turkmenistan). A conceptual framework for malaria elimination programme management was developed and data were extracted and synthesized. Findings were reviewed at a consultative workshop, which led to a revision of the framework and further data extraction and synthesis. Success factors of implementation, programme choices and changes, and enabling factors were distilled. RESULTS: Decentralized programmes enhanced engagement in malaria elimination by sub-national units and communities. Integration of the malaria programme into other health services was also common. Decentralization and integration were often challenging due to the skill and experience levels of newly tasked staff. Accountability for programme impact was not clarified for most programmes. Motivation of work force was a key factor in maintaining programme quality but there were few clear, detailed strategies provided. Different incentive schemes targeted various stakeholders. Training and supervision, although not well described, were prioritized by most programmes. Multi-sectoral collaboration helped some programmes share information, build strategies and interventions and achieve a higher quality of implementation. In most cases programme action was spurred by malaria outbreaks or a new elimination goal with strong leadership. Some programmes showed high capacity for flexibility through introduction of new strategies and tools. Several case-studies described methods for monitoring implementation quality and coverage; however analysis and feedback to those implementing malaria elimination in the periphery was not well described. Political commitment and sustained financing contributed to malaria programme success. Consistency of malaria programmes depends on political commitment, human and financial resources, and leadership. Operational capacity of the programme and the overall health system structure and strength are also important aspects. CONCLUSIONS: Malaria eradication will require adaptive, well-managed malaria programmes that are able to tailor implementation of evidence-based strategies, founded upon strong sub-national surveillance and response, with adequate funding and human resources.

Strategies and approaches to vector control in nine malaria-eliminating countries: a cross-case study analysis.

BACKGROUND: There has been progress towards malaria elimination in the last decade. In response, WHO launched the Global Technical Strategy (GTS), in which vector surveillance and control play important roles. Country experiences in the Eliminating Malaria Case Study Series were reviewed to identify success factors on the road to elimination using a cross-case study analytic approach. METHODS: Reports were included in the analysis if final English language draft reports or publications were available at the time of analysis (Bhutan, Cape Verde, Malaysia, Mauritius, Namibia, Philippines, Sri Lanka, Turkey, Turkmenistan). A conceptual framework for vector control in malaria elimination was developed, reviewed, formatted as a matrix, and case study data was extracted and entered into the matrix. A workshop was convened during which participants conducted reviews of the case studies and matrices and arrived at a consensus on the evidence and lessons. The framework was revised and a second round of data extraction, synthesis and summary of the case study reports was conducted. RESULTS: Countries implemented a range of vector control interventions. Most countries aligned with integrated vector management, however its impact was not well articulated. All programmes conducted entomological surveillance, but the response (i.e., stratification and targeting of interventions, outbreak forecasting and strategy) was limited or not described. Indoor residual spraying (IRS) was commonly used by countries. There were several examples of severe reductions or halting of IRS coverage and subsequent resurgence of malaria. Funding and operational constraints and poor implementation had roles. Bed nets were commonly used by most programmes; coverage and effectiveness were either not measured or not articulated. Larval control was an important intervention for several countries, preventing re-introduction, however coverage and impact on incidence were not described. Across all interventions, coverage indicators were incomparable, and the rationale for which tools were used and which were not used appeared to be a function of the availability of funding, operational issues and cost instead of evidence of effectiveness to reduce incidence. CONCLUSIONS: More work is required to fill gaps in programme guidance, clarify the best methods for choosing and targeting vector control interventions, and support to measure cost, cost-effectiveness and cost-benefit of vector surveillance and control interventions.

Fine-scale malaria risk mapping from routine aggregated case data.

BACKGROUND: Mapping malaria risk is an integral component of efficient resource allocation. Routine health facility data are convenient to collect, but without information on the locations at which transmission occurred, their utility for predicting variation in risk at a sub-catchment level is presently unclear. METHODS: Using routinely collected health facility level case data in Swaziland between 2011-2013, and fine scale environmental and ecological variables, this study explores the use of a hierarchical Bayesian modelling framework for downscaling risk maps from health facility catchment level to a fine scale (1 km x 1 km). Fine scale predictions were validated using known household locations of cases and a random sample of points to act as pseudo-controls. RESULTS: Results show that fine-scale predictions were able to discriminate between cases and pseudo-controls with an AUC value of 0.84. When scaled up to catchment level, predicted numbers of cases per health facility showed broad correspondence with observed numbers of cases with little bias, with 84 of the 101 health facilities with zero cases correctly predicted as having zero cases. CONCLUSIONS: This method holds promise for helping countries in pre-elimination and elimination stages use health facility level data to produce accurate risk maps at finer scales. Further validation in other transmission settings and an evaluation of the operational value of the approach is necessary.

The independent effect of living in malaria hotspots on future malaria infection: an observational study from Misungwi, Tanzania.

BACKGROUND: As malaria transmission declines, continued improvements of prevention and control interventions will increasingly rely on accurate knowledge of risk factors and an ability to define high-risk areas and populations at risk for focal targeting of interventions. This paper explores the independent association between living in a hotspot and prospective risk of malaria infection. METHODS: Malaria infection status defined by nPCR and AMA-1 status in year 1 were used to define geographic hotspots using two geospatial statistical methods (SaTScan and Kernel density smoothing). Other malaria risk factors for malaria infection were explored by fitting a multivariable model. RESULTS: This study demonstrated that residing in infection hotspot of malaria transmission is an independent predictor of malaria infection in the future. CONCLUSION: It is likely that targeting such hotspots with better coverage and improved malaria control strategies will result in more cost-efficient uses of resources to move towards malaria elimination.

Hot spot or not: a comparison of spatial statistical methods to predict prospective malaria infections.

BACKGROUND: Within affected communities, Plasmodium falciparum infections may be skewed in distribution such that single or small clusters of households consistently harbour a disproportionate number of infected individuals throughout the year. Identifying these hotspots of malaria transmission would permit targeting of interventions and a more rapid reduction in malaria burden across the whole community. This study set out to compare different statistical methods of hotspot detection (SaTScan, kernel smoothing, weighted local prevalence) using different indicators (PCR positivity, AMA-1 and MSP-1 antibodies) for prediction of infection the following year. METHODS: Two full surveys of four villages in Mwanza, Tanzania were completed over consecutive years, 2010-2011. In both surveys, infection was assessed using nested polymerase chain reaction (nPCR). In addition in 2010, serologic markers (AMA-1 and MSP-119 antibodies) of exposure were assessed. Baseline clustering of infection and serological markers were assessed using three geospatial methods: spatial scan statistics, kernel analysis and weighted local prevalence analysis. Methods were compared in their ability to predict infection in the second year of the study using random effects logistic regression models, and comparisons of the area under the receiver operating curve (AUC) for each model. Sensitivity analysis was conducted to explore the effect of varying radius size for the kernel and weighted local prevalence methods and maximum population size for the spatial scan statistic. RESULTS: Guided by AUC values, the kernel method and spatial scan statistics appeared to be more predictive of infection in the following year. Hotspots of PCR-detected infection and seropositivity to AMA-1 were predictive of subsequent infection. For the kernel method, a 1 km window was optimal. Similarly, allowing hotspots to contain up to 50% of the population was a better predictor of infection in the second year using spatial scan statistics than smaller maximum population sizes. CONCLUSIONS: Clusters of AMA-1 seroprevalence or parasite prevalence that are predictive of infection a year later can be identified using geospatial models. Kernel smoothing using a 1 km window and spatial scan statistics both provided accurate prediction of future infection.

A reiterative method for calculating the early bactericidal activity of antituberculosis drugs.

Studies of early bactericidal activity (EBA) are important in the rapid evaluation of new antituberculosis drugs. Historically, these have concentrated on the log fall in the viable count in sputum during the first 48 hours of therapy. In this paper, we provide a mathematical model that suggests that the viable count in sputum follows an exponential decay curve with the equation V = S + Me(-kt) (where V is the viable count, M the population of bacteria susceptible to the test drug, S the population susceptible only to sterilizing agents, t the day of sputum collection as related to start of therapy, k the rate constant for the bacteria killed each day, and e the Napierian constant). We demonstrate that data from clinical trials fits the exponential decay model. We propose that future EBA studies should be performed by measuring daily quantitative counts for at least 5 days. We also propose that the comparison of the early bactericidal activity of antituberculosis drugs should be evaluated using the time taken to reduce the viable count by 50% (vt(50)). A further reiterative refinement following a rule set based on statistically the best fit to the exponential decay model is described that will allow investigators to identify anomalous results and thus enhance the accuracy in measuring early bactericidal activity.