Impact of satellite clinics on geographic access to assisted reproductive technology services in the United States.

BACKGROUND: Many assisted reproductive technology (ART) centers utilize satellite clinics to expand reach and access to clinical services, but their contribution to lowering geographic barriers in access to care has not been examined. This study's purpose is to determine the extent to which satellite clinics impact geographic access to ART and estimate the percentage of reproductive-age women who have geographic access to ART services. METHODS: A systematic web-search collected the locations of all main and satellite ART clinics in the United States (US). Driving times were calculated between satellite clinics and main clinics. The percentage of women with geographic access to care was characterized by clinic type using US Census Core Based Statistical Areas (CBSAs). Logistic regression was used to statistically model the presence of main and satellite clinics as a function of CBSA median income and female reproductive-age population. RESULTS: Four hundred sixty-nine main clinics with embryology labs and 583 satellite clinics were found in the US. Practices with satellite clinics tend to perform more ART cycles. Satellite clinics are located on average 66 minutes from their practice's main clinic and 31 minutes from any main clinic. 22% of satellite clinics were in CBSAs without a main clinic. 46 M (72%) US reproductive-age women live in a CBSA with a main clinic, 5.1 M (8%) women live in a CBSA without a main clinic but at least one satellite clinic, and 13 M (20%) women live in an area with no ART clinic of either type. Female reproductive-age population was found to be a more important predictor of clinic presence than median income. CONCLUSIONS: The majority of satellite clinics in the US are positioned in relative proximity to a main clinic. 85% of satellite clinics are located closer to the main clinic of other practices than to their own main clinic. Less than a quarter of ART satellite clinics expand geographic access to ART services by being located in areas without a main clinic, and the vast majority of practices with satellite clinics position their satellite clinics close to another practice's main clinic. TRIAL REGISTRATION: Not applicable.

Grid-Scale Life Cycle Greenhouse Gas Implications of Renewable, Storage, and Carbon Pricing Options.

Models that characterize life cycle greenhouse gases from electricity generation are limited in their capability to estimate emissions changes at scales that capture the grid-scale benefits of technologies and policies that enhance renewable systems integration. National assumptions about generation mixes are often applied at annual time steps, neglecting spatiotemporal resolutions that provide insights on impacts from time-variable emissions. Our grid-scale model incorporates details of transmission and generation planning that allows a geographically and temporally textured and more realistic assessment of the life cycle greenhouse gas emissions outcomes, using a case study of the Western Interconnection of North America. Results from a co-optimized model of generation, transmission, and operations-the Johns Hopkins Stochastic Multistage Integrated Network Expansion Model-provide a detailed characterization of twenty-one scenarios with different configurations of storage additions, new renewable capacity, and carbon prices. Life cycle results suggest that optimization models that focus on generation alone may underestimate emissions by 18-29% because only emissions from power generation are quantified (i.e., supply chain emissions are omitted) but also that carbon pricing is the predominant driver of reducing emissions in the scenarios we examine. Life cycle assessment of electricity generation should move beyond individual technologies toward capturing the influence of policies at the system level to better understand technology-policy dynamics for the grid.

The full Low-carbon Expansion Generation Optimization (LEGO) model.

This paper introduces the full Low-carbon Expansion Generation Optimization (LEGO) model available on Github (https://github.com/wogrin/LEGO). LEGO is a mixed-integer quadratically constrained optimization problem and has been designed to be a multi-purpose tool, like a Swiss army knife, that can be employed to study many different aspects of the energy sector. Ranging from short-term unit commitment to long-term generation and transmission expansion planning. The underlying modeling philosophies are: modularity and flexibility. Its unique temporal structure allows LEGO to function with either chronological hourly data, or all kinds of representative periods. LEGO is also composed of thematic modules that can be added or removed from the model easily via data options depending on the scope of the study. Those modules include: unit commitment constraints; DC- or AC-OPF formulations; battery degradation; rate of change of frequency inertia constraints; demand-side management; or the hydrogen sector. LEGO also provides a plethora of model outputs (both primal and dual), which is the basis for both technical but also economic analyses. To our knowledge, there is no model that combines all of these capabilities, which we hereby make freely available to the scientific community.

Linking energy sector and air quality models through downscaling: Long-run siting of electricity generators to account for spatial variability and technological innovation.

Modeling the air pollution implications of long-term energy transitions requires a downscaling process as an intermediate step between national-scale energy models and fine-scaled air quality models. Traditional "Grow-in-Place" (GIP) downscaling methods assume that future patterns of generator siting and emissions will be similar to those in the past. However, rapid technological change and shifting policy might yield very different future spatial patterns of power emissions. Here, we propose a "Site-and-Grow" (SAG) downscaling framework to couple the Electricity Market Module (EMM) of the National Energy Modeling System (NEMS) with the Community Multi-scale Air Quality (CMAQ) model to simulate future changes in emissions from power sector. The SAG framework consists of two steps. First, we downscale regional energy information to subregions using a modified generation expansion model under the assumption that economic fundamentals drive decisions at that scale. Second, we use GIS-based screening to locate potential sites for new power plants, and specify the final county-level placement using a multicriteria value function, assuming that land use and environmental constraints are most influential. The method is implemented in one EMM region (Carolinas and Virginia) as a case study. We compare spatial and temporal variability of downscaled emissions using both GIP and SAG methods, as well as emissions differences among four NEMS scenarios (base case, high natural gas consumption, high penetration of electric vehicles, and marine vessel electrification in ports). The results indicate that coal power plant emissions such as SO2 and NOx continue to dominate emissions from all other traditional power plants even in 2040, which suggests that emission changes will mainly be determined by where old coal plants are retired. An ANOVA (analysis of variance) comparison of four energy scenarios with two downscaling methods shows that the choice of downscaling method can contribute as much to emissions patterns as much as the choice of scenario.

Building Healthy Community Environments: A Public Health Approach.

Environmental quality has a profound effect on health and the burden of disease. In the United States, the environment-related burden of disease is increasingly dominated by chronic diseases. At the local level, public health practitioners realize that many policy decisions affecting environmental quality and health transcend the authorities of traditional health department programs. Healthy decisions about the built environment, including housing, transportation, and energy, require broad collaborative efforts. Environmental health professionals have an opportunity to address the shift in public health burden toward chronic diseases and play an important role in the design of healthy communities by bringing data and tools to decision makers. This article provides a guide for community leaders to consider the public health effects of decisions about the built environment. We present a conceptual framework that represents a shift from compartmentalized solutions toward an inclusive systems approach that encourages partnership across disciplines and sectors. We discuss practical tools to assist with environmental decision making, such as Health Impact Assessments, environmental public health tracking, and cumulative risk assessment. We also identify priorities in research, practice, and education to advance the role of public health in decision making to improve health, such as the Health Impact Assessment, as a core competency for environmental health practitioners. We encourage cross-disciplinary communication, research, and education that bring the fields of planning, transportation, and energy in closer collaboration with public health to jointly advance the systems approach to today's environmental challenges.

System dynamics-based evaluation of interventions to promote appropriate waste disposal behaviors in low-income urban areas: A Baltimore case study.

Inappropriate waste disposal is a serious issue in many urban neighborhoods, exacerbating environmental, rodent, and public health problems. Governments all over the world have been developing interventions to reduce inappropriate waste disposal. A system dynamics model is proposed to quantify the impacts of interventions on residential waste related behavior. In contrast to other models of municipal solid waste management, the structure of our model is based on sociological and economic studies on how incentives and social norms interactively affect waste disposal behavior, and its parameterization is informed by field work. A case study of low-income urban neighborhoods in Baltimore, MD, USA is presented. The simulation results show the effects of individual interventions, and also identify positive interactions among some potential interventions, especially information and incentive-based policies, as well as their limitations. The model can help policy analysts identify the most promising intervention packages, and then field test those few, rather than having to pilot test all combinations. Sensitivity analyses demonstrate large uncertainties about behavioral responses to some interventions, showing where information from survey research and social experiments would improve policy making.

Metrics matter: conflicting air quality rankings from different indices of air pollution.

Comparisons of air quality policies involve numerous considerations such as cost, health, effects on vegetation and materials, and aesthetics. Such assessments require difficult scientific and value judgments. These difficulties can also characterize comparisons that consider only physical and chemical air quality indices. We compare ambient tropospheric ozone concentrations from a baseline scenario and seven emissions scenarios for a case study. The resulting air qualities are evaluated based upon spatial and temporal distribution of impacts, exceedances of regulatory standards, concentrations weighted by population density, and a variety of averaging times. Results reveal that even when only a single pollutant is considered, comparisons of air quality can be ambiguous. Which scenario has better air quality depends on how (e.g., choice of averaging times, absolute vs. relative changes in concentrations), where (e.g., effects in specific areas vs. effects over the entire region), and when (e.g., the percent of time for which one alternative has higher concentrations than another) the comparison is made. This indicates that general descriptors of air quality such as the annual average ozone concentration do not fully describe the complexity of air quality. Use of such averages can result in different policy rankings than consideration of the full distribution of impacts.

Modeling the effects of changes in new source review on national SO2 and NOx emissions from electricity-generating units.

The Clean Air Act establishes New Source Review (NSR) programs that apply to construction or modification of major stationary sources. In 2002 and 2003, EPA revised its rules to narrow NSR's coverage of renovations. Congress mandated a National Research Council study of the revisions' impacts. In that study, we used an electricity-sector model to explore possible effects of the equipment replacement provision (ERP), the principal NSR change directed at power plants. We find that, assuming implementation of the Clean Air Interstate Rule (CAIR), tight enforcement of the prerevision NSR rules would likely lead to no or limited decreases in national emissions compared to policies such as ERP. However, emissions might shift forward in time because the previous NSR rules would depress allowance prices, discouraging banking and encouraging allowance use. Only under the most aggressive prerevision NSR enforcement scenario, in which essentially all coal capacity is compelled to retrofit controls by 2020, do NOx emissions fall below ERP levels. Even then, total 2007-2020 SO2 emissions are unaffected. Further decreases in national emissions could be accomplished more cheaply by tighter emissions caps than through NSR because caps provide incentives for efficient operating strategies, such as fuel switching, as well as retrofits.