\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{\textbf{Pregnancy- Associated Diabetes and Stillbirths by Race and
Ethnicity Among Hospitalized Pregnant Women in the United States: A
Retrospective Cross-Sectional Analysis}}



\author[1]{Jonnae Atkinson}%
\author[1]{Deepa Dongawar}%
\author[1]{Vicki Mercado-Evans}%
\author[1]{Ayleen Hernandez}%
\author[1]{Aisha Deslandes}%
\author[1]{Monica Gonzalez}%
\author[1]{Danielle Sherman}%
\author[1]{Hamisu Salihu}%
\affil[1]{Baylor College of Medicine}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
\textbf{Objective}: Racial disparities in pre-existing diabetes (PDM)
and gestational diabetes (GDM) remain largely unexplored. We examine
national PDM and GDM prevalence trends by race/ethnicity, and the
association between these conditions and fetal death. \textbf{Design}:
Retrospective cross-sectional analysis \textbf{Setting}: United States
Nationwide Inpatient Sample Survey \textbf{Population/Sample}: A total
of 69,539,875 pregnancy-related hospitalization from 2002 to 2017
including 674,040 women with PDM (1.0\%) and 2,960,797 (4.3\%) with GDM.
\textbf{Methods}: Joinpoint regression was used to evaluate trends in
prevalence. Survey logistic regression was used to evaluate the
association between exposures (PDM and GDM) and outcome. \textbf{Main
Outcome Measures}: National trends in PDM and GDM prevalence by
race/ethnicity, and the association between these conditions and fetal
death \textbf{Results}: Overall, the average annual increase in
prevalence was 5.2\% (95\% CI {[}4.2, 6.2{]}) for GDM and 1.0\% (95\% CI
{[}-0.1, 2.0{]}) for PDM, over the study period. Hispanic (AAPC 5.3;
95\% CI {[}3.6, 7.1{]}) and NH-Black (AAPC 0.9; 95\% CI {[}0.1, 1.7{]})
women had the highest average annual percent increase in prevalence of
GDM and PDM, respectively. After adjustment, odds of stillbirth were
highest for Hispanic (OR 2.41:95\% CI {[}2.23, 2.60{]}) women with PDM
and decreased for women with GDM (OR 0.51;95\% CI {[}0.50, 0.53),
irrespective of race/ethnicity. \textbf{Conclusions and Global Health
Implications}: PDM and GDM prevalence is increasing in the U.S. with
highest average annual percent changes seen among minority women.
Further, reasons for variation in occurrence of stillbirths among
mothers with PDM and GDM by race/ethnicity are not clear and warrant
additional research.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Diabetes is recognized as one of the most common metabolic disorders of
pregnancy affecting 17\% of pregnancies
globally.\textsuperscript{1}Gestational diabetes mellitus (GDM), a
condition defined by the American College of Obstetrics and Gynecology
as carbohydrate intolerance during pregnancy, comprises most cases of
pregnancy-associated diabetes.\textsuperscript{2} Data suggest that
approximately 86\% of pregnancy-associated diabetes is caused by GDM,
while about 14\% is due to diabetes diagnosed prior to
pregnancy.\textsuperscript{1} In part due to the greater prevalence of
GDM compared to pre-pregnancy diabetes (PDM), the majority of research
on pregnancy-associated diabetes has focused on maternal morbidity and
fetal outcomes among women with GDM. We know from previously published
data that women diagnosed with GDM have an increased risk for several
adverse outcomes including stillbirth, fetal overgrowth, preterm birth,
preeclampsia and progression to type 2 diabetes later in
life.\textsuperscript{3--5} Data also suggest that children born to
mothers with GDM are at increased risk for obesity, cardiovascular
disease and type 2 diabetes later in life.\textsuperscript{3,4} Women
with PDM are reported to be at a significantly increased risk for
preeclampsia, congenital malformations, fetal overgrowth and fetal
death.\textsuperscript{6-16} These data raise serious concerns given
that the number of women with PDM is increasing, irrespective of
diabetes subtype (type 1 insulin-dependent, or type 2 non-insulin
dependent).\textsuperscript{6,9,10,17}

The risks accompanying pregnancy-associated diabetes have been reported
to disproportionately impact women from different races/ethnicities.
Some studies have indicated that non-Caucasian races/ethnicities have a
higher prevalence of PDM and GDM than Caucasian
women.\textsuperscript{9,18--20} Consequently, women of different racial
and ethnic backgrounds, such as Black and South Asian with PDM and GDM,
have also been shown to be at an increased risk for adverse fetal
outcomes including perinatal loss, preterm delivery, respiratory
distress syndrome and fetal anomalies.\textsuperscript{21--23} However,
the majority of these studies focused solely on GDM and assessed women
from a limited geographic region. As a result, these data highlight the
need for further research to better characterize existing racial/ethnic
disparities.

We sought to expand the depth and breadth of the current understanding
of racial and ethnic differences in the prevalence of both PDM and GDM,
by using hospital data to examine national trends in prevalence by
race/ethnicity. We also examined the association between these
conditions, race/ethnicity and stillbirth.

\textbf{Methods}

\emph{Data Source and Sample}

We conducted a retrospective cross-sectional analysis using hospital
records from January 1, 2002 through December 31, 2017 that were
contained in the Nationwide Inpatient Sample (NIS). The NIS is made
available by the Healthcare Cost and Utilization Project (HCUP) and is
currently the largest all-payer, publicly available inpatient database
in the United States. (Healthcare Cost and Utilization Project (HCUP))
The two-staged cluster sampling design ensures that hospitalizations in
the NIS are representative of the population on important factors
including month of admission, primary reason for hospitalization,
hospital size, location, ownership, and teaching status.
Hospitalization-level weights are provided with each annual database
which allow national estimates to be generated. In 2017, the NIS
contained approximately seven million inpatient hospitalizations each
year (35 million when weighted) from 47 participating states. HCUP
transitioned from ICD-9-CM to ICD-10-CM format on October 1, 2015.

Our study sample included pregnancy-related hospitalizations among women
aged 15 to 49 years, identified using an HCUP-created variable
``NEOMAT'' which captures maternal diagnosis records with diagnosis and
procedure codes for pregnancy and delivery in the ICD-9-CM era. In the
ICD-10-CM period, diagnoses codes `Oxx.x' were used to identify
pregnancy and delivery related hospitalizations. To assess the study's
primary exposure, we first scanned ICD-9-CM codes (the principal
diagnosis and up to 29 secondary diagnoses) in each woman's discharge
record for an indication of gestational diabetes mellitus (GDM)
(ICD-9-CM: 648.8x, ICD-10-CM: O24.xx or O99.81) and/or pre-existing
(i.e., diagnosis of type 1 or 2 diabetes prior to the pregnancy)
(ICD-9-CM:249.xx, 250.xx, 648.0x; ICD-10-CM: E10.xx, E11.xx, E13.xx).
The outcome of interest was stillbirth (ICD-9-CM: 656.4x, V27.1x,
V27.3x, V27.4x, V27.6x, V27.7x, 768.0, 768.1, 779.9, 632; ICD-10-CM:
O36.4x, P95.xx, P96.9, Z37.1, Z37.3, Z37.4, Z37.6, Z37.7).

Individual-level sociodemographic and behavioral characteristics were
also extracted from the NIS databases. Maternal age in years was
classified into three categories: 15-24, 25--34, and 35-49 years.
Self-reported maternal race/ethnicity was first based on ethnicity
(Hispanic or non-Hispanic {[}NH{]}), and the NH group, further
subdivided by race (White, Black, or other). As the `other' racial group
is not clearly defined in the dataset, our analyses were limited to
NH-White, NH-Black and Hispanics pregnant women. Median household
income, which served as a proxy for socio-economic status, was estimated
using the patient's zip code and subsequently grouped into quartiles. We
classified the primary payer for hospital admission into following
categories: Medicare, Medicaid, private (commercial carriers, private
health maintenance organization {[}HMOs{]}, and preferred provider
organization {[}PPOs{]}); and self-pay. We also considered several
hospital characteristics including teaching status (teaching vs.
non-teaching), location (urban vs. rural), and US region (Northeast,
Midwest, South, or West).

Information for this study was obtained from a retrospective secondary
de-identified data source. Therefore, patient involvement and core
outcome set requirements are not applicable to this analysis.

\emph{Statistical Analysis}

Joinpoint regression was used to identify and describe temporal changes
in the rates of PDM and GDM among pregnancy-related hospitalizations
during the 15-year study period. This type of statistical regression
analysis is valuable in identifying key periods that denote a
statistically significant change in the rate of events over
time\textbf{.} \textsuperscript{24} The iterative model-building process
began by fitting the annual rate data to a straight line with no
joinpoints, which assumed a single trend best described the data. Then a
joinpoint, reflecting a change in the trend, was added to the model and
a Monte Carlo permutation test assessed the improvement in model fit.
The process continued until a final model with an optimal (best-fitting)
number of joinpoints was selected, with each joinpoint indicating a
change in the trend, and an annual percent change (APC) estimated to
characterize how the rate was changing within each distinct trend
segment.

Descriptive statistics, including frequencies and percentages, were used
to describe the distribution of pregnancy-related hospitalizations
across patient- and hospital-level characteristics, stratified by
exposure group (PDM and GDM) across racial/ethnic groups: NH-White,
NH-Blacks and Hispanics. Since national estimates were desired, all
statistical analyses were weighted using an HCUP-provided
discharge-level weight that accounted for the sampling design and
appropriately generated variance estimates. Furthermore, we calculated
the stillbirth rates in women with PDM and GDM across various
racial/ethnic groups.

Multivariable survey logistic regression was also used to produce
adjusted odds ratios (OR) that quantified the magnitude of the
association between the exposures, PDM and GDM, and the outcome
stillbirth, across various racial/ethnic groups. Statistical analyses
were performed with R version 3[?]5[?] 1 (University of Auckland, Auckland,
New Zealand) and R Studio Version 1[?]1[?] 423 (Boston, MA). We assumed a
5\% type I error rate for all hypothesis tests (two-sided). Due to the
de-identified, publicly available nature of NIS data, the analyses
performed for this study were considered exempt by the Baylor College of
Medicine Institutional Review Board.

\textbf{Results}

We analyzed a total of 69,539,875 hospitalizations from 2002 to 2017
including 674,040 women diagnosed with PDM (1.0\%) and 2,960,797 (4.3\%)
women diagnosed with GDM.

Figure 1 displays the temporal trends in the rates of PDM and GDM in
hospitalized pregnant women by race and ethnicity from 2002 to 2017.
Overall, the prevalence of~PDM and GDM~increased over the 15-year study
period from 11.1 per 1,000 hospitalizations to 12.8 per 1,000
hospitalizations and from 42.7 per 1,000 hospitalizations to 91.6 per
1,000 hospitalizations, respectively. The average annual increase in
prevalence was 5.2\% (95\% CI {[}4.2, 6.2{]}) for GDM and 1.0\% (95\% CI
{[}-0.1, 2.0{]}) for PDM over the study period. Hispanic women had the
highest average annual percent increase in prevalence of GDM (AAPC 5.3;
95\% CI {[}3.6, 7.1{]}). The prevalence of GDM among Hispanic women
increased from 50\% in 2002 to 109.5\% in 2017. NH-Black women had the
highest average annual percent increase in the prevalence of PDM over
the study period (AAPC 0.9; 95\% CI {[}0.1, 1.7{]}). The prevalence of
PDM among NH-Black women increased from~18.9\% in 2002 to 21.7\% in
2017. The lowest average annual percent increase in the prevalence of
GDM (AAPC 5.1; 95\% CI {[}3.9, 6.2{]}) and PDM (AAPC 0.3; 95\% CI
{[}-1.4, 2.0{]}) was observed among NH-Whites. The prevalence of GDM
among NH-Whites increased from 37.7\% in 2002 to 78.9\% in 2017, while
the prevalence of PDM increased from 9.2\% to 9.5\% over the same time.

Table 1 shows the relationship between socio-demographic factors and
diagnosis of PDM and GDM among hospitalized pregnant women by
race/ethnicity. The age distribution of PDM and GDM was similar across
groups. The prevalence of PDM and GDM increased with age in each
race/ethnic group. The prevalence of PDM was highest among NH-Black
hospitalized women in each age group ranging from 1.1\% for those less
than 24 years of age to 4.3\% for those 35 to 49 years of age. The
prevalence of GDM was highest among Hispanic women across all age groups
ranging from 2.9\% for those less than 24 years of age to 15.8\% for
those 35 to 49 years of age.

Among hospitalized pregnant women for which discharge status was known,
there were notable differences in the prevalence of pregnancy-associated
diabetes by race/ethnicity (Table 1). The highest prevalence rates for
pregnancy-associated diabetes were observed among NH-Black (9.8\%) and
Hispanic (9.9\%) women who were discharged against medical advice.
Additionally, the prevalence of PDM among NH-Black mothers who died in
the hospital was 7.9\%, notably higher than the prevalence in other
racial/ethnic groups. The prevalence of GDM among mothers who died
during hospitalization was highest for Hispanic women (5.5\%).

The diagnosis of PDM was more common among hospitalized women who
reported a residence in a low-income zip code for all race/ethnicity
groups (Table 1). In contrast, the prevalence of GDM was highest among
hospitalized Hispanic (8.1\%) and NH-Black (6.4\%) women with residences
in high-income zip codes. There were also differences in primary payer
for health care services by race/ethnicity. Most NH-White hospitalized
pregnant women used private insurance to pay for care, while the
majority of NH-Black and Hispanic women had Medicaid as their primary
payor. Among hospitalized pregnant women for which payor source was
known, the prevalence of PDM and GDM was highest among women who had
Medicare as their primary payor in each race and ethnic group.

With respect to hospital characteristics, the diagnosis of PDM and GDM
was similar across regions for race/ethnic groups with one exception
(Table 1). The prevalence of PDM among mothers increased with hospital
size in each race/ethnic group but was similar across racial/ethnic
groups for GDM. The prevalence of PDM and GDM was highest in urban
teaching hospitals in each race/ethnic group, where most women were
admitted for care.

Figure 2 depicts the rates for stillbirth among women with PDM and GDM
by race and ethnicity. Overall, the rate of stillbirth was low for both
groups, but the occurrence of stillbirth among hospitalized women with
PDM (2.40\%) was about 4.4 times higher than that for hospitalized women
with GDM (0.54\%). The highest frequency of stillbirth among women with
PDM and GDM was in the NH-Black (0.9\% and 3.13\%, respectively) group
and the lowest in the NH-White (0.54\% and 2.40\%, respectively) group.

Figure 3 depicts the association between pregnancy-associated diabetes
and stillbirth by race/ethnicity. Odds ratios for stillbirth adjusted
for sociodemographic hospital characteristics are provided for
hospitalized women diagnosed with PDM and GDM by race/ethnicity.
Unadjusted odds ratios are provided in Table S1 for PDM and in Table S2
for GDM. Compared to hospitalized pregnant women who did not have
diabetes, the adjusted odds of stillbirth more than doubled for women
diagnosed with PDM (OR=2.14; 95\% CI {[}2.07,2.22{]}). The increase in
the adjusted odds of stillbirth observed among hospitalized mothers with
PDM was consistent for each race/ethnic group. The adjusted odds of
stillbirth among hospitalized women with PDM was highest for Hispanic
women (OR=2.41; 95\% CI {[}2.23,2.60{]}) and lowest for NH Black women
(OR=1.81; 95\% CI {[}1.71,1.94{]}).

In contrast, the adjusted odds of stillbirth for hospitalized mothers
diagnosed with GDM was lower than that for hospitalized pregnant women
without diabetes. Hospitalized pregnant woman diagnosed with GDM were
49\% (OR=0.51; 95\% CI {[}0.50,0.53{]}) less likely to have a
stillbirth. The lower adjusted odds of stillbirth observed among
hospitalized women with GDM was consistent across race/ethnic groups.
The adjusted odds of stillbirth among hospitalized women with GDM were
the same for NH-White (OR=0.52; 95\% CI {[}0.49,0.55{]}), NH-Black
(OR=0.52; 95\% CI {[}0.48 -0.56{]}) and Hispanic OR=0.52; 95\% CI
{[}0.49,0.56{]}) women. The data show that GDM is protective for
stillbirth among hospitalized pregnant women when compared to their
respective non-diabetic counterparts regardless of race/ethnicity.

\textbf{Discussion}

\emph{Main Findings and Interpretation}

As reported in previous studies, results of our analyses suggest that
there are racial/ethnic differences in the prevalence of PDM and GDM.
However, our findings expand on previous studies by reporting data on
trends in pregnancy-associated diabetes by race/ethnicity. Consistent
with earlier HCUP NIS studies, our results show an increasing trend in
the prevalence of PDM and GDM from 2002 to 2017.\textsuperscript{10} In
addition, our data suggest the prevalence of PDM and GDM is increasing
among NH-White, NH-Black and Hispanic hospitalized mothers. These
findings correlate with increasing trends in risk factors for diabetes
such as obesity, inactivity, and hypertension observed among specific
racial/ethnic minority women.\textsuperscript{25--30} Another possible
contributing factor is an increase in advanced maternal age
pregnancies.\textsuperscript{31--33}

Note that the graphs in Figure 1 show an inflection point in trends for
PDM and GDM in 2015. This is likely caused by two factors. First, the
implementation of U.S. Prevent Services Task Force recommendations for
routine GDM screening in asymptomatic pregnant women after 24 weeks of
pregnancy in 2014.\textsuperscript{34} Secondly, a change in disease
reporting in 2015 due to the transition from the ICD-9 to ICD-10 coding
set.\textsuperscript{35} These changes likely increased screening for
PDM and GDM as well as disease reporting.

The majority of associations we found between pregnancy-associated
diabetes, race/ethnicity and maternal characteristics are consistent
with previously reported data.\textsuperscript{10,36,37} One exception
is the increase in GDM prevalence with increasing zip code income
observed among hospitalized NH-Black and Hispanic women. A previously
published study conducted in California found that high-income zip codes
had a lower prevalence of GDM compared to low-income zip
codes.\textsuperscript{38} These conflicting results suggest that
factors such as diabetes screening, exercise, and diet could be more
important than previously suspected to the prevalence of GDM in certain
racial/ethnic groups.

The highest prevalence rates for PDM and GDM were observed among
Hispanic and NH-Black pregnant mothers discharged against medical advice
(DAMA). Previously published data suggest DAMA is more common among
hospitalized pregnant women with PDM and GDM compared to those without
diabetes.\textsuperscript{39} DAMA among hospitalized pregnant women is
also associated with Black race, public insurance, substance abuse,
mental illness, chronic hypertension, neonatal morbidity and fetal
death.\textsuperscript{40} Additional research is needed to better
characterize this vulnerable group and develop screening tools for
intervention.

Another notable finding is the prevalence of PDM among NH-Black pregnant
women who died during hospitalization. Among pregnant women who died
during hospitalization, NH-Black had the highest prevalence of PDM when
compared to the other race/ethnic groups. Our results are consistent
with previously published NIS data.\textsuperscript{41} The causal
pathway between PDM and maternal mortality among hospitalized NH-Black
women is not clear but studies report African American race/ethnicity is
a significant independent risk-factor for pregnancy-related
cardiovascular conditions including venous thromboembolism and stroke
that can be fatal.\textsuperscript{42,43} Further, type 1 diabetes has
been associated with an increased risk of maternal mortality due to
complications such as hypoglycemia.\textsuperscript{44} These data
suggest NH-Black pregnant women with PDM could be predisposed to
glycemic and cardiovascular conditions that increase risk for maternal
mortality. However, additional research is needed.

Consistent with previously published data, stillbirth rates for
hospitalized pregnant women in our study were higher for women with PDM
than those with GDM.\textsuperscript{12,14} Unlike previous studies,
results of our analyses show that racial/ethnic differences in
stillbirth rates exist. Additionally, we are the first to report
adjusted odds of stillbirth for Hispanic mothers with PDM. Hispanic
mothers with PDM had the highest adjusted odds of stillbirth when
compared to their non-diabetic counterparts in our study, a previously
unreported finding. Reasons for differences across racial/ethnic groups
are likely underlying maternal and fetal characteristics that have not
been well-studied. Previously reported risks factors for stillbirth
among woman with PDM include maternal age, BMI, gestational infant size,
male gender, parity, tobacco use and type of
delivery.\textsuperscript{18,35,37,45} The extent to which these factors
are influenced by race/ethnicity is not clear and should be taken into
consideration.

Interestingly, after adjustment for maternal and hospital
characteristics, we found that GDM was protective for stillbirth in each
race/ethnic group, decreasing the odds of stillbirth by approximately
50\%. Similar findings are reported by Lavery et
al.\textsuperscript{46}One possible explanation could be related to the
large percentage (70-78\%) of women in our study with private or public
medical coverage. Medical coverage may result in more aggressive
diabetes screening during pregnancy and better access to follow-up care
decreasing the occurrence of stillbirth.

\emph{Strength and Limitations}

This study has several strengths and limitations. Strengths of this
study include a sample size, study design and time of data collection.
The HCUP NIS is designed to produce regional and national estimates of
hospitalization utilization and outcomes. The analyses are conducted
using weighted estimates that represent more than 35 million annual
hospitalizations nationally. The large sample size allowed for robust
analysis of rare exposures like PDM and outcomes like stillbirth in
specific racial/ethnic groups at a national level using a standardized
dataset. Use of this data reduces selection bias and increases precision
of our analyses. Further, our findings are based on data from 2002 to
2017, more recent and comprehensive than previous national
studies.\textsuperscript{7,10,47}

Limitations of this study include the data source. HCUP NIS is limited
to an inpatient population and does not account for women who delivered
outside of the hospital. Additionally, results of our studies are
limited to the analysis of only three racial/ethnic groups despite
differences in prevalence of pregnancy-associated diabetes in other
race/ethnic groups including Native Americans and
NH-Asians.\textsuperscript{7,31,33}

Another limitation of this study is variation in data reporting for PDM
and GDM. Despite efforts to standardized documentation, there are
differences in reporting across data sources likely due to changes in
diagnosis criteria over time, coding, and health care provider
experience.\textsuperscript{48} The extent to which variation in
reporting is likely to influence our results is not clear. However, a
study that compared birth certificate data to other government collected
data found the HCUP NIS survey to be a more acceptable
source.\textsuperscript{48}

Furthermore, our study is limited by the inability to~account for
sociodemographic characteristics such as education level, employment,
and marital status. Additionally, we could not account for factors such
as physical activity, diet, hemoglobin a1c level and diabetes type which
are associated with pregnancy-associated diabetes and/or stillbirth
because of NIS HCUP data limitations.\textsuperscript{49,50} Lastly,
given the design of this study, a causal relationship between PDM and
stillbirth could not be determined.

\textbf{Conclusion and Implications for Translation}

We observed that PDM and GDM is increasing in the U.S. among minority
women. Additionally, our data highlight the increased prevalence of
stillbirths and maternal mortality among women with PDM, especially
Hispanic and NH-Black mothers, respectively.

Our results emphasize the need for the following: 1) broader
implementation of standardized guidelines for reproductive age women
diagnosed with diabetes with clear pre-conception treatment goals; 2)
greater public health efforts to reach women at high risk for diabetes
and GDM for screening and glucose monitoring; 3) additional research to
better understand race/ethnicity specific risk-factors for PDM and the
causal pathway for stillbirth and maternal death; and 4) allocation of
resources is needed to address maternal risk factors and social
determinants of health necessary to mitigate racial/ethnic differences
and target effective interventions.

\textbf{Disclosure of Interests}

The authors have no competing interest to declare.

\textbf{Contribution of Authorship}

JA conceived main conceptual idea for the project. DD and HMS secured
access to the dataset. DD performed computations and verified analytical
methods. All authors were involved in interpretation of the data. JA,
VM, AAH, AVD, GAD and DAS were involved in drafting the article. All
authors were involved in critical revision of the article and
contributed to the final approval of the manuscript.

\textbf{Details of Ethics Approval}

Due to the de-identified, publicly available nature of NIS data, the
analyses performed for this study were considered exempt by the Baylor
College of Medicine Institutional Review Board.

\textbf{Funding}

This work was supported by the Health Resources and Services
Administration (HRSA) {[}grant number 1 D34HP31024-01-00{]} for the
project titled Baylor College of Medicine (BCM) Center of Excellence in
Health Equity, Training \& Research.

\textbf{Acknowledgements}

This study was presented as an abstract at the Baylor College of
Medicine Center of Excellence in Health Equity, Training and Research
Summer Research Summit in Houston, Texas.

\emph{References}

1. Lapolla A, Metzger BE. No Title. In: \emph{Gestational Diabetes. A
Decade after the HAPO Study.} vol 28. Front Diabetes.; 2020:1-10.
doi:doi: 10.1159/000480161

2. ACOG Practice Bulletin No. 190: Gestational Diabetes
Mellitus.\emph{Obstet Gynecol} . 2018;131(2):e49-e64.
doi:10.1097/AOG.0000000000002501

3. Plows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The
pathophysiology of gestational diabetes mellitus. \emph{Int J Mol Sci} .
2018;19(11). doi:10.3390/ijms19113342

4. Szmuilowicz ED, Josefson JL, Metzger BE. Gestational Diabetes
Mellitus. \emph{Endocrinol Metab Clin North Am} . 2019;48(3):479-493.
doi:10.1016/j.ecl.2019.05.001

5. Vounzoulaki E, Khunti K, Abner SC, Tan BK, Davies MJ, Gillies CL.
Progression to type 2 diabetes in women with a known history of
gestational diabetes: Systematic review and meta-analysis. \emph{BMJ} .
2020;369. doi:10.1136/bmj.m1361

6. Peng TY, Ehrlich SF, Crites Y, et al. Trends and racial and ethnic
disparities in the prevalence of pregestational type 1 and type 2
diabetes in Northern California: 1996--2014. In: \emph{American Journal
of Obstetrics and Gynecology} . Vol 216. Mosby Inc.; 2017:177.e1-177.e8.
doi:10.1016/j.ajog.2016.10.007

7. Deputy NP, Kim SY, Conrey EJ, Bullard KM. Prevalence and Changes in
Preexisting Diabetes and Gestational Diabetes Among Women Who Had a Live
Birth --- United States, 2012--2016. \emph{MMWR Morb Mortal Wkly Rep} .
2018;67(43):1201-1207. doi:10.15585/mmwr.mm6743a2

8. Ikedionwu CA, Dongarwar D, Yusuf KK, Ibrahimi S, Salinas-Miranda AA,
Salihu HM. Pre-pregnancy maternal obesity, macrosomia, and risk of
stillbirth: A population-based study. \emph{Eur J Obstet Gynecol Reprod
Biol} . 2020;252:1-6. doi:10.1016/j.ejogrb.2020.06.004

9. Bardenheier BH, Imperatore G, Devlin HM, Kim SY, Cho P, Geiss LS.
Trends in Pre-Pregnancy Diabetes Among Deliveries in 19 U.S. States,
2000-2010. \emph{Am J Prev Med} . 2015;48(2).
doi:10.1016/j.amepre.2014.08.031

10. Correa A, Bardenheier B, Elixhauser A, Geiss LS, Gregg E. Trends in
Prevalence of Diabetes Among Delivery Hospitalizations, United States,
1993--2009. \emph{Matern Child Health J} . 2015;19(3):635-642.
doi:10.1007/s10995-014-1553-5

11. Rackham O, Paize F, Weindling AM. Cause of Death in Infants of Women
with Pregestational Diabetes Mellitus and the Relationship with Glycemic
Control. \emph{Postgrad Med} . 2009;121(4). doi:10.3810/pgm.2009.07.2026

12. Tennant PWG, Glinianaia S V., Bilous RW, Rankin J, Bell R.
Pre-existing diabetes, maternal glycated haemoglobin, and the risks of
fetal and infant death: a population-based study. \emph{Diabetologia} .
2014;57(2):285-294. doi:10.1007/s00125-013-3108-5

13. Starikov R, Dudley D, Reddy UM. Stillbirth in the Pregnancy
Complicated by Diabetes. \emph{Curr Diab Rep} . 2015;15(3):1-9.
doi:10.1007/s11892-015-0580-y

14. Page JM, Allshouse AA, Cassimatis I, et al. Characteristics of
Stillbirths Associated With Diabetes in a Diverse U.S.
Cohort.\emph{Obstet Gynecol} . 2020;136(6):1095-1102.
doi:10.1097/AOG.0000000000004117

15. Gortazar L, Goday A, Flores-Le Roux JA, et al. Trends in prevalence
of pre-existing diabetes and perinatal outcomes: A large,
population-based study in Catalonia, Spain, 2006-2015. \emph{BMJ Open
Diabetes Res Care} . 2020;8(1):1254. doi:10.1136/bmjdrc-2020-001254

16. Kim SY, Sharma AJ, Sappenfield W, Salihu HM. Preventing large birth
size in women with preexisting diabetes mellitus: The benefit of
appropriate gestational weight gain. \emph{Prev Med (Baltim)} .
2016;91:164-168. doi:10.1016/j.ypmed.2016.08.026

17. Kitzmiller JL, Ferrara A, Peng T, Cissell MA, Kim C. Preexisting
Diabetes and Pregnancy. In: \emph{Diabetes in America} . ; 2018.
https://www.ncbi.nlm.nih.gov/books/NBK567999/?report=reader. Accessed
June 3, 2021.

18. Hedderson M, Ehrlich S, Sridhar S, Darbinian J, Moore S, Ferrara A.
Racial/ethnic disparities in the prevalence of gestational diabetes
mellitus by BMI. \emph{Diabetes Care} . 2012;35(7):1492-1498.
doi:10.2337/dc11-2267

19. Pu J, Zhao B, Wang EJ, et al. Racial/Ethnic Differences in
Gestational Diabetes Prevalence and Contribution of Common Risk
Factors.\emph{Paediatr Perinat Epidemiol} . 2015;29(5):436-443.
doi:10.1111/ppe.12209

20. Mayorga ME, Reifsnider OS, Neyens DM, Gebregziabher MG, Hunt KJ.
Simulated Estimates of Pre-Pregnancy and Gestational Diabetes Mellitus
in the US: 1980 to 2008. \emph{PLoS One} . 2013;8(9):73437.
doi:10.1371/journal.pone.0073437

21. Nguyen BT, Cheng YW, Snowden JM, Esakoff TF, Frias AE, Caughey AB.
The effect of race/ethnicity on adverse perinatal outcomes among
patients with gestational diabetes mellitus. \emph{Am J Obstet Gynecol}
. 2012;207(4). doi:10.1016/j.ajog.2012.06.049

22. Verheijen ECJ, Critchley JA, Whitelaw DC, Tuffnell DJ. Outcomes of
pregnancies in women with pre-existing type 1 or type 2 diabetes, in an
ethnically mixed population. \emph{BJOG An Int J Obstet Gynaecol} .
2005;112(11). doi:10.1111/j.1471-0528.2005.00747.x

23. Esakoff TF, Caughey AB, Block-Kurbisch I, Inturrisi M, Cheng YW.
Perinatal outcomes in patients with gestational diabetes mellitus by
race/ethnicity. \emph{J Matern Neonatal Med} . 2011;24(3):422-426.
doi:10.3109/14767058.2010.504287

24. Institute NC. Joinpoint Regression Program. \emph{Stat Methodol Appl
Branch, Surveill Res Progr} . http://surveillance.cancer.gov/joinpoint/.
Accessed March 11, 2021.

25. Arroyo-Johnson C, Mincey KD. Obesity Epidemiology
Worldwide.\emph{Gastroenterol Clin North Am} . 2016;45(4):571-579.
doi:10.1016/j.gtc.2016.07.012

26. Ibe A, Smith TC. Diabetes in US women on the rise independent of
increasing BMI and other risk factors; a trend investigation of serial
cross-sections. \emph{BMC Public Health} . 2014;14(1).
doi:10.1186/1471-2458-14-954

27. Fryar CD, Ostchega Y, Hales CM, Zhang G, Kruszon-Moran D.
Hypertension Prevalence and Control Among Adults: United States,
2015-2016. \emph{NCHS Data Brief} . 2017;(289):1-8.
https://europepmc.org/article/med/29155682. Accessed June 17, 2021.

28. Cameron NA, Molsberry R, Pierce JB, et al. Pre-Pregnancy
Hypertension Among Women in Rural and Urban Areas of the United
States.\emph{J Am Coll Cardiol} . 2020;76(22).
doi:10.1016/j.jacc.2020.09.601

29. Saffer H, Dave D, Grossman M, Ann Leung L. Racial, Ethnic, and
Gender Differences in Physical Activity. \emph{J Hum Cap} . 2013;7(4).
doi:10.1086/671200

30. Siahpush M, Robbins RE, Ramos AK, Michaud TL, Clarke MA, King KM.
Does Difference in Physical Activity Between Blacks and Whites Vary by
Sex, Income, Education, and Region of Residence? Results from 2008 to
2017 National Health Interview Surveys. \emph{J Racial Ethn Heal
Disparities} . 2019;6(5). doi:10.1007/s40615-019-00586-9

31. Choe S-A, Eliot M, Savitz D, Wellenius G. Ambient Air Pollution
During Pregnancy and Risk of Gestational Diabetes in New York
City.\emph{Environ Res} . 2019;175:414-420.
doi:10.1016/j.envres.2019.04.030.Ambient

32. Rewers M, Ludvigsson J. Environmental risk factors for type 1
diabetes. \emph{Lancet} . 2016;387(10035).
doi:10.1016/S0140-6736(16)30507-4

33. Cabacungan ET, Ngui EM, McGinley EL. Racial/ethnic disparities in
maternal morbidities: A statewide study of labor and delivery
hospitalizations in Wisconsin. \emph{Matern Child Health J} .
2012;16(7). doi:10.1007/s10995-011-0914-6

34. Moyer VA. Screening for gestational diabetes mellitus: U.S.
Preventive Services Task Force recommendation statement. \emph{Ann
Intern Med} . 2014;160(6):414-420. doi:10.7326/m13-2905

35. Martin JA, Hamilton BE, D P, et al. National Vital Statistics
Reports Births : Final Data for 2013. \emph{Statistics (Ber)} .
2015;64(1):1-104.

36. Albrecht SS, Kuklina E V., Bansil P, et al. Diabetes trends among
delivery hospitalizations in the U.S., 1994-2004. \emph{Diabetes Care} .
2010;33(4):768-773. doi:10.2337/dc09-1801

37. Williams MA, Emanuel I, Kimpo C, Leisenring WM, Hale CB. A
population-based cohort study of the relation between maternal
birthweight and risk of gestational diabetes mellitus in four
racial/ethnic groups. \emph{Paediatr Perinat Epidemiol} .
1999;13(4):452-465. doi:10.1046/j.1365-3016.1999.00219.x

38. Young C, Laurent O, Chung JH, Wu J. Geographic Distribution of
Healthy Resources and Adverse Pregnancy Outcomes. \emph{Matern Child
Health J} . 2016;20(8). doi:10.1007/s10995-016-1966-4

39. Wier LM, Witt E, Burgess J, Elixhauser A. \emph{Hospitalizations
Related to Diabetes in Pregnancy, 2008: Statistical Brief \#102} .
Agency for Healthcare Research and Quality (US); 2006.
http://www.ncbi.nlm.nih.gov/pubmed/21413204. Accessed June 17, 2021.

40. Tucker Edmonds B, Ahlberg C, McPherson K, Srinivas S, Lorch S.
Predictors and Adverse Pregnancy Outcomes Associated with Antepartum
Discharge Against Medical Advice. \emph{Matern Child Health J} .
2014;18(3). doi:10.1007/s10995-013-1288-8

41. Bateman BT, Bansil P, Hernandez-Diaz S, Mhyre JM, Callaghan WM,
Kuklina E V. Prevalence, trends, and outcomes of chronic hypertension: A
nationwide sample of delivery admissions. \emph{Am J Obstet Gynecol} .
2012;206(2). doi:10.1016/j.ajog.2011.10.878

42. James AH, Jamison MG, Brancazio LR, Myers ER. Venous thromboembolism
during pregnancy and the postpartum period: Incidence, risk factors, and
mortality. \emph{Am J Obstet Gynecol} . 2006;194(5):1311-1315.
doi:10.1016/j.ajog.2005.11.008

43. James AH, Bushnell CD, Jamison MG, Myers ER. Incidence and risk
factors for stroke in pregnancy and the puerperium. \emph{Obstet
Gynecol} . 2005;106(3):509-516. doi:10.1097/01.AOG.0000172428.78411.b0

44. Kitzmiller JL, Ferrara A, Peng T, Cissell MA, Kim
C.\emph{Preexisting Diabetes and Pregnancy} . National Institute of
Diabetes and Digestive and Kidney Diseases (US); 2018.
http://www.ncbi.nlm.nih.gov/pubmed/33651557. Accessed June 9, 2021.

45. Mackin ST, Nelson SM, Wild SH, Colhoun HM, Wood R, Lindsay RS.
Factors associated with stillbirth in women with
diabetes.\emph{Diabetologia} . 2019;62(10):1938-1947.
doi:10.1007/s00125-019-4943-9

46. Lavery J, Friedman A, Keyes K, Wright J, Ananth C. Gestational
diabetes in the United States: temporal changes in prevalence rates
between 1979 and 2010. \emph{BJOG An Int J Obstet Gynaecol} .
2017;124(5):804-813. doi:10.1111/1471-0528.14236

47. Lawrence JM, Contreras R, Chen W, Sacks DA. Trends in the prevalence
of preexisting diabetes and gestational diabetes mellitus among a
racially/ethnically diverse population of pregnant women,
1999-2005.\emph{Diabetes Care} . 2008;31(5):899-904.
doi:10.2337/dc07-2345

48. Lynch CP, Baker N, Korte JE, Mauldin JG, Mayorga ME, Hunt KJ.
Increasing prevalence of diabetes during pregnancy in South
Carolina.\emph{J Women's Heal} . 2015;24(4). doi:10.1089/jwh.2014.4968

49. Murphy HR, Rayman G, Duffield K, et al. Changes in the glycemic
profiles of women with type 1 and type 2 diabetes during
pregnancy.\emph{Diabetes Care} . 2007;30(11). doi:10.2337/dc07-0500

50. Khera R, Angraal S, Couch T, et al. Adherence to methodological
standards in research using the National Inpatient Sample. \emph{JAMA -
J Am Med Assoc} . 2017;318(20). doi:10.1001/jama.2017.17653

\textbf{Table/Figure Captions List}

Table 1: Patient characteristics of mothers diagnosed with pre-pregnancy
and gestational diabetes by race/ethnicity

Figure 1: Temporal trends in the rates of pre-pregnancy and gestational
diabetes, overall and by race/ethnicity: 2002-2017

Figure 2: Rates of stillbirth among mothers with pre-pregnancy and
gestational diabetes, overall and by race/ethnicity

Figure 3: Forest plot showing association between pre-pregnancy and
gestational diabetes (exposure) and stillbirth (outcome), overall and by
race/ethnicity

Table 1: Patient characteristics of mothers diagnosed with pre-pregnancy
and gestational diabetes by race/ethnicity\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
& \textbf{NH-White} & \textbf{NH-White} & \textbf{NH-White} &
\textbf{NH-White} & \textbf{NH-Black} & \textbf{NH-Black} &
\textbf{NH-Black} & \textbf{NH-Black} & \textbf{Hispanic} &
\textbf{Hispanic} & \textbf{Hispanic} & \textbf{Hispanic}\tabularnewline
\midrule
\endhead
& \textbf{Pre-pregnancy diabetes} & & & & & & & & & & &\tabularnewline
N=304710 & \textbf{Pre-pregnancy diabetes} & & & & & & & & & &
&\tabularnewline
N=304710 & \textbf{Gestational Diabetes} & & & & & & & & & &
&\tabularnewline
N=1586776 & \textbf{Gestational Diabetes} & & & & & & & & & &
&\tabularnewline
N=1586776 & \textbf{Pre-pregnancy diabetes} & & & & & & & & & &
&\tabularnewline
N=175832 & \textbf{Pre-pregnancy diabetes} & & & & & & & & & &
&\tabularnewline
N=175832 & \textbf{Gestational Diabetes} & & & & & & & & & &
&\tabularnewline
N=439638 & \textbf{Gestational Diabetes} & & & & & & & & & &
&\tabularnewline
N=439638 & \textbf{Pre-pregnancy diabetes} & & & & & & & & & &
&\tabularnewline
N=193498 & \textbf{Pre-pregnancy diabetes} & & & & & & & & & &
&\tabularnewline
N=193498 & \textbf{Gestational Diabetes} & & & & & & & & & &
&\tabularnewline
N=934383 & \textbf{Gestational Diabetes} & & & & & & & & & &
&\tabularnewline
N=934383 & & & & & & & & & & & &\tabularnewline
& \textbf{\%=100} & \textbf{Prevalence} & \textbf{\%=100} &
\textbf{Prevalence} & \textbf{\%=100} & \textbf{Prevalence} &
\textbf{\%=100} & \textbf{Prevalence} & \textbf{\%=100} &
\textbf{Prevalence} & \textbf{\%=100} &
\textbf{Prevalence}\tabularnewline
\textbf{Age} & & & & & & & & & & & &\tabularnewline
\textless{}24 years & 21.2\% & 0.8\% & 14.5\% & 2.7\% & 23.8\% & 1.1\% &
20.0\% & 2.3\% & 17.9\% & 0.7\% & 15.8\% & 2.9\%\tabularnewline
25-34 years & 54.7\% & 1.0\% & 58.0\% & 5.5\% & 50.7\% & 2.3\% & 54.1\%
& 6.2\% & 51.6\% & 1.6\% & 54.7\% & 8.1\%\tabularnewline
35-49 years & 24.1\% & 1.5\% & 27.5\% & 8.9\% & 25.5\% & 4.3\% & 25.9\%
& 10.9\% & 30.5\% & 3.4\% & 29.4\% & 15.8\%\tabularnewline
\textbf{Discharge Status} & \textbf{Discharge Status} & & & & & & & & &
& &\tabularnewline
Routine & 94.7\% & 1.0\% & 97.4\% & 5.3\% & 91.7\% & 1.9\% & 96.1\% &
5.1\% & 95.9\% & 1.4\% & 98.2\% & 7.1\%\tabularnewline
Transfer & 1.5\% & 2.7\% & 0.5\% & 5.0\% & 1.7\% & 4.8\% & 0.6\% & 4.3\%
& 1.0\% & 3.7\% & 0.4\% & 6.8\%\tabularnewline
Died & 0.0\% & 3.5\% & 0.0\% & 4.3\% & 0.1\% & 7.9\% & 0.0\% & 2.8\% &
0.0\% & 5.0\% & 0.0\% & 5.5\%\tabularnewline
DAMA & 1.4\% & 6.4\% & 0.2\% & 4.9\% & 3.1\% & 9.8\% & 0.8\% & 6.5\% &
1.5\% & 9.9\% & 0.2\% & 7.5\%\tabularnewline
Other & 2.4\% & 1.3\% & 1.8\% & 5.2\% & 3.3\% & 3.0\% & 2.4\% & 5.3\% &
1.6\% & 2.1\% & 1.1\% & 7.1\%\tabularnewline
Missing & 0.0\% & 0.7\% & 0.0\% & 6.4\% & 0.0\% & 1.0\% & 0.0\% & 7.6\%
& 0.0\% & 1.0\% & 0.0\% & 4.9\%\tabularnewline
\textbf{Zip Income quartile} & \textbf{Zip Income quartile} & & & & & &
& & & & &\tabularnewline
Lowest quartile & 21.8\% & 1.3\% & 17.6\% & 5.7\% & 41.8\% & 2.3\% &
36.4\% & 4.9\% & 33.3\% & 1.8\% & 28.4\% & 7.2\%\tabularnewline
2nd quartile & 21.8\% & 1.1\% & 21.5\% & 5.7\% & 17.4\% & 2.0\% & 19.0\%
& 5.6\% & 19.6\% & 1.5\% & 20.7\% & 7.7\%\tabularnewline
3rd quartile & 19.9\% & 1.0\% & 22.3\% & 5.8\% & 12.2\% & 1.9\% & 15.4\%
& 5.9\% & 16.5\% & 1.4\% & 19.1\% & 8.0\%\tabularnewline
Highest quartile & 14.5\% & 0.7\% & 20.5\% & 5.4\% & 6.9\% & 1.6\% &
10.8\% & 6.4\% & 8.0\% & 1.1\% & 12.2\% & 8.1\%\tabularnewline
Missing & 22.0\% & 0.9\% & 18.1\% & 4.0\% & 21.7\% & 1.9\% & 18.3\% &
4.1\% & 22.6\% & 1.3\% & 19.7\% & 5.4\%\tabularnewline
\textbf{Primary Payer} & & & & & & & & & & & &\tabularnewline
Medicare & 4.8\% & 1.4\% & 4.2\% & 6.5\% & 6.7\% & 3.2\% & 5.1\% & 6.0\%
& 3.5\% & 1.6\% & 3.8\% & 8.6\%\tabularnewline
Medicaid & 31.6\% & 1.2\% & 22.2\% & 4.5\% & 50.1\% & 2.0\% & 40.5\% &
4.0\% & 54.3\% & 1.5\% & 49.3\% & 6.4\%\tabularnewline
Private Insurance & 47.2\% & 0.9\% & 52.1\% & 5.1\% & 26.0\% & 2.0\% &
30.1\% & 5.7\% & 23.1\% & 1.4\% & 24.6\% & 7.2\%\tabularnewline
Self-Pay & 4.3\% & 1.0\% & 3.4\% & 4.1\% & 4.2\% & 1.8\% & 3.5\% & 3.8\%
& 6.9\% & 1.4\% & 5.8\% & 5.6\%\tabularnewline
Missing & 12.1\% & 1.0\% & 18.0\% & 7.8\% & 13.0\% & 2.1\% & 20.7\% &
8.2\% & 12.1\% & 1.6\% & 16.5\% & 10.7\%\tabularnewline
\textbf{Hospital Characteristics} & \textbf{Hospital Characteristics} &
& & & & & & & & & &\tabularnewline
\textbf{Hospital Region} & & & & & & & & & & & &\tabularnewline
Northeast & 17.6\% & 0.9\% & 20.7\% & 5.2\% & 18.2\% & 2.0\% & 19.8\% &
5.4\% & 11.7\% & 1.3\% & 11.5\% & 6.2\%\tabularnewline
Midwest & 20.8\% & 1.0\% & 22.5\% & 5.6\% & 17.0\% & 2.2\% & 15.3\% &
5.0\% & 5.7\% & 1.6\% & 6.3\% & 8.7\%\tabularnewline
South & 43.5\% & 1.2\% & 37.9\% & 5.2\% & 57.0\% & 2.0\% & 55.7\% &
5.0\% & 39.4\% & 1.5\% & 36.2\% & 6.5\%\tabularnewline
West & 18.0\% & 0.9\% & 18.9\% & 5.0\% & 7.9\% & 1.8\% & 9.3\% & 5.4\% &
43.1\% & 1.5\% & 46.0\% & 7.6\%\tabularnewline
\textbf{Hospital Bed Size} & \textbf{Hospital Bed Size} & & & & & & & &
& & &\tabularnewline
Small & 10.8\% & 0.8\% & 14.5\% & 5.3\% & 7.7\% & 1.5\% & 10.9\% & 5.4\%
& 9.4\% & 1.2\% & 12.5\% & 7.6\%\tabularnewline
Medium & 25.0\% & 0.9\% & 27.0\% & 5.2\% & 25.1\% & 1.8\% & 27.9\% &
4.9\% & 23.7\% & 1.2\% & 26.6\% & 6.8\%\tabularnewline
Large & 63.8\% & 1.1\% & 58.1\% & 5.3\% & 66.7\% & 2.3\% & 60.7\% &
5.1\% & 66.6\% & 1.6\% & 60.5\% & 7.1\%\tabularnewline
Missing & 0.4\% & 1.2\% & 0.3\% & 5.1\% & 0.4\% & 1.7\% & 0.4\% & 4.3\%
& 0.3\% & 1.6\% & 0.3\% & 7.8\%\tabularnewline
\textbf{Hospital Location and Teaching Status} & \textbf{Hospital
Location and Teaching Status} & \textbf{Hospital Location and Teaching
Status} & & & & & & & & & &\tabularnewline
Rural & 10.8\% & 0.8\% & 12.0\% & 4.6\% & 5.1\% & 1.7\% & 4.8\% & 4.0\%
& 3.6\% & 1.2\% & 3.8\% & 6.0\%\tabularnewline
Urban non-teaching & 30.8\% & 0.8\% & 35.9\% & 4.8\% & 19.6\% & 1.4\% &
25.1\% & 4.5\% & 30.8\% & 1.1\% & 36.3\% & 6.1\%\tabularnewline
Urban teaching & 58.0\% & 1.2\% & 51.8\% & 5.8\% & 74.9\% & 2.4\% &
69.7\% & 5.5\% & 65.3\% & 1.8\% & 59.5\% & 7.9\%\tabularnewline
Missing & 0.4\% & 1.2\% & 0.3\% & 5.1\% & 0.4\% & 1.7\% & 0.4\% & 4.3\%
& 0.3\% & 1.6\% & 0.3\% & 7.8\%\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{a}DAMA- Discharged against medical advice

\textbf{Hosted file}

\verb`Pregnancy-Asscociated Diabetes and Stillbirth Figure 1.docx`  available at \url{https://authorea.com/users/734246/articles/711391-pregnancy-associated-diabetes-and-stillbirths-by-race-and-ethnicity-among-hospitalized-pregnant-women-in-the-united-states-a-retrospective-cross-sectional-analysis}

\textbf{Hosted file}

\verb`Pregnancy-Asscociated Diabetes and Stillbirth Figure 2.docx`  available at \url{https://authorea.com/users/734246/articles/711391-pregnancy-associated-diabetes-and-stillbirths-by-race-and-ethnicity-among-hospitalized-pregnant-women-in-the-united-states-a-retrospective-cross-sectional-analysis}

\textbf{Hosted file}

\verb`Pregnancy-Asscociated Diabetes and Stillbirth Figure 3.docx`  available at \url{https://authorea.com/users/734246/articles/711391-pregnancy-associated-diabetes-and-stillbirths-by-race-and-ethnicity-among-hospitalized-pregnant-women-in-the-united-states-a-retrospective-cross-sectional-analysis}

\selectlanguage{english}
\FloatBarrier
\end{document}

