Complex Drivers Research Articles

Increased Spending on Low-Value Care During the COVID-19 Pandemic in Virginia

Abstract Characterizing the value and equity of care delivered during the COVID-19 pandemic is crucial to uncovering health system vulnerabilities and informing post-pandemic recovery. We used insurance claims to evaluate low-value (no clinical benefit, potentially harmful) and clinically-indicated utilization of a subset of 11 ambulatory services within a cohort of ∼2 million Virginia adults during the first two years of the pandemic (3/1/2020-12/31/21). In 2020, low-value and clinically-indicated utilization decreased similarly, while in 2021, low-value and clinically-indicated utilization were 7% higher and 4% lower than pre-pandemic rates, respectively. Extrapolated to Virginia’s population of insured adults, ∼$1.3 billion in spending was associated with low-value utilization of a subset of ambulatory services, reflecting 6% higher spending than pre-pandemic rates in 2021. Compared with pre-pandemic rates, low-value and clinically-indicated utilization of the 11 services were 15% and 16% lower, respectively, during 3/1/2020-12/31/21 among patients with the greatest socioeconomic deprivation. Meanwhile, low-value and clinically-indicated utilization remained closer to pre-pandemic rates among patients with the least socioeconomic deprivation during the same timeframe. These results highlight widening healthcare disparities and underscore the need for policy-level efforts to address the complex drivers of low-value care and equitably redistribute expenditures to services that enhance health.

Conceptual Complexity About Return Migration of Refugees/Asylum Seekers

Return migration is one of the complex categories of migration, which becomes even more complicated in the context of refugees’ return to their country of origin. A plethora of terms is used to describe the return of refugees/asylum seekers, having strong political and policy relevance. To unpack the features and drivers of conceptual complexity, I propose three interrelated arguments. The first is that labelling – the choice of one concept over another – in return migration often depends on who uses the term (e.g. scholars, policymakers, practitioners, migrants) and in which part of the world. The second argument is that return, similar to binaries in other areas of migration, is often associated with binaries, such as voluntary and forced return. Despite policy categories that reiterate the ‘voluntary’ character of return, the actual practices rarely confirm that return is voluntary and often remain in grey areas, as the emerging literature on bordering practices shows. The third argument is that there are multiple levels at which labelling and binaries are constructed in relation to each other. The identifiable scales include the academic level, the policy level and the migrants themselves.

The CHD severity classification system: development of a tool to assist with disease stratification for CHD research.

Complexity stratification for CHD is an integral part of clinical research due to its heterogenous clinical presentation and outcomes. To support our ongoing research efforts into CHD requiring disease severity stratifications, a simplified CHD severity classification system was developed and verified, with potential utility for clinical researchers without specialist CHD knowledge or access to clinical/medical records. A two-tiered analysis approach was undertaken. First-tier analysis included the audit of a comprehensive system based on: i) timing of intervention, ii) cardiac morphology, and iii) cardiovascular physiology using real patient data (n = 30), across 10 common CHD lesions. Second-tier analysis allowed for a simplified version of the classification system using morphology as a stand-alone predictor. Twelve clinicians of varying specialities involved in CHD care ranked 10 common lesions from least to most severe based on typical presentation and clinical course. First-tier analysis identified that cardiac morphology was the principal driver of complexity. Second-tier analysis largely confirmed the ranking and classification of the lesions into the broad CHD severity groups, although some variation was noted, specifically among non-cardiac specialists. This simplified version of the classicisation system, with morphology as a stand-alone predictor of severity, allowed for effective stratification for the purposes of analysis. The findings presented here support this comprehensive and simple CHD severity classification system with broad utility in CHD research, particularly among clinicians and researchers with limited knowledge of CHD. The model may be applied to produce locally relevant research tools.

Social comparison and self‐determination in luxury consumption motivations

AbstractThis study integrates social comparison theory) and self‐determination theory to examine the roles of escapism, conformity, and individual cultural values in shaping luxury brand attitudes. Concepts related to consumer upward and downward social comparison are complemented by aspects related to self‐determination and autonomy. Drawing on survey data from the United States and Colombia, the research employs partial least squares structural equation modeling and fuzzy‐set qualitative comparative analysis (fsQCA) to investigate the complex drivers of luxury consumption. The findings reveal that escapism and conformity, complementary motivations grounded in social comparison and self‐determination theories, positively influence luxury brand attitudes. Furthermore, individual cultural values, such as self‐enhancement and self‐transcendence, moderate the relationships between these consumer motivations and attitudes. The cross‐cultural comparison highlights differences in the influence of cultural values on luxury consumption between two distinct cultures. The fsQCA—results uncover distinct configurations of motivations and values sufficient for driving positive luxury attitudes, extending the understanding of the complex interplay of factors shaping luxury consumption. By integrating social comparison theory and self‐determination theory perspectives, examining cultural values, and employing advanced methodological approaches, this study contributes to the theoretical understanding of luxury consumption. It also offers valuable insights for luxury brand managers navigating the evolving global market. Future research directions emphasize the potential for further integrating these two theoretical frameworks in the luxury domain.

The granular landscape of health disparities: a socio-spatial examination of chronic disease prevalence within New York City's community districts

This study investigates the relationship between neighborhood Socioeconomic Position (SEP) and the prevalence of hypertension and coronary heart disease (CHD) across New York City's community districts. By using a composite SEP index derived from multiple socioeconomic indicators, the research seeks to uncover health disparities that may be obscured in broader geographic analyses. A primary objective is to identify community districts where hypertension and CHD prevalence is significantly higher than expected, even after adjusting for SEP. This focus helps pinpoint areas where targeted public health interventions may be urgently required, especially when the anticipated inverse relationship between SEP and health outcomes does not hold. A linear mixed-effects model (LMM) was used to analyze the association between SEP and these health outcomes, incorporating both fixed and random effects to account for variation across the city's 59 community districts. The analysis revealed a significant negative association between SEP and both hypertension and CHD, with higher SEP levels generally correlating with lower prevalence rates. However, some districts exhibited high random effects, indicating a higher-than-expected prevalence of these conditions, even after controlling for SEP. The authors suspect that additional factors-such as environmental exposures, healthcare accessibility, or community infrastructure-may contribute to these unexplained disparities. The findings emphasize the need for public health strategies that are sensitive to local contexts and the multifactorial nature of health inequities. Understanding the complex drivers behind these disparities can inform policies aimed at reducing health inequities in diverse urban environments.

Influence of Rivers, Tides, and Tidal Wetlands on Estuarine Carbonate System Dynamics

Variations in estuarine carbonate chemistry can have critical impacts on marine calcifying organisms, yet the drivers of this variability are difficult to quantify from observations alone, due to the strong spatiotemporal variability of these systems. Terrestrial runoff and wetland processes vary year to year based on local precipitation, and estuarine processes are often strongly modulated by tides. In this study, a 3D-coupled hydrodynamic-biogeochemical model is used to quantify the controls on the carbonate system of a coastal plain estuary, specifically the York River estuary. Experiments were conducted both with and without tidal wetlands. Results show that on average, wetlands account for 20–30% of total alkalinity (TA) and dissolved inorganic carbon (DIC) fluxes into the estuary, and double-estuarine CO2 outgassing. Strong quasi-monthly variability is driven by the tides and causes fluctuations between net heterotrophy and net autotrophy. On longer time scales, model results show that in wetter years, lower light availability decreases primary production relative to biological respiration (i.e., greater net heterotrophy) resulting in substantial increases in CO2 outgassing. Additionally, in wetter years, advective exports of DIC and TA to the Chesapeake Bay increase by a factor of three to four, resulting in lower concentrations of DIC and TA within the estuary. Quantifying the impacts of these complex drivers is not only essential for a better understanding of coastal carbon and alkalinity cycling, but also leads to an improved assessment of the health and functioning of coastal ecosystems both in the present day and under future climate change.

Abstract P307: A Review Of The Burden, Management And Outcomes Of Patients With Aldosterone Dysregulation

Introduction: Aldosterone dysregulation (excess aldosterone production) is a complex physiological driver of uncontrolled hypertension as well as cardiovascular (CV) and kidney outcomes. Understanding the clinical and economic burden of excess aldosterone in routine practice is critical to optimizing treatment and improving long-term outcomes. This review examines the burden of excess aldosterone production as a driver of disease and its impact on clinical and economic outcomes. Hypothesis: Excess aldosterone production has a broad impact on long-term outcomes, and left untreated, contributes to worsened patient outcomes. Methods: A targeted literature review was conducted to identify articles on the burden, management and outcomes of patients with excess aldosterone production (2013 – 2024) with 27 manuscripts included. Results: More studies document the impact of excess aldosterone on organ function in the cardiovascular system and kidney (10, 8 manuscripts) compared to liver (1 manuscript). Excess aldosterone levels and aldosterone-induced oxidative stress and inflammation are associated with increased risk of CV events including myocardial infarction, heart failure, stroke, and increased morbidity and mortality. For example, Framingham Offspring Study showed increased CV disease risk with each rise in serum aldosterone levels (HR, 1.11; 95% CI, 1.02–1.21). Kidney, hepatic and metabolic manifestations are also well-documented, with implications for progression to chronic kidney disease, liver disease and metabolic syndrome. Another study reported an 11% increased risk of CKD progression associated with each doubling of serum aldosterone. Current pharmacological interventions for hypertension, including mineralocorticoid receptor antagonists, were not reported to restore normal aldosterone levels. No literature was found on the economic burden of excess aldosterone or the association between excess aldosterone production and clinical outcomes independent of blood pressure control. Conclusions: Currently available literature emphasizes the broad impact of excess aldosterone production on CV, kidney, hepatic, and metabolic outcomes. The impact on outcomes indicates that excess aldosterone production is underappreciated, with prognostic implications beyond primary aldosteronism. Current hypertension treatments do not address excess aldosterone production. These findings underscore the unmet need for novel management strategies that target excess aldosterone production.

Paradoxical Effects of Income and Income Inequality on Racial Health Disparities.

The intersection of race and place in shaping health disparities presents complex dynamics, as evidenced by studies in cities like Detroit, Baltimore, and Philadelphia, where predominantly Black and economically disadvantaged populations experience high overall rates of health problems. Surprisingly, these cities do not exhibit the most pronounced racial disparities. In contrast, areas with a higher percentage of White residents, indicative of greater income inequality, show stark differences in health outcomes between Black and White populations. This disparity underscores how conditions diverge more sharply between Black and White individuals in wealthier urban areas. This phenomenon suggests a complex and sometimes counterintuitive relationship among race, place, income, and income inequality in shaping racial health disparities. These dynamics have significant policy implications. Addressing health disparities requires nuanced strategies that recognize the multiplicative effects of race and income inequality on health outcomes. Policies focusing on areas with a high disease burden, such as Detroit, Philadelphia, and Baltimore can effectively mitigate disparities both locally and more broadly. Conversely, interventions targeting regions with lower disease prevalence, but higher racial disparities must be approached carefully to avoid exacerbating inequalities. In conclusion, understanding and addressing the complex drivers of health disparities demand comprehensive approaches that acknowledge the intertwined influences of race, income, and place. By prioritizing interventions that address economic disparities alongside health initiatives, policymakers can foster more equitable health outcomes across diverse communities.

Multi-Tier Land Use and Land Cover Mapping Framework and Its Application in Urbanization Analysis in Three African Countries

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa’s anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous “developed” or “built-up” area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses.

Life’s building blocks: the modular path to multiscale complexity

Modularity, the structuring of systems into discrete, interconnected units, is a fundamental organizing principle in biology across multiple scales. Recent progress in understanding the role of modularity as an evolutionary mechanism and a key driver of biological complexity has highlighted its importance in shaping the structure and function of living systems. Here, we propose a unifying framework that identifies the potential evolutionary advantages of modularity in systems ranging from molecular networks to ecologies, such as facilitating evolvability, enhancing robustness, improving information flows, and enabling the emergence of higher-level functions. Our analysis reveals the pervasiveness of modularity in living systems and highlights its crucial role in the evolution of multiscale hierarchies of increasing complexity.

A Comprehensive Study on the Emerging Role of Neuroeconomics Dynamics in Cybersecurity

As cyber threats continue to evolve, there is growing recognition that effective security requires a deeper scientific understanding of the complex drivers of human judgment and decision-making. The emerging field of neuroeconomics, combining economics, psychology, and neuroscience, provides new theoretical frameworks, measurement techniques, and models to elucidate the neural foundations underpinning human motivations and behaviors critical to cyber contexts. This paper reviews key neuroeconomic concepts including dual-process thinking, prospect theory, and social decision neuroscience and highlights their potential for generating new insights and interventions to strengthen cybersecurity. Ethical considerations surrounding neuroeconomic monitoring and potential manipulations also demand careful governance. Overall, neuroeconomic research promises to advance cybersecurity practices and policies by grounding them in realistic neural models of human cognition, emotions, and social dynamics. Careful interdisciplinary collaboration will be key to validating applications and avoiding pitfalls as neuroeconomic tools and theories are integrated into both cybersecurity scholarship and practice. This neuro-cognitive approach represents a compelling frontier with immense opportunities to transform cybersecurity through enhanced appreciation of the human dimension.

Exploring Reasons and Perspectives Behind Decisions to Forego Hospital Births Among Remote Rural Populations in Bokeo Province, Lao PDR

Maternal mortality remains high among marginalized rural populations in Laos. Remote mountain villages in Bokeo Province typify persistent health access divides facing minor subgroups amidst infrastructure gaps and scattered habitation. This study explores complex drivers behind shunning hospital births using focused rural ethnography. Between October and November 2023, this qualitative study interviewed 40 participants including pregnant women, postpartum women, and their relatives across 5 villages in Bokeo Province regarding barriers to hospital births. Open-ended discussions aimed to elucidate motivations and barriers embedded in their decision to avoid formal delivery care, despite mortality awareness. Transcriptions from Lao were translated to Thai, maintaining cultural nuances. Thematic analysis identified patterns in women’s attitudes and barriers to hospital childbirth. A collaborative coding process revealed constraints on access, affordability, and autonomy, explaining why vulnerable groups often forego hospital births despite risks. The result founded that prohibitive transportation deficiencies including impassable roads, exorbitant driver fees and unreliable conveyance options were primary obstacles. Prohibitive direct and indirect costs compounded struggles for impoverished families. Restrictive traditions persisted among certain ethnicities. Discrimination fears and hospital foreignness also provoked wariness among a minority. Multidimensional geographical, financial and sociocultural barriers intersect to obstruct rural groups from accessible emergency obstetric care in Laos. Infrastructural strengthening, financial protection and public education can relieve these pronounced constraints denying the marginalized realization of maternal health rights.

Behavioural response to gastrointestinal parasites of yearling dairy calves at pasture

ABSTRACT Aims To investigate the association between gastrointestinal parasites (GIP) and animal behaviour in dairy calves under New Zealand pastoral conditions, using animal-mounted, accelerometer-based sensors. Methods Thirty-six, 5–6-month-old, Friesian-Jersey, heifer calves fitted with animal activity sensors to track behaviour were randomly allocated to one of two treatment groups. Half the animals were challenged with an oral dose of 20,000 larvae of Ostertagia ostertagi and Cooperia oncophera once a week for 3 weeks and half were unchallenged. Five weeks after the last dose, seven infected and nine uninfected animals were treated with an oral anthelmintic (AHC) and data collected for a further week. Accelerometer data were classified into minutes per day eating, ruminating, in moderate–high activity or in low activity. Live weight and faecal egg counts (FEC) were recorded weekly over the study period. All animals co-grazed a newly sown pasture not previously grazed by ruminants and were moved every week to fresh grazing. Treatment status was blinded to those managing the animals which were otherwise treated identically. Results Complete behavioural records were available from 30/36 calves, (13 challenged and 17 unchallenged). Before treatment with AHC, FEC increased in infected and un-treated calves over the study, while uninfected animals maintained a near zero FEC. There was no difference in live weight gain between the two groups over the study period. Bayesian, multinomial regression predicted differences in animal behaviour between infected and uninfected animals that were not treated with AHC over the 7 weeks following initial infection. Parasitised calves not treated with AHC were less active and spent up to 6 (95% highest density interval (HDI) = 1–11) minutes/day less in low level activity and up to 15 (95% HDI = 7–20) minutes/day less in moderate to high level activity. They ruminated up to 9 (95% HDI = 2–15) minutes/day more and ate up to 10 (95% HDI = 2–19) minutes/day more than control calves that were not treated with AHC. The effect of AHC on time spent in each behaviour differed between infected and uninfected calves and increased the coefficient of dispersion of the behavioural data. Conclusions and clinical relevance Small differences in animal behaviour can be measured in calves with GIP. However, to use this to target treatment, further validation studies are required to confirm the accuracy of behavioural classification and understand the complex drivers of animal behaviour in a dynamic and variable pasture-parasite–host environment.

Clinical trial participation and end-of-life care among older adults: A multi-center longitudinal observational cohort analysis of 121,717 patients with cancer.

11181 Background: Patients’ access to clinical trials remains significantly disparate across the US & world. Complex drivers, some rooted in evidence while others in perceptions, impact shared decision making about trial participation esp in older adults (65y+). To that end, we investigated clinical trial participation (CTP) and unplanned healthcare utilization & hospice use in patients w cancer. Methods: We queried the prospective longitudinal observational cohort of all patients with cancer receiving care at any of the 323 practice locations of 14 multi-site community cancer centers across 11 US states, all within the US Oncology Network and participating in the Oncology Care Model (OCM), an alternative payment model pilot from CMS to promote quality cancer care while reducing costs. We extracted data from OCM episode claims, medical records from 7/1/17-6/30/22 and used propensity score matching by time period, age, gender, date of death, cancer type to identify matched episodes for further stratification by CTP, to compare hospice care & unplanned ER utilization by non-trial usual care vs CTP. Results: Overall, 121,717 pts received cancer care across 282,604 OCM care episodes in the community during this 5-yr period. Most (94%) were older adults aged 65y+ with breast, lung, multiple myeloma being the most common cancers. Of 121,717 pts, 4800 (3.9%) participated in a cancer treatment clinical trial in at least one care episode. Analysis of the 13,260 propensity score-matched episodes (6630 usual care vs 6630 CTP) shows: 1. ER/Observation visits: Patients on clinical trials did nothave any more ER/Obs visits vs usual care (ER/Obs episodes: CTP 23.4%, usual care 22.3%, p0.11). 2. Hospitalizations: The proportion of care episodes with hospitalization was higher in CTP vs usual care (25.9% vs 21.9%, p<0.001). 3. Hospice enrollment: Patients on clinical trials were no less likely to enroll in hospice >3 days prior to death, compared to non-trial usual care cohort (CTP 56.8%, usual care 52.6%, OR 1.18, p 0.13). Furthermore, median duration of hospice in patients on trials was not different from usual care (CTP 8 days, usual care 9 days, p 0.16). Conclusions: Our findings show that while more hospitalizations were observed, clinical trials participation is not associated with suboptimal end of life care outcomes pertaining to hospice enrollment & duration as well as unplanned ER/healthcare utilization. Further efforts to integrate access to clinical trials and evidence-based supportive/palliative care in a scalable sustainable model of goal-concordant, patient-center cancer care are ongoing. CMMI Disclaimer: The statements contained in this document are solely those of the authors and do not necessarily reflect the views or policies of CMS. The authors assume responsibility for the accuracy and completeness of the information contained in this document.

The effect of terrain on the fine-scale genetic diversity of sub-Antarctic Collembola: A landscape genetics approach.

Biodiversity patterns are shaped by the interplay between geodiversity and organismal characteristics. Superimposing genetic structure onto landscape heterogeneity (i.e., landscape genetics) can help to disentangle their interactions and better understand population dynamics. Previous studies on the sub-Antarctic Prince Edward Islands (located midway between Antarctica and Africa) have highlighted the importance of landscape and climatic barriers in shaping spatial genetic patterns and have drawn attention to the value of these islands as natural laboratories for studying fundamental concepts in biology. Here, we assessed the fine-scale spatial genetic structure of the springtail, Cryptopygus antarcticus travei, which is endemic to Marion Island, in tandem with high-resolution geological data. Using a species-specific suite of microsatellite markers, a fine-scale sampling design incorporating landscape complexity and generalised linear models (GLMs), we examined genetic patterns overlaid onto high-resolution digital surface models and surface geology data across two 1-km sampling transects. The GLMs revealed that genetic patterns across the landscape closely track landscape resistance data in concert with landscape discontinuities and barriers to gene flow identified at a scale of a few metres. These results show that the island's geodiversity plays an important role in shaping biodiversity patterns and intraspecific genetic diversity. This study illustrates that fine-scale genetic patterns in soil arthropods are markedly more structured than anticipated, given that previous studies have reported high levels of genetic diversity and evidence of genetic structing linked to landscape changes for springtail species and considering the homogeneity of the vegetation complexes characteristic of the island at the scale of tens to hundreds of metres. By incorporating fine-scale and high-resolution landscape features into our study, we were able to explain much of the observed spatial genetic patterns. Our study highlights geodiversity as a driver of spatial complexity. More widely, it holds important implications for the conservation and management of the sub-Antarctic islands.

The Drivers of Complexity in Inventory Management Within the Healthcare Industry

The purpose of this study is to investigate the complexities of inventory management in the healthcare industry for improving efficiency and resilience in healthcare supply chains. A Systematic Literature review using Scopus database and PRISMA approach was performed. Driven by innovation and technology, new developments are changing the face of inventory management. Predictive analytics and stock optimization are made possible by the increasing deployment of AI and machine learning. Autonomous Mobile Robots and RFID technology are being used more for quick identification and data collection, while Internet-of-Things devices are used for real-time tracking. Blockchain technology to guarantee supply chain traceability and transparency is also being investigated. Another is automation through robots, which lowers human error and increases warehouse operation efficiency. Finally, the incorporation of cloud-based systems such as SMART logistics and E-logistics platforms enables distant access to inventory information, encouraging adaptability and cooperation across various sites.

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source–flow–sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

Comparison of two statistical measures of complexity applied to ecological bipartite networks

Networks are a convenient way to represent many interactions among different entities as they provide an efficient and clear methodology to evaluate and organize relevant data. While there are many features for characterizing networks, a quantity seems rather elusive: Complexity. The quantification of the complexity of networks is nowadays a fundamental problem. Here, we present a novel tool for identifying the complexity of ecological networks. We compare the behavior of two relevant indices of complexity: K-complexity and Single Value Decomposition (SVD) entropy. For that, we use real data and two types of null models. Both null models consist of randomized networks built by swapping a controlled number of links of the original ones. We analyze 23 plant–pollinator and 19 host-parasite networks as case studies. Our results show that (a) it is necessary to calculate, not only the original network K-complexity and SVD entropy but also to calculate the corresponding indices of the randomized networks (b) the density and degree distribution are essential in the characterization of a network and the randomized networks are a suitable tool to detect the network complexity, and (c) plant–pollinator networks are more complex than host-parasite networks. We found that, for the first null model, K-complexity and SVD did not change with link swapping in both pollinator-plant and host-parasite networks. For the second null model, K-complexity for pollinator-plant networks generally decreased with an increasing number of links swapped (i.e. negative slope), showing that plant–pollinator networks lose complexity with increasing link swapping. In contrast, there was a positive slope between K-complexity and link swapping for host-parasite networks, showing that these networks are less complex than plant–pollinator networks. For both types of networks, in general, the slope between K-complexity and the number of links swapped became more positive with network density. Overall, SVD entropy was less responsive to link swapping. Our analyses show that although SVD entropy has been widely used to characterize network complexity, K-complexity is a more reliable tool. Additionally, they show that degree distribution and density are important drivers of network complexity and should be accounted for in future studies.

Elucidate the complex drivers of significant greening on the Loess Plateau from 2000 to 2020

Greening and its drivers in the Loess Plateau of China have been extensively studied, but attributional explorations regarding vegetation restoration in different climatic zones remain unclear. This study quantitatively evaluated greening on the Loess Plateau based on Fraction Vegetation Cover (FVC) calculated by MOD13Q1 dataset, compared FVC in different climatic zones, and explored the relationship between greening and precipitation, evaporation, soil water content (SWC), surface water bodies, and human activities. The study found that FVC on the Loess Plateau increased significantly at a rate of 0.538% yr−1 from 2000 to 2020, resulting in 94% greening. The greening rate was higher in the middle temperate zones (0.557% yr−1) than in the southern temperate zones (0.532% yr−1), but its average FVC was lower. This suggests that the greening rate (0.551% yr−1) is higher within 5 km of water bodies. Cropland (15.26%), grassland (13.66%) and trees (12.57%) had higher FVC increment than other types, while the six-fold increase in urban area is a significant impediment to greening. Additionally, there is a significant positive correlation with FVC when the urban economy exceeds 100 billion. The interaction of natural factors and human activities made the greatest contribution to greening, as calculated by the GeoDetector. It is hypothesized that there is significant potential for greening in the middle temperate zones. However, large-scale tree planting may not have a catalytic effect and could even be counterproductive. To achieve sustainable greening of the Loess Plateau in the future, shrubs and artificial water storage facilities may be key.

Social, behavioral and environmental determinants of vector-borne diseases: A narrative review of evidence and implications for integrated control approaches.

Vector-borne diseases exert immense health burdens worldwide. Malaria alone causes over 200 million cases and 600,000 deaths annually. Transmission involves complex drivers requiring examination beyond entomological factors. A systematic literature search across databases identified relevant studies on vector-borne disease determinants published in the last two decades, with priority given to rigorous designs like longitudinal analyses, reviews, and meta-analyses from diverse epidemic regions, allowing narrative synthesis of key determinants, relationships, and gaps. This narrative review synthesizes evidence on social, behavioral, and environmental determinants of major vector-borne disease risks over the past decade. Lower education, poverty, unplanned urbanization, gender inequities, inadequate water and waste management, climate variations, and land-use changes stood out as key determinants. However, significant knowledge gaps remain around quantifying precise threshold effects and impacts of tailored interventions across contexts. A social-ecological perspective recognizing interdependencies between determinants should frame integrated control programs. Multicomponent strategies addressing environmental modifications, protective measures, behavioral motivators, and infrastructure alongside governance and community engagement require implementation research and impact evaluation. Overall, this review highlights priority areas for advancing evidence-based vector control through contextualized, determinant-targeted policies and interventions. Further research incorporating modeling, trials, and cost-effectiveness analyses is critical to validate approaches that address influential drivers, leverage motivators, and reduce the global burden.