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In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g. in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g. means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone. [ABSTRACT FROM AUTHOR]

Loss to follow-up is an endemic feature of time-to-event analyses that precludes observation of the event of interest. To our knowledge, in typical cohort studies with encounters occurring at regular or irregular intervals, there is no consensus on how to handle person-time between participants' last study encounter and the point at which they meet a definition of loss to follow-up. We demonstrate, using simulation and an example, that when the event of interest is captured outside of a study encounter (e.g., in a registry), person-time should be censored when the study-defined criterion for loss to follow-up is met (e.g., 1 year after last encounter), rather than at the last study encounter. Conversely, when the event of interest must be measured within the context of a study encounter (e.g., a biomarker value), person-time should be censored at the last study encounter. An inappropriate censoring scheme has the potential to result in substantial bias that may not be easily corrected. [ABSTRACT FROM AUTHOR]

The exposome has been defined as the totality of exposures individuals experience over the course of their lives and how those exposures affect health. Three domains of the exposome have been identified: internal, specific external, and general external. Internal factors are those that are unique to the individual, and specific external factors include occupational exposures and lifestyle factors. The general external domain includes sociodemographic factors such as educational level and financial status. Eliciting information on the exposome is daunting and not feasible at present; the undertaking may never be fully realized. A variety of tools have been identified to measure the exposome. Biomarker measurements will be one of the major tools in exposomic studies. However, exposure data can also be obtained from other sources such as sensors, geographic information systems, and conventional tools such as survey instruments. Proof-of-concept studies are being conducted that show the promise of exposomic investigation and the integration of different kinds of data. The inherent value of exposomic data in epidemiologic studies is that they can provide greater understanding of the relationships among a broad range of chemical and other risk factors and health conditions and ultimately lead to more effective and efficient disease prevention and control. [ABSTRACT FROM AUTHOR]

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care.We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues.We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations. [ABSTRACT FROM AUTHOR]

Although investigators have assessed the relationship between self-reported cigarette smoking and biomarker levels, the validity of self-reported information on smokeless tobacco (SLT) use is uncertain. We used aggregated data from the 2003–2004, 2005–2006, 2007–2008, and 2009–2010 administrations of the National Health and Nutrition Examination Survey (NHANES) to compare self-reported SLT use with serum concentrations of cotinine, a metabolite of nicotine, among US adults aged ≥18 years. Receiver operating characteristic analysis was used to determine the optimal serum cotinine cutpoint for discriminating SLT users from nonusers of tobacco, and concordance analysis was used to compare self-reported SLT use with cotinine levels. Among the 30,298 adult respondents who completed the NHANES during 2003–2010, 418 reported having exclusively used SLT and no other type of tobacco (cigarettes, cigars, or pipes) during the past 5 days, while 23,457 reported not using any tobacco. The optimal cotinine cutpoint for discriminating SLT users from non–tobacco users was 3.0 ng/mL (sensitivity = 97.0%, specificity = 93.0%), which was comparable to a revised cutpoint recommended for identifying adult cigarette smokers. Concordance with cotinine was 96.4% and 93.7% for self-reported SLT use and tobacco nonuse, respectively. These findings indicate that self-reported SLT use among adults correlates highly with serum cotinine levels and that the optimal cutpoint for minimizing misclassification of self-reported use is a serum cotinine concentration of 3.0 ng/mL. [ABSTRACT FROM PUBLISHER]

Second-generation assays for anti-cyclic citrullinated peptide (anti-CCP), a highly sensitive and specific marker for rheumatoid arthritis (RA), have redefined the epidemiology of RA. In the Women's Health Initiative (WHI) RA study (2009–2011), we evaluated the prevalence of anti-CCP positivity among 15,691 (10.2% of 161,808) WHI participants aged 50–79 years who reported RA. Using stored baseline specimens, we measured serum anti-CCP, rheumatoid factor (RF), and antinuclear antibody in a defined sample of 9,988 of black, white, and Hispanic women. In a subset of women, we measured plasma cytokine levels and number of copies of the human leukocyte antigen (HLA)-DRB1 (HLA-DRB1) shared epitope in DNA by means of Luminex polymerase chain reaction typing (Luminex Corporation, Austin, Texas). We validated classification of probable clinical RA in 2 clinics as anti-CCP positivity or self-reported validated use of disease-modifying antirheumatic drugs (DMARDs). The prevalence of anti-CCP positivity was 8.1%, and the prevalence of RF positivity was approximately 16.0%. DMARD use including prednisone was reported by 1,140 (11.4%) participants (841 excluding prednisone) but by 57.5% of anti-CCP-positive women. The prevalence of 2 shared epitopes was also much higher for anti-CCP-positive women (18.2%, as opposed to only 5.5% for women with anti-CCP-negative DMARD-positive RA and 6.6% for anti-CCP-negative, RF-negative DMARD nonusers). Median cytokine levels were much higher for anti-CCP-positive/RF-positive women. Women with anti-CCP-positive RA and anti-CCP-negative RA had different characteristics with regard to HLA shared epitope, cigarette smoking, and inflammation (cytokines). [ABSTRACT FROM AUTHOR]

The linear mixed model (LMM), which is routinely used to describe change in outcomes over time and its association with risk factors, assumes that a unit change in any predictor is associated with a constant change in the outcome. When it is used on psychometric tests, this assumption may not hold. Indeed, psychometric tests usually suffer from ceiling and/or floor effects and curvilinearity (i.e., varying sensitivity to change). The authors aimed to determine the consequences of such misspecification when evaluating predictors of cognitive decline. As an alternative to the LMM, they considered 2 mixed models based on latent processes that handle discrete and bounded outcomes. Model differences are illustrated here using data on 4 psychometric tests from the Personnes Agées QUID (PAQUID) Study (1989–2004). The type I error of the Wald test for risk-factor regression parameters was formally assessed in a simulation study. It demonstrated that type I errors in the LMM could be dramatically inflated for some tests, such that spurious associations with risk factors were found. In particular, confusion between effects on mean level and effects on change over time was highlighted. The authors recommend use of the alternative mixed models when studying psychometric tests and more generally quantitative scales (quality of life, activities of daily living). [ABSTRACT FROM AUTHOR]

Background: In epidemiologic research, little emphasis has been placed on methods to account for left-hand censoring of 'exposures' due to a limit of detection (LOD).Methods: We calculate the odds of anti-HIV therapy naiveté in 45 HIV-infected men as a function of measured log(10) plasma HIV RNA viral load using five approaches including ad hoc methods as well as a maximum likelihood estimate (MLE). We also generated simulations of a binary outcome with 10% incidence and a 1.5-fold increased odds per log increase in a log-normally distributed exposure with 25, 50 and 75% of exposure data below LOD. Simulated data were analysed using the same five methods, as well as the full data.Results: In the example, the estimated odds ratio (OR) varied by 1.22-fold across methods, from 1.45 to 1.77 per log(10) copies of viral load and the standard error for the log OR varied by 1.52-fold across methods, from 0.31 to 0.47. In the simulations, use of full data or the MLE was unbiased with appropriate confidence interval (CI) coverage. However, as the proportion of exposure below LOD increased, substituting LOD, LOD/ radical 2 or LOD/2 was increasingly biased with increasingly inappropriate CI coverage. Finally, exclusion of values below LOD was unbiased but imprecise.Conclusions: In this example and the settings explored by simulation, and among methods readily available to investigators (i.e. sans full data), the MLE provided an unbiased and appropriately precise estimate of the exposure-outcome OR. [ABSTRACT FROM AUTHOR]

Abstract: We conducted an interlaboratory study which differed from the typical study of this type because of its emphasis on comparing intralaboratory variability in results. We sent specimens to six laboratories experienced in the analysis of perfluorinated alkyl compounds in blood matrices and that use stringent procedures to control and assure accuracy and precision. Each received an identical set of 60 plasma specimens that were analyzed in six completely independent batches. Split specimens were included so that within- and between-batch coefficients of variation could be calculated. All laboratories used liquid chromatography–tandem mass spectrometry (LC–MS/MS). The concentrations of perfluorooctanesulfonate (PFOS), perfluorooctanoate (PFOA), and perfluorohexanesulfonate (PFHxS) measured in the specimens in general showed a high level of agreement, although in some cases the agreement was only moderate. The average within- and between-batch coefficient of variation for PFOS was 9.1% and 9.3%; for PFOA was 14.5% and 14.5%; and for PFHxS was 14.5% and 17.0%. The recent availability of labeled internal standards, among other advances, has facilitated improvement in the accuracy and precision of the assays. Considering the degree of between-subject variation in levels among people in background-exposed populations, the results indicate that biomarker-based epidemiologic studies of associations with health could have reasonable precision. [Copyright &y& Elsevier]

We review several issues of broad relevance to the interpretation of epidemiologic evidence concerning the toxicity of lead in adults, particularly regarding cognitive function and the cardiovascular system, which are the subjects of two systematic reviews that are also part of this mini-monograph. Chief among the recent developments in methodologic advances has been the refinement of concepts and methods for measuring individual lead dose in terms of appreciating distinctions between recent versus cumulative doses and the use of biological markers to measure these parameters in epidemiologic studies of chronic disease. Attention is focused particularly on bone lead levels measured by K-shell X-ray fluorescence as a relatively new biological marker of cumulative dose that has been used in many recent epidemiologic studies to generate insights into lead's impact on cognition and risk of hypertension, as well as the alternative method of estimating cumulative dose using available repeated measures of blood lead to calculate an individual's cumulative blood lead index. We review the relevance and interpretation of these lead biomarkers in the context of the toxicokinetics of lead. In addition, we also discuss methodologic challenges that arise in studies of occupationally and environmentally exposed subjects and those concerning race/ethnicity and socioeconomic status and other important covariates. [ABSTRACT FROM AUTHOR]