Association of Redlining and Natural Environment with Depressive Symptoms in Women in the Sister Study.

BACKGROUND: Improving mental health is recognized as an important factor for achieving global development goals. Despite strong evidence that neighborhood greenery promotes better mental health, there are environmental justice concerns over the distribution of neighborhood greenery. Underlying these concerns are present-day consequences of historical discriminatory financial investment practices, such as redlining which was established by the U.S. Federal Home Owners' Loan Corporation (HOLC) in the 1930s. The impacts of redlining on environmental and health disparities have been researched extensively. However, the influences of redlining on the associations between neighborhood environment and health outcomes have not been fully assessed. OBJECTIVES: The aim of this study was to examine whether associations between residential tree cover and depressive symptoms vary across areas subject to HOLC practices. METHODS: Depressive symptoms were defined by the 10-item Center for Epidemiologic Studies Depression Scale collected during the period 2008-2012 for 3,555 women in the Sister Study cohort residing in cities subject to HOLC practices across the United States. HOLC rating maps were obtained from the Mapping Inequality Project, University of Richmond, with neighborhoods graded as A (best for financial investment, green), B (still desirable, blue), C (declining, yellow), and D (hazardous, red-known as redlined). Tree cover within 500m and 2,000m from residences was estimated using 2011 U.S. Forest Service Percent Tree Canopy Cover. Mixed model using climate zone as the random effect was applied to evaluate the associations with adjustments for potential covariates. Analyses were stratified by HOLC grade. RESULTS: Tree cover was significantly higher in neighborhoods with better HOLC grades. A 10% increase in tree cover was associated with reduced odds of depressive symptoms for the full study population, with adjusted odds ratios (AORs) of 0.93 [95% confidence interval (CI): 0.88, 0.99], and 0.91 (0.85, 0.97) for 500-m and 2,000-m buffer, respectively. Across HOLC grades, the strongest associations were observed in redlined neighborhoods, with respective AORs of 0.72 (95% CI: 0.52, 0.99) and 0.63 (95% CI: 0.45, 0.90) for 500-m and 2,000-m buffer. DISCUSSION: Findings support a remediation strategy focused on neighborhood greenery that would address multiple public health priorities, including mental health and environmental justice. https://doi.org/10.1289/EHP12212.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency.

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Integrating Personal Air Sensor and GPS to Determine Microenvironment-Specific Exposures to Volatile Organic Compounds.

Personal exposure to volatile organic compounds (VOCs) from indoor sources including consumer products is an understudied public health concern. To develop and evaluate methods for monitoring personal VOC exposures, we performed a pilot study and examined time-resolved sensor-based measurements of geocoded total VOC (TVOC) exposures across individuals and microenvironments (MEs). We integrated continuous (1 min) data from a personal TVOC sensor and a global positioning system (GPS) logger, with a GPS-based ME classification model, to determine TVOC exposures in four MEs, including indoors at home (Home-In), indoors at other buildings (Other-In), inside vehicles (In-Vehicle), and outdoors (Out), across 45 participant-days for five participants. To help identify places with large emission sources, we identified high-exposure events (HEEs; TVOC > 500 ppb) using geocoded TVOC time-course data overlaid on Google Earth maps. Across the 45 participant-days, the MEs ranked from highest to lowest median TVOC were: Home-In (165 ppb), Other-In (86 ppb), In-Vehicle (52 ppb), and Out (46 ppb). For the two participants living in single-family houses with attached garages, the median exposures for Home-In were substantially higher (209, 416 ppb) than the three participant homes without attached garages: one living in a single-family house (129 ppb), and two living in apartments (38, 60 ppb). The daily average Home-In exposures exceeded the estimated Leadership in Energy and Environmental Design (LEED) building guideline of 108 ppb for 60% of the participant-days. We identified 94 HEEs across all participant-days, and 67% of the corresponding peak levels exceeded 1000 ppb. The MEs ranked from the highest to the lowest number of HEEs were: Home-In (60), Other-In (13), In-Vehicle (12), and Out (9). For Other-In and Out, most HEEs occurred indoors at fast food restaurants and retail stores, and outdoors in parking lots, respectively. For Home-In HEEs, the median TVOC emission and removal rates were 5.4 g h-1 and 1.1 h-1, respectively. Our study demonstrates the ability to determine individual sensor-based time-resolved TVOC exposures in different MEs, in support of identifying potential sources and exposure factors that can inform exposure mitigation strategies.

Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures.

BACKGROUND: Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge. OBJECTIVES: We aimed to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products. METHODS: We applied frequent itemset mining to an integrated data set linking consumer product chemical ingredient data with product purchasing data from 60,000 households to identify chemical combinations resulting from co-use of consumer products. RESULTS: We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Last, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways. DISCUSSION: Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. https://doi.org/10.1289/EHP8610.

Types and spatial contexts of neighborhood greenery matter in associations with weight status in women across 28 U.S. communities.

Excess body weight is a risk factor for many chronic diseases. Studies have identified neighborhood greenery as supportive of healthy weight. However, few have considered plausible effect pathways for ecosystem services (e.g., heat mitigation, landscape aesthetics, and venues for physical activities) or potential variations by climate. This study examined associations between weight status and neighborhood greenery that capture ecosystem services most relevant to weight status across 28 U.S. communities. Weight status was defined by body mass index (BMI) reported for 6591 women from the U.S. Sister Study cohort. Measures of greenery within street and circular areas at 500 m and 2000 m buffer distances from homes were derived for each participant using 1 m land cover data. Street area was defined as a 25 m-wide zone on both sides of street centerlines multiplied by the buffer distances, and circular area was the area of the circle centered on a home within each of the buffer distances. Measures of street greenery characterized the pedestrian environment to capture physically and visually accessible greenery for shade and aesthetics. Circular greenery was generated for comparison. Greenery types of tree and herbaceous cover were quantified separately, and a combined measure of tree and herbaceous cover (i.e., aggregate greenery) was also included. Mixed models accounting for the clustering at the community level were applied to evaluate the associations between neighborhood greenery and the odds of being overweight or obese (BMI > 25) with adjustment for covariates selected using gradient boosted regression trees. Analyses were stratified by climate zone (arid, continental, and temperate). Tree cover was consistently associated with decreased odds of being overweight or obese. For example, the adjusted odds ratio [AOR] was 0.92, 95% Confidence Interval [CI]: 0.88-0.96, given a 10% increase in street tree cover at the 2000 m buffer across the 28 U.S. communities. These associations held across climate zones, with the lowest AOR in the arid climate (AOR: 0.74, 95% CI: 0.54-1.01). In contrast, associations with herbaceous cover varied by climate zone. For the arid climate, a 10% increase in street herbaceous cover at the 2000 m buffer was associated with lower odds of being overweight or obese (AOR: 0.75, 95% CI: 0.55-1.03), whereas the association was reversed for the temperate climate, the odds increased (AOR: 1.19, 95% CI: 1.05-1.35). Associations between greenery and overweight/obesity varied by type and spatial context of greenery, and climate. Our findings add to a growing body of evidence that greenery design in urban planning can support public health. These findings also justify further defining the mechanism that underlies the observed associations.

Data Mining Approaches for Assessing Chemical Coexposures Using Consumer Product Purchase Data.

The use of consumer products presents a potential for chemical exposures to humans. Toxicity testing and exposure models are routinely employed to estimate risks from their use; however, a key challenge is the sparseness of information concerning who uses products and which products are used contemporaneously. Our goal was to demonstrate a method to infer use patterns by way of purchase data. We examined purchase patterns for three types of personal care products (cosmetics, hair care, and skin care) and two household care products (household cleaners and laundry supplies) using data from 60,000 households collected over a one-year period in 2012. The market basket analysis methodology frequent itemset mining (FIM) was used to identify co-occurring sets of product purchases for all households and demographic groups based on income, education, race/ethnicity, and family composition. Our methodology captured robust co-occurrence patterns for personal and household products, globally and for different demographic groups. FIM identified cosmetic co-occurrence patterns captured in prior surveys of cosmetic use, as well as a trend of increased diversity of cosmetic purchases as children mature to teenage years. We propose that consumer product purchase data can be mined to inform person-oriented use patterns for high-throughput chemical screening applications, for aggregate and combined chemical risk evaluations.

How do natural features in the residential environment influence women's self-reported general health? Results from cross-sectional analyses of a U.S. national cohort.

BACKGROUND: The relationship between health and human interaction with nature is complex. Here we conduct analyses to provide insights into potential health benefits related to residential proximity to nature. OBJECTIVES: We aimed to examine associations between measures of residential nature and self-reported general health (SRGH), and to explore mediation roles of behavioral, social, and air quality factors, and variations in these relationships by urbanicity and regional climate. METHODS: Using residential addresses for 41,127 women from the Sister Study, a U.S.-based national cohort, we derived two nature exposure metrics, canopy and non-gray cover, using Percent Tree Canopy and Percent Developed Imperviousness from the National Land Cover Database. Residential circular buffers of 250 m and 1250 m were considered. Gradient boosted regression trees were used to model the effects of nature exposure on the odds of reporting better SRGH (Excellent/Very Good versus the referent, Good/Fair/Poor). Analyses stratified by urbanicity and regional climate (arid, continental, temperate) and mediation by physical activity, social support, and air quality were conducted. RESULTS: A 10% increase in canopy and non-gray cover within 1250 m buffer was associated with 1.02 (95% CI: 1.00-1.03) and 1.03 (95% CI: 1.01-1.04) times the odds of reporting better SRGH, respectively. Stronger associations were observed for the urban group and for continental climate relative to other strata. Social support and physical activity played a more significant mediation role than air quality for the full study population. DISCUSSION: Findings from this study identified a small but important beneficial association between residential nature and general health. These findings could inform community planning and investments in neighborhood nature for targeted health improvements and potential societal and environmental co-benefits.

The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency.

The U.S. Environmental Protection Agency (EPA) is faced with the challenge of efficiently and credibly evaluating chemical safety often with limited or no available toxicity data. The expanding number of chemicals found in commerce and the environment, coupled with time and resource requirements for traditional toxicity testing and exposure characterization, continue to underscore the need for new approaches. In 2005, EPA charted a new course to address this challenge by embracing computational toxicology (CompTox) and investing in the technologies and capabilities to push the field forward. The return on this investment has been demonstrated through results and applications across a range of human and environmental health problems, as well as initial application to regulatory decision-making within programs such as the EPA's Endocrine Disruptor Screening Program. The CompTox initiative at EPA is more than a decade old. This manuscript presents a blueprint to guide the strategic and operational direction over the next 5 years. The primary goal is to obtain broader acceptance of the CompTox approaches for application to higher tier regulatory decisions, such as chemical assessments. To achieve this goal, the blueprint expands and refines the use of high-throughput and computational modeling approaches to transform the components in chemical risk assessment, while systematically addressing key challenges that have hindered progress. In addition, the blueprint outlines additional investments in cross-cutting efforts to characterize uncertainty and variability, develop software and information technology tools, provide outreach and training, and establish scientific confidence for application to different public health and environmental regulatory decisions.

Advancing Environmental Epidemiology to Assess the Beneficial Influence of the Natural Environment on Human Health and Well-Being.

Environmental health research can be oriented across a continuum of effects ranging from adverse to cobenefits to salutogenic. We argue that the salutogenic end of the continuum is insufficiently represented in research and as a basis for environmental protection, even though there is growing evidence that the natural environment plays a critical role in blunting adverse effects and promoting human health and well-being. Thus, we advocate for advancing environmental health research through environmental epidemiology that more fully and directly accounts for the salutogenic effects of the natural environment on individual well-being by (1) defining "natural environments" broadly, from pristine natural areas to urban green infrastructure; (2) considering exposure comprehensively to encompass residential, occupational, and recreational settings, local and distant, day-to-day and occasional; (3) doing individual-level assessments that include both health and well-being outcomes and one's experience of nature, including potential mediation by connectedness to nature and individual perceptions and preferences, as well as sociocultural and demographic effect modifiers; and (4) collecting longitudinal and nationally representative data.

Assessment of temperature and ultraviolet radiation effects on sunburn incidence at an inland U.S. Beach: A cohort study.

BACKGROUND: Increases in outdoor temperature may lead to increases in sunburn, outdoor exposure, and skin cancer in human populations. OBJECTIVE: This study aimed to quantify sunburn incidence and risk for Ohio beachgoers exposed to varying outdoor conditions. METHODS: Sunburn incidence data were obtained through a prospective cohort study at East Fork Lake (Cincinnati, Ohio, USA). Recruitment occurred over 26 weekend days. Beach interviews and follow-up telephone interviews obtained exposure and health information. New sunburns were self-reported 8-9 days post-enrollment. Survey data were paired with ultraviolet radiation (UVR) index and temperature data for statistical analysis. RESULTS: Among 947 beachgoers, new sunburns were reported in 18% of swimmers. Sunburn incidence was associated with temperature (odds ratio = 1.2; 95% CI: 1.1 - 1.4) and UVR index (odds ratio = 1.6; 95% CI: 1.0 - 2.5) in models adjusted for water exposure, arrival time, and beach visit frequency. Some evidence of a temperature+UVR interaction was observed. LIMITATIONS: Exposure and sunburn data were self-reported without clinical diagnosis and date of onset. The follow-up period enabled sunburns to be reported from a variety of days rather than only the beach visit day thereby limiting interpretation. Sun protection behaviors were not evaluated. CONCLUSIONS: Temperature and UVR influence sunburn frequency. Temperature, however was more strongly associated with sunburn in beachgoers than the nearest measured UVR index, suggesting future investigations are needed to better understand how temperature effects sunburn development. Interventions for decreasing sunburn are needed.

Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment.

BACKGROUND: Computational exposure science represents a frontier of environmental science that is emerging and quickly evolving. OBJECTIVES: In this commentary, we define this burgeoning discipline, describe a framework for implementation, and review some key ongoing research elements that are advancing the science with respect to exposure to chemicals in consumer products. DISCUSSION: The fundamental elements of computational exposure science include the development of reliable, computationally efficient predictive exposure models; the identification, acquisition, and application of data to support and evaluate these models; and generation of improved methods for extrapolating across chemicals. We describe our efforts in each of these areas and provide examples that demonstrate both progress and potential. CONCLUSIONS: Computational exposure science, linked with comparable efforts in toxicology, is ushering in a new era of risk assessment that greatly expands our ability to evaluate chemical safety and sustainability and to protect public health. CITATION: Egeghy PP, Sheldon LS, Isaacs KK, Özkaynak H, Goldsmith M-R, Wambaugh JF, Judson RS, Buckley TJ. 2016. Computational exposure science: an emerging discipline to support 21st-century risk assessment. Environ Health Perspect 124:697-702; http://dx.doi.org/10.1289/ehp.1509748.

Integrating bacterial and viral water quality assessment to predict swimming-associated illness at a freshwater beach: a cohort study.

BACKGROUND & OBJECTIVE: Recreational waters impacted by fecal contamination have been linked to gastrointestinal illness in swimmer populations. To date, few epidemiologic studies examine the risk for swimming-related illnesses based upon simultaneous exposure to more than one microbial surrogate (e.g. culturable E. coli densities, genetic markers). We addressed this research gap by investigating the association between swimming-related illness frequency and water quality determined from multiple bacterial and viral genetic markers. METHODS: Viral and bacterial genetic marker densities were determined from beach water samples collected over 23 weekend days and were quantified using quantitative polymerase chain reaction (qPCR). These genetic marker data were paired with previously determined human exposure data gathered as part of a cohort study carried out among beach users at East Fork Lake in Ohio, USA in 2009. Using previously unavailable genetic marker data in logistic regression models, single- and multi-marker/multi-water quality indicator approaches for predicting swimming-related illness were evaluated for associations with swimming-associated gastrointestinal illness. RESULTS: Data pertaining to genetic marker exposure and 8- or 9-day health outcomes were available for a total of 600 healthy susceptible swimmers, and with this population we observed a significant positive association between human adenovirus (HAdV) exposure and diarrhea (odds ratio  = 1.6; 95% confidence interval: 1.1-2.3) as well as gastrointestinal illness (OR  = 1.5; 95% CI: 1.0-2.2) upon adjusting for culturable E. coli densities in multivariable models. No significant associations between bacterial genetic markers and swimming-associated illness were observed. CONCLUSIONS: This study provides evidence that a combined measure of recreational water quality that simultaneously considers both bacterial and viral densities, particularly HAdV, may improve prediction of disease risk than a measure of a single agent in a beach environment likely influenced by nonpoint source human fecal contamination.

GPS-based microenvironment tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation in central North Carolina.

A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time-location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies.

Evidence of high rates of undiagnosed asthma in central Ohio elementary schoolchildren.

BACKGROUND: In Ohio, 14.5% of 5- to 9-year-olds and 17.3% of 10- to 17-year-olds have asthma. Moreover, there is concern that these numbers may underestimate the true disease burden. We sought to evaluate variability in asthma rates and respiratory symptoms among central Ohio fourth graders as a means to assess potential undiagnosed and undertreated asthma and its determinants. METHODS: We recruited 13 central Ohio elementary schools representing a broad range of nonurban settings and surveyed fourth graders to estimate school-level physician-diagnosed asthma (PDA), respiratory morbidity, and home exposures to smoking and pets. We used generalized linear mixed models with random intercept for school to examine relationships among exposures, respiratory symptoms, and PDA. RESULTS: Across the 13 schools, 94% of students participated in the survey, and the estimated asthma prevalence rate was 10.2% (N = 101 of 987). An additional 41% reported not having PDA but then went on to report symptoms consistent with asthma potentially suggestive of undiagnosed asthma. Of students with PDA, 21% reported symptoms suggestive of poorly controlled asthma. High levels of secondhand smoke (SHS) exposure were associated both with PDA (p = .05) and with respiratory symptoms (p < .0001). Students who owned a cat or a bird were more likely to report respiratory symptoms (p = .02 and p = .04, respectively). CONCLUSIONS: We provide evidence that the already high childhood asthma public health burden in central Ohio may be underreported. Schools may be an ideal location to conduct screenings and implement environmental interventions oriented toward SHS and household pets that will yield respiratory morbidity benefits.

Ambient air toxics and asthma prevalence among a representative sample of US kindergarten-age children.

BACKGROUND: Criteria pollutants have been associated with exacerbation of children's asthma, but the role of air toxics in relation to asthma is less clear. Our objective was to evaluate whether exposure to outdoor air toxics in early childhood increased asthma risk or severity. METHODS: Air toxics exposure was estimated using the 2002 National Air toxics Assessment (NATA) and linked to longitudinal data (n=6950) from a representative sample of US children born in 2001 and followed through kindergarten-age in the Early Child Longitudinal Study-Birth Cohort (ECLS-B). RESULTS: Overall, 17.7% of 5.5 year-olds had ever been told by a healthcare professional they had asthma, and 6.8% had been hospitalized or visited an emergency room for an asthma attack. Higher rates of asthma were observed among boys (20.1%), low-income (24.8%), and non-Hispanic black children (30.0%) (p≤0.05). Air toxics exposure was greater for minority race/ethnicity (p<0.0001), low income (p<0.0001), non-rural area (p<0.001). Across all analyses, greater air toxics exposure, as represented by total NATA respiratory hazard index, or when limited to respiratory hazard index from onroad mobile sources or diesel PM, was not associated with a greater prevalence of asthma or hospitalizations (p trend >0.05). In adjusted logistic regression models, children exposed to the highest respiratory hazard index were not more likely to have asthma compared to those exposed to the lowest respiratory hazard index of total, onroad sources, or diesel PM. CONCLUSIONS: Early childhood exposure to outdoor air toxics in a national sample has not previously been studied relative to children's asthma. Within the constraints of the study, we found no evidence that early childhood exposure to outdoor air toxics increased risk for asthma. As has been previously reported, it is evident that there are environmental justice and disparity concerns for exposure to air toxics and asthma prevalence in US children.

Nonoutbreak-related airborne Staphylococcus spp in a veterinary hospital.

BACKGROUND: Methicillin-resistant Staphylococcus spp (MRS), such as methicillin-resistant Staphylococcus aureus (MRSA), is a nosocomial pathogen of significant concern. This study evaluates the prevalence and determinants of airborne MRS and methicillin-susceptible Staphylococcus spp (MSS) in a veterinary teaching hospital during periods of no known active clinical cases of Staphylococci infections. METHODS: Airborne MRS and MRSA were sampled from 10 areas on 3 days each over 3 consecutive months in a veterinary teaching hospital. Each location was sampled twice each day (daytime and evening). Hospital clinical personnel were surveyed for activity levels across the areas sampled. RESULTS: Airborne MSS was detected in 52% (25/48) of samples and mecA-confirmed airborne MRS in 12.5% (6/48) of samples. Among daytime air samples, Staphylococcus spp varied by functional area (P < .001), and airborne MRS varied by human activity level (P = .011) with the hallway posing the greatest risk for both. CONCLUSION: Our results indicate that (1) Staphylococcus spp is present in air in clinical environments during periods of no known clinical Staphylococci cases and (2) levels of airborne Staphylococcus spp are associated with functional space and human activity level. Applying these findings to MRSA surveillance creates opportunities for improving the accuracy and precision of exposure classification and risk mitigation.

Comparability and repeatability of methods for estimating the dietary intake of the heterocyclic amine contaminant 2-amino-1-methyl-6-phenylimidazo[4,5b]pyridine (PhIP).

Inconsistent risk estimates for dietary heterocyclic amine (HCA) exposure and cancers may be due to differences in exposure assessment methods and the associated measurement error. We evaluated repeatability and comparability of intake estimates of the HCA 2-amino-1-methyl-6-phenylimidazo[4,5b]pyridine (PhIP) among two food frequency questionnaire (FFQ) collections, three diary collections, and three measurements of urinary PhIP and its metabolites in 36 non-smokers in Baltimore, Maryland, during 2004-2005. Collections spanned ∼9 months. Method repeatability was characterised with intraclass correlation coefficients (ICCs). Comparability among methods was assessed with Spearman correlation coefficients. Within-subject variability in PhIP intake was comparably high across all methods (ICCs of 0.20, 0.30, and 0.15 for FFQ, diary, and creatinine-adjusted urinary PhIP, respectively). Mean diary-based PhIP intake and mean urinary PhIP concentration were strongly correlated when restricting the analysis to participants with at least one non-zero diary-based estimate of PhIP intake (n = 15, r = 0.75, p = 0.001), but not in the full study population (n = 36, r = 0.18, p = 0.28). Mean PhIP intake from the FFQ was not associated with that either based on the diary or urinary PhIP separately, but was modestly correlated with a metric that combined the diary- and biomarker-based approaches (r = 0.30, p = 0.08). The high within-subject variability will result in significantly attenuated associations if a single measure is used to estimate exposure within an epidemiologic study. Improved HCA assessment tools, such as a combination of methods or validated biomarkers that capture long term exposure, are needed.

In vivo phycocyanin flourometry as a potential rapid screening tool for predicting elevated microcystin concentrations at eutrophic lakes.

Current approaches for assessing human health risks associated with cyanotoxins often rely on the quantification of microcystin. Significant limitations of current approaches are cost and time to obtain a result. To address these challenges, a numerical index for screening microcystin risks above the World Health Organization's (WHO) low-risk threshold for microcystin was developed for eutrophic Midwestern U.S. lakes based on water quality results from 182 beach water samples collected from seven Ohio lakes. In 48 (26.4%) samples we observed microcystin concentrations as measured by ELISA that exceeded the 4 µg/L microcystin threshold. A multivariable logistic regression model using practical real-time measures of in vivo phycocyanin (by fluorometry) and secchi depth was constructed to estimate the probability of a beach sample exceeding 4 µg/L microcystin. The final model achieved statistical significance (p = 0.030) as well as good calibration (as measured by the goodness-of-fit test comparing observed to expected counts within deciles of risk based on the model, p = 0.329) and discrimination (as indicated by the area under the receiver-operator-curve (0.795)). These results demonstrate two rapid and practical measures of recreational water quality are effective in identifying "at risk" lake conditions warranting additional management (e.g., advisory and/or advanced testing).