True Exposure Research Articles

Assessing the degree of personal exposure to PM2.5 in growing cities of Rwanda based on time-activity patterns and microenvironments

Long-term exposure to fine particulate matter (PM2.5) is strongly linked with a wide range of diverse health effects making it a substantial global threat and a critical concern for public health. While studies have been conducted on personal exposure (PE) to PM2.5 in specific environments, contributions of different microenvironments and activities to overall daily PE remain unclear. This study evaluates the degree of PE to PM2.5 in five growing cities of Rwanda based on individual’s time-activity patterns and visited microenvironments. A total of 150 participants were recruited to collect real-time personal and ambient PM2.5 measurements during their routine activities in different outdoor and indoor microenvironments for five consecutive days in dry season. Each participant was an employee in one of the six most prevalent urban economic activities found in their city of residence: workshops, flour mill plants, near road activities, garages, markets, kitchens and motorcycle taxi services. The participant’s day was categorized into three most distinct microenvironments grouped under home, work, and other microenvironments (other MEs). PE to PM2.5 assessed for all participants showed significant variability among types of activities and categories of microenvironments (p < 0.05). The work microenvironment experienced the highest daily mean PM2.5 exposures ranging from 12.67 μg/m3 to 192.64 μg/m3, followed by other MEs ranging from 13.25 μg/m3 to 113.58 μg/m3, while the lowest exposures observed at home microenvironment with concentrations ranging from 11.69 μg/m3 to 72.54 μg/m3 among 7 monitored activities in 5 cities. Exposure contributions and personal-ambient differences were dominated by the work microenvironment, with a daily contribution of flour milling activities reaching up to 51.55 %; and some participants were exposed to maximum PM2.5 concentrations up to 22 times higher than ambient levels while in the kitchen activities. This study highlights the significant effects of daily personal activities and visited microenvironments on personal PM2.5 exposure, and the importance of considering a personal lifestyle in understanding the true personal exposure.

Evaluating radon concentration and temporal correction factors in residential and workplace buildings: A comparison of passive and active methods

Effective mitigation of the health impacts of radon exposure begins with accurate measurement of this environmental contaminant. Typically, radon surveys require measurements over a period of several months. This process involves the application of temporal correction factors (TCF). Disparities in indoor radon concentration (IRC) are evident across building types. While the integrated technique has traditionally been considered the most reliable for measuring IRC, the active method is becoming more prevalent due to the availability of commercial radon measurement instruments. The aim of this study is to compare IRC using passive (CR-39) and active (ICA device) methods across 69 indoor spaces, including 35 workplaces and 34 residential buildings. The investigation was conducted over a span of one year and included 966 CR-39 detectors that were replaced every 3 and 6 months, respectively, to assess seasonal fluctuations and facilitate the computation of TCF. Statistically significant differences in IRC were observed between residential and workplace buildings (p < 0.001). Among workplaces, educational and research institutions showed the highest average IRC (166 Bq/m3), while hospitals exhibited the lowest (25 Bq/m3). Significant differences in TCF were found between the two measurement methods (p < 0.05), making TCF specific to the passive method inapplicable to active method. Moreover, distinctions between workplace and residential buildings, including the presence of air conditioning units and differing occupancy patterns, lead to substantial differences in both IRC (p < 0.001) and TCF. The assessment of radon exposure based on room occupancy duration revealed substantial variations: workplaces showed lower actual exposure (62 Bq/m3 vs. 75 Bq/m3, p < 0.001), while residential settings, particularly at night, displayed higher exposure (278 Bq/m3 vs. 245 Bq/m3, p = 0.02) than integrated measurements suggest. Continuous monitoring systems offer critical insights into true radon exposure levels.

Quantifying the diurnal variation in atmospheric NO2 from Geostationary Environment Monitoring Spectrometer (GEMS) observations

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation in several important trace gas and aerosol pollutants including nitrogen dioxide (NO2), which is the focus of this work. NO2 is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of NO2 tropospheric column diurnal variation which can be greater than 50 % of the column amount, especially in polluted environments. The NO2 distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low-Earth-orbit satellite observation. We use GEMS data in combination with TROPOspheric Monitoring Instrument (TROPOMI) satellite and Pandora ground-based remote sensing measurements and Multi-Scale Infrastructure for Chemistry and Aerosols (Version 0, MUSICAv0) 3D chemical transport model analysis to examine the NO2 diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be key to including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar NO2 diurnal cycles in the new Earth Venture Instrument-1 Tropospheric Emissions: Monitoring Pollution (TEMPO) data over North America, and later over Europe with Sentinel-4.

Exposure variables in veterinary epidemiology: are they telling us what we think they are?

This manuscript summarizes a presentation delivered by the first author at the 2024 symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to Dr. Jan Sargeant. Epidemiologic research plays a crucial role in understanding the complex relationships between exposures and health outcomes. However, the accuracy of the conclusions drawn from these investigations relies upon the meticulous selection and measurement of exposure variables. Appropriate exposure variable selection is crucial for understanding disease etiologies, but it is often the case that we are not able to directly measure the exposure variable of interest and use proxy measures to assess exposures instead. Inappropriate use of proxy measures can lead to erroneous conclusions being made about the true exposure of interest. These errors may lead to biased estimates of associations between exposures and outcomes. The consequences of such biases extend beyond research concerns as health decisions can be made based on flawed evidence. Recognizing and mitigating these biases are essential for producing reliable evidence that informs health policies and interventions, ultimately contributing to improved population health outcomes. To address these challenges, researchers must adopt rigorous methodologies for exposure variable selection and validation studies to minimize measurement errors.

Disinfection Byproducts in Drinking Water from the Tap: Variability in Household Calculated Additive Toxicity (CAT).

Recent studies have implemented a calculated additive toxicity (CAT) approach that sums measured disinfection byproduct (DBP) concentrations weighted by their respective in vitro bioassay potencies to estimate their associated risk in disinfected waters. In this study, the CAT approach was used to systematically investigate 21 regulated and unregulated DBPs measured in drinking water at the household level. Water samples from the tap were collected from over 120 randomly selected participants supplied by eight public water systems using four distinct source water types, two types of disinfection processes, and across two seasons. The purpose of this study was to compare CAT using multiple biological end points, examine household variability, identify DBPs driving toxicity, and assess if current regulated DBPs are adequate predictors of unregulated DBPs. Our results support the significance of unregulated DBPs, particularly haloacetonitriles and iodoacetic acid, as drivers of toxicity. Simple linear models between regulated versus unregulated concentrations and CAT were overall weak with 67% considered poor (r 2 < 0.3). These results reveal that current regulatory monitoring approaches may not be adequately capturing true household exposure due to higher contribution of unregulated DBPs to CAT and poor predictability between regulated and unregulated DBP-mediated CAT.

Pharmacogenetic Testing or Therapeutic Drug Monitoring: A Quantitative Framework.

Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]). Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV. Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred. For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.

Comparison of chemical contaminant measurements using CLAM, POCIS, and silicone band samplers in estuarine mesocosms.

Discrete water samples represent a snapshot of conditions at a particular moment in time and may not represent a true chemical exposure caused by changes in chemical input, tide, flow, and precipitation. Sampling technologies have been engineered to better estimate time-weighted concentrations. In this study, we consider the utility of three integrative sampling platforms: polar organic chemical integrative sampler (POCIS), silicone bands (SBs), and continuous, low-level aquatic monitoring (CLAM). This experiment used simulated southeastern salt marsh mesocosm systems to evaluate the response of passive (POCIS, SBs) and active sampling (CLAM) devices along with discrete sampling methodologies. Three systems were assigned to each passive sampler technology. Initially, all tanks were dosed at nominal (low) bifenthrin, pyrene, and triclosan concentrations of 0.02, 2.2, and 100 µg/L, respectively. After 28 days, the same treatment systems were dosed a second time (high) with bifenthrin, pyrene, and triclosan at 0.08, 8.8, and 200 µg/L, respectively. For passive samplers, estimated water concentrations were calculated using published or laboratory-derived sampling rate constants. Chemical residues measured from SBs resulted in high/low ratios of approximately 2x, approximately 3x, and 1x for bifenthrin, pyrene, and triclosan. A similar pattern was calculated using data from POCIS samples (~4x, ~3x, ~1x). Results from this study will help users of CLAM, POCIS, and SB data to better evaluate water concentrations from sampling events that are integrated across time. Integr Environ Assess Manag 2024;20:1384-1395. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Postmortem concentrations for total blood carbon monoxide (TBCO) as novel biomarker for carbon monoxide (CO) poisonings.

Total Blood Carbon Monoxide (TBCO) showed promising results in improving accuracy of CO determinations in blood and presenting better stability to different storage conditions. Therefore, it was proposed as alternative biomarker to carboxyhemoglobin (COHb) for CO poisoning diagnosis. However, given that current interpretation reference values exist for COHb only, it is difficult to implement TBCO analysis in routine. Therefore, we aimed at determining TBCO reference values for postmortem CO poisoning cases. A previously validated method for TBCO analysis via gas chromatography-mass spectrometry was applied to cardiac, peripheral, cranial and spleen blood samples collected from 92 autopsies. Autopsy cases included 21 non-CO related and 71 CO-related cases with varying postmortem intervals (PMI). Statistical analyses were performed using statistical software R Studio. When comparing lower to higher PMI for non-CO related cases, no significant differences were found, which suggests that CO formation or degradation at low PMIs does not occur. Spleen blood showed potential as alternative matrix for CO determinations in cases with sample availability issues, but needs to be evaluated for CO positive cases. Results for cardiac blood in CO-related autopsies showed a positive correlation between COHb and TBCO values (R = 0.78). This value is lower than what is found in the literature, suggesting that even though COHb and TBCO are correlated, a potential underestimation of the true CO exposure might occur if only COHb values are taken into consideration. Samples were divided into CO exposure groups based on COHb concentrations and with the data obtained, classification into following TBCO concentration groups are proposed: no significant CO exposure case <6 µmol/mL, medium CO exposure case 6-20 µmol/mL, high CO exposure case >20µmol/mL. Even if a higher number of samples in each group would enable to increase the confidence, these results are very promising and highlight the importance of TBCO measurement.

Research on Occupational Poisoning and Biological Monitoring

A new system for the regulation of chemical substances was introduced in Japan in April 2023. Unlike delineated rules, the new system does not specify any specific measures for individual substances, but is rather based on the management of health through the voluntary implementation of measures in order to reduce exposure to all dangerous/hazardous chemical substances. Regarding specific methods, exposures will be mainly elucidated through the measurement of a work environment. However, the necessity of biological monitoring should also be considered. This study aimed to identify the importance of biological monitoring by reviewing changes made over time in biological monitoring methods used for occupational poisoning. The study presents occupational poisoning by several compounds (methyl bromide, polycyclic aromatic hydrocarbons, and MOCA) and the corresponding biological monitoring methods utilized, as researched by the author. Changes in biological monitoring based on the history of the Study Group on Occupational Poisoning and Biological Monitoring of the Japan Society for Occupational Health are also introduced. The areas of occupational poisoning at different times and cases of occupational cancer caused by exposure to chemical substances were presented in lectures held by the Study Group on Occupational Poisoning and Biological Monitoring. These lectures showed that although biological monitoring was previously implemented primarily by measuring the urinary metabolites of the exposed substance, the monitoring methods used have changed as the nature of exposure has changed, leading to the development of new tools that detect trace, low concentration, and mixed exposures. The health management of workers handling chemical substances at occupational sites will be shifted to autonomous management. However, it should be noted that only biological monitoring can detect and prove true exposure. Particularly, risk assessment by biological monitoring is necessary for substances that are suspected to be absorbed through the skin, and the measurement methods used should continue to be developed and refined. The significance of biological monitoring will continue to increase.

Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure.

Regression calibration as developed by Rosner, Spiegelman, and Willett is used to adjust the bias in effect estimates due to measurement error in continuous exposures. The method involves two models: a measurement error model relating the mismeasured exposure to the true (or gold-standard) exposure and an outcome model relating the mismeasured exposure to the outcome. However, no comprehensive guidance exists for determining which covariates should be included in each model. In this article, we investigate the selection of the minimal and most efficient covariate adjustment sets under a causal inference framework. We show that to address the measurement error, researchers must adjust for, in both measurement error and outcome models, any common causes (1) of true exposure and the outcome and (2) of measurement error and the outcome. We also show that adjusting for so-called prognostic variables that are independent of true exposure and measurement error in the outcome model, may increase efficiency, while adjusting for any covariates that are associated only with true exposure generally results in efficiency loss in realistic settings. We apply the proposed covariate selection approach to the Health Professional Follow-up Study dataset to study the effect of fiber intake on cardiovascular disease. Finally, we extend the originally proposed estimators to a nonparametric setting where effect modification by covariates is allowed.

Design and evaluation of a noise dosimeter integrated into an ear-muff hearing protector

In impulse-noise environments, such as during weapons training, it can be difficult to characterize an individual’s true cumulative exposure, and consequently their risk of hearing loss. Direct measurements of sound pressure in the ear canal underneath hearing protection are ideal for assessing the protected noise exposure because the effects of distance, orientation, protection, etc., are automatically included in the measurement. However, these types of measurements are not commonly collected outside the laboratory. In this research, we develop and evaluate a portable a noise dosimeter that is integrated into a commonly used muff-based hearing protector (3M™ PELTOR™ ComTac™ V). The design of the device aims to simultaneously measure both the external and the under-the-muff sound pressures in a portable, wearable form-factor. First, we demonstrate that the modifications do not affect the hearing protector attenuation using an acoustic test fixture and a fit-check system across 10 refits (p = 0.14). Next, we show through testing with a shock-tube and acoustic test fixture that it is possible to obtain valid pressure measurements through this system. Finally, we report on initial field testing during weapons training. Future application of this technology can be used to develop damage risk criteria based on in-ear measurements that will provide for more accurate individualized risk assessment.

A search for factors associated with reduced carbohydrate intake and NTD risk in two population-based studies.

Two population-based case-control studies have reported an increased risk of neural tube defect (NTD)-affected pregnancies among women with low carbohydrate diet in the periconceptional period. Given that only two studies have investigated this association, it is unclear to what degree the findings could be impacted by residual confounding. Here, we further interrogated both studies that observed this association with the objective to identify factors from a much larger number of factors that might explain the association. By employing a machine learning algorithm (random forest), we investigated a baseline set of over 200 variables. These analyses produced the top 10 variables in each data set for cases and controls that predicted periconceptional low carbohydrate intake. Examining those prediction variables with logistic regression modeling, we did not observe any particular variable that substantially contributed to the NTD-low carbohydrate association in either data set. If there are underlying factors that explain the association, our findings suggest that none of the 200+ variables we examined were sufficiently correlated with what that true explanatory exposure may be. Alternatively, our findings may suggest that there are other unidentified factor(s) at play, or the association observed in two independent data sets is directly related to low carbohydrate intake.

Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status.

To improve user experience, recommender systems have been widely used on many online platforms. In these systems, recommendation models are typically learned from positive/negative feedback that are collected automatically. Notably, recommender systems are a little different from general supervised learning tasks. In recommender systems, there are some factors (e.g., previous recommendation models or operation strategies of a online platform) that determine which items can be exposed to each individual user. Normally, the previous exposure results are not only relevant to the instances' features (i.e., user or item), but also affect their feedback ratings, thus leading to confounding bias in the recommendation models. To mitigate this bias, researchers have already provided a variety of strategies. However, there are still two issues that are underappreciated: 1) previous debiased RS approaches cannot effectively capture recommendation-specific, exposure-specific and their common knowledge simultaneously; 2) the true exposure results of the user-item pairs are partially inaccessible, so there would be some noises if we use their observability to approximate it as existing approaches. Motivated by this, we develop a novel debiasing recommendation approach. More specifically, we first propose a mutual information-based counterfactual learning framework based on the causal relationship among the instance features, exposure status, and ratings. This framework can 1) capture recommendation-specific, exposure-specific and their common knowledge by explicitly modeling the relationship among the causal factors, and 2) achieve robustness towards partially inaccessible exposure results by a pairwise learning strategy. Under such a framework, we implement an optimizable loss function with theoretical analysis. By minimizing this loss, we expect to obtain an unbiased recommendation model that reflects the users' real interests. Meanwhile, we also prove that our loss function has robustness towards the partial inaccessibility of the exposure status. Finally, extensive experiments on public datasets manifest the superiority of our proposed method in boosting the recommendation performance.

Estimating the effect of latent time-varying count exposures using multiple lists.

A major challenge in longitudinal built-environment health studies is the accuracy of commercial business databases that are used to characterize dynamic food environments. Different databases often provide conflicting exposure measures on the same subject due to different source credibilities. As on-site verification is not feasible for historical data, we suggest combining multiple databases to correct the bias in health effect estimates due to measurement error in any 1 datasource. We propose a joint model for the time-varying health outcomes, observed count exposures, and latent true count exposures. Our model estimates the time-specific quality of sources and incorporates time dependence of true count exposure by Poisson integer-valued first-order autoregressive process. We take a Bayesian nonparametric approach to flexibly account for location-specific exposures. By resolving the discordance between different databases, our method reduces the bias in the longitudinal health effect of the true exposures. Our method is demonstrated with childhood obesity data in California public schools with respect to convenience store exposures in school neighborhoods from 2001 to 2008.

Generalized Linear Models with Covariate Measurement Error and Zero-Inflated Surrogates.

Epidemiological studies often encounter a challenge due to exposure measurement error when estimating an exposure-disease association. A surrogate variable may be available for the true unobserved exposure variable. However, zero-inflated data are encountered frequently in the surrogate variables. For example, many nutrient or physical activity measures may have a zero value (or a low detectable value) among a group of individuals. In this paper, we investigate regression analysis when the observed surrogates may have zero values among some individuals of the whole study cohort. A naive regression calibration without taking into account a probability mass of the surrogate variable at 0 (or a low detectable value) will be biased. We developed a regression calibration estimator which typically can have smaller biases than the naive regression calibration estimator. We propose an expected estimating equation estimator which is consistent under the zero-inflated surrogate regression model. Extensive simulations show that the proposed estimator performs well in terms of bias correction. These methods are applied to a physical activity intervention study.

A Comparison of Measured Airborne and Self-Reported Secondhand Smoke Exposure in the MADRES Pregnancy Cohort Study.

Secondhand smoke (SHS) exposure during pregnancy is linked to adverse birth outcomes, such as low birth weight and preterm birth. While questionnaires are commonly used to assess SHS exposure, their ability to capture true exposure can vary, making it difficult for researchers to harmonize SHS measures. This study aimed to compare self-reported SHS exposure with measurements of airborne SHS in personal samples of pregnant women. SHS was measured on 48-hour integrated personal PM2.5 Teflon filters collected from 204 pregnant women, and self-reported SHS exposure measures were obtained via questionnaires. Descriptive statistics were calculated for airborne SHS measures, and analysis of variance tests assessed group differences in airborne SHS concentrations by self-reported SHS exposure. Participants were 81% Hispanic, with a mean (standard deviation [SD]) age of 28.2 (6.0) years. Geometric mean (SD) personal airborne SHS concentrations were 0.14 (9.41) µg/m3. Participants reporting lower education have significantly higher airborne SHS exposure (p = .015). Mean airborne SHS concentrations were greater in those reporting longer duration with windows open in the home. There was no association between airborne SHS and self-reported SHS exposure; however, asking about the number of smokers nearby in the 48-hour monitoring period was most correlated with measured airborne SHS (Two + smokers: 0.30 µg/m3 vs. One: 0.12 µg/m3 and Zero: 0.15 µg/m3; p = .230). Self-reported SHS exposure was not associated with measured airborne SHS in personal PM2.5 samples. This suggests exposure misclassification using SHS questionnaires and the need for harmonized and validated questions to characterize this exposure in health studies. This study adds to the growing body of evidence that measurement error is a major concern in pregnancy research, particularly in studies that rely on self-report questionnaires to measure SHS exposure. The study introduces an alternative method of SHS exposure assessment using objective optical measurements, which can help improve the accuracy of exposure assessment. The findings emphasize the importance of using harmonized and validated SHS questionnaires in pregnancy health research to avoid biased effect estimates. This study can inform future research, practice, and policy development to reduce SHS exposure and its adverse health effects.

Core concepts in pharmacoepidemiology: Measurement of medication exposure in routinely collected healthcare data for causal inference studies in pharmacoepidemiology.

Observational designs can complement evidence from randomized controlled trials not only in situations when randomization is not feasible, but also by evaluating drug effects in real-world, considering a broader spectrum of users and clinical scenarios. However, use of such real-world scenarios captured in routinely collected clinical or administrative data also comes with specific challenges. Unlike in trials, medication use is not protocol based. Instead, exposure is determined by a multitude of factors involving patients, providers, healthcare access, and other policies. Accurate measurement of medication exposure relies on a similar broad set of factors which, if not understood and appropriately addressed, can lead to exposure misclassification and bias. To describe core considerations for measurement of medication exposure in routinely collected healthcare data. We describe the strengths and weaknesses of the two main types of routinely collected healthcare data (electronic health records and administrative claims) used in pharmacoepidemiologic research. We introduce key elements in those data sources and issues in the curation process that should be considered when developing exposure definitions. We present challenges in exposure measurement such as the appropriate determination of exposure time windows or the delineation of concomitant medication use versus switching of therapy, and related implications for bias. We note that true exposure patterns are typically unknown when using routinely collected healthcare data and that an in-depth understanding of healthcare delivery, patient and provider decision-making, data documentation and governance, as well as pharmacology are needed to ensure unbiased approaches to measuring exposure. Various assumptions are made with the goal that the chosen exposure definition can approximate true exposure. However, the possibility of exposure misclassification remains, and sensitivity analyses that can test the impact of such assumptions on the robustness of estimated medication effects are necessary to support causal inferences.

Risk perception or hazard perception? Examining misperceptions of miners' personal exposures to noise.

While perceptions of risk have been examined in the workplace to understand safety behavior, hazard perception has been overlooked, particularly for chemical, physical, and biological agents. This study sought to establish the prevalence of one type of mismatch in hazard perception, - noise misperception - among miners, to examine whether different types of noisy environments (e.g., continuous, highly variable, etc.) alter workers' misperception of their noise exposures, and to evaluate whether noise misperception is associated with hearing protection device (HPD) use behavior. In this cross-sectional study across 10 surface mines in the USA, 135 normal-hearing participants were surveyed on their perceptions of exposure to noise at work and were monitored for three shifts, each with personal noise dosimetry, to examine which workers had a mismatch in perceived versus true noise exposure by 8-hr, time-weighted average, NIOSH exposure limits (TWANIOSH). Mixed effects logistic regression and probit Bayesian Kernel Machine Regression (BKMR) models examining on the odds of noise misperception associated with four different noise metrics (kurtosis, crest factor, variability, and number of peaks >135dB) were used to determine which types of noisy environments may influence noise misperception. The relationship between noise misperception and odds of not wearing HPDs during a work shift was further examined. Our findings showed that nearly 1 in 3 workers underestimated their exposure to noise when their true exposure was in fact hazardous (TWANIOSH≥85 dBA) for at least one shift, and 6% misperceived hazardous exposures for all shifts. Work shifts with highly kurtotic noise distributions (>3) had 3.1 (95% CI: 1.1 to 8.4) times significantly higher odds of resulting in misperceived noise; no other noise metric was significantly associated with noise misperception. BKMR modeling provided further evidence that kurtosis dominates this relationship, with an IQR increase in kurtosis significantly associated with 1.68 (95% CI: 1.13 to 2.50) higher odds of noise misperception. Although not statistically significant, misperception of hazardous noise exposure was associated with 3.2 (95% CI: 0.8 to 12.5) times higher odds of not using earplugs during a work shift. Misperception of noise occurs in the workplace, and likely occurs for other physical, chemical, and biological exposures. This hazard misperception may influence risk perceptions and worker behavior and reduce the effectiveness of behavior-related training. Elimination, substitution, or engineering controls of exposures is the best way to prevent hazard misperceptions and exposure-related diseases.

Issues in Implementing Regression Calibration Analyses.

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.

The social patterning of vicarious discrimination: Implications for health equity

BackgroundMost research on discrimination and health operationalizes discrimination as direct individual experiences. Here, we examine the social patterning of vicarious discrimination, an important but largely overlooked dimension of discrimination. MethodsDrawing on community-based participatory research with a multi-stage probability sample (n = 178) of African Americans in Tallahassee, Florida, we measured vicarious discrimination, or exposure to discrimination through one's family and friends. We used chi-square tests to examine gender differences in the social domains and relational sources of vicarious discrimination. Negative binomial regression models were fit to identify predictors of exposure to vicarious discrimination. ResultsVicarious discrimination is more prevalent than direct experiences of discrimination (73 versus 61%) and more than 20% of participants report vicarious discrimination in the absence of direct discrimination. For women, vicarious discrimination most often involved the workplace; for men, police. However, gender differences are smaller for vicarious versus direct discrimination. Close friends and children were top relational sources of vicarious discrimination for men and women, respectively. Middle-aged participants reported the most vicarious discrimination. ConclusionsOverall, our data show that vicarious discrimination is more common than widely understood and associated with individual-level sociodemographic characteristics that index one's position in broader social systems. The prevalence of vicarious discrimination in the absence of direct discrimination suggests that standard approaches, which measure individual exposures in isolation, are subject to misclassification bias. Our results imply that existing research on discrimination and health, which already demonstrates substantial harm, underestimates African Americans' true exposures to salient aspects of discrimination.