Welcome to CDC Stacks | Impact of Missing Data for Body Mass Index in an Epidemiologic Study - 39989 | CDC Public Access
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All Simple Search
Advanced Search
Impact of Missing Data for Body Mass Index in an Epidemiologic Study
  • Published Date:
    Jul 2016
  • Source:
    Matern Child Health J. 20(7):1497-1505.


Public Access Version Available on: July 01, 2017 information icon
Please check back on the date listed above.
Details:
  • Pubmed ID:
    27029540
  • Pubmed Central ID:
    PMC4911272
  • Funding:
    CC999999/Intramural CDC HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    Objective

    To assess the potential impact of missing data on body mass index (BMI) on the association between prepregnancy obesity and specific birth defects.

    Methods

    Data from the National Birth Defects Prevention Study (NBDPS) were analyzed. We assessed the factors associated with missing BMI data among mothers of infants without birth defects. Four analytic methods were then used to assess the impact of missing BMI data on the association between maternal prepregnancy obesity and three birth defects; spina bifida, gastroschisis, and cleft lip with/without cleft palate. The analytic methods were: (1) complete case analysis; (2) assignment of missing values to either obese or normal BMI; (3) multiple imputation; and (4) probabilistic sensitivity analysis. Logistic regression was used to estimate crude and adjusted odds ratios (aOR) and 95 % confidence intervals (CI).

    Results

    Of NBDPS control mothers 4.6 % were missing BMI data, and most of the missing values were attributable to missing height (~90 %). Missing BMI data was associated with birth outside of the US (aOR 8.6; 95 % CI 5.5, 13.4), interview in Spanish (aOR 2.4; 95 % CI 1.8, 3.2), Hispanic ethnicity (aOR 2.0; 95 % CI 1.2, 3.4), and <12 years education (aOR 2.3; 95 % CI 1.7, 3.1). Overall the results of the multiple imputation and probabilistic sensitivity analysis were similar to the complete case analysis.

    Conclusions

    Although in some scenarios missing BMI data can bias the magnitude of association, it does not appear likely to have impacted conclusions from a traditional complete case analysis of these data.

  • Supporting Files:
    No Additional Files