Literature DB >> 33929655

Bias in Self-reported Prepregnancy Weight Across Maternal and Clinical Characteristics.

Andrea J Sharma1,2, Joanna E Bulkley3, Ashley B Stoneburner3, Padmavati Dandamudi3, Michael Leo3, Williams M Callaghan4, Kimberly K Vesco3,5.   

Abstract

OBJECTIVES: Prepregnancy body mass index (BMI) and gestational weight gain (GWG) are known determinants of maternal and child health; calculating both requires an accurate measure of prepregnancy weight. We compared self-reported prepregnancy weight to measured weights to assess reporting bias by maternal and clinical characteristics.
METHODS: We conducted a retrospective cohort study among pregnant women using electronic health records (EHR) data from Kaiser Permanente Northwest, a non-profit integrated health care system in Oregon and southwest Washington State. We identified women age ≥ 18 years who were pregnant between 2000 and 2010 with self-reported prepregnancy weight, ≥ 2 measured weights between ≤ 365-days-prior-to and ≤ 42-days-after conception, and measured height in their EHR. We compared absolute and relative difference between self-reported weight and two "gold-standards": (1) weight measured closest to conception, and (2) usual weight (mean of weights measured 6-months-prior-to and ≤ 42-days-after conception). Generalized-estimating equations were used to assess predictors of misreport controlling for covariates, which were obtained from the EHR or linkage to birth certificate.
RESULTS: Among the 16,227 included pregnancies, close agreement (± 1 kg or ≤ 2%) between self-reported and closest-measured weight was 44% and 59%, respectively. Overall, self-reported weight averaged 1.3 kg (SD 3.8) less than measured weight. Underreporting was higher among women with elevated BMI category, late prenatal care entry, and pregnancy outcome other than live/stillbirth (p < .05). Using self-reported weight, BMI was correctly classified for 91% of pregnancies, but ranged from 70 to 98% among those with underweight or obesity, respectively. Results were similar using usual weight as gold standard. CONCLUSIONS FOR PRACTICE: Accurate measure of prepregnancy weight is essential for clinical guidance and surveillance efforts that monitor maternal health and evaluate public-health programs. Identification of characteristics associated with misreport of self-reported weight can inform understanding of bias when assessing the influence of prepregnancy BMI or GWG on health outcomes.
© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

Entities:  

Keywords:  Body mass index; Body weights and measures; Gestational weight gain; Pregnancy; Validation studies

Mesh:

Year:  2021        PMID: 33929655      PMCID: PMC8924792          DOI: 10.1007/s10995-021-03149-9

Source DB:  PubMed          Journal:  Matern Child Health J        ISSN: 1092-7875


  19 in total

1.  Methods to apply probabilistic bias analysis to summary estimates of association.

Authors:  Timothy L Lash; Morten Schmidt; Annette Østergaard Jensen; Malene Cramer Engebjerg
Journal:  Pharmacoepidemiol Drug Saf       Date:  2010-06       Impact factor: 2.890

2.  Relationship of self-reported prepregnant weight and weight gain during pregnancy to maternal body habitus and age.

Authors:  C Stevens-Simon; K J Roghmann; E R McAnarney
Journal:  J Am Diet Assoc       Date:  1992-01

3.  Development of an algorithm to identify pregnancy episodes in an integrated health care delivery system.

Authors:  Mark C Hornbrook; Evelyn P Whitlock; Cynthia J Berg; William M Callaghan; Donald J Bachman; Rachel Gold; F Carol Bruce; Patricia M Dietz; Selvi B Williams
Journal:  Health Serv Res       Date:  2007-04       Impact factor: 3.402

4.  Good practices for quantitative bias analysis.

Authors:  Timothy L Lash; Matthew P Fox; Richard F MacLehose; George Maldonado; Lawrence C McCandless; Sander Greenland
Journal:  Int J Epidemiol       Date:  2014-07-30       Impact factor: 7.196

Review 5.  Information bias in epidemiological studies with a special focus on obstetrics and gynecology.

Authors:  Ulrik S Kesmodel
Journal:  Acta Obstet Gynecol Scand       Date:  2018-04       Impact factor: 3.636

6.  Is probabilistic bias analysis approximately Bayesian?

Authors:  Richard F MacLehose; Paul Gustafson
Journal:  Epidemiology       Date:  2012-01       Impact factor: 4.822

7.  Pregnancy plasma glucose levels exceeding the American Diabetes Association thresholds, but below the National Diabetes Data Group thresholds for gestational diabetes mellitus, are related to the risk of neonatal macrosomia, hypoglycaemia and hyperbilirubinaemia.

Authors:  A Ferrara; N S Weiss; M M Hedderson; C P Quesenberry; J V Selby; I J Ergas; T Peng; G J Escobar; D J Pettitt; D A Sacks
Journal:  Diabetologia       Date:  2006-11-14       Impact factor: 10.122

Review 8.  Maternal obesity and pregnancy.

Authors:  Hemant K Satpathy; Alfred Fleming; Donald Frey; Michael Barsoom; Chabi Satpathy; Jimmy Khandalavala
Journal:  Postgrad Med       Date:  2008-09-15       Impact factor: 3.840

9.  Trends and factors associated with self-reported receipt of preconception care: PRAMS, 2004-2010.

Authors:  Reena Oza-Frank; Elizabeth Gilson; Sarah A Keim; Courtney D Lynch; Mark A Klebanoff
Journal:  Birth       Date:  2014-07-04       Impact factor: 3.689

10.  Weight rhythms: weight increases during weekends and decreases during weekdays.

Authors:  Anna-Leena Orsama; Elina Mattila; Miikka Ermes; Mark van Gils; Brian Wansink; Ilkka Korhonen
Journal:  Obes Facts       Date:  2014-01-31       Impact factor: 3.942

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