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Quantifying Benefits of Using Satellite Derived Early Warning System to Predict Cholera in Bangladesh
Last registered on March 12, 2021

Pre-Trial

Trial Information
General Information
Title
Quantifying Benefits of Using Satellite Derived Early Warning System to Predict Cholera in Bangladesh
RCT ID
AEARCTR-0006900
Initial registration date
February 02, 2021
Last updated
March 12, 2021 8:22 AM EST
Location(s)

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Primary Investigator
Affiliation
Penn State University
Other Primary Investigator(s)
PI Affiliation
Virginia Tech
PI Affiliation
University of Rhode Island
PI Affiliation
Moravian College
Additional Trial Information
Status
In development
Start date
2021-01-17
End date
2022-01-01
Secondary IDs
Abstract
In Bangladesh, cholera poses a significant health risk to local populations. Individuals and households can reduce their risks of contracting cholera through safe water, sanitation, and hygiene behaviors and by limiting contact with potentially-contaminated water. Yet, as the Bangladeshi population faces both endemic and epidemic cholera, households may make suboptimal investments in cholera risk reduction if risk levels are unknown and unstable. If, however, households had access to early warning systems alerting them of periods of increased cholera risk in their communities, this information might shift their investments in risk-reducing behaviors such as boiling or filtering drinking water. We explore these ideas by developing a smartphone app to disseminate cholera risk information to households in Matlab Bangladesh. We develop two versions of this app: (1) an app with temporally and locationally specific cholera risk information--developed through a risk model that utilizes satellite data inputs--that is personalized specifically for an end user and (2) an app with publicly available information about averting cholera risk. We randomize access to each version of the app (as well as maintain a pure control group without access to either app) to measure whether providing personalized information about household cholera risk shifts cholera averting behaviors and cholera incidence within our study population. In addition, we examine the value associated with providing households with this type of personalized cholera risk information.
External Link(s)
Registration Citation
Citation
Pakhtigian, Emily et al. 2021. "Quantifying Benefits of Using Satellite Derived Early Warning System to Predict Cholera in Bangladesh." AEA RCT Registry. March 12. https://doi.org/10.1257/rct.6900-2.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
The intervention involves installing a cell phone app that contains information about cholera risk on smartphones owned by individuals in treatment households. Half of the treatment households will receive a cell phone app that contains personalized cholera risk information; the other half will receive a cell phone app that contains publicly available information about averting cholera risk. There will also be a pure control group--without access to either app--for comparison.
Intervention Start Date
2021-02-15
Intervention End Date
2021-10-29
Primary Outcomes
Primary Outcomes (end points)
Household drinking water treatment and storage; Household water security; Cholera incidence
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We conduct an information experiment using two versions of a cell phone app developed for this trial. One version of the app provides information about cholera risk in the area surrounding the user's home; the risk information is updated monthly. The other version of the app is static, and it contains publicly available information about ways that users can protect themselves and their households from cholera. We experimentally vary access to each version of the cell phone app, and we maintain a third arm of study participants without access to either app.
Experimental Design Details
Not available
Randomization Method
Randomization is conducted using Stata.
Randomization Unit
The unit of randomization is the village. Villages will be randomly selected into one of three study arms: public health app, cholera risk model app, or control.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
40 villages
Sample size: planned number of observations
2000 households (1 respondent per household)
Sample size (or number of clusters) by treatment arms
750 households (across 15 villages) in cholera risk model app treatment arm; 750 households (across 15 villages) in public health app treatment arm; 500 households (across 10 villages) in control arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Moravian College
IRB Approval Date
2021-01-30
IRB Approval Number
21-0003