Behavioral Response to COVID-19 Antibody Testing

Last registered on October 06, 2020

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Pre-Trial

Trial Information

General Information

Title

Behavioral Response to COVID-19 Antibody Testing

RCT ID

AEARCTR-0006119

Initial registration date

July 10, 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 13, 2020, 3:42 PM EDT

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

Last updated

October 06, 2020, 3:53 PM EDT

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

Locations

Country

United States of America

Region

California

Primary Investigator

Name

Mireille Jacobson

Affiliation

USC

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Other Primary Investigator(s)

PI Name

Tom Chang

PI Affiliation

USC

Contact Investigator

PI Name

Manisha Shah

PI Affiliation

UCLA

Contact Investigator

Additional Trial Information

Status

In development

Start date

2020-07-06

End date

2021-02-28

Keywords

Health

Additional Keywords

JEL code(s)

I18, I12

Secondary IDs

Abstract

This study aims to shed light on how people response to COVID-19 antibody testing. In collaboration with Contra Costa County Health Services (CCHS), we will field two nearly identical online surveys designed to capture both individual beliefs about COVID-19 and behavior taken to mitigate the spread of the disease. The surveys will be fielded to asymptomatic people who show up to a phlebotomy clinic for routine clinician-ordered lab tests and are not explicitly coming for COVID-19 testing. By (randomly) offering some people coming in for lab work a free blood antibody test at the same time, we will be able to study not only how testing affects beliefs about exposure as well as preventive behavior. We can also characterize who takes up the offer of the antibody test.

External Link(s)

Registration Citation

Citation

Chang, Tom, Mireille Jacobson and Manisha Shah. 2020. "Behavioral Response to COVID-19 Antibody Testing." AEA RCT Registry. October 06. https://doi.org/10.1257/rct.6119-2.0

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Experimental Details

Interventions

Intervention(s)

Randomized offer of a free COVID-19 antibody test to individuals showing up to CCHS for routine blood lab work.

Intervention Start Date

2020-07-06

Intervention End Date

2020-10-16

Primary Outcomes

Primary Outcomes (end points)

Percent chance you had COVID-19; uncertainty about chance you have COVID-19; index of positive preventive health behavior in the past 7 days.

Primary Outcomes (explanation)

Our survey asks individuals the percent chance they have had COVID-19 as well as their confidence in their estimate. It also captures the following behaviors in the past 7 days -- wore a mask; washed hands with soap or used hand sanitizer several times per day; canceled or postponed personal or social activities; avoided eating at restaurants; self-isolating; avoided attending social gatherings; avoided contact with high risk individuals. We will combine these outcomes into an index that is the sum of responses.

We will measure these outcomes at two points in time, first within 24 hours of recruitment and second about 2 weeks later.

Secondary Outcomes

Secondary Outcomes (end points)

Selection into testing among those in the treatment group. Other behaviors in the past 7 days such as whether individuals are stockpiling food, working for pay, exercising, attending religious services, and getting help with domestic chores; media consumption; beliefs about getting COVID-19, about someone the respondent knows getting COVID-19, about someone the respondent’s age dying from COVID-19 if infected and about COVID-19 affecting earnings.

Secondary Outcomes (explanation)

We will study test take-up. Specifically, among those who received a (random) offer for an antibody test, what are the observable characteristics of those who select in to testing. We will also analyze the effect of assignment to testing on changes in the secondary outcomes listed above. We will also analyze the effect of test offer on changes in (1) other behaviors in the past 7 days such as whether individuals are stockpiling food, working for pay, exercising, attending religious services, and getting help with domestic chores; (2) media consumption and (3) beliefs about getting COVID-19, about someone the respondent knows getting COVID-19, about someone the respondent’s age dying from COVID-19 if infected and about COVID-19 affecting earnings.

Experimental Design

Individuals will be randomized based on clinic x time of day and day of week to the offer of a free COVID-19 antibody test. All individuals recruited at the lab will answer surveys.

Experimental Design Details

Randomization Method

Randomization was done in an office using excel and imperfect latin squares. Over a 5 week period, we randomized offers of testing by clinic (3 sites), time of day (8am-12pm and 1-5pm) and day of week within study week and randomized the order of the week.

After an initial 5 weeks, we extended the intervention period another 6 weeks, through the week of September 18, 2020. As before, we randomized offers of testing by clinic (3 sites), time of day (8am-12pm and 1-5pm) and day of week within study week and randomized the order of the week.

Randomization Unit

Clinic x Time of Day (8am-12pm and 1-5pm) x day week.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

180 clusters, where 1 cluster is a clinic x Time of Day (8am-12pm and 1-5pm) x day of week x week of study

Sample size: planned number of observations

5,000

Sample size (or number of clusters) by treatment arms

90 clusters receive antibody test offers

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

We use data collected from another study "Monitoring elderly psychological well-being during the COVID-19 pandemic" for inputs into our calculations. Although the survey was fielded to a different sample (seniors nationwide), preventive health behaviors were measured identically. Thus, we power the current study using the index of preventive health measures described above. For respondents in California, we have a mean index of 5.375 (out of a possible score of 7) with a standard deviation of 1.69. Given $5 incentives for survey completion, we have a budget to field about 5000 surveys. With 2 clusters, assuming a 50% correlation within clusters, we can detect a change in the index of about 0.51 or about 9.4% of the mean. Assuming a 0% correlation within clusters, we can we can detect a change in the index of about 0.13 or about 2.4% of the mean. Since individuals are not coming to CCHS for COVID-19 testing, the intra-cluster correlation in COVID-19 beliefs and behaviors is likely between these two thresholds. Thus, realistically, we should have sample size to detect changes of closer to about 5% of the mean index.

Supporting Documents and Materials

IRB