Sudan M.,University of California at Los Angeles |
Kheifets L.I.,University of California at Los Angeles |
Arah O.A.,University of California at Los Angeles |
Arah O.A.,University of Amsterdam |
And 2 more authors.
Journal of Exposure Science and Environmental Epidemiology
In this study, we demonstrate the complexities of performing a sibling analysis with a re-examination of associations between cell phone exposures and behavioral problems observed previously in the Danish National Birth Cohort. Children (52,680; including 5441 siblings) followed up to age 7 were included. We examined differences in exposures and behavioral problems between siblings and non-siblings and by birth order and birth year. We estimated associations between cell phone exposures and behavioral problems while accounting for the random family effect among siblings. The association of behavioral problems with both prenatal and postnatal exposure differed between siblings (odds ratio (OR): 1.07; 95% confidence interval (CI): 0.69-1.66) and non-siblings (OR: 1.54; 95% CI: 1.36-1.74) and within siblings by birth order; the association was strongest for first-born siblings (OR: 1.72; 95% CI: 0.86-3.42) and negative for later-born siblings (OR: 0.63; 95% CI: 0.31-1.25), which may be because of increases in cell phone use with later birth year. Sibling analysis can be a powerful tool for (partially) accounting for confounding by invariant unmeasured within-family factors, but it cannot account for uncontrolled confounding by varying family-level factors, such as those that vary with time and birth order. © 2014 Nature America, Inc. All rights reserved. Source
Panaccio M.P.,Sanofi S.A. |
Cummins G.,Quintiles |
Wentworth C.,Evidera |
Lanes S.,HealthCore Inc. andover |
And 4 more authors.
Purpose: Atrial fibrillation/flutter (AF) is frequently associated with cardiovascular comorbidities. Observational health care databases are commonly used for research purposes in studies of quality of care, health economics, outcomes research, drug safety, and epidemiology. This retrospective cohort study applied a common data model to administrative claims data (Truven Health Analytics MarketScan® claims databases [MS-Claims]) and electronic medical records data (Geisinger Health System's MedMining electronic medical record database [MG-EMR]) to examine the risk of cardiovascular hospitalization and all-cause mortality in relation to clinical risk factors in recent-onset AF and to assess the consistency of analyses for each data source.Methods: Cohorts of patients with newly diagnosed AF (n=105,262 [MS-Claims] and n=3,919 [MG-EMR]) and demographically similar patients without AF (n=105,262 [MS-Claims] and n=3,872 [MG-EMR]) were followed from the qualifying AF diagnosis until cardiovascular hospitalization, death, database disenrollment, or study completion. A common data model standardized the data in structure, format, content, and nomenclature to allow for systematic assessment and comparison of outcomes from two disparate data sets.Results: In both databases, AF patients had greater overall baseline comorbidity and higher incidence rates of cardiovascular hospitalization (threefold higher) and all-cause mortality (46% higher) than non-AF patients. For AF patients, incidence rates of cardiovascular hospitalization and all-cause mortality were increased by the concomitant presence of coronary disease, chronic obstructive pulmonary disease, and stroke at baseline. Overall, the pattern of cardiovascular hospitalization in the MS-Claims database was similar to that in the MG-EMR database. Compared with the MS-Claims database, the use of cardiovascular medications and the capture of certain comorbidities among AF patients appeared to be higher in the MG-EMR data set.Conclusion: Similar standardized analyses across EMR and Claims databases were consistent in the association of AF with acute morbidity and an increased risk of all-cause mortality. Areas of inconsistency were due to differences in underlying population demographics and cardiovascular risks and completeness of certain data fields. © 2015 Panaccio et al. Source
McAuliffe M.E.,Takeda Cambridge |
Lanes S.,HealthCore Inc. andover |
Leach T.,Takeda Cambridge |
Parikh A.,Takeda Pharmaceuticals International |
And 6 more authors.
Current Medical Research and Opinion
Objectives: Inflammatory bowel disease (IBD) is a chronic condition commonly requiring lifelong care. Both IBD and IBDrelated treatments can cause significant morbidity, and it is often difficult to differentiate their relative etiologic contribution to adverse events (AEs). The objectives of this study were to assess the rates of select AEs among patients with IBD as a function of disease severity and of the use of anti-tumor necrosis factor alpha (anti-TNFα) medications. Methods: We conducted a retrospective cohort study of IBD patients in the HealthCore Integrated Research Database (HIRD™) between January 2004 and January 2011 to determine rates of AEs in patients with mild and moderate to severe IBD. Key study endpoints were select prespecified malignant neoplasms, infections, and other AEs of interest. Results: A total of 33,386 IBD patients (52.7% ulcerative colitis; 47.3% Crohn's disease) met the inclusion criteria, and 60% had been followed for ≥1 year. Patients with moderate to severe IBD had increased rates of infections, lymphatic and digestive tract cancers, gastrointestinal (GI) perforations, and myocardial infarctions versus patients with mild IBD. Patients with IBD who used anti-TNFα therapies during the study had increased incidence of many types of infections, certain GI cancers (including rectal and anal cancer), intestinal perforations, and kidney stones compared with patients who had never used anti-TNFa therapies. Conclusions: Results from this large US cohort provide descriptive information on AE rates in a population of IBD patients undergoing routine care, estimating background incidence rates of AEs that are not readily available in the published literature. Our study findings may be limited owing to a lack of generalizability and potential for misclassification due to reliance on medical diagnosis and treatment and procedure codes to identify disease, comorbidities, and treatments. Further research and validation of our findings in other populations and databases are needed. © 2015 Informa UK Ltd. Source