Geographic Units Research Articles

Abstract B080: Examining racial differences in area-level factors associated with prostate cancer stage at diagnosis: A systematic review

Abstract In the United States, prostate cancer is the most diagnosed cancer and the second highest cause of cancer deaths in men. Despite the high survival rate for prostate cancer compared to other cancers, racial and ethnic disparities persist. Black males are more likely to be diagnosed at an advanced stage, with more aggressive forms, and have two-fold higher age-adjusted mortality rates than non-Hispanic White males. Research studies show that the stage at diagnosis is a critical prognostic factor for survival and access to quality health care. There is increasing evidence of an association between neighborhood area-level contextual factors (e.g., social and physical environments) and prostate cancer stage at diagnosis, but very few systematic reviews. This systematic review examined White and Black racial differences in the association between area-level factors and prostate cancer stage diagnosis in the United States. We systematically obtained relevant peer-reviewed articles published in English until August 30, 2022, from three databases: PubMed, Embase, and Scopus. The relevant studies included prostate cancer observational studies in adult men (18 years and older) conducted in the United States that measured one or more area-level factors at the level of a geographic unit (e.g., census tract, block group, and zip codes). We used the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies to review the internal validity of the final studies. The reviewers identified 41 articles with a stage at diagnosis as an outcome, of which 11 articles (26%) statistically examined White-Black differences for the association between stage at diagnosis and an area-level factor. Most of the final 11 studies (73%) used advanced stage (regional, distant/metastatic stage) as the primary indicator variable, and two studies looked at two different outcomes (e.g., localized/early stage and tumor aggressiveness). The observed area-level factors were grouped as social (62.5%), physical/built environment (18.8%), and health service determinants (18.8%). Neighborhood socioeconomic status (SES) or deprivation composite scores (12.5%) and poverty (12.5%) were the most area-level factors reported in the studies. There were differences in area-level factors associated with prostate cancer severity for Black and White males. Overall, the results showed that Black men were more likely to be diagnosed with advanced-stage or aggressive tumors if they resided in areas with low neighborhood SES, low environmental quality, and the highest deprived and racially segregated areas compared to Whites. The study summarized results from individual studies exploring the role of neighborhood area-level factors and prostate cancer stage racial disparities in the United States. Future studies can provide an understanding of the impact and mechanisms of the area-level social and physical environments in cancer disparities. Citation Format: Ifeanyi B. Chukwudozie, Elle R. Martell, Ravneet Kaur, Rosie Hanneke, Vincent Freeman. Examining racial differences in area-level factors associated with prostate cancer stage at diagnosis: A systematic review [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B080.

Integrating a non-gridded space representation into a graph neural networks model for citywide short-term crash risk prediction

Current applications of Graph Neural Networks in citywide short-term crash risk prediction have been limited by a gridded representation of space, which restricts the network’s capability to effectively capture the spatial and temporal dependency of crash occurrences. In addition, a grided representation does not match most geographic units used for administrative purposes, limiting the use of crash risk predictions by practitioners. This paper applies a gated localised diffusion graph neural network (GLDNet) model to compare the use of two alternative geographic units, Mesh Block (MB) and grid, to forecast locations where crashes are likely to occur in a future time window. The GLDNet relies on a graph-based representation of geographic units and a weighted loss function to address the sparsity of crash occurrences. The tests are performed using crash data from the City of Melbourne, Australia, over a period of one year. The predictions are made at six-hour intervals, and the results show that the GLDNet consistently outperforms baseline methods, with differences in prediction accuracy from 10% to 21% in relation to historical average and benchmark deep learning models. In terms of geographic units, the MB-based GLDNet performed better than its grid counterpart, with differences in prediction accuracy of up to 12.3%. The better performance stems from the underlying information attached to the MB units (i.e., land use) and the network properties (i.e., degree of centrality), which enhance the GLDNet capability to identify crash risk in both central and peripherical areas. Regarding its applicability, the MB-based GLDNet directly integrates with other data sources, which provides contextual information about crash hotspots that helps decision-makers develop police patrolling and rescuing strategies.

The evolution of artificial oasis over two millennia in Hexi corridor, China

Artificial oasis is an important geographical unit that is formed by the combination of natural disturbances and human interventions in a fragile ecosystem of arid regions. Therefore, there are important theoretical and practical significance to estimate long term artificial oasis changes and its driving mechanism. We took Hexi Corridor in northwest of China as a case study, based on multi-types of data included documentary reconstruction records, historical archives data and published articles to quantitative analyze the relationship between artificial oasis evolution and a set of natural and human factors over the past two millennia (0-1990 AD). Results showed that artificial oases had a tortuous evolutionary history, experienced three periods of rapid development in the Hexi Corridor. The structural equation model revealed that two human related factors (population density and grain yield) have a significantly greater influence on artificial oasis development than natural variables (temperature). According to current conditions, paradigm shift in human behavior are the prerequisite for oasis evolution and its transition. Furthermore, assessment the scale of artificial oasis based on the human-land relationship is a future direction for research and practice, for allocating water and soil resources rationally, improving the quality of oasis, and controlling the scale of oasis.

Trapped in dilemma: Inverted N-shaped EKC evidence of economic growth and ecological land in a spatial spillover perspective

The environmental Kuznets relationship between economic growth and the area of ecological land using refined data has been relatively underexplored. Previous studies on the economic growth-ecological environment relationship primarily focused on cities or larger geographical units, resulting in inconsistent findings. Using the STIRPAT model, we investigate the impact of economic growth and its decomposition effects on ecological land area, and employ the Spatial Durbin Model to empirically analyze district- and county-level panel data within the Beijing-Tianjin-Hebei urban agglomeration. Our findings reveal an inverted “N" shaped association between real per capita GDP and ecological land area, woodlands area, and grasslands area, with the second turning point located at real per capita GDP values of $179,169, $39,778, and $36,793, respectively. The strong spillover effects of per capita GDP accelerate the occurrence of the tipping point where the impact of per capita GDP shifts from positive to negative across all ecological land types. To address this, we propose strategies like delaying the second turning point, smoothing the third stage curve, and mitigating ecological service loss. These results offer valuable policy insights, not only for the Beijing-Tianjin-Hebei region but also for other areas grappling with the conflict between economic development and ecological land use.

Challenges and Opportunities in One Health: Google Trends Search Data.

Google Trends data can be informative for zoonotic disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we demonstrate the potential to use Google Trends for zoonotic disease prediction by predicting monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010-2021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms to Lyme disease. For each search term, we built an expanding window negative binomial model that adjusted for seasonal differences using a lag term. Performance was measured by Root Mean Squared Errors (RMSEs) and the visual associations between observed and predicted case counts. The highest performing model had excellent predictive ability in some states, but performance varied across states. The highest performing models were for Lyme disease search terms, which indicates the high specificity of search terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data for One Health research, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data. Lastly, we recommend that Google Trends be explored as an option for predicting other zoonotic diseases and incorporate other data streams that may improve predictive performance.

Predicting hurricane evacuation for local neighborhoods across a metropolitan region

To mitigate the devastating impacts of hurricanes on people's lives, communities, and societal infrastructures, disaster management would benefit considerably from a detailed understanding of evacuation, including the socio-demographics of the populations that evacuate, or remain, down to disaggregated geographic levels such as local neighborhoods. A detailed household evacuation prediction model for local neighborhoods requires both a robust household evacuation decision model and individual household data for small geographic units. This paper utilizes a recently published statistical meta-analysis for the first requirement and then conducts a rigorous population synthesis procedure for the second. Our model produces predicted non-evacuation rates for all US Census block groups for the Tampa-St. Petersburg-Clearwater Metropolitan Statistical Area for a Hurricane Irma-like storm along with their socio-demographic and hurricane impact risk profiles. Our model predictions indicate that non-evacuation rates are likely to vary considerably, even across neighboring block groups, driven by the variability in evacuation risk profiles. Our results also demonstrate how different predictors may come to the fore in influencing non-evacuation in different block groups, and that predictors which may have an outsize impact on individual household evacuation decisions, such as Race, are not necessarily associated with the greatest differentials in non-evacuation rates when we aggregate households to block group level and above. Our research is intended to provide a framework for the design of hurricane evacuation prediction tools that could be used in disaster management.

Bayesian Disease Mapping to Identify High-Risk Population for Oral Cancer: A Retrospective Spatiotemporal Analysis

Objectives. Bayesian mapping is an effective spatiotemporal approach to identify high-risk geographic areas for diseases and has not been used to identify oral cancer hotspots in Australia previously. This retrospective disease mapping study was undertaken to identify the oral cancer trends and patterns within the Queensland state in Australia. Methods. This study included data obtained from Queensland state Cancer Registry from 1982 to 2018. Domains mapped included the oral cancer incidence and mortality in Queensland (QLD). Local government areas (LGAs) and suburbs were utilized as geographical units for the estimation using Bayesian mapping approach. Results. Of the 78 LGAs, 21 showed high-oral cancer incidence as measured using higher median smoothed incidence risk (SIR), above the state average. Specifically, nine LGAs within predominantly rural areas had SIR above 100% of the state average. Of these, only one LGA (Mount Isa City) had a median smoothed SIR and 95% CI of 2.61 (2.14–3.15) which was constantly above 100% of the state average. Furthermore, mortality risk estimated using smoothed mortality risk (SMR), were significantly higher than the state average in 31 LGAs. Seventeen LGAs had a median SMR above 100% of the state average while three LGAs had the highest overall, 3- and 5-year mortality risks. Considering the 95% credible interval which is indicative of the uncertainty around the estimates, three LGAs had the highest overall mortality risks—Yarrabah Aboriginal Shire (3.80 (2.16–6.39)), Cook Shire (3.37 (2.21–5.06)), and Mount Isa City (3.04 (2.40–3.80)). Conclusion. Bayesian disease mapping approach identified multiple incidence and mortality hotspots within regional areas of the Queensland. Findings from our study can aid in designing targeted public health screening and interventions for primary prevention of oral cancer in regional and remote communities.

Beyond the Farm to Fork Strategy: Methodology for designing a European agro-ecological future

The publication of the European Commission's Farm to Fork Strategy has sparked a heated debate between those who advocate the intensification of agriculture in the name of food security and those who recommend its de-intensification for environmental reasons. The design of quantified scenarios is a key approach to objectively evaluate the arguments of the two sides. To this end, we used the accounting methodology GRAFS (Generalized Representation of Agri-Food Systems) to describe the agri-food system of Europe divided into 127 geographical units of similar agricultural area, in terms of nitrogen (N) fluxes across cropland, grassland, livestock, and human consumption. This analysis reveals, in current European agriculture, a high level of territorial specialization, a strong dependence on long distance trade, and environmental N losses amounting to about 14 TgN/yr, i.e. nearly 70 % of the annual N input (including N synthetic fertilizers, symbiotic N fixation, oxidized N deposition and import of food and feed). Based on the analysis of the yield-fertilization relationship of cropping systems at the scale of their full rotation cycle, and on a simplified model of livestock ingestion, excretion and production, we advanced the GRAFS methodology for prospective scenario design. Three scenarios for the European agri-food system were explored for 2050: a business-as-usual (BAU) scenario, a scenario based on the measures considered by the EU Farm to Fork Strategy (F2F), and a fully agro-ecological scenario (AE). The results show that the F2F scenario reduces the dependence of Europe on imports of synthetic fertilizers and feed resources by 40 % as well as the environmental N losses by 30 %, but not to the level of its claimed ambitions as N lost to the environment still amounts to about 10 TgN/yr, i.e. 67 % of N inputs. Of the three scenarios studied, only in the AE scenario, involving the relocation of feed production, the generalization of organic crop rotations with N fixing legume crops, and a shift of agricultural production and food consumption toward less animal-based products, would Europe be able to dispense with N imports, still being able to export some cereals, meat, and milk products to the rest of the world, while halving today's reactive N emissions to the environment.

Four Decades of Landscape Change on a Granite Dome (Enchanted Rock, Texas): A Photographic Field Work Analysis

This study focuses on photographic analysis of Enchanted Rock, a granite dome in central Texas; fourteen photo pairs compare contrasting views after—thirty-six to forty-six years, and assess changes in geomorphology, sediments, and vegetation on miniature landforms, especially erosional depressions—gnammas and vernal pools. Several distinct landscape changes occurred during the study period, but these affected sites unevenly. Vegetation displayed noticeable differences in plant density and species, particularly a pronounced increase of Opuntia cacti and woody shrubs on several depressions; these changes, however, are not considered permanent, and are interpreted as representing periodic fluctuations due to climatic oscillations such as recurrent drought. Among geomorphic features, a rock pedestal and a tafoni panel—displaying friable, crumbling, material—are seemingly being rapidly obliterated by weathering; their low compressive strength (Schmidt hammer) R values support this idea. In contrast, a large, isolated, boulder near the dome base did not—as anticipated—display any obvious changes. Large stones and gravelly sediments, accumulated on vernal pools, or deposited along rill channels between depressions, showed the most conspicuous alterations. The observed changes are arranged along a presumed developmental sequence portraying subsequent geomorphic stages during pool development, which occur as detached stones gradually weather and eventually disintegrate; both coarse and fine sediments are transported downslope during intense, infrequent rainfall events. Pools, gnammas, rills, sediments, and vegetation function on Enchanted Rock as interconnected geographical units and components of an elaborate drainage network, steadily affecting each other over space and time.

Identifying Spatiotemporal Patterns of Multiscale Connectivity in the Flow Space of Urban Agglomeration in the Yellow River Basin

The United Nations Sustainable Development Goals (SDGs) and the rise of global sustainability science have led to the increasing recognition of basins as the key natural geographical units for human–land system coupling and spatial coordinated development. The effective measurement of spatiotemporal patterns of urban connectivity within a basin has become a key issue in achieving basin-related SDGs. Meanwhile, China has been actively working toward co-ordinated regional development through in-depth implementation of the Yellow River Basin’s ecological protection and high-quality development. Urban connectivity has been trending in urban planning, and significant progress has been made on different scales according to the flow space theory. Nevertheless, few studies have been conducted on the multiscale spatiotemporal patterns of urban agglomeration connectivity. In this study, the urban network in the Yellow River Basin was constructed using Tencent population migration data from 2015 and 2019. It was then divided into seven distinct communities to enable analysis at both the basin and community scales. Centrality, symmetry, and polycentricity indices were employed, and the multiscale spatiotemporal patterns of urban agglomerations in the Yellow River Basin were identified using community detection, complex networks, and the migration kaleidoscope method. Community connectivity was notably concentrated at the basin scale with a centripetal pattern and spatial heterogeneity. Additionally, there was a symmetrical and co-ordinated relationship in population migration between the eastern and western regions of the basin, as well as between the internal and external parts of the basin. At the community scale, there was significant variation in the extent of central agglomeration among different communities, with few instances of similar-level, long-distance, and interregional bilateral links. The utilization of multiscale spatiotemporal patterns has the potential to enhance the comprehension of economic cooperation between various cities and urban agglomerations. This understanding can aid decision-makers in formulating sustainable development policies that foster the spatial integration of the basin.

The Spatio-Temporal Patterns of Regional Development in Shandong Province of China from 2012 to 2021 Based on Nighttime Light Remote Sensing.

As a major coastal economic province in the east of China, it is of great significance to clarify the temporal and spatial patterns of regional development in Shandong Province in recent years to support regional high-quality development. Nightlight remote sensing data can reveal the spatio-temporal patterns of social and economic activities on a fine pixel scale. We based the nighttime light patterns at three spatial scales in three geographical regions on monthly nighttime light remote sensing data and social statistics. Different cities and different counties in Shandong Province in the last 10 years were studied by using the methods of trend analysis, stability analysis and correlation analysis. The results show that: (1) The nighttime light pattern was generally consistent with the spatial pattern of construction land. The nighttime light intensity of most urban, built-up areas showed an increasing trend, while the old urban areas of Qingdao and Yantai showed a weakening trend. (2) At the geographical unit scale, the total nighttime light in south-central Shandong was significantly higher than that in eastern and northwest Shandong, while the nighttime light growth rate in northwest Shandong was significantly highest. At the urban scale, Liaocheng had the highest nighttime light growth rate. At the county scale, the nighttime light growth rate of counties with a better economy was lower, while that of counties with a backward economy was higher. (3) The nighttime light growth was significantly correlated with Gross Domestic Product (GDP) and population growth, indicating that regional economic development and population growth were the main causes of nighttime light change.

Comparative Assessment of the Spatiotemporal Dynamics and Driving Forces of Natural and Constructed Wetlands in Arid and Semiarid Areas of Northern China

Arid and semiarid wetlands, the core geographical unit of desert oases, significantly benefit and improve the ecological environment. In this study, we systematically compared the spatiotemporal dynamics and driving forces of natural and constructed wetlands in arid and semiarid regions of northern China from 1995 to 2019. For these comparisons, we utilized a land-use transition matrix, partial least-squares–structural equation model (PLS-SEM), and geographically weighted regression (GWR) model. The results showed that (1) the area of wetlands as a whole showed an upward trend, with natural and constructed wetlands increasing by 4.16% and 11.86%, respectively. The increases mainly resulted from conversions of grassland and other lands (shrub, sparse vegetation, and bare land). (2) The direct dominant factors that drove natural wetland changes were soil and terrain, while those that drove constructed wetlands were human disturbances. Human disturbance, by affecting soil, had a higher significant indirect effect on natural wetlands. Heat, by affecting moisture, had the greatest indirect effect on constructed wetlands. (3) The sensitivity of natural and constructed wetlands to the responses of different drivers showed significant spatial heterogeneity. This study explores the interaction and driving mechanisms of human and natural attributes on natural and constructed wetlands and provides a scientific foundation for the restoration and sustainable development of wetlands in arid and semiarid areas.

Examining Built Environment Effects on Metro Ridership at Station-to-Station Level considering Circle Heterogeneity: A Case Study from Xi’an, China

Transit-oriented development is described as a geographic unit with multicircle structures. Most studies have analysed the impact of the built environment within station catchment areas on metro passenger flows from a macro perspective and have lacked analysis of the circle heterogeneity. Few relevant studies have independently investigated the impact of the built environment on the passenger flow in each circle and indeed neglected the systematic interaction between inner circles and circles in the TOD area. In this study, the 800 m buffer from the station was equally divided into four circles. Based on the gravity model, the representative built environment features around the metro stations on both sides were extracted using the block attention module (BAM). Subsequently, Shapley Additive exPlanation (SHAP) was used to explore the influence of different built environment variables on passenger flow at each circle between the origin and destination stations. The results indicate the following: (1) the station-to-station passenger flow is significantly affected by the availability of transfers and the distance between the origin and destination stations; (2) the impact of different built environments on ridership significantly varies within different circles; and (3) the built environment has a similar impact on average daily passenger flow on both sides. Therefore, this study proposes strategies to optimize the metro passenger flow by developing different land use in different circles and updating the urban spatial structure.

Diversity patterns of echinoids (Echinodermata: Echinoidea) in shallow waters of the Mexican Pacific

The Mexican Pacific hosts a large diversity of echinoids occupying different habitats and ecological niches; nevertheless, the group remains scarcely studied in terms of their spatial patterns of biodiversity and variation in the region. This work evaluated the alpha, beta, and gamma diversity of echinoids living in the Mexican Pacific's shallow waters (0–200 m) at three spatial scales: Operational geographic units (OGUs), ecoregions, and provinces. Alpha diversity was evaluated through the standardized coverage values of 0D, 1D, and 2D, while the contribution of alpha and beta diversity components to gamma diversity was estimated with additive partitioning. Finally, we evaluated whether beta diversity resulted from overlap, species replacement, or richness differences. A total of 45 species and 2,232 incidences were registered in the 150 OGUs distributed throughout the Mexican Pacific. Cluster completeness in the ecoregions of the Warm Temperate Northeast Pacific (WTNP) province was higher (> 95 %) than in the ecoregions of the Eastern Tropical Pacific (TEaP) province. Standardized coverage values of the 0D, 1D, and 2D indices showed differences between the ecoregions Cortezian and Magdalena Transition in the WTNP province. At the ecoregion and province level, alpha diversity might be influenced by sampling techniques and habitat availability, which limits the distribution of regular and irregular echinoids. Gamma diversity is mainly determined by beta diversity at the ecoregion scale, where species overlap and richness differences were the major contributing components, probably resulting from substrate dependence, habitat availability, and mesoscale ocean circulation patterns.

Small Area Geographic Estimates of Cardiovascular Disease Risk Factors in India

With an aging population, India is facing a growing burden of cardiovascular diseases (CVDs). Existing programs on CVD risk factors are mostly based on state and district data, which overlook health disparities within macro units. To quantify and geovisualize the extent of small area variability within districts in CVD risk factors (hypertension, diabetes, and obesity) in India. This cross-sectional study analyzed nationally representative data from the National Family Health Survey 2019-2021, encompassing individuals aged 15 years or older, for hypertension (n = 1 715 895), diabetes (n = 1 807 566), and obesity (n = 776 023). Data analyses were conducted from July 1, 2022, through August 1, 2023. Geographic units consisting of more than 30 000 small areas, 707 districts, and 36 states or Union Territories across India. For primary outcomes, CVD risk factors, including hypertension, diabetes, and obesity, were considered. Four-level logistic regression models were used to partition the geographic variability in each outcome by state or Union Territory (level 4), district (level 3), and small area (level 2) and compute precision-weighted small area estimates. Spatial distribution of district-wide means, within-district small area variability, and their correlation were estimated. The final analytic sample consisted of 1 715 895 individuals analyzed for hypertension (mean [SD] age, 39.8 [17.3] years; 921 779 [53.7%] female), 1 807 566 for diabetes (mean [SD] age, 39.5 [17.2] years; 961 977 [53.2%] female), and 776 023 for obesity (mean [SD] age, 30.9 [10.2] years; 678 782 [87.5%] women). Overall, 21.2% of female and 24.1% of male participants had hypertension, 5.0% of female and 5.4% of men had diabetes, and 6.3% of female and 4.0% of male participants had obesity. For female participants, small areas (32.0% for diabetes, 34.5% for obesity, and 56.2% for hypertension) and states (30.0% for hypertension, 46.6% for obesity, and 52.8% for diabetes) accounted for the majority of the total geographic variability, while districts accounted for the least (13.8% for hypertension, 15.2% for diabetes, and 18.9% for obesity). There were moderate to strong positive correlations between district-wide mean and within-district variability (r = 0.66 for hypertension, 0.94 for obesity, and 0.96 for diabetes). For hypertension, a significant discordance between district-wide mean and within-district small area variability was found. Results were largely similar for male participants across all categories. This cross-sectional study found a substantial small area variability, suggesting the necessity of precise policy attention specifically to small areas in program formulation and intervention to prevent and manage CVD risk factors. Targeted action on policy-priority districts with high prevalence and substantial inequality is required for accelerating India's efforts to reduce the burden of noncommunicable diseases.

A 20-Year Update on the Practice of Thoracic Surgery in Canada: A Survey of the Canadian Association of Thoracic Surgeons.

This study provides an update to a landmark 2004 report describing demographics, training, and trends in adherence to thoracic surgery practice standards in Canada. An updated questionnaire was administered to all members of the Canadian Association of Thoracic Surgeons via email (n=142, compared to n=68 in 2004). Our report incorporates internal data from Ontario Health and the Canadian Partnership Against Cancer. Forty-eight surgeons completed the survey (male, 70.8%; mean±standard deviation age, 50.3±9.3 years). This represents a 33.8% response rate, compared to 64.7% in 2004. Most surgeons (69%) served a patient population of over 1 million per center; 32%-34% reported an on-call ratio of 1:4-1:5 days, and the average weekly hours worked was 56.4±11.9. Greater access to dedicated geographic units per center (73% in 2021 vs. 53% in 2004) has improved thoracic-associated services and house staff, notably endoscopy units (100% vs. 91%), with 73% of respondents having access to both endobronchial and endoscopic ultrasound. Access to thoracic radiology has also improved, particularly regarding positron emission tomography scanners per center (76.9% vs. 13%). Annual case volumes for lung (255 vs. 128), esophageal (41 vs. 19), and mediastinal resections (30 vs. 13), along with hiatal hernia repair (45 vs. 20), have increased substantially despite reports of operating room availability and radiology as rate-limiting steps. This survey characterizes compliance with current practice standards, addressing the needs of thoracic surgeons across Canada. Over 85% of respondents were aware of the 2004 compliance paper, and 35% had applied for resources and equipment in response.

Beetle evolution illuminates the geological history of the World's most diverse tropical archipelago

The geologically‐complex Indo–Australian–Melanesian archipelago (IAMA) hosts extraordinarily high levels of species richness and endemism and has long served as a natural laboratory for studying biogeography and evolution. Nonetheless, its geological history and the provenance and evolution of its biodiversity remain poorly understood. Here, we provide a geological scenario for the IAMA informed by a time‐calibrated molecular phylogeny of 1006 species of Trigonopterus weevils – an exceptionally diverse radiation of regionally‐endemic flightless beetles. Moreover, we performed a statistical biogeographic analysis and examined timing and patterns in the accumulation of lineages residing in a priori‐defined geographic units comprising the IAMA. We estimate that Trigonopterus originated in Australia during the early Paleogene. Subsequent rapid diversification in the area of the present‐day Papuan Peninsula suggests the presence of proto‐Papuan islands by the middle Eocene; the New Guinea North Coast Ranges were colonized in the late Eocene, followed by the New Guinea Highlands and the Bird's Head Peninsula. We inferred the presence of terrestrial habitat in the North Moluccas and Sulawesi in the late Oligocene and the subsequent rapid colonization of Sundaland and the Lesser Sunda Islands. New Caledonia and Samoa were colonized from the Papuan Peninsula, and their faunas also diverged in the late Oligocene. These biota‐informed time estimates are compatible with geological data from the region and shed new light on IAMA paleogeography, even where geological evidence has been lost to erosion. Beetle evolution thus appears to have closely tracked the geological evolution of the IAMA, revealing a uniquely well‐resolved view of regional biogeography.

Use of geographically weighted regression models to inform retail endgame strategies in South Korea: application to cigarette and ENDS prevalence

BackgroundReducing tobacco retailer availability is a key tobacco endgame policy. The development and evaluation of retail-based policies require spatial methodologies. We modelled the prevalence of adult cigarette and electronic nicotine...

Karst landform classification considering surface flow characteristics derived from digital elevation models

AbstractKarst landforms are one of the most typical geographical units with a specific physical process on the earth's surface. The classification of karst landforms is an important aspect for understanding their landform processes and mechanisms. However, influenced by various interior and external forces, karst landforms have an extremely complex surface morphology, increasing the difficulty of their automatic classification. In this study, we considered hydrological features as an important factor in characterizing karst landforms and proposed a method that considers surface flow for karst landform classification. In this method, terrain was reversed for hydrological analysis to achieve the landform units. Then, the watershed boundary of the reversed terrain is extracted by hydrological analysis. The boundary of the karst landform unit is determined by erasing the plain area from the watershed boundary. Thereafter, the graph theory segmentation method is employed to merge the landform units belonging to the same karst landform entity. The proposed approach is validated and applied in two sample karst areas, Fenglin and Fengcong, located in Guilin, China, using digital elevation model data with 30 m spatial resolution. In addition, a comparative analysis is conducted to evaluate the accuracy of the proposed method. The results demonstrated that the typical karst landform units of Fenglin and Fengcong can be effectively classified. The overall classification accuracy is 94.44%. The proposed method produced more reasonable and accurate boundaries compared with the contour tree and terrain feature point methods. Furthermore, the classification results indicate various landform development stages of the karst landform process in the study area. The proposed method considering surface flow characteristics can be further extended to other landform types with highly complex landforms.

Water resource sustainable use assessment methodology and an impact factor analysis framework for SDG 6-oriented river basins: evidence from the Yellow River basin (Shaanxi section) in China.

The rapid growth of developing countries has placed unprecedented pressure on water resources, severely hindering the realization of sustainable development goal 6 (SDG 6) in river basins. In this study, sustainable water resource utilization (SWRU) in the Yellow River basin (Shaanxi section) from 2005 to 2019 is evaluated through an analysis of water resource overload combined with the water footprint (WF) and the water planetary boundary (WPB) and an analysis of water resource utilization quality combined with the WF and city development index (CDI) based on the coupled coordination model. Then, the results are incorporated into the drive-pressure-state-impact-response framework to analyze the impacts of the socioeconomic system on SWRU and the feedback effect of related policies. The results show that there were obvious differences in the spatiotemporal evolution characteristics of the WF in different geographical units. The WF of Guanzhong first increased and then decreased, and the WF of Northern Shaanxi grew continuously. The water deficit state is increasing. Although the coordination level between the WF and CDI in the basin increased by 500.31%, it was characterized by nonequilibrium and volatility. Compared to water resource endowment, socioeconomic development and government policies have greater impacts on SWRU; furthermore, the influencing factors demonstrate spatial variability, revealing the complexity of achieving SDG 6 in the basin. As policy implications, adaptive water resource policies should be formulated on the basis of strengthening the overall basin management. This study provides a scientific basis for promoting the realization of SDG 6 through watershed water management.