Using Dynamic Incentives to Encourage Abstinence in a Drug Epidemic

Last registered on November 01, 2021

Pre-Trial

Trial Information

General Information

Title
Using Dynamic Incentives to Encourage Abstinence in a Drug Epidemic
RCT ID
AEARCTR-0008314
Initial registration date
October 08, 2021
Last updated
November 01, 2021, 6:29 PM EDT

Locations

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Primary Investigator

Affiliation
University of Chicago

Other Primary Investigator(s)

PI Affiliation
University of California Santa Cruz
PI Affiliation
Advocate Aurora Health

Additional Trial Information

Status
In development
Start date
2021-09-22
End date
2023-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Combatting the rise of the opioid epidemic is a central challenge of U.S. health care policy. A promising approach for improving welfare and decreasing medical costs of people with substance abuse disorders is offering incentive payments for healthy behaviors. This approach, broadly known as “contingency management” in the medical literature, has repeatedly shown to be effective in treating substance abuse.
However, the use of incentives by treatment facilities remains extremely low. Furthermore, it is not well understood how to design optimal incentives to treat opioid abuse. This project will conduct a randomized evaluation of two types of dynamically adjusting incentive schedules for people with opioid use disorders or cocaine use disorders: “escalating” schedules where incentive amounts increase with success to increase
incentive power, and “de-escalating” schedules where incentive amounts decrease with success to improve incentive targeting. Both schemes are implemented with a novel “turnkey” mobile application, making them uniquely low-cost, low-hassle, and scalable. Effects will be measured on abstinence outcomes, including longest duration of abstinence and the percentage of negative drug tests. In combination with survey data,
variation from the experiment will shed light on the barriers to abstinence more broadly and inform our understanding of optimal incentive design.
External Link(s)

Registration Citation

Citation
Dizon-Ross, Rebecca, Mindy Waite and Ariel Zucker. 2021. "Using Dynamic Incentives to Encourage Abstinence in a Drug Epidemic." AEA RCT Registry. November 01. https://doi.org/10.1257/rct.8314-1.2000000000000002
Experimental Details

Interventions

Intervention(s)
Our primary intervention is a 12-week incentives intervention which provides participants with incentives for abstaining from drug use. Participants will be registered for a mobile app provided by DynamiCare Health and a provided with linked debit card. The app will prompt patients to submit saliva drug tests through their mobile phones on a random schedule (averaging three tests per week). For these randomly scheduled “contingent” tests, patients will receive immediate financial rewards in exchange for submitting drug-negative samples.

Within the intervention group, we will randomize six total incentive schedules: both a “High” and “Low” version of Escalating, De-escalating, and constant incentives. The schedules are designed so that both High schedules will pay similar amounts on average, as will both Low schedules.
• Escalating Groups, High and Low: In the Escalating groups, incentive amounts increase with every negative drug test up to a ceiling, and “reset” to the lowest amount when a test is positive or missed. Incentives in the High group will range from $4-$16, and $2-$8 in the Low group.
• De-escalating Groups, High and Low: In the De-escalating groups, incentive amounts increase with every positive or missed drug test (up to a ceiling), and decrease by the same increment with every negative drug test (down to a floor). Incentives in the High group will range from $10-$20, and $6-$12 in the Low group.
• Constant Groups, High and Low: In the constant groups
Intervention Start Date
2021-09-29
Intervention End Date
2022-12-31

Primary Outcomes

Primary Outcomes (end points)
Our first primary outcome is the percent of follow-up tests that are negative, i.e., tests negative for opioids for people with opioid use disorder, or OUD; negative for cocaine for people with cocaine use disorder, or CoUD; and negative for opioids and cocaine for people with OUD and CoUD.
- To answer the research question “Do incentives increase abstinence?” we compare the control and incentives groups (pooled).
- To assess the most effective incentive (or incentive combination), we will implement the “smart pooling and pruning” method of Banerjee et al. 2021 among all treatment groups.


Our second primary outcome is the percent of contingent tests that are negative (where negative is defined as above).
- To answer the research question “Is high stakes or targeting more important for improving average compliance?” we will compare the escalating and de-escalating subgroups (pooled across high and low incentive levels).
- To answer the question “Do larger incentives matter?” we will compare the high and low incentive subgroups (pooled across escalating and de-escalating subgroups).

To answer the research question “Do high-stakes incentives come at the cost of increased variance of behavioral compliance?” we will again compare the escalating and de-escalating subgroups (pooled across high and low incentive levels). Our primary outcome will be:
- The variance of the individual-level proportions of contingent tests that are negative

To answer the research question “Do individuals respond to dynamic incentives?” we will compare the escalating, constant, and de-escalating subgroups (pooled across high and low incentive levels). This allows a clean test for whether there is an effect of dynamic incentives since all else is constant across these groups at the first test (including the payment amount). Our primary outcome will be:
- An indicator for whether the first contingent test is negative.
Primary Outcomes (explanation)
The primary form of missing data will be follow-up tests that are not taken. Our main approach will be to code these tests as “not negative” rather than “missing”. This lumps together missing and positive tests (a common approach in the literature). However, we will show the relationship between treatment assignment and test taking and utilize bounding exercises (e.g. Lee bounds) to clearly demonstrate the range of potential impacts missing data could be having on our results. This applies to all test results, both contingent and follow-up.

Secondary Outcomes

Secondary Outcomes (end points)
We will assess differences between the control group and incentive groups (pooled), as well as between the control group and the most effective incentive group (or incentive combination), as chosen by the “smart pooling and pruning method”, in the following secondary outcomes:

- Duration of time in treatment program
- Earnings
- Employment (dummy)
- Medical care utilization
o ED visits
o Inpatient stays
o Primary care utilization
- Total cost of care
- Incentive amount per negative follow-up test (i.e., “cost-effectiveness”)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We enroll adults adults with cocaine or opioid use disorders in urban Wisconsin. We begin with a one-week induction period, during which all patients will be registered for a mobile app provided by DynamiCare health and asked to complete an initial “non-contingent” saliva drug test. Patients who submit a first test will then be randomly assigned to one of our intervention groups, or to a control group that simply receives outpatient treatment as usual, for a 12-week intervention period. During this time, the intervention groups will receive their assigned intervention and the control group will receive treatment as usual. For all participants, the mobile app provided by DynamiCare Health will prompt remote “non-contingent follow-up” saliva drug tests every four weeks (including at the 12 week mark), which will be rewarded with $20 gift cards, to monitor abstinence behavior for study purposes. We will also conduct an endline survey with all participants at the end of the 12-week period.
Experimental Design Details
Not available
Randomization Method
Randomization was conducted by loading randomized allocation tables into the RedCap system
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
300
Sample size: planned number of observations
300
Sample size (or number of clusters) by treatment arms
The sample will be divided between treatment arms in the following proportions:
1. Control 0.28
2. Escalating high 0.16
3. Escalating low 0.16
4. Deescalating high 0.16
5. Deescalating low 0.16
6. Constant high 0.04
7. Constant low 0.04
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Aurora IRB (Registration # IRB00001266)
IRB Approval Date
2021-06-27
IRB Approval Number
21-102E
Analysis Plan

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