Climate and cultural evolution drove Holocene cropland change in the Huai River Valley, China.

As an important way of maximizing land productivity by growing more than one crop type in the same field, mixed cropping has been an effective option for sustaining population growth under different climatic conditions since prehistoric period. We used a combination of archaeological data and an improved prehistoric land use model (PLUM) to quantitatively reconstruct spatiotemporal changes in cropland types and areas in the Huai River Valley of China, a core region of mixed cropping during the Holocene. The total cropland area increased more than 25 times during 8-2 ka BP, with northward expansion of rice-dominated cultivation during 5-4 ka BP and southward expansion of dry-dominated cultivation after 4 ka BP. Temperature and precipitation determined cropland types distribution, while that of cropland area was controlled by cultural development. The interplay between past climate, culture, and cultivation potentially provides useful insights into mitigating future population pressures with climate change.

Species-level characterization of the core microbiome in healthy dogs using full-length 16S rRNA gene sequencing.

Despite considerable interest and research in the canine fecal microbiome, our understanding of its species-level composition remains incomplete, as the majority of studies have only provided genus-level resolution. Here, we used full-length 16S rRNA gene sequencing to characterize the fecal microbiomes of 286 presumed healthy dogs living in homes in North America who are devoid of clinical signs, physical conditions, medication use, and behavioral problems. We identified the bacterial species comprising the core microbiome and investigated whether a dog's sex & neuter status, age, body weight, diet, and geographic region predicted microbiome variation. Our analysis revealed that 23 bacterial species comprised the core microbiome, among them Collinsella intestinalis, Megamonas funiformis, Peptacetobacter hiranonis, Prevotella copri, and Turicibacter sanguinis. The 23 taxa comprised 75% of the microbiome on average. Sterilized females, dogs of intermediate body sizes, and those exclusively fed kibble tended to harbor the most core taxa. Host diet category, geographic region, and body weight predicted microbiome beta-diversity, but the effect sizes were modest. Specifically, the fecal microbiomes of dogs fed kibble were enriched in several core taxa, including C. intestinalis, P. copri, and Holdemanella biformis, compared to those fed raw or cooked food. Conversely, dogs on a raw food diet exhibited higher abundances of Bacteroides vulgatus, Caballeronia sordicola, and Enterococcus faecium, among others. In summary, our study provides novel insights into the species-level composition and drivers of the fecal microbiome in healthy dogs living in homes; however, extrapolation of our findings to different dog populations will require further study.

Urban meteorological drought comprehensive index based on a composite fuzzy matter element-moment estimation weighting model.

Due to rapid urbanization and climate change, cities face hidden drought risks. A single drought index may inadequately reflect urban meteorological drought. The indicator weight combination method does not fully consider index correlation and weight. This study constructed an urban meteorological drought evaluation index system and developed the Composite Fuzzy Matter Element Meteorological drought Comprehensive Index (CFEMCI) by combining the moment estimation weighting model. Analyzing Zhengzhou City from 2000 to 2019, CFEMCI effectively captured meteorological drought events, with a probability of detection (POD) > 0.78, critical success index (CSI) > 0.70, false alarm rate (FAR) < 0.13 and failure ratio (FR) < 0.22. Most meteorological droughts were classified as Grade I (no drought), with 26% being light and moderate (Grades II-III). Droughts mainly occurred in spring, and the summer drought showed a more significantly aggravating trend. This index provides reliable urban drought monitoring and supports disaster prevention and mitigation efforts.

Hyper pooling private trips into high occupancy transit like attractive shared rides.

The size of the solution space associated with the trip-matching problem has made the search for high-order ride-pooling prohibitive. We introduce hyper-pooled rides along with a method to identify them within urban demand patterns. Travellers of hyper-pooled rides walk to common pick-up points, travel with a shared vehicle along a sequence of stops and are dropped off at stops from which they walk to their destinations. While closely resembling classical mass transit, hyper-pooled rides are purely demand-driven, with itineraries (stop locations, sequences, timings) optimised for all co-travellers. For 2000 trips in Amsterdam the algorithm generated 40 hyper-pooled rides transporting 225 travellers. They would require 52.5 vehicle hours to travel solo, whereas in the hyper-pooled multi-stop rides, it is reduced sixfold to 9 vehicle hours only. This efficiency gain is made possible by achieving an average occupancy of 5.8 (and a maximum of 14) while remaining attractive for all co-travellers.

Causal discovery reveals complex patterns of drought-induced displacement.

The increasing frequency and severity of droughts present a significant risk to vulnerable regions of the globe, potentially leading to substantial human displacement in extreme situations. Drought-induced displacement is a complex and multifaceted issue that can perpetuate cycles of poverty, exacerbate food and water scarcity, and reinforce socio-economic inequalities. However, our understanding of human mobility in drought scenarios is currently limited, inhibiting accurate predictions and effective policy responses. Drought-induced displacement is driven by numerous factors and identifying its key drivers, causal-effect lags, and consequential effects is often challenging, typically relying on mechanistic models and qualitative assumptions. This paper presents a novel, data-driven methodology, grounded in causal discovery, to retrieve the drivers of drought-induced displacement within Somalia from 2016 to 2023. Our model exposes the intertwined vulnerabilities and the leading times that connect drought impacts, water and food security systems along with episodes of violent conflict, emphasizing that causal mechanisms change across districts. These findings pave the way for the development of algorithms with the ability to learn from human mobility data, enhancing anticipatory action, policy formulation, and humanitarian aid.

Combining short- and long-read sequencing unveils geographically structured diversity in Borrelia miyamotoi.

Borrelia miyamotoi is an emerging Ixodes tick-borne human pathogen in the Northern hemisphere. The aim of the current study was to compare whole genome sequences of B. miyamotoi isolates from different continents. Using a combination of Illumina and PacBio platforms and a novel genome assembly and plasmid typing pipeline, we reveal that the 21 sequenced B. miyamotoi isolates and publically available B. miyamotoi genomes from North America, Asia, and Europe form genetically distinct populations and cluster according to their geographical origin, where distinct Ixodes species are endemic. We identified 20 linear and 17 circular plasmid types and the presence of specific plasmids for isolates originating from different continents. Linear plasmids lp12, lp23, lp41, and lp72 were core plasmids found in all isolates, with lp41 consistently containing the vmp expression site. Our data provide insights into the genetic basis of vector competence, virulence, and pathogenesis of B. miyamotoi.

A place to start?

How do economic geographers determine where to begin their research projects, where to locate and delimit their case studies, where and how to "cut in" to problems? In the absence of self-evident or pregiven answers to these questions, the problem-cum-choice of where and how to start is inescapably tangled up with issues of preliminary conceptualization and indeed theorization, since cases are not so much found as made, being in various ways coproduced with different "theory-method packages." There is (and can be) no singular or universal answer to these questions. Instead, this brief intervention outlines one rationale for getting "started," founded as such rationales should be with reference a particular approach or mode of theorization. The approach here centers on the problematic of recombinant development, on the role of extended case-study designs, and on the still sparsely realized potential of conjunctural modes of analysis.

Applying a Bayesian Spatiotemporal Model to Examine Excess County-Level Cardiovascular Disease Death Rates During the COVID-19 Pandemic.

Amid the COVID-19 pandemic, national cardiovascular disease (CVD) death rates increased, especially among younger adults. County-level variation has not been documented. Using county-level CVD deaths (ICD-10 codes: I00-I99) from the US National Vital Statistics System, we developed a Bayesian multivariate spatiotemporal model to estimate excess CVD death rates in 2020 based on trends from 2010-2019 for adults aged 35-64 and ≥65 years. Among adults aged 35-64 years, 64.7% of counties experienced significant excess CVD death rates. The median county-level CVD death rate in 2020 was 150 per 100,000 persons, which exceeded the predicted rate for 2020 (median excess death rate: 11 per 100,000; median excess rate ratio: 1.08). Among adults aged ≥65 years, 15.2% of counties experienced significant excess CVD death rates. The median county-level CVD death rate was 1,546 per 100,000 in 2020, which exceeded the predicted rate in 2020 (median excess death rate: 48 per 100,000, median excess rate ratio: 1.03). Counties with significant excess death rates in 2020 were geographically dispersed. In 2020, disruptions of county-level CVD death rates were widespread, especially among younger adults, suggesting the continued importance of CVD prevention and treatment in younger adults in communities across the country.

Mapping the Hidden Terrain of Hepatocellular Carcinoma: Exploring Regional Differences in Incidence and Mortality across Two Decades by Using the Largest US Datasets.

Background: There is an observed variation in the burden of hepatocellular carcinoma (HCC) across different US populations. Our study aims to comprehensively assess variations in HCC incidence and mortality rates across different regions of the US. Understanding these geographical differences is crucial, given prior evidence indicating variations in the incidence of viral hepatitis and metabolic dysfunction-associated steatotic liver disease and varying access to curative HCC treatment among states. Methods: HCC age-adjusted incidence rates between 2001 and 2021 were obtained from the United States Cancer Statistics (USCS) database (which covers approximately 98% of the US population). HCC age-adjusted mortality rates between 2000 and 2022 were obtained from the National Center of Health Statistics (NCHS) database (covering approximately 100% of the US population). The rates were categorized by US geographical region into West, Midwest, Northeast, and South. Incidence rates were also categorized by race/ethnicity. Time trends [annual percentage change (APC) and average APC (AAPC)] were estimated by using Joinpoint Regression via the weighted Bayesian Information Criteria (p < 0.05). Results: Between 2001 and 2021, there were 491,039 patients diagnosed with HCC in the US (74.2% males). The highest incidence rate per 100,000 population was noted in the West (7.38), followed by the South (6.85). Overall incidence rates increased between 2001 and 2015 and then significantly decreased until 2021 (APC = -2.29). Most cases were in the South (38.8%), which also had the greatest increase in incidence (AAPC = 2.74). All four geographical regions exhibited an overall similar trend with an increase in incidence over the first 10-15 years followed by stable or decreasing rates. While stratification of the trends by race/ethnicity showed slight variations among the regions and groups, the findings are largely similar to all race/ethnic groups combined. Between 2000 and 2022, there were 370,450 patients whose death was attributed to HCC in the US (71.6% males). The highest mortality rate per 100,000 population was noted in the South (5.02), followed by the West (4.99). Overall mortality rates significantly increased between 2000 and 2013 (APC = 1.90), then stabilized between 2013 and 2016, and then significantly decreased till 2022 (APC = -1.59). Most deaths occurred in the South (35.8%), which also had the greatest increase in mortality (AAPC = 1.33). All four geographical regions followed an overall similar trend, with an increase in mortality over the first 10-15 years, followed by stable or decreasing rates. Conclusions: Our analysis, capturing about 98% of the US population, demonstrates an increase in HCC incidence and mortality rates in all geographical regions from 2000 to around 2014-2016, followed by stabilizing and decreasing incidence and mortality rates. We observed regional variations, with the highest incidence and mortality rates noted in the West and South regions and the fastest increase in both incidence and mortality noted in the South. Our findings are likely attributable to the introduction of antiviral therapy. Furthermore, demographic, socioeconomic, and comorbid variability across geographical regions in the US might also play a role in the observed trends. We provide important epidemiologic data for HCC in the US, prompting further studies to investigate the underlying factors responsible for the observed regional variations in HCC incidence and mortality.

Individual socioeconomic status, neighborhood disadvantage, and cognitive aging: A longitudinal analysis of the CLSA.

BACKGROUND: There are likely many contributors to variation in the rate of cognitive decline in middle and late adulthood, including individual and neighborhood socio-economic factors. This study examines whether individual socio-economic factors, namely income and wealth, correlate with cognitive decline, in part, through neighborhood-level social and material disadvantage. METHODS: Using the three waves of data collection from the Canadian Longitudinal Study on Aging (CLSA), this study included 51,338 participants between the age of 45 and 85 years at baseline (51% female). Individual socio-economic status (SES) was assessed by annual household income and by the current value of savings and investments. Neighborhood disadvantage was measured by area-based material and social deprivation indices. Cognition was measured at each wave using verbal fluency, mental alternations, and delayed word recall. Latent change score models, incorporating direct and indirect pathways, were constructed to estimate the indirect effect of individual SES on cognitive change through area-level disadvantage. Multi-group models were constructed on the basis of age-group (45-64 years; 65-74 years; or 75+ years) to allow for varying estimates across age. RESULTS: Among 45-64-year-olds, income and wealth had indirect effects on initial cognitive level and on rate of cognitive decline through material disadvantage (standardized indirect effects = 0.01, p < 0.001), but only wealth had an indirect effect through social disadvantage (p = 0.019). Among 65-74-year-olds, income and wealth had indirect effects on initial cognitive level (p < 0.01) but not on rate of cognitive decline (p > 0.05), and among 75+ year-olds, no indirect effects were observed (p > 0.05). Wealth and income had direct effects, independent of neighborhood disadvantage, on cognition in all age groups (p < 0.05). CONCLUSIONS: Among middle-aged adults, greater individual SES may mitigate cognitive decline, in part, by allowing individuals to live in more materially and socially advantaged neighborhoods.

Higher ultraviolet light exposure is associated with lower mortality: An analysis of data from the UK biobank cohort study.

We aimed to examine associations between ultraviolet (UV) exposure and mortality among older adults in the United Kingdom (UK). We used data from UK Biobank participants with two UV exposures, validated with measured vitamin D levels: solarium use and annual average residential shortwave radiation. Associations between the UV exposures, all-cause and cause-specific mortality were examined as adjusted hazard ratios. The UV exposures were inversely associated with all-cause, cardiovascular disease (CVD) and cancer mortality. Solarium users were also at a lower risk of non-CVD/non-cancer mortality. The benefits of UV exposure may outweigh the risks in low-sunlight countries.

Nutritional quality of food purchases during the COVID-19 public health crisis: An analysis of geographic disparities in North Carolina.

Objective: To examine geographic disparities in the nutritional quality of food purchases during the COVID-19 public health crisis in North Carolina (NC). Methods: Using shopper-level longitudinal transaction records between October 2019- and December 2020 from NC's largest grocery retailer, we fit mixed-effect models to examine disparities in the nutritional quality of food purchases among shoppers in counties with different levels of socioeconomic development and how such disparities changed after March 2020, accounting for other observed and contextual factors. Results: Shoppers in counties with lower development levels purchased a larger share of calories from least healthy foods and a smaller share from healthier foods compared to shoppers in counties with higher development levels. These disparities were slightly attenuated for the least healthy foods and did not change for healthier foods after the onset of the COVID crisis. Conclusion: Despite existing nutritional disparities among shoppers in counties with different levels of socioeconomic development, we did not observe a large-scale accentuation of inequities in dietary quality during the COVID-19 crisis. This pattern may have resulted from programmatic responses to mitigate the adverse effects of the COVID crisis on vulnerable populations. Future work should further explore the role of such responses.

Deep transformer-based heterogeneous spatiotemporal graph learning for geographical traffic forecasting.

Accurate geographical traffic forecasting plays a critical role in urban transportation planning, traffic management, and geospatial artificial intelligence (GeoAI). Although deep learning models have made significant progress in geographical traffic forecasting, they still face challenges in effectively capturing long-term temporal dependencies and modeling heterogeneous dynamic spatial dependencies. To address these issues, we propose a novel deep transformer-based heterogeneous spatiotemporal graph learning model for geographical traffic forecasting. Our model incorporates a temporal transformer that captures long-term temporal patterns in traffic data without simple data fusion. Furthermore, we introduce adaptive normalized graph structures within different graph layers, enabling the model to capture dynamic spatial dependencies and adapt to diverse traffic scenarios, especially for the heterogeneous relationship. We conduct comprehensive experiments and visualization on four primary public datasets and demonstrate that our model achieves state-of-the-art results in comparison to existing methods.

From across the globe - traumatic injuries are an international concern at the US-Mexico border wall.

Introduction: Socio-economic and political events of recent years have caused a significant increase in immigrants attempting to illegally cross the United States (US)-Mexico border. While a 30-foot border wall separates the US and Mexico, immigrants from around the world have used this location as their point of entry to the US. These border crossings have led to a dramatic increase in major trauma resulting in increased inpatient resource utilization and the need for comprehensive hospital services. The aim of this study was to describe the nationality of injured immigrants admitted to a Trauma Center serving a segment of the US-Mexico border wall and to report their ultimate destinations after discharge. Methods: We performed a retrospective review of patients admitted to an academic, Level 1 Trauma Center after injury at the US-Mexico border wall from 2021 to 2022. Demographic information was obtained from the trauma registry. The electronic medical record was searched to identify each patient's self-reported country of origin. Patients' nationality was then stratified by region of the world to understand geographic representation of border injury admissions. Results: We identified 597 patients injured while crossing the US-Mexico border wall representing 38 different countries. The mean age of patients was 32.2±10.4 years and 446 (75%) were male. Most patients (405, 67.8%) were Mexican, followed by 23 (3.9%) patients from Peru, 17 (2.8%) patients from India, 14 (2.3%) patients from El Salvador, 13 (2.2%) patients from Cuba and 12 (2.0%) patients from Jamaica. When considering regions of the world other than Mexico, patients were most commonly from Africa, South America and Central America. Conclusion: The increased volume of trauma associated with the US-Mexico border wall is a humanitarian and health crisis.(1) The diverse national origin of patients admitted after injury from border wall falls has shed new light on the social and interpreter services needed to care for these border injury patients and the challenges that exist in their post-discharge care.

An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study.

BACKGROUND: A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs. OBJECTIVE: We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities. METHODS: Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model. RESULTS: In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the "NWO Navigate Stroke" system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes. CONCLUSIONS: The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.

Rural-urban disparities in cardiovascular disease mortality vary by poverty level and region.

PURPOSE: To examine rural and urban disparities in cardiovascular disease (CVD) death rates by poverty level and region. METHODS: Using 2021 county-level population and mortality data for CVD deaths listed as the underlying cause among adults aged 35-64 years, we calculated age-standardized CVD death rates and rate ratios (RR) for 4 categories of counties: high-poverty rural, high-poverty urban, low-poverty rural, and low-poverty urban (referent). Results are presented nationally and by US Census region. FINDINGS: Rural and urban disparities in CVD mortality varied markedly by poverty and region. Nationally, the CVD death rate was highest among high-poverty rural areas (191 deaths per 100,000, RR: 1.76, CI: 1.73-1.78). By region, Southern high-poverty rural areas had the highest CVD death rate (256 deaths per 100,000) and largest disparity relative to low-poverty urban areas (RR: 2.05; CI: 2.01-2.09). In the Midwest and West, CVD death rates among high-poverty areas were higher than low-poverty areas, regardless of rural or urban classification. CONCLUSIONS: Results reinforce the importance of prioritizing high-poverty rural areas, especially in the South, in efforts to reduce CVD mortality. These efforts may need to consider socioeconomic conditions and region, in addition to rural and urban disparities.

Portraying the developing PCK of Dutch pre-service geography teachers.

Pedagogical Content Knowledge (PCK) is used to describe the knowledge teachers use to teach a specific subject to a specific audience. Although PCK is linked to student success and motivation, relatively little is known about the PCK of geography teachers. Through a mixed methods approach, we surveyed a group of 73 Dutch pre-service teachers in their final year of geography teacher education. We used the PCK-consensus model to address both PCK-on action (teacher knowledge) and PCK-in action (teacher practice). We investigated the former through a CoRe-assignment and the latter through a quantitative survey. Teacher's PCK-in action focussed on teacher-centred lessons with ample attention for visualisations, current events, and efforts to engage students. The results for PCK-on action confirmed the content dependency of PCK. Pre-service teachers chose different geographical topics and used different goals and strategies when teaching these topics. In this context, we also found that they experienced difficulties when teaching controversial issues. In a final step, we combined the results of both methods for 9 teachers in individual PCK portraits. These portraits show that coherence between PCK-elements and, therefore, PCK-quality is still weak for most pre-service teachers. Consequently, their fragile subject matter knowledge seems to influence their developing PCK.

Impact of Location of Residence and Distance to Cancer Centre on Medical Oncology Consultation and Neoadjuvant Chemotherapy for Triple-Negative and HER2-Positive Breast Cancer.

Despite consensus guidelines, most patients with early-stage triple-negative (TN) and HER2-positive (HER2+) breast cancer do not see a medical oncologist prior to surgery and do not receive neoadjuvant chemotherapy (NAC). To understand barriers to care, we aimed to characterize the relationship between geography (region of residence and cancer centre proximity) and receipt of a pre-treatment medical oncology consultation and NAC for patients with TN and HER2+ breast cancer. Using linked administrative datasets in Ontario, Canada, we performed a retrospective population-based analysis of women diagnosed with stage I-III TN or HER2+ breast cancer from 2012 to 2020. The outcomes were a pre-treatment medical oncology consultation and the initiation of NAC. We created choropleth maps to assess the distribution of the outcomes and cancer centres across census divisions. To assess the relationship between distance to the nearest cancer centre and outcomes, we performed multivariable regression analyses adjusted for relevant factors, including tumour extent and nodal status. Of 14,647 patients, 29.9% received a pre-treatment medical oncology consultation and 77.7% received NAC. Mapping demonstrated high interregional variability, ranging across census divisions from 12.5% to 64.3% for medical oncology consultation and from 8.8% to 64.3% for NAC. In the full cohort, compared to a distance of ≤5 km from the nearest cancer centre, only 10-25 km was significantly associated with lower odds of NAC (OR 0.83, 95% CI 0.70-0.99). Greater distances were not associated with pre-treatment medical oncology consultation. The interregional variability in medical oncology consultation and NAC for patients with TN and HER2+ breast cancer suggests that regional and/or provider practice patterns underlie discrepancies in the referral for and receipt of NAC. These findings can inform interventions to improve equitable access to NAC for eligible patients.

Comparison of Asthma Phenotypes in Severe Asthma Cohorts (SARP, U-BIOPRED, ProAR and COREA) From 4 Continents.

PURPOSE: Asthma is a clinical syndrome with various underlying pathomechanisms and clinical phenotypes. Genetic, ethnic, and geographic factors may influence the differences in clinical presentation, severity, and prognosis. We compared the characteristics of asthma based on the geographical background by analyzing representative cohorts from the United States, Europe, South America, and Asia using the Severe Asthma Research Program (SARP), Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED), Program for Control of Asthma in Bahia (ProAR), and Cohort for Reality and Evolution of Adult Asthma in Korea (COREA), respectively. METHODS: The clinical characteristics and medications for the SARP (n = 669), U-BIOPRED (n = 509), ProAR (n = 996), and COREA (n = 3,748) were analyzed. Subgroup analysis was performed for severe asthma. RESULTS: The mean age was highest and lowest in the COREA and SARP, respectively. The asthma onset age was lowest in the ProAR. The mean body mass index was highest and lowest in the SARP and COREA, respectively. Baseline pulmonary function was lowest and highest in the U-BIOPRED and COREA, respectively. The number of patients with acute exacerbation in the previous year was highest in U-BIOPRED. The mean blood eosinophil count was highest in COREA. The total immunoglobulin E was highest in the ProAR. The frequency of atopy was highest in the SARP. The principal component analysis plot revealed differences among all cohorts. CONCLUSIONS: The cohorts from 4 different continents exhibited different clinical and physiological characteristics, probably resulting from the interplay between genetic susceptibility and geographical factors.

Evaluation of weather and environmental factors and their association with cutaneous melanoma incidence: A national ecological study.

Background: Cutaneous melanoma (CM) is a significant contributor to skin cancer-related mortality globally and in Canada. Despite the well-established link between ultraviolet (UV) radiation exposure and skin cancer risk, there remains a gap in population-level interventions and persistent misconceptions about sun exposure and impact of environment on individual behavior. Objective: The current study provides an ecological analysis using latest available data (2011-2017) to define geographic/environmental contributors to the CM landscape in Canada. Methods: Utilizing Canadian Cancer Registry and Canadian Urban Environmental Health Research Consortium data, we analyzed 39,605 CM cases occurring in Canada from 2011 to 2017. Environmental data, including UV radiation, greenspace (normalized difference vegetation index), temperature, heat events, and precipitation was used to evaluate the effect of environment on CM incidence rates across Forward Sortation Area postal codes. Results: Forward Sortation Areas with increased CM incidence were associated with higher annual average temperature, snowfall, heat events, normalized difference vegetation index, and vitamin D-weighted UV exposure. Conversely, factors associated with decreased incidence included an increased annual highest temperature, rain precipitation, and a longer duration of heat events. Limitations: This study is subject to ecological bias and findings should be interpreted with caution. Conclusion: This study further substantiates associations between specific environmental factors and CM incidence.