Thomas McCurdy

Frequency of mouthing behavior in young children

Young children may be more likely than adults to be exposed to pesticides following a residential application as a result of hand- and object-to-mouth contacts in contaminated areas. However, relatively few studies have specifically evaluated mouthing behavior in children less than 5 years of age. Previously unpublished data collected by the Fred Hutchinson Cancer Research Center (FHCRC) were analyzed to assess the mouthing behavior of 72 children (37 males/35 females). Total mouthing behavior data included the daily frequency of both mouth and tongue contacts with hands, other body parts, surfaces, natural objects, and toys. Eating events were excluded. Children ranged in age from 11 to 60 months. Observations for more than 1 day were available for 78% of the children. The total data set was disaggregated by gender into five age groups (10–20, 20–30, 30–40, 40–50, 50–60 months). Statistical analyses of the data were then undertaken to determine if significant differences existed among the age/gender subgroups in the sample. A mixed effects linear model was used to test the associations among age, gender, and mouthing frequencies. Subjects were treated as random and independent, and intrasubject variability was accounted for with an autocorrelation function. Results indicated that there was no association between mouthing frequency and gender. However, a clear relationship was observed between mouthing frequency and age. Using a tree analysis, two distinct groups could be identified: children ≤24 and children >24 months of age. Children ≤24 months exhibited the highest frequency of mouthing behavior with 81±7 events/h (mean±SE) (n=28 subjects, 69 observations). Children >24 months exhibited the lowest frequency of mouthing behavior with 42±4 events/h (n=44 subjects, 117 observations). These results suggest that children are less likely to place objects into their mouths as they age. These changes in mouthing behavior as a child ages should be accounted for when assessing aggregate exposure to pesticides in the residential environment.

The national exposure research laboratory's consolidated human activity database.

EPAs National Exposure Research Laboratory (NERL) has combined data from 12 U.S. studies related to human activities into one comprehensive data system that can be accessed via the Internet. The data system is called the Consolidated Human Activity Database (CHAD) and is available at E-mail: http://www.epa.gov/nerl/. CHAD contains 22,968 person days of activity and is designed to assist exposure assessors and modelers in constructing population “cohorts” of people with specified characteristics that are suitable for subsequent analysis or modeling. This paper describes the studies comprising CHAD and the various intellectual foundations that underlay the gathering of human activity pattern data. Next, it provides a brief overview of the Internet version of CHAD, and discusses how the program was formulated. Emphasis is placed on how activity-specific energy expenditure estimates were developed. Finally, the paper recommends steps that should be taken to improve the collection of activity data that would improve energy expenditure estimates and related information needed for physiologically based exposure–dose modeling efforts.

Using human activity data in exposure models: Analysis of discriminating factors

This paper tests factors thought to be important in explaining the choices people make in where they spend time. Three aggregate locations are analyzed: outdoors, indoors, and in-vehicles for two different sample groups: a year-long (longitudinal) sample of one individual and a cross-sectional sample of 169 individuals from the US Environmental Protection Agencys Consolidated Human Activity Database (CHAD). The cross-sectional sample consists of persons similar to the longitudinal subject in terms of age, work status, education, and residential type. The sample groups are remarkably similar in the time spent per day in the tested locations, although there are differences in participation rates: the percentage of days frequenting a particular location. Time spent outdoors exhibits the most relative variability of any location tested, with in-vehicle time being the next. The factors found to be most important in explaining daily time usage in both sample groups are: season of the year, season/temperature combinations, precipitation levels, and day-type (work/nonwork is the most distinct, but weekday/weekend is also significant). Season, season/temperature, and day-type are also important for explaining time spent indoors. None of the variables tested are consistent in explaining in-vehicle time in either the cross-sectional or longitudinal samples. Given these findings, we recommend that exposure modelers subdivide their population activity data into at least season/temperature, precipitation, and day-type “cohorts” as these factors are important discriminating variables affecting where people spend their time.

A source-to-dose assessment of population exposures to fine PM and ozone in Philadelphia, PA, during a summer 1999 episode

A novel source-to-dose modeling study of population exposures to fine particulate matter (PM2.5) and ozone (O3) was conducted for urban Philadelphia. The study focused on a 2-week episode, 11–24 July 1999, and employed the new integrated and mechanistically consistent source-to-dose modeling framework of MENTOR/SHEDS (Modeling Environment for Total Risk studies/Stochastic Human Exposure and Dose Simulation). The MENTOR/SHEDS application presented here consists of four components involved in estimating population exposure/dose: (1) calculation of ambient outdoor concentrations using emission-based photochemical modeling, (2) spatiotemporal interpolation for developing census-tract level outdoor concentration fields, (3) calculation of microenvironmental concentrations that match activity patterns of the individuals in the population of each census tract in the study area, and (4) population-based dosimetry modeling. It was found that the 50th percentiles of calculated microenvironmental concentrations of PM2.5 and O3 were significantly correlated with census-tract level outdoor concentrations, respectively. However, while the 95th percentiles of O3 microenvironmental concentrations were strongly correlated with outdoor concentrations, this was not the case for PM2.5. By further examining the modeled estimates of the 24-h aggregated PM2.5 and O3 doses, it was found that indoor PM2.5 sources dominated the contributions to the total PM2.5 doses for the upper 5 percentiles, Environmental Tobacco Smoking (ETS) being the most significant source while O3 doses due to time spent outdoors dominated the contributions to the total O3 doses for the upper 5 percentiles. The MENTOR/SHEDS system presented in this study is capable of estimating intake dose based on activity level and inhalation rate, thus completing the source-to-dose modeling sequence. The MENTOR/SHEDS system also utilizes a consistent basis of source characterization, exposure factors, and human activity patterns in conducting population exposure assessment of multiple co-occurring air pollutants, and this constitutes a primary distinction from previous studies of population exposure assessment, where different exposure factors and activity patterns would be used for different pollutants. Future work will focus on incorporating the effects of commuting patterns on population exposure/dose assessments as well as on extending the MENTOR/SHEDS applications to seasonal/annual studies and to other areas in the U.S.

Developing meaningful cohorts for human exposure models

This paper summarizes numerous statistical analyses focused on the US Environmental Protection Agencys Consolidated Human Activity Database (CHAD), used by many exposure modelers as the basis for data on what people do and where they spend their time. In doing so, modelers tend to divide the total population being analyzed into “cohorts”, to reduce extraneous interindividual variability by focusing on people with common characteristics. Age and gender are typically used as the primary cohort-defining attributes, but more complex exposure models also use weather, day-of-the-week, and employment attributes for this purpose. We analyzed all of these attributes and others to determine if statistically significant differences exist among them to warrant their being used to define distinct cohort groups. We focused our attention mostly on the relationship between cohort attributes and the time spent outdoors, indoors, and in motor vehicles. Our results indicate that besides age and gender, other important attributes for defining cohorts are the physical activity level of individuals, weather factors such as daily maximum temperature in combination with months of the year, and combined weekday/weekend with employment status. Less important are precipitation and ethnic data. While statistically significant, the collective set of attributes does not explain a large amount of variance in outdoor, indoor, or in-vehicle locational decisions. Based on other research, parameters such as lifestyle and life stages that are absent from CHAD might have reduced the amount of unexplained variance. At this time, we recommend that exposure modelers use age and gender as “first-order” attributes to define cohorts followed by physical activity level, daily maximum temperature or other suitable weather parameters, and day type possibly beyond a simple weekday/weekend classification.

Concentrations and phase distributions of nitrated and oxygenated polycyclic aromatic hydrocarbons in ambient air

Abstract The concentrations of nitrated and oxygenated polycyclic aromatic hydrocarbons (PAH) in ambient air, both in the vapor phase and adsorbed on airborne particles, were measured over a 12-month period in Houston, Texas. Seasonal variations in the levels of the target compounds were weakly related to changes in ambient temperature, but more strongly related to fluctuations in the levels of ozone (O3), nitrogen dioxide (NO2), and other oxides of nitrogen (NOx). Phase distributions of the target compounds were determined by the denuder difference method. These phase distributions varied greatly over 12 months and were related to the molecular sizes, hence vapor pressures, of the compounds, but few significant associations were found between the percentages of compounds present in the vapor phase and ambient temperatures.

Understanding variability in time spent in selected locations for 7–12-year old children

This paper summarizes a series of analyses of clustered, sequential activity/location data collected by Harvard University for 160 children aged 7–12 years in Southern California (Geyh et al., 2000). The main purpose of the paper is to understand intra- and inter-variability in the time spent by the sample in the outdoor location, the location exhibiting the most variability of the ones evaluated. The data were analyzed using distribution-free hypothesis-testing (K–S tests of the distributions), generalized linear modeling techniques, and random-sampling schemes that produced “cohorts” whose descriptive statistical characteristics were evaluated against the original dataset. Most importantly, our analyses indicate that subdividing the population into appropriate cohorts better replicates parameters of the original data, including the interclass correlation coefficient (ICC), which is a relative measure of the intra- and inter-individual variability inherent in the original data. While the findings of our analyses are consistent with previous assessments of “time budget” and physical activity data, they are constrained by the rather homogeneous sample available to us. Owing to a general lack of longitudinal human activity/location data available for other age/gender cohorts, we are unable to generalize our findings to other population subgroups.

Biologically based modeling of multimedia, multipathway, multiroute population exposures to arsenic

This article presents an integrated, biologically based, source-to-dose assessment framework for modeling multimedia/multipathway/multiroute exposures to arsenic. Case studies demonstrating this framework are presented for three US counties (Hunderton County, NJ; Pima County, AZ; and Franklin County, OH), representing substantially different conditions of exposure. The approach taken utilizes the Modeling ENvironment for TOtal Risk studies (MENTOR) in an implementation that incorporates and extends the approach pioneered by Stochastic Human Exposure and Dose Simulation (SHEDS), in conjunction with a number of available databases, including NATA, NHEXAS, CSFII, and CHAD, and extends modeling techniques that have been developed in recent years. Model results indicate that, in most cases, the food intake pathway is the dominant contributor to total exposure and dose to arsenic. Model predictions are evaluated qualitatively by comparing distributions of predicted total arsenic amounts in urine with those derived using biomarker measurements from the NHEXAS — Region V study: the population distributions of urinary total arsenic levels calculated through MENTOR and from the NHEXAS measurements are in general qualitative agreement. Observed differences are due to various factors, such as interindividual variation in arsenic metabolism in humans, that are not fully accounted for in the current model implementation but can be incorporated in the future, in the open framework of MENTOR. The present study demonstrates that integrated source-to-dose modeling for arsenic can not only provide estimates of the relative contributions of multipathway exposure routes to the total exposure estimates, but can also estimate internal target tissue doses for speciated organic and inorganic arsenic, which can eventually be used to improve evaluation of health risks associated with exposures to arsenic from multiple sources, routes, and pathways.

Examining variations of resting metabolic rate of adults: a public health perspective.

PURPOSE There has not been a recent comprehensive effort to examine existing studies on the resting metabolic rate (RMR) of adults to identify the effect of common population demographic and anthropometric characteristics. Thus, we reviewed the literature on RMR (kcal·kg(-1)·h(-1)) to determine the relationship of age, sex, and obesity status to RMR as compared with the commonly accepted value for the metabolic equivalent (MET; e.g., 1.0 kcal·kg(-1)·h(-1)). METHODS Using several databases, scientific articles published from 1980 to 2011 were identified that measured RMR, and from those, others dating back to 1920 were identified. One hundred and ninety-seven studies were identified, resulting in 397 publication estimates of RMR that could represent a population subgroup. Inverse variance weighting technique was applied to compute means and 95% confidence intervals (CI). RESULTS The mean value for RMR was 0.863 kcal·kg(-1)·h(-1) (95% CI = 0.852-0.874), higher for men than women, decreasing with increasing age, and less in overweight than normal weight adults. Regardless of sex, adults with BMI ≥ 30 kg·m(-2) had the lowest RMR (<0.741 kcal·kg(-1)·h(-1)). CONCLUSIONS No single value for RMR is appropriate for all adults. Adhering to the nearly universally accepted MET convention may lead to the overestimation of the RMR of approximately 10% for men and almost 15% for women and be as high as 20%-30% for some demographic and anthropometric combinations. These large errors raise questions about the longstanding adherence to the conventional MET value for RMR. Failure to recognize this discrepancy may result in important miscalculations of energy expended from interventions using physical activity for diabetes and other chronic disease prevention efforts.

A new method of longitudinal diary assembly for human exposure modeling.

Human exposure time-series modeling requires longitudinal time–activity diaries to evaluate the sequence of concentrations encountered, and hence, pollutant exposure for the simulated individuals. However, most of the available data on human activities are from cross-sectional surveys that typically sample 1 day per person. A procedure is needed for combining cross-sectional activity data into multiple-day (longitudinal) sequences that can capture day-to-day variability in human exposures. Properly accounting for intra- and interindividual variability in these sequences can have a significant effect on exposure estimates and on the resulting health risk assessments. This paper describes a new method of developing such longitudinal sequences, based on ranking 1-day activity diaries with respect to a user-chosen key variable. Two statistics, “D” and “A”, are targeted. The D statistic reflects the relative importance of within- and between-person variance with respect to the key variable. The A statistic quantifies the day-to-day (lag-one) autocorrelation. The user selects appropriate target values for both D and A. The new method then stochastically assembles longitudinal diaries that collectively meet these targets. On the basis of numerous simulations, the D and A targets are closely attained for exposure analysis periods >30 days in duration, and reasonably well for shorter simulation periods. Longitudinal diary data from a field study suggest that D and A are stable over time, and perhaps over cohorts as well. The new method can be used with any cohort definitions and diary pool assignments, making it easily adaptable to most exposure models. Implementation of the new method in its basic form is described, and various extensions beyond the basic form are discussed.