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.10 The COVID-19 rate, defined as the number of confirmed cases per capita provided from the Massachusetts Department of Public Health,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.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;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.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)14was 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.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.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.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.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).