American Journal of Epidemiology (AM J EPIDEMIOL)

Publisher: Johns Hopkins University. School of Hygiene and Public Health; Society for Epidemiologic Research (U.S.), Oxford University Press (OUP)

Journal description

The American Journal of Epidemiology is the premiere epidemiological journal devoted to the publication of empirical research findings methodological developments in the field of epidemiological research and opinion pieces. It is aimed at both fellow epidemiologists and those who use epidemiological data including public health workers and clinicians.

Current impact factor: 5.23

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 5.23
2013 Impact Factor 4.975
2012 Impact Factor 4.78
2011 Impact Factor 5.216
2010 Impact Factor 5.745
2009 Impact Factor 5.589
2008 Impact Factor 5.454
2007 Impact Factor 5.285
2006 Impact Factor 5.241
2005 Impact Factor 5.068
2004 Impact Factor 4.933
2003 Impact Factor 4.486
2002 Impact Factor 4.189
2001 Impact Factor 3.948
2000 Impact Factor 3.87
1999 Impact Factor 3.978
1998 Impact Factor 3.699
1997 Impact Factor 3.773
1996 Impact Factor 4.112
1995 Impact Factor 3.712
1994 Impact Factor 3.482
1993 Impact Factor 3.081
1992 Impact Factor 3.135

Impact factor over time

Impact factor

Additional details

5-year impact 5.63
Cited half-life >10.0
Immediacy index 1.40
Eigenfactor 0.06
Article influence 2.38
Website American Journal of Epidemiology website
Other titles American journal of epidemiology
ISSN 0002-9262
OCLC 1480139
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Oxford University Press (OUP)

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author cannot archive a post-print version
  • Restrictions
    • 12 months embargo
  • Conditions
    • Pre-print can only be posted prior to acceptance
    • Pre-print must be accompanied by set statement (see link)
    • Pre-print must not be replaced with post-print, instead a link to published version with amended set statement should be made
    • Pre-print on author's personal website, employer website, free public server or pre-prints in subject area
    • Post-print in Institutional repositories or Central repositories
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany archived copy (see policy)
    • Eligible authors may deposit in OpenDepot
    • The publisher will deposit in PubMed Central on behalf of NIH authors
    • Publisher last contacted on 19/02/2015
    • This policy is an exception to the default policies of 'Oxford University Press (OUP)'
  • Classification
    ​ yellow

Publications in this journal

  • American Journal of Epidemiology 09/2015; DOI:10.1093/aje/kwv209
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    ABSTRACT: Married couples might be an appropriate target for obesity prevention interventions. In the present study, we aimed to evaluate whether an individual's risk of obesity is associated with spousal risk of obesity and whether an individual's change in body mass index (BMI; weight in kilograms divided by height in meters squared) is associated with spousal BMI change. We analyzed data from 3,889 spouse pairs in the Atherosclerosis Risk in Communities Study cohort who were sampled at ages 45-65 years from 1986 to 1989 and followed for up to 25 years. We estimated hazard ratios for incident obesity by whether spouses remained nonobese, became obese, remained obese, or became nonobese. We estimated the association of participants' BMI changes with concurrent spousal BMI changes using linear mixed models. Analyses were stratified by sex. At baseline, 22.6% of men and 24.7% of women were obese. Nonobese participants whose spouses became obese were more likely to become obese themselves (for men, hazard ratio = 1.78, 95% confidence interval: 1.30, 2.43; for women, hazard ratio = 1.89, 95% confidence interval: 1.39, 2.57). With each 1-unit increase in spousal BMI change, women's BMI change increased by 0.15 (95% confidence interval: 0.13, 0.18) and men's BMI change increased by 0.10 (95% confidence interval: 0.09, 0.12). Having a spouse become obese nearly doubles one's risk of becoming obese. Future research should consider exploring the efficacy of obesity prevention interventions in couples.
    American Journal of Epidemiology 09/2015; DOI:10.1093/aje/kwv112
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    ABSTRACT: A challenge for population health surveillance systems using telephone methodologies is to maintain representative estimates as response rates decrease. Raked weighting, rather than conventional poststratification methodologies, has been developed to improve representativeness of estimates produced from telephone-based surveillance systems by incorporating a wider range of sociodemographic variables using an iterative proportional fitting process. This study examines this alternative weighting methodology with the monthly South Australian population health surveillance system report of randomly selected people of all ages in 2013 (n = 7,193) using computer-assisted telephone interviewing. Poststratification weighting used age groups, sex, and area of residence. Raked weights included an additional 6 variables: dwelling status, number of people in household, country of birth, marital status, educational level, and highest employment status. Most prevalence estimates (e.g., diabetes and asthma) did not change when raked weights were applied. Estimates that changed by at least 2 percentage points (e.g., tobacco smoking and mental health conditions) were associated with socioeconomic circumstances, such as dwelling status, which were included in the raked-weighting methodology. Raking methodology has overcome, to some extent, nonresponse bias associated with the sampling methodology by incorporating lower socioeconomic groups and those who are routinely not participating in population surveys into the weighting formula. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:
    American Journal of Epidemiology 08/2015; DOI:10.1093/aje/kwv080
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    ABSTRACT: This study applied socioecological and cumulative risk exposure frameworks to test the hypotheses that 1) the experience of poverty is associated with feeling less safe at school, and 2) feeling less safe is associated with engaging in poorer weight-related behaviors, as well as an increased probability of being overweight or obese. Data were from the ongoing Québec Longitudinal Study of Child Development, initiated in 1998 with a population-based cohort of 2,120 Québec (Canada) infants 5 months of age and their parent or primary caregiver. Measures of youths' (age, 13 years) self-reported feelings of safety, screen time, physical activity, and objectively assessed not overweight/obese (70%), overweight (22%), and obese (8%) weight status were collected in 2011. Family poverty trajectory from birth was assessed by using latent growth modeling. As hypothesized, exposure to poverty was associated with feeling less safe at school and, in turn, with an increased probability of being overweight or obese. The association was most pronounced for youths who experienced chronic poverty. Compared with youths who experienced no poverty and felt unsafe, those who experienced chronic poverty and felt unsafe were nearly 18% more likely to be obese (9.2% vs. 11.2%). Although feeling unsafe was associated with screen time, screen time did not predict weight status.
    American Journal of Epidemiology 04/2015; DOI:10.1093/aje/kwv005
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    ABSTRACT: Selenium has been linked to a reduced risk of bladder cancer in some studies. Smoking, a well-established risk factor for bladder cancer, has been associated with lower selenium levels in the body. We investigated the selenium-bladder cancer association in subjects from Maine, New Hampshire, and Vermont in the New England Bladder Cancer Case-Control Study. At interview (2001-2005), participants provided information on a variety of factors, including a comprehensive smoking history, and submitted toenail samples, from which we measured selenium levels. We estimated odds ratios and 95% confidence intervals among 1,058 cases and 1,271 controls using logistic regression. After controlling for smoking, we saw no evidence of an association between selenium levels and bladder cancer (for fourth quartile vs. first quartile, odds ratio (OR) = 0.98, 95% confidence interval (CI): 0.77, 1.25). When results were restricted to regular smokers, there appeared to be an inverse association (OR = 0.76, 95% CI: 0.58, 0.99); however, when pack-years of smoking were considered, this association was attenuated (OR = 0.91, 95% CI: 0.68, 1.20), indicating potential confounding by smoking. Despite some reports of an inverse association between selenium and bladder cancer overall, our results, combined with an in-depth evaluation of other studies, suggested that confounding from smoking intensity or duration could explain this association. Our study highlights the need to carefully evaluate the confounding association of smoking in the selenium-bladder cancer association. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.
    American Journal of Epidemiology 03/2015; 181(7). DOI:10.1093/aje/kwu324
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    ABSTRACT: Epidemiologic studies utilizing source apportionment (SA) of fine particulate matter have shown that particles from certain sources might be more detrimental to health than others; however, it is difficult to quantify the uncertainty associated with a given SA approach. In the present study, we examined associations between source contributions of fine particulate matter and emergency department visits for pediatric asthma in Atlanta, Georgia (2002-2010) using a novel ensemble-based SA technique. Six daily source contributions from 4 SA approaches were combined into an ensemble source contribution. To better account for exposure uncertainty, 10 source profiles were sampled from their posterior distributions, resulting in 10 time series with daily SA concentrations. For each of these time series, Poisson generalized linear models with varying lag structures were used to estimate the health associations for the 6 sources. The rate ratios for the source-specific health associations from the 10 imputed source contribution time series were combined, resulting in health associations with inflated confidence intervals to better account for exposure uncertainty. Adverse associations with pediatric asthma were observed for 8-day exposure to particles generated from diesel-fueled vehicles (rate ratio = 1.06, 95% confidence interval: 1.01, 1.10) and gasoline-fueled vehicles (rate ratio = 1.10, 95% confidence interval: 1.04, 1.17). © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:
    American Journal of Epidemiology 03/2015; 181(7). DOI:10.1093/aje/kwu305
  • American Journal of Epidemiology 03/2015; 181(8). DOI:10.1093/aje/kwv031
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    ABSTRACT: The Postlicensure Rapid Immunization Safety Monitoring Program, the vaccination safety monitoring component of the US Food and Drug Administration's Mini-Sentinel project, is currently the largest cohort in the US general population for vaccine safety surveillance. We developed a study design selection framework to provide a roadmap and description of methods that may be utilized to evaluate potential associations between vaccines and health outcomes of interest in the Postlicensure Rapid Immunization Safety Monitoring Program and other systems using administrative data. The strengths and weaknesses of designs for vaccine safety monitoring, including the cohort design, the case-centered design, the risk interval design, the case-control design, the self-controlled risk interval design, the self-controlled case series method, and the case-crossover design, are described and summarized in tabular form. A structured decision table is provided to aid in planning of future vaccine safety monitoring activities, and the data components comprising the structured decision table are delineated. The study design selection framework provides a starting point for planning vaccine safety evaluations using claims-based data sources. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:
    American Journal of Epidemiology 03/2015; 181(8). DOI:10.1093/aje/kwu322
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    ABSTRACT: The concept of translational cancer epidemiology has evolved since its early beginnings in 1937 with the establishment of the National Cancer Institute. Conceptual models of cancer control research have also evolved over the last 30 years, to the point where we now have 4 stages of translational research (T0-T4). The current review by Lam et al. (Am J Epidemiol. 2015;000(00):000-000) covers cancer epidemiology research supported by the National Cancer Institute and a selected sample of the cancer epidemiology literature. It suggests that most cancer epidemiology in the last 10 years has been in pure discovery research. Current "drivers" of cancer epidemiology research, including new technologies, team science multilevel research, and knowledge integration, are not strongly represented in the review. However, the use of epidemiology in the latter stages of translation may not have been captured by the scope of this review. The closer epidemiologists get to advanced stages of translation, the more likely they are to work with investigators in other disciplines in other sectors of society. An argument can be made that regardless of whether this kind of research is not happening or was just missed by the current review, the field of cancer epidemiology can expand its scope and further evolve towards more effective applications in population health. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:
    American Journal of Epidemiology 03/2015; 181(7). DOI:10.1093/aje/kwu476
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    American Journal of Epidemiology 03/2015; 181(6). DOI:10.1093/aje/kwv009
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    ABSTRACT: In 2013, the National Heart, Lung, and Blood Institute assembled a working group on epidemiology and population sciences from its Advisory Council and Board of External Experts. The working group was charged with making recommendations to the National Heart, Lung, and Blood Advisory Council about how the National Heart, Lung, and Blood Institute could take advantage of new scientific opportunities and delineate future directions for the epidemiology of heart, lung, blood, and sleep diseases. Seven actionable recommendations were proposed for consideration. The themes included 1) defining the compelling scientific questions and challenges in population sciences and epidemiology of heart, lung, blood, and sleep diseases; 2) developing methods and training mechanisms to integrate "big data" science into the practice of epidemiology; 3) creating a cohort consortium and inventory of major studies to optimize the efficient use of data and specimens; and 4) fostering a more open, competitive approach to evaluating large-scale longitudinal epidemiology and population studies. By building on the track record of success of the heart, lung, blood, and sleep cohorts to leverage new data science opportunities and encourage broad research and training partnerships, these recommendations lay a strong foundation for the transformation of heart, lung, blood, and sleep epidemiology. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:
    American Journal of Epidemiology 03/2015; 181(6). DOI:10.1093/aje/kwv011
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    ABSTRACT: In this issue of the Journal, an expert panel offers 7 recommendations on how population studies supported by National Heart, Lung, and Blood Institute contracts might be strategically transformed (Am J Epidemiol. 2015;181(00):0000-0000). The Institute and its external advisors seemingly established this panel of epidemiologists and nonepidemiologists primarily to find ways to save research costs. Although the working group's recommendations offer reasonable approaches, we believe that, even in tough fiscal times, the main drivers of cardiovascular epidemiologic research must remain 1) scientific questions that are important and 2) study designs to match these. Although cardiovascular epidemiology admittedly is often redundant and needs to be more efficient, undue focus on administrative efficiency and cost savings will not necessarily guarantee cutting-edge population research. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:
    American Journal of Epidemiology 03/2015; 181(6). DOI:10.1093/aje/kwv012
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    ABSTRACT: Pooling specimens prior to performing laboratory assays has various benefits. Pooling can help to reduce cost, preserve irreplaceable specimens, meet minimal volume requirements for certain lab tests, and even reduce information loss when a limit of detection is present. Regardless of the motivation for pooling, appropriate analytical techniques must be applied in order to obtain valid inference from composite specimens. When biomarkers are treated as the outcome in a regression model, techniques applicable to individually measured specimens may not be valid when measurements are taken from pooled specimens, particularly when the biomarker is positive and right skewed. In this paper, we propose a novel semiparametric estimation method based on an adaptation of the quasi-likelihood approach that can be applied to a right-skewed outcome subject to pooling. We use simulation studies to compare this method with an existing estimation technique that provides valid estimates only when pools are formed from specimens with identical predictor values. Simulation results and analysis of a motivating example demonstrate that, when appropriate estimation techniques are applied to strategically formed pools, valid and efficient estimation of the regression coefficients can be achieved. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:
    American Journal of Epidemiology 03/2015; 181(7). DOI:10.1093/aje/kwu301