Summary

for people ages 6 months and up (full criteria)
healthy people welcome
at Irvine, California and other locations
study started
estimated completion
Guiyun Yan

Description

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 ecologies 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.

Official Title

Environmental Modifications in Sub-Saharan Africa: Changing Epidemiology, Transmission and Pathogenesis of Plasmodium Falciparum and P. Vivax Malaria

Details

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, we 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, socio-economic, 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 two high transmission areas in Kenya and Ethiopia to evaluate the impact of adaptive interventions that involve sequential and combinational 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.

Keywords

LLIN, PBO LLIN, IRS, Larviciding Adaptive intervention Multiple assignment randomized trial (SMART) Cluster-randomized SMART trial Optimal intervention strategy Clinical malaria incidence Cost-effectiveness Malaria Permethrin Regular long-lasting insecticidal nets LLIN plus Piperonyl butoxide-treated LLIN Long-lasting microbial larvicide Indoor residual spraying with micro-encapsulated pirimiphos-methyl PBO-LLIN plus larval source management PBO-LLIN plus enhanced methods LLIN plus indoor residual spraying LLIN+IRS+LSM LLIN+IRS plus emhanced method

Eligibility

For people ages 6 months and up

Household inclusion criteria:

  • Households with residents at the time of survey
  • Agreement of the adult resident to provide informed consent for the intervention and survey

Study subjects inclusion criteria:

  • Passive case detection by health facilities will include all residents in the study clusters; active case detection will include residents of >6 months
  • Agreement of parent/guardian to provide informed consent and minors to provide assent.

Household exclusion criteria:

  • Household vacant
  • No adult resident home on more than 3 occasions

Study subjects exclusion criteria:

• Participants not home on day of survey

Locations

  • Program in Public Health
    Irvine California 92697 United States
  • Tom-Mboya University College, Maseno University
    Homa Bay Homa Bay County Kenya

Lead Scientist

Details

Status
not yet accepting patients
Start Date
Completion Date
(estimated)
Sponsor
University of California, Irvine
ID
NCT04182126
Study Type
Interventional
Last Updated