Background: Research on health effects and potential harms of electronic cigarette (EC) use during pregnancy is limited. We sought to determine the risks of pregnancy EC use on pregnancy-related adverse birth outcomes and assess whether quitting ECs reduces the risks. Methods: Women with singleton live births who participated in the US Pregnancy Risk Assessment Monitoring System (PRAMS) survey study 2016–2020 were classified into four mutually exclusive groups, by their use of ECs and combustible cigarettes (CCs) during pregnancy: non-use, EC only use, CC only use, and dual use. We determined the risk of preterm birth, low birth weight, and small-for-gestational-age (SGA) by comparing cigarette users to non-users with a modified Poisson regression model adjusting for covariates. In a subset of women who all used ECs prior to pregnancy, we determined whether quitting EC use reduces the risk of preterm birth, low birth weight, and SGA by comparing to those who continued its use. All analyses were weighted to account for the PRAMS survey design and non-response rate. Results: Of the 190,707 women (weighted N = 10,202,413) included, 92.1% reported cigarette non-use, 0.5% EC only use, 6.7% CC only use, and 0.7% dual use during pregnancy. Compared with non-use, EC only use was associated with a significantly increased risk of preterm birth (adjusted risk ratio [aRR]: 1.29, 95% confidence interval [CI]: 1.00, 1.65) and low birth weight (aRR: 1.38, 95%CI: 1.09, 1.75), but not SGA (aRR: 1.04, 95%CI: 0.76, 1.44). Among 7,877 (weighted N = 422,533) women EC users, quitting use was associated with a significantly reduced risk of low birth weight (aRR: 0.76, 95%CI: 0.62, 0.94) and SGA (aRR: 0.77, 95%CI: 0.62, 0.94) compared to those who continued to use ECs during pregnancy. Conclusions: Pregnancy EC use, by itself or dual use with CC, is associated with preterm birth and low birth weight. Quitting use reduces that risk. ECs should not be considered as a safe alternative nor a viable gestational smoking cessation strategy.

Objective: We aimed to develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data. Methods: We identified a cohort of children born between 2000 and 2017, enrolled in the Tennessee Medicaid Program (TennCare), who either had DS coded on their birth certificate or had a diagnosis listed using an International Classification of Diseases (ICD) code (suspected DS), and who received care at Vanderbilt University Medical Center, a comprehensive academic medical center, in the United States. Children with suspected DS were defined as having DS if they had (a) karyotype-confirmed DS indicated on their birth certificate; (b) karyotype-pending DS indicated on their birth certificate (or just DS if test type was not specified) and at least two healthcare encounters for DS during the first 6 years of life; or (c) at least three healthcare encounters for DS, with the first and last encounter separated by at least 30 days, during the first six years of life. The positive predictive value (PPV) of the algorithm and 95% confidence interval (CI) were reported. Results: Of the 411 children with suspected DS, 354 (86.1%) were defined as having DS by the algorithm. According to medical chart review, the algorithm correctly identified 347 children with DS (PPV = 98%, 95%CI: 96.0–99.0%). Of the 57 children the algorithm defined as not having DS, 50 (97.7%, 95%CI: 76.8–93.9%) were confirmed as not having DS by medical chart review. Conclusions: An algorithm that accurately identifies individuals with DS using birth certificate data and/or ICD codes provides a valuable tool to study DS using administrative data.

Objective: To develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data. Methods: We identified a cohort of children born 2000-2017, enrolled in a state Medicaid program, who either had DS coded on the birth certificate or had a diagnosis listed using an International Classification of Diseases (ICD) code (suspected DS), and who received care at a comprehensive academic medical center in the United States. Children with suspected DS were defined as having DS if they had a) karyotype confirmed DS indicated on their birth certificate; b) karyotype pending DS indicated on their birth certificate (or just DS if test type was not specified) and at least two healthcare encounters for DS during the first 6 years of life; or c) at least three healthcare encounters for DS, with the first and last encounter separated by at least 30 days, during the first six years of life. Positive predictive value (PPV) of the algorithm and 95% confidence interval (CI) were reported. Results: Of the 411 children with suspected DS, 354 (86.1%) were defined as having DS by the algorithm. According to medical chart review, the algorithm correctly identified 347 children with DS (PPV=98%, 95%CI: 96.0%-99.0%). Of the 57 children the algorithm defined as not having DS, 50 (97.7%, 95%CI: 76.8%-93.9%) were confirmed not having DS by medical chart review. Conclusions: An algorithm that accurately identifies individuals with DS using birth certificate data and/or ICD codes provides a valuable tool to study DS using administrative data.