Ethical Considerations of Declining Surgical Intervention: Balancing Patient Wishes with Fiduciary Responsibility.

ABSTRACT: Orthopaedic surgeons face increasing pressure to meet quality metrics due to regulatory changes and payment policies. Poor outcomes, including patient mortality, can result in financial penalties and negative ratings. Importantly, adverse outcomes often increase surgeon stress level and lead to job dissatisfaction and burnout. Despite optimization efforts, some orthopaedic patients remain at high risk for complications. In this article, we explore the ethical considerations when surgeons are presented with high-risk surgical candidates. We examine how the ethical tenets of patient interests, namely beneficence, nonmaleficence, autonomy, and justice, apply to such patients. We discuss external forces such as the malpractice environment, financial challenges in health-care delivery, and quality rankings. Informed consent and the challenges of communicating risks to patients are discussed, as well as the role of modifiable and nonmodifiable risk factors. Case examples with varied outcomes highlight the complexities of decision-making with high-risk patients and the potential role of palliative care. We provide recommendations for surgeons and care teams, including the importance of justifiable reasons for not operating, the utilization of institutional resources to help make care decisions, and the robust communication of risks to patients.

Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry.

We updated the chemical mechanism of the GEOS-Chem global 3-D model of atmospheric chemistry to include new recommendations from the NASA Jet Propulsion Laboratory (JPL) chemical kinetics Data Evaluation 19-5 and from the International Union of Pure and Applied Chemistry (IUPAC) and to balance carbon and nitrogen. We examined the impact of these updates on the GEOS-Chem version 14.0.1 simulation. Notable changes include 11 updates to reactions of reactive nitrogen species, resulting in a 7% net increase in the stratospheric NOx (NO + NO2) burden; an updated CO + OH rate formula leading to a 2.7% reduction in total tropospheric CO; adjustments to the rate coefficient and branching ratios of propane + OH, leading to reduced tropospheric propane (-17%) and increased acetone (+3.5%) burdens; a 41% increase in the tropospheric burden of peroxyacetic acid due to a decrease in the rate coefficient for its reaction with OH, further contributing to reductions in peroxyacetyl nitrate (PAN; -3.8%) and acetic acid (-3.4%); and a number of minor adjustments to halogen radical cycling. Changes to the global tropospheric burdens of other species include -0.7% for ozone, +0.3% for OH (-0.4% for methane lifetime against oxidation by tropospheric OH), +0.8% for formaldehyde, and -1.7% for NOx. The updated mechanism reflects the current state of the science, including complex chemical dependencies of key atmospheric species on temperature, pressure, and concentrations of other compounds. The improved conservation of carbon and nitrogen will facilitate future studies of their overall atmospheric budgets.

Public Health Benefits From Improved Identification of Severe Air Pollution Events With Geostationary Satellite Data.

Despite improvements in ambient air quality in the US in recent decades, many people still experience unhealthy levels of pollution. At present, national-level alert-day identification relies predominately on surface monitor networks and forecasters. Satellite-based estimates of surface air quality have rapidly advanced and have the capability to inform exposure-reducing actions to protect public health. At present, we lack a robust framework to quantify public health benefits of these advances in applications of satellite-based atmospheric composition data. Here, we assess possible health benefits of using geostationary satellite data, over polar orbiting satellite data, for identifying particulate air quality alert days (24hr PM2.5 > 35 µg m-3) in 2020. We find the more extensive spatiotemporal coverage of geostationary satellite data leads to a 60% increase in identification of person-alerts (alert days × population) in 2020 over polar-orbiting satellite data. We apply pre-existing estimates of PM2.5 exposure reduction by individual behavior modification and find these additional person-alerts may lead to 1,200 (800-1,500) or 54% more averted PM2.5-attributable premature deaths per year, if geostationary, instead of polar orbiting, satellite data alone are used to identify alert days. These health benefits have an associated economic value of 13 (8.8-17) billion dollars ($2019) per year. Our results highlight one of many potential applications of atmospheric composition data from geostationary satellites for improving public health. Identifying these applications has important implications for guiding use of current satellite data and planning future geostationary satellite missions.

Impact of particulate nitrate photolysis on air quality over the Northern Hemisphere.

We use the Community Multiscale Air Quality (CMAQv5.4) model to examine the potential impact of particulate nitrate (pNO3-) photolysis on air quality over the Northern Hemisphere. We estimate the photolysis frequency of pNO3- by scaling the photolysis frequency of nitric acid (HNO3) with an enhancement factor that varies between 10 and 100 depending on pNO3- and sea-salt aerosol concentrations and then perform CMAQ simulations without and with pNO3- photolysis to quantify the range of impacts on tropospheric composition. The photolysis of pNO3- produces gaseous nitrous acid (HONO) and nitrogen dioxide (NO2) over seawater thereby increasing atmospheric HONO and NO2 mixing ratios. HONO subsequently undergoes photolysis, producing hydroxyl radicals (OH). The increase in NO2 and OH alters atmospheric chemistry and enhances the atmospheric ozone (O3) mixing ratio over seawater, which is subsequently transported to downwind continental regions. Seasonal mean model O3 vertical column densities without pNO3- photolysis are lower than the Ozone Monitoring Instrument (OMI) retrievals, while the column densities with the pNO3- photolysis agree better with the OMI retrievals of tropospheric O3 burden. We compare model O3 mixing ratios with available surface observed data from the U.S., Japan, the Tropospheric Ozone Assessment Report - Phase II, and OpenAQ; and find that the model without pNO3- photolysis underestimates the observed data in winter and spring seasons and the model with pNO3- photolysis improves the comparison in both seasons, largely rectifying the pronounced underestimation in spring. Compared to measurements from the western U.S., model O3 mixing ratios with pNO3- photolysis agree better with observed data in all months due to the persistent underestimation of O3 without pNO3- photolysis. Compared to the ozonesonde measurements, model O3 mixing ratios with pNO3- photolysis also agree better with observed data than the model O3 without pNO3- photolysis.

The Social Cost of Ozone-Related Mortality Impacts From Methane Emissions.

Atmospheric methane directly affects surface temperatures and indirectly affects ozone, impacting human welfare, the economy, and environment. The social cost of methane (SC-CH4) metric estimates the costs associated with an additional marginal metric ton of emissions. Current SC-CH4 estimates do not consider the indirect impacts associated with ozone production from changes in methane. We use global model simulations and a new BenMAP webtool to estimate respiratory-related deaths associated with increases in ozone from a pulse of methane emissions in 2020. By using an approach consistent with the current SC-CH4 framework, we monetize and discount annual damages back to present day values. We estimate that the methane-ozone mechanism is attributable to 760 (95% CI: 330-1200) respiratory-related deaths per million metric tons of methane globally, for a global net present damage of $1800/mT (95% CI: $760-$2800/Mt CH4; 2% Ramsey discount rate); this would double the current SC-CH4 if included. These physical impacts are consistent with recent studies, but comparing direct costs is challenging. Economic damages are sensitive to uncertainties in the exposure and health risks associated with tropospheric ozone, assumptions about future projections of NOx emissions, socioeconomic conditions, and mortality rates, monetization parameters, and other factors. Our estimates are highly sensitive to uncertainties in ozone health risks. We also develop a reduced form model to test sensitivities to other parameters. The reduced form tool runs with a user-supplied emissions pulse, as well as socioeconomic and precursor projections, enabling future integration of the methane-ozone mechanism into the SC-CH4 modeling framework.

Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors.

In the United States (U.S.), studies on nitrogen dioxide (NO2) trends and pollution-attributable health effects have historically used measurements from in situ monitors, which have limited geographical coverage and leave 66% of urban areas unmonitored. Novel tools, including remotely sensed NO2 measurements and estimates of NO2 estimates from land-use regression and photochemical models, can aid in assessing NO2 exposure gradients, leveraging their complete spatial coverage. Using these data sets, we find that Black, Hispanic, Asian, and multiracial populations experience NO2 levels 15-50% higher than the national average in 2019, whereas the non-Hispanic White population is consistently exposed to levels that are 5-15% lower than the national average. By contrast, the in situ monitoring network indicates more moderate ethnoracial NO2 disparities and different rankings of the least- to most-exposed ethnoracial population subgroup. Validating these spatially complete data sets against in situ observations reveals similar performance, indicating that all these data sets can be used to understand spatial variations in NO2. Integrating in situ monitoring, satellite data, statistical models, and photochemical models can provide a semiobservational record, complete geospatial coverage, and increasingly high spatial resolution, enhancing future efforts to characterize, map, and track exposure and inequality for highly spatially heterogeneous pollutants like NO2.

Characterizing variations in ambient PM2.5 concentrations at the U.S. Embassy in Dhaka, Bangladesh using observations and the CMAQ modeling system.

We analyze hourly PM2.5 (particles with an aerodynamic diameter of ≤ 2.5 µm) concentrations measured at the U.S. Embassy in Dhaka over the 2016 - 2021 time period and find that concentrations are seasonally dependent with the highest occurring in winter and the lowest in monsoon seasons. Mean winter PM2.5 concentrations reached ~165-175 µg/m3 while monsoon concentrations remained ~30-35 µg/m3. Annual mean PM2.5 concentration reached ~5-6 times greater than the Bangladesh annual PM2.5 standard of 15 µg/m3. The number of days exceeding the daily PM2.5 standard of 65 µg/m3 in a year approached nearly 50%. Daily-mean PM2.5 concentrations remained elevated (>65 µg/m3) for more than 80 consecutive days. Night-time concentrations were greater than daytime concentrations. The comparison of results obtained from the Community Multiscale Air Quality (CMAQ) model simulations over the Northern Hemisphere using 108-km horizontal grids with observed data suggests that the model can reproduce the seasonal variation of observed data but underpredicts observed PM2.5 in winter months with a normalized mean bias of 13-32%. In the model, organic aerosol is the largest component of PM2.5, of which secondary organic aerosol plays a dominant role. Transboundary pollution has a large impact on the PM2.5 concentration in Dhaka, with an annual mean contribution of ~40 µg/m3.

Examining the impact of dimethyl sulfide emissions on atmospheric sulfate over the continental U.S.

We examine the impact of dimethylsulfide (DMS) emissions on sulfate concentrations over the continental U.S. by using the Community Multiscale Air Quality (CMAQ) model version 5.4 and performing annual simulations without and with DMS emissions for 2018. DMS emissions enhance sulfate not only over seawater but also over land, although to a lesser extent. On an annual basis, the inclusion of DMS emissions increase sulfate concentrations by 36% over seawater and 9% over land. The largest impacts over land occur in California, Oregon, Washington, and Florida, where the annual mean sulfate concentrations increase by ~25%. The increase in sulfate causes a decrease in nitrate concentration due to limited ammonia concentration especially over seawater and an increase in ammonium concentration with a net effect of increased inorganic particles. The largest sulfate enhancement occurs near the surface (over seawater) and the enhancement decreases with altitude, diminishing to 10-20% at an altitude of ~5 km. Seasonally, the largest enhancement of sulfate over seawater occurs in summer, and the lowest in winter. In contrast, the largest enhancements over land occur in spring and fall due to higher wind speeds that can transport more sulfate from seawater into land.

2002-2017 anthropogenic emissions data for air quality modeling over the United States.

The United States Environmental Protection Agency (US EPA) has developed a set of annual North American emissions data for multiple air pollutants across 18 broad source categories for 2002 through 2017. The sixteen new annual emissions inventories were developed using consistent input data and methods across all years. When a consistent method or tool was not available for a source category, emissions were estimated by scaling data from the EPA's 2017 National Emissions Inventory with scaling factors based on activity data and/or emissions control information. The emissions datasets are designed to support regional air quality modeling for a wide variety of human health and ecological applications. The data were developed to support simulations of the EPA's Community Multiscale Air Quality model but can also be used by other regional scale air quality models. The emissions data are one component of EPA's Air Quality Time Series Project which also includes air quality modeling inputs (meteorology, initial conditions, boundary conditions) and outputs (e.g., ozone, PM2.5 and constituent species, wet and dry deposition) for the Conterminous US at a 12 km horizontal grid spacing.

How have Divergent Global Emission Trends Influenced Long-range Transported Ozone to North America?

Several locations across the United States in non-compliance with the national standard for ground-level ozone (O3) are thought to have sizeable influences from distant extra-regional emission sources or natural stratospheric O3, which complicates design of local emission control measures. To quantify the amount of long-range transported O3 (LRT O3), its origin, and change over time, we conduct and analyze detailed sensitivity calculations characterizing the response of O3 to emissions from different source regions across the Northern Hemisphere in conjunction with multi-decadal simulations of tropospheric O3 distributions and changes. Model calculations show that the amount of O3 at any location attributable to sources outside North America varies both spatially and seasonally. On a seasonal-mean basis, during 1990-2010, LRT O3 attributable to international sources steadily increased by 0.06-0.2 ppb yr-1 at locations across the United States and arose from superposition of unequal and contrasting trends in individual source-region contributions, which help inform attribution of the trend evident in O3 measurements. Contributions of emissions from Europe steadily declined through 2010, while those from Asian emissions increased and remained dominant. Steadily rising NOx emissions from international shipping resulted in increasing contributions to LRT O3, comparable to those from Asian emissions in recent years. Central American emissions contribute a significant fraction of LRT O3 in southwestern United States. In addition to the LRT O3 attributable to emissions outside of North America, background O3 across the continental United States is comprised of a sizeable and spatially variable fraction that is of stratospheric origin (29-78%).

Wet nitrogen (N) deposition to urban Latin America: filling in the gaps with GEOS-Chem.

In Latin America, atmospheric deposition is a major vector of nitrogen (N) input to urban systems. Yet, measurements of N deposition are sparse, precluding analysis of spatial patterns, temporal trends, and ecosystem impacts. Chemical transport models can be used to fill these gaps in the absence of dense measurements. Here, we evaluate the performance of a global 3-D chemical transport model in simulating spatial and interannual variation in wet inorganic N (NH4-N + NO3-N) deposition across urban areas in Latin America. Monthly wet and dry inorganic N deposition to Latin America were simulated for the period 2006-2010 using the GEOS-Chem Chemical Transport Model. Published estimates of observed wet or bulk inorganic N deposition measured between 2006-2010 were compiled for 16 urban areas and then compared with model output from GEOS-Chem. Observed mean annual inorganic N deposition to the urban study sites ranged from 5.7-14.2 kg ha-1 yr-1, with NH4-N comprising 48-90% of the total. Results show that simulated N deposition was highly correlated with observed N deposition across sites (R2 = 0.83, NMB = -50%). However, GEOS-Chem generally underestimated N deposition to urban areas in Latin America compared to observations. Underestimation due to bulk sampler dry deposition artifacts was considered and improved bias without improving correlation. In contrast to spatial variation, the model did not capture year-to-year variation well. Discrepancies between modeled and observed values exist, in part, because of uncertainties in Latin American N emissions inventories. Our findings indicate that even at coarse spatial resolution, GEOS-Chem can be used to simulate N deposition to urban Latin America, improving understanding of regional deposition patterns and potential ecological effects.

What Constitutes Evidence? Colorectal Cancer Screening and the U.S. Preventive Services Task Force.

The United States Preventive Services Task Force is perhaps America's best-known source of evidence-based medicine (EBM) recommendations. This paper reviews aspects of the history of one such recommendation-screening for colorectal cancer (CRC)-to explore how the Task Force evaluates the best available evidence to reach its conclusions.Although the Task Force initially believed there was inadequate evidence to recommend CRC screening in the 1980s, it later changed its mind. Indeed, by 2002, it was recommending screening colonoscopy for those aged 50 and older, "extrapolating" from the existing evidence as there were no randomized controlled trials of the procedure. By 2016, due in part to the use of an emerging analytic modality known as modeling, the Task Force supported four additional CRC screening tests that lacked randomized data. Among the reasons the Task Force gave for these decisions was the desire to improve adherence for a strategy-screening healthy, asymptomatic individuals-that it believed saved lives.During these same years, the Task Force diverged from other organizations by declining to advocate screening otherwise healthy Black patients earlier than age 50-despite the fact that such individuals had higher rates of CRC than the general population, higher mortality from the disease and earlier onset of the disease. In declining to extrapolate in this instance, the Task Force underscored the lack of reliable data that proved that the benefits of such testing would outweigh the harms.The history of CRC screening reminds us that scientific evaluation relies not only on methodological sophistication but also on a combination of intellectual, cognitive and social processes. General internists-and their patients-should realize that EBM recommendations are often not definitive but rather thoughtful data-based advice.

Global Emissions of Hydrogen Chloride and Particulate Chloride from Continental Sources.

Gaseous and particulate chlorine species play an important role in modulating tropospheric oxidation capacity, aerosol water uptake, visibility degradation, and human health. The lack of recent global continental chlorine emissions has hindered modeling studies of the role of chlorine in the atmosphere. Here, we develop a comprehensive global emission inventory of gaseous HCl and particulate Cl- (pCl), including 35 sources categorized in six source sectors based on published up-to-date activity data and emission factors. These emissions are gridded at a spatial resolution of 0.1° × 0.1° for the years 1960 to 2014. The estimated emissions of HCl and pCl in 2014 are 2354 (1661-3201) and 2321 (930-3264) Gg Cl a-1, respectively. Emissions of HCl are mostly from open waste burning (38%), open biomass burning (19%), energy (19%), and residential (13%) sectors, and the major sources classified by fuel type are combustion of waste (43%), biomass (32%), and coal (25%). Emissions of pCl are mostly from biofuel (29%) and open biomass burning processes (44%). The sectoral and spatial distributions of HCl and pCl emissions are very heterogeneous along the study period, and the temporal trends are mainly driven by the changes in emission factors, energy intensity, economy, and population.

Nowcasting Applications of Geostationary Satellite Hourly Surface PM2.5 Data.

The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 µm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States (US) were derived from NOAA's operational geostationary satellites Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of -21.4% and -15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 µg/m3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the US, but with pronounced differences in the western US due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (> 35 µg/m3), the hourly estimates of PM2.5 used in Nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality ten times more than standard ground observations (1.8 vs. 0.17 million people per hour).

Estimating US Background Ozone Using Data Fusion.

US background (US-B) ozone (O3) is the O3 that would be present in the absence of US anthropogenic (US-A) emissions. US-B O3 varies by location and season and can make up a large, sometimes dominant, portion of total O3. Typically, US-B O3 is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O3. We develop and apply a method to fuse observed O3 with US-B O3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O3. Trends in O3 bias are explored across different simulation years and varying model scales. We found that the CTM US-B O3 estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O3 was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O3. Across the four estimates, we found annual mean CTM-simulated US-B O3 ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O3 ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.

Impact of dimethylsulfide chemistry on air quality over the Northern Hemisphere.

We implement oceanic dimethylsulfide (DMS) emissions and its atmospheric chemical reactions into the Community Multiscale Air Quality (CMAQv53) model and perform annual simulations without and with DMS chemistry to quantify its impact on tropospheric composition and air quality over the Northern Hemisphere. DMS chemistry enhances both sulfur dioxide (SO2) and sulfate ( S O 4 2 - ) over seawater and coastal areas. It enhances annual mean surface SO2 concentration by +46 pptv and S O 4 2 - by +0.33 µg/m3 and decreases aerosol nitrate concentration by -0.07 µg/m3 over seawater compared to the simulation without DMS chemistry. The changes decrease with altitude and are limited to the lower atmosphere. Impacts of DMS chemistry on S O 4 2 - are largest in the summer and lowest in the fall due to the seasonality of DMS emissions, atmospheric photochemistry and resultant oxidant levels. Hydroxyl and nitrate radical-initiated pathways oxidize 75% of the DMS while halogen-initiated pathways oxidize 25%. DMS chemistry leads to more acidic particles over seawater by decreasing aerosol pH. Increased S O 4 2 - from DMS enhances atmospheric extinction while lower aerosol nitrate reduces the extinction so that the net effect of DMS chemistry on visibility tends to remain unchanged over most of the seawater.