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A modified cohort method has been proposed for estimating the relative incidence of rare adverse reactions after vaccination. The method requires only a sample of the cases, thus avoiding the need for following large population cohorts or selecting controls. This case series method has statistical power equivalent to that of the full cohort method when the risk periods after vaccination are short and vaccine coverage is high. The method also eliminates confounding by variables associated with both the outcome and avoidance of vaccination. In this paper, the cohort, case-control, and case senes methods are reviewed, and their underlying assumptions and performances are compared. Theoretical results are illustrated using data on febnle convulsions after measles-mumps-rubella vaccination in the United Kingdom. Am J Epidemiol 1996; 143: 1165–73. [ABSTRACT FROM AUTHOR]

The optimal allocation of available resources is the concern of every investigator in choosing a study design. The recent development of statistical methods for the analysis of two-stage data makes these study designs attractive for their economy and efficiency. However, little work has been done on deriving two-stage designs that are optimal under the kinds of constraints encountered in practice. The methods presented in this paper provide a means of deriving designs that will maximize precision for a fixed total budget or minimize the study cost necessary to achieve a desired precision. These optimal designs depend on the relative information content and the relative cost of gathering the first- and second-stage data. In place of the usual sample size calculations, the investigator can use pilot data to estimate the study size and second-stage sampling fractions. The gains in efficiency that can result from such carefully designed studies are illustrated here by deriving and implementing optimal designs using data from the Coronary Artery Surgery Study (Circulation 980:62:254–61). Am J Epidemiol 1996:143:92–100 [ABSTRACT FROM AUTHOR]

An introduction is presented in which the author discusses various papers within the supplement to this issue related to the epidemic-assistance investigations (Epi-Aids) that the Centers for Disease Control and Prevention (CDC) conducted during the first 60 years of its existence (1946–2005).

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]

On this, the 100th anniversary of Johns Hopkins Bloomberg School of Public Health, we take the opportunity to reflect on the ties between the School, the American Journal of Epidemiology, and the Society for Epidemiologic Research. We discuss briefly the intersection of the School, the Journal, and the Society throughout their histories, with the aim of providing some insight into how the Journal and the Society have contributed to the evolution of the field. In so doing, we articulate the challenges that the Journal and the Society jointly face today, with an eye to finding opportunities in these challenges that can be helpful in coming decades. [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]

Epidemiologic investigations often report dose-response associations, which may be combined in meta-analyses. The authors examined how often the log odds, risk, or hazard ratio per unit increase in exposure, and its standard error, can be estimated from results reported from observational studies of diet and prostate or bladder cancer so that results are usable in meta-analyses estimating dose-response associations. Eight electronic databases were searched for studies reporting on the association of diet, nutrition, or physical activity with these cancers. A total of 767 papers reported 3,284 results; 1,999 (61%) results, reported in 545 (71%) papers, were usable in dose-response meta-analyses. The most important reason that results were not usable was the absence of sufficient information on exposure levels in the different groups. The proportion of results usable in “high-low” meta-analyses (comparisons of extreme categories) was similar (62%). Results that showed evidence of an association were more likely to be usable than results that found no such evidence. Insufficient detail in reporting of results of observational studies can lead to exclusion of these results from meta-analyses and is an important threat to the validity of systematic reviews of such research. Results providing evidence of associations may be overrepresented in meta-analyses of observational studies. [ABSTRACT FROM AUTHOR]

The authors offer a response to a commentary made by J. Zelner and colleagues on their article "Sociological Imagination and Infectious Disease." Zelner and colleagues highlight the importance of considering structural factors as fundamental causes of disease. The authors offer follow-up points that clarify the reasoning behind their perspective and highlight how failure to consider both frameworks proposed by B. G. Link and J. Phelan limits research and furthers health inequities.

The authors offer a response to A. R. Cartus and B. D. L. Marshall's commentary on their study on choosing medication type for patients with opioid use disorder (OUD). Topics include the interesting set of issues raised by Cartus and Marshall regarding the potential trade-offs between investing in technical sophistication and public health considerations and their agreement with Cartus and Marshall on the need to focus on OUD patient-centered outcomes.

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]

An introduction is presented in which the editor discusses various reports within the issue on topics including emerging challenges for prison health, adverse effects of incarceration on families of prisoners, and the prevalence of treatable mental disorders.

Radiation from nuclear weapons or power plants has caused great concern among the public—concern that needs to be addressed with the best available data. Among the concerns associated with ionizing radiation are possible serious and far-reaching effects on reproductive health. Relevant data that can be used to address these concerns are scarce. The Hiroshima and Nagasaki bombings of World War II and the 1986 Chernobyl disaster in Ukraine are probably among the most dramatic and important sources of information on health effects, but much of the information is historical, and the exposed cohorts are getting old. In their accompanying article, Yamada et al. (Am J Epidemiol. 2021;190(11):2323–2333) revisit data on reproductive health outcomes in survivors of the Hiroshima and Nagasaki bombings during the years after the blasts. Exposure levels were very high, but after Yamada et al.'s reanalysis, effect estimates were low, and the evidence for overall effects on birth defects and perinatal mortality is still weak. The authors acknowledge that their data have limitations and that the generalizability of the findings is limited by the devastating conditions that prevailed in the 2 Japanese cities after the blasts. [ABSTRACT FROM AUTHOR]

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368–375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields. [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]

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]

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]

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 very insightful and clear paper by VanderWeele and Vansteelandt in this issue of the Journal (Am J Epidemiol. 2010;172(12):1339–1348) bridges the gap between biostatistics methodologists focusing on causal methods for mediation analyses and the practitioners of mediational analyses to the benefit of both groups. In an effort to continue the bridging of this gap, this invited commentary relates the important issue of “natural direct effects” to the well-known epidemiologic method of direct standardization. Additionally, attention is paid to the importance of temporal sequencing to help substantiate the mediation relations among the exposure, mediation, and outcome. A crucial mathematical distortion under the logistics model, called “absence of collapsibility,” is noted in motivating VanderWeele and Vansteelandt's use of the log-linear model for comparing the effect of exposure adjusted for the mediator with the effect of exposure unadjusted for the mediator. It is also noted that this issue applies to one approach to assessing confounding. Finally, some issues are raised for consideration when testing the interaction between the exposure and mediator before assessing mediation. [ABSTRACT FROM PUBLISHER]

The article offers the author's views on a paper presented by A.F. Subar on dietary questionnaire validation. According to him, the paper is a valuable contribution and provides further insights into the progress in the development of dietary assessment methods as well as limitations in the methods to assess validity. Moreover, he opines that Subar's report adds to the medical literature indicating that carefully designed questionnaires can provide reasonably good measure of dietary composition.

The Epidemic Intelligence Service (EIS) was the vision of Alexander Langmuir, who developed a program with a vital mission to address an unmet need in the United States. The Communicable Disease Center, now the Centers for Disease Control and Prevention (CDC; Atlanta, Georgia), and the EIS steadily expanded from focusing on infectious disease to address chronic diseases, health statistics, occupational and environmental health and safety, injury prevention and control, and reproductive health. Langmuir recognized the need for epidemiologists to collaborate with others, initially from the laboratory and later including veterinarians, demographers, statisticians, nutritionists, behavioral and social scientists, industrial hygienists, and sanitarians. These partnerships stimulated the further evolution of the EIS Program to include sophisticated statistical analysis, economics, and the tools of the behavioral and social sciences. A mixture of analytical rigor and practical application characterizes the practice of epidemiology at CDC and in the EIS. Thus, the “significant” in the title of this paper refers to the analytical rigor of the public health approach and the validity of the results, while the “consequential” reflects the practical application of the results, trying to make a difference in health outcomes. [ABSTRACT FROM PUBLISHER]

In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post– Modern Epidemiology world. [ABSTRACT FROM AUTHOR]

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 issue of the Journal, Isanaka et al. (Am J Epidemiol. 2016;184(12):861-869) set out to update an incidence correction factor used for estimating numbers of cases of severe acute malnutrition (SAM) in children aged 6-59 months. The total number of current SAM cases (prevalent cases) increases by the number of new (incident) cases and decreases as a result of recovery or death. Prevalence estimates are obtained from cross-sectional surveys. Calculation of incidence typically requires longitudinal data, which evidently are rarely collected for SAM, and so a correction factor is applied instead. Isanaka et al. pool and meta-analyze data from longitudinal and community programs in 3 West African countries (Mali, Niger, and Burkina Faso), covering the period 2009-2012. Heterogeneity and the ongoing lack of data undermine the use of a single incidence correction factor for SAM estimates. Routine data collection is recommended as a way forward and aligns with recommendations of the World Health Organization. This commentary helps to outline a context for the use of such data and provide some perspective on the inadequacy of data, relative to the importance of the issue. [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 letter to the editor is presented in response to the article "Leukemia following occupational exposure to 60-Hz electric and magnetic fields among Ontario electric utility workers," by A.B. Miller and colleagues.

Dr. Dieter Flesch-Janys et al. have informed the Journal that some of the results published in their paper on polychlorinated dioxins and furans (PCDD/F) and mortality in German workers from a herbicide-producing plant (1) contain a computational error. It was detected while preparing new data analyses in collaboration with Dr. H. Becher and Dr. K. Steindorf of the German Cancer Research Center, Heidelberg, Germany, who also computed the corrected estimates. The error had occurred in the computation of the time between birth and the end of observation for the PCDD/F-exposed cohort of chemical workers (the old follow-up date, December 31, 1989, was used instead of the new one, December 31, 1992). The error affected the estimated relative risks using an unexposed cohort of workers as the reference group (tables 3 and 4 in the originally published paper). The relative risk estimates for the internal comparison (tables 5-7) remain substantially unchanged.The corrected estimates for table 4 of the original paper are presented in the accompanying table.The corrected relative risk estimates are slightly lower than those originally reported. Thus, some confidence intervals in the lower dose regions now include one. However, the pattern of the dose-response relations for total and cancer mortality remains unchanged, and the trend tests still are significant. For mortality due to ischemic heart diseases, a numeric increase now is first observed for the subgroup of toxic equivalencies of polychlorinated dibenzo-p-dioxins and -furans (TOTTEQ) with more than 278 ng/kg of blood fat.The conclusion of the paper, “the results of this cohort study support the hypothesis of a dose-related effect of PCDD/F on cancer and ischemic heart diseases mortality” (1, p. 1174), remains unaffected.A corrected version of the paper can be requested from Dr. D. Flesch-Janys, Medical Center for Chemical Workers' Health, Fuhlsbuttler Str. 401, D-22309 Hamburg, Germany.The Journal regrets this computational error and apologizes for any inconvenience this may have caused readers. [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]

Secondary trait genetic association provides insight into the genetic architecture of disease etiology but requires caution in estimation. Ignoring case-control sampling may introduce bias into secondary trait association. In this paper, we compare the efficiency and robustness of various inverse probability weighted (IPW) estimators and maximum likelihood (ML) estimators. ML methods have been proposed but require correct modeling of both the secondary and the primary trait associations for valid inference. We show that ML methods using a misspecified primary trait model can severely inflate the type I error. IPW estimators are typically less efficient than ML estimators but are robust against model misspecification. When the secondary trait is available for the entire cohort, the IPW estimator with selection probabilities estimated nonparametrically and the augmented IPW estimator improve efficiency over the simple IPW estimator. We conclude that in large genetic association studies with complex sampling schemes, IPW-based estimators offer flexibility and robustness, and therefore are a viable option for analysis. [ABSTRACT FROM PUBLISHER]