A Prescription for Health Equity: A Healthcare Provider-based Produce Prescription Program for People With Prediabetes and Type 2 Diabetes

Diabetes Mellitus, Type 2 Clinical Trial

Official title:

A Prescription for Health Equity: A Healthcare Provider-based Produce Prescription Program for People With Prediabetes and Type 2 Diabetes

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05939349
• Other study ID #: 2022-06
• Status: Not yet recruiting
• Phase: N/A
• Start date: September 1, 2023
• Est. completion date: August 31, 2025

Study information

• Verified date: August 2023
• Source: Griffin Hospital
• Contact: Rockiy G Ayettey, MS, DHSc
• Phone: 2037321125
• Email: rockiy.ayettey[@]yalegriffinprc.org
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The goal of this project is to co-design a healthcare provider-based produce prescription program (PPR) in partnership with the community served to improve participants' food security status, diet quality, and cardio-metabolic health outcomes, and to reduce healthcare costs, specifically related to medication use and hospital visits. Novel to this study is an implementation of a community co-designed randomized controlled trial (RCT) with a delayed intervention control group focused of equity (i.e., including the target population in the intervention designed for them) in design, implementation, and evaluation. The project will be conducted in 3 phases. Phase 1 will involve formative research and PPR co-design with community partners and potential participants through listening sessions, partner meetings, and community advisory group sessions to finalize the intervention protocol and components, for which investigators will then request IRB approval. Phase 2 will involve the implementation of a delayed intervention RCT PPR. Data analysis and final reporting will be conducted during Phase 3.

Specific Aims:

In collaboration with community partners and community members, utilize implementation science strategies to identify and address community, systemic, and structural barriers and assets to co-design a tailored produce prescription program (PPR) intervention that emphasizes health equity in a low-income population served by Griffin Hospital (GH) and/or Griffin Faculty Physicians (GFP).

Hypothesis: Collaborating with our community partners on the design and implementation of a PPR will lead to a successful design and implementation of the PPR to our population of focus, as evidenced by satisfaction, retention, experiences of dignity/respect, improved self-efficacy related to fruit and vegetable consumption, and diet quality.

Demonstrate improvements, in intervention group vs delayed intervention control group, in food security status, diet quality, and cardio-metabolic outcomes in individuals with prediabetes or type 2 diabetes through implementation of a tailored PPR in a low-income population served by GH and/or GFP.

Hypothesis: The PPR designed with community input will improve food security status, diet quality, self-reported health related quality of life and cardio-metabolic outcomes (Hemoglobin A1C, weight/body mass index, lipids, blood pressure), among our intervention participants compared with a control over a 6-month period.

Evaluate the impact of a tailored PPR on healthcare cost among low-income participants with prediabetes or type 2 diabetes.

Hypothesis: The successful implementation of the tailored PPR will lead to a reduction in certain healthcare cost specifically related to medication usage (including dose) and reduction in emergency department visit and/or hospitalization among intervention participants compared with a control over a 6-month period.

Description:

The risk of nutrition-related chronic diseases is significantly higher in low-income populations when compared with those with higher incomes. Over the course of the last century, the American diet has shifted toward higher consumption of sugar, saturated fat, added sodium, processed foods, and overall calories, while intake of fresh fruits and vegetables has starkly declined. These dietary shifts parallel the rapid rise in the national prevalence of obesity, cardiovascular disease and type 2 diabetes. With among 1 in 3 adults in the United States currently affected by obesity and over 30 million Americans (1 in 10) with type 2 diabetes, there is a growing need to invest resources in prevention and treatment. Diabetes is considered the seventh leading cause of death and is associated with $327 billion yearly in excess medical costs and lost productivity. Obesity, diabetes rates and diet quality are all associated with socioeconomic status and disparately impact low-income, Black and Hispanic persons compared to their higher-income or white counterparts.

Increasing the consumption of fruits and vegetables can improve health by promoting healthy weight and reducing blood sugar, the risk of heart disease, stroke, some types of cancer, and digestive problems. Studies have shown that even without reducing caloric consumption, increasing fruit and vegetable consumption can provide independent benefits by increasing fiber intake, reducing sodium and fat intake, and increasing the micronutrient content of the consumer's diet. There are grave disparities in access to healthy foods based on income, race, geographic location, and immigration status, which in turn drive disparities in obesity, diabetes, overall health, and quality of life.

Fruit and vegetable incentive programs are a prudent method of improving food security, while not investing in or allocating money for poor nutritional quality food. They guarantee that the money spent goes to fruits and vegetables known to improve nutritional status, and a growing body of evidence demonstrates that money allocated to the purchase of fruits and vegetables can improve diet quality, purchasing patterns and sometimes health in a statistically and clinically significant manner. Produce prescription programs have the potential to reduce health disparities resulting from differential access to healthy food. In addition to providing free or discounted access to produce, many PPRs also attempt to address other cultural and socio-contextual barriers to accessing healthy food by providing educational and skill-building programming such as cooking demonstrations, suggested family meal plans, and nutritional information at the point of purchase. The few studies that examine the effectiveness of PPRs suggest that they are associated with dietary improvements and reductions in food insecurity. Overall, fruit and vegetable incentive programs have demonstrated positive impact (with few studies reporting no impact or minimal impact) on fruit and vegetable intake and or diet quality, reduced hemoglobin A1C, and reduced body weight.

Fruit and vegetable incentive programs are designed to increase the budget share available to a household for the purchase of fruits and vegetables.14 Incentive programs have the ability to immediately increase fruit and vegetable purchases, although the benefits of increasing fruit and vegetable purchases and consumption might take longer to realize. Incentives make it possible to encourage healthier purchasing patterns that are necessary for longer term behavior change that leads to improved health status. According to a 2020 meta-analysis conducted by Engel and Ruder, overall, fruit and vegetable incentive programs have a demonstrated benefit on fruit and vegetable purchase patterns.14 In Connecticut's Lower Naugatuck Valley (LNV), poverty and low-income rates (8% and 21%, respectively, in 2017) have been increasing across the region since 2000, according to a 2019 report on community well-being in the LNV. The seven communities (Ansonia, Beacon Falls, Derby, Naugatuck, Oxford, Seymour, and Shelton) of the LNV have a combined population of approximately 140,000, with increasing racial and ethnic diversity. Food insecurity and nutrition-related chronic disease are significant issues among the area's residents, with 12% of adults reporting food insecurity. Nutrition related chronic diseases such as obesity (affecting 28% of the population) are high. Heart disease and diabetes are among the eight leading causes of premature death and account for an average of 14 and 15 years of potential life lost per disease, respectively.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 134
• Est. completion date: August 31, 2025
• Est. primary completion date: August 31, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. age 18 years or older

2. a patient of GH and/or GFP

3. diagnosis of prediabetes or Type 2 diabetes consistent with the American Diabetes Association diagnostic criteria

4. low-income and eligible for SNAP (Supplemental Nutrition Assistance Program, formerly known as Food Stamps) and/or Medicaid.

Exclusion Criteria:

1. inability to speak English or Spanish

2. having had gastric bypass or other bariatric surgeries

3. having an eating disorder, or other substantial, clinical dietary restrictions.

Related Conditions & MeSH terms

• Diabetes Mellitus
• Diabetes Mellitus, Type 2
• Pre Diabetes
• Prediabetic State

Intervention

Other:
• Produce Prescription Group
Participants will receive vouchers equal to $40/household/month, with an additional $5/month per additional household member, for the purchase of fresh fruits and vegetables during the six-month intervention period. The vouchers will be administered in the form of a restricted Mastercard debit card. A variety of nutrition education options will be offered throughout the intervention period and participation will be tracked. The Nutrition education options will include a periodic newsletter to participants that will include nutrition and diabetes prevention and management opportunities available through Griffin Hospital Population Health Team, the local health department, program and education opportunities available through the Registered Dietitians, local offerings by SNAP-Education and The Expanded Food and Nutrition Education Program.

Locations

Country	Name	City	State
n/a

Sponsors (3)

Lead Sponsor	Collaborator
Griffin Hospital	About Fresh, Griffin Faculty Physicians

References & Publications (29)

2019 Valley Community Index: Understanding the Valley Region | DataHaven. Accessed June 30, 2022. https://www.ctdatahaven.org/reports/2019-valley-community-index-understanding-valley-region

Abdullah MM, Gyles CL, Marinangeli CP, Carlberg JG, Jones PJ. Cost-of-illness analysis reveals potential healthcare savings with reductions in type 2 diabetes and cardiovascular disease following recommended intakes of dietary fiber in Canada. Front Pharmacol. 2015 Aug 11;6:167. doi: 10.3389/fphar.2015.00167. eCollection 2015. — View Citation

Aiyer JN, Raber M, Bello RS, Brewster A, Caballero E, Chennisi C, Durand C, Galindez M, Oestman K, Saifuddin M, Tektiridis J, Young R, Sharma SV. A pilot food prescription program promotes produce intake and decreases food insecurity. Transl Behav Med. 2019 Oct 1;9(5):922-930. doi: 10.1093/tbm/ibz112. — View Citation

American Diabetes Association. Economic Costs of Diabetes in the U.S. in 2017. Diabetes Care. 2018 May;41(5):917-928. doi: 10.2337/dci18-0007. Epub 2018 Mar 22. — View Citation

Andreyeva T, Tripp AS, Schwartz MB. Dietary Quality of Americans by Supplemental Nutrition Assistance Program Participation Status: A Systematic Review. Am J Prev Med. 2015 Oct;49(4):594-604. doi: 10.1016/j.amepre.2015.04.035. Epub 2015 Aug 1. — View Citation

ASA24® Dietary Assessment Tool | EGRP/DCCPS/NCI/NIH. Accessed June 30, 2022. https://epi.grants.cancer.gov/asa24/

Beckles GL, Chou CF. Disparities in the Prevalence of Diagnosed Diabetes - United States, 1999-2002 and 2011-2014. MMWR Morb Mortal Wkly Rep. 2016 Nov 18;65(45):1265-1269. doi: 10.15585/mmwr.mm6545a4. — View Citation

Bhat S, Coyle DH, Trieu K, Neal B, Mozaffarian D, Marklund M, Wu JHY. Healthy Food Prescription Programs and their Impact on Dietary Behavior and Cardiometabolic Risk Factors: A Systematic Review and Meta-Analysis. Adv Nutr. 2021 Oct 1;12(5):1944-1956. doi: 10.1093/advances/nmab039. — View Citation

Bickel G, Nord M, Price C, Hamilton W, Cook J. Guide to Measuring Household Food Security. USDA; Food and Nutrition Services; Office of Analysis, Nutrition, and Evaluation; 2000:1-82. https://fns-prod.azureedge.net/sites/default/files/FSGuide_0.pdf

Bowling AB, Moretti M, Ringelheim K, Tran A, Davison K. Healthy Foods, Healthy Families: combining incentives and exposure interventions at urban farmers' markets to improve nutrition among recipients of US federal food assistance. Health Promot Perspect. 2016 Mar 31;6(1):10-6. doi: 10.15171/hpp.2016.02. eCollection 2016. — View Citation

Bridle C, Riemsma RP, Pattenden J, et al. Systematic review of the effectiveness of health behavior interventions based on the transtheoretical model. Psychol Health. 2005;20(3):283-301. doi:10.1080/08870440512331333997

Cavanagh M, Jurkowski J, Bozlak C, Hastings J, Klein A. Veggie Rx: an outcome evaluation of a healthy food incentive programme. Public Health Nutr. 2017 Oct;20(14):2636-2641. doi: 10.1017/S1368980016002081. Epub 2016 Aug 19. — View Citation

Chinchanachokchai S, Jamelske EM, Owens D. Tracking the Use of Free Produce Coupons Given to Families and the Impact on Children's Consumption. WMJ. 2017 Feb;116(1):40-3. — View Citation

Cooksey-Stowers K, Schwartz MB, Brownell KD. Food Swamps Predict Obesity Rates Better Than Food Deserts in the United States. Int J Environ Res Public Health. 2017 Nov 14;14(11):1366. doi: 10.3390/ijerph14111366. — View Citation

Dodd AH, Briefel R, Cabili C, Wilson A, Crepinsek MK. Disparities in consumption of sugar-sweetened and other beverages by race/ethnicity and obesity status among United States schoolchildren. J Nutr Educ Behav. 2013 May-Jun;45(3):240-9. doi: 10.1016/j.jneb.2012.11.005. Epub 2013 Feb 13. — View Citation

Engel K, Ruder EH. Fruit and Vegetable Incentive Programs for Supplemental Nutrition Assistance Program (SNAP) Participants: A Scoping Review of Program Structure. Nutrients. 2020 Jun 4;12(6):1676. doi: 10.3390/nu12061676. — View Citation

Ghosh-Dastidar B, Cohen D, Hunter G, Zenk SN, Huang C, Beckman R, Dubowitz T. Distance to store, food prices, and obesity in urban food deserts. Am J Prev Med. 2014 Nov;47(5):587-95. doi: 10.1016/j.amepre.2014.07.005. Epub 2014 Sep 10. — View Citation

Guenther PM, Casavale KO, Reedy J, Kirkpatrick SI, Hiza HA, Kuczynski KJ, Kahle LL, Krebs-Smith SM. Update of the Healthy Eating Index: HEI-2010. J Acad Nutr Diet. 2013 Apr;113(4):569-80. doi: 10.1016/j.jand.2012.12.016. Epub 2013 Feb 13. Erratum In: J Acad Nutr Diet. 2016 Jan;116(1):170. — View Citation

Guidelines for assessing nutrition-related knowledge, attitudes and practices. Accessed June 30, 2022. https://www.fao.org/3/i3545e/i3545e00.htm

Hennessy CH, Moriarty DG, Zack MM, Scherr PA, Brackbill R. Measuring health-related quality of life for public health surveillance. Public Health Rep. 1994 Sep-Oct;109(5):665-72. — View Citation

Katie Garfield, Sarah Downer, Rachel Landauer, et al. Produce-RX-March-2021.Pdf.; 2021. Accessed August 19, 2021. https://www.chlpi.org/wp-content/uploads/2013/12/Produce-RX-March-2021.pdf

Lagisetty PA, Priyadarshini S, Terrell S, Hamati M, Landgraf J, Chopra V, Heisler M. Culturally Targeted Strategies for Diabetes Prevention in Minority Population. Diabetes Educ. 2017 Feb;43(1):54-77. doi: 10.1177/0145721716683811. — View Citation

Li R, Zhang P, Barker LE, Chowdhury FM, Zhang X. Cost-effectiveness of interventions to prevent and control diabetes mellitus: a systematic review. Diabetes Care. 2010 Aug;33(8):1872-94. doi: 10.2337/dc10-0843. — View Citation

Liu J, Rehm CD, Onopa J, Mozaffarian D. Trends in Diet Quality Among Youth in the United States, 1999-2016. JAMA. 2020 Mar 24;323(12):1161-1174. doi: 10.1001/jama.2020.0878. — View Citation

Mainstreaming Produce Prescriptions - Center For Health Law and Policy Innovation. Accessed October 7, 2021. https://www.chlpi.org/health-law-and-policy/projects/mainstreaming-producerx/

Mozaffarian D, Liu J, Sy S, Huang Y, Rehm C, Lee Y, Wilde P, Abrahams-Gessel S, de Souza Veiga Jardim T, Gaziano T, Micha R. Cost-effectiveness of financial incentives and disincentives for improving food purchases and health through the US Supplemental Nutrition Assistance Program (SNAP): A microsimulation study. PLoS Med. 2018 Oct 2;15(10):e1002661. doi: 10.1371/journal.pmed.1002661. eCollection 2018 Oct. — View Citation

Ridberg RA, Yaroch AL, Nugent NB, Byker Shanks C, Seligman H. A Case for Using Electronic Health Record Data in the Evaluation of Produce Prescription Programs. J Prim Care Community Health. 2022 Jan-Dec;13:21501319221101849. doi: 10.1177/21501319221101849. — View Citation

Teutsch S. The cost-effectiveness of preventing diabetes. Diabetes Care. 2003 Sep;26(9):2693-4. doi: 10.2337/diacare.26.9.2693. No abstract available. — View Citation

Trapl ES, Joshi K, Taggart M, Patrick A, Meschkat E, Freedman DA. Mixed Methods Evaluation of a Produce Prescription Program for Pregnant Women. J Hunger Environ Nutr. 2017;12(4):529-543. doi:10.1080/19320248.2016.1227749

* Note: There are 29 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Tracking Study Participation Inputs	Data will be collected and monitored related to compliance with voucher spending and seeking tech support related to reloads/utilization of the debit card. Additionally, participation and attendance in nutrition education opportunities will be recorded to understand intervention fidelity.; Field notes: the study core team will have access to a field note data collection survey in Qualtrics. This survey will be a single, open-ended survey question designed to collect contextual information or anecdotes that will help the study team later contextualize findings.; Additionally, investigators plan to use a provider satisfaction survey to assess healthcare providers and those affiliated with the medical practices. This tool was developed internally, and feedback was provided from the team.	Change from Baseline at 6 months and 9 months
Other	Medication use	Medication use during the study will be collected by the study coordinator from the electronic medical record. A medication log will be completed by participants and reviewed with the study coordinator at each assessment time point. This information will also be confirmed with the participant's physician/or by electronic data review.	Change from Baseline at 6 months and 9 months
Other	Emergency room visits	Emergency Department visits and/or hospitalizations will be confirmed via the hospital's Population Health Department project staff.	Change from Baseline at 6 months and 9 months
Other	Hospitalizations	Hospitalizations will be confirmed via the hospital's Population Health Department project staff.	Change from Baseline at 6 months and 9 months
Other	Field notes	The study core team will have access to a field note data collection survey in Qualtrics. This survey will be a single, open-ended survey question designed to collect contextual information or anecdotes that will help the study team later contextualize findings.	Change from Baseline at 6 months and 9 months
Other	Provider satisfaction survey	A provider satisfaction survey to assess healthcare providers and those affiliated with the medical practices. This tool was developed internally, and feedback was provided from the team.	9 months
Primary	Glycemic control	Glycated hemoglobin A1c (HbA1c) will be collected by Griffin's Community Health RN or Yale-Griffin Prevention Research Center Staff (will be obtained by direct measurements from the participants' finger stick blood sample) at baseline, 6 months and 9 months for both groups, while the intervention group is participating in the intervention and while the delayed group. HbA1c will be used to measure the average plasma glucose concentration and will be measured using a finger-prick test.	Change from Baseline at 6 months and 9 months
Secondary	Diet quality	Diet quality will be measured using the Global Diet Quality Projects Diet Quality Questionnaire (DQQ) the survey can be found here (https://drive.google.com/drive/folders/16rpv-sr05HXTs3NSZmvFbRaL6dFfcuAs,	Change from Baseline at 6 months and 9 months
Secondary	Food Insecurity	Food insecurity will be assessed using the U.S. Department of Agriculture (USDA) household food security survey module 18-item survey modified for a 30 days time reference point instead of the standard past 12-months.27	Change from Baseline at 6 months and 9 months
Secondary	Respect and Dignity Scale	This survey, developed with community input from the PRCs CAG and PPR participants in other US projects, will be used to assess participant experiences related to respect and dignity when participating in the various program components.	Change from Baseline at 6 months and 9 months
Secondary	Body weight	Body weight will be measured using a calibrated Tanita electronic scale.	Change from Baseline at 6 months and 9 months
Secondary	Blood Pressure	Blood pressure will be measured by using a Dinamap Monitor Pro 100 (GE Healthcare, Piscataway, NJ) after sitting for 5 minutes by a registered nurse.	Change from Baseline at 6 months and 9 months
Secondary	Serum lipids	Values of total cholesterol (Tchol), triglycerides (TG), and high-density lipoprotein (HDL) will be obtained by direct measurements from the participants' finger stick blood sample. Serum low-density lipoprotein (LDL) will be calculated using the following formula LDL = Tchol - (TG/5 + HDL). HDL:Tchol ratio will also be computed.	Change from Baseline at 6 months and 9 months
Secondary	Socio-demographics	Socio-demographics will be measured using standard questionnaires developed by the funder and internally developed items.	Change from Baseline at 6 months and 9 months
Secondary	Health related quality of life	Health related quality of life will be measured using a single item consistent with the CDC Behavioral Risk Factor Surveillance System.28 plus additional items collect self-reported diagnosis of nutrition-related nutrition disease.	Change from Baseline at 6 months and 9 months
Secondary	Participation in SNAP, food assistance	Participation in SNAP, food assistance will be measured using items standard to the funder,	Change from Baseline at 6 months and 9 months
Secondary	Nutrition knowledge	Nutrition knowledge will be measured using an internally developed survey based on the Dietary Guidelines for Americans and MyPlate.	Change from Baseline at 6 months and 9 months