Search

Excessive gestational weight gain (GWG) predicts adverse pregnancy outcomes and later obesity risk for both mother and child. Women who receive GWG advice from their obstetric clinicians are more likely to gain the recommended amount, but many clinicians do not counsel their patients on GWG, pointing to the need for new strategies. Electronic medical records (EMRs) are a useful tool for tracking weight and supporting guideline-concordant care, but their use for care related to GWG has not been evaluated. We performed in-depth interviews with 16 obstetric clinicians from a multi-site group practice in Massachusetts that uses an EMR. We recorded, transcribed, coded, and analyzed the interviews using immersion-crystallization. Many respondents believed that GWG had 'a lot' of influence on pregnancy and child health outcomes but that their patients did not consider it important. Most indicated that excessive GWG was a big or moderate problem in their practice, and that inadequate GWG was rarely a problem. All used an EMR feature that calculates total GWG at each visit. Many were enthusiastic about additional EMR-based supports, such as a reference for recommended GWG for each patient based on prepregnancy body mass index, a 'growth chart' to plot actual and recommended GWG, and an alert to identify out-of-range gains, features which many felt would remind them to counsel patients about excessive weight gain. Additional decision support tools within EMRs would be well received by many clinicians and may help improve the frequency and accuracy of GWG tracking and counseling. Keywords Gestational weight gain * Obstetrics * Electronic medical record * Counseling, Introduction Gestational weight gain (GWG) is an important predictor of short- and long-term outcomes of pregnancy for both mother and child [1]. Compared with adequate gain, mothers with excessive GWG [...]

Abstract Background Multicenter study designs have several advantages, but the possibility of non-random measurement error resulting from procedural differences between the centers is a special concern. While it is possible to address and correct for some measurement error through statistical analysis, proactive data monitoring is essential to ensure high-quality data collection. Methods In this article, we describe quality assurance efforts aimed at reducing the effect of measurement error in a recent follow-up of a large cluster-randomized controlled trial through periodic evaluation of intraclass correlation coefficients (ICCs) for continuous measurements. An ICC of 0 indicates the variance in the data is not due to variation between the centers, and thus the data are not clustered by center. Results Through our review of early data downloads, we identified several outcomes (including sitting height, waist circumference, and systolic blood pressure) with higher than expected ICC values. Further investigation revealed variations in the procedures used by pediatricians to measure these outcomes. We addressed these procedural inconsistencies through written clarification of the protocol and refresher training workshops with the pediatricians. Further data monitoring at subsequent downloads showed that these efforts had a beneficial effect on data quality (sitting height ICC decreased from 0.92 to 0.03, waist circumference from 0.10 to 0.07, and systolic blood pressure from 0.16 to 0.12). Conclusions We describe a simple but formal mechanism for identifying ongoing problems during data collection. The calculation of the ICC can easily be programmed and the mechanism has wide applicability, not just to cluster randomized controlled trials but to any study with multiple centers or with multiple observers.

ObjectiveFish consumption influences a number of health outcomes. Few studies have directly compared dietary assessment methods to determine the best approach to estimating intakes of fish and its component nutrients, including DHA, and toxicants, including methylmercury. Our objective was to compare three methods of assessing fish intake.DesignWe assessed 30 d fish intake using three approaches: (i) a single question on total fish consumption; (ii) a brief comprehensive FFQ that included four questions about fish; and (iii) a focused FFQ with thirty-six questions about different finfish and shellfish.SettingObstetrics practices in Boston, MA, USA.SubjectsFifty-nine pregnant women who consumed ≤2 monthly fish servings.ResultsEstimated intakes of fish, DHA and Hg were lowest with the one-question screener and highest with the thirty-six-item fish questionnaire. Estimated intake of DHA with the thirty-six-item questionnaire was 4·4-fold higher (97 v. 22 mg/d), and intake of Hg was 3·8-fold higher (1·6 v. 0·42 μg/d), compared with the one-question screener. Plasma DHA concentration was correlated with fish intake assessed with the one-question screener (Spearman r = 0·27, P = 0·04), but not with the four-item FFQ (r = 0·08, P = 0·54) or the thirty-six-item fish questionnaire (r = 0·01, P = 0·93). In contrast, blood and hair Hg concentrations were similarly correlated with fish and Hg intakes regardless of the assessment method (r = 0·35 to 0·52).ConclusionsA longer questionnaire provides no advantage over shorter questionnaires in ranking intakes of fish, DHA and Hg compared with biomarkers, but estimates of absolute intakes can vary by as much as fourfold across methods. [ABSTRACT FROM AUTHOR]

Background: Multicenter study designs have several advantages, but the possibility of non-random measurement error resulting from procedural differences between the centers is a special concern. While it is possible to address and correct for some measurement error through statistical analysis, proactive data monitoring is essential to ensure high-quality data collection. Methods: In this article, we describe quality assurance efforts aimed at reducing the effect of measurement error in a recent follow-up of a large cluster-randomized controlled trial through periodic evaluation of intraclass correlation coefficients (ICCs) for continuous measurements. An ICC of 0 indicates the variance in the data is not due to variation between the centers, and thus the data are not clustered by center. Results: Through our review of early data downloads, we identified several outcomes (including sitting height, waist circumference, and systolic blood pressure) with higher than expected ICC values. Further investigation revealed variations in the procedures used by pediatricians to measure these outcomes. We addressed these procedural inconsistencies through written clarification of the protocol and refresher training workshops with the pediatricians. Further data monitoring at subsequent downloads showed that these efforts had a beneficial effect on data quality (sitting height ICC decreased from 0.92 to 0.03, waist circumference from 0.10 to 0.07, and systolic blood pressure from 0.16 to 0.12). Conclusions: We describe a simple but formal mechanism for identifying ongoing problems during data collection. The calculation of the ICC can easily be programmed and the mechanism has wide applicability, not just to cluster randomized controlled trials but to any study with multiple centers or with multiple observers.