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Field
Last Published
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Before
March 26, 2021 10:49 AM
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After
May 05, 2021 02:03 PM
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Intervention (Public)
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Before
We will test the impact of financial incentives, messages from a community leader and messages from race and gender concordant vs. discordant providers on COVID-19 vaccine uptake.
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After
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.
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Field
Experimental Design (Public)
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Before
We will test the impact of financial incentives, messages from a community leader and messages from race and gender concordant vs. discordant providers on COVID-19 vaccine uptake.
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After
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.
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Sample size (or number of clusters) by treatment arms
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Before
2,500 control
1250 culturally appropriate community message + incentive,
1250 culturally appropriate community message (no incentive)
625 Race concordant, male doctor + incentive
625 Race concordant, female doctor + incentive,
625 Race disconcordant, male doctor + incentive
625 Race disconcordant, female doctor + incentive
625 Race concordant, male doctor (no incentive)
625 Race concordant, female doctor (no incentive)
625 Race disconcordant, male doctor (no incentive)
625 Race disconcordant, female doctor (no incentive)
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After
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.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
We power our study for our primary outcome, vaccine take-up. For the purposes of this power calculation, we assume that, similar to recent estimates of vaccination intentions, only 50% of the population will get vaccinated without any outside incentive (see Szilagyi et al 2020). Using standard assumptions of 80% power, 5% alpha, we will be able to detect a change in vaccinations of 3.24 percentage points (6.5% off the mean) for our information treatment (7,500) vs. control (2,500).
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After
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.
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Keyword(s)
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Before
Health
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After
Health
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Secondary Outcomes (End Points)
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Before
Vaccine take-up beyond one month
For the subset of people who are part of the survey panel, we will also see if there is a change in preventive health behaviors and labor supply (assume follow-up surveys).
From administrative data, we will also study health care utilization and COVID-19 testing.
We will also study heterogeneous treatment effects by race, gender and age.
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After
Vaccine take-up at 6-months and at other intervals
For the subset of people who are part of the survey panel, we will also see if there is a change in preventive health behaviors and labor supply (assume follow-up surveys).
From administrative data, we will also study health care utilization and COVID-19 testing.
We will also study heterogeneous treatment effects by race, gender and age.
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