Evaluating the influence of climate inputs on WEPP predictions for a forest road in the Florida Panhandle, USA

This study aims to use routinely collected health data and trial emulation methodology to inform the design of a pragmatic randomized controlled trial (RCT) in people requiring multivessel coronary revascularization with severe symptomatic multivessel disease and high-risk characteristics, typically underrepresented in previous RCTs. Hospital episode statistics (HES) linked to Office for National Statistics will be the main data source. The study population is patients who require multivessel myocardial revascularization with at least one of the following high-risk characteristics: age >75 years, female, diagnosed with acute coronary syndrome, heart failure, chronic kidney disease, peripheral vascular disease, or intermediate frailty risk. The intervention procedure is coronary artery bypass grafting (CABG) and the control (reference) is percutaneous coronary intervention (PCI). Outcomes include all-cause and cardiovascular (CV) death, CV hospitalization, major adverse cardiovascular events, and major vascular complications or bleeding within 5 years of the index procedure. This study includes 3 stages of statistical analyses: (1) latent class analysis (LCA) to identify mutually exclusive patient clusters (latent classes) representing different clinical phenotypes, (2) instrumental variable analysis (IVA) to estimate the average treatment effect (ATE) in the whole population and each patient cluster; and (3) repeating stage 2 in an emulated trial population obtained by matching the HES population with individual participant data from an RCT. We will then co-design the protocol for a definitive clinical trial in partnership with patients, public, and stakeholders. This study introduces a novel, stepwise data science framework that integrates machine learning (unsupervised learning through LCA), causal inference, and trial emulation methods applied in big data, to design a future stratified and adaptive RCT of CABG versus PCI in high-risk patients. Our proposed approach fosters new collaborations among data scientists, trial methodologists, clinicians, and patient and public representatives in complex trial designs for diverse, high-risk populations. This study represents a new framework for co-production in trials of cardiovascular interventions, which offers a scalable model and has the potential to transfer to other disease areas. URL: https://www. gov/study/NCT05853536. Unique identifier: NCT05853536.

We evaluate the performance of targeted maximum likelihood estimation (TMLE) for estimating the average treatment effect in missing data scenarios under varying levels of positivity violations. We employ model- and design-based simulations, with the latter using undersmoothed highly adaptive lasso on the "WASH Benefits Bangladesh" data set to mimic real-world complexities. Five missingness-directed acyclic graphs are considered, capturing common missing data mechanisms in epidemiological research, particularly in one-point exposure studies. These mechanisms include also not-at-random missingness in the exposure, outcome, and confounders. We compare eight missing data methods in conjunction with TMLE as the analysis method, distinguishing between non-multiple imputation (non-MI) and multiple imputation (MI) approaches. The MI approaches use both parametric and machine learning models. Results show that non-MI methods, particularly complete cases with TMLE incorporating an outcome-missingness model, exhibit lower bias compared to all other evaluated missing data methods and greater robustness against positivity violations across. In comparison MI with classification and regression trees (CART) achieve lower root mean squared error, while often maintaining nominal coverage rates. Our findings highlight the trade-offs between bias and coverage, and we recommend using complete cases with TMLE incorporating an outcome-missingness model for bias reduction and MI CART when accurate confidence intervals are thepriority.

Caries in primary teeth and caries in permanent teeth: association and effect modifiers.

Who thrives against the odds? Mapping adolescent trait heterogeneity of emotional maltreatment effects with causal forests DML.

In causal inference, estimating the average treatment effects (ATE) for competing risk outcomes requires robust adjustment for confounding and specialized methods to handle competing events. Although doubly robust estimators offer protection against single-model misspecification, they become inconsistent if both the propensity score and outcome regression models are misspecified. To enhance reliability in observational studies, there is a critical need for methods that accommodate multiple candidate models, providing stronger protection against model misspecification in complex competing risks settings. We propose a new multiply robust (MR) estimator for the difference in cause-specific cumulative incidence function (CIF) with right-censored competing risks data to estimate ATE in competing risks data. The proposed framework integrates the pseudo-value approach, which transforms the censored, time-dependent CIF into a complete-data outcome, with the multiply robust estimation framework. This framework not only avoids reliance on the proportional hazards assumption and effectively addresses right censoring, but also enhances the robustness of the estimation. By specifying multiple candidate models for both the propensity score and the outcome regression, the resulting estimator is consistent and asymptotically unbiased, provided that at least one of the multiple propensity score or outcome regression models is correctly specified. Monte Carlo simulations demonstrated that the proposed MR estimator maintains negligible bias and near-nominal 95% coverage probabilities, provided at least one candidate model is correctly specified. This robust performance was consistent across all investigated censoring rates. Notably, combinations featuring heterogeneous misspecifications exhibited superior performance compared to those with homogeneous error. Application to the Right Heart Catheterization dataset yielded treatment effect estimates consistent with existing literature, confirming the practical utility and reliability of the method in real-world observational studies. The multiply robust framework ensures superior robustness and consistency for estimating cause-specific CIFs, even under high censoring. This methodology offers a more resilient alternative to traditional estimators, providing researchers with a dependable solution for treatment effect estimation in the presence of competing events and complex confounding.

Challenging a Gold Standard in Rehabilitation Research: The Limits of Outcome-Centered Trials in Pain Science.

ABSTRACT We investigate food security implications of integrating timber-tree species into cocoa farms as climate-smart agriculture. Food security is measured using household dietary diversity score (HDDS), household food insecurity access scale (HFIAS), and food expenditure. Probit, multinomial probit, negative binomial and beta regressions, and average treatment-effect are estimated. Farmers perceive timber-tree integration to mitigate adverse ramifications of climate change. Realization of climate change, positive tree-planting perceptions, Environmental and Social Sustainability Projects (ESSP) awareness, and residing in ESSP communities boost timber-tree integration. Tree integration have positive associations with food expenditure, food security status, HDDS, while lessening food insecurity and HFIAS severities.

Multiple morphological abnormalities of the sperm flagella (MMAF) compromise intracytoplasmic sperm injection (ICSI) outcomes. Testicular sperm aspiration (TESA) may be beneficial, but its impact on early embryonic development and pregnancy in MMAF remains uncertain. This study evaluated the effect of TESA on fertilization, cleavage, on-time Day-3 embryo development, and clinical pregnancy and explored modification by female age and MMAF genotype. In this retrospective cohort, 196 MMAF patients undergoing ICSI (January 2019-December 2024) without female-factor infertility were analyzed: TESA group (n = 65) versus non-TESA group (n = 131). Only fresh embryo transfer cycles were included. Outcomes were fertilization, cleavage, on-time Day-3 embryo development, and pregnancy. Multivariable regression was complemented by inverse probability of treatment weighting, propensity score matching, and stratified analyses by female age and genotype. Robustness was examined using bootstrap resampling and outlier-sensitivity analyses. Notably, TESA was associated with higher fertilization rates (adjusted coefficient: +5.63%, P = 0.022; inverse probability of treatment weighting average treatment effect [IPTW-ATE]: +5.9%, P = 0.022; Cohen's d: 0.371). Cleavage gains were smaller (IPTW-ATE: +3.8%, P = 0.013; Cohen's d: 0.246) and method-sensitive. No significant differences were found for on-time Day-3 development or pregnancy; power was limited for these endpoints. TESA mostly improved fertilization in women aged 30-35 years (IPTW-ATE: +9.5%, P = 0.017) and cleavage in genotype-negative patients (IPTW-ATE: +10.1%, P = 0.012). Propensity models showed good balance (area under the receiver operating characteristic curve: 0.638, and standardized differences <0.1); power was adequate for fertilization (0.825). Overall, TESA is associated with higher fertilization rates in MMAF-ICSI, with modest and method-sensitive evidence for cleavage, supporting selective use. Prospective, multi-center validation is needed. Limited downstream effects suggest a role primarily at early stages.

Efficiently quantifying thermodynamic and kinetic effects of biochar pyrolysis parameters on antibiotic adsorption from water using interpretable causal machine learning.

BackgroundDysmenorrhea is the most common menstrual disorder among young women and often disrupts daily activities and well-being. Although pharmacological management is widely used, sustainable non-pharmacological strategies remain underexplored, particularly in low-resource settings. This study assessed the association between a behavioral change communication (BCC) module and dysmenorrhea among female university students in Bangladesh.MethodsA matched cross-sectional comparative study initially recruited 498 female students from three public universities. Students attending three BCC sessions were classified as exposed group, while those who did not attend served as non-exposed group. After exclusions, 472 participants were analyzed. Propensity score matching (1:1 nearest-neighbor, caliper of 0.01 and no replacement) yielded 98 matched pairs. The primary measure was the average treatment effect on the treated (ATT), estimated using a doubly robust method to evaluate the association between BCC exposure and dysmenorrhea prevalence in the matched samples.ResultsIn matched samples, the overall prevalence of dysmenorrhea was 69.4%. Prevalence was higher among non-exposed participants (77.6%) compared with those exposed to the BCC module (61.2%). Using the doubly robust estimator, BCC-exposed participants had a 23 percentage-point lower prevalence of dysmenorrhea than non-exposed participants (ATT = −0.23; 95% CI: −0.33 to −0.13; p < 0.001), after adjustment for observed covariates. This association remained consistent and statistically significant across regression adjustment and inverse probability weighting estimators of the ATT. Participants exposed to BCC module more frequently reported regular physical activity and higher dietary diversity, both associated with lower odds of dysmenorrhea in post-matching analyses.ConclusionExposure to a theory-driven BCC module was associated with lower reported dysmenorrhea prevalence and healthier lifestyle behaviors among female university students in Bangladesh. Due to non-random design and lack of baseline outcome data, results are associative rather than causal, highlighting the need for further longitudinal or randomized studies to evaluate the impact of BCC programs.

Violence interruption programmes are a common type of community violence intervention (CVI), but there is no empirical evidence on programme impacts on youth violence specifically. This study evaluates the effectiveness of Safe Streets, a violence interruption CVI programme in Baltimore, Maryland, in reducing homicides and non-fatal shootings (NFS) among youth ages 15-24. The synthetic control method was used to assess site level and programme level average treatment effects for 11 Safe Streets sites between 2007 and 2023. Models compared smoothed monthly rates of youth homicides and NFS in treated areas with their synthetic controls. Models were estimated using multiple inference approaches, treatment times and control pool eligibility definitions. Safe Streets overall was associated with a 42% reduction in youth homicides and a 21% reduction in youth NFS, though neither estimate was statistically significant. Uncensored site-specific models estimate large reductions in youth homicides in five sites and youth NFS in seven sites and large increases in youth homicides in two sites and youth NFS in two sites. No site-specific estimates were statistically significant using primary inference methods. The rarity of outcomes and sensitivity to modelling decisions warrants cautious interpretation of findings. Differences in programme effectiveness across sites suggest opportunities to strengthen youth engagement in some sites. Changing youth violence dynamics underscore a need to augment and adapt CVI models to match these cultural shifts. Continued work to integrate Safe Streets with other violence prevention approaches, support implementation fidelity and invest in structural change may improve programme outcomes further.

ABSTRACT When making career-defining decisions, individuals should be well-informed. This study examines the impact of a low-cost personalised information intervention on occupational choices for students in lower-secondary education in Switzerland. Using data from an online platform and a regression discontinuity design (RDD), we analyse how a tool called occupation finder affects the number of occupations students apply to. The findings show that tailored information significantly increases the number of occupations students apply to. The intention-to-treat effect suggests every fifth student applies to an additional occupation, while the local average treatment effect indicates an increase of applications to three additional occupations for those students using the tool.

BackgroundAllergic bronchopulmonary aspergillosis/mycosis (ABPA/ABPM) is frequently misdiagnosed as pulmonary tuberculosis due to overlapping clinical and radiological features.MethodsIn a retrospective cohort of 89 multidisciplinary team (MDT)–confirmed ABPA/ABPM patients, we investigated determinants of misdiagnosis and disentangled causal drivers from spurious associations. Machine learning models with SHAP interpretation were utilized alongside a Double Machine Learning framework to estimate the average treatment effects of five key features while adjusting for major confounders.ResultsImmunological markers were identified as the dominant contributors to misdiagnosis. Total serum IgE showed the strongest protective causal effect against misdiagnosis, followed by Aspergillus fumigatus–specific IgE. Bronchiectasis demonstrated a modest protective effect. These findings were robust across covariate balance, overlap, and placebo analyses.ConclusionOur results indicate that ABPA/ABPM misdiagnosis is driven primarily by underrecognition of immunological features rather than imaging findings, underscoring the importance of systematic immunologic assessment to reduce diagnostic delay and unnecessary anti-tuberculosis treatment, particularly in tuberculosis-endemic settings.

Sacubitril/valsartan shows benefits in heart failure and may have kidney protective effects. Its impact on kidney tubular health in chronic kidney disease (CKD) remains unclear. We evaluated the effects of sacubitril/valsartan on urinary markers of tubular dysfunction and injury in UK Heart and Renal Protection-III (HARP III, ISRCTN:11958993). Urine tubular biomarkers were measured at baseline, 3 and 6 months using first morning void or spot urine samples among 411 participants from UK HARP III. A mixed model repeated measures approach was used to quantify the study average effect of treatment on the urine biomarkers. Compared to allocation to irbesartan, allocation to sacubitril/valsartan reduced neutrophil-gelatinase associated lipocalin (NGAL), a marker secreted in the distal tubules after ischemia and reperfusion, by 18% (95% CI: -32% to -1%). No significant changes were observed for the other biomarkers, and there was no evidence of effect modification by key baseline characteristics across all biomarkers studied. In UK HARP-III, sacubitril/valsartan reduced urinary NGAL compared with irbesartan but did not affect other tubular biomarkers of injury, ischemia and fibrosis, suggesting limited tubular benefits, consistent with no observed effect on kidney function.

ABSTRACT This paper examines digital finance’s impact on manufacturing value-added using 2011–2022 panel data for China and ten Association of Southeast Asian Nations (ASEAN) economies. We identify significant Heterogeneous Treatment Effects (HTE): divergent forces across development gradients result in a statistical offset, rendering the average treatment effect (ATE) insignificant. In underdeveloped economies, while the direct impact is statistically insignificant (0.104), digital finance triggers conditional empowerment by significantly mitigating credit friction (0.022*). Developed economies experience significant structural upgrading and servitization (−0.052*). Results are robust to Bartik instrumental variable (IV) and PWT-based human capital estimations.

ABSTRACT This paper discusses the use of double/debiased machine learning (DML) for estimating the average treatment effect (ATE) on a survival function using pseudo-observations. Through simulations, we demonstrate the advantage of our method compared to existing estimators in the presence of many covariates. This method is applied in evaluating the effect of e-learning programme participation on the job-finding rate among individuals who are seeking employment.

Abstract Background and aims Stroke is a major global health burden and a leading cause of adult disability. Endovascular therapy (EVT) significantly improves outcome and survival in patients with large vessel occlusion strokes. However, many stroke patients have pre-existing cardiovascular disease requiring antiplatelet therapy, including dual antiplatelet therapy (DAPT). While DAPT is well established for secondary stroke prevention, its impact on EVT remains uncertain. Methods We analysed data from a prospective multicentre EVT-cohort, the German Stroke Registry. We examined baseline characteristics, haemorrhagic complications, and functional outcome of patients on pre-stroke DAPT undergoing EVT. The average treatment effect of DAPT was double-robust estimated using propensity score weighting with outcome regression analysis. Results Among 13,082 patients, 312 (2.38%) underwent EVT on pre-stroke DAPT (median age 74 years [IQR 66.5–82], 43.2% female). Median pre-admission mRS was 1 (IQR 0–2), median admission NIHSS score was 15 (IQR 8–19), and successful reperfusion (mTICI ≥2b) was achieved in 83.2%. Any intracranial haemorrhage (ICH) occurred in 23.0%. Median 90-day mRS was 4 (IQR 2–6). DAPT was not associated with significant differences in functional outcome or survival, but was associated with an approximately threefold increased risk of ICH compared to both single APT and to no APT. Updated analyses will be presented at the meeting. Conclusions Pre-stroke DAPT was associated with a higher rate of any ICH after EVT but did not adversely affect functional outcome or survival. EVT appears to be effective in patients on DAPT despite increased haemorrhagic risk. Conflict of interest The authors report no conflicts of interest related to this project.

ABSTRACT This paper examines the unintended consequences of the 2018–2019 US‐China trade war for Vietnam, a key bystander economy, using quarterly HS 8‐digit US import data from 2017Q1 to 2020Q4. While the average treatment effect on Vietnam's exports to the US is small and statistically insignificant, substantial heterogeneity arises from pre‐war revealed comparative advantages (RCAs). Vietnam experienced markedly stronger export growth in sectors where China held high pre‐war RCA, consistent with trade diversion and substitution effects. These gains were nonlinear and most pronounced in sectors where Vietnam initially had low comparative advantage, reflecting the exploitation of previously untapped export opportunities. Consequently, Vietnam captured meaningful trade‐diversion benefits primarily outside its pre‐existing areas of comparative strength. In addition, we provide consistent evidence that the relocation of MNCs is strongly associated with the observed export surge. By quantifying these spillovers, our study contributes to the literature on global value chain dynamics and trade protectionism.

To evaluate the causal effect of ceftazidime/avibactam (C/A) combination therapy versus monotherapy on mortality and clinical success in patients with KPC-producing Klebsiella pneumoniae (KPC-Kp) infections in intensive care unit. This multi-centre, retrospective observational study (2021-2023) included adults with KPC-Kp bloodstream infections or pneumonia treated with C/A-based regimens. We employed a counterfactual framework using inverse probability of treatment weighting (IPTW) to estimate the average treatment effect on 30-day mortality. Clinical success was further assessed using Desirability of Outcome Ranking (DOOR) analysis and partial credit scoring based on patient-perspective scenarios. Among 123 included patients, 77 (62.6%) received monotherapy and 46 (37.4%) received combination therapy. The combination group presented with significantly higher baseline severity, including higher APACHE II scores and rates of septic shock. In the IPTW-adjusted analysis, 30-day survival was 73.8% (95% CI: 56-92%) with combination therapy compared with 60.8% (95% CI: 46.8-77%) with monotherapy. The survival probability ratio was 1.21 (95% CI: 0.80-1.45), indicating no statistically significant survival benefit. The DOOR analysis showed a 54.7% (95% CI: 48.9%-60.4%) probability of a more favourable outcome with combination therapy, which was not statistically significant. Mean partial credit scores did not differ significantly across scenarios prioritizing survival or adverse event avoidance. In this cohort, C/A-based combination therapy did not provide a significant survival advantage or an improved clinical desirability ranking compared with monotherapy, after adjusting for confounding factors.