Human population size for each cluster, stratified into three age groups will be ascertained from our existing data. We will obtain age-group level aggregated morbidity data from local hospitals and clinics where the sampled residents seek treatment. These clinical data are reported to the Ministry of Health of Kenya and hence are publicly available. We will determine vector abundance using CO2-baited CDC light traps for 16 trap-nights per cluster per month in each of the indoor and outdoor environments. Using these data, each cluster will be allocated to either treatment or control through randomization using the following procedures. First, each of the four parameters listed above will be standardized with the highest cluster as 1. Second, we will assign the highest weight for clinical malaria cases , the lowest weight for human population size , and intermediate weights for expected vector density and larval habitats , following the method of Corbel et al.. For each cluster a rank score will be computed as the sum of weighted clinical malaria incidence, vector density, habitat abundance, and human population size. Finally, the 14 clusters within each county will be sequentially numbered according to their rank scores and sorted into seven blocks of two clusters having successive rank scores. We expect the two clusters within each block to have similar risk characteristics for clinical malaria, vector abundance, larval habitats, and human population size. In each block,vertical farming equipment suppliers the ranks of the two clusters are put into two sealed envelopes, one cluster will be randomly allocated to treatment and another to control, using computer-generated random numbers .
After the larvicide application optimization and study cluster randomization, we will treat each treatment cluster with LLML at the time interval of 4 or 5 months . The first treatment will be conducted in February-March about 1 month before the beginning of the long rainy season which usually starts in April. After three treatments, we will perform no treatments for the next 8 months. This will provide useful data on the dynamics of action of the LLML and the waning efficacy of LLML over time. These data will be important in analyzing cost-effectiveness to help optimize the timing of re-treatments. After 8 months, a total washout of the LLMLs will be assumed to have taken place. Next, we will perform a crossover and switch of the control and treatment clusters. Former control clusters will receive three rounds of LLML treatment at appropriate time intervals, and the former treatment clusters will receive no LLMLs. This strategy will minimize ascertainment biases that might be attributed to care-seeking behaviors of the population or to malaria detection and reporting by malaria treatment clinics. We will test LLMLs manufactured by Central Life Sciences. The larvicide application regime is as follows: temporary, semipermanent, and permanent habitats will be treated with FourStar controlled release granule formulation, 90-day briquettes, and 180-day briquettes, respectively. Application dosage will follow the recommendation of the manufacturer: 10 lbs per acre of water surface for the granule formulation, and 100 ft2 water surface per briquette. We will conduct monthly vector surveys throughout the study period to determine indoor- and outdoor-biting vector abundance, using the same sample size of 64 trap-nights per cluster per month, and sporozoite infection and mosquito blood meal analysis will be conducted on all collected specimens.
To confirm larviciding efficacy, we will examine larval abundance, age structure, and pupal productivity on a monthly basis in 100 randomly selected larval habitats each from treatment and control sites using our GIS maps and data on sites where LLML was applied.Sample size was calculated based on 2010 and 2011 active case surveillance results from Iguhu and Emutete areas. Then the number of clusters required and the number of individuals required for each cluster were calculated following the methods developed by Hayes and Bennett based on cluster-randomized trials assuming equal population for each cluster. The observed malaria incidence rate was 52.7 cases per 1000 people year in 2011. We calculated the numbers of clusters and individuals required for epidemiological assessment of the long-lasting larvicide treatments to detect a 50 % protective efficacy conferred by the treatment compared with the reference group , with a power of 80 %, significance level of 5 % and the coefficient of variation of true proportions between clusters within each treatment was assumed to be 0.15. The estimated number of clusters for the intervention will be five and the required number of individuals for each matched-pair will be 1196; assuming a design effect of 0.25 and 20 % of subjects lost to follow-up. The estimated number of clusters for the intervention will be seven and the required number of individuals for each of the matched-pairs will be fewer than 2000. The 28 clusters proposed in the randomized cluster study will detect 50 % malaria incidence reduction with 99.9 % power and 30 % incidence reduction with 85.3 % power. This is based on the current malaria incidence rate in the study sites and a two-tailed alpha with a human population size of 2000 per cluster .
If the malaria incidence is 50 % lower than the current value, the design will still detect 50 % incidence reduction with 99.7 % power and 40 % reduction with 95.2 % power .We will monitor primary and secondary endpoint outcomes throughout the 5-year study period ; data analysis will be conducted in year 5. The difference in clinical malaria incidence between treatment and control groups will be compared using Poisson multivariate regression models with intervention, age, and calendar time as covariates, using a generalized estimating equations approach. GEE is necessary since incidence will be modeled monthly as a temporally-correlated repeated measure using grouped data. Intervention will be a time-varying covariate since the treatment crosses over after three intervention rounds. Since there is no intervention in the 8 months during the washout period, interval censoring will be performed to exclude the second 4 months of data during this period. The odds ratio and the 95 % confidence interval for clinical malaria rates between treatment and control groups will be calculated. Difference in vector density and EIR will be analyzed using a negative binomial regression model and the GEE approach. In all these analyses, clusters will be indicated as intervention and control, calendar time will be categorized into: pre intervention, intervention, post intervention , washout , crossover intervention, post intervention , and nonintervention, and months since intervention will also be included as an independent variable. These variables will allow for comparison between intervention and control clusters based on baseline observations, e.g., relative reduction in vector density, and allow for evaluation of cumulative effect,e.g., the second round of treatment may produce add edeffect following first-round treatment. Finally, for the economic evaluation, we will calculate incremental cost-effectiveness ratios based on the primary endpoint and on long-term health outcomes including malaria deaths averted. Using the “ingredients approach”, costs will be classified according to: initial setup investment , running costs , and costs of program management and quality control . Cost data will be estimated from health facility and Ministry of Health records, LLML manufacturers and financial accounts of the research project. One-way and multi-way sensitivity analysis will be undertaken to examine the implications of potential changes in variables such as larvicide price and larviciding application frequency. ICERs will be reported from both provider and societal perspectives for different transmission intensity scenarios.Larval control and environmental management have played very important roles in malaria elimination in the United States and Europe, where today larval control using biological larvicides is the primary vector control method. Larvicides target mosquito larvae,grow light shelves representing a major advantage over adult control, in which changes in biting and resting behaviors can lead adult mosquitoes to evade control activities. In addition, microbial larvicides from bacteria Bti and Bs have different modes of action than pyrethroid insecticides; therefore, microbial larvicides do not aggravate pyrethroid resistance.
Furthermore, larval control does not conflict with but rather complements the front-line ITN and IRS malaria control programs. Larval control may now be timelier than ever, since pyrethroid resistance and outdoor malaria transmission are increasing in Africa. However, there are some potential limitations of larviciding as it is practiced today. Although there are three formulations of long-lasting larvicide available for use in different habitat types , the classification of habitats is primarily based on the longevity of the aquatic period and productivity of the habitat. The longevity of the aquatic period may be visually identified; however, the productivity of a habitat may change over time. Canopy cover in the habitat, such as grasses in the water, may affect the spread of Bti/Bs [Zhou, personal observations]. Furthermore, heavy rainfall may wash away Bti/Bs and create new habitat; therefore, additional Bti/Bs may need to be applied at an unplanned time after the rain. There are also limitations for the design. The incidence of clinical malaria is essential for the evaluation of intervention success. However, as pointed out by previous studies, crude health facility records are not always a reliable source of such information and may in fact under estimate the true clinical incidence rate. However, as long as clinical malaria was diagnosed the same way across all health care facilities, comparison between intervention and control groups is justified. EIR is a good measure of reduction in transmission since larval control reduces overall vector population density and EIR is measured based on vector population density. Additional indicators, such as clinical incidence through active case surveillance, can be a more accurate estimate of incidence, and parasite prevalence through cross-sectional surveillance may be helpful. However, as per restrictions imposed by the funding policy, direct measures of human subjects are restricted. Despite very high bed net coverage, malaria incidence in many African sites is resurging after a short-time reduction when ITN and IRS scale-up was initially rolled out. This malaria resurgence is caused primarily by increases in insecticide resistance and outdoor transmission. New cost-effective methods beyond bed nets and IRS are urgently needed. Long-lasting microbial larviciding represents a promising new tool that can target both indoor and outdoor transmission and alleviate the problem of pyrethroid resistance. Comprehensive evaluation of potentially cost-effective LLML will provide critically needed data for determining whether LLML can be used as a supplemental malaria control tool to further reduce malaria incidence in Africa.Aerobic exercise and physical activity have been strongly recommended as health promotion and disease prevention approaches by the World Health Organization and the Department of Health and Human Services . Exercise also has been shown to positively promote psychological wellness , as well as effectively reduce and lower symptoms associated with a variety of disorders, including mood disorders , panic disorder , dysphoric moods , and cognitive disorders . A growing body of studies lend support for the use of structured exercise as an alternative intervention for substance use disorders, including promoting smoking cessation , reducing cannabis craving , and promoting sustained recovery among those in recovery . Some studies also have examined the effectiveness of exercise as an adjunct therapy for the treatment of substance use disorders ; however, to date, there are no studies on the utility of exercise for addressing methamphetamine dependence, a significant public health problem . This is a gap in the field, considering that problems with anxiety and depression have been shown to be associated with MA relapse and treatment retention, especially for newly abstinent MA abusers during the early phases of the recovery process . Given that previous studies have shown that exercise is useful for reducing mood-related symptoms of anxiety and depression, in particular, this study sought to examine the impact of an 8-week structured exercise intervention on reducing depression and anxiety symptoms among a newly abstinent sample of MA-dependent adults compared to a health education condition. It was hypothesized that participants assigned to the exercise condition would demonstrate statistically lower psychological symptoms during the 8-week intervention period compared to participants assigned to health education. Under the approval of the Institutional Review Board of the University of California, Los Angeles , the present study includes 135 MA-dependent adults who voluntarily participated in a randomized controlled trial of exercise compared to health education that took place at a publicly funded residential treatment program that had a small gym between 2010 and 2013 in Southern California.