Exposure Assessment In Epidemiology Research Articles

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- = 0.51 (with LCS). We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology.

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.

Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment.

Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces. We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model. The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts. Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

Field Performance of a Novel Passive Bioaerosol Sampler using Polarized Ferroelectric Polymer Films.

Passive bioaerosol samplers can improve environmental and health protection by enhancing the practicality and cost-effectiveness of air sampling. Here, we present the outdoor field testing of a novel, passive bioaerosol sampler, the Rutgers Electrostatic Passive Sampler (REPS), based on the use of polarized, ferroelectric polymer film (poly(vinylidene fluoride)). Four 10-day-long field campaigns were conducted to compare total (culturable + non-culturable) and culturable bioaerosol collection efficiencies of REPS to passive samplers (PTFE settling filters and agar settling plates). These collection efficiencies were calculated relative to performance of an active, reference Button Sampler. Compared to passive PTFE filters, which exclusively rely on gravitational particle deposition, REPS collected a 7-fold higher total microorganism quantity. Relative to the Button Sampler, REPS collected 25% of the total number of bacteria and fungi and 65% of the culturable bacteria. Furthermore, REPS achieved this performance without any air movers, pumps, batteries or external power. Since the Button Samplers operated at 4 L/min, REPS was calibrated to have equivalent sampling rates of 2.6 L/min and 1.0 L/min for culturable bacteria and total microorganisms, respectively. These results suggest that REPS can passively collect airborne microorganisms, including culturable bacteria, with high efficiency over long-term sampling durations. REPS can provide better preservation of bacterial culturability because it has no active airflow, which desiccates microbes in active samplers. Since there are limited options available for long-term, unattended bioaerosol sampling, REPS can complement currently available bioaerosol sampling technologies for numerous environmental health applications, such as exposure assessment for epidemiology and monitoring aeroallergen trends.

A hybrid land use regression/AERMOD model for predicting intra-urban variation in PM2.5

Characterizing near-source spatio-temporal variation is a long -standing challenge in air pollution epidemiology, and common intra-urban modeling approaches [e.g., land use regression (LUR)], do not account for short-term meteorological variation. Atmospheric dispersion modeling approaches, such as AERMOD, can account for near-source pollutant behavior by capturing source-meteorological interactions, but requires external validation and resolved background concentrations. In this study, we integrate AERMOD-based predictions for source-specific fine particle (PM2.5) concentrations into LUR models derived from total ambient PM2.5 measured at 36 unique sites selected to represent different source and elevation profiles, during summer and winter, 2012–2013 in Pittsburgh, Pennsylvania (PA). We modeled PM2.5 emissions from 207 local stationary sources in AERMOD, utilizing the monitoring locations as receptors, and hourly meteorological information matching each sampling period. Finally, we compare results of the integrated LUR/AERMOD hybrid model to those of the AERMOD + background and standard LUR models, at the full domain scale and within a 5 km2 sub-domain surrounding a large industrial facility. The hybrid model improved out-of-sample prediction accuracy by 2–10% over LUR alone, though performance differed by season, in part due to within-season temporal variability. We found differences up to 10 μg/m3 in predicted concentrations, and observed the largest differences within the industrial sub-domain. LUR underestimated concentrations from 500 to 2500 m downwind of major sources. The hybrid modeling approach we developed may help to improve intra-urban exposure estimates, particularly in regions of large industrial sources, sharp elevation gradients, or complex meteorology (e.g., frequent inversion events), such as Pittsburgh, PA. More broadly, the approach may inform the development of spatio-temporal modeling frameworks for air pollution exposure assessment for epidemiology.

Challenges in Dietary Exposure Assessment in Epidemiology: Research Trends.

Although exposure assessment of the usual diet is an essential component of nutrition epidemiology, it remains one of the most challenging issues in the field. Dietary exposure is widely measured using Food Frequency Questionnaires (FFQs), which impose a low burden on respondents and are inexpensive in large-scale studies. FFQs have long satisfied the needs of epidemiological research, and have helped deliver the tremendous recent growth in knowledge of the diet-disease association. However, issues surrounding measurement errors with FFQs have attracted substantial research interest around the world. Attenuation of the diet-disease association due to measurement errors identified in Western populations has led to extended methodological investigations comparing the performance of FFQs with biomarkers. The need for better dietary assessment methods has increased. Dietary records or recall provide relatively accurate estimates of intake for specific days, and of the usual diet if collected on multiple days. Until recently, however, their use in large-scale studies was not feasible, mainly due to cost. One innovative tool which may overcome the limitations of dietary records or recall is computerized 24-h dietary recall systems. These systems have been demonstrated to provide high-quality dietary intake data among Western populations. Incorporation of such new technology into large-scale epidemiological studies would make multiple-day administration of 24-h recall feasible in terms of cost. Research efforts to improve dietary assessment among Japanese and Asian populations are still under development. The development of innovative methods for Japanese remains an urgent research challenge.

Evaluation of a spatially resolved forest fire smoke model for population-based epidemiologic exposure assessment.

Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory. Findings for cardiovascular effects have been inconsistent, possibly related to the limitations of conventional methods to assess FFS exposure. In previous work, we developed an empirical model to estimate smoke-related fine particulate matter (PM2.5) for all populated areas in British Columbia (BC), Canada. Here, we evaluate the utility of our model by comparing epidemiologic associations between modeled and measured PM2.5. For each local health area (LHA), we used Poisson regression to estimate the effects of PM2.5 estimates and measurements on counts of medication dispensations and outpatient physician visits. We then used meta-regression to estimate the overall effects. A 10 μg/m3 increase in modeled PM2.5 was associated with increased sabutamol dispensations (RR=1.04, 95% CI 1.03–1.06), and physician visits for asthma (1.06, 1.04–1.08), COPD (1.02, 1.00–1.03), lower respiratory infections (1.03, 1.00–1.05), and otitis media (1.05, 1.03–1.07), all comparable to measured PM2.5. Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days. This suggests that the exposure model is a promising tool for increasing the power of epidemiologic studies to detect the health effects of FFS via improved spatial coverage and resolution.

0249 Determinants of occupational hygiene expert judgment accuracy

ObjectivesA cross sectional study was performed to determine the applicability and accuracy of expert judgment in occupational exposure assessment. The roles of educational session and determining factors were also realised.MethodThirteen...

Combining information from two data sources with misreporting and incompleteness to assess hospice-use among cancer patients: a multiple imputation approach.

Combining information from multiple data sources can enhance estimates of health-related measures by using one source to supply information that is lacking in another, assuming the former has accurate and complete data. However, there is little research conducted on combining methods when each source might be imperfect, for example, subject to measurement errors and/or missing data. In a multisite study of hospice-use by late-stage cancer patients, this variable was available from patients' abstracted medical records, which may be considerably underreported because of incomplete acquisition of these records. Therefore, data for Medicare-eligible patients were supplemented with their Medicare claims that contained information on hospice-use, which may also be subject to underreporting yet to a lesser degree. In addition, both sources suffered from missing data because of unit nonresponse from medical record abstraction and sample undercoverage for Medicare claims. We treat the true hospice-use status from these patients as a latent variable and propose to multiply impute it using information from both data sources, borrowing the strength from each. We characterize the complete-data model as a product of an 'outcome' model for the probability of hospice-use and a 'reporting' model for the probability of underreporting from both sources, adjusting for other covariates. Assuming the reports of hospice-use from both sources are missing at random and the underreporting are conditionally independent, we develop a Bayesian multiple imputation algorithm and conduct multiple imputation analyses of patient hospice-use in demographic and clinical subgroups. The proposed approach yields more sensible results than alternative methods in our example. Our model is also related to dual system estimation in population censuses and dual exposure assessment in epidemiology.

Simulation of NO2 commute exposure – Investigation of its contribution to total exposure and comparison of air pollution models

Background: Little is known about the commute exposure to traffic-related air pollution of a general population and its contribution to total daily exposure. Aims: Our aim is to estimate population based NO2 exposure during commute in Basel, Switzerland, using detailed information on commute behavior and commute route and three air pollution models of varying spatial resolutions. Methods: Firstly, trips between home and work places are simulated by means of a Geographic Information System model using extensive time-activity information from the Swiss 2010 Microcensus database, which provides temporally and spatially resolved data on travel behavior. Secondly, exposure to air pollution is estimated by combining the commute patterns and home and work locations with estimates from air pollution models. Thirdly, the contribution of time-weighted commute exposure to annual NO2 estimates is assessed. Simulating variations of commute patterns (e.g. avoiding main roads) allows us to test scenarios of change in commute behavior on exposure distribution. Finally, three air pollution models (land use regression model, a high and low resolution dispersion model) are evaluated for estimating commute and total NO2 exposures as a marker of traffic-related air pollution in epidemiological exposure assessment studies. Results: The main mode of travel to work in Basel was public transport (52%), followed by car (18%) and bike (17%).Within the Basel area, the average daily time spent in transit between home and work locations was 52 ±37min. Our first results reveal significantly higher average NO2 concentrations during commute (28.2µg/m³, range: 11.6-54.5µg/m³) than at home outdoor locations (25.5µg/m³, range: 11.8-43.4µg/m³). Conclusions: The results will be further examined and contribution of commute to total daily exposure will be quantified. The study will contribute to a better understanding of population commute exposure to NO2 and its relevance in long-term epidemiological exposure assessments.

A comparison of two methods for ecologic classification of radon exposure in British Columbia: residential observations and the radon potential map of Canada

OBJECTIVE: To compare ecologic classification of radon exposure from observed residential concentrations in BC with classifications based on a map that shows geological radon potential, with particular attention to high-smoking populations. METHODS: First, residential radon measurements from four health agencies were used to classify 74 local health areas (LHAs) as low, moderate, or high exposure based on the number of homes with concentrations greater than 200 and 600 Bq/m3. Second, the Zone 1 (high), Zone 2 (elevated), and Zone 3 (guarded) risk categories of the radon potential map of Canada were used to make the same exposure classifications based on the population-weighted area of each zone in each LHA. Agreement was compared and quantified. Average smoking rates in each LHA were used to further assess agreement for smokers, who are a high-risk group. RESULTS: Both methods showed a range of exposure across LHAs. The radon potential map classified more areas as high exposure than the observed radon concentrations, and the methods agreed in 30 of 74 LHAs. The radon potential map identified much of the southern coastal region as high exposure, but 617 of the 621 observed concentrations were £200 Bq/m3, and no observations were >600 Bq/m3. An estimated 36% of the BC population and 35% of BC smokers live in the southern coastal region. CONCLUSIONS: The radon potential map of Canada may communicate potential radon risk, but it was not designed for epidemiologic exposure assessment. Overall, the potential map classified 34 LHAs as higher than observed, and 10 LHAs as lower than observed. The potential map should only be used to inform exposure assessment in conjunction with observed radon concentrations.

A Comparison of Two Methods for Ecologic Classification of Radon Exposure in British Columbia: Residential Observations and the Radon Potential Map of Canada

To compare ecologic classification of radon exposure from observed residential concentrations in BC with classifications based on a map that shows geological radon potential, with particular attention to high-smoking populations. First, residential radon measurements from four health agencies were used to classify 74 local health areas (LHAs) as low, moderate, or high exposure based on the number of homes with concentrations greater than 200 and 600 Bq/m³. Second, the Zone 1 (high), Zone 2 (elevated), and Zone 3 (guarded) risk categories of the radon potential map of Canada were used to make the same exposure classifications based on the population-weighted area of each zone in each LHA. Agreement was compared and quantified. Average smoking rates in each LHA were used to further assess agreement for smokers, who are a high-risk group. Both methods showed a range of exposure across LHAs. The radon potential map classified more areas as high exposure than the observed radon concentrations, and the methods agreed in 30 of 74 LHAs. The radon potential map identified much of the southern coastal region as high exposure, but 617 of the 621 observed concentrations were ≤200 Bq/m³, and no observations were >600 Bq/m³. An estimated 36% of the BC population and 35% of BC smokers live in the southern coastal region. The radon potential map of Canada may communicate potential radon risk, but it was not designed for epidemiologic exposure assessment. Overall, the potential map classified 34 LHAs as higher than observed, and 10 LHAs as lower than observed. The potential map should only be used to inform exposure assessment in conjunction with observed radon concentrations.

An assessment of air pollution and its attributable mortality in Ulaanbaatar, Mongolia

Epidemiologic studies have consistently reported associations between outdoor fine particulate matter (PM2.5) air pollution and adverse health effects. Although Asia bears the majority of the public health burden from air pollution, few epidemiologic studies have been conducted outside of North America and Europe due in part to challenges in population exposure assessment. We assessed the feasibility of two current exposure assessment techniques, land use regression (LUR) modeling and mobile monitoring, and estimated the mortality attributable to air pollution in Ulaanbaatar, Mongolia. We developed LUR models for predicting wintertime spatial patterns of NO2 and SO2 based on 2-week passive Ogawa measurements at 37 locations and freely available geographic predictors. The models explained 74% and 78% of the variance in NO2 and SO2, respectively. Land cover characteristics derived from satellite images were useful predictors of both pollutants. Mobile PM2.5 monitoring with an integrating nephelometer also showed promise, capturing substantial spatial variation in PM2.5 concentrations. The spatial patterns in SO2 and PM, seasonal and diurnal patterns in PM2.5, and high wintertime PM2.5/PM10 ratios were consistent with a major impact from coal and wood combustion in the city’s low-income traditional housing (ger) areas. The annual average concentration of PM2.5 measured at a centrally located government monitoring site was 75 μg/m3 or more than seven times the World Health Organization’s PM2.5 air quality guideline, driven by a wintertime average concentration of 148 μg/m3. PM2.5 concentrations measured in a traditional housing area were higher, with a wintertime mean PM2.5 concentration of 250 μg/m3. We conservatively estimated that 29% (95% CI, 12–43%) of cardiopulmonary deaths and 40% (95% CI, 17–56%) of lung cancer deaths in the city are attributable to outdoor air pollution. These deaths correspond to nearly 10% of the city’s total mortality, with estimates ranging to more than 13% of mortality under less conservative model assumptions. LUR models and mobile monitoring can be successfully implemented in developing country cities, thus cost-effectively improving exposure assessment for epidemiology and risk assessment. Air pollution represents a major threat to public health in Ulaanbaatar, Mongolia, and reducing home heating emissions in traditional housing areas should be the primary focus of air pollution control efforts.

The influence of sociodemographic characteristics on agreement between self-reports and expert exposure assessments

Often in exposure assessment for epidemiology, there are no highly accurate exposure data and different measurement methods are considered. The objective of this study was to use various statistical techniques to explore agreement between individual reports and expert ratings of workplace exposures in several industries and investigate the sociodemographic influences on this agreement. A cohort of 1,282 employees at 4 industries/14 worksites answered questions on workplace physical, chemical, and psychosocial exposures over the past 12 months. Occupational hygienists constructed job exposure matrices (JEMs) based on worksite walkthrough exposure evaluations. Worker self-reports were compared with the JEMs using multivariable analyses to explore discord. There was poor agreement between the self-reported and expert exposure assessments, but there was evidence that agreement was modified by sociodemographic characteristics. Several characteristics including gender, age, race/ethnicity, hourly wage and nativity strongly affected the degree of discord between self-reports and expert raters across a wide array of different exposures. Agreement between exposure assessment tools may be affected by sociodemographic characteristics. This study is cross-sectional and therefore, a snapshot of potential exposures in the workplace. Nevertheless, future studies should take into account the social contexts within which workplace exposures occur.

Measured radiofrequency exposure during various mobile-phone use scenarios

Epidemiologic studies of mobile phone users have relied on self reporting or billing records to assess exposure. Herein, we report quantitative measurements of mobile-phone power output as a function of phone technology, environmental terrain, and handset design. Radiofrequency (RF) output data were collected using software-modified phones that recorded power control settings, coupled with a mobile system that recorded and analyzed RF fields measured in a phantom head placed in a vehicle. Data collected from three distinct routes (urban, suburban, and rural) were summarized as averages of peak levels and overall averages of RF power output, and were analyzed using analysis of variance methods. Technology was the strongest predictor of RF power output. The older analog technology produced the highest RF levels, whereas CDMA had the lowest, with GSM and TDMA showing similar intermediate levels. We observed generally higher RF power output in rural areas. There was good correlation between average power control settings in the software-modified phones and power measurements in the phantoms. Our findings suggest that phone technology, and to a lesser extent, degree of urbanization, are the two stronger influences on RF power output. Software-modified phones should be useful for improving epidemiologic exposure assessment.

A model for radiofrequency electromagnetic field predictions at outdoor and indoor locations in the context of epidemiological research

We present a geospatial model to predict the radiofrequency electromagnetic field from fixed site transmitters for use in epidemiological exposure assessment. The proposed model extends an existing model toward the prediction of indoor exposure, that is, at the homes of potential study participants. The model is based on accurate operation parameters of all stationary transmitters of mobile communication base stations, and radio broadcast and television transmitters for an extended urban and suburban region in the Basel area (Switzerland). The model was evaluated by calculating Spearman rank correlations and weighted Cohen's kappa (kappa) statistics between the model predictions and measurements obtained at street level, in the homes of volunteers, and in front of the windows of these homes. The correlation coefficients of the numerical predictions with street level measurements were 0.64, with indoor measurements 0.66, and with window measurements 0.67. The kappa coefficients were 0.48 (95%-confidence interval: 0.35-0.61) for street level measurements, 0.44 (95%-CI: 0.32-0.57) for indoor measurements, and 0.53 (95%-CI: 0.42-0.65) for window measurements. Although the modeling of shielding effects by walls and roofs requires considerable simplifications of a complex environment, we found a comparable accuracy of the model for indoor and outdoor points.

Human Exposure to Selected Animal Neurocarcinogens: A Biomarker-Based Assessment and Implications for Brain Tumor Epidemiology

This review is based on the proceedings from the Second Lebow Conference, held in Chicago in 2007. The conference concentrated on developing a framework for innovative studies in the epidemiology of environmental exposures, focusing specifically on the potential relationship with brain tumors. Researchers with different perspectives, including toxicology, pharmacokinetics, and epidemiological exposure assessment, exchanged information and ideas on the use of biomarkers of exposure in molecular epidemiology studies and summarized the current knowledge on methods and approaches for biomarker-based exposure assessment. This report presents the state of science regarding biomarker-based exposure assessment of the four most common neurocarcinogens: acrylamide, 1,3-butadiene, N-nitroso compounds, and polycyclic aromatic hydrocarbons. Importantly, these chemicals are also carcinogenic in other organs; therefore, this discussion is useful for environmental epidemiologists studying all cancer types.

Statistical Modelling of Formaldehyde Occupational Exposure Levels in French Industries, 1986–2003

Occupational exposure databanks (OEDBs) have been cited as sources of exposure data for exposure surveillance and exposure assessment in epidemiology. In 2003, an extract was made from COLCHIC, the French national OEDB, of all concentrations of formaldehyde. The data were analysed with extended linear mixed-effects models in order to identify influent variables and elaborate a multi-sector picture of formaldehyde exposures. Respectively, 1401 and 1448 personal and area concentrations were available for the analysis. The fixed effects of the personal and area models explained, respectively, 57 and 53% of the total variance. Personal concentrations were related to the sampling duration (short-term higher than TWA levels), decreased with the year of sampling (-9% per year) and were higher when local exhaust ventilation was present. Personal levels taken during planned visits and for occupational illness notification purpose were consistently lower than those taken during ventilation modification programmes or because the hygienist suspected the presence of significant risk or exposure. Area concentrations were related to the sampling duration (short-term higher than TWA levels), and decreased with the year of sampling (-7% per year) and when the measurement sampling flow increased. Significant within-facility (correlation coefficient 0.4-0.5) and within-sampling campaign correlation (correlation coefficient 0.8) was found for both area and personal data. The industry/task classification appeared to have the greatest influence on exposure variability while the sample duration and the sampling flow were significant in some cases. Estimates made from the models for year 2002 showed elevated formaldehyde exposure in the fields of anatomopathological and biological analyses, operation of gluing machinery in the wood industry, operation and monitoring of mixers in the pharmaceutical industry, and garages and warehouses in urban transit authorities.

Mechanical exposure concepts using force as the agent.

This paper presents a model that addresses mechanical exposure with regard to the development of musculoskeletal disorders, defines exposure concepts, unifies a variety of exposures, and includes the concept of human activity. When force is used as an agent, concepts related to the measurement, transformation, and interaction of the agent with tissues can be developed for use in epidemiologic exposure assessment and hazard assessment. The importance of tissue response in the exposure modeling process and in the creation of exposure indices is highlighted. Unfortunately, the response of tissue to forces of varying amplitudes and time variation patterns are largely unknown and thus reduce the possibility to develop optimal exposure assessment metrics. Although the paper argues that an exposure index at the tissue level may be the most powerful, considerations of resources and current knowledge make exposure indices based on external exposure or internal exposure preferable choices.

Accuracy of task recall for epidemiological exposure assessment to construction noise

Aims: To validate the accuracy of construction worker recall of task and environment based information; and to evaluate the effect of task recall on estimates of noise exposure. Methods: A...