Evaluation of Medicaid Grocery Gift Cards & Nutrition Education Program for Pregnant Women on Maternal and Infant Outcomes
Phase: NA
Conditions: Pregnancy Related, Financial Gift, Nutrition, Healthy
Sponsor: University of Texas at Austin

 
FORM: IRB Proposal – Secondary Use Submission 
NUMBER 
VERSION DATE 
HRP-UT903 
4/27/2023 
 
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Clinicaltrials.gov cover page 
 
Official Title of the study 
Evaluation of Driscoll Health Plan's Nurture program on nutrition education and food support for 
pregnant women 
 
NCT number  
To be assigned 
 
Date of the document 
January 24th, 2024 
 
Unique Protocol ID: 
STUDY00005622 (IRB Review Exempt) 
 
Principal Investigator: 
Maninder “Mini” Kahlon  
Associate Professor 
Population Health, Dell Medical School, University of Texas at Austin 
 
 
 

 
Page 2 of 16 
 
STUDY INFORMATION 
Submit this form when seeking determinations for human subject research using secondary data or biospecimens. 
 
1 
Confirmation of Secondary Use of Data or Specimen 
Click on the following check box (or double click and type an “X” if using Google Docs). 
 
 
☒ The data or specimens have been or will be collected for purposes unrelated to 
the purpose of this study. 
If this is not the case, please complete HRP-UT901 IRB Proposal Standard Submission or HRP-UT902 IRB 
Proposal Exempt Submission form.  
 
2 
Study Information 
Describe the purpose of the research, study background, and hypotheses. 
To input text click in the light grey area below. 
Purpose: 
To assess the impact of Driscoll Health Plan’s Nurture program on pregnancy outcomes among 
participating members. 
 
Background: 
Driscoll Health Plan (DHP) is a non-profit, community-based health insurance plan that 
provides coverage to South Texas and the Rio Grande Valley communities. Their insurance 
products include STAR Medicaid, STAR Kids, CHIP, and CHIP Perinatal. Affiliated with Driscoll 
Children’s Hospital, DHP offers healthcare services at specialty centers and clinics in various 
locations, including McAllen, Harlingen, Brownsville, Laredo, Rio Grande City, Eagle Pass, 
Edinburg, Victoria, and Weslaco. 
 
Maternal health has gained increasing attention. Senate Bill (S.B.) 750, 86th Legislature, 
Regular Session, 2019, Section 3, for example, mandates the Texas Health and Human Services 
Commission (HHSC) to develop or enhance statewide initiatives for improving the quality of 
maternal health care services (Kolkhorst, 2019). It specifies initiatives that contracted managed 
care organizations (MCOs) must implement and requires progress reporting to the legislature 
MCOs are also encouraged to incorporate their own initiatives to enhance maternal healthcare 
services. 
 
In response, DHP, with input from our Factor Health lab at Dell Medical School, introduced the 
Nurture program for its members as a quality improvement initiative, designed to expand 
members’ access to nutrition education and food purchase support to improve health and 
pregnancy outcomes. 
 
DHP’s Nurture Program: 

 
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The Nurture program focused on promoting healthy eating through nutrition education and 
food purchasing support with monthly gift cards for a main local grocery store at the Corpus 
Christi area. Specifically, the program includes the following components: 
 
1. Nurture Monthly Packages: Sent every four weeks until the member reaches the end of 
pregnancy (delivery or other reasons, e.g., miscarriage), with a maximum of eight 
packages. Each package includes: 
• A US$40 gift card (2 x $20 each) to a grocery store. 
• A healthy eating information sheet selected from Brighter Bites project 
(https://brighterbites.org/).  
• A set of three recipes. 
• Nutrition-related Frequently Asked Questions (included in four out of the eight 
packages). 
 
2. Two 30-Minute Nutritional Consultations over the phone: Designed by a Registered 
Dietitian Nutritionist (RDN) who trained a bilingual nutrition specialist to deliver the 
consultations. Topics revolved around healthy eating during pregnancy, such as food 
choices, portion sizes, budget-friendly shopping, food safety, and food preparation. No 
nutrition therapy advice to manage diseases was provided. 
 
Members had the flexibility to opt out of the program at any time and discontinue receiving 
packages. Consultations were optional. If they opted out, they continued to receive the DHP 
benefits as part of their regular coverage. 
 
Secondary Data Analysis Proposal: With funding from the Episcopal Health Foundation, we 
will conduct a secondary data analysis to evaluate the DHP’s Nurture program using their 
existing administrative data from claims. 
 
Kolkhorst (2019). Relating to maternal and newborn health care and the quality of services provided to women in this state 
under certain health care programs. (S.B. 750). 86th Legislature, Regular Session. 
https://capitol.texas.gov/BillLookup/History.aspx?LegSess=86R&Bill=SB750.   
 
 
3 
Design, Methodology, and Data Analysis 
 
Provide information regarding study design or data collection methodologies. Describe the data analysis plan, 
including any statistical procedures or power analysis.  
 
To input text, click in the light grey area below. 
 
 
We will evaluate the impact of DHP’s quality improvement pilot program (called, 
Nurture), which included nutrition education and food support offered to a subset of 
pregnant women aged 30 years and above and were covered by the Medicaid Managed 

 
Page 4 of 16 
 
Care Organization (MCO) plan in their “STAR” and “CHIP” programs during October 
2022 and July 2023.  
 
From this population, DHP randomly selected a subset of approximately 500 women 
during that period, from which approximately 200 were in the program, the “exposed 
group.” The women who were covered during the same period but were not selected 
were not exposed to the program and will compose the “comparison group.” 
 
The objective is to compare maternal and neonatal outcomes between those who 
participated in the program (exposed group) and those who did not (comparison 
group). 
 
Analysis Plan: 
Claims data will be pulled retrospectively from the insurer's database and other 
administrative sources. Information will include healthcare utilization, maternal 
outcomes during pregnancy, and neonatal outcomes at and post-delivery. The primary 
analysis involves comparing the outcomes of the exposed group with the comparison 
group. Statistical methods appropriate for this type of administrative data will be 
employed to assess the differences between the two groups, adjusting for potential 
confounders. 
 
The Nurture program’s impact will be evaluated using claims data received from DHP 
concerning two composite outcomes: adverse maternal health outcomes (Composite 1) 
and adverse neonatal outcomes (Composite 2), as shown in Table 1 below. In addition, 
we will explore single outcomes, such as delivery mode (vaginal vs c-section), cost data, 
and birthweight, adding nuance to our understanding of the program's impact.  
 
We will primarily use logistic regressions to predict the primary, binary composite 
outcomes for mothers and newborns, adjusted for risk factors such as maternal age, 
gestational age at the time of enrollment, pre-existing diabetes, hypertension, and 
being overweight pre-pregnancy (shown in Table 2). The program “dose” variables are 
anticipated to be continuous predictors that include both ‘usual care’ (i.e., prenatal care 
visits) and Nurture program elements (the dollar value of the gift cards spent up to 30 
days after stopping the program, total number of nutrition consult minutes); see Table 
3.  
Because we expect cost data to be continuously distributed, linear regressions are 
expected to be more appropriate. The predictors will remain the same in all models 
irrespective of outcome, including the “dose” variables. 
 

 
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Table 1. Program outcomes from health claims 
Composite outcome 1:  
Adverse health events during 
pregnancy 
 
Composite outcome 2:  
Adverse health events at 
birth and newborn-related 
 
Exploratory outcomes: 
 
• Gestational Diabetes Mellitus  
• Excessive weight gain during 
pregnancy 
• Pregnancy-associated 
hypertension 
 
• Pre-term birth (<37 weeks) 
• Neonatal Intensive Care 
Unit (NICU) 
• Small for gestational age 
• Large for gestational age 
• Stillbirths 
 
• Emergency Department 
visits 
• Cost (outpatient, 
inpatient, pharmacy, 
total) 
• Delivery mode (vaginal, 
c-section) 
• Birthweight collected 
from hospitals 
 
Table 2. Population descriptors and demographics from health claims 
Demographics 
 
Health history (dichotomous) 
• Age (years) 
 
• Family history of diabetes mellitus 
• Ethnicity 
 
• Pre-pregnancy diabetes mellitus 
• Location (Nueces/Hidalgo) 
 
• Pre-pregnancy overweight/obesity 
• Gestational age at enrollment 
 
• History of gestational diabetes 
• Gestational age at delivery 
 
• Chronic hypertension 
 
Table 3. “Dose” variables from program implementation tracking and health claims 
Nurture Program uptake 
 
Care Received 
Gift card usage (dollar amount) 
 
Prenatal visits (counts) 
Packages received (counts) 
 
Referral to Case Management (dichotomous) 
Nutritional consults (duration) 
 
 
 
DHP’s Nurture enrollment: 
1. DHP receives daily new health plan enrollees from the TX State. Their data 
analytics team identified new pregnant members aged 30 or older in bi-weekly 
“cycles” and ran an algorithm to randomly select a list of names to be notified 
and contacted by DHP’s Community Health Workers (CHWs). 
2. Notifications: Members on the roster to be called received an SMS text with a 
URL to an informational page, which was also mailed as a hard copy to the 
member’s address on file. 
3. Welcome calls: DHP’s CHWs called up to 3 times on consecutive days to welcome 
the women to the Nurture program, explain what to expect, confirm address for 
mailing out the packages, and book two phone nutritional consultations. 
o Individuals who could not be reached by the CHW did not receive the 
packages and the consultations but continued to receive their regular 
benefits as part of their DHP membership. 

 
Page 6 of 16 
 
 
The end of participation in the program is marked by the termination of pregnancy (in 
the form of a claim to the health plan that would flag delivery or a 
miscarriage/abortion/stillbirth). If such a claim does not reach DHP’s system in a timely 
manner, the woman in the Nurture program could receive up to 8 mailout packages.  
 
The Texas Health and Human Services Commission (HHSC) reviewed and approved key 
member-facing materials, such as SMS text and the informational letter, as part of their 
regular review process of member-facing materials. 
 
Third group: The women who were selected to be offered the program, but DHP staff 
could not connect during telephonic outreach, compose a third group. Administrative 
data on this group will enable us to explore how they compare with the other two 
groups in terms of their population descriptors and demographics shown in Table 2 
above. Answering evaluation questions such as how do those who were reached by the 
CHWs differ from those who weren’t, helping to assess reach from an equity 
perspective. 
 
 
 
 
 
4 
Data or Specimen Information 
Provide information about where the data or specimens will come from (e.g., 
pathology lab, commercial sources) and what type of data or specimens you will 
obtain. 
To input text click in the light grey area below. 
 
DHP data analysis team will leverage internal databases to compile the required 
administrative data for analyses, which will be shared as Excel spreadsheets via secured 
File Transfer Protocol (sFTP). 
 
DHP also contracted the Dell Med lab (Factor Health) services for the delivery of the 
nutritional consultations and to track gift card balances. These data are owned by 
Driscoll, who will receive the full data set, de-identify and anonymize it, and merge it to 
the other relevant sets they will generate for analysis. 
 
The staff involved in the delivery of the nutrition consultation service will not be 
involved in the data analysis in any way. 

 
Page 7 of 16 
 
 
 
5 
Identifiers 
List all identifiers the researcher will obtain even if the researcher plans to make the 
data or specimens anonymous. Include information that can be linked directly or 
indirectly to the subject. 
To input text click in the light grey area below. List all identifiers collected. 
 
No identifier will be shared for this data analysis. Only basic demographics information 
(age, ethnicity, and service location [Nueces/Hidalgo]) will be shared. 
 
DHP analytics team will anonymize and de-identify the data before sharing them with 
our team for the analysis. Anonymization will be done in a way so that no information 
can be linked back to the individuals.  
 
 
Re-identification and DUAs 
Review all statements and check those that apply. 
 
Click on the following check box (or double click and type an “X” if using Google Docs).   
 
6 
☐ The researcher will receive/collect directly identifiable data or specimens with 
identifiers. 
7 
☒ The researcher will only receive/collect coded or pseudonymized data or 
specimens. 
8 
☐ The researcher will have the ability to re-identify participants. 
9 
☐ The researcher will sign a DUA or MTA with the entity(ies) providing the data or 
specimens. 
 9a 
☐  
If so, the DUA(s) or MTA(s) prohibit the researcher from ever receiving 
identifiers from the entity(ies) providing the data or specimens and 
prohibit the researcher from seeking to learn the identities of 
participants. 

 
Page 8 of 16 
 
If available, upload a copy of the DUA(s) or MTA(s) to the RMS system under “Other 
Documents.” 
 
10 
☐ The secondary data or specimens are publicly available. 
11 
☒ 
The secondary data or specimens are not publicly available. 
 
 
12 
Describe the restrictions (if any) that apply to this data source (e.g., access to data 
requires log in, one must join an online community to access the data, one must sign 
a data use agreement) 
To input text click in the light grey area below. 
 
A secured File Transfer Protocol via UT Box will be put in place for the data transfer. 
Only the individuals directly involved in the analysis will have access to the transfer 
folder. 
 
The administrative data belong to Driscoll Health Plan and are not publicly available. 
 
 
 
13 
☐ 
Subjects provided consent for their participation in research when the data or 
specimens were originally collected. 
13a  
☐ 
If so, the consent form authorizes researchers to share data or specimens 
for future research.  
If available, upload a copy of the original consent forms to the RMS system under “Other 
Documents.” 
 
14 Research Participant Information 
Provide information regarding the participant population (e.g., age, 
gender, inclusion/exclusion criteria, disease state). 
To input text click in the light grey area below. 
 
Members of the Driscoll Health Plan could be enrolled in the program if they were 
pregnant and aged 30 years or older at the time of DHP enrollment. 
 
15 Total Sample Size 
To input text click in the light grey area below. 
 
We expect to receive data from approximately 1700 DHP members broken down as the 
following (approximate numbers): 

 
Page 9 of 16 
 
• Exposed group: DHP selected 511 members between October 2022 and July 
2023, of whom 224 were contacted and opted into the Nurture program.   
o CHWs could not reach the remaining 287 women on the list and, therefore, 
could not confirm address and book consultations. We will receive the 
demographic data on this group to explore how they compare with the 
other two groups in terms of their population descriptors (Table 2 in 
section 3). 
• Comparison group: approximately 1200 women who were 30 years old or older 
and pregnant during the same enrollment period and were not selected during 
the DHP’s enrollment process. 
 
 
16 Sample Size Rationale  
Provide justification for the sample size and its adequacy for answering the 
research question. 
To input text click in the light grey area below. 
 
Data coming from claims are mostly binary, which is characterized by limited variability 
and increased uncertainty in proportions. This fact makes detecting statistically 
significant differences more challenging, requiring larger sample sizes to ensure the 
reliability of the findings in such studies using these binary outcomes—which is the case 
of Nurture.  
 
Statistical power is critically dependent on the base rate of the outcome metrics among 
the comparison subjects. But, when the comparison group is composed of unmatched 
subjects that is at least four times the size of the exposure group, statistical power 
tends to be maximized.  
 
That being said, the analysis needs to include all women as described in the previous 
section.  
 
Because we do not know the incidence rate of the outcomes of interest, we estimated 
the effect sizes we can capture at 80% power with alpha = 0.05 assuming different rates 
among comparison subjects (30% and 35%) and four different rates among exposed 
subjects (25%, 23%, 20%, 18%).  
 
Table 4 demonstrates the statistical power to detect differences between the exposed 
and comparison groups, assuming Nurture produces certain effects. If the composite 
outcome rate is around 30% among comparison subjects, we have enough power to 

 
Page 10 of 16 
 
detect rates of 20% or lower among exposed subjects (yellow highlight). If the rate is 
closer to 35% among comparison subjects, we have enough power to detect rates of 
25% or lower among exposed subjects (blue highlight).  
 
Table 4. Statistical power at alpha = 0.05 to detect the program effects of interest 
assuming two different base rates among comparison subjects.  
 
% rate among exposed 
 
25% 
23% 
20% 
18% 
% rate among comparison 
subjects 
35% 
78 
90 
98 
99 
30% 
28 
46 
80 
93 
 
 
 
17 Confidentiality and Data Security Plan 
Describe how you will protect confidentiality of the subjects and the security of the 
data or specimens. Describe where you will store the data or specimens, who will 
have access to data or specimens; and the data and specimen retention and 
destruction timelines. 
To input text click in the light grey area below. 
 
DHP’s data analysis team will anonymize and remove all PHI from all data sets prior to 
sharing in a way so that no information can be linked back to the individuals. 
 
Data for analysis will be saved as Excel files and, subsequently, in formats specific for 
analyses using specialized statistical software, in a UT Box folder accessible only to team 
members involved in the data analysis, such as the PI and the biostatistician.  
 
Data sets will be destroyed (permanently deleted) 3 years after all analyses are 
completed. 
 
 
 
 
WAIVER OF INFORMED CONSENT 
Complete this section if you are seeking a waiver of informed consent for the collection of identifiable data or 
biospecimens. 
 

 
Page 11 of 16 
 
To approve a waiver or alteration of informed consent all of the following criteria below must be justified by the 
researcher. 
 
Only complete the sections below if requesting a waiver of informed consent for the use of readily identifiable data or 
specimens and the data use is not regulated by HIPAA. 
 
If research solely involves HIPAA protected data, skip this section and complete the Waiver of HIPAA Authorization 
section below. 
 
 
 
 
 
PROTECTED HEALTH INFORMATION 
Complete this section if you need to attest to the first (21) question. 
18 
The research involves no more than minimal risk to the subjects. 
To input text click in the light grey area below. Provide protocol specific rationale as to how this study meets this 
requirement. 
 
 
 
19 
The waiver or alteration will not adversely affect the rights and welfare of the 
subjects. 
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requirement. 
 
 
 
20 
The research could not practicably be carried out without the waiver or alteration (it 
is impracticable to perform the research if obtaining informed consent is required and 
not just impracticable to obtain consent). 
To input text click in the light grey area below. Provide protocol specific rationale as to how this study meets this 
requirement. 
 
 
 
21 
If the research involves using identifiable private information or identifiable 
biospecimens, the research could not practicably be carried out without using such 
information or biospecimens in an identifiable format. 
To input text click in the light grey area below. Provide protocol specific rationale as to how this study meets this 
requirement. 
 
 
 

 
Page 12 of 16 
 
 
22 ☐ 
As part of this research, the research will obtain, use, or disclose Protected 
Health Information (PHI). 
 
23 Type of Authorization(s) Requested 
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a ☐ 
Obtaining participant authorization for access to PHI 
b ☐ 
Waiver or alteration of HIPAA authorization 
c ☐ 
Partial waiver for subject identification or recruitment 
 
24 Health Information Use Request 
 
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25 HIPAA Defined Identifiers Recorded 
Select all identifier associated with the PHI. 
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☐ 
Names 
 
 
☐ 
Dates (including month/year): 
Admission, birth, death, or procedure 
date, ages over 89 
 
 
☐ 
Telephone numbers 
 
 
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Vehicle identifiers and serial numbers 
 
 
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Facsimile numbers 
 
 
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Web universal resource locators (URLs) 
 
 
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Electronic mail addresses 
 
 
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Internet protocol (IP) address numbers 
 
 
☐ 
Medical record numbers 
 
 
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and voiceprints 
 
☐ 
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numbers 
 
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E.g. (addresses, census block, zip codes, city, state) 
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Page 13 of 16 
 
 
☐ 
Account numbers 
 
 
☐ 
Certificate/license numbers 
 
 
☐ 
Any other unique identifying number, characteristic, or code, unless otherwise 
permitted by the Privacy Rule for re-identification 
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List the data points or attach a list of the data to be collected about each subject 
from their medical records or other HIPAA protected data sources.  
Select either question 26a or 26b and provide additional information as requested. 
 
Click on the following checkboxes (or double click and type an “X” if using Google Docs). 
 
26a ☐ 
Attach a list of all data points collected from PHI. 
Upload a document with data points collected from PHI to the UT RMS system under “Local Site 
Documents” 
 
26b ☐ 
Or, list the data points. 
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•  
 
 
 
Waiver or Partial Waiver of HIPAA authorization (as required) 
Complete this section if seeking a waiver of HIPAA authorization or a partial waiver to identify potential participants. 
 
27 
Describe why the research could not practically be conducted without access to and 
use of the PHI 
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Date Range of Records 
 
28 
Start Date 
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29 
End Date 
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30 
State why obtaining written authorization is impracticable 
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Data Retention and Security 
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31 
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Attest that the protected health information will not be reused or 
disclosed to any other person or entity, except as required by law, for 
authorized oversight of the research study, or for other research where the 
use or disclosure of protected health information meets IRB and HIPAA 
requirements 
 
31a ☐ 
Identifiers will be destroyed at earliest opportunity 
 
 
 
Describe the destruction plan and timeline 
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31b ☐ 
Identifiers will be retained indefinitely 
 
 
 
State rationale for retaining identifiers indefinitely 
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Partial Waiver of HIPAA Authorization  
For Activities Preparatory to Research and Subject Identification 
Attest to questions 32 and 33 if seeking a partial waiver of HIPAA authorization to identify potential participants and 
collect their contact information to obtain authorization at a later date. 

 
Page 15 of 16 
 
 
32 ☐ 
Attest that PHI will be accessed only by individuals who have authorization to 
access the records outside of the research context. 
 
33 ☐ 
Attest that researchers will not move or transmit identifiable PHI from the 
covered entity. 
 
 
CONFLICTS OF INTEREST 
This section is required for all studies. Please confirm that all research personnel who meet the definition of “covered 
individuals” are designated as such in the Local Study Team Members section of the SmartForm application in UTRMS-
IRB. 
 
34 
Financial Conflicts of Interest 
Financial interest includes utilizing your licensed intellectual property in the study; serving as a paid consultant, or 
advisory board member, or officer/director with a related entity; and equity or business ownership in a company 
that is related to this project.  Additional guidance on financial conflicts of interest is available on the COI website 
 
A or B must be checked.  
 
 
 
A 
☐ 
The PI and/or other covered individual(s) has/have a financial interest 
related to this study  
i 
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individuals involved, and briefly describe the interest: 
 
 
 
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B 
☐ 
To the best of your knowledge, no one on the study team has financial 
interest related to this study  
35 
Non-financial Conflicts of Interest 
Non-financial Interests could include such things as: 
- utilizing your unlicensed intellectual property in the study, 
- serving as an unpaid advisory board member or officer/director with a related entity,  
- equity or business ownership in a company that has yet to make a profit and is related to this project, 
- conflict of time/effort, 
- personal and professional relationships/affiliations, 
- intellectual passions or personal beliefs 
- other factors that could create bias in the study  
 
A or B must be checked.  

 
Page 16 of 16 
 
 
 
 
 
 
A 
☐ 
The PI and/or other covered individual(s) has/have a non-financial interest 
related to this study  
i 
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individuals involved, and briefly describe the interest: 
 
 
 
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B 
☐ 
To the best of your knowledge, no one on the study team has non-
financial interest related to this study