Maternal characteristics and pregnancy outcomes in the NICE birth cohort: an assessment of self-selection bias
Open accessAbstract
BACKGROUND: Prospective birth cohorts are essential for identifying associations between exposures and outcomes. However, voluntary participation introduces a potential bias due to self selection since the persons that chose to participate may differ in background characteristics and behaviors. OBJECTIVES: (NICE) birth cohort in northern Sweden. METHODS: = 4976) regarding maternal characteristics and lifestyle. Maternal characteristics and pregnancy outcomes were compared between the groups and associations between exposures (smoking, folic acid, BMI, parity, education) and pregnancy outcomes (birth weight and gestational age) were analyzed by linear regression analyses, examining any interaction with the group. RESULTS: NICE participants were more highly educated, older and more likely to cohabit than the non-participants. They more often took folic acid and multivitamin supplements and less often smoked during early pregnancy. Pregnancy outcomes (mode of delivery, gestational age at delivery, birth weight and APGAR score) did, however, not differ significantly between participants and non-participants. Smoking, BMI, education and parity affected gestational age and birth weight, but the associations were of similar magnitude in participants and non-participants, with no significant effect on the group. CONCLUSION: Self-selection to the NICE study was evident in some factors related to lifestyle and socioeconomic characteristics but did not appear to skew pregnancy outcomes or alter well-known effects of certain lifestyle parameters on pregnancy outcomes.
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If an online health forum primarily attracts individuals with a particular condition who are actively seeking information and support, the data collected from that platform may not reflect the experiences and perspectives of individuals who do not actively engage in online discussions.
“For example, if an online health forum primarily attracts individuals with a particular condition who are actively seeking information and support, the data collected from that platform may not reflect the experiences and perspectives of individuals who do not actively engage in online discussions [87].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [87]
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- Authors
- Ögge, L. E., Murray, F., Modzelewska, D., Lundqvist, R., Nilsson, S., Carré, H., Kippler, M., Wold, A. E., Sandberg, A.-S., Sandin, A., Jacobsson, B., & Barman, M.
- Affiliations
- Department of Obstetrics and Gynecology, Sahlgrenska University Hospital
- Umeå University (SE)
- Sahlgrenska University Hospital (SE)
- Chalmers University of Technology (SE)
- Karolinska Institutet (SE)
- University of Gothenburg (SE)
- ORCID
- Venue
- The Journal of Maternal-Fetal & Neonatal Medicine
- Publisher
- Informa UK Limited
- Published
- 2022-01-03
- Volume
- 35
- Issue
- 25
- Pages
- 9014-9022
- ISSN
- 1476-4954 (ISSN-L)
- 1476-7058
- OpenAlex
- W4205584489
- Cited by
- 22
- References
- 35
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 8 · 2023: 3 · 2022: 3 · 2021: 1
- FWCI
- 5.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Journal of Maternal-Fetal & Neonatal Medicine (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DiVA at Umeå University (Umeå University) (submittedVersion, other-oa)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- Swedish Research Council Formas (2018-02275)
- Swedish Research Council (521-2013-3154, 2019-01317)
- Swedish Research Council for Health and Jane and Dan Olsson Foundation
- Topics
- Health Sciences › Health Professions › General Health Professions › Maternal and Neonatal Healthcare
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
- Keywords
- self-selection bias, maternal characteristics, pregnancy outcomes, birth weight, gestational age, maternal smoking, folic acid supplementation, maternal body mass index, parity, maternal education, socioeconomic status, mode of delivery, Apgar score
- MeSH terms
- Birth Weight, Female, Folic Acid, Humans, Pregnancy, Pregnancy Outcome, Prospective Studies, Selection Bias
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