Epidemiology of mental disorders during pregnancy and link to birth outcome: a large-scale retrospective observational database study including 38,000 pregnancies.

PURPOSE: To investigate the real-life epidemiology of mental disorders during pregnancy and their impact on birth outcome in an unselected low-risk population in Germany. METHODS: Claims data of the Techniker Krankenkasse (TK) were analyzed as part of a retrospective observational study over a one-year period from 01/2008 to 12/2008 including 38,174 pregnant women. ICD-10 codes were clustered into four diagnostic groups: depression, anxiety disorders, somatoform/dissociative disorders and acute stress reactions. The relationship between mental disorders, birth mode and infant weight was tested using chi-squared tests and multivariate logistic regression. Main outcome measures included the prevalence of mental disorders during pregnancy, performed cesarean sections and infants born underweight. RESULTS: N = 16,639 cases with at least one diagnosis from the four mental disorder diagnostic groups were identified: 9.3% cases of depression, 16.9% cases with an anxiety disorder, 24.2% cases with a somatoform/dissociative disorder, and 11.7% cases of acute stress reactions. Women diagnosed with a mental disorder were more likely to deliver their child by cesarean section ([depression: OR =1.26 (95% CI 1.14-1.39); anxiety: OR 1.11 (95% CI 1.02-1.19); somatoform disorders: OR 1.12 (95% CI 1.05-1.20); acute stress reactions: OR 1.17 (95% CI 1.07-1.28)]. Furthermore, infants of women diagnosed with an ICD-10 code for depression during pregnancy were more likely to be underweight and/or delivered preterm [OR =1.34 (95% CI 1.06-1.69)]. CONCLUSIONS: We observed substantially high prevalence rates of mental disorders during pregnancy which urgently warrant more awareness for validated screening and adequate treatment options.

Being macrosomic at birth is an independent predictor of overweight in children: results from the IDEFICS study.

Fetal macrosomia is a risk factor for the development of obesity late in childhood. We retrospectively evaluated the relationship between maternal conditions associated with fetal macrosomia and actual overweight/obesity in the European cohort of children participating in the IDEFICS study. Anthropometric variables, blood pressure and plasma lipids and glucose were measured. Socio-demographic data, medical history and perinatal factors, familiar and gestational history, maternal and/or gestational diabetes were assessed by a questionnaire. Variables of interest were reported for 10,468 children (M/F = 5,294/5,174; age 6.0 ± 1.8 years, M ± SD). The sample was divided in four groups according to child birth weight (BW) and maternal diabetes: (1) adequate for gestational age offspring (BW between the 10th and 90th percentiles for gestational age) of mothers without diabetes (AGA-ND); (2) adequate for gestational age offspring of mothers with diabetes (AGA-D); (3) macrosomic offspring (BW > 90th percentile for gestational age) of mothers without diabetes (Macro-ND); (4) macrosomic offspring of mothers with diabetes (Macro-D). Children macrosomic at birth showed significantly higher actual values of body mass index, waist circumference, and sum of skinfold thickness. In both boys and girls, Macro-ND was an independent determinant of overweight/obesity, after the adjustment for confounders [Boys: OR = 1.7 95 % CI (1.3;2.2); Girls: OR = 1.6 95 % CI (1.3;2.0)], while Macro-D showed a significant association only in girls [OR = 2.6 95 % CI (1.1;6.4)]. Fetal macrosomia, also in the absence of maternal/gestational diabetes, is independently associated with the development of overweight/obesity during childhood. Improving the understanding of fetal programming will contribute to the early prevention of childhood overweight/obesity.

Linkage of mother-baby pairs in the German Pharmacoepidemiological Research Database.

PURPOSE: Administrative healthcare databases are increasingly being used to investigate potential drug risks in pregnancy. Our study aimed to develop an algorithm for linkage of mother-baby pairs (MBPs) in the German Pharmacoepidemiological Research Database (GePaRD) as a prerequisite for such studies. METHODS: GePaRD contains sociodemographic data, drug dispensations, ambulatory, and hospital information on more than 14 million insurants from four German statutory health insurances (SHIs) covering all regions in Germany. Linkage was based on co-insurance information of the newborn with the potential mother (direct linkage) or of both potential mother and newborn with the potential father (indirect linkage). Linkage is not possible if the baby is co-insured with the father and the mother is self-insured. Further information on birth or childbed was used to validate the potential mother as true mother in MBP. Descriptive comparisons between linked and unlinked mothers were conducted. RESULTS: Of 323,993 newborns identified between 2004 and 2006, 250,355 (77.3%) could be linked in MBP. Of those, 189,702 (75.8%) MBP were based on direct linkage. Mean age was similar in linked (31.1 years, standard deviation (SD = 5.4) and unlinked (31.8 years, SD = 5.5) mothers as was the proportion of caesarean sections in both groups (28.9% vs. 29.3%). CONCLUSIONS: The developed algorithm permits linkage of a great number of newborns with their mothers and creates a potential data source for investigation of drug risks in pregnancy. Further validation studies are needed also including information on pregnancies not resulting in live births.

The Contribution of National Spontaneous Reporting Systems to Detect Signals of Torsadogenicity: Issues Emerging from the ARITMO Project.

INTRODUCTION: Spontaneous reporting systems (SRSs) are pivotal for signal detection, especially for rare events with a high drug-attributable component, such as torsade de pointes (TdP). Use of different national SRSs is rarely attempted because of inherent difficulties, but should be considered on the assumption that rare events are diluted in international databases. OBJECTIVE: The aim was to describe TdP-related events associated with antipsychotics, H1-antihistamines and anti-infectives in three national SRSs (in Italy, Germany and France) and highlight potential signals of torsadogenicity through a combined literature evaluation. METHODS: A common search strategy was applied to extract TdP-related events: (1) TdP, (2) QT interval abnormalities, (3) ventricular fibrillation/tachycardia, and (4) sudden cardiac death. Signals of disproportionate reporting (SDRs) were calculated for TdP + QT interval abnormalities and defined by a lower limit of the 95 % confidence interval of the reporting odds ratio (ROR) >1. Among SDRs with at least three cases without concomitant pro-arrhythmic drugs, we defined potential new signal of torsadogenicity as drugs with no published evidence from (a) the crediblemeds(®) website ( http://www.crediblemeds.com , as of November 1st, 2014); (b) studies on the FDA Adverse Event Reporting System (FAERS); and (c) safety trials or pharmaco-epidemiological studies (as of December 16th, 2014). RESULTS: Overall, 3505 cases were retrieved (1372, 1468, and 801 for France, Germany and Italy, respectively). Antipsychotics were mainly recorded in Germany (792 cases), whereas antibiotics peaked at 515 and 491 (France and Italy, respectively). Forty-one drugs met criteria for SDRs in at least one single source, of which 31 were detected only from one single SRS: 18, ten and three (French, German and Italian SRS, respectively). By contrast, only five SDRs were detected in all national data sources (amisulpride, aripiprazole, haloperidol, olanzapine, risperidone). Overall, five potential new signals of torsadogenicity were identified: flupentixol, ganciclovir, levocetirizine, oxatomide and tiapride. CONCLUSIONS: We found differences across and within national SRSs in the reporting of drug-induced TdP, which finally resulted in five potential new signals of torsadogenicity. These findings warrant targeted pharmacovigilance studies to formally assess the existence of actual drug-event associations.

Signal detection and monitoring based on longitudinal healthcare data.

Post-marketing detection and surveillance of potential safety hazards are crucial tasks in pharmacovigilance. To uncover such safety risks, a wide set of techniques has been developed for spontaneous reporting data and, more recently, for longitudinal data. This paper gives a broad overview of the signal detection process and introduces some types of data sources typically used. The most commonly applied signal detection algorithms are presented, covering simple frequentistic methods like the proportional reporting rate or the reporting odds ratio, more advanced Bayesian techniques for spontaneous and longitudinal data, e.g., the Bayesian Confidence Propagation Neural Network or the Multi-item Gamma-Poisson Shrinker and methods developed for longitudinal data only, like the IC temporal pattern detection. Additionally, the problem of adjustment for underlying confounding is discussed and the most common strategies to automatically identify false-positive signals are addressed. A drug monitoring technique based on Wald's sequential probability ratio test is presented. For each method, a real-life application is given, and a wide set of literature for further reading is referenced.