Proxy Information Research Articles

Neighborhood Awareness and Stock Return Synchronicity: Evidence from Satellite Nighttime Light Data

AbstractThis paper examines the impact on stock return synchronicity of information provided by people who neighbor firms. Neighboring people have inherent advantages in acquiring and interpreting information. We employed novel satellite nighttime light data as a proxy for information held by neighboring people. Our analyses confirmed that brighter nighttime light was related to greater and better‐quality information production. Using a sample of 18,963 firm–year observations over the 2000–2013 period, we found that information from nearby people facilitated the incorporation of firm‐specific information into stock prices, resulting in lower stock return synchronicity. The results were robust when using the slope as an instrumental variable and were supported by various sensitivity checks. The effect of nighttime light intensity was more pronounced for firms operating across multiple geographic regions or diverse industries, those situated near more parks and shopping malls, and those with fewer institutional investors and less media coverage.

The moderating effect of private information on the relation between financial reporting quality and corporate dividend efficiency

AbstractWe examine the effect of private information on the linkage between financial reporting quality and dividends. While prior studies show that better reporting quality mitigates agency problems regarding dividends, this effect can depend on capital suppliers' private information sources. In a sample of Japanese firms, we measure dividend efficiency regarding over‐ and underpayments and use unique main bank relationships as a proxy for private information. Reporting quality enhances dividend efficiency, and this linkage is observed only for firms without main bank relationships. The results suggest that high‐quality reporting streamlines dividends, particularly when capital suppliers do not have private information sources.

Development of stakeholder-informed recommendations for inclusion of family spillover effects in health technology assessment.

The impacts of disease and treatment on a patient's family members and informal caregivers are known as "family spillover effects." Although many formal value frameworks call for the consideration of these effects, they are often not included in health technology assessments (HTAs) and cost-effectiveness analyses (CEAs). A formal evaluation of stakeholder perspectives may help address the disconnect for inclusion of family spillover effects observed in practice. To develop stakeholder-driven recommendations for the measurement and use of family spillover effects in the United States and to identify research opportunities. We first conducted a targeted literature review of US-based CEAs and HTA reports from the past 10 years to assess the current use of family spillover effects. We then used a purposeful sampling technique to conduct 25 qualitative interviews with outcomes researchers, patient advocates, health economists, and health policy and payer experts to gather perspectives on when and how family spillover effects should be considered in HTA processes. We conducted a thematic analysis of the interview transcripts to identify key themes and develop preliminary recommendations. Finally, we conducted an online workshop with 8 stakeholders to discuss, rate, and refine preliminary recommendations to develop final recommendations. A key theme identified in the stakeholder interviews was the role that data availability, analyst preferences, and prior precedence play in limiting the inclusion of spillover effects in HTAs. Additional themes included support for the inclusion of both qualitative and quantitative spillover effects and the need to capture broad and diverse impacts across populations. We developed 15 recommendations from the consensus building workshop addressing measurement, CEA modeling, and HTA processes. Key recommendations included (1) a transparent process for deciding when family spillover effects should be included, (2) measurement of direct and indirect costs with priority based on the magnitude of impact, (3) the use of validated measures, (4) the use of proxy information and expert elicitation when quality data are unavailable, and (5) the use of a modified impact inventory table for transparency of included effects. Research opportunities included patient involvement in family spillover effect research and HTAs, mapping algorithms and non-preference-based caregiver measures to generate utilities, and consensus best practices for modeling. The inconsistent inclusion of family spillover effects in HTAs and CEAs remains a persistent challenge. The stakeholder-driven recommendations and research opportunities identified in this study may help improve the transparency, measurement, and use of family spillover effects in assessing the clinical and economic value of novel medical technologies.

High-Dimensional Propensity Score and Its Machine Learning Extensions in Residual Confounding Control

“The use of health care claims datasets often encounters criticism due to the pervasive issues of omitted variables and inaccuracies or mis-measurements in available confounders. Ultimately, the treatment effects estimated using such data sources may be subject to residual confounding. Digital electronic administrative records routinely collect a large volume of health-related information; and many of which are usually not considered in conventional pharmacoepidemiological studies. A high-dimensional propensity score (hdPS) algorithm was proposed that uses such information as surrogates or proxies for mismeasured and unobserved confounders in an effort to reduce residual confounding bias. Since then, many machine learning and semi-parametric extensions of this algorithm have been proposed to better exploit the wealth of high-dimensional proxy information. In this tutorial, we will (i) demonstrate logic, steps and implementation guidelines of hdPS using an open data source as an example (using reproducible R codes), (ii) familiarize readers with the key difference between propensity score versus hdPS, as well as the requisite sensitivity analyses, (iii) explain the rationale for using the machine learning and double robust extensions of hdPS, and (iv) discuss advantages, controversies, and hdPS reporting guidelines while writing amanuscript.

Solar global irradiance from actinometric degree data for Filaret Observatory (Bucharest), 1892–1903

Long-term series of solar irradiation measured at ground level are not available in the old times. However, long-term series of actinometric degree data obtained by using the Arago-Davy instrument have been recorded in the second half of the 19th century in several locations of the world. We have developed recently a method to estimate global solar irradiance on horizontal surface from actinometric degree data (doi.org/10.1007/s00704-023-04485-2). This opens a way of finding proxy information about the incident solar irradiance on various areas of the globe before the 20th century. Hourly actinometric degree data for the years 1892–1903 are available at the Filaret Observatory (Bucharest, Romania, South-Eastern Europe). The observed series have a systematic decreasing tendency, which has been removed by using a correction procedure. The proposed method is used here to evaluate solar global irradiance at Filaret Observatory. Solar irradiance data provided by the Twentieth Century Reanalysis Project version 3 (20CRv3) are used as a reference. The expected hourly and daily average solar irradiance values show reasonable qualitative consistency with the 20CRv3 data. This consistency is notably stronger during the warmer months from April to September. Much better agreement is found for the monthly averaged solar global irradiance values. At this level, the proposed method and the procedure of the 20CRv3 project seem to validate each other.

Mental health among centenarians living in Switzerland.

Given the increasing number of people achieving exceptionally long lifespans, there is an urgent need for a better understanding of mental health in centenarians. This study aimed to understand the prevalence of mental health conditions-depressive symptoms, anxiety, sleep disturbances, disinhibition, and aberrant motor behaviour-among centenarians in Switzerland. Data were collected from N = 169 participants via telephone interviews or paper questionnaires, either directly from centenarians or through proxy informants. Half the data were collected during a period when protective measures were imposed due to the COVID-19 pandemic, and half were collected after the measures were lifted. Mental health conditions were prevalent in our sample, particularly depressive symptoms (44.51%) and anxiety (42.17%). Significant positive associations were found between depressive symptoms and anxiety, and between disinhibition and aberrant motor behaviour. Furthermore, we identified statistical predictors for the occurrence of mental health conditions. Notably, institutionalised living increased the odds of depressive symptomatology, while those with higher education levels or an absence of cognitive impairment experienced more sleep disturbances. Finally, cognitive impairment was linked to increased disinhibition and aberrant motor behaviour. The high prevalence of mental health conditions underscores the need for proactive mental health care strategies in advanced old age. Moreover, it is vital to consider the interconnected nature of mental health conditions and to prioritise vulnerable groups, such as centenarians in institutional settings.

Lead Independent Directors and Internal Information Environment

ABSTRACTResearch Question/IssueThis study explores the relationship between the presence of a lead independent director (LID) and firms' internal information environment. LIDs are elected independent members of the board who perform key duties for the independent directors and the board, including reviewing and approving board meeting agendas, chairing non‐executive board meetings, and acting as a liaison between the CEO and other independent directors. We hypothesize that LID presence lowers information barriers between the CEO and the rest of the board members, enabling more rapid information acquisition and integration and enhancing the internal information environment of the firm.Research Findings/InsightsUsing a sample of US publicly listed companies from 2001 to 2019, we document that LID presence on the board is positively associated with proxies of internal information quality that reflect better information acquisition and information integration: accuracy and precision of management earnings forecasts, speed of earnings announcement, and absence of material weaknesses in internal controls. These results are robust to alternative model specifications, including entropy balancing, Heckman two‐step correction for self‐selection bias, firm fixed effects, and placebo tests. Further analyses suggest that LIDs with financial expertise and audit committee memberships are more effective in positively influencing internal information quality. We also show that LID presence is positively associated with several proxies of external information quality.Theoretical/Academic ImplicationsWe build on agency theory to argue that LIDs improve internal information quality by reinforcing the information quality benefits of unified leadership while mitigating potential compromises in information quality arising from entrenchment. Similarly, we use arguments emanating from the novel strategic leadership systems theory to posit that a LID appointment facilitates the tasks of the CEO and the board, enhancing the effectiveness of both groups in their respective roles: the CEO in making operating and investment decisions and the board in strengthening oversight while bringing cohesion in their shared role of strategy visioning and implementation.Practitioner/Policy ImplicationsOur findings suggest that there is scope for shareholders to consider LID appointments as an addition to their firms' corporate governance structures to enhance the internal information environment and decision‐making efficiency. Policymakers can also encourage LID appointments on the board when promoting best practices in corporate governance through regulatory guidelines.

Racial Differences in Treatment Intensity at the End of Life Among Older Adults with Heart Failure: Evidence from the Health and Retirement Study.

Background: Black Americans experience the highest prevalence of heart failure (HF) and the worst clinical outcomes of any racial or ethnic group, but little is known about end-of-life care for this population. Objective: Compare treatment intensity between Black and White older adults with HF near the end of life. Design: Negative binomial and logistic regression analyses of pooled, cross-sectional data from the Health and Retirement Study (HRS). Setting/Subjects: A total of 1607 U.S. adults aged 65 years and older with HF who identify as Black or White, and whose proxy informant participated in an HRS exit interview between 2002 and 2016. Measurements: We compared four common measures of treatment intensity at the end of life (number of hospital admissions, receipt of care in an intensive care unit (ICU), utilization of life support, and whether the decedent died in a hospital) between Black and White HF patients, controlling for demographic, social, and health characteristics. Results: Racial identity was not significantly associated with the number of hospital admissions or admission to an ICU in the last 24 months of life. However, Black HF patients were more likely to spend time on life support (odds ratio [OR] = 2.16, confidence interval [CI] = 1.35-3.44, p = 0.00) and more likely to die in a hospital (OR = 1.53, CI = 1.03-2.28, p = 0.04) than White HF patients. Conclusion: Black HF patients were more likely to die in a hospital and to spend time on life support than White HF patients. Thoughtful and consistent engagement with HF patients regarding treatment preferences is an important step in addressing inequities.

Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency

AbstractThis paper proposes the use of social media as a proxy for financial information. Using an extended sample of 53,580,759 tweets and employing text analysis tools (Latent Dirichlet Allocation and Term Frequency–Inverse Document Frequency), we determine the information being exchanged on any given day. We train machine‐learning classifiers and forecast crypto price movements for more than 8000 cryptocurrencies and gauge market efficiency through successful forecasts based on public information. We propose various metrics of market efficiency for cryptocurrency assets and demonstrate that market efficiency is higher during the first 6 months after the Initial Coin Offering. We also examine the efficiency behavior of individual currencies during crisis periods.

Time to Change How We Measure Quality of Life and Well-Being in Autism: A Systematic Review

AbstractThis review examines methods used to report on the QoL and/or well-being of autistic people. A search of four databases (June 2023) identified 256 studies that reported on the Qol and/or well-being of at least one autistic person. The quality of studies varied. Results were synthesised on who reported, who was reported on, how information was obtained including accommodations and community involvement. One-third used proxy informants, over 80% used generic measures of QoL and/or well-being, autistic people with intellectual impairment and communication disorders were underrepresented, one quarter included accommodations, and few studies included community involvement. Despite the growing research on QoL and well-being of autistic people, more accessible and inclusive research is required to understand autistic people’s experience.

Successful Life Conduct in Very Old Age: Theoretical Implications and Empirical Support from a Population-Based Study

Social survey data on those aged 80 years or older is sparse. Based on a representative sample, this paper validates a multidimensional model proposed for understanding of quality of life (QoL) in very old age. Towards this goal, this paper estimated levels and heterogeneity of personal and environmental resources, well-being, autonomy, and perceived appreciation by society in the population of the very old in Germany. Next, the contribution of personal and environmental resources to QoL outcomes and overlap between these outcomes was estimated using a multivariate approach. Results were based on a representative survey on QoL of the very old in North Rhine-Westphalia, Germany’s most populous state. The survey included comprehensive information on socio-demographics, health, social and personal QoL resources including objective testing of cognitive function. The sample comprised 1,863 individuals (mean age 86.5 years, range 80–102 years), including 211 individuals residing in non-private dwellings. Interviews with proxy informants were conducted for 176 individuals that were willing to participate but could not conduct the interview themselves due to limited ability to communicate (PLC). Pronounced differences were found for PLC with respect to environmental and personal resources and QoL outcomes. Pronounced differences were also found both with respect to lower observed levels of QoL outcomes (e.g., autonomy) and predictors of QoL outcomes (e.g., effect of negative “external” appreciation on subjective well-being). Contrary to the deficit-oriented model of old age, a high degree of autonomy was observed. However, substantial and consequential negative “external” appreciation of very old age was also apparent.

Growth Increments of Coralline Red Alga Clathromorphum Compactum Capture Sea‐Ice Variability Links to Arctic and Atlantic Multidecadal Oscillations (1805–2015)

AbstractGiven sea ice's importance in global climate regulation, fully understanding the role of natural temperature and atmospheric patterns like the Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO) in its variability is critical. While instrumental AMO and reliable AO records are available since the mid‐1800s and 1958, respectively, satellite sea‐ice concentration data sets start only in 1979, limiting the shared timespan to study their interplay. Growth increments of the coralline algae, Clathromorphum compactum, can provide sea‐ice proxy information for years prior to 1979. We present a seasonal 210‐year algal record from Lancaster Sound in the Canadian Arctic Archipelago capturing low frequency AMO/NAO variability and high frequency interannual AO/NAO prior to 2000. We suggest that sea‐ice variability here is strongly coupled to these large‐scale climate processes, and that sea‐ice cover was greater and the AO more negative in the early and late 19th century compared to the 20th.

Do analysts provide information about other comprehensive income in book value forecasts for financial firms?

Analysts' earnings forecasts exclude other comprehensive income (OCI). However, OCI affects firm value on a dollar-for-dollar basis and can enhance investors' assessments of the riskiness of firms' equity capital. Focusing on financial firms and using analysts' book value per share (BVPS) forecasts as a proxy for forward-looking information about OCI, we examine whether analysts provide information about future OCI via BVPS forecasts, whether investors respond to BVPS innovations (which should include OCI innovations), and whether such innovations are more useful to investors in financial firms with difficult-to-value financial assets. We find evidence consistent with: 1) Analysts' BVPS forecasts generally conveying at least some information about future OCI; and, 2) The market responding to whether firms miss analysts' consensus BVPS expectations (which should include OCI expectations), with stronger evidence for firms with larger holdings of difficult-to-value financial assets. The evidence supports the intuition that analysts provide at least some information about future OCI in their BVPS forecasts.

Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning.

Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of "Bovine," exposure of "Statistical Analysis or Machine Learning," and outcome of "Methane Emissions". The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.

Health support of people with intellectual disability and the crucial role of support workers

BackgroundPeople with intellectual disability have a poorer health status than the general population. In The Netherlands, support workers play a key role in meeting health support needs of people with intellectual disability. Research on how people with intellectual disability and their support workers experience the support worker’s role in preventing, identifying, and following up health needs of people with intellectual disability is scarce. To enhance health support of people with intellectual disability it is crucial that we understand how health support is delivered in everyday practice. Therefore, this study investigated experiences of people with intellectual disability and support workers with the health support of people with intellectual disability.MethodData collection consisted of six focus group (FG) discussions with between four and six participants (N = 27). The FGs consisted of three groups with support workers (n = 15), two groups with participants with mild to moderate intellectual disability (n = 8), and one group with family members as proxy informants who represented their relative with severe to profound intellectual disability (n = 4). The data was analysed thematically on aspects relating to health support.ResultsWe identified three main themes relevant to the health support of people with intellectual disability: 1) dependence on health support, 2) communication practices in health support, and 3) organizational context of health support. Dependence on health support adresses the way in which support workers meet a need that people with intellectual disability cannot meet themselves, and communication practices and organizational context are identified as systems in which health support takes place.ConclusionThis study investigated experiences with the health support of people with intellectual disability from the perspectives of people with intellectual disability and support workers. We discuss the dependence of people with intellectual disability and the complexity of health support in everyday practice. We provide practical implications that can strengthen support workers in the provision of health support for people with intellectual disability in everyday practice. The findings of this study emphasize the need for intellectual disability care-provider organizations to establish policies around consistency in support staff to make it easier to identify and follow up health needs, and an environment where support staff can develop their expertise concerning communication practices, lifestyle choices, and identifying and following up health needs.

Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms.

In livestock production, animal-related data are often registered in specialised databases and are usually not interconnected, except for a common identifier. Analysis of combined datasets and the possible inclusion of third-party information can provide a more complete picture or reveal complex relationships. The aim of this study was to develop a risk index to predict farms with an increased likelihood for animal welfare violations, defined as non-compliance during on-farm welfare inspections. A data-driven approach was chosen for this purpose, focusing on the combination of existing Swiss government databases and registers. Individual animal-level data were aggregated at the herd level. Since data collection and availability were best for cattle and pigs, the focus was on these two livestock species. We present machine learning models that can be used as a tool to plan and optimise risk-based on-farm welfare inspections by proposing a consolidated list of priority holdings to be visited. The results of previous on-farm welfare inspections were used to calibrate a binary welfare index, which is the prediction goal. The risk index is based on proxy information, such as the participation in animal welfare programmes with structured housing and outdoor access, herd type and size, or animal movement data. Since transparency of the model is critical both for public acceptance of such a data-driven index and farm control planning, the Random Forest model, for which the decision process can be illustrated, was investigated in depth. Using historical inspection data with an overall low prevalence of violations of approximately 4% for both species, the developed index was able to predict violations with a sensitivity of 81.2 and 79.5% for cattle and pig farms, respectively. The study has shown that combining multiple and heterogeneous data sources improves the quality of the models. Furthermore, privacy-preserving methods are applied to a research environment to explore the available data before restricting the feature space to the most relevant. This study demonstrates that data-driven monitoring of livestock populations is already possible with the existing datasets and the models developed can be a useful tool to plan and conduct risk-based animal welfare inspection.

Building a multistate model from electronic health records data for modeling long-term diabetes complications

Abstract Objective: The progression of long-term diabetes complications has led to a decreased quality of life. Our objective was to evaluate the adverse outcomes associated with diabetes based on a patient’s clinical profile by utilizing a multistate modeling approach. Methods: This was a retrospective study of diabetes patients seen in primary care practices from 2013 to 2017. We implemented a five-state model to examine the progression of patients transitioning from one complication to having multiple complications. Our model incorporated high dimensional covariates from multisource data to investigate the possible effects of different types of factors that are associated with the progression of diabetes. Results: The cohort consisted of 10,596 patients diagnosed with diabetes and no previous complications associated with the disease. Most of the patients in our study were female, White, and had type 2 diabetes. During our study period, 5928 did not develop complications, 3323 developed microvascular complications, 1313 developed macrovascular complications, and 1129 developed both micro- and macrovascular complications. From our model, we determined that patients had a 0.1334 [0.1284, .1386] rate of developing a microvascular complication compared to 0.0508 [0.0479, .0540] rate of developing a macrovascular complication. The area deprivation index score we incorporated as a proxy for socioeconomic information indicated that patients who reside in more disadvantaged areas have a higher rate of developing a complication compared to those who reside in least disadvantaged areas. Conclusions: Our work demonstrates how a multistate modeling framework is a comprehensive approach to analyzing the progression of long-term complications associated with diabetes.

Gender and couple status differences in advance care planning: a cross-sectional study.

Past studies have shown mixed results on how gender and living with a spouse or partner impact advance care planning (ACP). Few if any have tested for the interaction between these two variables. We examined how gender and couple status interact to impact the use of ACP practices including written instructions, designating a durable power of attorney for healthcare (DPOAHC), and discussing one's decisions with others. We used cross-sectional data taken from the Health and Retirement Study, a longitudinal study of adults over the age of 50 in the United States. Data are from 632 respondents who died between the 2016 core survey and the 2018 exit survey. Participants had completed the 2016 survey and had a proxy informant complete the 2018 exit survey after their death. Generalized linear mixed models were used to test for main effects and interactions. Women were more likely than men to designate a DPOAHC and to discuss their wishes with others. Women living without a partner were more likely than men living without a partner and coupled households to discuss their wishes with family or others. Both gender and couple status are important variables associated with ACP practices. Healthcare providers may want to reach out to women living within a coupled household and men living without a partner to ensure that they know the benefits of ACP.

Provider Perspectives on Adolescent Confidentiality and the Electronic Health Record Postimplementation of the 21st Century Cures Act Final Rule

PurposeThe 21st Century Cures Act Final Rule mandates increased transparency and accessibility of the Electronic Health Record (EHR). This mandate has exacerbated tensions in adolescent healthcare between transparency and confidentiality. This study evaluates clinicians' perspectives on how well the EHR maintains confidentiality for adolescents in the post-Cures Act era. MethodsA web-based survey was distributed via the national listserv of the Society for Adolescent Health and Medicine in March–April 2022. English-speaking members practicing in the Unites States who used an EHR to document clinical visits with minors were eligible. The survey included questions about EHR training, features, and approach to information sharing with adolescent patients and proxies. ResultsParticipants included 97 respondents from 32 states. Most participants were physicians (98%) and identified themselves as attending (89%). Several EHR vendors were represented, although the majority used Epic (76%). As has been seen in prior studies, there remains significant variability in approaches to patient portal access for adolescent patients and their adult proxies. Respondents report that training around adolescent-specific privacy issues remains infrequent and is perceived as inadequate. Adolescent providers, despite feeling confident in navigating the EHR generally, continue to report low rates of confidence in how well their EHR may protect adolescent privacy. DiscussionClinicians have persistent concerns about adolescent confidentiality postimplementation of the 21st Century Cures Act. Sharing sufficient information while protecting adolescent confidentiality might require standardization by EHR vendors to improve granularity of proxy information sharing. Healthcare institutions must also commit to training providers on management of adolescent confidentiality.

The admixture of Quercus sp. in Pinus sylvestris stands influences wood anatomical trait responses to climatic variability and drought events.

Forests are threatened by increasingly severe and more frequent drought events worldwide. Mono-specific forests, developed as a consequence of widespread management practices established early last century, seem particularly susceptible to global warming and drought compared with mixed-species forests. Although, in several contexts, mixed-species forests display higher species diversity, higher productivity, and higher resilience, previous studies highlighted contrasting findings, with not only many positive but also neutral or negative effects on tree performance that could be related to tree species diversity. Processes underlying this relationship need to be investigated. Wood anatomical traits are informative proxies of tree functioning, and they can potentially provide novel long-term insights in this regard. However, wood anatomical traits are critically understudied in such a context. Here, we assess the role of tree admixture on Pinus sylvestris L. xylem traits such as mean hydraulic diameter, cell wall thickness, and anatomical wood density, and we test the variability of these traits in response to climatic parameters such as temperature, precipitation, and drought event frequency and intensity. Three monocultural plots of P. sylvestris and three mixed-stand plots of P. sylvestris and Quercus sp. were identified in Poland and Spain, representing Continental and Mediterranean climate types, respectively. In each plot, we analyzed xylem traits from three P. sylvestris trees, for a total of nine trees in monocultures and nine in mixed stands per study location. The results highlighted that anatomical wood density was one of the most sensitive traits to detect tree responses to climatic conditions and drought under different climate and forest types. Inter-specific facilitation mechanisms were detected in the admixture between P. sylvestris and Quercus sp., especially during the early growing season and during stressful events such as spring droughts, although they had negligible effects in the late growing season. Our findings suggest that the admixture between P. sylvestris and Quercus sp. increases the resilience of P. sylvestris to extreme droughts. In a global warming scenario, this admixture could represent a useful adaptive management option.