18 research outputs found
Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model
In this study, learning modalities offered by public schools across the
United States were investigated to track changes in the proportion of schools
offering fully in-person, hybrid and fully remote learning over time. Learning
modalities from 14,688 unique school districts from September 2020 to June 2021
were reported by Burbio, MCH Strategic Data, the American Enterprise
Institute's Return to Learn Tracker and individual state dashboards. A model
was needed to combine and deconflict these data to provide a more complete
description of modalities nationwide.
A hidden Markov model (HMM) was used to infer the most likely learning
modality for each district on a weekly basis. This method yielded higher
spatiotemporal coverage than any individual data source and higher agreement
with three of the four data sources than any other single source. The model
output revealed that the percentage of districts offering fully in-person
learning rose from 40.3% in September 2020 to 54.7% in June of 2021 with
increases across 45 states and in both urban and rural districts. This type of
probabilistic model can serve as a tool for fusion of incomplete and
contradictory data sources in support of public health surveillance and
research efforts.Comment: 25 pages, 4 figure
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Prevalence of Adverse Pregnancy Outcomes, by Maternal Diabetes Status at First and Second Deliveries, Massachusetts, 1998–2007
Introduction: Understanding patterns of diabetes prevalence and diabetes-related complications across pregnancies could inform chronic disease prevention efforts. We examined adverse birth outcomes by diabetes status among women with sequential, live singleton deliveries. Methods: We used data from the 1998–2007 Massachusetts Pregnancy to Early Life Longitudinal Data System, a population-based cohort of deliveries. We restricted the sample to sets of parity 1 and 2 deliveries. We created 8 diabetes categories using gestational diabetes mellitus (GDM) and chronic diabetes mellitus (CDM) status for the 2 deliveries. Adverse outcomes included large for gestational age (LGA), macrosomia, preterm birth, and cesarean delivery. We computed prevalence estimates for each outcome by diabetes status. Results: We identified 133,633 women with both parity 1 and 2 deliveries. Compared with women who had no diabetes in either pregnancy, women with GDM or CDM during any pregnancy had increased risk for adverse birth outcomes; the prevalence of adverse outcomes was higher in parity 1 deliveries among women with no diabetes in parity 1 and GDM in parity 2 (for LGA [8.5% vs 15.1%], macrosomia [9.7% vs. 14.9%], cesarean delivery [24.7% vs 31.3%], and preterm birth [7.7% vs 12.9%]); and higher in parity 2 deliveries among those with GDM in parity 1 and no diabetes in parity 2 (for LGA [12.3% vs 18.2%], macrosomia [12.3% vs 17.2%], and cesarean delivery [27.0% vs 37.9%]). Conclusions: Women with GDM during one of 2 sequential pregnancies had elevated risk for adverse outcomes in the unaffected pregnancy, whether the diabetes-affected pregnancy preceded or followed it