Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data
Abstract BackgroundPreterm birth, defined as birth at <37 weeks of gestation, is the leading cause of neonatal death globally and the second leading cause of infant mortality in the United States. There is mounting evidence that COVID-19 infection during pregnancy is associ...
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JMIR Publications
2025-07-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e66097 |
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| author | Ari Z Klein Shriya Kunatharaju Su Golder Lisa D Levine Jane C Figueiredo Graciela Gonzalez-Hernandez |
| author_facet | Ari Z Klein Shriya Kunatharaju Su Golder Lisa D Levine Jane C Figueiredo Graciela Gonzalez-Hernandez |
| author_sort | Ari Z Klein |
| collection | DOAJ |
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Abstract
BackgroundPreterm birth, defined as birth at <37 weeks of gestation, is the leading cause of neonatal death globally and the second leading cause of infant mortality in the United States. There is mounting evidence that COVID-19 infection during pregnancy is associated with an increased risk of preterm birth; however, data remain limited by trimester of infection. The ability to study COVID-19 infection during the earlier stages of pregnancy has been limited by available sources of data.
ObjectiveThe objective of this study was to use self-reports in large-scale social media data to assess the association between the trimester of COVID-19 infection and preterm birth.
MethodsIn this retrospective cohort study, we used natural language processing and machine learning, followed by manual validation, to identify self-reports of pregnancy on Twitter and to search these users’ collection of publicly available tweets for self-reports of COVID-19 infection during pregnancy and, subsequently, a preterm birth or term birth outcome. Among the users who reported their pregnancy on Twitter, we also identified a 1:1 age-matched control group, consisting of users with a due date before January 1, 2020—that is, without COVID-19 infection during pregnancy. We calculated the odds ratios (ORs) with 95% CIs to compare the frequency of preterm birth for pregnancies with and without COVID-19 infection and by the timing of infection: first trimester (1‐13 weeks), second trimester (14‐27 weeks), or third trimester (28‐36 weeks).
ResultsThrough August 2022, we identified 298 Twitter users who reported COVID-19 infection during pregnancy, a preterm birth or term birth outcome, and maternal age: 94 (31.5%) with first-trimester infection, 110 (36.9%) with second-trimester infection, and 95 (31.9%) with third-trimester infection. In total, 26 (8.8%) of these 298 users reported preterm birth: 8 (8.5%) with first-trimester infection, 7 (6.4%) with second-trimester infection, and 12 (12.6%) with third-trimester infection. In the 1:1 age-matched control group, 13 (4.4%) of the 298 users reported preterm birth. Overall, the odds of preterm birth were significantly higher for pregnancies with COVID-19 infection compared to those without (OR 2.08, 95% CI 1.06‐4.28; PPPP
ConclusionsBased on self-reports in large-scale social media data, the results of our study suggest that COVID-19 infection particularly during the third trimester is associated with higher odds of preterm birth. |
| format | Article |
| id | doaj-art-a3b04e96fec241f1aec52ebe6be5937b |
| institution | DOAJ |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj-art-a3b04e96fec241f1aec52ebe6be5937b2025-08-20T02:40:30ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-07-0127e66097e6609710.2196/66097Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media DataAri Z Kleinhttp://orcid.org/0000-0002-8281-3464Shriya Kunatharajuhttp://orcid.org/0000-0001-6042-1745Su Golderhttp://orcid.org/0000-0002-8987-5211Lisa D Levinehttp://orcid.org/0000-0002-6811-7980Jane C Figueiredohttp://orcid.org/0000-0001-8040-3341Graciela Gonzalez-Hernandezhttp://orcid.org/0000-0002-6416-9556 Abstract BackgroundPreterm birth, defined as birth at <37 weeks of gestation, is the leading cause of neonatal death globally and the second leading cause of infant mortality in the United States. There is mounting evidence that COVID-19 infection during pregnancy is associated with an increased risk of preterm birth; however, data remain limited by trimester of infection. The ability to study COVID-19 infection during the earlier stages of pregnancy has been limited by available sources of data. ObjectiveThe objective of this study was to use self-reports in large-scale social media data to assess the association between the trimester of COVID-19 infection and preterm birth. MethodsIn this retrospective cohort study, we used natural language processing and machine learning, followed by manual validation, to identify self-reports of pregnancy on Twitter and to search these users’ collection of publicly available tweets for self-reports of COVID-19 infection during pregnancy and, subsequently, a preterm birth or term birth outcome. Among the users who reported their pregnancy on Twitter, we also identified a 1:1 age-matched control group, consisting of users with a due date before January 1, 2020—that is, without COVID-19 infection during pregnancy. We calculated the odds ratios (ORs) with 95% CIs to compare the frequency of preterm birth for pregnancies with and without COVID-19 infection and by the timing of infection: first trimester (1‐13 weeks), second trimester (14‐27 weeks), or third trimester (28‐36 weeks). ResultsThrough August 2022, we identified 298 Twitter users who reported COVID-19 infection during pregnancy, a preterm birth or term birth outcome, and maternal age: 94 (31.5%) with first-trimester infection, 110 (36.9%) with second-trimester infection, and 95 (31.9%) with third-trimester infection. In total, 26 (8.8%) of these 298 users reported preterm birth: 8 (8.5%) with first-trimester infection, 7 (6.4%) with second-trimester infection, and 12 (12.6%) with third-trimester infection. In the 1:1 age-matched control group, 13 (4.4%) of the 298 users reported preterm birth. Overall, the odds of preterm birth were significantly higher for pregnancies with COVID-19 infection compared to those without (OR 2.08, 95% CI 1.06‐4.28; PPPP ConclusionsBased on self-reports in large-scale social media data, the results of our study suggest that COVID-19 infection particularly during the third trimester is associated with higher odds of preterm birth.https://www.jmir.org/2025/1/e66097 |
| spellingShingle | Ari Z Klein Shriya Kunatharaju Su Golder Lisa D Levine Jane C Figueiredo Graciela Gonzalez-Hernandez Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data Journal of Medical Internet Research |
| title | Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data |
| title_full | Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data |
| title_fullStr | Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data |
| title_full_unstemmed | Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data |
| title_short | Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data |
| title_sort | association between covid 19 during pregnancy and preterm birth by trimester of infection retrospective cohort study using large scale social media data |
| url | https://www.jmir.org/2025/1/e66097 |
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