Image_1_Reading Profiles in Multi-Site Data With Missingness.pdf

<p>Children with reading disability exhibit varied deficits in reading and cognitive abilities that contribute to their reading comprehension problems. Some children exhibit primary deficits in phonological processing, while others can exhibit deficits in oral language and executive functions that affect comprehension. This behavioral heterogeneity is problematic when missing data prevent the characterization of different reading profiles, which often occurs in retrospective data sharing initiatives without coordinated data collection. Here we show that reading profiles can be reliably identified based on Random Forest classification of incomplete behavioral datasets, after the missForest method is used to multiply impute missing values. Results from simulation analyses showed that reading profiles could be accurately classified across degrees of missingness (e.g., ∼5% classification error for 30% missingness across the sample). The application of missForest to a real multi-site dataset with missingness (n = 924) showed that reading disability profiles significantly and consistently differed in reading and cognitive abilities for cases with and without missing data. The results of validation analyses indicated that the reading profiles (cases with and without missing data) exhibited significant differences for an independent set of behavioral variables that were not used to classify reading profiles. Together, the results show how multiple imputation can be applied to the classification of cases with missing data and can increase the integrity of results from multi-site open access datasets.</p

Quantifying the Impact of Gestational Diabetes Mellitus, Maternal Weight and Race on Birthweight via Quantile Regression

<div><p>Background</p><p>Quantile regression, a robust semi-parametric approach, was used to examine the impact of gestational diabetes mellitus (GDM) across birthweight quantiles with a focus on maternal prepregnancy body mass index (BMI) and gestational weight gain (GWG).</p><p>Methods</p><p>Using linked birth certificate, inpatient hospital and prenatal claims data we examined live singleton births to non-Hispanic white (NHW, 135,119) and non-Hispanic black (NHB, 76,675) women in South Carolina who delivered 28–44 weeks gestation in 2004–2008.</p><p>Results</p><p>At a maternal BMI of 30 kg/m² at the 90^th quantile of birthweight, exposure to GDM was associated with birthweights 84 grams (95% CI 57, 112) higher in NHW and 132 grams (95% CI: 104, 161) higher in NHB. Results at the 50^th quantile were 34 grams (95% CI: 17, 51) and 78 grams (95% CI: 56, 100), respectively. At a maternal GWG of 13.5 kg at the 90^th quantile of birthweight, exposure to GDM was associated with birthweights 83 grams (95% CI: 57, 109) higher in NHW and 135 grams (95% CI: 103, 167) higher in NHB. Results at the 50^th quantile were 55 grams (95% CI: 40, 71) and 69 grams (95% CI: 46, 92), respectively.</p><p>Summary</p><p>Our findings indicate that GDM, maternal prepregnancy BMI and GWG increase birthweight more in NHW and NHB infants who are already at the greatest risk of macrosomia or being large for gestational age (LGA), that is those at the 90^th rather than the median of the birthweight distribution.</p></div

Predicted Infant Birthweight using model 2.

<p>Results pictured for mother’s age 26, gestational age 38 weeks, prepregnancy BMI 30, averaged over effects of infant sex, prenatal care, smoking, hypertension, first born, and availability of prenatal information at the 50^th (A), 75^th (B), and 90^th (C) quantile.</p

Predicted Infant Birthweight using model 1.

<p>Results pictured for mother’s age 26, gestational age 38 weeks, 11.3 kg (25 lbs) weight gain, averaged over effects of infant sex, prenatal care, smoking, hypertension, first born, and availability of prenatal information at the 50^th (A), 75^th (B), and 90^th (C) quantile.</p

Characteristics of the study population stratified by race and diabetes status for singleton live births of 28 through 44 weeks gestational age in South Carolina 2004–2008.

<p>Statistics reported are means (sd) or percentages.</p

Model Two: Predicted infant birth weights (gm) and birth weight differences (with 95% CI) at gestational weight gain (GWG) of 9, 13.5, 18, and 30 kg in NHW and NHB with and without diabetes.

<p>Results listed for maternal age of 26, gestational age 38 weeks, prepregnancy BMI 30, averaged over effects for dichotomous factors.</p

Flow chart defining study population and exclusions.

<p>Flow chart defining study population and exclusions.</p

Effect of GDM on Birthweight by Quantile.

<p>Effect of GDM on birthweight in Model One (A) and Model Two (B). The figure presents the additional birthweight associated with a mother having GDM in NHW and NHB for a maternal BMI of 30 or 35 (A) or gestational weight gain of 13.5 or 18 kg (B).</p

Model One: Predicted infant birth weights (gm) and birth weight differences (with 95% CI) at a maternal pre-pregnancy body mass index of 25, 30, 35, and 45 kg/m² in NHW and NHB with and without diabetes.

<p>Results listed for maternal age of 26, gestational age 38 weeks, gestational weight gain 11.25 kg, averaged over effects for dichotomous factors.</p

Estimated VA Savings (Losses) From 0 MHV Veterans Receiving 3 or more MHV in December 31, 2012 Value.

<p>Note: Low number of Veterans impacted refers to the number of Veterans with estimated positive costs in all three categories. High number of Veterans impacted refers to the total number of Veterans in the sample with a non-zero cost in at least one category. Low estimated Savings (Losses) refer to the low number of Veterans impacted while high estimated Savings (Losses ) refer to the high number of Veterans. Mean estimated cost in each category refers to the 5 year least-square mean estimate from the joint model multiplied by the consumer price index multiplier to reflect December 2012 dollar values.</p