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COVID-19 Vaccination Take-Up in a County-Run Medicaid Managed Care Population
Last registered on June 10, 2021

Pre-Trial

Trial Information
General Information
Title
COVID-19 Vaccination Take-Up in a County-Run Medicaid Managed Care Population
RCT ID
AEARCTR-0007405
Initial registration date
March 25, 2021
Last updated
June 10, 2021 1:42 PM EDT
Location(s)

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Primary Investigator
Affiliation
USC
Other Primary Investigator(s)
PI Affiliation
UCLA
PI Affiliation
USC
Additional Trial Information
Status
On going
Start date
2021-05-24
End date
2022-05-15
Secondary IDs
Abstract
In this work, we are partnering with Contra Costa Health Services (CCHS), the department of health in Contra Costa County, CA, to measure COVID-19 vaccinations and other COVID-19 related preventive health behaviors in the county’s Medicaid managed care population. Our work will test ways to increase COVID-19 vaccine uptake. We hypothesize that small financial incentives and other low-cost behavioral nudges can be used to increase vaccine uptake and reduce disparities in uptake among diverse racial/ethnic minority populations.
External Link(s)
Registration Citation
Citation
Chang, Tom, Mireille Jacobson and Manisha Shah. 2021. "COVID-19 Vaccination Take-Up in a County-Run Medicaid Managed Care Population." AEA RCT Registry. June 10. https://doi.org/10.1257/rct.7405-3.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
We will test the impact of financial incentives, the highlighting of a convenient scheduling link, messaging from race and gender concordant vs. discordant providers and message type on COVID-19 vaccine uptake.
Intervention Start Date
2021-05-24
Intervention End Date
2021-09-20
Primary Outcomes
Primary Outcomes (end points)
Vaccine uptake at 1-month
Vaccine intentions at time of survey
Primary Outcomes (explanation)
Vaccine uptake at 1-month is measured from a state registry that feeds into our partner's data system.
Vaccine intentions are measured in our survey
Secondary Outcomes
Secondary Outcomes (end points)
Vaccine take-up at 6 months; vaccine take-up at 1-year.

Difference between vaccine intention and vaccination

Time to first vaccination

Full vaccination status

We will also study heterogeneous treatment effects by race, gender, age and other characteristics.

From administrative data, we may study health care utilization and COVID-19 testing.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We will test the impact of financial incentives, messages from race and gender concordant vs. discordant providers, as well as message type on COVID-19 vaccine uptake.
Experimental Design Details
Not available
Randomization Method
computer
Randomization Unit
members of Contra Costa Health Plan
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
The goal is to recruit 10,000 unvaccinated members of Contra Costa Health Plan
Sample size: planned number of observations
10,000
Sample size (or number of clusters) by treatment arms
We will randomize the 10,000 respondents to one of four arms:

1. Control arm [2,500]
2. Messaging/Information Arm 1: no information/emotional message [N=2,500]
3. Messaging/Information Arm 2: provider safety and effectiveness information [N=2,500]
4. Messaging/Information Arm 3: information on consequences of going unvaccinated [N=2,500].

Each of these four arms will be interacted with a financial incentive of $10 (N=2,500) or $50 (N=2,500) and, separately with a convenient link to the county public vaccine appointment scheduling system highlighted for participants (N=5,000). Provider messages will also be randomized by race and gender concordance.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We power our study for our primary outcome, vaccine take-up. For the purposes of this power calculation, we assume that among the vaccine hesitant only 10% of the population will get vaccinated in the absence if our interventions. In unadjusted comparisons, we will be able to detect a change in vaccinations of 1.68 percentage points (16.8% off the mean) for our financial incentive (5,000) vs. control (5,000) or scheduling link vs. control. For any message (7,500) vs. no message (N=2,500) we can detect take-up changes of 1.94 percentage points (19.4% off the mean). For our 3-message type or race or gender concordance (N=2,500 each) comparisons we can detect take-up changes of 2.38 percentage points (23.8% off the mean). Accounting for statistical controls as well as our randomization strata and assuming these increase the R-squared on take-up to 0.25, the MDEs decline to 0.146 percentage points for financial incentives/scheduling link, 1.68 percentage points for any message and 2.1 percentage points for message type or race or gender concordance comparisons. To benchmark these comparisons, we note that Alsan et al. (2019) finds increases in flu shot take-up among African American men, a vaccine hesitant group, of about 22 percentage points for a $5 or $10 incentive. A key difference with our study is Alsan et al. (2019) provided vaccinations on site. Nonetheless, our study will be well-powered if our interventions have impacts even 1/10th the size as those in Alsan et al. (2019). Alsan M., Owen G. and Graziani G. 2019. "Does Diversity Matter for Health? Experimental Evidence from Oakland." American Economic Review 109(12): 4071-4111. DOI: 10.1257/aer.20181446.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Southern California Institutional Review Board
IRB Approval Date
2021-03-10
IRB Approval Number
UP-21-00030
Analysis Plan

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