De-biasing people's over-optimism about their health risk

Last registered on September 25, 2020


Trial Information

General Information

De-biasing people's over-optimism about their health risk
Initial registration date
July 16, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
July 21, 2020, 11:51 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
September 25, 2020, 12:51 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Northwestern University

Other Primary Investigator(s)

PI Affiliation
UC Berkeley
PI Affiliation
University of Chicago

Additional Trial Information

In development
Start date
End date
Secondary IDs
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

Dizon-Ross, Rebecca , Seema Jayachandran and Ariel Zucker. 2020. "De-biasing people's over-optimism about their health risk." AEA RCT Registry. September 25.
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Experimental Details


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
Intervention End Date

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
Note about design change: After enrolling participants beginning on July 31, we decided after a short while to pause and (a) shorten the baseline survey (b) change the intervention script to be explicit that the international statistics we cite are likely relevant to India (c) shorten the information scripts (d) rearrange the order of the information (e) add extra explanation about probability (f) and get IRB approval for the modifications. The goal of the changes was to make the T1, T2, and T3 scripts more relevant and clearer to participants, and to deliver them before participants' attention faded. The decision was prompted by feedback from surveyors who said that participants were paying less attention by the time the treatment scripts came and that some participants said that India has especially low rates of Covid mortality so isn't like other countries. In addition, we analyzed belief updating in T1 compared to control. Importantly, this is a comparison that is not testing a primary hypothesis. Our key interest is T2 versus T1 and T3 versus T1 (The hypotheses H1 and H2 laid out above), and we did not analyze these comparisons when deciding how to proceed. The T1 versus C comparison showed that many participants were not updating their beliefs about others' risk based on the information on fatality risk we delivered, or were updating in the wrong direction. We thus turned this initial sample into a pilot, changed the scripts as described, pre-tested the revised survey and script, waited for IRB approval, and then relaunched in late September. To ensure a sufficient sample size, we decided at this point to plan to enroll people with hypertension too, unless the enrollment rate is higher than expected and we can reach our target sample size of 2400 to 2600 people with just diabetics.
Randomization Method
in office by computer
Randomization Unit
Was the treatment clustered?

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)

Institutional Review Boards (IRBs)

IRB Name
Northwestern University
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials