Considering the monthly reapplication interval, this still may not be a cost-effective product for large-scale application. The new US EPA-approved long-lasting formulation, FourStar Microbial Briquets , is potentially effective for up to 6 months , and preliminary data suggest that it is effective in malaria mosquito control [GZ, unpublished data]. Field-testing is needed to determine the efficacy and cost-effectiveness of this long-lasting larvicide. The central objective of this study is to determine the effectiveness and cost-effectiveness of long-lasting microbial larviciding on the incidence of clinical malaria and the reduction of transmission intensity. The hypothesis is that adding LLML to ongoing ITN and IRS programs will lead to significant reductions in both indoor and outdoor malaria transmission and malaria incidence as well as cost savings. This paper describes a protocol for evaluating the impact of LLML in reducing malaria vector populations and clinical malaria incidence.We will conduct our study in 28 randomly selected clusters in the highland localities of Kakamega and Vihiga counties, western Kenya . A cluster typically consists of an area of approximately 4 km2 in size and comprises 400–700 households and about 2000–3000 residents. The catchment population of the study area, including intervention clusters, control clusters, and buffer zones, is estimated as 250,000 according to 2010 census data. Local residents are predominantly farmers and depend upon farming, cattle and goat herding for subsistence. Malaria transmission is seasonal, with two peaks in vector abundance reflecting the bimodal rainfall pattern: a major peak between April and June and a minor peak between October and November. Most malaria is caused by Plasmodium falciparum. The main malaria vectors in the area are An. gambiae s.s., An. arabiensis, and An. funestus s.l.. Malaria vector density was high in the early 2000s,weed growing systems decreased substantially during 2006–2008 after the first round of mass distribution of ITNs in 2006, and has gradually increased since 2008.
Pyrethrum spray collections of indoor-resting Anopheles were about 1.0 females/house/ night in 2014 compared to 0.1 females/house/night in 2007. Cross-sectional community-level surveys in May 2011 indicated that parasite prevalence averaged 11.8 % in the general population but varied between localities from 3.3 % to 25.4 %. In school children aged 6–13 years, surveys in 2012 found an average parasite prevalence of 27.2 %, which varied from 18.8 to 35.4 % among villages. Active case surveillance through bi-weekly home visits in May 2011 indicated an average annual clinical malaria incidence rate of 31.4 cases per 1000 people in the general population, varying from 28.9 to 36.2 between villages. Ownership of ITNs ranged from 78.3 to 84.2 % in 2013. There have been several attempts in the past 10 years to control malaria vectors in the study area using conventional formulations of Bti/Bs and IRS. The last community-wide mass distribution of ITNs was undertaken by the Division of Malaria Control of Kenya in 2014. Currently there is no mass distribution of ITNs or IRS and no larviciding in the proposed study area.For purposes of planning and conducting an evaluation of the intervention, we will subdivide the field area into villages , which is the smallest administrative unit in Kenya. Using villages as clusters has advantages over random sampling. First, the clinical records in health centers or hospitals in Kenya generally include the name of the village and sub-location ; therefore, clinical malaria cases can be traced back to the village level. Second, villages have been conveniently used as intervention/ control clusters in previous trials. Our field team will conduct the demographic surveys before the start of the intervention. Each team will be provided with a printed overview map and a handheld Google Nexus 7 tablet.
A surveillance team, comprising a field technician, a reporter, and a local guide, will visit every compound to explain the study procedures, tally inhabitants, and collect information on house characteristics. If the head of the compound agrees to participate, we will record the geographical coordinates of the main house of the compound and compound codes will be written in permanent marker on the front wall next to the door. We will record the genders and ages of all compound members on questionnaire forms using the on-site Google Nexus , which will update the database in real time together with the GPS coordinates of the surveyed compound. We will map the locations of all compounds using ArcGIS. Demographic surveillance will be done in year 1, 6–12 months prior to intervention . We will draw village boundaries based on the demographic surveys and confirm it with the field teams and the database manager. If a village is too small , we will combine the village with a neighboring village to form one cluster. Total and age- and gender-specific populations will be aggregated at the cluster level.Clinical malaria records will be collected from 8 to 12 months prior to intervention, to calculate baseline incidence rate at each cluster for cluster randomization, through to 8 to 12 months after all interventions . We will collect information on clinical malaria cases retrospectively from all government-run hospitals, health care centers, and clinics located either within the study area itself or within catchment areas overlapping the study area. We will obtain clinical data from the treatment centers through the malaria control office of Kakamega and Vihiga counties, Kenya. We will also collect patient- and treatment-related information, including age, gender, date of diagnosis, parasite species, village of patient , and prescriptions given. All personal identifiers will be excluded from this study. A clinical malaria case is defined as an individual with fever and other related symptoms such as chills, severe malaise, headache, or vomiting in the presence of a Plasmodium-positive blood smear.
The clinical malaria incidence rate is calculated as the number of clinical malaria episodes divided by the total person time at risk based on demographic surveys. We will also collect the aggregated monthly diarrhea data at each site along with clinical malaria records from local health clinics and hospitals. We will not conduct prospective passive surveillance, active home visits, or cross-sectional blood surveys. We will calculate the clinical malaria incidence rate separately for each cluster, different study period and different age group . We will include all clinical malaria cases in our study, including cases diagnosed during the four study periods : preintervention period: baseline clinical malaria records started at least 8–12 months prior to the application of long-lasting microbial larvicides till intervention, intervention period: all clinical records during the intervention period, the 8-month wash-out period, and post intervention period: clinical malaria records till 8–12 months after the last round of larvicide application.Permission to use microbial larvicides for malaria vector control has been obtained from the Pest Control Products Board of Kenya. Ethical clearance has been approved by the Scientific and Ethical Unit of the Kenya Medical Research Institute . As described, aggregated clinical data will be obtained from the treatment centers through the malaria control offices of Kakamega and Vihiga counties, Kenya. According to US Department of Health and Human Services Code of Federal Regulations 45 CFR 46.101 part 4 , these data are in the category of exempt human subjects research, which involves the study of existing data, documents, or records, with no collection of subject-level information. Informed consent will be obtained from each participant. All investigative team members in the United States, Kenya, and Australia have no financial conflict of interest with the larvicide manufacturer, Central Life Sciences.We will conduct baseline malaria vector surveillance at least 4 months prior to any application of LLMLs . We will conduct malaria vector population surveillance on a monthly basis continuously till at least 8 months after the last round of larvicide application . We will monitor both indoor- and outdoor-biting mosquito abundance using CO2-baited Centers for Disease Control light traps equipped with collection bottle rotators . The collection bottle rotator,indoor farming systems which has eight separate plastic collection bottles, will be programmed to collect active mosquitoes at 2-h intervals between 16:00–08:00. We will place two traps within each sampling compound: one inside the living room, the other outside the house 5 m away. We will conduct a total of 64 trap-nights of vector sampling per cluster per month. This will provide an estimation precision of 0.2 mosquitoes using the previously determined standard deviation. Species of collected mosquitoes will be identified and blood-feeding status will be recorded. We will test for P. falciparum sporozoite infection and blood meal source using an enzyme-linked immunosorbent assay on all specimens. For each house where the vector population was sampled, we will record the number of sleeping persons at each house on the same day as the vector survey. We will calculate sporozoite rate and EIR for each cluster. EIRs will be calculated as × × , and standardized to a monthly basis.
The trapping method will allow for comparison of indoor- and outdoor-biting mosquito abundance and determination of nightly biting activity patterns. We will calculate indoor and outdoor transmission intensities separately assuming that all mosquitoes collected from a compound had their blood meal from the same household. We will calculate EIR for the four study periods as describe above: preintervention period: baseline vector surveillance started at least 6 months prior to the application of long-lasting microbial larvicides till intervention, intervention period, the 8-month washout period, and post intervention period: vector surveillance continued till 8 months after the last round of larvicide application. To determine whether new malaria vector species are present in the study sites, we will sequence the ribosomal second internal transcribed spacer and mitochondrial CO1 gene in anopheline specimens that are not amplified by the recombinant deoxyribonucleic acid polymerase chain reaction method, and we will conduct phylogenetic analysis to determine whether the new species found by Stevenson et al. are also present in the study sites.We will conduct the intervention using a two-step approach. First, we will conduct a small-scale four-cluster trial to optimize the time, duration, and quantity of LLML application. Second, we will conduct a clusterrandomized trial to test the effectiveness and cost effectiveness of LLML. The design has two parallel arms, i.e., control and intervention, and allows for baseline survey without intervention and crossover .We will select four clusters, two in each county, for an entomological evaluation of the optimal larvicide application scheme . We will randomly select two clusters, one in each county, treated with larvicides and the other two sites will serve as controls . We will treat temporary habitats with FourStar controlled release granule formulation, which maintains effectiveness through wet and dry periods for up to 1 month. We will treat semipermanent habitats with FourStar 90-day briquettes and permanent habitats with FourStar 180-day briquettes. Application dosage will follow the recommendation of the manufacturer, Central Life Sciences: 10 lbs per acre of water surface for the granule formulation, and one briquette per 100 ft2 of water surface for the briquette formulations, regardless of water depth. We will re-treat the habitats every 4 to 5 months. On a weekly basis in the treatment and control sites, we will use aerial samplers to determine habitat pupal productivity, and use standard dippers to determine larval abundance. This will allow for determination of habitat productivity with a tolerable error of 0.5 mosquitoes, based on the standard deviation identified in previous studies. We will monitor indoor and outdoor vector abundance using 64 trapnights per cluster per month. This sample size will allow detection of a difference in average vector abundance of 0.12 mosquitoes with 80 % statistical power and 0.05 type-I error. We will use ELISA methods to determine Anopheles mosquitoes’ sporozoite infection and blood feeding host preference. We will analyze the data immediately after the small scale trial using analysis of variance with repeated measures and appropriate transformation to determine the effects of habitat larviciding on mosquito abundance and transmission intensity. We will assign fourteen clusters each in the two counties to intervention or no intervention by a block randomization method on the basis of clinical malaria incidence, vector density, and human population size per site. Year 1 will focus on preparing the study sites and working with clinics and hospitals to help them improve their routine malaria surveillance . In year 2, we will conduct preliminary surveys on all 28 sites to determine clinical malaria incidence, vector density, geographic information system coordinates of larval habitats, and human population size.