Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT04182126 |
| Other study ID # |
20173512 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
December 1, 2019 |
| Est. completion date |
August 31, 2024 |
Study information
| Verified date |
May 2024 |
| Source |
University of California, Irvine |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
In the past decade, massive scale-up of long-lasting insecticidal nets (LLINs) and indoor
residual spraying (IRS) have led to significant reductions in malaria mortality and
morbidity. Nonetheless, malaria burden remains high, and a dozen countries in Africa show a
trend of increasing malaria incidence over the past several years. The high malaria burden in
many areas of Africa underscores the need to improve the effectiveness of intervention tools
by optimizing first-line intervention tools and integrating newly approved products into
control programs. Vector control is an important component of the national malaria control
strategy in Africa. Because transmission settings and vector ecology vary among countries or
among districts within a country, interventions that work in one setting may not work well in
all settings. Malaria interventions should be adapted and re-adapted over time in response to
evolving malaria risks and changing vector ecology and behavior. The central objective of
this application is to design optimal adaptive combinations of vector control interventions
to maximize reductions in malaria burden based on local malaria transmission risks, changing
vector ecology, and available mix of interventions approved by the Ministry of Health in each
target country. The central hypothesis is that an adaptive approach based on local malaria
risk and changing vector ecology will lead to significant reductions in malaria incidence and
transmission risk. The aim of this study is to use a cluster-randomized sequential, multiple
assignment randomized trial (SMART) design to compare various vector control methods
implemented by the Ministry of Health of Kenya in reducing malaria incidence and infection,
and develop an optimal intervention strategy tailored toward to local epidemiological and
vector conditions.
Description:
In the past decade, massive scale-up of long-lasting insecticide-treated nets (LLINs) and
indoor residual spraying (IRS) in Africa have led to significant reductions in malaria
mortality and mobility. However, current first-line interventions are not sufficient to
eliminate malaria in most countries. The widespread use of pyrethroid insecticides has
resulted in resistant vector populations, and high coverage of LLINs and IRS has led to
increased outdoor human feeding behavior and resting behavior. These changes in vector
ecology and behaviors have significantly limited the effectiveness of current first-line
interventions that target indoor biting and resting mosquitoes. Furthermore, as a result of
ecological changes and intervention measures, malaria risk in a locality is dynamic, and the
utility of malaria intervention tools may vary as new tools are being approved and introduced
and the cost of each tool differs among locations and over time. Such variations in malaria
risk, vector ecology, and utility of intervention tools exemplify the need to develop optimal
adaptive interventions tailored to local malaria risks, vector ecology and supply chains. The
central objective of this application is to design optimal adaptive combinations of vector
control interventions to maximize reductions in malaria burden based on local malaria
transmission risks, changing vector ecology, and available mix of interventions approved by
the Ministry of Health in each target country. The central hypothesis is that an adaptive
approach based on local malaria risk and changing vector ecology will lead to significant
reductions in malaria incidence and transmission risk. To accomplish this objective, the
investigators propose the following three specific aims:
1. Measure malaria incidence and predict risk using environmental, biological, social, and
climatic features with machine learning approaches. Hypothesis: Malaria risk prediction
can be improved through the use of machine learning techniques that include
environmental, biological, socioeconomic, and climatic features. Approach: Each site
will measure malaria incidence, prevalence and social economic factors through community
surveys. Classification-based and regression-based approaches will be used to develop
malaria risk predictive models, and model performance will be validated. Outcome: This
Aim will establish improved malaria risk prediction models and lay an important
foundation for developing intervention strategies adaptive to local vector ecology and
future malaria risks using reinforced machine learning approaches.
2. Use a cluster-randomized sequential, multiple assignment randomized trial (SMART) design
to develop an optimal adaptive intervention strategy. Hypothesis: Malaria control
interventions that are adapted to local malaria risk and vector ecology and are cost
effective can be identified using a cluster-randomized SMART design. Approach:
Cluster-randomized SMART design will be used in a high transmission areas in Kenya to
evaluate the impact of adaptive interventions that involve sequential and combination
use of next-generation nets, indoor spraying of non-pyrethroid insecticides, and larval
source management for malaria control.
3. Evaluate the cost-effectiveness and impact of an adaptive intervention approach on
secondary endpoints related to malaria risk and transmission. Hypothesis: Intervention
strategies adapted to local malaria risk and vector ecology will be more cost-effective
in reducing malaria incidence and transmission risk than the currently-used LLIN
intervention. Approach: The economic costs of individual interventions or combinations
thereof will be assessed from both a provider and societal perspective using standard
economic evaluation methodologies. Cost-effectiveness will be measured in terms of cost
per person protected. The study will examine changes in drug and insecticide resistance
and infection prevalence attributable to the adaptive interventions.
Malaria interventions adapted to rapidly changing malaria risk and vector ecologies are
critically needed to improve the effectiveness of malaria control measures. This study will
use new techniques, including machine learning and a novel cluster-randomized SMART design,
to develop optimal adaptive malaria intervention strategies.
The investigators will use 84 clusters in Kisumu County in Western Kenya to conduct the
trial. Since it is a sequential multiple assignment randomized trail, the trial will include
several intervention stages. At each stage there will be different interventions. If an
intervention is effective (i.e., yields an above threshold reduction in malaria incidence) at
Stage 1, the intervention will be continued, otherwise, the intervention will be replaced by
another one at Stage 2. The replacement intervention may be decided by different ways, e.g.,
an known effective intervention or an intervention determined by a machine learning
algorithm. Since interventions in some clusters may be continued (i.e., effective) by next
stage, other interventions may be replaced by different interventions, the number of
interventions arms can vary from stage to stage. This is very different from ordinary cluster
randomized trials. In this trial, the investigators planned to start with piperonyl butoxide
(PBO) treated long-lasting insecticidal nets (PBO LLIN), indoor residual spraying with
Actellic(R) insecticide, and using the routine LLIN intervention as control. Both Actellic
IRS and PBO LLIN have been tested to be effective against pyrethroid resistant Anopheles
malaria vectors and reduce clinical malaria. Therefore, the initial stage will have three
arms, i.e., regular LLIN, PBO LLIN, and regular LLIN plus Actellic IRS. Since the
investigators don't know if the effectiveness of these interventions in different clusters,
the stage 2 interventions may include up to 7 arms, i.e., some arms may be split into two
arms, based on the evaluation at the end of Stage 1 intervention.
The investigators will begin the trial with a two-year smaller scale trial using 36 cluster
and randomly assign the three interventions, i.e., regular LLIN, PBO LLIN and regular LLIN
plus Actellic IRS, into these 36 cluster, with 12 clusters for each intervention. This
pre-trial trial is to determine the optimal way for conducting the full-scale 84 cluster
trial, including operational and effectiveness evaluation procedures, as well as
cost-effectiveness analysis. The full scale 84 cluster trial will be started by Year 3. The
full trial will be started from fresh, i.e., the same three interventions will be randomly
assigned to the 84 clusters with 28 clusters for each interventions. Clinical malaria will be
monitored using a cohort active case surveillance, parasite prevalence and vector density
will be monitored using cross-sectional samplings. The results of these surveillance at the
end of Stage 1 trial will be used to evaluate the effectiveness of interventions at each
cluster for the Stage 1 interventions. Stage 2 interventions will be determined for each
cluster based on the above evaluations, e.g., continue the same intervention or replace the
intervention with different ones.
