dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series Presenter: Rie Sakai-Bizmark, PhD. Assistant Professor, The Lundquist Institute at Harbor-UCLA Medical Center, David Geffen School of Medicine at UCLA Abstract Women with gestational diabetes mellitus (GDM) are at high risk of developing glucose intolerance after delivery. In the long term, women with GDM have a nearly 10-fold higher risk of developing type 2 diabetes mellitus (T2D) than women without GDM. The American Diabetes Association (ADA) and the American College of Obstetrics and Gynecology (ACOG) recommend that women with GDM undergo a 75-g oral glucose tolerance test (OGTT) between four and 12 weeks postpartum, and periodically thereafter. However, postpartum glucose screening (PGS) rate is historically low despite of various interventions to improve such rate. We hypothesized that PGS rate is lower among postpartum homeless women than their housed counterparts, and that interventions to improve PGS rate among postpartum homeless women with GDM should be tailored to their unique circumstances. The Japanese Society of Diabetes and Pregnancy (JSDP) modified the method to perform PGS with random plasma glucose (RPG) and glycated hemoglobin (HbA1c), which are simple and less invasive, to reduce the risk of COVID-19 infection by shortening the time spent in the hospital. RPG or HbA1c test do not require fasting. Therefore, homeless women who utilized care for other reasons could have the test as PGS. Given the barriers faced by homeless individuals, we hypothesize that RPG and HbA1c at healthcare utilizations during the postpartum period could be one of the strategies to identify high-risk individuals early because 1] healthcare utilizations are an opportunity for healthcare providers and social workers to educate homeless patients on GDM and their insurance eligibility and coverage for the screening, and 2] the physical barriers to health care access, which are often cited as a reason for the low PGS rate, are removed. This proposed study will use administrative data from five states (AZ, CO, NC, NJ, and OR), which collectively include 9.3% of the US female homeless population. Each state will provide detailed, linked, multi-level, anonymized data for postpartum homeless women from four sources: 1] Medicaid claims; 2] Homeless Management Information System (HMIS); 3] birth records; and 4] the American Hospital Association (AHA) database to obtain hospital characteristics. With data from 2013 to 2020, an estimated sample size of 24,000 homeless women who delivered babies and 3,290 postpartum homeless women with GDM will be included. First, we will estimate rates of GDM and PGS among homeless women. Second, we will estimate the cost-effectiveness of performing RPG and HbA1c tests...[Full abstract: https://dknet.org/about/blog/2581] Upcoming webinars schedule: https://dknet.org/about/webinar

1. Postpartum Glucose
Screening among
homeless women with
gestational diabetes
Rie Sakai Bizmark, MD, MPH, PhD
rsakaibizmark@Lundquist.org
The dkNET Pilot Program in Bioinformatics
Webinar Series
February 24, 2023

2. Outline
§ Background
§ Aims and Hypotheses
§ Innovation
§ Preliminary Study
§ Data Source
§ Statistical Analyses
§ Progress
§ Future Directions

3. Homelessness and Health
§ On a single night in 2022, roughly 582,500 people were
experiencing homelessness in the United States.
§ people experiencing homelessness have twice the odds
of unmet medical needs related to chronic disease
compared to housed individuals
§ Limited preventive care, poor nutrition and inadequate
access to prenatal care potentially contribute to the
development of gestational diabetes mellitus (GDM)
and type 2 diabetes mellitus (TD2)

4. Homelessness and Pregnancy
§ Among people were experiencing homelessness in 2022,
222,970 (38.3%) were female.
§ Infancy (under age one) is the age at which a person is
most likely to experience homelessness in the US.
§ As infants are typically accompanied by their mother, this
statistic illustrates that women in the postpartum period are
also at high risk of homelessness.

5. More about Homelessness and
Pregnancy
§ 4% of mothers experience homelessness during
pregnancy
§ Pregnancy increases the risk of homelessness and
conversely, homelessness also increases the risk of
pregnancy.
§ Higher rates of unintended pregnancy
§ 73%, homeless vs 49%, domiciled

6. § Women with a history of GDM have a nearly 10 times
higher risk of subsequently developing T2D than
women with no history of GDM
§ The American College of Obstetrics and Gynecology
(ACOG) recommend that women with GDM have a
75-g oral glucose tolerance test (OGTT) between four
and 12 weeks postpartum
§ The postpartum glucose screening (PGS) rate is
historically low despite various interventions to
improve the rate.
Relationship Between Gestational and
Type 2 Diabetes

7. Postpartum Glucose Screening
among Homeless
§ Commonly cited barriers to PGS
§ Cost
§ lack of access to care
§ poor communication with healthcare professionals
§ uncertain recommendations
§ lack of transportation and convenient testing locations
§ low risk awareness
§ These are also key barriers that prevent the homeless
population from seeking general preventative care

8. Testing Opportunities during
Healthcare Utilization
§ Providing glucose screening during any utilization of
healthcare (AUHC) within the postpartum period for
women who missed the recommended PGS screening
could be an efficient strategy for early identification of
high-risk individuals
§ AUHC includes:
1) Inpatient care
2) Emergency department visits
3) Outpatient visits

9. Advantages and Disadvantages of
Screening at AUHC
§ Advantages
1) Opportunity for healthcare providers and social workers
to educate homeless patients on GDM and their insurance
eligibility and coverage for the screening
2) Physical barriers to health care access are removed
§ Disadvantages
1) OGTT cannot be used because it requires fasting from
the night before

10. Use of RPG and A1c Tests
§ Random plasma glucose (RPG) and glycated
hemoglobin (A1c) tests
§ they can be performed quickly without fasting
§ JSDP Modification: Use of RPG and A1c as a PGS was
implemented by the Japanese Society of Diabetes
and Pregnancy (JSDP)
§ In order to reduce the risk of COVID-19 infection by
limiting time spent with medical personnel

11. § Estimate rates of gestational diabetes (GDM) and
postpartum glucose screening (PGS) among homeless
women
§ Housed peers will serve as the comparison population
§ Hypothesis:
Rate of GDM is higher and rate of PGS is lower among
homeless women than housed women
AIM 1

12. AIM 2
§ Estimate the cost-effectiveness of providing RPG and
A1c tests during AUHC for homeless women with GDM
who missed the recommended PGS
§ Compare outcomes between the current standard of care
and the JSDP modification for those who missed PGS before
12 weeks postpartum and utilized care 12 weeks to 1 year
after delivery
§ Hypothesis:
Given the barriers faced by homeless individuals, this modified
protocol could be a cost-effective strategy for early
identification of high-risk individuals

13. § AZ, CO, NC, NJ and OR
§ Medicaid claims
§ State Medicaid program: AZ, NC, and NJ
§ All Payers Claims Data (APCD): CO and OR
§ Homeless Management information system (HMIS)
§ Birth records
§ Years 2013 - 2020 chosen based on data availability
Database
We contacted 17 states, which had recorded populations of homeless
females greater than 3,000 (point-in-time count) based on the U.S.
Department of Housing and Urban Development (HUD)
Cohort selection was based on:
• Data availability
• Length of time required to process data requests
• Data quality

14. Data Linkage
§ Linkage between Medicaid
claims data, HMIS data, and
infant birth records will be
performed by statisticians from
state data organizations
§ Linkages will be performed using
protected patient attributes
such as social security number,
name, and birth date
§ To protect human subject
privacy, direct identifiers will be
encrypted or removed before
data delivery
§ Instead of date elements, we
requested number of days
between events
State Linkage Organization
AZ
Center for Health Information and Research (CHIR) at
Arizona State University (ASU)
CO Center for Improving Value in Health Care
NC North Carolina State Center for Health Services
NJ Trusted third party (e.g., Optum)
OR Integrated Client Services (ICS)

15. Innovation
§ First multi-state study to link medical and Homeless
Management Information System (HMIS) records to identify
homeless individuals
§ Some hospital records and insurance claims data have homeless
indicators, but the definition of homelessness is not consistent
across hospitals and databases
§ Utilizing the HMIS database to identify homeless individuals will
allow us to use one consistent definition of homelessness
§ We will provide the first U.S. based estimates of GDM and PGS
among homeless women
§ We will evaluate the life-long effect of the
modification in PGS

16. Preliminary Study (Colorado)
§ Linked Medicaid and HMIS records from Colorado
§ A total of 1,176 and 187,435 homeless and housed women delivered
babies between 2017-2019
§ Identified 83 homeless and 16,282 housed women with GDM
§ Rates and odds ratios (OR) of GDM adjusted for age and race/ethnicity
were higher among homeless (no statistical differences)
Adjusted
Rate
95%CI aORa 95%CI P value
Homeless 12.9% 10.3% 15.4% 1.16 0.92 1.46 .20
Housed 11.3% 11.1% 11.5% Reference
a:adjusted odds ratio

17. § Homeless women with GDM were
more likely to be younger and non-
Hispanic White than their housed
counterparts (both Ps < .01)
§ Rates of PGS were low in both
groups of women with GDM
§ Rates higher among those who had
a postpartum checkup
§ 62.5% of homeless and 41.2% of
housed women who had a
postpartum checkup had PGS
Preliminary Study (Colorado)

18. Identification of Patients
§ Using linked data, we will identify women who delivered babies
between January 2014 and December 2019, and examine their
healthcare utilization (AUHC) within one year after delivery
§ The first and last possible dates for this longitudinal study will be
January 2014 and December 2020, respectively
§ Deliveries will be identified based on the International Classification
of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)
diagnostic and procedure codes until September 2015, and ICD-10-
CM diagnostic and procedure codes after October 2015
Delivery
Postpartum period
1 year
Timeframe to assess AUHC

19. Exposure Variable: Homelessness
§ Homeless defined as individuals who stayed in shelters at
least one time during the observation period
§ Individuals who never used shelters and had valid zip codes for
their home addresses will be defined as housed individuals
§ People who utilized shelters could have been housed at
other times during the observation period, but shelter use
indicates housing insecurity

20. Primary Outcomes
1) Prevalence of GDM
§ Identified using ICD-9-CM 648.8 and ICD-10-CM O24.4
§ Exclusions: Pre-existing DM coded at delivery
2) PGS screening rate
§ Measured by Current Procedural Terminology (CPT) codes for
glucose tolerance test (28084-28086 and 82950-82952) and fasting
plasma glucose test (82947 and 82962)

21. Secondary Outcomes
1) Glucose intolerance (GI) at postpartum
§ Identified with an elevated blood glucose (ICD-9-CM 790.2 or
ICD-10-CM R73)
2) Presence of DM
§ Identified with ICD-9-CM code 250 or ICD-10-CM codes E10 or E11
in any of the diagnostic fields

22. Adjustment Variables
The models will be
adjusted for individual
characteristics, clinical
factors, and hospital
characteristics
Variables Explanation Source
Individual Characteristics
Age Categorized into quintile Medicaid
Race/ethnicity
Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Asian
or Pacific Islander, Hispanic and other
Medicaid
Length of homelessness
We will estimate the length of homelessness from the date of
entry into housing services
HMIS
Clinical Factors
Body Mass Index
BMI will be calculated from mother’s height and weight
available in infant’s birth record
Birth
Insulin use
Women with insulin-treated GDM are more likely to follow up
than women with diet-controlled GDM
Medicaid
Maternal comorbidity
Index
Validated index score created from 21 comorbidities, such as
preeclampsia, existing diabetes mellitus (DM), multiple
gestation, hypertension, sickle cell disease, etc.
Medicaid
Hospital Characteristics
Hospital size
1] large (≥400 beds), 2] medium (100-399 beds), and 3] small
(<100 beds)
AHA
Teaching status 1] major, 2] minor, and 3] nonteaching AHA
Control type
1] investor owned and for profit, 2] nongovernment owned and
not for profit, 3] government owned and nonfederal, 4] safety-
net status (A hospital will be considered a safety-net if the
percentage of Medicaid patients was in the highest quartile for
each state. This categorization will be refined after we obtain
the database. For example, the majority of our study
population likely utilized safety-net hospitals. Therefore, it
could be categorized into two groups: 1] safety-net hospital
and 2] other.)
AHA
Location 1] urban, 2] rural AHA
Geographic location
State
Dummy variables for each state will be included (location fixed
effect). This will adjust for all location-specific time-invariant
effects
Medicaid

23. Statistical Analysis: AIM 1
AIM 1: Estimate rates of GDM and PGS among homeless women
§ Number of GDM and number of PGS among women who delivered
babies will be identified.
§ Multivariable logistic regression will be used to evaluate the association
between homelessness and each outcome (GDM, PGS)
§ Residual correlations are possible for the following two reasons
1] Individuals who had multiple deliveries between 2014 and 2019 will
be included in the study multiple times
2] Multiple records from the same hospitals will be included
§ Individual- and hospital-level random effects will be included
§ Cross-classified multilevel models (CCMM) will be used instead of
traditional multilevel models, which assume a structure in which
observations are hierarchically nested, e.g., individuals always had
deliveries at the same hospital

24. Statistical Analysis: AIM 2
AIM 2: To estimate the cost-effectiveness of providing RPG and A1c
tests during AUHC for homeless women with GDM who missed the PGS
§ Hypothetical cohort of 3,290 postpartum homeless women with
GDM
§ Compare outcomes between the current standard of care for PGS
and providing RPG and A1c tests (JSDP Modification) for those who
missed PGS before 12 weeks postpartum and utilized care 12 weeks
to 1 year after delivery

25. Model Structure
Decision Tree

26. Model Structure
1. Estimate the rate of PGS among homeless women with GDM
2. For those who were not screened, model the performance of JSDP
modification during healthcare utilization 12 weeks - 1 year after delivery
3. For those identified as having abnormal glucose metabolism by RPG and
A1c tests, perform an OGTT to confirm
4. For those with abnormal OGTT, begin intervention
Decision Tree

27. § Long-term impact will be estimated using quality-adjusted life-years (QALYs)
§ Since GI also affects subsequent deliveries, the delivery rate will be
accounted for
§ Separate utilities (i.e., a measure to evaluate the relative quality of life as compared
with perfect health) will be estimated for each pregnant woman and her offspring
§ These QALYs will be summed
§ Healthy mothers and surviving offspring will be assumed to have life
expectancy of 78 years
§ Patients with treated and untreated diabetes will have their life expectancy
shortened by 8 and 9 years, respectively
§ Health risk of the offspring of a GDM mother will also be accounted for
§ All costs will be presented in 2020 U.S. dollars and adjusted based on the
medical care component of the Consumer Price Index
§ Costs and utilities will be discounted at a baseline rate of 3% based on
average inflation
§ Incremental cost-effective ratios (ICER), will be
calculated as [incremental cost]/[incremental QALYs]
Estimating Long-Term Impact

28. § We also consider
§ How many people will have OGTT after they had the
abnormal results in JSDP modification test
§ How many people will stay in the intervention
§ Existing literature focusing on homeless population
showed that more than 80% of people stayed in the
intervention
§ After they develop the condition, how many people
adhere to the treatment

29. Parameter Estimates
§ We will use previously reported estimates for most of the
parameters, including performance of JSDP modification
(e.g., true positive/negative rates, etc.)
§ Risk adjusted rates will be estimated specifically among
postpartum homeless women with GDM using the data
(Those rates are different from the general population of
postpartum women with GDM)
§ 1] having PGS between 4 and 12 weeks after delivery
§ 2] AUHC between 12 weeks and 1 year after delivery
§ 3] a diagnosis of GI at postpartum
§ 4] delivery

30. Sensitivity Analyses
§ The SA will include
1) Implementation rate of having an OGTT after identified as having AGM by JSDP modification
2) Implementation rate of having the intervention after an OGTT confirmed GI
§ Parameter estimates will be altered by 50% in both directions for most inputs
§ Includes providing the JSDP modification during AUHC occurring within two years of delivery,
rather than one year
§ The following three parameter estimates, which have high uncertainty, will be altered
by 90% in the lower direction
1) Performance of the JDSP modification
Ø Performance was reported among Japanese women, but not American
2) The effect of the intervention
Ø Due to limited nutritious food options and an inadequate exercise environment, the effect of diet and
exercise counseling is likely to be smaller among homeless
3) Engagement and retention rates
§ One-way SA with Tornado plot and probabilistic SA of 1,000 simulations using Monte
Carlo simulation will be conducted

31. Limitations
§ Limitation #1: We will define the homeless population based on the
utilization of housing services.
§ Response: It is estimated that more than 80% of the homeless
population in the states included in this study are sheltered.
Additionally, if homeless individuals are categorized as housed, the
direction of bias will be toward the null.
§ Limitation #2: As 9.3% of the homeless population in the U.S. will be
included in the analyses, the results may not be generalizable to
other parts of the country.
§ Response: Effective postpartum care approaches for homeless
individuals can differ by state, but our approach is novel because
unmet PGS needs among the homeless have not been
reliably assessed.

32. § AZ: Completed and signed the DUA
§ The data from HMIS was transferred to Center for Health Information &
Research in Arizona State University (ASU)
§ CO: Database obtained
§ NC
§ Working on DUAs with Medicaid and two HMIS
§ NJ
§ Working on DUAs
§ OR
§ Waiting for the DUA from HMIS
§ APCD wants to wait for the DUA from HMIS as their process is quick
§ MI also expressed the interest
Progress

33. Future Directions
§ We will perform additional cost-effectiveness analyses using other PGS
strategies
§ For example, providing an OGTT during the delivery hospitalization when
postpartum follow-up is challenging
§ We will collaborate with stakeholders in the local area to develop
strategies to increase the utilization of PGS
§ We will evaluate other healthcare interventions
§ For example, efforts to increase the diabetic retinopathy screening rate

34. Thank you
Any questions or comments?
rsakaibizmark@lundquist.org