The combined use of Indoor Residual Spraying and Long-Lasting Insecticidal Nets for malaria reduction in endemic rural Tanzania: A cross sectional entomological survey dataset

Research outputs produced through repeated cross sectional entomological surveys in rural Tanzania for the purpose of evaluating the compbined impact of Long-Lasting Insecticidal Nets (LLIN) and Indoor Residual Spraying on malaria transmission, in comparison to sole use of LLIN. Information collected includes: total mosquitoes collected inside the household, details of house structure (type of wall and roof, presence of open eaves, ceiling and window screens), presence of livestock, whether the house was sprayed, as well as bed net ownership and usage. Information on Anopheles species and sporozoite rates is also provided

Accounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control

BACKGROUND: The effectiveness of malaria vector control interventions is often evaluated using cluster randomized trials (CRT) with outcomes assessed using repeated cross-sectional surveys. A key requirement for appropriate design and analysis of longitudinal CRTs is accounting for the intra-cluster correlation coefficient (ICC). In addition to exchangeable correlation (constant ICC over time), correlation structures proposed for longitudinal CRT are block exchangeable (allows a different within- and between-period ICC) and exponential decay (allows between-period ICC to decay exponentially). More flexible correlation structures are available in statistical software packages and, although not formally proposed for longitudinal CRTs, may offer some advantages. Our objectives were to empirically explore the impact of these correlation structures on treatment effect inferences, identify gaps in the methodological literature, and make practical recommendations. METHODS: We obtained data from a parallel-arm CRT conducted in Tanzania to compare four different types of insecticide-treated bed-nets. Malaria prevalence was assessed in cross-sectional surveys of 45 households in each of 84 villages at baseline, 12-, 18- and 24-months post-randomization. We re-analyzed the data using mixed-effects logistic regression according to a prespecified analysis plan but under five different correlation structures as well as a robust variance estimator under exchangeable correlation and compared the estimated correlations and treatment effects. A proof-of-concept simulation was conducted to explore general conclusions. RESULTS: The estimated correlation structures varied substantially across different models. The unstructured model was the best-fitting model based on information criteria. Although point estimates and confidence intervals for the treatment effect were similar, allowing for more flexible correlation structures led to different conclusions based on statistical significance. Use of robust variance estimators generally led to wider confidence intervals. Simulation results showed that under-specification can lead to coverage probabilities much lower than nominal levels, but over-specification is more likely to maintain nominal coverage. CONCLUSION: More flexible correlation structures should not be ruled out in longitudinal CRTs. This may be particularly important in malaria trials where outcomes may fluctuate over time. In the absence of robust methods for selecting the best-fitting correlation structure, researchers should examine sensitivity of results to different assumptions about the ICC and consider robust variance estimators

Combination of Insecticide Treated Nets and Indoor Residual Spraying in Northern Tanzania Provides Additional Reduction in Vector Population Density and Malaria Transmission Rates Compared to Insecticide Treated Nets Alone: A Randomised Control Trial.

Indoor residual spraying (IRS) combined with insecticide treated nets (ITN) has been implemented together in several sub-Saharan countries with inconclusive evidence that the combined intervention provides added benefit. The impact on malaria transmission was evaluated in a cluster randomised trial comparing two rounds of IRS with bendiocarb plus universal coverage ITNs, with ITNs alone in northern Tanzania. From April 2011 to December 2012, eight houses in 20 clusters per study arm were sampled monthly for one night with CDC light trap collections. Anopheles gambiae s.l. were identified to species using real time PCR Taq Man and tested for the presence of Plasmodium falciparum circumsporozoite protein. ITN and IRS coverage was estimated from household surveys. IRS coverage was more than 85% in two rounds of spraying in January and April 2012. Household coverage with at least one ITN per house was 94.7% after the universal coverage net campaign in the baseline year and the proportion of household with all sleeping places covered by LLIN was 50.1% decreasing to 39.1% by the end of the intervention year. An.gambiae s.s. comprised 80% and An.arabiensis 18.3% of the anopheline collection in the baseline year. Mean An.gambiae s.l. density in the ITN+IRS arm was reduced by 84% (95%CI: 56%-94%, p = 0.001) relative to the ITN arm. In the stratum of clusters categorised as high anopheline density at baseline EIR was lower in the ITN+IRS arm compared to the ITN arm (0.5 versus 5.4 per house per month, Incidence Rate Ratio: 0.10, 95%CI: 0.01-0.66, p-value for interaction <0.001). This trial provides conclusive evidence that combining carbamate IRS and ITNs produces major reduction in Anopheles density and entomological inoculation rate compared to ITN alone in an area of moderate coverage of LLIN and high pyrethroid resistance in An.gambiae s.s

Ranking Malaria Risk Factors to Guide Malaria Control Efforts in African Highlands

INTRODUCTION: Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies. METHODS AND FINDINGS: A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through "classification and regression trees", an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density. CONCLUSIONS: In Burundi highlands monitoring Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors

'Repel all biters': an enhanced collection of endophilic Anopheles gambiae and Anopheles arabiensis in CDC light-traps, from the Kagera Region of Tanzania, in the presence of a combination mosquito net impregnated with piperonyl butoxide and permethrin.

BACKGROUND: Mosquito nets containing synergists designed to overcome metabolic resistance mechanisms in vectors have been developed. These may enhance excitability in the mosquitoes and affect how they respond to CDC light-traps. Investigating the behaviour of vectors of disease in relation to novel mosquito nets is, therefore, essential for the design of sampling and surveillance systems. METHODS: In an initial experiment in Muleba, Tanzania, nine bedrooms from three housing clusters were sampled. CDC light-traps were operated indoors next to occupied untreated nets (UTN), Olyset® long lasting insecticidal net (LLIN) and Olyset Plus® LLIN containing piperonyl butoxide (PBO) synergist. Nets were rotated daily between the nine rooms over nine nights. A further series of experiments using the nets on alternate nights in a single room was undertaken during the short rains. Anopheles gambiae s.l. were collected in CDC light-traps, a window-trap and Furvela tent-trap. Anopheles gambiae s.l. were identified to species by polymerase chain reaction (PCR). RESULTS: In the initial experiment 97.7% of the 310 An. gambiae s.l. were An. gambiae s.s., the remainder being Anopheles arabiensis. The number of mosquitoes collected from 81 light-trap collections was greater in the presence of an Olyset [density rate ratio 1.81, 95% CI (1.22-2.67), p = 0.003] relative to an UTN. In a second experiment, in the wet season 84% of the 180 An. gambiae s.l. identified were An. arabiensis. The number of An. gambiae s.l. collected from a light-trap compared to a tent-trap was significantly higher when an Olyset Plus net was used compared to an UTN. Survival of the mosquitoes in the window trap was not reduced by the use of an Olyset Plus net in the bedroom relative to an Olyset net. CONCLUSION: Mosquitoes entering bedrooms, even those susceptible to pyrethroids, were not killed by contact with an Olyset Plus LLIN. The enhanced numbers of An. gambiae or An. arabiensis collected in light-traps when a treated net is used requires further experimentation and may be because of a heightened escape reaction on the part of the mosquito

10 Years of Environmental Change on the Slopes of Mount Kilimanjaro and Its Associated Shift in Malaria Vector Distributions.

INTRODUCTION: Malaria prevalence has declined in the Kilimanjaro region of Tanzania over the past 10 years, particularly at lower altitudes. While this decline has been related to the scale-up of long-lasting insecticidal nets to achieve universal coverage targets, it has also been attributed to changes in environmental factors that are important for enabling and sustaining malaria transmission. OBJECTIVES: Herein, we apply spatial analytical approaches to investigate the impact of environmental and demographic changes, including changes in temperature, precipitation, land cover, and population density, on the range of the major malaria vector species Anopheles arabiensis in two districts of Tanzania, situated on the southern slope of Mount Kilimanjaro. These models are used to identify environmental changes that have occurred over a 10-year period and highlight the implications for malaria transmission in this highland region. METHODS: Entomological data were collected from the Hai and Lower Moshi districts of Tanzania in 2001-2004 and 2014-2015. Vector occurrence data were applied alongside satellite remote sensing indices of climate and land cover, and gridded population data, to develop species distribution models for An. arabiensis for the 2004 and 2014 periods using maximum entropy. Models were compared to assess the relative contribution of different environmental and demographic factors to observed trends in vector species distribution in lowland and highland areas. RESULTS: Changes in land cover were observed in addition to increased population densities, increased warm season temperature, and decreased wetness at low altitudes. The predicted area and extent of suitable habitat for An. arabiensis declined across the study area over the 10-year period, with notable contraction at lower altitudes, while species range in higher altitude zones expanded. Importantly, deforestation and warmer temperatures at higher altitudes may have created stable areas of suitable vector habitat in the highlands capable of sustaining malaria transmission. CONCLUSION: We show that environmental changes have had an important influence on the distribution of malaria vector species in a highland area of northern Tanzania. Highland areas may be at continued risk for sporadic malaria outbreaks despite the overall range contraction of principal vector species at lower altitudes, where malaria transmission remains at low intensity

The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries.

Pyriproxyfen (PPF) may become an alternative insecticide for areas where pyrethroid-resistant vectors are prevalent. The efficacy of PPF can be assessed through the dissection and assessment of vector ovaries. However, this reliance on expertise is subject to limitations. We show here that these limitations can be overcome using a convolutional neural network (CNN) to automate the classification of egg development and thus fertility status. Using TensorFlow, a resnet-50 CNN was pretrained with the ImageNet dataset. This CNN architecture was then retrained using a novel dataset of 524 dissected ovary images from An. gambiae s.l. An. gambiae Akron, and An. funestus s.l., whose fertility status and PPF exposure were known. Data augmentation increased the training set to 6973 images. A test set of 157 images was used to measure accuracy. This CNN model achieved an accuracy score of 94%, and application took a mean time of 38.5 s. Such a CNN can achieve an acceptable level of precision in a quick, robust format and can be distributed in a practical, accessible, and free manner. Furthermore, this approach is useful for measuring the efficacy and durability of PPF treated bednets, and it is applicable to any PPF-treated tool or similarly acting insecticide