Robert A. Canales

Relative Pesticide and Exposure Route Contribution to Aggregate and Cumulative Dose in Young Farmworker Children

The Child-Specific Aggregate Cumulative Human Exposure and Dose (CACHED) framework integrates micro-level activity time series with mechanistic exposure equations, environmental concentration distributions, and physiologically-based pharmacokinetic components to estimate exposure for multiple routes and chemicals. CACHED was utilized to quantify cumulative and aggregate exposure and dose estimates for a population of young farmworker children and to evaluate the model for chlorpyrifos and diazinon. Micro-activities of farmworker children collected concurrently with residential measurements of pesticides were used in the CACHED framework to simulate 115,000 exposure scenarios and quantify cumulative and aggregate exposure and dose estimates. Modeled metabolite urine concentrations were not statistically different than concentrations measured in the urine of children, indicating that CACHED can provide realistic biomarker estimates. Analysis of the relative contribution of exposure route and pesticide indicates that in general, chlorpyrifos non-dietary ingestion exposure accounts for the largest dose, confirming the importance of the micro-activity approach. The risk metrics computed from the 115,000 simulations, indicate that greater than 95% of these scenarios might pose a risk to children’s health from aggregate chlorpyrifos exposure. The variability observed in the route and pesticide contributions to urine biomarker levels demonstrate the importance of accounting for aggregate and cumulative exposure in establishing pesticide residue tolerances in food.

Quantified outdoor micro-activity data for children aged 7-12-years old

Estimation of aggregate exposure and risk requires detailed information regarding dermal contact and mouthing activity. We analyzed micro-level activity time series (MLATS) of children aged 7–12 years to quantify these contact behaviors and evaluate differences by age and gender. In all, 18 children, aged 7–12 years, were videotaped while playing outdoors. Video footage was transcribed via Virtual Timing Device (VTD) software. We calculated the hand and mouth contact frequency, hourly duration and median duration of contact with 16 object categories. Median mouthing frequencies were 12.6 events/h and 2.6 events/h for hands and non-dietary objects, respectively. Median hourly mouthing duration was 0.4 min/h and 0.1 min/h with hands and objects. Median mouthing contact duration was 1 s and 1.5 s with hands and objects, respectively. The median object contact frequency for both the hands combined was 537.3 events/h with an hourly contact duration of 81.8 min/h and a median contact duration of 3 s. There were no significant differences in the mouthing activity between genders or age groups. Female children had longer and more frequent hand contacts with several surface types. Age was negatively correlated with hand contacts of floor and wood surfaces. Contact frequencies in this study are higher than current regulatory recommendations for this age group.

Association of Children’s Urinary CC16 Levels with Arsenic Concentrations in Multiple Environmental Media

Arsenic exposure has been associated with decreased club cell secretory protein (CC16) levels in adults. Further, both arsenic exposure and decreased levels of CC16 in childhood have been associated with decreased adult lung function. Our objective was to determine if urinary CC16 levels in children are associated with arsenic concentrations in environmental media collected from their homes. Yard soil, house dust, and tap water were taken from 34 homes. Urine and toenail samples were collected from 68 children. All concentrations were natural log-transformed prior to data analysis. There were associations between urinary CC16 and arsenic concentration in soil (b = −0.43, p = 0.001, R2 = 0.08), water (b = −0.22, p = 0.07, R2 = 0.03), house dust (b = −0.37, p = 0.07, R2 = 0.04), and dust loading (b = −0.21, p = 0.04, R2 = 0.04). In multiple analyses, only the concentration of arsenic in soil was associated with urinary CC16 levels (b = −0.42, p = 0.02, R2 = 0.14 (full model)) after accounting for other factors. The association between urinary CC16 and soil arsenic may suggest that localized arsenic exposure in the lungs could damage the airway epithelium and predispose children for diminished lung function. Future work to assess this possible mechanism should examine potential associations between airborne arsenic exposures, CC16 levels, lung function, and other possible confounders in children in arsenic-impacted communities.

Cryptosporidium risk from swimming pool exposures.

BACKGROUND Infection risk estimates from swimming in treated recreational water venues are lacking and needed to prioritize public health interventions in swimming pools. Quantitative infection risk estimates among different age groups are needed to identify vulnerable populations. High risk populations can be targeted during public health interventions, like education campaigns and pool operation improvements. OBJECTIVES This study estimated per-swim and annual Cryptosporidium infection risks in adults (>18) and children (≤18) using new experimental data collected in the U.S. on swimmer behavior. METHODS Risks were estimated using oocyst concentration data from the literature, and data collected in this study on pool water ingestion, swim duration and pool use frequency. A sensitivity analysis identified the most influential model variables on infection probability. RESULTS The average estimated risk of Cryptosporidium infection was 2.6×10-4 infections/swim event. The per-swim risk estimate in the present study differed from others because behavior data (ingestion rates, swim duration, and visit frequency) were collected in different countries and varied from U.S. estimates. We found swimmer behaviors influence infection risk. This is the first study to report annual risk of Cryptosporidium infection among swimmers by age group. Using U.S. exposure data, annual risk was estimated at 2.9×10-2 infections/year for children and 2.2×10-2 infections/year for adults. Annual risk for all swimmers was estimated at 2.5×10-2 infections/year from swimming in treated recreational water venues. Due to increased ingestion and swim duration, child swimmers had the highest annual risk estimate. Cryptosporidium concentration is the most influential variable on infection probability. CONCLUSIONS Results suggest the need for standardized pool water quality monitoring for Cryptosporidium, education, development of interventions to reduce ingestion, consideration of behaviors unique to swimming populations in future risk assessments and improvement of oocyst removal from pool water. Child swimmers were the most vulnerable sub-population, and should be targeted in healthy swimming education campaigns.

Using Contact-Specific Surface Area Estimates in Exposure Models

ABSTRACTThe objectives of this work are to demonstrate methods for the collection and incorporation of contact-specific surface area measurements in dermal exposure assessments and illustrate the potential difference in resulting dermal and non-dietary ingestion estimates using this type of surface area data. Continuing the work of Stanfords Exposure Research Group, categorical surface area data contained in childrens sequential microlevel activity patterns were converted into quantitative coordinates, which in turn provided a foundation to map data on the skin surface. Programs were constructed to establish an accounting system of spatial coordinates, governed by categorical surface area data, to map exposure estimates or activity statistics on the skin. An illustrative example is provided that estimates the spatial variability of chlorpyrifos on the palm of a hand using contact-specific surface area data. Results show a maximum value of 14.6 ng on the fingertips and no chemical exposure along the edge...

Methodology to Capture Children's Non-Dietary Ingestion Exposure Activities During Meal Events

ABSTRACT During meal events, a childs food can be contaminated through contacts with objects and surfaces, and/or unwashed hands that have chemical residues, increasing ingestion exposure of contaminants for the child. This is not surprising, given that very young children eat more with the hands than adults, are active, and play with toys and objects while eating. In addition, childrens unwashed hands and toys are commonly inserted into their mouths during meal events, increasing exposure. By observing children during their meal events, information can be gathered on the frequency and duration of contacts between objects, foods, and hands, and the sequence of events before the hands, foods, or objects are inserted into the mouth. This article describes the process of refining a videotaping and video-translation methodology to capture micro-level activity time series (MLATS), in order to better quantify total exposure for young children as a result of their behavior during meal events and cross-contamination of foods and hands. These MLATS can be seen as detailed activity patterns that provide useful data, along with transfer coefficients and environmental concentration to estimate exposures.

Modeling the role of fomites in a norovirus outbreak

Abstract Norovirus accounts for a large portion of the gastroenteritis disease burden, and outbreaks have occurred in a wide variety of environments. Understanding the role of fomites in norovirus transmission will inform behavioral interventions, such as hand washing and surface disinfection. The purpose of this study was to estimate the contribution of fomite-mediated exposures to infection and illness risks in outbreaks. A simulation model in discrete time that accounted for hand-to-porous surfaces, hand-to-nonporous surfaces, hand-to-mouth, -eyes, -nose, and hand washing events was used to predict 17 hr of simulated human behavior. Norovirus concentrations originated from monitoring contamination levels on surfaces during an outbreak on houseboats. To predict infection risk, two dose-response models (fractional Poisson and 2F1 hypergeometric) were used to capture a range of infection risks. A triangular distribution describing the conditional probability of illness given an infection was multiplied by modeled infection risks to estimate illness risks. Infection risks ranged from 70.22% to 72.20% and illness risks ranged from 21.29% to 70.36%. A sensitivity analysis revealed that the number of hand-to-mouth contacts and the number of hand washing events had strong relationships with model-predicted doses. Predicted illness risks overlapped with leisure setting and environmental attack rates reported in the literature. In the outbreak associated with the viral concentrations used in this study, attack rates ranged from 50% to 86%. This model suggests that fomites may have accounted for 25% to 82% of illnesses in this outbreak. Fomite-mediated exposures may contribute to a large portion of total attack rates in outbreaks involving multiple transmission modes. The findings of this study reinforce the importance of frequent fomite cleaning and hand washing, especially when ill persons are present.

Quantifying human-environment interactions using videography in the context of infectious disease transmission

Quantitative data on human-environment interactions are needed to fully understand infectious disease transmission processes and conduct accurate risk assessments. Interaction events occur during an individuals movement through, and contact with, the environment, and can be quantified using diverse methodologies. Methods that utilize videography, coupled with specialized software, can provide a permanent record of events, collect detailed interactions in high resolution, be reviewed for accuracy, capture events difficult to observe in real-time, and gather multiple concurrent phenomena. In the accompanying video, the use of specialized software to capture humanenvironment interactions for human exposure and disease transmission is highlighted. Use of videography, combined with specialized software, allows for the collection of accurate quantitative representations of human-environment interactions in high resolution. Two specialized programs include the Virtual Timing Device for the Personal Computer, which collects sequential microlevel activity time series of contact events and interactions, and LiveTrak, which is optimized to facilitate annotation of events in real-time. Opportunities to annotate behaviors at high resolution using these tools are promising, permitting detailed records that can be summarized to gain information on infectious disease transmission and incorporated into more complex models of human exposure and risk.

Methods for Handling Left-Censored Data in Quantitative Microbial Risk Assessment

This study evaluates methods for handling data with low (10%) to severe (90%) left-censoring within an environmental microbiology context and demonstrates that some of these methods may be appropriate when using data containing concentrations below a limit of detection to estimate infection risks. Additionally, this study uses a skewed data set, which is an issue typically faced by environmental microbiologists. ABSTRACT Data below detection limits, left-censored data, are common in environmental microbiology, and decisions in handling censored data may have implications for quantitative microbial risk assessment (QMRA). In this paper, we utilize simulated data sets informed by real-world enterovirus water data to evaluate methods for handling left-censored data. Data sets were simulated with four censoring degrees (low [10%], medium [35%], high [65%], and severe [90%]) and one real-life censoring example (97%) and were informed by enterovirus data assuming a lognormal distribution with a limit of detection (LOD) of 2.3 genome copies/liter. For each data set, five methods for handling left-censored data were applied: (i) substitution with LOD/2 , (ii) lognormal maximum likelihood estimation (MLE) to estimate mean and standard deviation, (iii) Kaplan-Meier estimation (KM), (iv) imputation method using MLE to estimate distribution parameters (MI method 1), and (v) imputation from a uniform distribution (MI method 2). Each data set mean was used to estimate enterovirus dose and infection risk. Root mean square error (RMSE) and bias were used to compare estimated and known doses and infection risks. MI method 1 resulted in the lowest dose and infection risk RMSE and bias ranges for most censoring degrees, predicting infection risks at most 1.17 × 10−2 from known values under 97% censoring. MI method 2 was the next overall best method. For medium to severe censoring, MI method 1 may result in the least error. If unsure of the distribution, MI method 2 may be a preferred method to avoid distribution misspecification. IMPORTANCE This study evaluates methods for handling data with low (10%) to severe (90%) left-censoring within an environmental microbiology context and demonstrates that some of these methods may be appropriate when using data containing concentrations below a limit of detection to estimate infection risks. Additionally, this study uses a skewed data set, which is an issue typically faced by environmental microbiologists.

Modeling Surface Disinfection Needs To Meet Microbial Risk Reduction Targets

It is known that the use of EPA-registered surface disinfectant sprays can reduce infection risk if used according to the manufacturers instructions. However, there are currently no standards for health care environments related to contamination levels on surfaces. The significance of this research is in quantifying needed reductions to meet various risk targets using realistic viral concentrations on surfaces for health care environments. This research informs the design of cleaning protocols by demonstrating that multiple applications may be needed to reduce risk and by highlighting a need for more models exploring the relationship among microbial contamination of surfaces, patient and health care worker behaviors, and infection risks. ABSTRACT Nosocomial viral infections are an important cause of health care-acquired infections where fomites have a role in transmission. Using stochastic modeling to quantify the effects of surface disinfection practices on nosocomial pathogen exposures and infection risk can inform cleaning practices. The purpose of this study was to predict the effect of surface disinfection on viral infection risks and to determine needed viral reductions to achieve risk targets. Rotavirus, rhinovirus, and influenza A virus infection risks for two cases were modeled. Case 1 utilized a single fomite contact approach, while case 2 assumed 6 h of contact activities. A 94.1% viral reduction on surfaces and hands was measured following a single cleaning round using an Environmental Protection Agency (EPA)-registered disinfectant in an urgent care facility. This value was used to model the effect of a surface disinfection intervention on infection risk. Risk reductions for other surface-cleaning efficacies were also simulated. Surface reductions required to achieve risk probability targets were estimated. Under case 1 conditions, a 94.1% reduction in virus surface concentration reduced infection risks by 94.1%. Under case 2 conditions, a 94.1% reduction on surfaces resulted in median viral infection risks being reduced by 92.96 to 94.1% and an influenza A virus infection risk below one in a million. Surface concentration in the equations was highly correlated with dose and infection risk outputs. For rotavirus and rhinovirus, a >99.99% viral surface reduction would be needed to achieve a one-in-a-million risk target. This study quantifies reductions of infection risk relative to surface disinfectant use and demonstrates that risk targets for low-infectious-dose organisms may be more challenging to achieve. IMPORTANCE It is known that the use of EPA-registered surface disinfectant sprays can reduce infection risk if used according to the manufacturers instructions. However, there are currently no standards for health care environments related to contamination levels on surfaces. The significance of this research is in quantifying needed reductions to meet various risk targets using realistic viral concentrations on surfaces for health care environments. This research informs the design of cleaning protocols by demonstrating that multiple applications may be needed to reduce risk and by highlighting a need for more models exploring the relationship among microbial contamination of surfaces, patient and health care worker behaviors, and infection risks.