Produce Rx: A randomized trial to evaluate vouchers to increase the consumption of fruits and vegetables and promote weight loss among food insecure individuals

Last registered on April 06, 2019


Trial Information

General Information

Produce Rx: A randomized trial to evaluate vouchers to increase the consumption of fruits and vegetables and promote weight loss among food insecure individuals
Initial registration date
April 02, 2019
Last updated
April 06, 2019, 3:15 PM EDT


Primary Investigator

Georgetown University

Other Primary Investigator(s)

PI Affiliation
IMPAQ International

Additional Trial Information

In development
Start date
End date
Secondary IDs
Washington, DC’s low-income communities – specifically Wards 5, 7, and 8 – are on the wrong side of nearly every documented social determinant of health. Of the more than 220,000 individuals in these three wards, 108,000 rely on SNAP, nearly 40% are obese, and 35% are unemployed. Today, there is a 15-year difference in life expectancy between residents of Ward 8 and residents of Ward 3, less than 10 miles away. The top four leading causes of death in DC are now diet-related chronic illnesses. Residents of these predominantly African American neighborhoods have acutely limited access to the resources essential to daily life, including the key need for affordable, healthy food. In clinical settings, patients often cite long distances to grocery stores and limited budgets for food as barriers in taking control of their diets and health.

Individuals that consume less expensive and more calorie-dense foods, which typically have lower nutritional value, are in a vicious cycle of hunger and over-consumption that impairs management of Type-2 diabetes, among other diet-related chronic conditions. Because obesity is a significant risk factor for chronic diseases, effective strategies for weight management are required.

This study will conduct a randomized controlled trial to evaluate the Produce Rx Grocery Store Model. The Produce Rx program aims to improve the nutritional status of Medicaid-enrolled adults diagnosed with at least one diet-related health condition in DC. Produce Rx specifically targets individuals who are currently experiencing or at risk for diet-related chronic diseases. This program combines vouchers for fruit and vegetables redeemable at a local supermarket with the offer of nutritional classes and an incentive for regular primary care visits.

We have designed an experiment to answer three primary research questions:
1. Does Produce Rx increase expenditure on fruit and vegetables?
2. Does participation in Produce Rx improve biomarkers among individuals with prediabetes, diabetes or hypertension?
3. Does Produce Rx decrease total health care expenditures and utilization (inpatient hospital and emergency department visits) among Medicaid managed care members?

We also address (as secondary hypotheses) questions of healthcare utilization and participant satisfaction, which have implications for the sustainability of the program.

4. What are the characteristics of those individuals with high voucher redemption rates versus those with lower rates? Do these differ by subgroups?
5. How do participants perceive the program? What barriers to participation do people report? How do they perceive food and nutrition resources in their neighborhoods and the relationship with their healthcare team compared to controls, after participation?
External Link(s)

Registration Citation

Alva, Maria and Andrew Zeitlin. 2019. "Produce Rx: A randomized trial to evaluate vouchers to increase the consumption of fruits and vegetables and promote weight loss among food insecure individuals." AEA RCT Registry. April 06.
Former Citation
Alva, Maria, Andrew Zeitlin and Andrew Zeitlin. 2019. "Produce Rx: A randomized trial to evaluate vouchers to increase the consumption of fruits and vegetables and promote weight loss among food insecure individuals." AEA RCT Registry. April 06.
Experimental Details


The Produce Rx prescription is equivalent to $20 per week per participant. This represents the total estimated amount a person would need to purchase the recommended amount of daily fruits and vegetables per USDA standards. USDA research using data from 2013 showed that it was possible for a person on a 2,000-calorie diet to eat enough quantity and variety of fruits and vegetables for about $2.10 to $2.60 per day, corresponding to $2.27 - $2.81 a day in 2018 dollars.

In addition, Produce Rx enrollees will be offered nutritional counselling services made available at the point of grocery sale.

These prescriptions must be redeemed every three months by a visit to the participant’s primary care physician.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes for the study are as follows:
1. Health biomarkers
2. Fruit and vegetable expenditure
3. Medical costs
Primary Outcomes (explanation)
1. Health biomarkers. These include BMI (weight/Kg^2), systolic blood pressure (SBP) and glycated hemoglobin test (HbA1c). Our primary test for this family of outcomes is a joint test of the null hypothesis that the treatment effect on all of them is zero for all units, conducted via randomization inference as in Young (2018).

2. The primary measure of fruit and vegetable expenditure will be derived from supermarket scanner data. We will use survey data at end line in the event that these data cannot be obtained for a representative sample of study participants.

3. Medical costs, as reported by the insurer. Our primary test for this outcome is a Kolmogorov-Smirnov test for differences in distributions between treatment and control. Inference regarding this test statistic will be undertaken by randomization inference.

Secondary Outcomes

Secondary Outcomes (end points)
We will conduct two types of secondary analyses: those that look at outcomes not defined as primary, and those that look at subgroup heterogeneity in treatment effects, with regard to primary outcomes.

The former includes:
- Self-reported total fruits and vegetables expenditure (one-week recall), using survey data collected at endline.

Subgroup analyses planned for the primary outcomes include heterogeneity by:
- Age
- Gender
- Severity of underlying medical condition based on the following categories:
(i) pre-diabetes in the 12-month pre-trial period (5.7 %<HbA1c < 6.5% or 100 mg/dl < FPG<126 mg/dl or 140 mg/dl <OGTT< 200 mg/dl);.
(ii) diagnosed with type-2 diabetes mellitus in the 12-month pre-trial period (HbA1c ≥ 6.5% or FPG ≥126 mg/dl or OGTT ≥ 200 mg/dl)
(iii) hypertension 130/80 but no other conditions
(iv) both diabetes type 2 and hypertension at enrollment.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Medicaid-enrolled participants with chronic diet-related health conditions, as measured in insurer data, will be individually (block) randomized into either the Produce Rx program or a control group. Analysis will be by intent-to-treat.
Experimental Design Details
Randomization Method
Randomization was done in office by computer.
Randomization Unit
Randomization is at the individual level. Individuals are randomly assigned to either the offer of the Produce Rx program or to a control group. As distance to the supermarket is an important determinant of access, and as three clinics are anticipated to participated in the study, randomization was blocked by zip code of residence and by the clinic in which the participant is enrolled.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
6,271 individuals.
Sample size: planned number of observations
6,271 individuals.
Sample size (or number of clusters) by treatment arms
3,152 individuals assigned to treatment; 3,119 individuals assigned to control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power for biomarker outcomes: We calculate power for the individually randomized design, with block indicators and pre-trial values of the outcomes. Given the persistence of biomarkers, our power calculations assume that these covariates explain 60 percent of the variation in these outcome at endline. This yields a minimum detectable effect size of 0.046 standard deviations. To translate this MDE into the natural units of the outcomes, we use available data from the 2012-2016 participating cohorts in DC Greens earlier programs. In that closely related population, the standard deviation (SD) for A1c was 2.89; the SD for BMI was 9.12, the SD for SBP was 17.25. These imply minimum detectable effects of 0.32 for A1c; of 1.02 for BMI; and of 1.93 for SBP. Power for medical expenditure We propose to use a two-part model for medical costs, as well as a Komolgorov-Smirnov test for equality in distributions (with the latter as primary, and with randomization inference used to conduct inference in both cases). Simulations demonstrate that these offer power advantages relative to OLS regressions for such skewed outcomes. However, since closed-form solutions do not exist for the power for these alternative specifications, we present power calculations for medical expenditures based on an ordinary least squares model. We consider these estimates to be conservative as a consequence. Medical costs will be analyzed on a monthly basis, with the primary analysis using 12 months of pre-intervention data and 8 months of post-intervention data. Projections about power depend on assumptions about two features of the data-generating process: (1) the share of variation in medical expenditures that can be explained by the combination of baseline covariates and block-month indicators; and (2) the autocorrelation in medical expenditure outcomes. Assuming that covariates explain 40 percent of the variation in outcomes – note that baseline biomarkers and other pre-intervention health measures can be combined with survey data to predict medical expenditures – and that the degree of autocorrelation in medical expenditures is 0.6 the study would be powered to detect an impact of 0.06 standard deviations. To map the MDE expressed in standard deviations to dollar terms, we note that according to Kaiser Family Foundation the average per member per month (PMPM) Medicaid spending is $750. We expect this value to be highly skewed (with a high fraction of zeros and a long right tail). Based on other studies conducted on the Medicaid population, standard deviations of monthly medical costs are approximately $2,000. An impact of 0.06 standard deviations therefore corresponds to $120 per month, or 16 percent of average monthly expenditure. A reduction in costs of $100 is approximately the effect size required to justify the cost of the program on a cost basis. As noted above, alternative specifications are likely to be substantially better powered to detect impacts on average expenditure than an OLS regression, in the presence of such skewed outcomes. For example, Komolgorov-Smirnov test can reduce the MDE to as little as a third of the MDE in an OLS regression for skewed data, as the PIs have shown elsewhere. Power for grocery expenditure Power for food expenditure outcomes, measured using point-of-sale data from Giant, is strengthened by the multiplicity of post-intervention time periods: we have a total of 36 post-intervention weeks (or equivalent, for individuals in the control group). This compensates considerably for the absence of pre-intervention point-of-sale data. We further assume that grocery expenditure data will be available only for a set of 200 individuals who can be enrolled (with the encouragement of a one-off voucher) to share their grocery expenditure data within the study. Statistical power for these outcomes is determined by (1) the share of expenditure outcomes explained by baseline covariates and by block-week fixed effects, which we denote collectively by R_X^2; and (2) and the extent of autocorrelation in an individual’s post-treatment expenditure outcomes, which we denote by ρ. We anticipate that food expenditures are highly correlated for a given household from week to week. Applying values of ρ=R_X^2=0.6 gives a minimum detectable effect size of 0.32 standard deviations in food expenditure. To map this minimum detectable effect size, expressed in standard deviations of food expenditure, into a dollar value, we require an estimate of the standard deviation of food expenditure. We derive this by combining data from two sources. First, the USDA-financed Evaluation of the Healthy Incentives Pilot (HIP): Final Report (Bartlett et al., 2014), which focuses on a similarly low-income (SNAP-eligible) population, reports a standard deviation of weekly household fruit and vegetable purchases – measured in cup equivalents – of 10.9. Second, USDA guidelines on fruit and vegetable consumption imply a current price of $1.00 per cup equivalent. Thus, we estimate the standard deviation of weekly fruit and vegetable expenditure as $10.9. This estimate for the standard deviation of weekly fruit and vegetable consumption, combined with our estimated minimum detectable effect size (in standard deviation units), implies that we are powered to detect an increase in fruit and vegetable expenditure of $3.54, with 80 percent probability. Compared with a weekly voucher of $20, this is a very small effect. This suggests that even under a range of alternative assumptions about the properties of measured expenditure, we will be highly powered to detect policy-relevant impacts.

Institutional Review Boards (IRBs)

IRB Name
DC Department of Health IRB
IRB Approval Date
IRB Approval Number
IRBPH # 2019-1


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