Evaluation of ULV applications against Old World sand fly (Diptera: Psychodidae) species in equatorial Kenya.

摘要

Reducing populations of phlebotomine sand flies in areas prevalent for human leishmaniases is of ongoing importance to United States military operations and civilian populations in endemic regions. However, not enough is known regarding the efficacy of Department of Defense-approved pesticides and equipment against sand flies; specifically, the potential for ultra-low volume (ULV) pesticide applications to control Old World sand fly vectors. In this study we examine two sprayers, the Terminator ULV and the Grizzly ULV, with UV-labeled Duet and Fyfanon in four combinations against caged Phlebotomus duboscqi (Neveu-Lemaire) and wild sand fly populations in a natural environment in western Kenya. All equipment and Fyfanon have United States military National Stock Numbers and both pesticides are registered with the United States Environmental Protection Agency. Caged sand flies were reared from local P. duboscqi and the area has long been studied because of high incidences of human cutaneous and visceral Leishmania. Patterns of mortality across grids of caged sand flies showed greater efficacy from the Grizzly ULV regardless of chemical. The Terminator ULV performed well with Duet but with a less uniform and overall lower rate of mortality across the spray grid. Sampling of wild populations before and after treatments suggested local population suppression from ULV treatments, as well as a possible repellent effect in nearby untreated areas. Surprisingly, ULV active ingredient deposition inferred from patterns of UV-labeled droplets captured on cotton ribbons adjacent to sand fly cages in spray plots did not match patterns of mortality. We discuss the implications of this study, the first of its kind, for future military preventive medicine activities, including relative performance costs and benefits of larger or smaller sprayers, and the relative stability of ULV-induced mortality patterns in varied or sub-optimal conditions.