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De-biasing people's over-optimism about their health risk
Last registered on September 25, 2020

Pre-Trial

Trial Information
General Information
Title
De-biasing people's over-optimism about their health risk
RCT ID
AEARCTR-0005951
Initial registration date
July 16, 2020
Last updated
September 25, 2020 12:51 PM EDT
Location(s)

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Primary Investigator
Affiliation
Northwestern University
Other Primary Investigator(s)
PI Affiliation
UC Berkeley
PI Affiliation
University of Chicago
Additional Trial Information
Status
In development
Start date
2020-07-18
End date
2021-12-06
Secondary IDs
Abstract
Providing people with information about their health risk is an important part of the policy response to a public health crisis. However, the most effective way to present such information is unknown, particularly in light of behavioral biases people have. One such bias is over-optimism about one's health risk (i.e., a tendency to believe that one's risk is lower than it is), which has been documented in many settings and shown to lead to riskier behaviors. This study aims to test whether interventions that offset people’s over-optimism can improve the effectiveness of information provision. We do so in the context of the COVID-19 pandemic, among a population that is particularly vulnerable to severe complications from COVID-19, namely diabetics and pre-diabetics and hypertensives, who represent a large and growing segment of the population in India.
External Link(s)
Registration Citation
Citation
Dizon-Ross, Rebecca , Seema Jayachandran and Ariel Zucker. 2020. "De-biasing people's over-optimism about their health risk." AEA RCT Registry. September 25. https://doi.org/10.1257/rct.5951-1.1.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
The study has 4 arms.
• Control
• T1: Deliver health risk information
• T2: Deliver health risk information + information that most people believe they are lower risk than other people
• T3: Deliver health risk information for similar but lower-risk sub-population

To ensure benefits for all participants, all study arms, including the control group, will receive basic information on how to protect themselves from becoming infected with Covid-19 and the importance of chronic disease management to reduce the risk of severe symptoms. They will also be offered a toll-free number they can call for further health information.

The basic treatment (T1) will deliver objective estimates of the fatality rate from Covid-19 for each person’s 10-year age group and co-morbidities, using cross-tabulated data from the US and elsewhere.

Those assigned to T2 will receive the basic information treatment on the infection fatality rate, plus will be told that people tend to systematically believe they are at lower Covid-19 risk than others like them.

Those assigned to T3 will receive the basic information treatment, and will also be told the infection fatality rate for the next youngest age group.
Intervention Start Date
2020-07-18
Intervention End Date
2020-11-13
Primary Outcomes
Primary Outcomes (end points)
We have two types of primary outcomes: beliefs about health risk and protective behavior to reduce Covid-19 infection and severity risk.

We will measure beliefs about absolute and relative risk of one’s infection fatality risk through survey questions.

We will measure behaviors to reduce Covid-19 risk two ways. First, we will ask about protective behaviors (e.g., social distancing, diabetes management). Second, we will use phone calls to a hotline we set up that offers additional information on Covid-19 prevention and recorded at-home exercises.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Self-reported health, which we will measure as an index of responses to survey questions
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Study participants will be randomized into one of 4 mutually exclusive groups: Control, T1, T2, and T3. The interventions, which are information scripts, will be delivered at the end of a phone-based baseline survey. We will collect outcomes through phone-based endline surveys, and by using data on phone calls to a toll-free number we set up.

We will test 4 main hypotheses.

H1: T2 will correct over-optimistic beliefs and increase risk-mitigating behaviors among the over-optimistic more than T1.

H2: T3 will correct over-optimistic beliefs and increase risk-mitigating behaviors among the over-optimistic more than T1.

H3: T1 will correct over-optimistic beliefs and increase risk-mitigating behaviors among the over-optimistic, relative to the control group.

H4: The pooled treatments (T1+T2+T3) will correct over-optimistic beliefs, increase risk-mitigating behavior, and improve health, relative to the control group.
Experimental Design Details
Not available
Randomization Method
in office by computer
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
About 2500 individuals; will depend on eligibility and consent rates.
Sample size: planned number of observations
Same as above
Sample size (or number of clusters) by treatment arms
Control group: 10% of sample
T1: 40% of sample
T2: 25% of sample
T3: 25% of sample
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Northwestern University
IRB Approval Date
2020-05-06
IRB Approval Number
STU00212568