Neonicotinoids

The ratings of 1, 3, and 5 in the EIQ method are surrogates for low, medium, and high risk or impact or toxicity or persistence, depending on the factor of interest. For demonstration purposes only, we show how converting the ratings to estimates of risk probabilities for only four of the factors limits the value of the EIQ method. The EIQ factors, “long-term health effects”, “leaching potential”, and “surface runoff potential”, and ratings of “little-none”, “possible”, “definite”, “small”, “medium”, and “large” imply that they are risks. Therefore, they have a probability of occurrence rather than an absolute certainty of occurring. Similarly, the factor “beneficial arthropod toxicity” has ratings of “low impact”, “moderate impact”, and “severe impact”. Degrees of impact also have associated uncertainty.
Because the ratings of 1, 3, and 5 are surrogates for risk, they can be converted to risk intervals that incorporate the underlying probabilities. Therefore, the simplest, yet coarse, way to do this is to assume the ratings of 1, 3, and 5 span the range of risk from 0 to 1 (or 0 to 100%). A rating of 1, when mapped onto an interval of risks would be 0 to 0.32. A score of 3 would be 0.33 to 0.66 and a score of 5 would be 0.67 to 1. Consequently, if a pesticide has a “surface runoff potential” factor that has a score of 3, it is at medium risk of runoff. However, a discrete score of 3 does not capture the probabilistic nature of risk, yet the score of 3 is intended to represent medium risk. Therefore, the score needs to be mapped to an estimate of risk. This can be done most simply by assuming a uniform probability density function of risk values from 0.32 to 0.66 for medium risk. Medium risk implies uncertainty and probability, but a score of 3 does not accommodate that risk estimate. An interval of 0.33 to 0.66, however crudely, accommodates the probability of occurrence.
To demonstrate the consequences of mapping discrete risk ratings to probabilities, we calculated adjusted EIQs for a group of 20 actual insecticide active ingredients with unadjusted EIQs ranging from 22.1 (methiocarb) to 44 (diazinon). The insecticides evaluated were chosen randomly from lists of active ingredients in Yu (2008) , who provides a relatively complete list of currently registered insecticides. Five insecticides each were chosen randomly from four chemical classes: carbamates, neonicotinoids, organophosphates, and pyrethroids. The unadjusted EIQs and ratings were obtained from the New York State Integrated Pest Management Program, Cornell University (www.nysipm.cornell.edu/publications/eiq/). The four factors discussed above were converted to probability ranges of risk and all other factors were held constant at their respective deterministic scores. To align those deterministic scores with the probability ranges mapped for the four factors, the ratings were converted to static probabilities proportional to the value of the scores. For example, a score of 3 for fish toxicity was converted to 0.5.
Using Monte Carlo simulation (Oracle Crystal Ball^® 11.2, Denver, CO), we calculated adjusted EIQs under different hypothetical scenarios by incorporating the probability ranges associated with the four factors (Fig. 1). Probabilities of occurrence of adjusted EIQ values were determined by incorporating sampling from the statistical probability density function of each input variable used to calculate the EIQ. Each of the four input variables was sampled 20,000 times. Then, the variability for each input was propagated into the output of the model so that the output reflected the probability of values that could occur.

The first analysis summarises and compares the initial impact of different IRS products. Data were restricted to initial timepoints collected within 2 months of IRS application as the active ingredient decays with time, so that averaging across the whole dataset may mis-represent the initial potency of IRS as studies had different durations. Statistical models were fit to generate overall estimates of the efficacy of the chemical class. These explanatory factors included the mosquito vectors (classified at the species complex level and species level where possible, i.e. A. arabiensis, A. funestus s.l. and A. gambiae s.l.), experimental hut type (West or East African design) and hut wall substrate (cement or mud) alongside the chemical class used for the IRS (carbamate, clothianidin, organophosphate and pyrethroid). Preliminary data exploration revealed that there were too few data to perform an extensive statistical test on all covariates. To overcome this a subset of the full database was generated by removing Ifakara hut studies, wall substrates that were not mud or cement and chemistries other than pyrethroids, organophosphates, carbamates or neonicotinoids. Binomial logistic regression models were fitted to the remaining count data (N = 78) to estimate the number of mosquitoes that were dead in 24-h, had exited, blood-fed or been deterred by the IRS product. The predicted value for the proportion of mosquitoes being killed, exiting, blood-fed or deterred is calculated as: $${\pi }_i=logi{t}^{-1}ln{\pi }_i∕\left(1-{\pi }_i\right)=\frac{\exp \left({\beta }_0+{\sum }_h{\beta }_h{\mathit{X}}_{\mathit{hi}}\right)}{\left(1-\exp \left({\beta }_0+{\sum }_h{\beta }_h{\mathit{X}}_{\mathit{hi}}\right)\right)}$$ where π_i is the estimated proportion for the ith data (e.g. the proportion of mosquitoes killed), β₀ is the intercept, the subscript h denotes the covariate of interest (taking number of 1 to H) and X_h is a matrix of explanatory factors (mosquito species, hut type, substrate and chemistry sprayed) with coefficients β_h^{59 (link)}. Bayesian models were fitted using Hamiltonian Monte Carlo sampling methods^{60 (link),61} . Four chains were initialised to assess the convergence of 2000 iterations, the first 1000 of each were discarded as burn in. The posterior distributions of parameters (4000 iterations) and 90% Bayesian credible intervals were estimated, posterior checks were performed using ShinyStan (version 1.0.0)⁶² and visually confirmed to fit the data (Supplementary Fig. 2–5).

ACh (#A6625) was purchased from MilliporeSigma (USA). The neonicotinoids (imidacloprid, #099–03771; thiacloprid, #205–19081; clothianidin, #034–22581) were purchased from FUJIFILM Wako Pure Chemical (Japan). These reagents were used without further purification.

Adult D. melanogaster flies were collected within a day following eclosion and placed in normal fly food (less than 30 flies/vial). Flies were transferred daily to new fly food. 2–5 days after eclosion, flies were briefly anesthetised with CO₂, and the sexes were separated and sorted into fly vials containing 1.0% agar food for starvation (10 flies/vial). After 16 h starvation, flies were transferred to vials containing neonicotinoid-containing food without anesthesia and cultured for 6 h (10 flies/vial). Neonicotinoid-containing foods were prepared by mixing 10 μL of diluted neonicotinoids dissolved in DMSO with 990 μL of a solution containing 1% agar and 5% sucrose for each vial. After 6 h cultured in vials containing neonicotinoid-containing food, flies were gently tapped down to the surface of the food, and flies that climbed within 20 s after tapping were recorded by a video camera (GZ-F270-W, JVCKENWOOD, Japan). The maximum climbing heights of the flies within 20 s after tapping were measured using ImageJ1.53v (National Institute of Health, USA). Since the height from the surface of the food to the vial top is 8 cm, the maximum climbing height is 8 cm.