Delivery Outcomes During the COVID-19 Pandemic as Reported in a Pregnancy Mobile App: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has presented obstacles for providers and patients in the maternal health care setting, causing changes to many pregnant women's birth plans, as well as abrupt changes in hospital labor and delivery policies and procedures. Few data exist on the effects of the COVID-19 pandemic on the maternal health care landscape at the national level in the United States. OBJECTIVE: The aim of this study is to assess the incidence of key obstetrics outcomes (preterm delivery, Cesarean sections, and home births) and length of hospital stay during the COVID-19 pandemic as compared to the 6 months prior. METHODS: We conducted a retrospective cohort study of women aged 18-44 years in the United States who delivered between October 1, 2019, and September 30, 2020, had singleton deliveries, and completed a birth report in the Ovia Pregnancy mobile app. Women were assigned to the prepandemic cohort if they delivered between October 2019 and March 2020, and the pandemic cohort if they delivered between April and September 2020. Gestational age at delivery, delivery method, delivery facility type, and length of hospital stay were compared. RESULTS: A total of 304,023 birth reports were collected, with 152,832 (50.26%) in the prepandemic cohort and 151,191 (49.73%) in the pandemic cohort. Compared to the prepandemic cohort, principal findings indicate a 5.67% decrease in preterm delivery rates in the pandemic cohort (P<.001; odds ratio [OR] 0.94, 95% CI 0.91-0.96), a 30.0% increase in home birth rates (P<.001; OR 1.3, 95% CI 1.23-1.4), and a 7.81% decrease in the average hospital length of stay postdelivery (mean 2.48 days, SD 1.35). There were no overall changes in Cesarean section rates between cohorts, but differences were observed between age, race, and ethnicity subgroups. CONCLUSIONS: Results suggest a need for continuous monitoring of maternal health trends as the COVID-19 pandemic progresses and underline the important role of digital data collection, particularly during the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".