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\begin{document}

\title{Patient Characteristics Associated with SARS-CoV-2 Infection in
Parturients Admitted for Labor and Delivery in Massachusetts: A
Prospective Cohort Study}



\author[1]{Sharon Reale}%
\author[1]{Mario Lumbreras-Marquez}%
\author[1]{Chih King}%
\author[1]{Stacey Burns}%
\author[1]{Kara Fields}%
\author[1]{Khady Diouf}%
\author[2]{Ilona Goldfarb}%
\author[2]{Andrea Ciaranello}%
\author[3]{Julian Robinson}%
\author[1]{Katherine Gregory}%
\author[4]{Krista Huybrechts}%
\author[1]{Brian T. Bateman}%
\affil[1]{Brigham and Women's Hospital and Harvard Medical School}%
\affil[2]{Massachusetts General Hospital and Harvard Medical School}%
\affil[3]{Brigham and Womens Hospital}%
\affil[4]{Brigham and Women's Hospital}%


\vspace{-1em}



  
  \date{June 15, 2024}


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\selectlanguage{english}
\begin{abstract}
Objective: Little is known about the demographic and clinical factors
associated with SARS-CoV-2 infection in pregnant women in the United
States. The objective of this study was to evaluate the factors
associated with SARS-CoV-2 infection in women admitted for labor and
delivery, in the context of universal screening. Design: Prospective
cohort study. Setting: Four Boston-area hospitals. Population:
Convenience sample of all women admitted for delivery (n= 1,153).
Methods: We reviewed the health records of all women admitted for
delivery at the largest health system in Massachusetts between April
19-May 16, 2020. Factors evaluated for potential association with
SARS-CoV-2 infection included age, BMI, race, co-morbidities, zip code,
infection in a household member, number of children in the household,
occupation, and insurance type. Main Outcome Measures: Risk of
SARS-CoV-2 infection and associations between SARS-CoV-2 infection and
clinical characteristics. Results: A total of 32 patients (2.8\%, 95\%
confidence interval 1.9-3.9) tested positive for SARS-CoV-2 infection on
admission for delivery; 24 (75.0\%) of the patients who tested positive
were asymptomatic. Factors associated with SARS-CoV-2 infection
included: younger age, obesity, African-American or Hispanic
race/ethnicity, residence in heavily affected communities, household
member with known infection, essential worker occupation, and Medicaid
insurance. 93.5\% of patients testing positive for SARS-CoV-2 had one or
more factors associated with disease acquisition. Conclusions: In this
large sample of deliveries, SARS-CoV-2 infection was largely
concentrated in patients with distinct demographic characteristics.
Understanding factors associated with infection may inform public health
efforts directed towards at risk populations and serve in counseling
pregnant women.%
\end{abstract}%



\sloppy




\textbf{Introduction}

With over 1,500 documented severe acute respiratory syndrome coronavirus
2 (SARS-CoV-2) cases per 100,000 residents as of June 22,
2020,\textsuperscript{1} the Commonwealth of Massachusetts has the third
highest rate of infection per capita in the United
States.\textsuperscript{2} Mass General Brigham is the largest network
of hospitals in Massachusetts. All birth hospitals within the network
implemented universal SARS-CoV-2 testing of all women admitted for labor
and delivery using polymerase chain reaction (PCR) testing for
SARS-CoV-2 on April 19, 2020.

While studies from large cities affected by coronavirus disease 2019
(COVID-19), including this population, have reported on the prevalence
of SARS-CoV-2 in the context of universal testing during admission for
delivery, little is known about the patient demographic, social, and
clinical factors associated with SARS-CoV-2 infection in pregnant women
in the United States.\textsuperscript{3-9} Understanding factors
associated with SARS-CoV-2 infection at the time of labor and delivery
is important for several reasons. First, it can highlight subgroups of
pregnant patients who are at particular risk for infection and can
inform the counseling of these women throughout pregnancy and as they
approach term, with the goal of implementing preventative strategies
needed to decrease the risk of infection. Second, in settings where
universal rapid testing is limited or not available, it can be used to
direct prevention efforts and inform which patients should be
prioritized for testing and the types of precautions that should be used
while testing is pending.

The aim of this study was therefore to determine the prevalence of, and
factors associated with, SARS-CoV-2 infection in patients admitted for
labor and delivery at four Boston-area hospitals.

\textbf{Methods}

Mass General Brigham Institutional Review Board approval was obtained
for this study, and the need for informed consent was waived (Protocol
\#2020P001079). Universal testing of all patients admitted for labor and
delivery at all Mass General Brigham hospitals began on April 19, 2020.
Mass General Brigham includes four large hospitals: two academic medical
centers and two community hospitals with a combined estimated annual
delivery volume of approximately 15,000 deliveries per year. All women
who were admitted for labor and delivery at all Mass General Brigham
hospitals between April 19, 2020 and May 16, 2020 and tested for
SARS-CoV-2 up to 48 hours before admission or upon admission were
included in this study. All SARS-CoV-2 testing was performed by
nasopharyngeal reverse transcription-PCR (RT-PCR) using assays approved
via United States Food and Drug Administration Emergency Use
Authorization. Electronic health records were reviewed for all patients
admitted to labor and delivery during the study period to abstract
SARS-CoV-2 test results, demographic data, and medical variables that
may be associated with SARS-CoV-2 infection.

We calculated the risk (and exact 95\% confidence interval) of a
positive SARS-CoV-2 test up to 48 hours before or on admission to labor
and delivery among our study population over the entire study population
and by study week. Patients not tested for SARS-CoV-2 were excluded from
these analyses, as were patients who tested positive earlier in
pregnancy but negative at the time of admission for labor and delivery,
given the focus of the study was on the risk of SARS-CoV-2 infection at
the time of delivery. We calculated the percentage of women testing
positive for SARS-CoV-2 on admission who were asymptomatic, with
symptoms defined as fever, chills, cough, dyspnea, myalgia, headache,
anosmia, ageusia, sore throat, rhinorrhea, nausea or vomiting, abdominal
pain, and diarrhea. The average daily positive tests per 100 admissions
to labor and delivery were compared to statewide data from the
Massachusetts Department of Public Health per 100,000 residents aged
20-39 by study week.

Demographic, socio-economic and clinical factors evaluated for their
association with infection included maternal age, body mass index (BMI),
race, co-morbidities (gestational diabetes, preexisting diabetes,
asthma, smoking, opioid use disorder), zip code, known SARS-CoV-2
infection in a household member, parity (as a surrogate for number
children in the household), occupation, and insurance type (MassHealth
or Medicaid vs. commercial insurance). We identified the factors most
strongly associated with SARS-CoV-2 infection and determined the risk of
infection, stratified based on the number of factors associated with
infection present.

The zip code of patient residence was mapped to the corresponding
towns.\textsuperscript{10} The COVID-19 rate, defined as the number of
confirmed cases per capita provided from the Massachusetts Department of
Public Health,\textsuperscript{1} was recorded on May 13, 2020.
Occupation for each patient was classified into categories based on the
United States Bureau of Labor Statistics 2018 Standard Occupation
Classification System.\textsuperscript{11} Occupations were then
classified as essential workers vs. nonessential workers, with
healthcare workers being a subset of essential workers. Occupations were
determined to be essential based on the emergency order enacted by the
governor of Massachusetts on March 23, 2020;\textsuperscript{12} those
included as essential were: building and grounds cleaning and
maintenance occupations, food preparation and serving related
occupations, healthcare practitioners and technical occupations,
healthcare support occupations, installation, maintenance, and repair
occupations, military support occupations, protective service
occupations, and transportation and material moving occupations. The
medical records of all essential workers were manually searched for
documentation of whether the patient was working from home or not
working. If patients whose job fell into the essential workers category
were specifically noted to be working from home or not working for over
two weeks prior to delivery, they were not included in the essential
worker category.

Due to the limited number of SARS-CoV-2 infections, estimating a full
multivariable logistic regression model including all covariates of
interest was not feasible.\textsuperscript{13} Therefore, a set of
simple logistic regression models was used to assess the univariate
association between each covariate of interest and the odds of
SARS-CoV-2 infection. As an exploratory analysis, multivariable logistic
regression with the least absolute shrinkage and selection operator
(lasso)\textsuperscript{14}was used to identify a small subset of
predictors with the strongest association with SARS-CoV-2 infection.
Lasso is a penalized regression method that constrains the sum of the
magnitude of regression model coefficients such that covariates that do
not improve prediction of the outcome are shrunk to zero, thus creating
a more parsimonious model.\textsuperscript{15} The degree of
penalization, lambda, was selected as the largest value that maintained
10-fold cross-validated prediction error within 1 standard error of the
minimum.\textsuperscript{14} The risk of SARS-CoV-2 infection and
corresponding 95\% confidence intervals were estimated amongst subgroups
of patients using Poisson regression with robust error variance. Missing
data on race (9.9\%), occupation category (7.6\%), and delivery BMI
(0.2\%) were addressed using multiple imputation by fully conditional
specification, assuming that data were missing at random given observed
data.\textsuperscript{16} Specifically, predictive mean matching,
logistic regression, and the discriminant function method were used to
impute continuous variables, binary and ordinal categorical variables,
and nominal categorical variables, respectively, to create 20 complete
datasets. Imputation models included all predictors assessed for
univariate association with SARS-CoV-2 infection, as well as delivery
hospital and SARS-CoV-2 test result. Odds ratio and risk estimates with
corresponding standard errors were obtained from each of the 20 complete
datasets and combined using Rubin's rules to produce pooled estimates
with 95\% confidence intervals.\textsuperscript{17} Lasso selected the
same set of predictors across all imputations, and final coefficients
were obtained by averaging across imputations. A complete case analysis
was performed as a sensitivity analysis. Statistical analyses were
performed using SAS software version 9.4 (SAS Institute Inc, Cary, NC,
USA) and R software version 3.6.1 (R Foundation for Statistical
Computing, Vienna, Austria).

\textbf{Results}

There were 1,153 deliveries at Mass General Brigham hospitals during the
study time period. Four patients who were not tested for SARS-CoV-2 and
six who tested positive earlier in pregnancy but negative at the time of
admission for labor and delivery were excluded from all analyses; 22
patients residing outside Massachusetts were excluded from any analysis
assessing COVID-19 rate by zip code (Figure 1). Thirty-two out of 1,143
women tested positive for SARS-CoV-2 on admission; the risk of
SARS-CoV-2 infection of patients tested in our sample was 2.8\% (95\%
confidence interval {[}CI{]}, 1.9-3.9). In Figure 2, new positive tests
per capita in our study are compared to age-specific statewide data from
the Massachusetts Department of Public Health by study week, with both
populations showing a gradual decline in cases over the four-week study
period.

Demographic and clinical factors strongly associated with SARS-CoV-2
infection include younger age (OR 4.01, 95\% CI 1.55-10.41 for age
\textless{}25 compared to age 25-35), obesity (OR 3.26, 95\% CI
0.76-14.03 for delivery BMI \emph{\textgreater{}} 30.0
kg/m\textsuperscript{2} compared to \emph{\textless{}} 24.9
kg/m\textsuperscript{2}), African American race (OR 6.54, 95\% CI
1.96-21.85), and Hispanic ethnicity (OR 19.87, 95\% CI 7.72-51.13)
(Table 1; complete case analysis, Appendix 1). Geographic and
occupational factors strongly associated with infection include an
increased rate of documented SARS-CoV-2 infections per capita in the
patient's town (OR 26.85, 95\% CI 7.86-91.66 for\emph{\textgreater{}}
95\textsuperscript{th} percentile compared to\emph{\textless{}}
90\textsuperscript{th}), a household member with known SARS-CoV-2
infection (OR 31.81, 95\% CI 13.37-75.65), essential worker occupation
excluding healthcare workers (OR 7.33, 95\% CI 3.16-16.99), and Medicaid
insurance vs. commercial (OR 10.87, 95\% CI 5.14-22.98) (Table 2;
complete case analysis, Appendix 1).

93.5\% of women who tested positive at the time of admission for labor
and delivery had at least one of these factors strongly associated with
SARS-CoV-2 infection; 54.8\% had four or more identifiable factors
(Table 3, Figure 3). In contrast, only 5.8\% of patients testing
negative for SARS-CoV-2 had four or more factors associated with
infection. While the number of outcome events precluded assessing the
independent effect of each of these factors, the lasso regression
identified COVID-19 rate in the
95\textsuperscript{th}-99\textsuperscript{th} percentile, Hispanic
ethnicity, household member with known SARS-CoV-2 infection, and
MassHealth or Medicaid insurance as the strongest predictors (Appendix
2). Among women testing positive on admission for labor and delivery,
75.0\% were asymptomatic (Table 4).

\textbf{Discussion}

Main Findings

In this large sample of patients admitted for labor and delivery
undergoing universal SARS-CoV-2 testing during the height of the initial
surge of infections in Massachusetts, we observed an overall 2.8\% risk
of infection, with three-quarters of infected patients being
asymptomatic. The risk was largely clustered in patients with distinct
demographic and occupational characteristics.

The risk of infection was 12\% for women living in the towns most
heavily affected by COVID-19 based on publicly reported case rates in
the general population, compared to 0.5\% for those living in towns with
the lowest infection rates. Approximately 14\% of Hispanic women and 5\%
of African American women were infected, versus 0.8\% of Caucasian and
Asian women. The risk of infection in public health beneficiaries was
more than ten-fold higher than in privately insured women. Essential
workers outside of healthcare had a markedly high risk of infection,
14\%. Furthermore, 93.5\% of patients with SARS-CoV-2 infection had at
least one of six factors associated with infection; over half had four
or more.

The factors associated with SARS-CoV2 infection may vary between
communities and are likely to evolve as the pandemic progresses in
various settings. Based on Massachusetts data at the peak of the spring
2020 surge, the strong association of infection with particular
demographic characteristics and neighborhoods suggest the need for
public health officials and clinicians to track and use this type of
data as outbreaks occur in order to implement interventions aimed at
decreasing infection rates in particular
communities.\textsuperscript{18} The high risk of infection in
non-healthcare essential workers suggests that directive work-related
precautions should be offered to women who work in non-healthcare
related high risk settings, if at all possible. As having a household
member with known SARS-CoV-2 infection was also strongly associated with
infection at the time of admission for labor and delivery, all household
members should be counseled to take precautions to avoid infection,
which may prevent transmission of the virus to the parturient. Risk
stratification of patients at risk for SARS-CoV-2 infection is also
instructive at the hospital level. Some hospitals may not have the
capacity to test all parturients or may not have rapid SARS-CoV-2
testing available, leading to a need to prioritize patients at highest
risk for infection for testing.

Our data demonstrate that pregnant women from vulnerable populations
were disproportionately affected by SARS-CoV2 infection during the first
wave of infection in Massachusetts. These trends are in line with the
widespread racial and socioeconomic disparities in COVID-19 seen in the
general population in several geographic areas, and though explanations
for these disparities are multifold, it is likely that residence in
crowded urban settings, poverty and employment in essential occupations,
and decreased access to care play a role.\textsuperscript{19,20} Thus,
there are profound racial and economic disparities in COVID-19 in
pregnant women in Massachusetts that track racial and economic
disparities in maternal health and obstetrical outcomes observed more
generally.\textsuperscript{21}

Compared to data from our study, the documented rates of new SARS-CoV-2
infection per capita in the state of Massachusetts for the people aged
20-39 was multi-fold lower,\textsuperscript{1} likely due to a
substantial undercounting of disease burden given widespread
asymptomatic disease and limited testing available for the general
population. Most SARS-CoV-2 testing is performed due to patient
symptoms; there are few settings where ongoing universal screening of
otherwise healthy patients is conducted. Thus, such universal testing
can provide valuable insight into the disease dynamics in the community
and can be used to monitor the burden of disease.\textsuperscript{3-6}

Strengths and Limitations

Strengths of our study include the high rate (\textgreater{}99\%) rate
of SARS-CoV-2 testing on admission, with a large study population of
over 1,000 patients included in a four-week time period. Furthermore,
manual chart review of all patients allowed for robust examination of
detailed demographic and clinical data. With 32 cases of infection in
our sample, it was not possible to perform extensive multivariable
adjustments, particularly given that a number of factors associated with
infection may be correlated, but an understanding of the factors
associated with infection remains relevant to recognizing which patients
may be at risk for SARS-CoV-2 infection. Moreover, the linear
relationship of risk of infection with each additional associated factor
suggests that the factors are not all correlated and that there is
additive risk with each added factor. In this study, we equate a
negative test with the lack of infection, but while the sensitivity of
the test is high, it may be imperfect. However, in the context of an
overall low prevalence of infection, false negatives are unlikely to
substantially bias the associations with risk factors reported. Finally,
the frequencies of SARS-CoV-2 infection in pregnant patients and factors
associated with infection may not be fully generalizable to the general
population, given physiologic changes specific to pregnancy and
potential variations in behavior of pregnant women compared to the
general population, particularly in the weeks leading up to delivery.
Nonetheless, our findings remain important in risk stratifying
parturients at risk for SARS-CoV-2 infection throughout pregnancy and at
the time of labor and delivery and in directing counseling and
prevention efforts.

Conclusion

In summary, in our large cohort of women admitted for labor and delivery
in Massachusetts undergoing universal SARS-CoV-2 infection screening,
there were multiple identifiable factors associated with infection.
Almost all patients who tested positive for infection had one or more
identifiable factors associated with disease. SARS-CoV-2 infection most
heavily affected pregnant women who were younger, African American or
Hispanic, non-healthcare essential workers, publicly insured, or from
heavily affected areas, underscoring another source of disparity in
obstetrics.

\textbf{Acknowledgements} : The authors thank Yonatan H Grad, MD, PhD,
for comments on an earlier version of this manuscript.

\textbf{Disclosure of interests:} The authors report no conflicts of
interest.

\textbf{Contributions to Authorship:}

SR helped in the conception, planning, carrying out, analyzing and
writing up of the manuscript. MLM helped in the conception, planning,
carrying out, analyzing and writing up of the manuscript. CK helped in
the planning and carrying out of the manuscript. SB helped in the
planning and carrying out of the manuscript. KF helped in planning,
carrying out, analyzing and writing up of the manuscript. KD helped in
planning, analyzing and writing up of the manuscript. IG helped in
planning, analyzing and writing up of the manuscript. AC helped in
planning, analyzing and writing up of the manuscript. JR helped in the
conception, planning, and writing up of the manuscript. KG helped in the
conception, planning, and writing up of the manuscript. KH helped in the
conception, planning, carrying out, analyzing and writing up of the
manuscript. BB helped in the conception, planning, carrying out,
analyzing and writing up of the manuscript.

\textbf{Details of ethics approval:} Mass General Brigham Institutional
Review Board approval was obtained for this study, and the need for
informed consent was waived (Protocol \#2020P001079).

\textbf{Funding} : None.

\textbf{Financial Disclosures:}

BTB is an investigator on grants to Brigham and Women's Hospital from
the NIH, FDA, CDC, Baxalta, Lilly, GSK, Pfizer, Takeda, and Pacira.~ He
is a consultant to Aetion, Inc and the Alosa Foundation.~ BTB served on
an expert panel for a postpartum hemorrhage quality improvement project
that was conducted by the Association of Women's Health, Obstetric and
Neonatal Nurses and funded by a grant from Merck for Mothers.

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\textbf{Table 1:} Demographics and Clinical Factors Associated with
SARS-CoV-2 infection.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
SARS-CoV-2 infection\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
SARS-CoV-2 infection\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Positive n=32\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Negative n=1111\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Odds ratio, 95\% confidence interval\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Maternal age, years, mean \selectlanguage{ngerman}± SD\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
28.8 \selectlanguage{ngerman}± 6.1\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
32.8 \selectlanguage{ngerman}± 4.8\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Age \textless{}25, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6 (18.8)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
56 (5.0)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
4.01 (1.55 - 10.41)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Age 25-35, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
20 (62.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
749 (67.4)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Reference\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Age \textgreater{}35, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6 (18.8)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
306 (27.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.73 (0.29 - 1.85)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
BMI, kg/m\textsuperscript{2}, mean \selectlanguage{ngerman}± SD\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
33.3 \selectlanguage{ngerman}± 6.1\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
30.7 \selectlanguage{ngerman}± 6.0\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Normal (\emph{\textless{}} 24.9 kg/m\textsuperscript{2}), n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2 (6.3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
156 (14.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Reference\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Overweight (25.0 - 29.9 kg/m\textsuperscript{2}), n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
8 (25.0)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
426 (38.4)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1.47 (0.31 - 6.98)*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Obese (\emph{\textgreater{}} 30.0 kg/m\textsuperscript{2}), n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
22 (68.8)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
527 (47.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
3.26 (0.76 -- 14.03)*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Race/Ethnicity, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
African American\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
5 (15.6)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
94 (8.6)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6.54 (1.96-21.85)*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Asian\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1 (3.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
122 (11.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1.01 (0.12-8.49)*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Caucasian\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6 (18.8)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
744 (67.8)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Reference\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Hispanic\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
18 (56.3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
112 (10.2)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
19.87 (7.72-51.13)*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Other\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
2 (6.3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
26 (2.4)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
9.53 (1.84-49.48)*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Comorbidities, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Gestational diabetes\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1 (3.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
99 (8.9)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.33 (0.04-2.45)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Preexisting diabetes\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0 (0.0)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6 (0.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
N/A+\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Asthma\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
6 (18.8)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
123 (11.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1.85 (0.75-4.60)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Tobacco exposure\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0 (0.0)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
11 (1.0)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
N/A+\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Opioid use disorder\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0 (0.0)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
12 (1.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
N/A+\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

SARS-CoV-2 = Severe acute respiratory syndrome coronavirus 2; SD =
Standard deviation; BMI = Body mass index; kg = Kilogram;
m\textsuperscript{2} = Square meter; N/A = Not applicable

* Missing data

+ Odds ratio could not be calculated due to zero prevalence of factor in
SARS-CoV-2 positive patients

\textbf{Table 2:} Geographic and Occupational Factors Associated with
SARS-CoV-2 infection.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
SARS-CoV-2 Testing\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
SARS-CoV-2 Testing\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Positive n=32\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Negative n=1111\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Odds ratio, 95\% confidence interval\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Percentile, COVID-19 rate among Massachusetts towns (based on
cases/100,000 residents)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textless{}90\textsuperscript{th}, n (\%)*\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
3 (9.4)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
600 (55.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Reference\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
90-94\textsuperscript{th}, n (\%)\textsuperscript{+}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
9 (28.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
340 (31.2)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
5.29 (1.42 - 19.72)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\emph{\textgreater{}}95\textsuperscript{th}, n
(\%)\textsuperscript{++}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
20 (62.5)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
149 (13.7)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
26.85 (7.86 - 91.66)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Household member with known SARS-CoV-2 infection, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
11 (34.4)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
18 (1.6)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
31.81 (13.37-75.65)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Number of children at home, median (Q1, Q3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.5 (0, 2)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
1 (0, 1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.86 (0.43-1.75)\selectlanguage{ngerman}\textsuperscript{§}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Occupation, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Nonessential workers\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
19 (61.3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
836 (81.6)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Reference\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Healthcare workers\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
3 (9.7)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
135 (13.2)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
0.98 (0.29-3.35)\textsuperscript{\textbar{}\textbar{}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Essential workers excluding healthcare workers\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
9 (29)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
54 (5.3)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
7.33 (3.16-16.99)\textsuperscript{\textbar{}\textbar{}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Insurance type, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
MassHealth or Medicaid\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
21 (65.6)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
166 (14.9)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
10.87 (5.14-22.98)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Commercial or out of pocket\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
11 (34.4)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
945 (85.1)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Reference\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

SARS-CoV-2 = Severe acute respiratory syndrome coronavirus 2; COVID-19 =
Coronavirus disease 2019; Q1 = First quartile; Q3 = Third quartile

* 0-1,1436 cases/100,000 residents

\textsuperscript{+} 1,437-1,791 cases/100,000 residents

++ 1,792-6404 cases/100,000 residents

\selectlanguage{ngerman}\textsuperscript{§} \selectlanguage{english}[?]1 vs. 0 children at home

\textsuperscript{\textbar{}\textbar{}} Missing data

\textbf{Table 3:} Risk of SARS-CoV-2 infection stratified by patient
characteristics.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& Positive n & Total n & Risk of SARS-CoV-2 infection (95\%
CI)\tabularnewline
\midrule
\endhead
Maternal age & & &\tabularnewline
Age \textless{}25 & 6 & 62 & 9.7 (4.5-20.7)\tabularnewline
Age 25-35 & 20 & 769 & 2.6 (1.7-4)\tabularnewline
Age \textgreater{}35 & 6 & 312 & 1.9 (0.9-4.2)\tabularnewline
BMI & & &\tabularnewline
Normal (\emph{\textless{}} 24.9 kg/m\textsuperscript{2}) & 2 & 158 & 1.3
(0.3-5.0)*\tabularnewline
Overweight (25.0 - 29.9 kg/m\textsuperscript{2}) & 8 & 434 & 1.8
(0.9-3.7)*\tabularnewline
Obese (\emph{\textgreater{}} 30.0 kg/m\textsuperscript{2}) & 22 & 549 &
4.0 (2.7-6.0)*\tabularnewline
Race/ethnicity & & &\tabularnewline
African American & 5 & 99 & 5.0 (2.1-11.8)*\tabularnewline
Asian & 1 & 123 & 0.8 (0.1-5.7)*\tabularnewline
Caucasian & 6 & 750 & 0.8 (0.4-1.8)*\tabularnewline
Hispanic & 18 & 130 & 13.7 (8.9-21.1)*\tabularnewline
Other & 2 & 28 & 7.1 (1.8-27.6)*\tabularnewline
COVID-19 rate category among Massachusetts towns (based on cases/100,000
residents) & & &\tabularnewline
\emph{\textless{}}90\textsuperscript{th} & 3 & 603 & 0.5 (0.2,
1.5)\tabularnewline
90-94\textsuperscript{th} & 9 & 349 & 2.6 (1.4, 4.9)\tabularnewline
\emph{\textgreater{}}95\textsuperscript{th} & 20 & 169 & 11.8 (7.8,
17.9)\tabularnewline
Household member with known SARS-CoV-2 infection & & &\tabularnewline
Yes & 11 & 29 & 37.9 (23.8-60.4)\tabularnewline
No & 21 & 1114 & 1.9 (1.2-2.9)\tabularnewline
Occupation & & &\tabularnewline
Nonessential workers & 19 & 855 & 2.1 (1.3-3.3)\tabularnewline
Healthcare workers & 3 & 138 & 2.1 (0.7-6.3)\tabularnewline
Essential workers excluding healthcare workers & 9 & 63 & 13.5
(7.4-24.8)*\tabularnewline
Insurance type & & &\tabularnewline
MassHealth or Medicaid & 21 & 187 & 11.2 (7.5-16.8)\tabularnewline
Commercial or out-of-pocket & 11 & 956 & 1.2 (0.6-2.1)\tabularnewline
\bottomrule
\end{longtable}

SARS-CoV-2 = Severe acute respiratory syndrome coronavirus 2; CI =
Confidence interval; BMI = Body mass index; kg = Kilogram;
m\textsuperscript{2} = Square meter; COVID-19 = Coronavirus disease 2019

* Missing data

\textbf{Table 4:} Characteristics of SARS-CoV-2 Positive Patients.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
& n=32\tabularnewline
\midrule
\endhead
Asymptomatic on admission, n (\%) & 24 (75)\tabularnewline
Symptoms, n (\%) &\tabularnewline
Fever & 5 (15.6)\tabularnewline
Chills & 1 (3.1)\tabularnewline
Cough & 4 (12.5)\tabularnewline
Dyspnea & 4 (12.5)\tabularnewline
Myalgia & 3 (9.4)\tabularnewline
Headache & 2 (6.3)\tabularnewline
Anosmia & 4 (12.5)\tabularnewline
Ageusia & 2 (6.3)\tabularnewline
Sore throat & 2 (6.3)\tabularnewline
Rhinorrhea & 2 (6.3)\tabularnewline
Nausea or vomiting & 1 (3.1)\tabularnewline
Abdominal pain & 1 (3.1)\tabularnewline
Diarrhea & 1 (3.1)\tabularnewline
Cesarean delivery, n (\%) & 14 (43.8)\tabularnewline
Gestational age at delivery, weeks, mean \selectlanguage{ngerman}± SD & 38.6 \selectlanguage{ngerman}±
2.2\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

SARS-CoV-2 = Severe acute respiratory syndrome coronavirus 2; SD =
Standard deviation

\textbf{Figure Legends}

Figure 2

\begin{itemize}
\tightlist
\item
  X axis: Study week.
\item
  Y axis: Average daily new coronavirus disease 2019 (COVID-19) cases
  per 100 admissions to Mass General Brigham Hospital Labor and Delivery
  with 95\% confidence intervals vs. average daily new COVID-19 cases
  per 100,000 Massachusetts residents.
\end{itemize}

Figure 3

\begin{itemize}
\tightlist
\item
  X axis: Number of factors associated with severe acute respiratory
  syndrome coronavirus 2 (SARS-CoV-2) infection. Factors included in
  analyses: age \textless{}25, body mass index
  (BMI)\emph{\textgreater{}} 30 kg/m\textsuperscript{2}, African
  American or Hispanic race/ethnicity, coronavirus disease 2019
  (COVID-19) rate in the patient's town \emph{\textgreater{}}
  95\textsuperscript{th}percentile, household member with known
  SARS-CoV-2 infection, essential worker occupation (excluding
  healthcare worker), and MassHealth or Medicaid insurance. Number of
  infected patients in each category: 0 factors associated with
  infection, 2 patients; 1 factor, 1 patient; 2 factors, 6 patients; 3
  factors, 5 patients; 4 factors, 10 patients; 5+ factors, 7 patients.
\item
  Y axis: Risk of SARS-CoV-2 infection with error bars representing 95\%
  confidence intervals for the risks.
\end{itemize}

\textbf{Hosted file}

\verb`Figure 1.docx`  available at \url{https://authorea.com/users/345887/articles/471971-patient-characteristics-associated-with-sars-cov-2-infection-in-parturients-admitted-for-labor-and-delivery-in-massachusetts-a-prospective-cohort-study}

\textbf{Hosted file}

\verb`Figure 2.docx`  available at \url{https://authorea.com/users/345887/articles/471971-patient-characteristics-associated-with-sars-cov-2-infection-in-parturients-admitted-for-labor-and-delivery-in-massachusetts-a-prospective-cohort-study}

\textbf{Hosted file}

\verb`Figure 3.docx`  available at \url{https://authorea.com/users/345887/articles/471971-patient-characteristics-associated-with-sars-cov-2-infection-in-parturients-admitted-for-labor-and-delivery-in-massachusetts-a-prospective-cohort-study}

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

