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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:prism="http://prismstandard.org/namespaces/1.2/basic/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns="http://purl.org/rss/1.0/"><channel rdf:about="http://www.annalsofepidemiology.org/?rss=yes"><title>Annals of Epidemiology</title><description>Annals of Epidemiology RSS feed: Current Issue. 
 Annals of Epidemiology  is a peer reviewed, international journal devoted to epidemiologic research and methodological development. 
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   acepidemiology.org  .</description><link>http://www.annalsofepidemiology.org/?rss=yes</link><dc:publisher>Elsevier Inc.</dc:publisher><dc:language>en</dc:language><dc:rights> © 2010 Published by Elsevier Inc. All rights reserved. </dc:rights><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:issn>1047-2797</prism:issn><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:publicationDate>March 2010</prism:publicationDate><prism:copyright> © 2010 Published by Elsevier Inc. All rights reserved. </prism:copyright><prism:rightsAgent>healthpermissions@elsevier.com</prism:rightsAgent><items><rdf:Seq><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279710000098/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003603/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003640/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003585/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003822/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003573/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003639/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003615/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003846/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279709003676/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279710000025/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279710000104/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279710000116/abstract?rss=yes"/><rdf:li rdf:resource="http://www.annalsofepidemiology.org/article/PIIS1047279710000128/abstract?rss=yes"/></rdf:Seq></items></channel><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279710000098/abstract?rss=yes"><title>Editorial Board</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279710000098/abstract?rss=yes</link><description></description><dc:title>Editorial Board</dc:title><dc:creator></dc:creator><dc:identifier>10.1016/S1047-2797(10)00009-8</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>IFC</prism:startingPage><prism:endingPage>IFC</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003603/abstract?rss=yes"><title>Neighborhood Disadvantage and Physical Activity: Baseline Results from the HABITAT Multilevel Longitudinal Study</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003603/abstract?rss=yes</link><description>Purpose: To examine the association between neighborhood disadvantage and physical activity (PA).Methods: We use data from the HABITAT multilevel longitudinal study of PA among middle-aged (40–65 years) men and women (N = 11,037, 68.5% response rate) living in 200 neighborhoods in Brisbane, Australia. PA was measured using three questions from the Active Australia Survey (general walking, moderate, and vigorous activity), one indicator of total activity, and two questions about walking and cycling for transport. The PA measures were operationalized by using multiple categories based on time and estimated energy expenditure that were interpretable with reference to the latest PA recommendations. The association between neighborhood disadvantage and PA was examined with the use of multilevel multinomial logistic regression and Markov chain Monte Carlo simulation. The contribution of neighborhood disadvantage to between-neighborhood variation in PA was assessed using the 80% interval odds ratio.Results: After adjustment for sex, age, living arrangement, education, occupation, and household income, reported participation in all measures and levels of PA varied significantly across Brisbane's neighborhoods, and neighborhood disadvantage accounted for some of this variation. Residents of advantaged neighborhoods reported significantly higher levels of total activity, general walking, moderate, and vigorous activity; however, they were less likely to walk for transport. There was no statistically significant association between neighborhood disadvantage and cycling for transport. In terms of total PA, residents of advantaged neighborhoods were more likely to exceed PA recommendations.Conclusions: Neighborhoods may exert a contextual effect on the likelihood of residents participating in PA. The greater propensity of residents in advantaged neighborhoods to do high levels of total PA may contribute to lower rates of cardiovascular disease and obesity in these areas.</description><dc:title>Neighborhood Disadvantage and Physical Activity: Baseline Results from the HABITAT Multilevel Longitudinal Study</dc:title><dc:creator>Gavin Turrell, Michele Haynes, Nicola W. Burton, Billie Giles-Corti, Brian Oldenburg, Lee-Ann Wilson, Katrina Giskes, Wendy J. Brown</dc:creator><dc:identifier>10.1016/j.annepidem.2009.11.004</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>171</prism:startingPage><prism:endingPage>181</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003640/abstract?rss=yes"><title>Lifestyle, Anthropometric, and Obesity-Related Physiologic Determinants of Insulin-like Growth Factor-1 in the Third National Health and Nutrition Examination Survey (1988–1994)</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003640/abstract?rss=yes</link><description>Purpose: Epidemiologic studies suggest that insulin-like growth factor-1 (IGF-1) is associated with obesity and, more recently, cancer. This study investigates multiple lifestyle, physiologic, and anthropometric determinants of circulating IGF-1 concentrations.Methods: Nationally representative data were used from the cross-sectional Third National Health and Nutrition Examination (NHANES III, 1988–1994) survey, which measured IGF-1 concentrations in blood, from a subsample of participants who were examined in the morning. After exclusion of persons with missing data, 6,058 men and women 20 years of age or older were included in the study.Results: The mean IGF-1 concentrations were 260 ng/mL in the entire population and were higher among men as compared with women (278.8 vs. 241.3 ng/mL; p&lt;0.0001). IGF-1 decreased with increasing age (p&lt;0.0001), body mass index (p&lt;0.0001), and waist circumference (p&lt;0.0001). Individuals with metabolic syndrome had lower IGF-1 concentrations after adjustment for covariates (p=0.0008). IGF-1 was inversely associated with increasing number of metabolic syndrome abnormalities (p=0.0008). All associations were stronger among women compared with men except across concentrations of glucose. IGF-1 concentrations did not vary by any other lifestyle or physiologic factors.Conclusions: Age, adiposity, hyperglycemia, and metabolic syndrome influenced circulating IGF-1 concentrations. Diet and physical activity had no impact on IGF-1 in this nationally representative population.</description><dc:title>Lifestyle, Anthropometric, and Obesity-Related Physiologic Determinants of Insulin-like Growth Factor-1 in the Third National Health and Nutrition Examination Survey (1988–1994)</dc:title><dc:creator>Niyati Parekh, Calpurnyia B. Roberts, Maya Vadiveloo, Thanusha Puvananayagam, Jeanine B. Albu, Grace L. Lu-Yao</dc:creator><dc:identifier>10.1016/j.annepidem.2009.11.008</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>182</prism:startingPage><prism:endingPage>193</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003585/abstract?rss=yes"><title>Factor Analysis of Metabolic Syndrome Components in the Coronary Artery Risk Development in Young Adults (CARDIA) Study: Examination of Factors by Race-Sex Groups and Across Time</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003585/abstract?rss=yes</link><description>Purpose: This study tests hypotheses of one-, two-, three-, and four-factor models of metabolic syndrome (MetS) components and assesses the consistency and fit of the factor models 10 years later using confirmatory factor analysis in a large biracial sample of men and women.Methods: With the use of data from the baseline and year-10 exams of the Coronary Artery Risk Development in Young Adults Study, confirmatory factor analysis was performed overall and for race- and sex-specific groups for one-, two-, three-, and four-factor MetS models in 3403 white and black men and women at baseline and in 2532 white and black men and women 10 years later. Metabolic risk variables used in the factor analysis were insulin resistance (HOMA-IR), fasting glucose, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, systolic and diastolic blood pressure, waist circumference, waist-hip ratio, triceps skinfolds, and uric acid.Results: Three- and four-factor models of MetS achieved excellent fits of the data, ranging from 0.92 to 0.96 for race- and sex-specific models and from the baseline to year-10 exams.Conclusions: The results suggest that MetS factors are consistent across time and race-sex groups. When investigating the MetS, it is necessary to evaluate race-sex groups.</description><dc:title>Factor Analysis of Metabolic Syndrome Components in the Coronary Artery Risk Development in Young Adults (CARDIA) Study: Examination of Factors by Race-Sex Groups and Across Time</dc:title><dc:creator>T. Freeman Ferguson, Ellen Funkhouser, Jeffrey Roseman</dc:creator><dc:identifier>10.1016/j.annepidem.2009.11.002</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-01-13</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-01-13</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>194</prism:startingPage><prism:endingPage>200</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003822/abstract?rss=yes"><title>Correlates of Weight Patterns during Middle Age Characterized by Functional Principal Components Analysis</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003822/abstract?rss=yes</link><description>Purpose: Describing weight trajectories using functional methods may further our understanding of how weight impacts health. We characterize weight patterns and describe correlates of these patterns.Methods: Using a subset of the Framingham Heart Study original cohort limited-access data set (n=1,429), we conducted a functional principal components analysis (PCA) of body mass index from 40 to 55 years of age. Scores from the principal component functions defined weight patterns. Gender-specific logistic regression models provided estimates of association with sociodemographic and lifestyle factors.Results: Overall weight status, weight changes, and cycling emerged as weight patterns during middle age. Overweight/obesity at 25 years was the most consistent correlate of weight patterns (adjusted odds ratios [AORs] for men and women were 14.2 and 3.7 for overall overweight, 99.5 and 28.3 for overall obese, and 1.4 and 3.9 for cycling). Weight status at 25 years was not associated with weight gain in either gender or weight loss in men; for women the AOR was 2.7 for weight loss.Conclusions: Functional PCA described weight patterns during middle age. The strong associations between weight status at 25 years and overall weight status and cycling during middle age underscore the importance of addressing weight earlier in life.</description><dc:title>Correlates of Weight Patterns during Middle Age Characterized by Functional Principal Components Analysis</dc:title><dc:creator>Molly E. Waring, Charles B. Eaton, Thomas M. Lasater, Kate L. Lapane</dc:creator><dc:identifier>10.1016/j.annepidem.2009.11.013</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>201</prism:startingPage><prism:endingPage>209</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003573/abstract?rss=yes"><title>Body Composition Among HIV-Seropositive and HIV-Seronegative Adult Patients with Pulmonary Tuberculosis in Uganda</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003573/abstract?rss=yes</link><description>Purpose: We determined whether human immunodeficiency virus (HIV) infection affects body cell mass and fat mass wasting among adults with pulmonary tuberculosis (PTB).Methods: We screened 967 Ugandan adults for PTB and HIV infection in a cross-sectional study. We compared anthropometric and bioelectric impedance analysis (BIA) body composition parameters among HIV-seropositive and HIV-seronegative men and women with or without PTB by using a non-parametric test.Results: We found that poor nutritional status associated with TB differed among men and women. Anthropometric and BIA body composition did not differ between HIV-seropositive and HIV-seronegative patients regardless of gender. Average weight group difference in men consisted of body cell mass and fat mass in equal proportions of 43%. In women, average weight group difference consisted predominantly of fat mass of 73% and body cell mass of 13%. Compared to individuals without TB, patients with TB had lower body mass index, weight, body cell mass, and fat mass regardless of gender and HIV status.Conclusions: Gender, but not HIV status, was associated with body composition changes in TB. TB appears to be the dominant factor driving the wasting process among co-infected patients.</description><dc:title>Body Composition Among HIV-Seropositive and HIV-Seronegative Adult Patients with Pulmonary Tuberculosis in Uganda</dc:title><dc:creator>Ezekiel Mupere, Sarah Zalwango, Allan Chiunda, Alphonse Okwera, Roy Mugerwa, Christopher Whalen</dc:creator><dc:identifier>10.1016/j.annepidem.2009.11.001</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>210</prism:startingPage><prism:endingPage>216</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003639/abstract?rss=yes"><title>Subsequent Autoimmune or Related Disease in Asthma Patients: Clustering of Diseases or Medical Care?</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003639/abstract?rss=yes</link><description>Purpose: Asthma includes immunological components that may share mechanisms with autoimmune diseases. We analyzed the subsequent occurrence of any of 22 autoimmune and related conditions in hospitalized asthma patients.Methods: A nationwide study was conducted in Sweden on subsequent diseases of asthma patients on the basis of the Hospital Discharge Register. Standardized incidence ratios (SIRs) were calculated for subsequent autoimmune diseases.Results: A total of 4006 patients were hospitalized for an autoimmune condition after last hospitalization for asthma. The SIRs were increased for 11 subsequent autoimmune conditions, diagnosed at least 5 years after asthma. The highest SIRs were noted for polyarteritis nodosa (4.29) and Addison disease (3.62). SIRs for these diseases and others, including the most common autoimmune disease rheumatoid arthritis, were increased even when the follow-up was started 5 years after the last asthma hospitalization. Addison disease and Crohn disease were increased in asthma patients hospitalized at various ages, whereas young asthma patients presented with celiac disease and immune thrombocytopenic purpura.Conclusions: Hospitalized asthma patients presented with a number of subsequent autoimmune and related diseases. Although we were unable to exclude the effects of environmental factors, the data suggest that shared genetic factors or gene-environment interactions may explain coexistence of some of these diseases.</description><dc:title>Subsequent Autoimmune or Related Disease in Asthma Patients: Clustering of Diseases or Medical Care?</dc:title><dc:creator>Kari Hemminki, Xinjun Li, Jan Sundquist, Kristina Sundquist</dc:creator><dc:identifier>10.1016/j.annepidem.2009.11.007</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2009-12-28</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2009-12-28</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>217</prism:startingPage><prism:endingPage>222</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003615/abstract?rss=yes"><title>Predictors of Mortality in Elderly Subjects with Obstructive Airway Disease: The PILE Score</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003615/abstract?rss=yes</link><description>Purpose: To identify significant covariates in addition to spirometry that predict mortality in elderly subjects with obstructive airway disease (OAD).Methods: Two hundred sixty-eight (268) participants with OAD from the Health, Aging and Body Composition study, a community-based observational cohort of well-functioning elderly aged 70-79 years, were followed on average for 6.1 years. Covariates related to pulmonary and physical function, comorbidity, demographics, and three inflammatory markers (interleukin-6, tumor necrosis factor-alpha, C-reactive protein) were evaluated for their association with all-cause mortality (31%) by means of Kaplan Meier analysis and Cox proportional hazards modeling.Results: Percent predicted forced expiratory volume in one second (PPFEV1; hazard ratio [HR] = 2.03, p &lt; 0.0001), knee extensor strength (HR = 1.36, p = 0.0002), interleukin-6 (HR = 1.37, p = 0.0002) and 400 m corridor walk time (HR = 1.24, p = 0.008) significantly predicted mortality. A multidimensional index, the PILE score, was constructed from PPFEV1, interleukin-6, and knee extensor strength. Each one-point increase in PILE score (range: 1-10) was associated with a 30% increase in mortality (95% confidence interval: 0.16-0.47) after adjusting for age, race, gender, smoking, and comorbidity, resulting in a 10.4-fold higher risk of death between the highest and lowest risk category.Conclusions: Subjects with OAD have a wide gradient of risk for mortality that can potentially be incorporated in clinical decision making.</description><dc:title>Predictors of Mortality in Elderly Subjects with Obstructive Airway Disease: The PILE Score</dc:title><dc:creator>Nitin Mehrotra, Amado X. Freire, Douglas C. Bauer, Tamara B. Harris, Anne B. Newman, Stephen B. Kritchevsky, Bernd Meibohm, Health ABC Study</dc:creator><dc:identifier>10.1016/j.annepidem.2009.11.005</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>223</prism:startingPage><prism:endingPage>232</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003846/abstract?rss=yes"><title>Periconceptional Multivitamin Use and Infant Birth Weight Disparities</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003846/abstract?rss=yes</link><description>Purpose: In the United States, African American women deliver preterm and low birth weight infants two to three times more frequently than their white counterparts. Our objective was to determine whether maternal periconceptional multivitamin (MVI) use is associated with this disparity.Methods: As a secondary analysis of previously collected data from mothers of non-malformed infants from the Slone Epidemiology Center Birth Defects Study, we conducted a retrospective cohort study of 2331 non-Hispanic white and 133 non-Hispanic black mother/infant pairs from 1998 through 2007. To estimate the effect of MVI use on birth outcomes, linear regression models were used.Results: In white subjects, MVI use was not associated with birth weight, gestational age, or weight-for-gestational-age. However, in black subjects, MVI use was associated with a 536-gram increased birth weight (p=0.001). Black MVI users also had longer gestations (although not statistically significant). When birth weights were adjusted for gestational age using z scores, MVI use was associated with increased fetal growth in black infants (+0.86 z score units, 95% confidence interval: 0.35–1.36).Conclusions: The present findings suggest MVI use may improve fetal growth and possibly gestational age in the offspring of African American women.</description><dc:title>Periconceptional Multivitamin Use and Infant Birth Weight Disparities</dc:title><dc:creator>Heather H. Burris, Allen A. Mitchell, Martha M. Werler</dc:creator><dc:identifier>10.1016/j.annepidem.2009.12.003</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>233</prism:startingPage><prism:endingPage>240</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279709003676/abstract?rss=yes"><title>Association of Paternal Age and Risk for Major Congenital Anomalies From the National Birth Defects Prevention Study, 1997 to 2004</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279709003676/abstract?rss=yes</link><description>Purpose: The objective of this study was to examine the associations between paternal age and birth defects of unknown etiologies while carefully controlling for maternal age.Methods: By using 1997 to 2004 data from the National Birth Defects Prevention Study, we fit logistic regression models with paternal and maternal age as continuous variables while adjusting for demographic and other factors.Results: Elevated odds ratios (ORs) for each year increase in paternal age were found for cleft palate (OR. 1.02, 95% confidence interval [95% CI], 1.00–1.04), diaphragmatic hernia (OR, 1.04; 95% CI, 1.02–1.06), right ventricular outflow tract obstruction (OR, 1.03; 95% CI, 1.01–1.04), and pulmonary valve stenosis (OR, 1.02, 95% CI, 1.01–1.04). At younger paternal ages, each year increase in paternal age correlated with increased odds of having offspring with encephalocele, cataract, esophageal atresia, anomalous pulmonary venous return, and coarctation of the aorta, but these increased odds were not observed at older paternal ages. The effect of paternal age was modified by maternal age for gastroschisis, omphalocele, spina bifida, all orofacial clefts, and septal heart defects.Conclusions: Our findings suggest that paternal age may be a risk factor for some multifactorial birth defects.</description><dc:title>Association of Paternal Age and Risk for Major Congenital Anomalies From the National Birth Defects Prevention Study, 1997 to 2004</dc:title><dc:creator>Ridgely Fisk Green, Owen Devine, Krista S. Crider, Richard S. Olney, Natalie Archer, Andrew F. Olshan, Stuart K. Shapira, The National Birth Defects Prevention Study</dc:creator><dc:identifier>10.1016/j.annepidem.2009.10.009</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-01-07</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-01-07</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section></prism:section><prism:startingPage>241</prism:startingPage><prism:endingPage>249</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279710000025/abstract?rss=yes"><title>Erratum</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279710000025/abstract?rss=yes</link><description>In the article entitled “Rising Social Inequalities in US Childhood Obesity, 2003–2007” by Singh et al., in the January 2010 issue of Annals of Epidemiology (Volume 20, Number 1, pages 40-52), the fourth sentence of the “Results” section of the abstract should read as follows:</description><dc:title>Erratum</dc:title><dc:creator></dc:creator><dc:identifier>10.1016/j.annepidem.2010.01.001</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section>Erratum</prism:section><prism:startingPage>250</prism:startingPage><prism:endingPage>250</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279710000104/abstract?rss=yes"><title>Table of Contents</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279710000104/abstract?rss=yes</link><description></description><dc:title>Table of Contents</dc:title><dc:creator></dc:creator><dc:identifier>10.1016/S1047-2797(10)00010-4</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section>Frontmatter</prism:section><prism:startingPage>A1</prism:startingPage><prism:endingPage>A1</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279710000116/abstract?rss=yes"><title>Masthead</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279710000116/abstract?rss=yes</link><description></description><dc:title>Masthead</dc:title><dc:creator></dc:creator><dc:identifier>10.1016/S1047-2797(10)00011-6</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section>Frontmatter</prism:section><prism:startingPage>A2</prism:startingPage><prism:endingPage>A2</prism:endingPage></item><item rdf:about="http://www.annalsofepidemiology.org/article/PIIS1047279710000128/abstract?rss=yes"><title>Information for Authors</title><link>http://www.annalsofepidemiology.org/article/PIIS1047279710000128/abstract?rss=yes</link><description></description><dc:title>Information for Authors</dc:title><dc:creator></dc:creator><dc:identifier>10.1016/S1047-2797(10)00012-8</dc:identifier><dc:source>Annals of Epidemiology 20, 3 (2010)</dc:source><dc:date>2010-03-01</dc:date><prism:publicationName>Annals of Epidemiology</prism:publicationName><prism:publicationDate>2010-03-01</prism:publicationDate><prism:volume>20</prism:volume><prism:number>3</prism:number><prism:issueIdentifier>S1047-2797(10)X0002-3</prism:issueIdentifier><prism:section>Frontmatter</prism:section><prism:startingPage>A3</prism:startingPage><prism:endingPage>A4</prism:endingPage></item></rdf:RDF>