Lessons learned and recommendations in conducting solutions-driven environmental and public health research.

Solutions-driven research is a transdisciplinary approach that incorporates diverse forms of expertise to identify solutions to stakeholder-identified environmental problems. This qualitative evaluation of early solutions-driven research projects provides transferable recommendations to improve researcher and stakeholder experiences and outcomes in transdisciplinary environmental research projects. Researchers with the U.S. Environmental Protection Agency (EPA) Office of Research and Development recently piloted a solutions-driven research approach in two parallel projects; one addressing nutrient management related to coastal waters and another studying wildland fire smoke impacts on indoor air quality. Studying the experiences of those involved with these pilots can enhance the integration of researcher and experiential expertise, improving solutions-driven research outcomes. Data collection included semi-structured interviews with 17 EPA researchers and 12 other stakeholders and reflective case narratives from the authors. We used conventional content analysis to qualitatively analyze perspectives on implementing innovative engagement and research approaches in a solutions-driven process. Findings that reflect common perspectives include the importance of continuous engagement, the challenges of differing timelines and priorities for researchers and stakeholders, and the need to define consistent markers of success across researchers and stakeholders. Key lessons to improve transdisciplinary research identified from the analysis are (1) improving clarity of roles and responsibilities; (2) planning to provide sufficient, continuous project funding over multiple years; (3) expecting research needs and plans to adapt to evolving circumstances; and (4) clearly defining the end of the project.

A value of information framework for assessing the trade-offs associated with uncertainty, duration, and cost of chemical toxicity testing.

A number of investigators have explored the use of value of information (VOI) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. This paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. The framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. The use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of VOI analysis. Specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the VOI of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. The framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. Our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. When the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial.

A Framework that Considers the Impacts of Time, Cost, and Uncertainty in the Determination of the Cost Effectiveness of Toxicity-Testing Methodologies.

Regulatory agencies are required to evaluate the impacts of thousands of chemicals. Toxicological tests currently used in such evaluations are time-consuming and resource intensive; however, advances in toxicology and related fields are providing new testing methodologies that reduce the cost and time required for testing. The selection of a preferred methodology is challenging because the new methodologies vary in duration and cost, and the data they generate vary in the level of uncertainty. This article presents a framework for performing cost-effectiveness analyses (CEAs) of toxicity tests that account for cost, duration, and uncertainty. This is achieved by using an output metric-the cost per correct regulatory decision-that reflects the three elements. The framework is demonstrated in two example CEAs, one for a simple decision of risk acceptability and a second, more complex decision, involving the selection of regulatory actions. Each example CEA evaluates five hypothetical toxicity-testing methodologies which differ with respect to cost, time, and uncertainty. The results of the examples indicate that either a fivefold reduction in cost or duration can be a larger driver of the selection of an optimal toxicity-testing methodology than a fivefold reduction in uncertainty. Uncertainty becomes of similar importance to cost and duration when decisionmakers are required to make more complex decisions that require the determination of small differences in risk predictions. The framework presented in this article may provide a useful basis for the identification of cost-effective methods for toxicity testing of large numbers of chemicals.

Exploration of PM mass, source, and component-related factors that might explain heterogeneity in daily PM2.5-mortality associations across the United States.

Multi-city epidemiologic studies examining short-term (daily) differences in fine particulate matter (PM2.5) provide evidence of substantial spatial heterogeneity in city-specific mortality risk estimates across the United States. Because PM2.5 is a mixture of particles, both directly emitted from sources or formed through atmospheric reactions, some of this heterogeneity may be due to regional variations in PM2.5 toxicity. Using inverse variance weighted linear regression, we examined change in percent change in mortality in association with 24 "exposure" determinants representing three basic groupings based on potential explanations for differences in PM toxicity - size, source, and composition. Percent changes in mortality for the PM2.5-mortality association for 313 core-based statistical areas and their metropolitan divisions over 1999-2005 were used as the outcome. Several determinants were identified as potential contributors to heterogeneity: all mass fraction determinants, vehicle miles traveled (VMT) for diesel total, VMT gas per capita, PM2.5 ammonium, PM2.5 nitrate, and PM2.5 sulfate. In multivariable models, only daily correlation of PM2.5 with PM10 and long-term average PM2.5 mass concentration were retained, explaining approximately 10% of total variability. The results of this analysis contribute to the growing body of literature specifically focusing on assessing the underlying basis of the observed spatial heterogeneity in PM2.5-mortality effect estimates, continuing to demonstrate that this heterogeneity is multifactorial and not attributable to a single aspect of PM.

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

Institutional insights on integrating social and environmental science for solutions-driven research.

Solving complex environmental problems requires interdisciplinary research involving the social and environmental sciences. The U.S. Environmental Protection Agency is working toward solutions-driven research and interdisciplinary integration within its Office of Research and Development. This article details the history of this process and discusses lessons learned from other federal agencies seeking to integrate social and biophysical research: finding the right combination of top down and bottom up approaches; balancing objectives of advancing science and/or supporting programmatic operations; using social science methods to inform the process; and engaging multiple stakeholders. Attention to the social context of scientific practice, including research processes and research use, fosters success. Three strategies for integrating social sciences to support solutions-driven environmental research are: weaving social science throughout the research process, strengthening social networks, and fostering interdisciplinary hubs. Integration into planning and carrying out research has greater transformative potential than integration into product development and distribution. This article provides insights into institutional considerations for advancing interdisciplinarity and the social context of scientific practice in government agencies. It illustrates the multiple decision contexts and inclusion of social science at the science-policy interface.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

BACKGROUND: Exposure to ambient air pollution increases morbidity and mortality, and is a leading contributor to global disease burden. We explored spatial and temporal trends in mortality and burden of disease attributable to ambient air pollution from 1990 to 2015 at global, regional, and country levels. METHODS: We estimated global population-weighted mean concentrations of particle mass with aerodynamic diameter less than 2·5 µm (PM2·5) and ozone at an approximate 11 km × 11 km resolution with satellite-based estimates, chemical transport models, and ground-level measurements. Using integrated exposure-response functions for each cause of death, we estimated the relative risk of mortality from ischaemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections from epidemiological studies using non-linear exposure-response functions spanning the global range of exposure. FINDINGS: Ambient PM2·5 was the fifth-ranking mortality risk factor in 2015. Exposure to PM2·5 caused 4·2 million (95% uncertainty interval [UI] 3·7 million to 4·8 million) deaths and 103·1 million (90·8 million 115·1 million) disability-adjusted life-years (DALYs) in 2015, representing 7·6% of total global deaths and 4·2% of global DALYs, 59% of these in east and south Asia. Deaths attributable to ambient PM2·5 increased from 3·5 million (95% UI 3·0 million to 4·0 million) in 1990 to 4·2 million (3·7 million to 4·8 million) in 2015. Exposure to ozone caused an additional 254 000 (95% UI 97 000-422 000) deaths and a loss of 4·1 million (1·6 million to 6·8 million) DALYs from chronic obstructive pulmonary disease in 2015. INTERPRETATION: Ambient air pollution contributed substantially to the global burden of disease in 2015, which increased over the past 25 years, due to population ageing, changes in non-communicable disease rates, and increasing air pollution in low-income and middle-income countries. Modest reductions in burden will occur in the most polluted countries unless PM2·5 values are decreased substantially, but there is potential for substantial health benefits from exposure reduction. FUNDING: Bill & Melinda Gates Foundation and Health Effects Institute.

The Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE): A tool to estimate the health and economic benefits of reducing air pollution.

A number of software tools exist to estimate the health and economic impacts associated with air quality changes. Over the past 15 years, the U.S. Environmental Protection Agency and its partners invested substantial time and resources in developing the Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE). BenMAP-CE is a publicly available, PC-based open source software program that can be configured to conduct health impact assessments to inform air quality policies anywhere in the world. The developers coded the platform in C# and made the source code available in GitHub, with the goal of building a collaborative relationship with programmers with expertise in other environmental modeling programs. The team recently improved the BenMAP-CE user experience and incorporated new features, while also building a cadre of analysts and BenMAP-CE training instructors in Latin America and Southeast Asia.

Meta-Analysis Methods to Estimate the Shape and Uncertainty in the Association Between Long-Term Exposure to Ambient Fine Particulate Matter and Cause-Specific Mortality Over the Global Concentration Range.

Estimates of excess mortality associated with exposure to ambient concentrations of fine particulate matter have been obtained from either a single cohort study or pooling information from a small number of studies. However, standard frequentist methods of pooling are known to underestimate statistical uncertainty in the true risk distribution when the number of studies pooled is small. Alternatively, Bayesian pooling methods using noninformative priors yield unrealistically large amounts of uncertainty in this case. We present a new hybrid frequentist-bayesian framework for meta-analysis that incorporates features of both frequentist and Bayesian approaches, yielding estimated uncertainty distributions that are more useful for burden estimation. We also present an example of mortality risk due to long-term exposure to ambient fine particulate matter obtained from a small number of cohort studies conducted in the United States and Europe. We compare our new risk uncertainty distribution to that obtained by the integrated exposure-response (IER) model used in the Global Burden of Disease 2010 project for which risk was modeled over the entire global concentration range. We suggest a method to incorporate our new risk uncertainty distribution based on the relatively low concentrations observed in the United States and western Europe into the IER model, thus extending risk estimation to the global concentration range.

Analysis of alternative pathways for reducing nitrogen oxide emissions.

UNLABELLED: Strategies for reducing tropospheric ozone (O3) typically include modifying combustion processes to reduce the formation of nitrogen oxides (NOx) and applying control devices that remove NOx from the exhaust gases of power plants, industrial sources and vehicles. For portions of the U.S., these traditional controls may not be sufficient to achieve the National Ambient Air Quality Standard for ozone. We apply the MARKet ALlocation (MARKAL) energy system model in a sensitivity analysis to explore whether additional NOx reductions can be achieved through extensive electrification of passenger vehicles, adoption of energy efficiency and conservation measures within buildings, and deployment of wind and solar power in the electric sector. Nationally and for each region of the country, we estimate the NOx implications of these measures. Energy efficiency and renewable electricity are shown to reduce NOx beyond traditional controls. Wide-spread light duty vehicle electrification produces varied results, with NOx increasing in some regions and decreasing in others. However, combining vehicle electrification with renewable electricity reduces NOx in all regions. IMPLICATIONS: State governments are charged with developing plans that demonstrate how air quality standards will be met and maintained. The results presented here provide an indication of the national and regional NOx reductions available beyond traditional controls via extensive adoption of energy efficiency, renewable electricity, and vehicle electrification.

Counterpoint: moving from potential-outcomes thinking to doing--changing research planning to enable successful health outcomes research.

The potential-outcomes framework is an appealing new approach that imposes a degree of formal conceptual modeling beyond traditional epidemiologic methods for assessing associations between air pollution and health. However, it introduces a number of additional factors to consider when selecting intervention and especially control conditions that call for forward-thinking research designs. We propose that researchers seeking to implement the potential-outcomes framework consider the use of prospective designs that provide more opportunities to establish well-defined intervention and control populations and determine causal relationships between air quality and health. In implementing these prospective research designs, collaboration between researchers and those who implement the interventions can improve the understanding of how a planned intervention actually occurs, thereby improving the characterization of emissions and air quality responses to the intervention. By looking ahead, epidemiologists can take advantage of upcoming regulatory interventions to design successful health outcomes research programs.

Outdoor fine particles and nonfatal strokes: systematic review and meta-analysis.

BACKGROUND: Epidemiologic studies find that long- and short-term exposure to fine particles (PM2.5) is associated with adverse cardiovascular outcomes, including ischemic and hemorrhagic strokes. However, few systematic reviews or meta-analyses have synthesized these results. METHODS: We reviewed epidemiologic studies that estimated the risks of nonfatal strokes attributable to ambient PM2.5. To pool risks among studies we used a random-effects model and 2 Bayesian approaches. The first Bayesian approach assumes a normal prior that allows risks to be zero, positive or negative. The second assumes a gamma prior, where risks can only be positive. This second approach is proposed when the number of studies pooled is small, and there is toxicological or clinical literature to support a causal relation. RESULTS: We identified 20 studies suitable for quantitative meta-analysis. Evidence for publication bias is limited. The frequentist meta-analysis produced pooled risk ratios of 1.06 (95% confidence interval = 1.00-1.13) and 1.007 (1.003-1.010) for long- and short-term effects, respectively. The Bayesian meta-analysis found a posterior mean risk ratio of 1.08 (95% posterior interval = 0.96-1.26) and 1.008 (1.003-1.013) from a normal prior, and of 1.05 (1.02-1.10) and 1.008 (1.004-1.013) from a gamma prior, for long- and short-term effects, respectively, per 10 µg/m PM2.5. CONCLUSIONS: Sufficient evidence exists to develop a concentration-response relation for short- and long-term exposures to PM2.5 and stroke incidence. Long-term exposures to PM2.5 result in a higher risk ratio than short-term exposures, regardless of the pooling method. The evidence for short-term PM2.5-related ischemic stroke is especially strong.

Estimating the national public health burden associated with exposure to ambient PM2.5 and ozone.

Ground-level ozone (O(3)) and fine particulate matter (PM(2.5)) are associated with increased risk of mortality. We quantify the burden of modeled 2005 concentrations of O(3) and PM(2.5) on health in the United States. We use the photochemical Community Multiscale Air Quality (CMAQ) model in conjunction with ambient monitored data to create fused surfaces of summer season average 8-hour ozone and annual mean PM(2.5) levels at a 12 km grid resolution across the continental United States. Employing spatially resolved demographic and concentration data, we assess the spatial and age distribution of air-pollution-related mortality and morbidity. For both PM(2.5) and O(3) we also estimate: the percentage of total deaths due to each pollutant; the reduction in life years and life expectancy; and the deaths avoided according to hypothetical air quality improvements. Using PM(2.5) and O(3) mortality risk coefficients drawn from the long-term American Cancer Society (ACS) cohort study and National Mortality and Morbidity Air Pollution Study (NMMAPS), respectively, we estimate 130,000 PM(2.5) -related deaths and 4,700 ozone-related deaths to result from 2005 air quality levels. Among populations aged 65-99, we estimate nearly 1.1 million life years lost from PM(2.5) exposure and approximately 36,000 life years lost from ozone exposure. Among the 10 most populous counties, the percentage of deaths attributable to PM(2.5) and ozone ranges from 3.5% in San Jose to 10% in Los Angeles. These results show that despite significant improvements in air quality in recent decades, recent levels of PM(2.5) and ozone still pose a nontrivial risk to public health.

Climate change-related temperature impacts on warm season heat mortality: a proof-of-concept methodology using BenMAP.

Climate change is anticipated to raise overall temperatures and is likely to increase heat-related human health morbidity and mortality risks. The objective of this work was to develop a proof-of-concept approach for estimating excess heat-related premature deaths in the continental United States resulting from potential changes in future temperature using the BenMAP model. In this approach we adapt the methods and tools that the US Environmental Protection Agency uses to assess air pollution health impacts by incorporating temperature modeling and heat mortality health impact functions. This new method demonstrates the ability to apply the existing temperature-health literature to quantify prospective changes in climate-sensitive heat-related mortality. We compared estimates of future temperature with and without climate change and applied heat-mortality health functions to estimate relative changes in heat-related premature mortality. Using the A1B emissions scenario, we applied the GISS-II global circulation model downscaled to 36-km using MM5 and formatted using the Meteorology-Chemistry Interface Processor. For averaged temperatures derived from the 5 years 2048-2052 relative to 1999-2003 we estimated for the warm season May-September a national U.S. estimate of annual incidence of heat-related mortality to be 3700-3800 from all causes, 3500 from cardiovascular disease, and 21 000-27 000 from nonaccidental death, applying various health impact functions. Our estimates of mortality, produced to validate the application of a new methodology, suggest the importance of quantifying heat impacts in economic assessments of climate change.

Maximizing health benefits and minimizing inequality: incorporating local-scale data in the design and evaluation of air quality policies.

The U.S. Environmental Protection Agency undertook a case study in the Detroit metropolitan area to test the viability of a new multipollutant risk-based (MP/RB) approach to air quality management, informed by spatially resolved air quality, population, and baseline health data. The case study demonstrated that the MP/RB approach approximately doubled the human health benefits achieved by the traditional approach while increasing cost less than 20%--moving closer to the objective of Executive Order 12866 to maximize net benefits. Less well understood is how the distribution of health benefits from the MP/RB and traditional strategies affect the existing inequalities in air-pollution-related risks in Detroit. In this article, we identify Detroit populations that may be both most susceptible to air pollution health impacts (based on local-scale baseline health data) and most vulnerable to air pollution (based on fine-scale PM(2.5) air quality modeling and socioeconomic characteristics). Using these susceptible/vulnerable subpopulation profiles, we assess the relative impacts of each control strategy on risk inequality, applying the Atkinson Index (AI) to quantify health risk inequality at baseline and with either risk management approach. We find that the MP/RB approach delivers greater air quality improvements among these subpopulations while also generating substantial benefits among lower-risk populations. Applying the AI, we confirm that the MP/RB strategy yields less PM(2.5) mortality and asthma hospitalization risk inequality than the traditional approach. We demonstrate the value of this approach to policymakers as they develop cost-effective air quality management plans that maximize risk reduction while minimizing health inequality.

Estimating Lifetime Cost of Illness. An Application to Asthma.

Rationale: Approximately 8% of the U.S. population suffers from asthma, a chronic condition. It poses a substantial economic burden to society in the form of lost productivity and healthcare costs.Objectives: We use the Medical Expenditure Panel Survey 2002-2010 to quantify the lifetime costs of asthma at each age, the impact of adult asthma on earnings and choice of occupation, and the impact of childhood asthma on parental income.Methods: We developed a framework to estimate the present discounted value of the cumulative lifetime asthma-related healthcare costs and lost productivity experienced by an individual after onset. This approach allows for age- and asthma duration-related variability in annual costs as well as for the intermittent nature of asthma symptoms.Results: Estimated asthma-related annual healthcare expenditures across all life stages are $700-$2,200 (2010 U.S. dollars). Lost annual earnings among individuals aged 30-49 are over $4,000 (2010 U.S. dollars). The present discounted value of the cumulative lifetime healthcare costs and lost productivity for a new case of asthma is estimated at $36,500 using the 3% discount rate (2010 U.S. dollars).Conclusions: The economic burden of asthma is substantial and larger than previously estimated, reflecting expenditures on treatment and lost earnings.

Scaling Up: Citizen Science Engagement and Impacts Beyond the Individual.

As the application of citizen science expands to address increasingly complex social problems (e.g., community health), there is opportunity to consider higher-order engagement beyond that of individual members of a community. The US Environmental Protection Agency (EPA) is working to foster public engagement in science through Smoke Sense, which is a citizen science research project that aims to reduce the public health burden of wildland fire smoke. Smoke Sense is facilitated by a mobile app that shares information on air quality, smoke, and health, and collects individual reports of smoke exposure and concurrent health symptoms. However, Smoke Sense is also generating interest among organizations that are working in this problem domain at the local, state, national, and tribal levels. The interest in the citizen science project at the organizational level led us to ask: What would motivate members of organizations to engage in a citizen science project like Smoke Sense, and how do they envision that engagement could improve public health? To explore these questions, we collected data through interviews with individuals who were engaged in Smoke Sense through their work at local, state, and tribal environmental and health agencies in the western US. An inductive, thematic analysis suggests that individuals' motivations stem from their experiences and challenges with smoke in their different roles (e.g., personal, professional, and community member). Respondents envisioned leveraging both the app itself and the data generated by individuals in their region to enhance and support their existing outreach and communication efforts related to smoke. Citizen science projects that aim to address issues in complex problem domains should be designed to complement the work of partner organizations.

Knowing Your Audience: A Typology of Smoke Sense Participants to Inform Wildfire Smoke Health Risk Communication.

Central to public health risk communication is understanding the perspectives and shared values among individuals who need the information. Using the responses from a Smoke Sense citizen science project, we examined perspectives on the issue of wildfire smoke as a health risk in relation to an individual's preparedness to adopt recommended health behaviors. The Smoke Sense smartphone application provides wildfire-related health risk resources and invites participants to record their perspectives on the issue of wildfire smoke. Within the app, participants can explore current and forecasted daily air quality, maps of fire locations, satellite images of smoke plumes, and learn about health consequences of wildfire smoke. We used cluster analysis to identify perspective trait-clusters based on health status, experience with fire smoke, risk perception, self-efficacy, access to exposure-reducing resources, health information needs, and openness to health risk messaging. Differences between traits were examined based on demographics, health status, activity level and engagement with the app. We mapped these traits to the Precaution Adoption Process Model (PAPM) to indicate where each trait lies in adopting recommended health behaviors. Finally, we suggest messaging strategies that may be suitable for each trait. We determined five distinct perspective traits which included individuals who were Protectors and have decided to engage on the issue by adopting new behaviors to protect their health; Cautious, Proactive, and Susceptible individuals who were at a Deciding stage but differed based on risk perceptions and information needs; and the Unengaged who did not perceive smoke as a health issue and were unlikely to change behavior in response to messaging. Across all five traits, the level of engagement and information needs differed substantially, but were not defined by demographics. Individuals in the Susceptible trait had the highest level of engagement and the highest information needs. Messaging that emphasizes self-efficacy and benefits of reducing exposure may be effective in motivating individuals from the deciding stage to taking health protective action. Shared perspectives define an individual's propensity for acting on recommended health behaviors, therefore, health risk message content should be tailored based on these perspectives.