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Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemio-logic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed healthcare databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases. [ABSTRACT FROM AUTHOR]

In this paper, we examine patterns of self-reported diagnosis of noncommunicable diseases (NCDs) and prevalences of algorithm/measured test-based, undiagnosed, and untreated NCDs in China, Ghana, India, Mexico, Russia, and South Africa. Nationally representative samples of older adults aged ≥50 years were analyzed from wave 1 of the World Health Organization's Study on Global Ageing and Adult Health (2007-2010; n = 34,149). Analyses focused on 6 conditions: angina, arthritis, asthma, chronic lung disease, depression, and hypertension. Outcomes for these NCDs were: 1) self-reported disease, 2) algorithm/measured test-based disease, 3) undiagnosed disease, and 4) untreated disease. Algorithm/measured test-based prevalence of NCDs was much higher than self-reported prevalence in all 6 countries, indicating underestimation of NCD prevalence in low- and middle-income countries. Undiagnosed prevalence of NCDs was highest for hypertension, ranging from 19.7% (95% confidence interval (CI): 18.1, 21.3) in India to 49.6% (95% CI: 46.2, 53.0) in South Africa. The proportion untreated among all diseases was highest for depression, ranging from 69.5% (95% CI: 57.1, 81.9) in South Africa to 93.2% (95% CI: 90.1, 95.7) in India. Higher levels of education and wealth significantly reduced the odds of an undiagnosed condition and untreated morbidity. A high prevalence of undiagnosed NCDs and an even higher proportion of untreated NCDs highlights the inadequacies in diagnosis and management of NCDs in local health-care systems. [ABSTRACT FROM AUTHOR]

Bias due to selective mortality is a potential concern in many studies and is especially relevant in cognitive aging research because cognitive impairment strongly predicts subsequent mortality. Biased estimation of the effect of an exposure on rate of cognitive decline can occur when mortality is a common effect of exposure and an unmeasured determinant of cognitive decline and in similar settings. This potential is often represented as collider-stratification bias in directed acyclic graphs, but it is difficult to anticipate the magnitude of bias. In this paper, we present a flexible simulation platform with which to quantify the expected bias in longitudinal studies of determinants of cognitive decline. We evaluated potential survival bias in naive analyses under several selective survival scenarios, assuming that exposure had no effect on cognitive decline for anyone in the population. Compared with the situation with no collider bias, the magnitude of bias was higher when exposure and an unmeasured determinant of cognitive decline interacted on the hazard ratio scale to influence mortality or when both exposure and rate of cognitive decline influenced mortality. Bias was, as expected, larger in high-mortality situations. This simulation platform provides a flexible tool for evaluating biases in studies with high mortality, as is common in cognitive aging research. [ABSTRACT FROM AUTHOR]

Influenza viruses undergo frequent antigenic changes. As a result, the viruses circulating change within and between seasons, and the composition of the influenza vaccine is updated annually. Thus, estimation of the vaccine's effectiveness is not constant across seasons. In order to provide annual estimates of the influenza vaccine's effectiveness, health departments have increasingly adopted the "test-negative design," using enhanced data from routine surveillance systems. In this design, patients presenting to participating general practitioners with influenza-like illness are swabbed for laboratory testing; those testing positive for influenza virus are defined as cases, and those testing negative form the comparison group. Data on patients' vaccination histories and confounder profiles are also collected. Vaccine effectiveness is estimated from the odds ratio comparing the odds of testing positive for influenza among vaccinated patients and unvaccinated patients, adjusting for confounders. The test-negative design is purported to reduce bias associated with confounding by health-care-seeking behavior and misclassification of cases. In this paper, we use directed acyclic graphs to characterize potential biases in studies of influenza vaccine effectiveness using the test-negative design. We show how studies using this design can avoid or minimize bias and where bias may be introduced with particular study design variations. [ABSTRACT FROM AUTHOR]

Ramadan is observed by 1.6 billion Muslims. In an accompanying article that uses data from the Nouna Health and Demographic Surveillance System in Burkina Faso, Schoeps et al. (Am J Epidemiol. 2018;187(10):2085–2092) find that exposure to Ramadan in early pregnancy is associated with an increased risk of mortality among children under age 5 years. Ramadan exposes observant individuals to a specific pattern of nutrition and other behaviors, including changes in sleep patterns. How these behaviors might result in child mortality is not yet understood, and the findings reported in this paper should be replicated in other settings. [ABSTRACT FROM AUTHOR]

Surveillance systems in public health practice have increased in number and sophistication with advances in data collection, analysis, and communication. When the Communicable Disease Center (now the Centers for Disease Control and Prevention) was founded some 70 years ago, surveillance referred to the close observation of individuals with suspected smallpox, plague, or cholera. Alexander Langmuir, head of the Epidemiology Branch, redefined surveillance as the epidemiology-based critical factor in infectious disease control. I joined Langmuir as assistant chief in 1955 and was appointed chief of the Surveillance Section in 1961. In this paper, I describe Langmuir's redefinition of surveillance. Langmuir asserted that its proper use in public health meant the systematic reporting of infectious diseases, the analysis and epidemiologic interpretation of data, and both prompt and widespread dissemination of results. I outline the Communicable Disease Center's first surveillance systems for malaria, poliomyelitis, and influenza. I also discuss the role of surveillance in the global smallpox eradication program, emphasizing that the establishment of systematic reporting systems and prompt action based on results were critical factors of the program. [ABSTRACT FROM AUTHOR]

In an attempt to reproduce the results of an inconclusive 1927 report by the British Medical Research Council on the hereditary versus social origins of rheumatic fever, Read, Ciocco, and Taussig, from Johns Hopkins University, with the support of Frost, conducted a case-control study in 1935 and 1936. Their study, which appeared in the American Journal of Hygiene in 1938, was outstanding for its clear and tidy rational for separating hereditary from environmental causes. The authors compared the prevalence of rheumatic fever among the relatives of 33 children admitted for "incident" rheumatic fever and 33 control children admitted in a tuberculosis clinic for reasons other than rheumatic fever. Both rheumatic fever (cases) and tuberculosis (controls) were diseases of the poor. All family members of both cases and controls, including uncles, aunts, and grandparents, were eligible for interview and physical examination. The results were compatible with the presence of an "inherited predisposition" to rheumatic fever because the disease was more prevalent among the uncles, aunts, and grandparents of case patients than among those of control patients. Methodologically, the paper by Read, Ciocco, and Taussig is an important but almost completely forgotten milestone in the evolution of case-control studies and of genetic epidemiology. [ABSTRACT FROM AUTHOR]

The idea that epidemiologic studies should start from first exposure onward has been advocated in the past few years. The study of incident exposures is contrasted with studies of prevalent exposures in which follow-up may commence after first exposure. The former approach is seen as a hallmark of a good study and necessary for causal inference. We argue that studying incident exposures may be necessary in some situations, but it is not always necessary and is not the preferred option in many instances. Conducting a study involves decisions as to which person-time experience should be included. Although studies of prevalent exposures involve left truncation (missingness on the left), studies of incident exposures may involve right censoring (missingness on the right) and therefore may not be able to assess the long-term effects of exposure. These considerations have consequences for studies of dynamic (open) populations that involve a mixture of prevalent and incident exposures. We argue that studies with prevalent exposures will remain a necessity for epidemiology. The purpose of this paper is to restore the balance between the emphasis on first exposure cohorts and the richness of epidemiologic information obtained when studying prevalent exposures. [ABSTRACT FROM AUTHOR]

Researchers conducting observational studies need to consider 3 types of biases: selection bias, information bias, and confounding bias. A whole arsenal of statistical tools can be used to deal with information and confounding biases. However, methods for addressing selection bias and unmeasured confounding are less developed. In this paper, we propose general bounding formulas for bias, including selection bias and unmeasured confounding. This should help researchers make more prudent interpretations of their (potentially biased) results. [ABSTRACT FROM AUTHOR]

Missing data are a commonly occurring threat to the validity and efficiency of epidemiologic studies. Perhaps the most common approach to handling missing data is to simply drop those records with 1 or more missing values, in so-called "complete records" or "complete case" analysis. In this paper, we bring together earlier-derived yet perhaps now somewhat neglected results which show that a logistic regression complete records analysis can provide asymptotically unbiased estimates of the association of an exposure of interest with an outcome, adjusted for a number of confounders, under a surprisingly wide range of missing-data assumptions. We give detailed guidance describing how the observed data can be used to judge the plausibility of these assumptions. The results mean that in large epidemiologic studies which are affected by missing data and analyzed by logistic regression, exposure associations may be estimated without bias in a number of settings where researchers might otherwise assume that bias would occur. [ABSTRACT FROM AUTHOR]

In cohort mortality studies, there often is interest in associations between an exposure of primary interest and mortality due to a range of different causes. A standard approach to such analyses involves fitting a separate regression model for each type of outcome. However, the statistical precision of some estimated associations may be poor because of sparse data. In this paper, we describe a hierarchical regression model for estimation of parameters describing outcome-specific relative rate functions and associated credible intervals. The proposed model uses background stratification to provide flexible control for the outcome-specific associations of potential confounders, and it employs a hierarchical “shrinkage” approach to stabilize estimates of an exposure’s associations with mortality due to different causes of death. The approach is illustrated in analyses of cancer mortality in 2 cohorts: a cohort of dioxin-exposed US chemical workers and a cohort of radiation-exposed Japanese atomic bomb survivors. Compared with standard regression estimates of associations, hierarchical regression yielded estimates with improved precision that tended to have less extreme values. The hierarchical regression approach also allowed the fitting of models with effect-measure modification. The proposed hierarchical approach can yield estimates of association that are more precise than conventional estimates when one wishes to estimate associations with multiple outcomes. [ABSTRACT FROM AUTHOR]

Increasingly often in epidemiologic research, associations between survival time and predictors of interest are measured by differences between distribution functions rather than hazard functions. For example, differences in percentiles of survival time, expressed in absolute time units (e.g., weeks), may complement the popular risk ratios, which are unitless measures. When analyzing time to an event of interest (e.g., death) in prospective cohort studies, the time scale can be set to start at birth or at study entry. The advantages of one time origin over the other have been thoroughly explored for the estimation of risks but not for the estimation of survival percentiles. In this paper, we analyze the use of different time scales in the estimation of survival percentiles with Laplace regression. Using this regression method, investigators can estimate percentiles of survival time over levels of an exposure of interest while adjusting for potential confounders. Our findings may help to improve modeling strategies and ease interpretation in the estimation of survival percentiles in prospective cohort studies. [ABSTRACT FROM AUTHOR]

After objections surfaced following a call for papers on "Prisoner Health," the editors of Epidemiologic Reviews decided to rename this year's volume "Incarceration and Health." In this commentary, we trace the origins of person-first language and explain why using appropriate terms in correctional health, including correctional health epidemiology, matters. We discuss the potential consequences of person-first language for justice-involved individuals and how inclusive language might affect the social, emotional, and physical well-being of individuals, families, and communities. Future directions may include measuring health outcomes when language is systematically changed. The barriers that thwart successful reentry may wane when dehumanizing language disappears. [ABSTRACT FROM AUTHOR]

In their accompanying article, Samoilenko and Lefebvre (Am J Epidemiol. 2019;188(7):1203–1205) correctly note 2 typographical errors in the formulas presented in a 2011 paper on placental abruption by Ananth and VanderWeele (Am J Epidemiol. 2011;174(1):99–108). Fortunately, to the best of our knowledge, researchers are using our methods papers and Dr. VanderWeele's 2015 book on mediation analysis (Explanation in Causal Inference ; Oxford University Press, New York, New York), rather than the paper on placental abruption, to carry out their direct and indirect effect analyses; and in our methods papers and the book, the formulas are correct. The formulas discussed by Samoilenko and Lefebvre and in our work make reference to a "rare outcome assumption." In evaluating this assumption, it is important to note that the outcome is to be relatively rare across all strata defined by the exposure and the mediator —a point that is often neglected. [ABSTRACT FROM AUTHOR]

Although the impact of interviewers on survey measurement has been studied for more than 85 years, such impacts are rarely considered in the analysis of health surveys. This issue is particularly important with single-stage surveys such as that used in the Behavioral Risk Factor Surveillance System(BRFSS),where there are no sampling clusters in which interviewer effects could be captured. The BRFSS involves an ongoing telephone survey of the health behaviors of US adults and was established in 1984 by the Centers for Disease Control and Prevention. Public-use BRFSS data files are widely used by epidemiologists and public health researchers to describe the health behaviors of adults in the United States. Since its onset, the BRFSS has provided identification codes for telephone interviewers completing BRFSS interviews in its public-use data files; however, a review of BRFSS publications shows no evidence that these codes have been used in estimating standard errors. In this paper we analyze data from the 2012 BRFSS, illustrate both design-based and model-based approaches to incorporating interviewer effects in variance estimation, and find evidence of substantial interviewer effects for 5 key estimates across states. These results suggest that BRFSS analysts should consider accounting for interviewer effects, and we provide example code enabling analysts to do so. We conclude with suggestions regarding possible directions for future research. [ABSTRACT FROM AUTHOR]

To detect gene-environment interactions, a logistic regression model is typically fitted to a set of case-control data, and the focus is on testing of the cross-product terms (gene × environment) in the model. A significant result is indicative of a gene-environment interaction under a multiplicative model for disease odds. Based on the sufficient-cause model for rates, in this paper we put forward a general approach to testing for sufficient-cause gene-environment interactions in case-control studies. The proposed tests can be tailored to detect a particular type of sufficient-cause gene-environment interaction with greater sensitivity. These tests include testing for autosomal dominant, autosomal recessive, and gene-dosage interactions. The tests can also detect trend interactions (e.g., a larger gene-environment interaction with a higher level of environmental exposure) and threshold interactions (e.g., gene-environment interaction occurs only when environmental exposure reaches a certain threshold level). Two assumptions are necessary for the validity of the tests: 1) the rare-disease assumption and 2) the no-redundancy assumption. Another 2 assumptions are optional but, if imposed correctly, can boost the statistical powers of the tests: 3) the gene-environment independence assumption and 4) the Hardy-Weinberg equilibrium assumption. SAS code (SAS Institute, Inc., Cary, North Carolina) for implementing the methods is provided. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cohort studies are often enriched for a primary exposure of interest to improve cost-effectiveness, which presents analytical challenges not commonly discussed in epidemiology. In this paper, we use causal diagrams to represent exposure-enriched cohort studies, illustrate a scenario wherein the risk ratio for the effect of a secondary exposure on an outcome is biased, and propose an analytical method for correcting for such bias. In our motivating example, maternal smoking (Z) is a cause of fetal growth restriction (X), which subsequently affects preterm birth (Y) (i.e., Z → X → Y); strong positive associations exist between both Z, X and X, Y; and enrichment for X increases its prevalence from 10% to 50%. In the X-enriched cohort, unadjusted and X-adjusted analyses lead to bias in the risk ratio for the total effect of Z on Y. After application of inverse probability weights, the bias is corrected, with a small loss of efficiency in comparison with a same-sized study without X-enrichment. With increasing interest in conducting secondary analyses to reduce research costs, caution should be employed when analyzing studies that have already been enriched, intentionally or unintentionally, for a primary exposure of interest. Causal diagrams can help identify scenarios in which secondary analyses may be biased. Inverse probability weights can be used to remove the bias. [ABSTRACT FROM AUTHOR]

Instrumental variable (IV) methods are increasingly being used in comparative effectiveness research. Studies using these methods often compare 2 particular treatments, and the researchers perform their IV analyses conditional on patients’ receiving this subset of treatments (while ignoring the third option of “neither treatment”). The ensuing selection bias that occurs due to this restriction has gone relatively unnoticed in interpretations and discussions of these studies’ results. In this paper we describe the structure of this selection bias with examples drawn from commonly proposed instruments such as calendar time and preference, illustrate the bias with causal diagrams, and estimate the magnitude and direction of possible bias using simulations. A noncausal association between the proposed instrument and the outcome can occur in analyses restricted to patients receiving a subset of the possible treatments. This results in bias in the numerator for the standard IV estimator; the bias is amplified in the treatment effect estimate. The direction and magnitude of the bias in the treatment effect estimate are functions of the distribution of and relationships between the proposed instrument, treatment values, unmeasured confounders, and outcome. IV methods used to compare a subset of treatment options are prone to substantial biases, even when the proposed instrument appears relatively strong. [ABSTRACT FROM AUTHOR]

In several studies of antiretroviral treatment (ART) programs for persons with human immunodeficiency virus infection, investigators have reported that there has been a higher rate of loss to follow-up (LTFU) among patients initiating ART in recent years than among patients who initiated ART during earlier time periods. This finding is frequently interpreted as reflecting deterioration of patient retention in the face of increasing patient loads. However, in this paper we demonstrate by simulation that transient gaps in follow-up could lead to bias when standard survival analysis techniques are applied. We created a simulated cohort of patients with different dates of ART initiation. Rates of ART interruption, ART resumption, and mortality were assumed to remain constant over time, but when we applied a standard definition of LTFU, the simulated probability of being classified LTFU at a particular ART duration was substantially higher in recently enrolled cohorts. This suggests that much of the apparent trend towards increased LTFU may be attributed to bias caused by transient interruptions in care. Alternative statistical techniques need to be used when analyzing predictors of LTFU—for example, using “prospective” definitions of LTFU in place of “retrospective” definitions. Similar considerations may apply when analyzing predictors of LTFU from treatment programs for other chronic diseases. [ABSTRACT FROM PUBLISHER]

Medical devices play a vital role in diagnosing, treating, and preventing diseases and are an integral part of the health-care system. Many devices, including implantable medical devices, enter the market through a regulatory pathway that was not designed to assure safety and effectiveness. Several recent studies and high-profile device recalls have demonstrated the need for well-designed, valid postmarketing studies of medical devices. Medical device epidemiology is a relatively new field compared with pharmacoepidemiology, which for decades has been developed to assess the safety and effectiveness of medications. Many methodological considerations in pharmacoepidemiology apply to medical device epidemiology. Fundamental differences in mechanisms of action and use and in how exposure data are captured mean that comparative effectiveness studies of medical devices often necessitate additional and different considerations. In this paper, we discuss some of the most salient issues encountered in conducting comparative effectiveness research on implantable devices. We discuss special methodological considerations regarding the use of data sources, exposure and outcome definitions, timing of exposure, and sources of bias. [ABSTRACT FROM PUBLISHER]

As pellagra reached epidemic proportions in the United States in the early 20th century, 2 teams of investigators assessed its incidence in cotton mill villages in South Carolina. The first, the Thompson-McFadden Commission, concluded that pellagra was likely infectious. The second, a Public Health Service investigation led by Joseph Goldberger, concluded that pellagra was caused by a dietary deficiency. In this paper, we recount the history of the 2 investigations and consider how the differences between the 2 studies' designs, measurements, analyses, and interpretations led to different conclusions. Because the novel dietary assessment strategy was a key feature of the Public Health Service's study design, we incorporated simulated measurement error in a reanalysis of the Public Health Service's data to assess whether this specific difference affected the divergent conclusions. [ABSTRACT FROM PUBLISHER]

The g-formula and agent-based models (ABMs) are 2 approaches used to estimate causal effects. In the current issue of the Journal, Murray et al. (Am J Epidemiol. 2017;186(2):131-142) compare the performance of the g-formula and ABMs to estimate causal effects in 3 target populations. In their thoughtful paper, the authors outline several reasons that a causal effect estimated using an ABM may be biased when parameterized from at least 1 source external to the target population. The authors have addressed an important issue in epidemiology: Often causal effect estimates are needed to inform public health decisions in settings without complete data. Because public health decisions are urgent, epidemiologists are frequently called upon to estimate a causal effect from existing data in a separate population rather than perform new data collection activities. The assumptions needed to transport causal effects to a specific target population must be carefully stated and assessed, just as one would explicitly state and analyze the assumptions required to draw internally valid causal inference in a specific study sample. Considering external validity in important target populations increases the impact of epidemiologic studies. [ABSTRACT FROM AUTHOR]

In public health, it has long been observed that "place"--specifically, where one lives--affects individual health, with the main research question distinguishing between the effects of "context" (defined as area characteristics) and "composition" (the characteristics of inhabitants) on health outcomes. There have been many studies in which the spatial patterning of disease has been explored, but they were often ecological in design, used broad census geographic levels, lacked individual-level data, or when available, did not simultaneously analyze community- and individual-level risk factors using appropriate modeling techniques. The paper by Diez-Roux et al. (Am J Epidemiol. 1997;146(1):48-63) represents an important expansion of the literature in terms of analytic methods used and level of geography studied. The authors demonstrated that both neighborhood- and individual-level measures of socioeconomic status work together to play an important role in shaping disease risk. Analyses incorporating both levels of data have the potential to provide epidemiologists with a deeper understanding of the divergent pathways via which neighborhood affects health. [ABSTRACT FROM AUTHOR]

In this commentary, I review the insights that have been gained using Rothman's sufficient cause model (Am J Epidemiol. 1976;104(6):587-592). Discussion pertains to the relations of the model to similar conceptualizations in other fields of study, the advances and extensions that have been made to the model since the paper's publication, and its relation to questions of actual causation, along with questions concerning the use of the model in the future of epidemiology. [ABSTRACT FROM AUTHOR]

In his 1976 paper "Estimability and Estimation in Case-Referent Studies" (Am J Epidemiol. 1976;103(2):226-235), Miettinen weaved together a patchwork of new ideas into a coherent view of case-control studies. His article spurred theoretical development in epidemiologic methods and became a platform for teaching about some key concepts in epidemiologic study design. [ABSTRACT FROM AUTHOR]

Published in 1999, McMichael's "Prisoners of the Proximate: Loosening the Constraints on Epidemiology in an Age of Change" (Am J Epidemiol. 1999;149(10):887-897) outlined an exciting vision for epidemiology as the field that that can help us better understand the drivers of population health so that we may intervene, paving the way for healthier populations. McMichael's paper remains today what it was when it was first published: clear, thoughtful, provocative, and usefully prescriptive in its call to action. McMichael identified 4 constraints facing epidemiology. The field has risen to this challenge and addressed some of these constraints. I discuss how successful (or not) we have been in addressing each of these 4 challenges. [ABSTRACT FROM AUTHOR]

The article discusses the time-dependent nature of influenza illness, immunization and pregnancy outcome in response to a commentary about a paper on maternal immunization and birth outcomes published in the 2016 issue. Several clarifications are made including the rates of influenza attack, seasonal and regional variability in influenza disease epidemiology, and the biological pathways linking influenza, immunization and adverse fetal outcomes.

A parameter in a statistical model is identified if its value can be uniquely determined from the distribution of the observable data. We consider the context of an instrumental variable analysis with a binary outcome for estimating a causal risk ratio. The semiparametric generalized method of moments and structural mean model frameworks use estimating equations for parameter estimation. In this paper, we demonstrate that lack of identification can occur in either of these frameworks, especially if the instrument is weak. In particular, the estimating equations may have no solution or multiple solutions. We investigate the relationship between the strength of the instrument and the proportion of simulated data sets for which there is a unique solution of the estimating equations. We see that this proportion does not appear to depend greatly on the sample size, particularly for weak instruments (p² ≤ 0.01). Poor identification was observed in a considerable proportion of simulated data sets for instruments explaining up to 10% of the variance in the exposure with sample sizes up to 1 million. In an applied example considering the causal effect of body mass index (weight (kg)/height (m)²) on the probability of early menarche, estimates and standard errors from an automated optimization routine were misleading. [ABSTRACT FROM AUTHOR]

Ozone is associated with adverse health; however, less is known about vulnerable/sensitive populations, which we refer to as sensitive populations. We systematically reviewed epidemiologic evidence (1988-2013) regarding sensitivity to mortality or hospital admission from short-term ozone exposure. We performed meta-analysis for overall associations by age and sex; assessed publication bias; and qualitatively assessed sensitivity to socioeconomic indicators, race/ethnicity, and air conditioning. The search identified 2,091 unique papers, with 167 meeting inclusion criteria (73 on mortality and 96 on hospitalizations and emergency department visits, including 2 examining both mortality and hospitalizations). The strongest evidence for ozone sensitivity was forage. Per 10-parts per billion increase in daily 8-hour ozone concentration, mortality risk for younger persons, at 0.60% (95% confidence interval (Cl): 0.40, 0.80), was statistically lower than that for older persons, at 1.27% (95% Cl: 0.76,1.78). Findings adjusted for publication bias were similar. Limited/suggestive evidence was found for higher associations among women; mortality risks were 0.39% (95% Cl: -0.22,1.00) higher than those for men. We identified strong evidence for higher associations with unemployment or lower occupational status and weak evidence of sensitivity for racial/ethnic minorities and persons with low education, in poverty, or without central air conditioning. Findings show that some populations, especially the elderly, are particularly sensitive to short-term ozone exposure. [ABSTRACT FROM AUTHOR]

In the article, the editor calls for submission of papers about epidemiology and epidemiologists, as well as the celebration of the "American Journal of Epidemiology" of its 100th anniversary.

The article presents an introduction to the issue, which contains a paper titled "Win-Win: Reconciling Social Epidemiology and Causal Inference," as well as commentaries for this work from several epidemiologists.

The article presents a response to commentaries to a paper proposing to reconcile social epidemiology and quantitative causal inference. Topics covered include the issue on whether identifying causes is a worthy scientific goal and evidence that complex processes of particular interest to social epidemiology have received less thoughtful attention.

The article presents the authors' response to Doctor A. I. Naimi's commentary about their paper which focused on the different method whose use is widespread in epidemiologic research. Topics discussed include his suggestion for the reporting of bounds for the natural effects that are compatible with a given data set and their alternative proposal to consider a subtly different counterfactual scenario where the exposure to present and the mediator for each individual to a random value are set.

The author features the operation of the "American Journal of Epidemiology" during his tenure as its editor-in-chief. He explores his journey as the journal's editor as he prepares for his resignation. He discusses some of the topics of papers published in the journal, including chronic disease, epidemiologic methods and nontransmissible diseases. In addition, the author recognizes the contributions of editorial board members, associate editors, reviewers, authors and readers.

The article presents the authors' response to the invited commentary of J. Merlo to the authors' paper tilted "Multilevel Analysis of Individual Heterogeneity - A Fundamental Critique of the Current Probabilistic Risk Factor Epidemiology," published in a previous issue of the journal. Topics discussed include accepted use of multilevel models. Also mentioned is the paper demonstrating the utility and adaptability of multilevel models for epidemiology.

The article presents the authors' response to D. Westreich and JK Edwards' commentary regarding their paper on estimating the efficacy of pre-exposure prophylaxis (PrEP) for human immunodeficiency virus (HIV) prevention in a randomized trial with imperfect adherence. They point out that real-world PrEP delivered with known efficacy counseling may be considered a different version of treatment than that used in randomized trials.

The article presents the authors' response to Dr.S. Harper's commentary on their paper and his effort to produce complementary absolute measures. Topics discussed include the use of the age-period-cohort (APC) toolbox to examine temporal evolution of a population health disparity, pitfalls in APC analysis and underlying assumptions necessary to gain useful information and their conclusion that cohort differences contributed more to racial disparities in heart disease mortality.

The article presents the authors' reply to a letter from D. Meyre regarding their systematic review and meta-analysis on the associations of variants of proprotein convertase subtilisine/kexin type I gene (PCSKI) with obesity. The reply addresses Meyre's criticisms of what he perceives as errors in the paper including an assertion that meta-analysis included 2 overlapping cohorts and suggestion that the analysis was underpowered to find significant association of rs632 with body mass index.

The authors comment on the paper "Is case-chaos methodology an appropriate alternative to conventional case-control studies for investigating outbreaks?" by M. Edelstein, A. Wallensten, and S. Kuhlmann-Berenzon in the 2014 issue. Topics covered include the limitation of the study due to available data sets, the case-chaos methodology's lack of sensitivity to identify foods that are rarely consumed and poorly recalled, and the useful applications of the case-chaos results.

A letter to the editor is presented in response to a paper by L. E. Beane Freeman which suggests that the inverse association between selenium and bladder cancer may be due to confounding by smoking.