Is maternal colonization with group B streptococci a risk factor for preeclampsia?

OBJECTIVE: To explore the association between maternal rectovaginal colonization with group B Streptococcus (GBS) and the outcome of preeclampsia, and to identify other factors such as maternal chocolate consumption that may be associated with preeclampsia on the Texas-Mexico border. STUDY DESIGN: A case-control study was conducted among 330 women who delivered at a teaching hospital in El Paso, Texas, during the time period April 2010 to April 2012. Preeclamptic cases (n = 165) and controls free of preeclampsia (n = 165) were matched by gestational age and date of delivery. Conditional logistic regression (with multiple imputation for missing data) was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) that were adjusted for maternal age and other factors. RESULTS: Cases (94.6%) and controls (97.0%) were predominantly Hispanic. GBS colonization was not associated with preeclampsia: adjusted OR = 1.73 (95% CI 0.63-4.74, p = 0.29). Maternal consumption of chocolate desserts once daily or more frequently as compared to < 7 times weekly was associated with a 76% reduction in the odds of preeclampsia: adjusted OR = 0.24 (95% CI 0.09-0.63, p = 0.004). CONCLUSION: Our study did not confirm the protective association between GBS and preeclampsia that was found in 2 existing state hospital datasets. Chocolate consumption during pregnancy was inversely associated with preeclampsia.

Prevalence of gestational fasting and postload single dysglycemia in Mexican-American women and their relative significance in identifying carbohydrate intolerance.

This study investigated the prevalence of gestational dysglycemia in a largely Hispanic population in a U.S.-Mexico border city and the influence of single plasma glucose (PG) result on the identification of gestational carbohydrate intolerance. Gestational dysglycemia was studied in a largely Mexican-American population using retrospective data. Gestational diabetes (GDM), gestational impaired fasting glucose (GIFG), and gestational impaired glucose tolerance (GIGT) were identified with Carpenter-Coustan thresholds. Glucose challenge test result was abnormal in 32.7% of 18307 women screened; 47% of them had one or more dysglycemic results in the confirmatory oral glucose tolerance test (OGTT). The prevalence of GDM, GIFG, and GIGT in these women was 8.7, 2.2, and 4.5%, respectively. Fasting, 1-hour, 2-hour, and 3-hour PGs were elevated in 20.5, 28.5, 25.0, and 15.0% of OGTT, respectively (GIFG: 6.0%; 1-hour GIGT: 6.5%; 2-hour GIGT: 4.4%; and 3-hour GIGT: 3.1%). Twelve percent of OGTTs showed dysglycemia at 1 hour with normal 2-hour PG. Isolated dysglycemia, similar to GDM, is prevalent in Mexican-American women. The minimal impact of 3-hour PG supports a 2-hour OGTT. But our results question the use of an "OGTT protocol without a first-hour specimen."

Multiple imputation for missing laboratory data: an example from infectious disease epidemiology.

PURPOSE: To present multiple imputation (MI) as an appropriate method to address missing values for a laboratory parameter (serum albumin) in an epidemiologic study. METHODS: A data set of patients who were hospitalized for invasive group A streptococcal infections was accessed. Age was the exposure of interest. The outcome was hospital mortality. Several variables, including serum albumin, were considered to be potential confounders. Of the 201 records, 91 had missing values for serum albumin. The MI procedure in SAS was used to perform 20 imputations of serum albumin by using a Markov chain Monte Carlo approach. Logistic regression was then performed on each of the 20 filled-in data sets, and the results were appropriately combined by using the MIANALYZE procedure. RESULTS: Age (> or = 55 years vs. 0-54 years) was not a risk factor for hospital mortality in the complete-case analysis (n = 110): adjusted odds ratio (OR) = 2.43 (95% confidence interval [CI]: 0.79-7.53). Age was a significant risk factor in the imputed data set (n = 201): adjusted OR = 3.08 (95% CI: 1.22-7.78). CONCLUSIONS: Epidemiologists frequently encounter data sets that contain missing values. Traditional missing data techniques such as the complete-subject analysis may lead to biased results. We have demonstrated the use of a novel technique, MI, to account for missing data.