Growing convergence research: Coproducing climate projections to inform proactive decisions for managing simultaneous wildfire risk.

We apply a convergence research approach to the urgent need for proactive management of long-term risk associated with wildfire in the United States. In this work we define convergence research in accordance with the US National Science Foundation-as a means of addressing a specific and compelling societal problem for which solutions require deep integration across disciplines and engagement of stakeholders. Our research team brings expertise in climate science, fire science, landscape ecology, and decision science to address the risk from simultaneous and impactful fires that compete for management resources, and leverages climate projections for decision support. In order to make progress toward convergence our team bridges spatial and temporal scale divides arising from differences in disciplinary and practice-based norms. We partner with stakeholders representing US governmental, tribal, and local decision contexts to coproduce a robust information base for support of decision making about wildfire preparedness and proactive land/fire management. Our approach ensures that coproduced information will be directly integrated into existing tools for application in operations and policy making. Coproduced visualizations and decision support information provide projections of the change in expected number of fires that compete for resources, the number of fire danger days per year relative to prior norms, and changes in the length and overlap of fire season in multiple US regions. Continuing phases of this work have been initiated both by stakeholder communities and by our research team, a demonstration of impact and value.

Social-ecological vulnerability of fishing communities to climate change: A U.S. West Coast case study.

Climate change is already impacting coastal communities, and ongoing and future shifts in fisheries species productivity from climate change have implications for the livelihoods and cultures of coastal communities. Harvested marine species in the California Current Large Marine Ecosystem support U.S. West Coast communities economically, socially, and culturally. Ecological vulnerability assessments exist for individual species in the California Current but ecological and human vulnerability are linked and vulnerability is expected to vary by community. Here, we present automatable, reproducible methods for assessing the vulnerability of U.S. West Coast fishing dependent communities to climate change within a social-ecological vulnerability framework. We first assessed the ecological risk of marine resources, on which fishing communities rely, to 50 years of climate change projections. We then combined this with the adaptive capacity of fishing communities, based on social indicators, to assess the potential ability of communities to cope with future changes. Specific communities (particularly in Washington state) were determined to be at risk to climate change mainly due to economic reliance on at risk marine fisheries species, like salmon, hake, or sea urchins. But, due to higher social adaptive capacity, these communities were often not found to be the most vulnerable overall. Conversely, certain communities that were not the most at risk, ecologically and economically, ranked in the category of highly vulnerable communities due to low adaptive capacity based on social indicators (particularly in Southern California). Certain communities were both ecologically at risk due to catch composition and socially vulnerable (low adaptive capacity) leading to the highest tier of vulnerability. The integration of climatic, ecological, economic, and societal data reveals that factors underlying vulnerability are variable across fishing communities on the U.S West Coast, and suggests the need to develop a variety of well-aligned strategies to adapt to the ecological impacts of climate change.

Perspectives on managing fisheries for community wellbeing in the face of climate change.

Coastal communities are being impacted by climate change, affecting the livelihoods, food security, and wellbeing of residents. Human wellbeing is influenced by the heath of the environment through numerous pathways and is increasingly being included as a desired outcome in environmental management. However, the contributors to wellbeing can be subjective and the values and perspectives of decision-makers can affect the aspects of wellbeing that are included in planning. We used Q methodology to examine how a group of individuals in fisheries management prioritize components of wellbeing that may be important to coastal communities in the California Current social-ecological system (SES). The California Current SES is an integrated system of ecological and human communities with complex linkages and connections where commercial fishing is part of the culture and an important livelihood. We asked individuals that sit on advisory bodies to the Pacific Fisheries Management Council to rank 36 statements about coastal community wellbeing, ultimately revealing three discourses about how we can best support or improve wellbeing in those communities. We examine how the priorities differ between the discourses, identify areas of consensus, and discuss how these perspectives may influence decision-making when it comes to tradeoffs inherent in climate adaptation in fisheries. Lastly, we consider if and how thoughts about priorities have been affected by the COVID-19 pandemic.

Reimagine fire science for the anthropocene.

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the "firehose" of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

Exposure therapy in a virtual environment: Validation in obsessive compulsive disorder.

Exposure and response prevention (ERP) is the current first-line psychological treatment for Obsessive-compulsive disorder (OCD). However, substantial inter-individual variability exists in treatment outcomes, including inadequate symptom improvements, and notable refusal and attrition rates. These are driven, in part, by impracticalities in simulating intrusive thoughts within clinical settings. Virtual reality (VR) offers the potential of overcoming these limitations in a manner that allows for finely controlled anxiety-provoking scenarios to be created within supportive clinical settings. To validate the potential of VR for treating contamination-based OCD, 22 patients undertook a VR ERP session and a matched session of the current gold-standard of in vivo ERP. In VR, patients were immersed within a contamination environment that permitted flexible delivery of customisable, graded exposure tasks. The VR environment utilised HTC Vive hardware, to allow for patients to both interact with, and physically move through the environment. Subjective and objective measures of distress were recorded, including heart and respiration rates. These measures indicate virtual and in vivo ERP sessions provoke consistent anxiety profiles across an exposure hierarchy. Virtual exposure was advantageous for engagement and adherence to tasks, and the therapeutic alliance was upheld. VR is a promising mechanism for ERP in contamination OCD.

Disease Surveillance Investments and Administration: Limits to Information Value in Pakistan Polio Eradication.

In Pakistan, annual poliovirus investment decisions drive quantities of supplemental immunization campaigns districts receive. In this article, we assess whether increased spending on poliovirus surveillance is associated with greater likelihood of correctly identifying districts at high risk of polio with assignment of an elevated "risk ranking." We reviewed programmatic documents from Pakistan for the period from 2012-2017, recording whether districts had been classified as "high risk" or "low risk" in each year. Through document review, we developed a decision tree to describe the ranking decisions. Then, integrating data from the World Health Organization and Global Polio Eradication Initiative, we constructed a Bayesian decision network reflecting investments in polio surveillance and immunization campaigns, surveillance metrics, disease incidence, immunization rates, and occurrence of polio cases. We test these factors for statistical association with the outcome of interest-a change in risk rank between the beginning and the end of the one-year time period. We simulate different spending scenarios and predict their impact on district risk ranking in future time periods. We find that per district spending increases are associated with increased identification of cases of acute flaccid paralysis (AFP). However, the low specificity of AFP investment and the largely invariant ranking of district risk means that even large increases in surveillance spending are unlikely to promote major changes in risk rankings at the current stage of the Pakistan polio eradication campaign.

Use of benchmark dose models in risk assessment for occupational handlers of eight pesticides used in pome fruit production.

The benchmark dose has been frequently recommended for the creation of points of departure for regulatory dose limits, but many regulations, including pesticide risk assessment and registration in the United States, continues to rely on NOAEL methods as the OECD toxicological standard methods recommend. This study used data from studies in support of pesticide registration for eight different compounds to build dose-response models and calculate benchmark doses and confidence limits. The results were compared to the NOAEL of the same study. A probabilistic estimate of dose was compared with all points of departure to demonstrate differences in the protective ability of each different selected limit. While neither the BMD/BMDL nor the NOAEL was consistently more protective, the advantage of using the BMD in quantifying the uncertainty of the point of departure is highlighted, and the feasibility of using current OECD-guideline studies for derivation of a BMD is demonstrated in these cases.

Variability in Cross-Domain Risk Perception among Smallholder Farmers in Mali by Gender and Other Demographic and Attitudinal Characteristics.

Previous research has shown that men and women, on average, have different risk attitudes and may therefore see different value propositions in response to new opportunities. We use data from smallholder farm households in Mali to test whether risk perceptions differ by gender and across domains. We model this potential association across six risks (work injury, extreme weather, community relationships, debt, lack of buyers, and conflict) while controlling for demographic and attitudinal characteristics. Factor analysis highlights extreme weather and conflict as eliciting the most distinct patterns of participant response. Regression analysis for Mali as a whole reveals an association between gender and risk perception, with women expressing more concern except in the extreme weather domain; however, the association with gender is largely absent when models control for geographic region. We also find lower risk perception associated with an individualistic and/or fatalistic worldview, a risk-tolerant outlook, and optimism about the future, while education, better health, a social orientation, self-efficacy, and access to information are generally associated with more frequent worry-with some inconsistency. Income, wealth, and time poverty exhibit complex associations with perception of risk. Understanding whether and how men's and women's risk preferences differ, and identifying other dominant predictors such as geographic region and worldview, could help development organizations to shape risk mitigation interventions to increase the likelihood of adoption, and to avoid inadvertently making certain subpopulations worse off by increasing the potential for negative outcomes.

Variability in metagenomic samples from the Puget Sound: Relationship to temporal and anthropogenic impacts.

Whole-metagenome sequencing (WMS) has emerged as a powerful tool to assess potential public health risks in marine environments by measuring changes in microbial community structure and function in uncultured bacteria. In addition to monitoring public health risks such as antibiotic resistance determinants, it is essential to measure predictors of microbial variation in order to identify natural versus anthropogenic factors as well as to evaluate reproducibility of metagenomic measurements.This study expands our previous metagenomic characterization of Puget Sound by sampling new nearshore environments including the Duwamish River, an EPA superfund site, and the Hood Canal, an area characterized by highly variable oxygen levels. We also resampled a wastewater treatment plant, nearshore and open ocean sites introducing a longitudinal component measuring seasonal and locational variations and establishing metagenomics sampling reproducibility. Microbial composition from samples collected in the open sound were highly similar within the same season and location across different years, while nearshore samples revealed multi-fold seasonal variation in microbial composition and diversity. Comparisons with recently sequenced predominant marine bacterial genomes helped provide much greater species level taxonomic detail compared to our previous study. Antibiotic resistance determinants and pollution and detoxification indicators largely grouped by location showing minor seasonal differences. Metal resistance, oxidative stress and detoxification systems showed no increase in samples proximal to an EPA superfund site indicating a lack of ecosystem adaptation to anthropogenic impacts. Taxonomic analysis of common sewage influent families showed a surprising similarity between wastewater treatment plant and open sound samples suggesting a low-level but pervasive sewage influent signature in Puget Sound surface waters. Our study shows reproducibility of metagenomic data sampling in multiple Puget Sound locations while establishing baseline measurements of antibiotic resistance determinants, pollution and detoxification systems. Combining seasonal and longitudinal data across these locations provides a foundation for evaluating variation in future studies.

Comparative Probabilistic Assessment of Occupational Pesticide Exposures Based on Regulatory Assessments.

Implementation of probabilistic analyses in exposure assessment can provide valuable insight into the risks of those at the extremes of population distributions, including more vulnerable or sensitive subgroups. Incorporation of these analyses into current regulatory methods for occupational pesticide exposure is enabled by the exposure data sets and associated data currently used in the risk assessment approach of the Environmental Protection Agency (EPA). Monte Carlo simulations were performed on exposure measurements from the Agricultural Handler Exposure Database and the Pesticide Handler Exposure Database along with data from the Exposure Factors Handbook and other sources to calculate exposure rates for three different neurotoxic compounds (azinphos methyl, acetamiprid, emamectin benzoate) across four pesticide-handling scenarios. Probabilistic estimates of doses were compared with the no observable effect levels used in the EPA occupational risk assessments. Some percentage of workers were predicted to exceed the level of concern for all three compounds: 54% for azinphos methyl, 5% for acetamiprid, and 20% for emamectin benzoate. This finding has implications for pesticide risk assessment and offers an alternative procedure that may be more protective of those at the extremes of exposure than the current approach.

Perception of Climate Risk among Rural Farmers in Vietnam: Consistency within Households and with the Empirical Record.

Rural farmers in Vietnamese communes perceive climate risk and potential impacts on livelihood within a complex context that may influence individual and household decisions. In a primary survey of 1,145 residents of the Thach Ha district of Ha Tinh province, we gathered data regarding perception about stability in climate, potential risks to livelihood, demographic characteristics, orientation toward risk, and interest in expanding economic activity. Temporal analysis of meteorological and economic indicator data forms an empirical basis for comparison with human perception. We ask the basic question: Are rural farmers' perceptions of climate consistent with the historical record and reproducible within households? We find that respondents do perceive climate anomalies, with some anchoring on recent extreme events as revealed by climate observational data, and further that spouses disproportionately share perceptions relative to randomly simulated pairings. To put climate-related risk perception in a larger context, we examine patterns across a range of risks to livelihood faced by farmers (livestock disease, pests, markets, health), using dimension reduction techniques. We find that our respondents distinguish among potential causes of low economic productivity, with substantial emphasis on climate-related impacts. They do not express uniform concern across risks, but rather average patterns reveal common modes and distinguish climate concern. Still, among those expressing concern about climate-related risks to livelihood we do not find an association with expressed intention to pursue changes in economic activity as a risk management response.

The Effect of Forest Management Strategy on Carbon Storage and Revenue in Western Washington: A Probabilistic Simulation of Tradeoffs.

The objectives of this study are to understand tradeoffs between forest carbon and timber values, and evaluate the impact of uncertainty in improved forest management (IFM) carbon offset projects to improve forest management decisions. The study uses probabilistic simulation of uncertainty in financial risk for three management scenarios (clearcutting in 45- and 65-year rotations and no harvest) under three carbon price schemes (historic voluntary market prices, cap and trade, and carbon prices set to equal net present value (NPV) from timber-oriented management). Uncertainty is modeled for value and amount of carbon credits and wood products, the accuracy of forest growth model forecasts, and four other variables relevant to American Carbon Registry methodology. Calculations use forest inventory data from a 1,740 ha forest in western Washington State, using the Forest Vegetation Simulator (FVS) growth model. Sensitivity analysis shows that FVS model uncertainty contributes more than 70% to overall NPV variance, followed in importance by variability in inventory sample (3-14%), and short-term prices for timber products (8%), while variability in carbon credit price has little influence (1.1%). At regional average land-holding costs, a no-harvest management scenario would become revenue-positive at a carbon credit break-point price of $14.17/Mg carbon dioxide equivalent (CO2 e). IFM carbon projects are associated with a greater chance of both large payouts and large losses to landowners. These results inform policymakers and forest owners of the carbon credit price necessary for IFM approaches to equal or better the business-as-usual strategy, while highlighting the magnitude of financial risk and reward through probabilistic simulation.

A Toxicological Framework for the Prioritization of Children's Safe Product Act Data.

In response to concerns over hazardous chemicals in children's products, Washington State passed the Children's Safe Product Act (CSPA). CSPA requires manufacturers to report the concentration of 66 chemicals in children's products. We describe a framework for the toxicological prioritization of the ten chemical groups most frequently reported under CSPA. The framework scores lifestage, exposure duration, primary, secondary and tertiary exposure routes, toxicokinetics and chemical properties to calculate an exposure score. Four toxicological endpoints were assessed based on curated national and international databases: reproductive and developmental toxicity, endocrine disruption, neurotoxicity and carcinogenicity. A total priority index was calculated from the product of the toxicity and exposure scores. The three highest priority chemicals were formaldehyde, dibutyl phthalate and styrene. Elements of the framework were compared to existing prioritization tools, such as the United States Environmental Protection Agency's (EPA) ExpoCast and Toxicological Prioritization Index (ToxPi). The CSPA framework allowed us to examine toxicity and exposure pathways in a lifestage-specific manner, providing a relatively high throughput approach to prioritizing hazardous chemicals found in children's products.

Can Carbon Nanomaterials Improve CZTS Photovoltaic Devices? Evaluation of Performance and Impacts Using Integrated Life-Cycle Assessment and Decision Analysis.

In emergent photovoltaics, nanoscale materials hold promise for optimizing device characteristics; however, the related impacts remain uncertain, resulting in challenges to decisions on strategic investment in technology innovation. We integrate multi-criteria decision analysis (MCDA) and life-cycle assessment (LCA) results (LCA-MCDA) as a method of incorporating values of a hypothetical federal acquisition manager into the assessment of risks and benefits of emerging photovoltaic materials. Specifically, we compare adoption of copper zinc tin sulfide (CZTS) devices with molybdenum back contacts to alternative devices employing graphite or graphene instead of molybdenum. LCA impact results are interpreted alongside benefits of substitution including cost reductions and performance improvements through application of multi-attribute utility theory. To assess the role of uncertainty we apply Monte Carlo simulation and sensitivity analysis. We find that graphene or graphite back contacts outperform molybdenum under most scenarios and assumptions. The use of decision analysis clarifies potential advantages of adopting graphite as a back contact while emphasizing the importance of mitigating conventional impacts of graphene production processes if graphene is used in emerging CZTS devices. Our research further demonstrates that a combination of LCA and MCDA increases the usability of LCA in assessing product sustainability. In particular, this approach identifies the most influential assumptions and data gaps in the analysis and the areas in which either engineering controls or further data collection may be necessary.

Metagenomic frameworks for monitoring antibiotic resistance in aquatic environments.

BACKGROUND: High-throughput genomic technologies offer new approaches for environmental health monitoring, including metagenomic surveillance of antibiotic resistance determinants (ARDs). Although natural environments serve as reservoirs for antibiotic resistance genes that can be transferred to pathogenic and human commensal bacteria, monitoring of these determinants has been infrequent and incomplete. Furthermore, surveillance efforts have not been integrated into public health decision making. OBJECTIVES: We used a metagenomic epidemiology-based approach to develop an ARD index that quantifies antibiotic resistance potential, and we analyzed this index for common modal patterns across environmental samples. We also explored how metagenomic data such as this index could be conceptually framed within an early risk management context. METHODS: We analyzed 25 published data sets from shotgun pyrosequencing projects. The samples consisted of microbial community DNA collected from marine and freshwater environments across a gradient of human impact. We used principal component analysis to identify index patterns across samples. RESULTS: We observed significant differences in the overall index and index subcategory levels when comparing ecosystems more proximal versus distal to human impact. The selection of different sequence similarity thresholds strongly influenced the index measurements. Unique index subcategory modes distinguished the different metagenomes. CONCLUSIONS: Broad-scale screening of ARD potential using this index revealed utility for framing environmental health monitoring and surveillance. This approach holds promise as a screening tool for establishing baseline ARD levels that can be used to inform and prioritize decision making regarding management of ARD sources and human exposure routes. CITATION: Port JA, Cullen AC, Wallace JC, Smith MN, Faustman EM. 2014. Metagenomic frameworks for monitoring antibiotic resistance in aquatic environments. Environ Health Perspect 122:222­228; http://dx.doi.org/10.1289/ehp.1307009

Spatial-temporal and cancer risk assessment of selected hazardous air pollutants in Seattle.

In the Seattle Air Toxics Monitoring Pilot Program, we measured 15 hazardous air pollutants (HAPs) at 6 sites for more than a year between 2000 and 2002. Spatial-temporal variations were evaluated with random-effects models and principal component analyses. The potential health risks were further estimated based on the monitored data, with the incorporation of the bootstrapping technique for the uncertainty analysis. It is found that the temporal variability was generally higher than the spatial variability for most air toxics. The highest temporal variability was observed for tetrachloroethylene (70% temporal vs. 34% spatial variability). Nevertheless, most air toxics still exhibited significant spatial variations, even after accounting for the temporal effects. These results suggest that it would require operating multiple air toxics monitoring sites over a significant period of time with proper monitoring frequency to better evaluate population exposure to HAPs. The median values of the estimated inhalation cancer risks ranged between 4.3 × 10⁻5 and 6.0 × 10⁻5, with the 5th and 95th percentile levels exceeding the 1 in a million level. VOCs as a whole contributed over 80% of the risk among the HAPs measured and arsenic contributed most substantially to the overall risk associated with metals.

Are women as likely to take risks and compete? Behavioural findings from central Vietnam.

Using controlled experiments to compare the risk attitude and willingness to compete of husbands and wives in 500 couples in rural Vietnam, we find that women are more risk averse than men and that, compared to men, women are less likely to choose to compete, irrespective of how likely they are to succeed. Relevant to development programmes concerned with lifting women out of poverty, our findings suggest that women may be more reluctant to adopt new technologies, take out loans, or engage in economic activities that offer higher expected returns, in order to avoid setups that require them to be more competitive or that have less predictable outcomes.

Probabilistic exposure analysis for chemical risk characterization.

This paper summarizes the state of the science of probabilistic exposure assessment (PEA) as applied to chemical risk characterization. Current probabilistic risk analysis methods applied to PEA are reviewed. PEA within the context of risk-based decision making is discussed, including probabilistic treatment of related uncertainty, interindividual heterogeneity, and other sources of variability. Key examples of recent experience gained in assessing human exposures to chemicals in the environment, and other applications to chemical risk characterization and assessment, are presented. It is concluded that, although improvements continue to be made, existing methods suffice for effective application of PEA to support quantitative analyses of the risk of chemically induced toxicity that play an increasing role in key decision-making objectives involving health protection, triage, civil justice, and criminal justice. Different types of information required to apply PEA to these different decision contexts are identified, and specific PEA methods are highlighted that are best suited to exposure assessment in these separate contexts.

Cancer risk assessment of selected hazardous air pollutants in Seattle.

The risk estimates calculated from the conventional risk assessment method usually are compound specific and provide limited information for source-specific air quality control. We used a risk apportionment approach, which is a combination of receptor modeling and risk assessment, to estimate source-specific lifetime excess cancer risks of selected hazardous air pollutants. We analyzed the speciated PM(2.5) and VOCs data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine to first quantify source contributions to the mixture of hazardous air pollutants (HAPs) in terms of mass concentrations. The cancer risk from exposure to each source was then calculated as the sum of all available species' cancer risks in the source feature. We also adopted the bootstrapping technique for the uncertainty analysis. The results showed that the overall cancer risk was 6.09 x 10(-5), with the background (1.61 x 10(-5)), diesel (9.82 x 10(-6)) and wood burning (9.45 x 10(-6)) sources being the primary risk sources. The PM(2.5) mass concentration contributed 20% of the total risk. The 5th percentile of the risk estimates of all sources other than marine and soil were higher than 110(-6). It was also found that the diesel and wood burning sources presented similar cancer risks although the diesel exhaust contributed less to the PM(2.5) mass concentration than the wood burning. This highlights the additional value from such a risk apportionment approach that could be utilized for prioritizing control strategies to reduce the highest population health risks from exposure to HAPs.

The application of genetic information for regulatory standard setting under the clean air act: a decision-analytic approach.

In 2002, the U.S. Environmental Protection Agency (EPA) released an "Interim Policy on Genomics," stating a commitment to developing guidance on the inclusion of genetic information in regulatory decision making. This statement was followed in 2004 by a document exploring the potential implications. Genetic information can play a key role in understanding and quantifying human susceptibility, an essential step in many of the risk assessments used to shape policy. For example, the federal Clean Air Act (CAA) requires EPA to set National Ambient Air Quality Standards (NAAQS) for criteria pollutants at levels to protect even sensitive populations from adverse health effects with an adequate margin of safety. Asthmatics are generally regarded as a sensitive population, yet substantial research gaps in understanding genetic susceptibility and disease have hindered quantitative risk analysis. This case study assesses the potential role of genomic information regarding susceptible populations in the NAAQS process for fine particulate matter (PM(2.5)) under the CAA. In this initial assessment, we model the contribution of a single polymorphism to asthma risk and mortality risk; however, multiple polymorphisms and interactions (gene-gene and gene-environment) are known to play key roles in the disease process. We show that the impact of new information about susceptibility on estimates of population risk or average risk derived from large epidemiological studies depends on the circumstances. We also suggest that analysis of a single polymorphism, or other risk factor such as health status, may or may not change estimates of individual risk enough to alter a particular regulatory decision, but this depends on specific characteristics of the decision and risk information. We also show how new information about susceptibility in the context of the NAAQS for PM(2.5) could have a large impact on the estimated distribution of individual risk. This would occur if a group were consequently identified (based on genetic and/or disease status), that accounted for a disproportionate share of observed effects. Our results highlight certain conditions under which genetic information is likely to have an impact on risk estimates and the balance of costs and benefits within groups, and highlight critical research needs. As future studies explore more fully the relationship between exposure, genetic makeup, and disease status, the opportunity for genetic information and disease status to play pivotal roles in regulation can only increase.