Abstract. Satellite observations are essential for global tropospheric ozone monitoring, but their ability to estimate ground-level ozone remains limited because of weak sensitivity and substantial uncertainty near the surface. In this study, we develop new methods for adjusting satellite ozone observations (IASI+GOME2, OMI/MLS, and CrIS) through chemistry-transport reanalysis and in situ ozone vertical profile measurements. Using these methods, we create global maps of ground-level ozone concentrations based on satellite observations. We use the Bayesian Maximum Entropy framework to horizontally interpolate the vertical profiles from ozonesondes and IAGOS and improve the accuracy of both the satellite column measurements and the surface-to-column ratios from a chemical reanalysis. This is done for monthly average maximum daily 8-hr ozone concentrations over several years. For the three satellites, surface ozone estimated from the BME-adjusted column-to-surface conversion showed improved agreement with TOAR-II observations. For IASI+GOME2 (2017–2020), global R2 increased from 0.25 to 0.51, and RMSE was reduced from 10.74 to 9.44 ppb. For OMI/MLS tropospheric column (2005–2022), global R2 increased from 0.26 to 0.57, and RMSE decreased from 22.21 to 7.79 ppb. For the CrIS 0–3 km ozone (2022), global R2 increased from 0.30 to 0.56, and RMSE decreased from 16.48 to 9.45 ppb. The method's efficacy was found to be highest within 6° of a vertical profile station and most impactful when the original satellite data quality was low. The resulting satellite-based monthly ground-level ozone estimates can be used further as an independent input to data fusion methods.

Anaemia, a deficiency of hemoglobin (Hb) or red blood cells, is a global public health crisis. It is especially prevalent in low- and middle-income countries (LMICs) like India, which has one of the highest burdens. While air pollution epidemiological studies in LMICs have focused mainly on fine particulate matter (PM _2.5 ), the health risks of other pollutants and their combined effects remain largely unexplored. This study aimed to estimate the association between long-term exposure to multiple air pollutants and anaemia among women of reproductive age (WRA) in India from 2015–2016. We first created a database of the air quality index (AQI) at a 1 km × 1 km spatial scale as a proxy for multi-pollutant exposure. We then combined this AQI data with health data from the fourth round of the National Family Health Survey (2015–2016). Using a logistic regression model adjusted for potential confounders, we conducted a cross-sectional analysis to assess the association between long-term AQI exposure (2007–2016) and anaemia prevalence among WRA, 15–49 years. We also examined the effect modification by various individual and socio-demographic factors, and performed analyses for single, two-, and three-pollutant models. The average AQI across India increased by 22%, from 122 in 2007–149 in 2016, with the most significant rise observed in the Indo-Gangetic Plain and western India. For each 10-unit increase in AQI, the odds ratio for anaemia among WRA was 1.061 (95% CI: 1.058, 1.063), and Hb levels decreased by 0.038 g dL ^−1 (95% CI: 0.036, 0.040). Effect modification was observed for several factors, including wealth index, daily iron intake, body mass index, education, place of residence, and cooking fuel. Our results imply that the anaemia prevalence is higher with a higher AQI, even within a single AQI category. Moreover, the prevalence of anaemia is comparable for exposure to PM and gaseous pollutants, and hence, in addition to the focus on PM _2.5 in the clean air action plan, gaseous pollutants also need to be controlled. The effect modification analysis would help prioritize targeted interventions for the more vulnerable group.

Air pollution and climate change are urgent global concerns, with urban areas contributing heavily to both air pollutants and greenhouse gas emissions. Here we calculate fine particulate matter, nitrogen dioxide, and ozone concentrations and fossil-fuel carbon dioxide emissions per capita in 13,189 urban areas worldwide from 2005 to 2019 and analyze correlations between trends for these pollutants, leveraging recently-developed global datasets. Globally, we found significant increases in ozone (+6%) and small, non-significant changes in fine particulate matter (+0%), nitrogen dioxide (-1%), and fossil-fuel carbon dioxide emissions (+4%). Also, over 50% of urban areas showed positive correlations for all pollutant pairs, though results varied by global region. High-income countries with strong mitigation policies experienced decreases in all pollutants, while regions with rapid economic growth had overall increases. This study shows the impacts of urban environmental initiatives in different regions and provides insights for reducing air pollution and carbon dioxide emissions simultaneously.

OBJECTIVES:Diabetes mellitus has been associated with greater difficulty of tracheal intubation in the operating room. This relationship has not been examined for tracheal intubation of critically ill adults. We examined whether diabetes mellitus was independently associated with the time from induction of anesthesia to intubation of the trachea among critically ill adults.DESIGN:A secondary analysis of data from five randomized trials completed by the Pragmatic Critical Care Research Group (PCCRG).SETTING:Emergency departments (EDs) or ICUs at 11 centers across the United States that enrolled in randomized trials of a pre-intubation checklist, fluid bolus administration, bag-mask ventilation between induction and laryngoscopy, and intubation using a bougie vs. stylet.PATIENTS:Critically ill adults undergoing tracheal intubation with a laryngoscope in an ED or an ICU.INTERVENTIONS:None.MEASUREMENTS AND MAIN RESULTS:A total of 2654 patients were included in this analysis, of whom 638 (24.0%) had diabetes mellitus. The mean time from induction of anesthesia to intubation of the trachea was 169 seconds (sd, 137s). Complications occurred during intubation in 1007 patients (37.9%). Diabetes mellitus was not associated with the time from induction of anesthesia to intubation of the trachea (-4.4 s compared with nondiabetes; 95% CI, -17.2 to 8.3 s; p = 0.50). Use of a video vs. direct laryngoscope did not modify the association between diabetes mellitus and the time from induction to intubation (p for interaction = 0.064). Diabetes mellitus was not associated with the probability of successful intubation on the first attempt (85.6% vs. 84.3%; p = 0.46) or complications during intubation (39.8% vs. 37.4%; p = 0.52).CONCLUSIONS:Among 2654 critically ill patients undergoing tracheal intubation in an ED or an ICU, diabetes mellitus was not independently associated with the time from induction to intubation, the probability of successful intubation on the first attempt, or the rate of complications during intubation.

Ground-level ozone is a significant air pollutant that detrimentally affects human health and agriculture. Global ground-level ozone concentrations have been estimated using chemical reanalyses, geostatistical methods, and machine learning, but these datasets have not been compared systematically. We compare six global ground-level ozone datasets (three chemical reanalyses, two machine learning, one geostatistics) relative to observations and against one another, for the ozone season daily maximum 8 h average mixing ratio, for 2006 to 2016. Comparing with global ground-level observations, most datasets overestimate ozone, particularly at lower observed concentrations. In 2016, across all stations, grid-to-grid R2 ranges from 0.50 to 0.75 and RMSE 4.25 to 12.22 ppb. Agreement with observed distributions is reduced at ozone concentrations above 50 ppb. Results show significant differences among datasets in global average ozone, as large as 5–10 ppb, multi-year trends, and regional distributions. For example, in Europe, the two chemical reanalyses show an increasing trend while other datasets show no increase. Among the six datasets, the share of population exposed to over 50 ppb varies from 61 % [28 %, 94 %] to 99 % [62 %, 100 %] in East Asia, 17 % [4 %, 72 %] to 88 % [53 %, 99 %] in North America, and 9 % [0 %, 58 %] to 76 % [22 %, 96 %] in Europe (2006–2016 average). Although sharing some of the same input data, we found important differences, likely from variations in approaches, resolution, and other input data, highlighting the importance of continued research on global ozone distributions. These discrepancies are large enough to impact assessments of health impacts and other applications.

Wildland fire (i.e., prescribed fire and wildfire) smoke exposure is an emerging public health threat, in part due to climate change. Previous research has demonstrated disparities in ambient fine particulate matter (PM2.5) exposure, with Black people, among others, exposed to higher concentrations; yet it remains unclear how wildland fire smoke may contribute to additional disproportionate exposure. Here, we investigate the additional PM2.5 burden contributed by wildland fire smoke in the contiguous United States by race and ethnicity, urbanicity, median household income, and language spoken at home, using modeled total, non-fire, and fire PM2.5 concentrations from 2007 to 2018. Wildland fires contributed 7% to 14% of total population weighted PM2.5 concentrations annually, while non-fire PM2.5 concentrations declined by 24% over the study period. Wildland fires contributed to greater PM2.5 exposure for Black and American Indian or Alaska Native people, and those who live in non-urban areas. Disproportionate mean non-fire PM2.5 concentrations for Black people (9.1 μg/m3, compared to 8.7 μg/m3 overall) were estimated to be further exacerbated by additional disproportionate concentrations from fires (1.0 μg/m3 , compared to 0.9 μg/m3 overall). These results can inform equitable strategies by public health agencies and air quality managers to reduce smoke exposure in the United States.

This commentary highlights the need for actionable and context-appropriate research on air pollution and health that will continue to drive policies to reduce exposures and disease burden. Research on air pollution and health has been substantial in high-income countries (HIC), leading to causal conclusions on the adverse effects of air pollution. Despite bearing the greatest disease burden from air pollution, low- and middle-income countries (LMICs) have had scant research funding, a trend that may well be aggravated due to changing political priorities in some HICs. High-quality data from LMICs is urgently needed to help motivate local, subnational, and national policies to raise awareness and identify priority actions to improve health. The new evidence will also provide a more complete understanding of air pollution and health globally. We highlight a framework for moving from research to action and address how this framework differs in HIC and LMIC contexts. We propose a hierarchy of research needs that begins with having the necessary air pollution monitoring and health data, and the capacity to use the data for informative analytics, risk assessment, valuation, and policy formulation. Building technical capacity may be needed for this purpose, as will development of a functioning regulatory system in parallel. We call for greater emphasis on surveillance studies to demonstrate the benefits of action and address barriers to action. The global community would benefit from a broad research agenda with priorities and adequate funding dedicated to building evidence that leads to positive policy change. We urge priority for advancing actionable research and improving research capacity in LMICs, including investments in routine collection of relevant data, emphasizing the foundation of risk monitoring and health data systems, and building a cadre of researchers and informed policy-makers.

Abstract The C4 Poaceae are a diverse group in terms of both evolutionary lineage and biochemistry. There is a distinct pattern in the distribution of C4 grass groups with aridity; however, the mechanistic basis for this distribution is not well understood. Additionally, few studies have investigated the functional strategies of co‐occurring C4 grass species for dealing with aridity in their natural environments. We explored the coordination of leaf‐level gas exchange, water use, and morphology among five co‐occurring semiarid C4 grasses belonging to divergent clades, biochemical subtypes, and size classes at three sites along a natural aridity gradient. More specifically, we measured predawn and midday water potential, stomatal conductance, water use efficiency, and photosynthesis. Leaf tissue was also collected for the analysis of stable isotopes of carbon and oxygen as well as for measurement of specific leaf area (SLA) and leaf width. Species differences in responsiveness of stomata to changes in vapor pressure deficit (VPD) were also assessed. It was expected that NAD‐me species would maintain higher rates of photosynthesis, higher water use efficiency, and have more responsive stomata than other co‐occurring species based on observed biogeographic patterns and past greenhouse studies. We found that Aristidoideae and Chloridoideae NAD‐me‐type grasses had greater stomatal sensitivity to VPD, consistent with a more isohydric strategy. However, midgrasses had both greater apparent water access and water use efficiency, regardless of subtype or lineage. PCK‐type species had less responsive stomata and maintained lower levels of photosynthesis with increasing aridity. There were strong interspecific differences in δ13C, leaf width, and SLA; however, these were not significantly correlated with water use efficiency. C4 grasses in our study did not fit discretely into functional groups as defined by lineage, biochemistry, or size class. Interspecific differences, evolutionary legacy, and biochemical pathway are likely to interact to determine water use and photosynthetic strategies of these plants. Control of water loss via highly responsive stomata may form the basis for dominance of certain C4 grass groups in arid environments. These findings build on our understanding of contrasting strategies of C4 grasses for dealing with aridity in their natural environments.

Long-term exposure to ambient ozone (O3) is associated with excess respiratory mortality. Pollution emissions, demographic, and climate changes are expected to drive future ozone-related mortality. Here, we assess global mortality attributable to ozone according to an Intergovernmental Panel on Climate Change (IPCC) Shared Socioeconomic Pathway (SSP) scenario applied in Coupled Model Intercomparison Project Phase 6 (CMIP6) models, projecting a temperature increase of about 3.6 degrees C by the end of the century. We estimated ozone-related mortality on a global scale up to 2090 following the Global Burden of Disease (GBD) 2019 approach, using bias-corrected simulations from three CMIP6 Earth System Models (ESMs) under the SSP3-7.0 emissions scenario. Based on the three ESMs simulations, global ozone-related mortality by 2090 will amount to 2.79 M [95% CI 0.97 M-5.23 M] to 3.12 M [95% CI 1.11 M-5.75 M] per year, approximately ninefold that of the 327 K [95% CI 103 K-652 K] deaths per year in 2000. Climate change alone may lead to an increase of ozone-related mortality in 2090 between 42 K [95% CI -37 K-122 K] and 217 K [95% CI 68 K-367 K] per year. Population growth and ageing are associated with an increase in global ozone-related mortality by a factor of 5.34, while the increase by ozone trends alone ranges between factors of 1.48 and 1.7. Ambient ozone pollution under the high-emissions SSP3-7.0 scenario is projected to become a significant human health risk factor. Yet, optimizing living conditions and healthcare standards worldwide to the optimal ones today (application of minimum baseline mortality rates) will help mitigate the adverse consequences associated with population growth and ageing, and ozone increases caused by pollution emissions and climate change.

Many United States (US) cities are experiencing urban heat islands (UHIs) and climate change-driven temperature increases. Extreme heat increases cardiovascular disease (CVD) risk, yet little is known about how this association varies with UHI intensity (UHII) within and between cities. We aimed to identify the urban populations most at-risk of and burdened by heat-related CVD morbidity in UHI-affected areas compared to unaffected areas. ZIP code-level daily counts of CVD hospitalizations among Medicare enrollees, aged 65-114, were obtained for 120 US metropolitan statistical areas (MSAs) between 2000 and 2017. Mean ambient temperature exposure was estimated by interpolating daily weather station observations. ZIP codes were classified as low and high UHII using the first and fourth quartiles of an existing surface UHII metric, weighted to each have 25% of all CVD hospitalizations. MSA-specific associations between ambient temperature and CVD hospitalization were estimated using quasi-Poisson regression with distributed lag non-linear models and pooled via multivariate meta-analyses. Across the US, extreme heat (MSA-specific 99th percentile, on average 28.6 °C) increased the risk of CVD hospitalization by 1.5% (95% CI: 0.4%, 2.6%), with considerable variation among MSAs. Extreme heat-related CVD hospitalization risk in high UHII areas (2.4% [95% CI: 0.4%, 4.3%]) exceeded that in low UHII areas (1.0% [95% CI: -0.8%, 2.8%]), with upwards of a 10% difference in some MSAs. During the 18-year study period, there were an estimated 37,028 (95% CI: 35,741, 37,988) heat-attributable CVD admissions. High UHII areas accounted for 35% of the total heat-related CVD burden, while low UHII areas accounted for 4%. High UHII disproportionately impacted already heat-vulnerable populations; females, individuals aged 75-114, and those with chronic conditions living in high UHII areas experienced the largest heat-related CVD impacts. Overall, extreme heat increased cardiovascular morbidity risk and burden in older urban populations, with UHIs exacerbating these impacts among those with existing vulnerabilities.

Tropospheric ozone is an important greenhouse gas, is detrimental to human health and crop and ecosystem productivity, and controls the oxidizing capacity of the troposphere. Previous studies, using models, aircraft and remote observation datasets, have shown that the tropospheric ozone has increased significantly in the tropical regions. Sensitivities studies have also showed that the ozone precursor emissions in these tropical regions have been increasing for the past three decades. For this paper, we will work with worldwide scientists to investigate how the emission evolves in the tropical regions from 1995 to 2019, and how these changes have contributed to the global and other receptor regions tropospheric ozone burden increases, by using ensemble state-of-the-art global and regional chemical transport models.

Elevated surface concentrations of ozone and fine particulate matter (PM2.5) can lead to poor air quality and detrimental impacts on human health. These pollutants are also termed Near-Term Climate Forcers (NTCFs) as they can also influence the Earth's radiative balance on timescales shorter than long-lived greenhouse gases. Here we use the Earth system model, UKESM1, to simulate the change in surface ozone and PM2.5 concentrations from different NTCF mitigation scenarios, conducted as part of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). These are then combined with relative risk estimates and projected changes in population demographics, to estimate the mortality burden attributable to long-term exposure to ambient air pollution. Scenarios that involve the strong mitigation of air pollutant emissions yield large future benefits to human health (25%), particularly across Asia for black carbon (7%), when compared to the future reference pathway. However, if anthropogenic emissions follow the reference pathway, then impacts to human health worsen over South Asia in the short term (11%) and across Africa (20%) in the longer term. Future climate change impacts on air pollutants can offset some of the health benefits achieved by emission mitigation measures over Europe for PM2.5 and East Asia for ozone. In addition, differences in the future chemical environment over regions are important considerations for mitigation measures to achieve the largest benefit to human health. Future policy measures to mitigate climate warming need to also consider the impact on air quality and human health across different regions to achieve the maximum co-benefits.

<p>The first phase of the Tropospheric Ozone Assessment Report (TOAR-I), an activity of the International Global Atmospheric Chemistry Project (IGAC), provided the first comprehensive view of surface ozone’s global distribution and trends, based on all available surface ozone observations.  TOAR-I focused on a present-day period of 2010-2014, and calculated trends for a range of periods, but primarily focused on the most recent years of 2000-2014, plus long-term trends from the 1970s/1980s through 2014.  Subsequent studies of ozone trends using data after the TOAR-I cut-off of 2014, have shown a wide range of trends, both positive and negative, at monitoring sites around the world.  To keep up with the rapid changes of ozone at urban, rural and remote locations this study provides current world-wide ozone trends using observations through 2021, archived in the newly updated TOAR-II Database of Surface Observations.  Focus is placed on two ozone metrics relevant to human health impacts:  1) the annual peak of the 6-month running mean of maximum daily 8-hour average ozone; this metric is used by Global Burden of Disease (GBD) to estimate mortality due to long-term ozone exposure; 2) the number of days per year that exceed 70 ppbv, based on the maximum daily 8-hour average ozone value; this value corresponds to the primary U.S. National Ambient Air Quality Standards for ozone and is relevant to short-term ozone exposure.  Global maps will indicate the regions of the world where the potential for ozone impacts on human health are greatest (and least), and will show regions where ozone air quality is either improving or degrading.  Despite our effort to use all available surface ozone observations, large data gaps exist across many regions of the world, especially in developing nations, and GBD maps generated by data fusion will be used to identify, and to estimate ozone levels in the data-poor regions. </p>

STUDY OBJECTIVE:To compare the effect of the use of a video laryngoscope versus a direct laryngoscope on each step of emergency intubation: laryngoscopy (step 1) and intubation of the trachea (step 2).METHODS:In a secondary observational analysis of data from 2 multicenter, randomized trials that enrolled critically ill adults undergoing tracheal intubation but did not control for laryngoscope type (video laryngoscope vs direct laryngoscope), we fit mixed-effects logistic regression models examining the 1) the association between laryngoscope type (video laryngoscope vs direct laryngoscope) and the Cormack-Lehane grade of view and 2) the interaction between grade of view, laryngoscope type (video laryngoscope vs direct laryngoscope), and the incidence of successful intubation on the first attempt.RESULTS:We analyzed 1,786 patients: 467 (26.2%) in the direct laryngoscope group and 1,319 (73.9%) in the video laryngoscope group. The use of a video laryngoscope was associated with an improved grade of view as compared with a direct laryngoscope (adjusted odds ratio for increasingly favorable grade of view 3.14, 95% confidence interval [CI] 2.47 to 3.99). Successful intubation on the first attempt occurred in 83.2% of patients in the video laryngoscope group and 72.2% of patients in the direct laryngoscope group (absolute difference 11.1%, 95% CI 6.5% to 15.6%). Video laryngoscope use modified the association between grade of view and successful intubation on the first attempt such that intubation on the first attempt was similar between video laryngoscope and direct laryngoscope at a grade 1 view and higher for video laryngoscope than direct laryngoscope at grade 2 to 4 views (P<.001 for interaction term).CONCLUSIONS:Among critically ill adults undergoing tracheal intubation, the use of a video laryngoscope was associated both with a better view of the vocal cords and with a higher probability of successfully intubating the trachea when the view of the vocal cords was incomplete in this observational analysis. However, a multicenter, randomized trial directly comparing the effect of a video laryngoscope with a direct laryngoscope on the grade of view, success, and complications is needed.

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m−3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

Rationale: A recent randomized trial found that using a bougie did not increase the incidence of successful intubation on first attempt in critically ill adults. The average effect of treatment in a trial population, however, may differ from effects for individuals. Objective: We hypothesized that application of a machine learning model to data from a clinical trial could estimate the effect of treatment (bougie vs. stylet) for individual patients based on their baseline characteristics ("individualized treatment effects"). Methods: This was a secondary analysis of the BOUGIE (Bougie or Stylet in Patients Undergoing Intubation Emergently) trial. A causal forest algorithm was used to model differences in outcome probabilities by randomized group assignment (bougie vs. stylet) for each patient in the first half of the trial (training cohort). This model was used to predict individualized treatment effects for each patient in the second half (validation cohort). Measurements and Main Results: Of 1,102 patients in the BOUGIE trial, 558 (50.6%) were the training cohort, and 544 (49.4%) were the validation cohort. In the validation cohort, individualized treatment effects predicted by the model significantly modified the effect of trial group assignment on the primary outcome (P value for interaction = 0.02; adjusted qini coefficient, 2.46). The most important model variables were difficult airway characteristics, body mass index, and Acute Physiology and Chronic Health Evaluation II score. Conclusions: In this hypothesis-generating secondary analysis of a randomized trial with no average treatment effect and no treatment effect in any prespecified subgroups, a causal forest machine learning algorithm identified patients who appeared to benefit from the use of a bougie over a stylet and from the use of a stylet over a bougie using complex interactions between baseline patient and operator characteristics.

Study objectives: Successful intubation on the first attempt has historically been defined as successful placement of an endotracheal tube (ETT) using a single laryngoscope insertion. More recent studies have defined successful placement of an ETT using a single laryngoscope insertion followed by a single ETT insertion. We sought to estimate the prevalence of first-attempt success using these 2 definitions and estimate their associations with the duration of intubation and serious complications. Methods: We performed a secondary analysis of data from 2 multicenter randomized trials of critically ill adults being intubated in the emergency department or ICU. We calculated the percent difference in successful intubations on the first attempt, median difference in the duration of intubation, and percent difference in the development of serious complications by definition. Results: The study population included 1,863 patients. Successful intubation on the first attempt decreased by 4.9% (95% confidence interval 2.5% to 7.3%) when defined as 1 laryngoscope insertion followed by 1 ETT insertion (81.2%) compared with when defined as only 1 laryngoscope insertion (86.0%). When successful intubation with 1 laryngoscope and 1 ETT insertion was compared with 1 laryngoscope and multiple ETT insertions, the median duration of intubation decreased by 35.0 seconds (95% confidence interval 8.9 to 61.1 seconds). Conclusion: Defining successful intubation on the first attempt as placement of an ETT in the trachea using 1 laryngoscope and 1 ETT insertion identifies attempts with the shortest apneic time. [Ann Emerg Med. 2023;82:432-437.]