The Hopkins Business of Health Initiative Health System Trust Index

Last registered on August 10, 2023


Trial Information

General Information

The Hopkins Business of Health Initiative Health System Trust Index
Initial registration date
July 31, 2023

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
August 10, 2023, 12:49 PM EDT

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



Primary Investigator

Johns Hopkins

Other Primary Investigator(s)

PI Affiliation
Johns Hopkins University
PI Affiliation
Johns Hopkins University

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
A unique feature of health care markets is that suppliers of medical services are, at least in principle, expected to behave in a patient’s best interest. Such relationships require trust. We are building a novel index of trust to measure and monitor Americans' level of confidence in the public and private institutions that influence the health of individuals and communities. Our index measures both the determinants of trust (e.g., personal experience with various components of the health system) and the implications of trust (e.g., propensity to get vaccinated, schedule annual physical, etc.). Our index is informed by a novel, representative survey of Americans that we are currently conducting. Importantly, our survey over-samples minority groups such that we can study disparities in trust across traditionally underserved groups. This pre-analysis plan documents our research design, survey methodology, and connection to the trust literature.

External Link(s)

Registration Citation

Darden, Michael, Theodore Iwashyna and Mario Macis. 2023. "The Hopkins Business of Health Initiative Health System Trust Index." AEA RCT Registry. August 10.
Sponsors & Partners


Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Primary Care Visit; COVID-19 Vaccination Status; Trust in the US health care system; Expectations: ability of local healthcare system to manage health
Primary Outcomes (explanation)
We directly ask respondents whether they have visited a primary care physician or nurse practitioner in the past two years. We also ask for a respondent's vaccination status to COVID-19.

Our trust measure asks respondents to rate the trust they hold in the overall United States health care system on a 10-point scale, where 0 = "I do not trust the US health care system at all" and 10 = "I trust the US health care system completely".

Our expectations measurements ask for a subjective probability that a respondent's local health care system can manage health and specific conditions effectively. We randomly assign the following conditions: common cold, broken arm, stroke. And we randomly vary language, focusing on the respondent in particular, other people like the respondent, or the average person in the respondent's area.

Secondary Outcomes

Secondary Outcomes (end points)
Trust in physicians, nurses, hospitals, health insurance companies, Medicare, Medicaid, pharmaceutical companies, medical device manufacturers, US Government, your state governor, the market system, other people in your neighborhood, other people in general. Time preference. Risk preference. Respondent's subjective health; their experience with COVID-19, their mental health (GAD2+PHQ2).
Secondary Outcomes (explanation)
The specific trust measures (e.g., in nurses) look like our overall trust measure. Time and risk preference questions follow from Falk et al. 2018, QJE.

Experimental Design

Experimental Design
We will survey a nationally representative sample of Americans about their trust in the US health care system. Respondents will complete a demographic and socioeconomic module that will capture health insurance coverage, income, education, and other relevant considerations. We will start our survey by directly asking respondents for their level of trust in the health care system and in various components of the system. Next, we will attempt to correlate trust with four factors that a simple model of care-seeking behavior suggest might translate in to differences in care-seeking by trust:
1. Expectations: We will expectations by directly asking respondent's about the perceived ability of a respondent's local health care system to manage health with the respondent's trust. In addition to a global question on the ability of the local system to manage health generically, we will randomize respondent's to three conditions: common cold, broken arm, and stroke, and repeat the ability question specifically for their hypothetical condition.

2. Time and Risk Preferences: This measure comes from Falk et al. (2018, QJE)

3. Value of Life: Several proxies for value of life include age, education, health status, etc.

4. Economic Considerations: Income, health insurance status, assets, etc.

5. Disutility of Care: We will directly ask respondents about the unpleasantness of visiting a doctor.

Our research also seeks to quantify disparities in trust. In addition to our 1,000 person sample of the general population, we will sample 100 African American and 100 Hispanic respondents.
Experimental Design Details
Our plan is to first classify individuals based on their levels of trust as measured by our general trust questions. Then, based on this classification, we will explore differences in expectations, time/risk preferences, value of life, economic considerations, and disutility of care by our trust classification. Finally, we will estimate regression models of the form:

Care = f(Trust, X)

Where, care is a measure of care-seeking behavior and trust is our classification of trust based on the general trust questions. The X vector includes demographic characteristics. We will iteratively add the 5 factors listed above in separate regressions. Our interest is in how the coefficient on our trust variable changes.

We also intend to interact our trust variable with demographic characteristics such as race.
Randomization Method
Randomization will be done in Qualtrics.
Randomization Unit
The unit of randomization will be the individual.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
A nationally representative survey of 1,200 individuals.
Sample size (or number of clusters) by treatment arms
One-third of the sample in each condition: common cold, broken arm, stroke.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Johns Hopkins Homewood IRB
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