Gestational Weight Gain and Long-term Maternal Obesity Risk: A Multiple-Bias Analysis

被引:17
作者
Hutchins, Franya [1 ]
Krafty, Robert [2 ]
El Khoudary, Samar R. [1 ]
Catov, Janet [1 ]
Colvin, Alicia [1 ]
Barinas-Mitchell, Emma [1 ]
Brooks, Maria M. [1 ]
机构
[1] Univ Pittsburgh, Dept Epidemiol, Grad Sch Publ Hlth, 130 DeSoto St, Pittsburgh, PA 15261 USA
[2] Univ Pittsburgh, Dept Biostat, Grad Sch Publ Hlth, Pittsburgh, PA 15261 USA
基金
美国国家卫生研究院;
关键词
epidemiologic methods; bias; selection bias; retrospective studies; women’ s health; gestational weight gain; obesity; pregnancy; BODY-MASS INDEX; LIFE-COURSE APPROACH; CARDIOVASCULAR-DISEASE; PREPREGNANCY WEIGHT; PRETERM DELIVERY; ATTRITION; IMPUTATION; PREGNANCY; STRATEGIES; HEALTH;
D O I
10.1097/EDE.0000000000001310
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: Lifecourse research provides an important framework for chronic disease epidemiology. However, data collection to observe health characteristics over long periods is vulnerable to systematic error and statistical bias. We present a multiple-bias analysis using real-world data to estimate associations between excessive gestational weight gain and mid-life obesity, accounting for confounding, selection, and misclassification biases. Methods: Participants were from the multiethnic Study of Women's Health Across the Nation. Obesity was defined by waist circumference measured in 1996-1997 when women were age 42-53. Gestational weight gain was measured retrospectively by self-recall and was missing for over 40% of participants. We estimated relative risk (RR) and 95% confidence intervals (CI) of obesity at mid-life for presence versus absence of excessive gestational weight gain in any pregnancy. We imputed missing data via multiple imputation and used weighted regression to account for misclassification. Results: Among the 2,339 women in this analysis, 937 (40%) experienced obesity in mid-life. In complete case analysis, women with excessive gestational weight gain had an estimated 39% greater risk of obesity (RR = 1.4, CI = 1.1, 1.7), covariate-adjusted. Imputing data, then weighting estimates at the guidepost values of sensitivity = 80% and specificity = 75%, increased the RR (95% CI) for obesity to 2.3 (2.0, 2.6). Only models assuming a 20-point difference in specificity between those with and without obesity decreased the RR. Conclusions: The inference of a positive association between excessive gestational weight gain and mid-life obesity is robust to methods accounting for selection and misclassification bias.
引用
收藏
页码:248 / 258
页数:11
相关论文
共 57 条
[1]  
[Anonymous], 2000, ASIA PACIFIC PERSPEC
[2]   Sociodemographic, clinical, and psychological factors associated with attrition in a prospective study of cardiovascular prevention: the Heart Strategies Concentrating on Risk Evaluation study [J].
Bambs, Claudia E. ;
Kip, Kevin E. ;
Mulukutla, Suresh R. ;
Aiyer, Aryan N. ;
Johnson, Cheryl ;
McDowell, Lee Ann ;
Matthews, Karen ;
Reis, Steven E. .
ANNALS OF EPIDEMIOLOGY, 2013, 23 (06) :328-333
[3]   Comparison of Self-reported and Measured Pre-pregnancy Weight: Implications for Gestational Weight Gain Counseling [J].
Bannon, Annika L. ;
Waring, Molly E. ;
Leung, Katherine ;
Masiero, Jessica V. ;
Stone, Julie M. ;
Scannell, Elizabeth C. ;
Simas, Tiffany A. Moore .
MATERNAL AND CHILD HEALTH JOURNAL, 2017, 21 (07) :1469-1478
[4]   A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives [J].
Ben-Shlomo, Y ;
Kuh, D .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2002, 31 (02) :285-293
[5]   Validity of Birth Certificate-Derived Maternal Weight Data [J].
Bodnar, Lisa M. ;
Abrams, Barbara ;
Bertolet, Marnie ;
Gernand, Alison D. ;
Parisi, Sara M. ;
Himes, Katherine P. ;
Lash, Timothy L. .
PAEDIATRIC AND PERINATAL EPIDEMIOLOGY, 2014, 28 (03) :203-212
[6]   Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models [J].
Bondarenko, Irina ;
Raghunathan, Trivellore .
STATISTICS IN MEDICINE, 2016, 35 (17) :3007-3020
[7]   Multiple Imputation for Missing Data via Sequential Regression Trees [J].
Burgette, Lane F. ;
Reiter, Jerome P. .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2010, 172 (09) :1070-1076
[8]  
Cacioppo John T, 2018, Arch Sci Psychol, V6, P21, DOI 10.1037/arc0000036
[9]   The Menopausal Transition and Cardiovascular Risk [J].
Chae, Claudia U. ;
Derby, Carol A. .
OBSTETRICS AND GYNECOLOGY CLINICS OF NORTH AMERICA, 2011, 38 (03) :477-+
[10]   A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable [J].
Corbin, Marine ;
Haslett, Stephen ;
Pearce, Neil ;
Maule, Milena ;
Greenland, Sander .
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2017, 46 (03) :1063-1072