Category: Statistics

Int J Health Econ Manag. 2026 Jul 24;26(3):16. doi: 10.1007/s10754-026-09420-2.

ABSTRACT

This article analyzes the economic responses of household heads confronted with SARS-CoV-2 symptoms during the partial lockdown period imposed in Cameroon in 2020. By isolating the specific effect of the individual symptomatic shock from that of general health restrictions, it fills a gap that the literature on the economic effects of COVID-19 has not yet directly addressed. The data come from a household survey conducted in Cameroon, and five economic responses are analyzed, namely over-indebtedness, active occupational mobility, and reductions in health, education, and sanitary protection expenditures. To address endogeneity, two complementary strategies are employed, namely the kinky least squares (KLS) method, which corrects endogeneity bias without resorting to an external instrument, and the recursive bivariate probit with instrumental variable, which jointly models binary treatment and outcome while accounting for the correlation between their unobserved error terms. Kinky least squares estimates establish a positive and statistically significant relationship between SARS-CoV-2 symptomatic manifestations and all of the selected economic responses. The recursive bivariate probit confirms these results, with the exception of active occupational mobility, whose coefficient remains positive but loses its statistical significance once non-linearity and the correlation of unobserved disturbances are fully accounted for. The decomposition of average treatment effects indicates that SARS-CoV-2 symptomatic manifestations increase the probability of reducing sanitary protection expenditures by 37.3% across the entire population (ATE) and by 38.1% among household heads who were actually affected (ATET). For education expenditures, these effects are 28.6% and 25.6%, respectively. For health expenditure reductions, these effects are 23.2% and 18.6%, respectively. These findings argue in favor of the free distribution of sanitary protection devices, the subsidization of medical consultations and community drug stockpiles, the maintenance of school transfer payments, and zero-interest emergency credit lines for informal workers experiencing temporary work incapacity. These measures will nonetheless remain insufficient as long as the health coverage system remains critically underdeveloped.

PMID:42496914 | DOI:10.1007/s10754-026-09420-2

Exp Brain Res. 2026 Jul 24;244(8):163. doi: 10.1007/s00221-026-07366-y.

ABSTRACT

Whether the cognitive benefits of athletic training transfer to specific academic domains, such as arithmetic, remains poorly understood. This study investigated the behavioral and electrophysiological (ERP) correlates of multiplication verification in 19 high school cycling athletes and 19 matched controls. Behaviorally, athletes exhibited significantly lower baseline mathematical proficiency (SATM scores), lower accuracy, slower reaction times and poorer processing efficiency. Electrophysiologically, athletes initially demonstrated significantly larger posterior P300 amplitudes than controls. Crucially, these group differences were substantially attenuated after statistically controlling for baseline mathematical proficiency using analysis of covariance (ANCOVA). The findings suggest that arithmetic-related neural processing differences in athletes may be associated with variability in mathematical proficiency rather than reflecting a generalized neurocognitive disadvantage. More broadly, the results support the view that cognitive adaptations associated with athletic expertise may be relatively domain-specific and may not automatically generalize to highly practiced academic skills such as arithmetic verification.

PMID:42496911 | DOI:10.1007/s00221-026-07366-y

Fish Physiol Biochem. 2026 Jul 24;52(4):126. doi: 10.1007/s10695-026-01753-6.

ABSTRACT

Aquaculture intensification is increasingly recognized as a strategic pathway to fight food insecurity. It is also harnessed to meet the rising global demand for high-quality animal protein. This has led to a growing need for fish seed and a heightened concerned for broodstock nutrition, reproductive efficiency, and quality of seeds. In the present study, reproductive responses of African catfish Heterobranchus bidorsalis broodstock were evaluated at graded levels of ethanolic Moringa oleifera leaf extract (EMOLE) as a phytogenic feed additive. A total of 84 broodstock (800.00 ± 150.00 g) were randomly assigned to 4 isonitrogenous (40% crude protein) diets containing 0.0% (control), 1.0%, 2.0% and 3.0% EMOLE in a completely randomized design with three replications. Fish were fed at 5% body weight, twice daily for 16 weeks. Reproductive indices assessed included milt motility, sperm morphology, milt volume and concentration, gonadosomatic index, egg diameter, fecundity, fertilization rate, hatching rate, and hatchling survival. Data were analyzed using descriptive statistics, one-way analysis of variance at α 0.05, using SPSS 20 software. Results showed that dietary EMOLE significantly improved reproductive performance. Fish fed 3.0% EMOLE exhibited the highest sperm motility (92.00 ± 2.31%), sperm concentration (225.33 ± 0.02 × 10⁶ ml⁻¹), fecundity (6118.00 ± 200.70), fertilization rate (78.63 ± 0.51%), hatchability (78.20 ± 0.67%), and larval survival (78.07 ± 1.27%). Sperm abnormalities were significantly reduced in supplemented groups. These findings indicate that 3.0% EMOLE can serve as a natural phytogenic feed additive for improving gonadal development and reproductive performance in Heterobranchus bidorsalis broodstock. This further places Moringa as a natural, cost-effective phytogenic additive for enhancing broodstock performance and sustainable seed production.

PMID:42496907 | DOI:10.1007/s10695-026-01753-6

Eur J Trauma Emerg Surg. 2026 Jul 24;52(1):229. doi: 10.1007/s00068-026-03277-3.

ABSTRACT

PURPOSE: Computed Tomography (CT) with intravenous (IV) contrast is critical to the evaluation of the trauma patient. A pandemic-related lockdown occurred in Spring 2022 in the city where iohexol iodinated contrast media) is manufactured, resulting in a global shortage. We explored the potential impact of this shortage on contrast utilization and patient outcomes.

METHODS: A retrospective study was performed of all trauma patients who underwent CT between January and December 2022 at a level I trauma center. Pre-contrast conservation (PRE) period was defined as January to April 2022, and post-conservation (POST) as May to December 2022. Demographics, utilization rates (bolus/patient) and outcomes were evaluated. Chi-square tests were performed for categorical variables and Mann-Whitney U tests for continuous variables. Statistical significance was set at p < 0.05.

RESULTS: 1,097 patients were included; 509 in the pre-shortage period, 588 post-shortage. 857 CTs with contrast (1.68 contrast bolus/patient) were performed pre-conservation and 979 post-conservation (1.66 contrast bolus/patient) (p = 0.04). Maximum Abbreviated Injury Scale (AIS) head, head/neck, and extremity were higher post-shortage (p < 0.05); however, mortality was higher in the pre-conservation group (p < 0.0012). There were 9 cases of Acute Kidney Injury (AKI, 8 PRE, 1 POST), 22 delayed diagnoses (14 PRE, 8 POST) and 2 missed injuries (PRE 2, POST 0).

CONCLUSIONS: Despite a nationwide IV contrast shortage and volume of contrast utilization per patient, we did not observe an increase in delayed diagnoses, missed injuries or AKI although these events remain small with no clear adverse signal. This may reflect the proactive institutional measures deployed to decrease nonurgent contrast use in the inpatient and outpatient settings in order to conserve contrast for more emergent settings. Based on the limited data set and truncated pre-shortage periods we were not able to reach any meaningful conclusions regarding quality of care and impact on outcomes. Next steps may include multi-center analysis to determine the larger clinical impact of this shortage.

PMID:42496906 | DOI:10.1007/s00068-026-03277-3

Neuroradiology. 2026 Jul 24. doi: 10.1007/s00234-026-04115-4. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate prognostication after aneurysmal subarachnoid hemorrhage (aSAH) remains challenging. Conventional clinical and radiological grading systems, including the World Federation of Neurosurgical Societies (WFNS), Hunt-Hess, and Fisher scales, are widely used but have limited discriminative capacity. This study aimed to systematically compare machine learning (ML)-based prognostic models with conventional grading systems for predicting functional outcomes and mortality after aSAH, and to evaluate factors influencing ML performance.

METHODS: A systematic review and meta-analysis were conducted according to PRISMA 2020 guidelines. PubMed, Embase, Scopus, Web of Science, and the Cochrane Library were searched for studies published between 2010 and 2025. Eligible studies evaluated ML-based models for outcome prediction in adult aSAH patients and reported performance of conventional grading systems. Prognostic discrimination was pooled using random-effects meta-analysis of the area under the receiver operating characteristic curve (AUC), with predefined subgroup analyses.

RESULTS: Fourteen studies including 6,247 patients were analyzed. ML models demonstrated good to excellent discrimination, with AUCs ranging from 0.81 to 0.97. The pooled ML AUC for predicting unfavourable neurological outcome was 0.86 (95% CI 0.83-0.89; p < 0.0001), with substantial heterogeneity (I² = 96.1%). ML models outperformed conventional grading systems in most studies and showed comparable performance in the remainder. Subgroup analyses confirmed statistically significant prognostic accuracy across clinical-only, imaging-based, and multimodal ML models.

CONCLUSION: Machine learning-based prognostic models demonstrate statistically significant and clinically meaningful performance for outcome prediction after aSAH, exceeding conventional grading systems and supporting their role as complementary risk stratification tools.

PMID:42496900 | DOI:10.1007/s00234-026-04115-4

Trop Anim Health Prod. 2026 Jul 24;58(7):446. doi: 10.1007/s11250-026-05261-w.

ABSTRACT

For the first time, this study employs a century-long dataset (1925-2024) to reveal key factors that would be in relationship with sheep population (NSheep) in Türkiye using state-of-the-art machine learning algorithms. Due to the existence of missing values in the original dataset, missing observations were addressed through four imputation techniques-Next Observation Carried Backward (NOCB), Mean, MIDASpy, and Random Forest (RF)-generating four distinct datasets for comparative analysis. For revealing the key production factors related with NSheep, Extreme Gradient Boosting (XGB) and Multilayer Perceptron (MLP) algorithms were modeled via 5-fold cross-validation and multiple performance metrics (R², MSE, RMSE, MAE, and MdAPE). MLP produced lower prediction errors than XGB across all imputation techniques, though this difference was statistically confirmed only under NOCB and RF imputation (Diebold-Mariano test, P < 0.01 and P < 0.05, respectively); differences under MEAN and MIDASpy imputation were not significant. The highest overall accuracy was achieved by MLP with NOCB imputation (R² = 0.975), while XGB with RF imputation showed the weakest fit (R² = 0.917). Feature importance analyses consistently identified cattle population (NBovine) as the dominant variable associated with NSheep across all four imputation techniques and both algorithms, followed by meadow and pasture area (M&PH) for XGBoost and a more evenly distributed set of variables (M&PH, sheep meat production, goat population) for MLP. Given that NBovine and NSheep both increased steadily over the study period, this association is interpreted as reflecting shared structural growth among livestock subsectors rather than a causal effect of cattle population on sheep numbers. These dataset-specific, associative findings support the value of combining multiple imputation strategies with flexible machine learning algorithms to characterize structural interdependencies in long-term agricultural production data. Future studies incorporating chronologically ordered validation schemes and explicitly modeling structural breaks and policy shifts could further clarify the robustness of these associations, and extending this approach to other livestock species and regions would help establish their broader generalizability.

PMID:42496899 | DOI:10.1007/s11250-026-05261-w

JMIR Mhealth Uhealth. 2026 Jul 24;14:e88382. doi: 10.2196/88382.

ABSTRACT

BACKGROUND: Engagement is a necessary precondition for the effectiveness of mobile health (mHealth) apps for long-term physical health conditions (LTCs), particularly as health systems increasingly prioritize the deployment of scalable, self-guided digital interventions. Outside controlled research settings, where clinician involvement often drives engagement, little is known about whether, how, and why people engage with mHealth apps based on intrinsic motivation alone. Existing systematic reviews have cataloged behavioral engagement indicators but rarely assess the mechanisms underlying engagement.

OBJECTIVE: This overview of systematic reviews aimed to (1) synthesize evidence on how engagement with mHealth apps for LTCs is defined, measured, and associated with health outcomes, (2) explore intrinsic and extrinsic motivational processes underlying engagement, and (3) provide practical guidance for developing scalable, user-centered digital health interventions that sustain sufficient engagement with minimal reliance on external drivers. Uniquely, we interpreted modifiable barriers and facilitators through a motivational lens that distinguishes extrinsic from intrinsic motives, mapping intrinsic motives onto autonomy, competence, and relatedness, as proposed by Self-Determination Theory (SDT).

METHODS: Searches of MEDLINE, Web of Science, Epistemonikos, and gray literature (inception to June 9, 2025) identified systematic reviews reporting engagement indicators, engagement-outcome associations, or barriers and facilitators among adults with LTCs. Quantitative reviews were appraised using AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews 2), and qualitative and mixed methods reviews were appraised using CASP (Critical Appraisal Skills Programme). A narrative synthesis was undertaken, and modifiable barriers and facilitators were independently mapped by 2 reviewers to extrinsic and intrinsic motivation and, for intrinsic factors, to the SDT constructs of autonomy, competence, and relatedness. Discrepancies were resolved through discussion with the wider research team.

RESULTS: Nineteen reviews (12 quantitative, 5 mixed methods, and 2 qualitative) were included from 4684 records. Fourteen (74%) reviews did not define engagement, and the remaining 5 equated it with “usage” or “adherence,” precluding meta-analysis. Fourteen reviews reported microlevel behavioral indicators, but none captured macrolevel or effective engagement. Eight assessed engagement-outcome links; 7 reported positive associations, and 1 reported no effect. Seven reviews included nonmodifiable factors that influence engagement (eg, ethnicity), while 13 included modifiable factors. SDT mapping revealed that modifiable factors influencing autonomy (eg, personal relevance, flexibility), competence (eg, usability, technical support), and relatedness (eg, clinician endorsement, peer connection) underpin intrinsic engagement, whereas extrinsic barriers include restrictions to access (including cost).

CONCLUSIONS: Current evidence on engagement with mHealth apps remains conceptually inconsistent and methodologically fragmented, but motivational patterns are clear: engagement depends largely on intrinsic motives once external conditions are satisfied. Applying SDT provides the first mechanism-oriented explanation of how engagement operates, enabling practical recommendations for evaluating existing apps and designing future mHealth interventions that support autonomy, competence, and relatedness.

PMID:42496884 | DOI:10.2196/88382

J Autism Dev Disord. 2026 Jul 24. doi: 10.1007/s10803-026-07459-1. Online ahead of print.

ABSTRACT

PURPOSE: Problem behaviours exacerbate the challenges associated with the core characteristics of autism during adolescence. Parenting interventions have demonstrated efficacy in addressing such behavioural challenges in younger autistic individuals, however, their evidence for autistic adolescents is sparse, particularly in low-income settings.

METHODS: This systematic review narratively synthesised studies outlining parenting interventions targeting problem behaviours in autistic adolescents.

RESULTS: Seven studies were included with three providing comparable quantitatively data for their efficacy in improving problem behaviours. Five out of seven studies reported positive evidence on reducing problem behaviours. Characteristics including fewer sessions, instructional mode of intervention delivery and use of behavioural management skills were most commonly shared among statistically significant studies. Parenting interventions had a positive effect on adaptive behaviours for children, and improved parent wellbeing and knowledge. The overall satisfaction with interventions was high, however, only one study was conducted in a lower- and middle-income country.

CONCLUSION: The findings underscore the encouraging evidence in an understudied area. The results emphasise the need to conduct further research for autistic adolescents and highlight potential parent-mediated interventions carry, given their acceptability and logistically scalable nature.

PMID:42496858 | DOI:10.1007/s10803-026-07459-1

Rheumatol Ther. 2026 Jul 24. doi: 10.1007/s40744-026-00861-2. Online ahead of print.

ABSTRACT

INTRODUCTION: Persistence to therapy to treat rheumatoid arthritis (RA) is an indirect measure of tolerability and effectiveness in the real-world setting. Previous clinical trials suggested that seropositive patients with RA may particularly benefit from abatacept. The risk of non-persistence after initiating abatacept, tumor necrosis factor inhibitors (TNFi), and Janus kinase inhibitors (JAKi) as first-line biologic or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) was examined.

METHODS: We utilized the 100% Medicare Fee-For-Service sample linked claims with Prognos laboratory data from 2012 to 2019. Newly-initiating, anti-cyclic citrullinated peptide (anti-CCP)-positive and rheumatoid factor-(RF) positive patients were identified. Index date was initiation of abatacept, TNFi, or JAKi as first-line b/tsDMARD treatment. Persistence (measured at 12 months post-index) was defined as the absence of a treatment gap ≥ 60 days, > 90 days off-treatment, or switch in therapy. Cox regression was used to investigate risk of non-persistence between the groups.

RESULTS: Of 3105 patients identified, 487 received abatacept (16%), 2330 TNFi (75%), and 288 JAKi (9%). Abatacept, TNFi, and JAKi twelve-month persistence was 48%, 33%, and 39%, respectively. Beneficiaries initiating with abatacept were more likely to be persistent than those initiating with TNFi or JAKi (hazard ratio [HR] for TNFi, 1.45, 95% CI 1.27-1.64; HR for JAKi, 1.43, 95% CI 1.19-1.72).

CONCLUSION: These real-world findings suggest that abatacept as a 1L treatment has the potential to improve persistence in patients with anti-CCP+ and RF+RA compared to the most commonly used 1L alternatives.

PMID:42496853 | DOI:10.1007/s40744-026-00861-2

Child Psychiatry Hum Dev. 2026 Jul 24. doi: 10.1007/s10578-026-02067-7. Online ahead of print.

ABSTRACT

In this article, we present a computational framework for predicting treatment response to neurofeedback (NF) among patients with Attention-Deficit/Hyperactivity Disorder (ADHD). The proposed framework uses functional brain connectivity analysis of electroencephalogram (EEG) signals acquired during an early-to-mid NF treatment window to classify participants as eventual responders or non-responders. The six-stage algorithm was evaluated using an open-access EEG dataset from the Mendeley Data repository comprising 60 children with ADHD aged 6-12 years. The framework includes a preprocessing pipeline designed to reduce EEG artifacts and noise. Next, spectral features, specifically of the alpha and beta frequency bands, were extracted from the noise-reduced signals. In the fourth stage, functional connectivity was estimated by calculating Phase Locking Value (PLV) between all electrode pairs, thereby quantifying inter-channel phase synchronization. The fifth stage, which is an essential stage, was dimensionality reduction to find the most discriminative features. Dimensionality reduction was achieved in a two-process manner; for the first process, statistical screening was performed using Welch’s t-test with FDR correction, followed by GA-based channel selection to identify the most discriminative electrode subset. The GA analysis identified a compact six-channel subset consisting of C3, C4, Cz, Fz, Fp1, and T6 from the original 32-channel montage. In the last classification stage, the reduced feature vectors were input as part of an ensemble of machine learning classifiers to achieve classification. Model performance was evaluated using subject-wise grouped cross-validation, with the best configuration achieving an accuracy of 84.72%. These results suggest that the proposed data-driven framework may support future research on individualized NF response prediction, pending validation on independent clinical datasets.

PMID:42496846 | DOI:10.1007/s10578-026-02067-7