Western US Research Articles

Intraspecific Variation and Recent Loss of Ancient, Conserved Effector Genes in the Sudden Oak Death Pathogen Phytophthora ramorum.

Members of the Phytophthora genus are responsible for many important diseases in agricultural and natural ecosystems. Phytophthora ramorum causes devastating diseases of oak, and tanoak stands in US forests and larch in the UK. The four evolutionary lineages involved express different virulence phenotypes on plant hosts, and characterization of gene content is foundational to understanding the basis for these differences. Recent discovery of P. ramorum at its candidate center of origin in Asia provides a new opportunity for investigating the evolutionary history of the species. We assembled, high-quality genome sequences of six P. ramorum isolates representing three lineages from Asia and three causing epidemics in western US forests. The six genomes were assembled into 13 putative chromosomes. Analysis of structural variation revealed multiple chromosome fusion and fission events. Analysis of putative virulence genes revealed variations in effector gene composition among the sequenced lineages. We further characterized their evolutionary history and inferred a contraction of crinkler-encoding genes in the subclade of Phytophthora containing P. ramorum. There were losses of multiple families and a near complete loss of paralogs in the largest core crinkler family in the ancestor of P. ramorum and sister species P. lateralis. Secreted glycoside hydrolase enzymes showed a similar degree of variation in abundance among genomes of P. ramorum lineages as that observed among several Phytophthora species. We found plasticity among genomes from multiple lineages in a Phytophthora species and provide insights into the evolutionary history of a class of anciently conserved effector genes.

Regional-specific trends of PM2.5 and O3 temperature sensitivity in the United States

Climate change poses direct and indirect threats to public health, including exacerbating air pollution. However, the influence of rising temperature on air quality remains highly uncertain in the United States, particularly under rapid reduction in anthropogenic emissions. Here, we examined the sensitivity of surface-level fine particulate matter (PM2.5) and ozone (O3) to summer temperature anomalies in the contiguous US as well as their decadal changes using high-resolution datasets generated by machine learning. Our findings demonstrate that in the eastern US, stringent emission control strategies have significantly reduced the positive responses of PM2.5 and O3 to summer temperature, thereby lowering the population exposure associated with warming-induced air quality deterioration. In contrast, PM2.5 in the western US became more sensitive to temperature, highlighting the urgent need to manage and mitigate the impact of worsening wildfires. Our results have important implications for air quality management and risk assessments of future climate change.

Interseeding cover crop into an irrigated sandy loam for 6 years: Soil, crop, and economic response

AbstractInterseeding cover crops (CCs) may be a potential strategy to manage sandy soils, which are highly prone to degradation. However, how this practice affects CC biomass production and other ecosystem services in sandy soils over the traditional CC planting system (post‐harvest drilling) is still unclear. We studied how broadcast interseeded (32–67 days before crop harvest) winter rye (Secale cereale L.) CC affected CC biomass production, nitrate leaching potential, soil properties, crop yields, and farm income compared with post‐harvest drilled CC in an on‐farm irrigated no‐till corn (Zea mays L.)–soybean (Glycine max L.) experiment in a sandy loam in the western US Corn Belt for 6 years. Across the 6 years, interseeded CC produced 0.57 Mg ha−1 of biomass, while post‐harvest drilled CC produced 0.37 Mg ha−1. Nitrate leaching is a concern in sandy soils, but interseeded CC had mixed effects on soil nitrate concentration. Interseeded CC did not affect soil properties (particulate organic matter and organic C concentrations, and wet aggregate stability) and crop yields. Further, interseeded CC did not reduce farm income more than post‐harvest drilled CC. The limited effect of interseeded CCs is likely due to the relatively small increase in CC biomass production over the traditional CC planting system. Additional strategies including irrigation, drill interseeding, and planting green may boost interseeded CC biomass production and thus soil services in sandy soils. After 6 years, interseeded CC slightly boosted CC biomass production but minimally affected soils and crops, and both interseeded and post‐harvest‐drilled CCs reduced net income.

Comparisons of Montane Snow Water Equivalent Projections: Calculating Total Snow Mass in Regions with Projection Agreement and Divergence in the Western US

Abstract Montane snowpack is a vital source of water in the Western United States. Here, we use a large-ensemble approach to evaluate the agreement across 124 snow water equivalent (SWE) projections with statistically downscaled forcing between end-of-century (2076 – 2095) and early 21st century (2106 – 2035) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and mid-spring snowmelt (15 May) in five western US domains. Using 1) the percent change to end-of-century SWE across different ensembles of snow projections, and 2) the shift between early 21st century and end-of-century SWE distributions for each snow projection, we identified relationships between projections that were consistent across each domain. In low to mid-elevations, end-of-century SWE decreases were 48% and larger on 15 April. These regions had projected changes to SWE that were both high-confidence and in relative agreement across projections. Despite this, the majority of 15 April SWE volume existed in higher elevations where the magnitude and direction (positive or negative) of SWE changes were most uncertain. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify 1) the highest-confidence changes to future snow water resources, and 2) the locations and periods where and when improvements to snow projections would most benefit estimates of future snow water resources.

Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM2.5: Implications for Assessment of Health Impacts.

Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of wildfire smoke on human health remains imprecisely understood due to uncertainties in both the measurement of exposure of population to wildfire smoke and dose-response functions linking exposure to health. Here, we compare daily wildfire smoke-related surface fine particulate matter (PM2.5) concentrations estimated using three approaches, including two chemical transport models (CTMs): GEOS-Chem and the Community Multiscale Air Quality (CMAQ) and one machine learning (ML) model over the contiguous US in 2020, a historically active fire year. In the western US, compared against surface PM2.5 measurements from the US Environmental Protection Agency (EPA) and PurpleAir sensors, we find that CTMs overestimate PM2.5 concentrations during extreme smoke episodes by up to 3-5 fold, while ML estimates are largely consistent with surface measurements. However, in the eastern US, where smoke levels were much lower in 2020, CTMs show modestly better agreement with surface measurements. We develop a calibration framework that integrates CTM- and ML-based approaches to yield estimates of smoke PM2.5 concentrations that outperform individual approach. When combining the estimated smoke PM2.5 concentrations with county-level mortality rates, we find consistent effects of low-level smoke on mortality but large discrepancies in effects of high-level smoke exposure across different methods. Our research highlights the differences across estimation methods for understanding the health impacts of wildfire smoke and demonstrates the importance of bench-marking estimates with available surface measurements.

Heritage on the High Plains: Motive-Based Market Segmentation for a US National Historic Site

Over the past several years, many iconic national parks in the US have experienced large increases in visitation. At the same time, lesser-known units have had declining numbers. Both issues challenge the sustainable management of parks. The purpose of this study is to use motivation theory to develop and test the differences between market segments at a western US national historic site. The ultimate goal is to contribute to sustainable visitation management by increasing visitation and enhancing the visitor experience. A market segmentation analysis can identify and describe promising target markets. The data were collected using an onsite exit survey, resulting in a sample of 570 visitors over the high season. Three clusters of visitors were identified: heritage immersers, history appreciators, and casual sightseers. The groups differed significantly with respect to visitation motives, travel, and visitation patterns. Segments are described and implications offered. The study confirms differences among cultural heritage visitors, with some being promising as target markets for the historic site. The study underscores the importance of tailoring site management and marketing strategies to meet the diverse needs of visitors and supports the use of motive-based segmentation as a means of better understanding and sustainably managing visitor experiences.

Investigation of awareness, sanitation, and customer education practices among employees of pet and animal feed stores that sell live animals in the United States

BackgroundNumerous zoonotic disease outbreaks have been associated with companion animals and poultry purchased at pet and animal feed stores. Employees are often the initial source of information for customers purchasing a new pet. Therefore, the objectives of this study were to: (1) investigate awareness, sanitation, and customer education practices related to zoonotic disease risk, and (2) identify predictors of providing customer education among employees of pet and animal feed stores that sell live animals.MethodsA survey of pet and animal feed store employees was conducted to evaluate sanitation practices, training, and awareness of zoonotic disease risk. Differences in proportions of categorical variables were assessed using Chi-square tests. Wilcoxon rank-sum and Kruskal–Wallis tests were used to assess for differences in the values of ordinal variables based on the values of categorical variables. A partial proportional odds model was used to identify predictors of providing customer education.ResultsSurveys were completed by 206 respondents from the Southeast, Midwest, Southwest, and Western US, 146 of whom reported that their workplace sold live animals. Handwashing was more frequent among employees whose workplace had handwashing policies related to handling animals and their habitats (p < 0.001). Perceived zoonotic disease risk was higher among those who had received workplace training (p = 0.007). Higher odds of providing customer education related to zoonotic disease risk were associated with serving in a supervisory role (p = 0.005), higher perceived zoonotic disease risk (p = 0.001), and more frequent sanitation practices (handwashing, p = 0.031; surface disinfection, p < 0.001; and glove use, p < 0.001).ConclusionsPet and animal feed stores play an important role in minimizing occupational health hazards for employees and providing education for customers. These retailers should implement clear biosecurity protocols and provide training about zoonotic disease risk associated with handling live animals.

Soil amendments alter tree growth and wood decay after forest thinning

AbstractForest soil amendments are increasingly used in western US forests to dispose of unmerchantable woody residues, reduce wildfire risk, and improve soil properties. Our objective was to determine the effect of fertilizer and organic amendments on tree growth and organic matter decomposition after thinning. Treatments were a control, three single soil amendments (wood chips, fertilizer, and biochar), and one combined soil amendment (biochar + fertilizer), each applied after thinning a ponderosa pine (Pinus ponderosa Dougl. ex. Laws) stand. After 10 years, amendment treatments had no effect on tree diameter increment (p = 0.600), but the biochar + fertilizer and wood chip treatments significantly increased height growth (p = 0.006). To estimate belowground biological changes, we used wood stakes made from aspen (Populus tremuloides Michx.) and loblolly pine (Pinus taeda L.) as an index of microbial activity. Stakes were placed: (1) on top of the litter/amendments, (2) at the interface between the litter/amendments and mineral soil, and (3) vertically inserted into the mineral soil, and stake mass loss was measured over 5 years. Stake mass loss of each species was least on the soil surface and increased with increasing depth. Aspen stakes generally had greater mass loss at all three soil locations in the fertilizer and biochar treatments. In contrast, pine stake mass loss was lower than aspen and less affected by fertilizer. Using thinned tree biomass to create amendments can improve forest productivity by enhancing soil conditions and mitigating wildfire. However, the impact of amendments on tree growth may take decades to be detectable.

The Collaborative Seismic Earth Model: Generation 2

AbstractGeological interpretations, earthquake source inversions and ground motion modeling, among other applications, require models that jointly resolve crustal and mantle structure. With the second generation of the Collaborative Seismic Earth Model (CSEM2), we present a global multi‐resolution tomographic Earth model that serves this purpose. The model evolves through successive regional‐ and global‐scale refinements. While the first generation aggregated regional models, with this study, we ensure consistency between all individual submodels, resulting in a model that accurately explains wave propagation across scales. Recent regional tomographic models were incorporated, comprising continental‐scale inversions for Asia and Africa, as well as regional inversions for the Western US, Central Andes, Iran, and Southeast Asia. Across all regional refinements, over 793,000 source‐receiver pairs contributed. Moreover, the long‐wavelength Earth model (LOWE) introduces large‐scale structures outside of pre‐existing local refinements. A full‐waveform inversion for global anisotropic P‐and S‐wave speed structure over a total of 194 iterations with a minimum period of 50 s on a large data set of 1 hr of waveform data from 2,423 earthquakes and over 6 million source‐receiver pairs ensures that regional updates in the crust and uppermost mantle translate into updates of deeper, global‐scale structure. To test the performance of CSEM2, we evaluate waveform fits between observed and synthetic seismograms at 50 s for an independent data set on the global scale, and on the regional scale for lower periods. We accurately simulate waveforms within and across regional refinements, maintaining the original resolution of the submodels embedded in the global framework.

Relationship Between Left Ventricular Ejection Fraction and ICD-10 Codes Among Patients Hospitalized With Heart Failure.

While left ventricular ejection fraction (LVEF) represents an important means by which to classify patients with heart failure (HF), relatively little is known about the distribution of LVEFs among patients hospitalized for HF based on their International Classification of Disease (ICD)-10 code. We performed a retrospective cross-sectional analysis of patients admitted to a large integrated health system within the western US between January 1, 2018 and October 1, 2022 with a principal diagnosis of HF (defined by ICD-10 codes: I50.2, systolic HF; I50.3, diastolic HF; I50.4, combined systolic and diastolic HF; I11.0, hypertensive heart disease with HF; and I13.0 and I13.2, hypertensive heart disease with HF and chronic kidney disease). Over nearly 5 years, 61,238 HF hospitalizations occurred, of which 49,772 (81%) had a LVEF available by echocardiography within the preceding 3 months. Whereas most patients hospitalized with systolic HF (n = 2220) as well as systolic and diastolic heart failure (n = 1582) had an LVEF ≤ 40% (86.2% and 74.8%, respectively), most patients hospitalized with diastolic HF (n = 1542) had an LVEF ≥ 50% (94.0%) (Figure). A much greater range of LVEFs were noted for those with hypertensive heart disease with HF (n = 18,092) and hypertensive heart disease with HF and CKD (n = 26,336) (Figure). While there was relatively good concordance between LVEF and the ICD-10 code-defined HF type for systolic HF, diastolic HF, and systolic and diastolic HF, these codes represent a small subset (~10%) of total HF hospitalizations.

SANCTIONS ON RUSSIA AND ITS EFFECT

In 2014 Europe Union and United States had implied Russia with sanction as a result of illegal annexation of Crimea. Those sanctions were just small wave and more sever sanctions had been added when Russia shooted down malaysian aircraft, after presense military operations in Donbass, and non-respect Minsk agreement. Even though restrictions were very serious and affected all sectors of Russian economy, sanctions had not brought any positive results. Huge mounts of banks and companies had suffered especially companies who had debt in dollars but had been earning in roubles in reason of depreciation of rouble. Russian economy had a serious hit after falling oil prices In the end of summer in 2014 Russian implied a counter-sanctions against EU and US that meant Russia refused the exports from Western Europe and US. Counter sanctions included extension and agricultural products. Russia had not suffered too much, on other hand developed countries like Germany, UK and Polland had got a huge damage.

Multi-scale forest heterogeneity promotes occupancy of dusky-footed woodrats in the Sierra Nevada

Forested landscapes are naturally heterogeneous, with the distribution of resources influencing animal habitat selection at multiple spatial scales. However, anthropogenic activities and changing disturbance regimes have reorganized how forests are structured from fine- to landscape-scales, generally with unknown consequences for forest-associated wildlife. For instance, fire suppression and selective logging in the western US has led to more homogeneous forests with fewer small patches of early-successional vegetation. As forest management aims to improve forest resilience to extreme fire and drought by restoring historical disturbance regimes and modifying forest structure through fuel management, there is a need for studies that evaluate how animals respond to forest heterogeneity at multiple scales. Here, we estimated occupancy for the dusky-footed woodrat (Neotoma fuscipes), an important prey species for many forest predators including the California spotted owl (Strix occidentalis occidentalis), relative to forest structure and composition at site-, patch-, and landscape-scales within landscapes where forest heterogeneity was created by even-aged timber management. Woodrats were more likely to occupy sites with greater canopy cover, understory cover, and hardwoods – particularly tanoak (Notholithocarpus densiflorus) – and smaller patches of young forest. Woodrats were also more likely to occupy mature forests in close proximity to younger forests, suggesting that young forest patches with more favorable local conditions can produce populations that recruit into adjacent, lower-quality mature forests. Our results suggest that creating small (∼2 ha) patches of high-quality woodrat habitat (i.e., young forests with dense understory and hardwoods) could provide “fishing holes” for spotted owls and other predators by supporting higher woodrat densities in surrounding mature forests managed for fuels – thus helping to meet both spotted owl conservation and forest resilience objectives. More broadly, we highlight the benefits of multi-scale studies and demonstrate that restoring landscape heterogeneity, including the creation of small early-successional forests, may benefit species conservation without compromising efforts to improve resilience in forest ecosystems globally.

Archaeological research at Kazan University in the context of Western European and North American archaeology

This article summarizes the history of archaeological research at Kazan University, from its establishment in 1804 to the present day. The first important milestone was in the 19th century when amateur collecting of artifacts and coins by the university professors in the early 1800s was replaced by professional archaeology, with the Society of Archaeology, History, and Ethnography founded in 1878 at Kazan University. The society published its own journal to raise awareness and foster curiosity, both locally and internationally, about archaeological discoveries in the Kazan Governorate and the surrounding territories. From the late 19th to the early 20th century, a fruitful cooperation developed between Russian archaeologists and Finnish scholars, which lasted until the 1930s and centered in their mutual interests revolving around the Ananyino culture and the origins of the Finno-Ugric peoples. In the aftermath of World War II, the university archaeological team took part in many projects: exploratory surveys for the construction of the Kuybyshev Hydroelectric Station (late 1940s–early 1950s), excavations of the early Bulgarian burials and proto-Bulgarian sites (1950s–early 1960s), unearthing of the Bilär settlement (late 1960s–1980s), fieldwork at the ancient Hungarian Bolshie Tigany burial and in Kazan (1974–1978). In the 1960s–1980s, foreign collaborations were limited to socialist countries (Bulgaria, Hungary), which kept the focus of their scholarly attention on the eastern regions. However, the intensive contacts with Finnish archaeologists continued. Starting from the 1990s–2000s, the results of archaeological research in Tatarstan finally became accessible to Western European and US scholars, who then relied on them to navigate their studies on the history of Islam and Islamization in Europe, sedentarization of nomads, and medieval trade. Now, the archaeology at Kazan University has advanced to a new stage by covering a much broader geographical scope and incorporating the latest technology into archaeological practices.

Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data

Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behavior. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations.

Encountering Prescribed Fire: Characterizing the Intersection of Prescribed Fire and Wildfire in the CONUS.

Prescribed fire is applied across the United States as a fuel treatment to manage the impact of wildfires and restore ecosystems. While the recent application of prescribed fire has largely been confined to the southeastern US, the increase in catastrophic wildfires has accelerated the growth of prescribed fire more broadly. To effectively achieve wildfire risk reduction benefits, which includes reducing the amount of smoke emitted, the area treated by prescribed fire must come into contact with a subsequent wildfire. In this study, we applied timely and consistent geospatially resolved data sets of prescribed fires and wildfires to estimate the rate at which an area treated by prescribed fire encounters a subsequent wildfire. We summarize these encounter rates across time intervals, prescribed fire treatment area, and number of previous prescribed fires and by region. On all U.S. Forest Service lands across the Conterminous US (CONUS) 6.2% of prescribed fire treated area from 2003-2022 encountered a subsequent wildfire in 2004-2023. Encounter rates were highest in western US forests, which tend to be more impacted by wildfire than the eastern US, and lower in the eastern US. Encounter rates increased with treatment area in the southeastern US but were relatively flat in the northwest. For the CONUS, encounter rates increased with longer time intervals, associated with diminished potential for reducing wildfire severity, between prescribed fire and the subsequent wildfire area burned. Our results provide timely information on prescribed fire and wildfire interactions that can be leveraged to optimize analyses of the trade-offs between prescribed fire and wildfire.

Application of stochastic storm transposition for hydrologic modeling in the mountainous western US

Recent studies have shown that stochastic storm transposition (SST) is an effective method for flood frequency analysis in small to medium sized watersheds. The effectiveness of this method has not been evaluated in larger watersheds in mountainous regions where snowmelt is the primary driver for peak flow events. In the western United States, several watersheds fall in an area where NOAA Atlas-14 precipitation frequency estimates do not exist. This data sparsity limits the available methods for developing flood frequency estimates. In this research, we present a case study for exploring the use of SST in the high desert of Wyoming, with challenges of scale, regulation, and snowmelt. Building on existing methods, a framework for performing SST in cold mountain regions is proposed, including incorporation of snowmelt functions, normalization fields in high altitude locations, and techniques for hydrologic model adjustment to account for epistemic uncertainty due to regulation. Results show that the foundational processes in the SST framework provide a valuable alternative for peak flow analysis in locations where frequency-based precipitation is not available.

Interpreting summertime hourly variation of NO2 columns with implications for geostationary satellite applications

Abstract. Accurate representation of the hourly variation in the NO2-column-to-surface relationship is essential for interpreting geostationary observations of NO2 columns. Previous research indicated inconsistencies in this hourly variation. This study employs the high-performance configuration of the GEOS-Chem model (GCHP) to analyze daytime hourly NO2 total columns and surface concentrations during summer. We use measurements from globally distributed Pandora sun photometers and aircraft observations over the United States. We correct Pandora total NO2 vertical columns for (1) hourly variations in effective temperature driven by vertically resolved contributions to the total column and (2) changes in local solar time along the Pandora line of sight. These corrections increase the total NO2 columns by 5–6 × 1014 molec. cm−2 at 09:00 and 18:00 across all sites. Fine-scale simulations from GHCP (∼12 km) reduce the normalized bias (NB) against Pandora total NO2 columns from 19 % to 10 % and against aircraft measurements from 25 % to 13 % in Maryland, Texas, and Colorado. Similar reductions are observed in NO2 columns over the eastern US (17 % to 9 %), the western US (22 % to 14 %), Europe (24 % to 15 %), and Asia (29 % to 21 %) when compared to 55 km simulations. Our analysis attributes the weaker hourly variability in the total NO2 column to (1) hourly variations in column effective temperature, (2) local solar time changes along the Pandora line of sight, and (3) differences in hourly NO2 variability from different atmospheric layers, with the lowest 500 m exhibiting greater variability, while the dominant residual column above 500 m exhibits weaker variability.

Abstract 4138301: Burden of Hyperlipidemia, Cardiovascular Mortality, and COVID-19: A Retrospective-Cohort Analysis of US Data

Background: Hyperlipidemia (HLD) is a major risk factor for cardiovascular disease (CVD). Little is known regarding temporal variation in CVD mortality related to HLD. The COVID-19 pandemic added complexity to factors influencing CVD mortality. Question: What are the yearly trends and impact of the COVID-19 pandemic on HLD-related CVD mortality in the United States? Methods: Mortality and demographic data for adults were obtained from CDC repository from 1999-2020, using ICD-10 codes HLD (E78.0-E78.5) and CVD (I00-I99). Age adjusted mortality rates (AAMR) per 1,000,000 population was standardized to the 2000 US population. Log-linear regression models evaluated mortality shifts. Average annual percentage change (AAPC) from 1999-2019 was used to calculate projected AAMR in 2020, subsequently compared to actual 2020 death rates to estimate pandemic-attributed excess deaths. Results: A total of 483,155 HLD-related CVD deaths were recorded between 1999-2020. Despite the CVD mortality decline in general population, HLD-related CVD AAMR rose from 36.33 [95% CI, 35.52-37.13] in 1999 to 99.77 [98.67-100.87] in 2019. Ischemic heart diseases (AAMR 49.39) were the most common causes of death while hypertension had the highest annual mortality increase (AAPC +10.23%) in populations with HLD. Higher HLD-related CVD mortality was observed in males (AAMR 104.87) than females (AAMR 61.93), in those ≥75 years (AAMR 646.45) than 35-75 years (AAMR 54.11), in non-Hispanic (NH) (AAMR 82.49) than Hispanic (AAMR 58.98) populations, and in rural (AAMR 89.98) than urban (AAMR 78.94) regions. NH Black populations (AAMR 84.35) and Western US regions (AAMR 96.88) had the highest HLD-related CVD. The first year of COVID-19 pandemic resulted in 10.55% excess HLD-related CVD death, with the most prominent increase in the 35-75 years age group (14.23%), Hispanic (17.96%), Black (14.82%), and urban (11.68%) populations. Conclusions: Our study revealed an increase in HLD-related CVD mortality which was exacerbated by the COVID-19 pandemic. Higher CVD mortality disproportionately affected males, Black, elderly (≥75 years), and rural populations with HLD. Further research is needed to validate our findings and identify contributing factors.

Abstract 4123308: Trends in Gestational Diabetes Mellitus By US State, 2019 -2023

Introduction/ Background: Gestational diabetes mellitus (GDM) is one of the most frequent adverse pregnancy outcomes and increases the risk of lifetime cardiometabolic disease. Given known geographic disparities in maternal morbidity and mortality in the US, we examined state-level prevalence and trends in GDM from 2019-2023. Methods: We conducted a serial, cross-sectional analysis of maternal health data recorded on birth certificates from all livebirths in the US using the National Center for Health Statistics Natality Files. We included pregnant individuals aged 15-44 years who gave birth from 2019-2023, had a singleton, live birth, and did not have pre-pregnancy diabetes. We calculated the age-standardized prevalence and average-annual percent change (AAPC) in GDM from 2019-2023, overall, by U.S. census region (Northeast, Midwest, South, and West), and in each US state and the District of Columbia (DC). Results: Of the 17,432,486 individuals with live births between 2019 and 2023, overall prevalence of GDM per 100 live births (95% confidence interval) was 6.9 (6.9, 6.9) in 2019 and 8.0 (8.0, 8.1) in 2023 (AAPC 3.2 [-1.7, 8.5]). There was significant geographic variation in overall prevalence of GDM by US region and state ( Figure 1 ). In 2023, prevalence was higher in the Midwest (8.8 [8.7, 8.9]) and Western states (8.6, [8.5,8.6]) compared with the Southern (7.3, [7.3, 7.4]) and Northeastern states (8.1, [8.0,8.2]), ranging from 5.5 (4.9,6.0) in DC to 13.0 (12.2,13.8) in Alaska. Prevalence was higher in 2023 compared with 2019 in all US regions and states and DC, except Alaska, Connecticut, Idaho, Maine, New Jersey, and Wyoming. Prevalence increased most rapidly in the Western US states (AAPC 4.2%/year [0.1, 8.4]) with the greatest change observed in Montana from 5.1 (4.7, 5.6) in 2019 to 8.0 (7.5, 8.5) in 2023 (AAPC 10.3%/year [5.6, 15.3]). Conclusions: GDM prevalence increased significantly from 2019-2023 with substantial heterogeneity by US region and state. More localized state-level analysis with context-specific polices that address factors associated with geographic differences in GDM are needed to promote maternal cardiometabolic health equity in the US.

Burden of Hyperlipidemia, Cardiovascular Mortality, and COVID-19: A Retrospective-Cohort Analysis of US Data.

Hyperlipidemia is a major cardiovascular disease (CVD) risk factor, but limited data on its mortality trends in CVD over time. We assessed annual hyperlipidemia-related CVD mortality trends in the United States, including the COVID-19 pandemic's impact. Mortality data were obtained from CDC repository between 1999 and 2020 among patients ≥15 years old, using ICD-10 codes hyperlipidemia (E78.0-E78.5) and CVD (I00-I99). Age-adjusted mortality rates (AAMR) per 1,000,000 population was standardized to the 2000 US population. Log-linear regression models were used to evaluate mortality shifts. Average annual percentage change (AAPC) from 1999-2019 was used to project 2020 AAMR, estimating pandemic-attributed excess deaths. From 1999 to 2020, 483,155 hyperlipidemia-related CVD deaths occurred. Despite a general CVD mortality decline, hyperlipidemia-related CVD AAMR rose from 36.33 in 1999 to 99.77 in 2019. Ischemic heart diseases (AAMR 49.39) were the leading cause while hypertension had the highest mortality increase (AAPC +10.23%). Mortality rates were higher in males (AAMR 104.87), non-Hispanic (AAMR 82.49), and rural populations (AAMR 89.98). Highest mortality was observed in Black populations (AAMR 84.35), those ≥75 years (AAMR 646.45), and Western US regions (AAMR 96.88). During the first pandemic year, deaths exceeded projections by 10.55%, with notable increases among ages 35-75 (14.23%), Hispanic (17.96%), Black (14.82%), and urban (11.68%) groups. Hyperlipidemia-related CVD mortality has risen over the past two decades, further heightened by the COVID-19 pandemic, with higher impact on males, Black Americans, the elderly, and rural residents. Further study is needed to understand contributing factors and mitigate disparities.