Tag: statistics

Int J Health Plann Manage. 2026 Aug 12. doi: 10.1002/hpm.70104. Online ahead of print.

ABSTRACT

Sub-Saharan Africa (SSA) continues to experience a high and uneven disease burden, mainly from HIV/AIDS, tuberculosis, and malaria. As external funding declines, the strategic allocation of domestic public health spending (DPHS) becomes crucial. This study assesses whether directing DPHS towards major diseases and high-burden areas improves health outcomes across 43 SSA countries between 2003 and 2022, using DALYs as the outcome measure. Results show a statistically significant positive relationship between DPHS and DALYs: HIV-specific models yield coefficients of 0.105-0.288 (p < 0.01), and all-cause models yield coefficients of 0.071-0.085 (p < 0.05), while other models are insignificant. The paradoxical positive association-stronger in high-burden settings-suggests that DPHS is reactive, mainly used to address existing disease burdens rather than prevent them. The significant persistence of DALYs, especially in low-burden and high-burden contexts, further indicates that spending is directed towards treatment and case management rather than preventive measures. The DPHS effect on tuberculosis or malaria outcomes in DALYs is slightly greater in low-burden areas. Beyond health spending, factors such as economic growth, inequality, population ageing, and governance substantially influence health outcomes. Overall, the findings highlight that while DPHS remains essential, its current reactive deployment limits effectiveness. A systemic shift is needed towards proactive, preventive approaches with targeted health interventions to reduce the region’s disease burden.

PMID:42585620 | DOI:10.1002/hpm.70104

JCO Clin Cancer Inform. 2026 Jul-Sep;10(3):e2600018. doi: 10.1200/CCI-26-00018. Epub 2026 Aug 12.

ABSTRACT

PURPOSE: AML is a highly aggressive hematologic cancer. During induction chemotherapy, up to 40% of patients experience complications resulting in unplanned readmissions or early death. This study aimed to identify the reasons for postinduction unplanned readmissions and to develop predictive models for unplanned readmissions or early death using structured and unstructured electronic health record (EHR) data to identify patients at highest risk.

METHODS: We retrospectively analyzed 1,111 inpatient encounters from 305 adult patients with AML treated at a Midwestern university hospital between 2006 and 2021. Inclusion criterion was adults with AML undergoing induction chemotherapy; exclusion criteria included acute promyelocytic leukemia, stem cell transplant recipients, and confirmed chronic myeloid leukemia. Adverse events-unplanned readmissions or mortality within 30 days of discharge from the initial induction hospitalization-were identified through chart review using predefined rules. Multiple logistic regression was used to identify risk factors. Variable selection was performed using the least absolute shrinkage and selection operator.

RESULTS: Within 30 days postdischarge, 22% of patients experienced an unplanned readmission. The most frequent reasons were fever/infection (53.5%), metabolic/GI/renal complications (16.2%), and pain/discomfort (14.1%). Mortality within 30 days was 22%. Predictive performance improved when comorbidities and symptom frequency from clinical notes were added (AUC increased from 0.67 to 0.74). Statistically significant predictors of increased adverse event risk included solid neoplasm (odds ratio [OR], 2.34), higher cardiopulmonary symptom burden per day (OR, 1.16), and a chemotherapy intensity moderated by age (OR, 0.90).

CONCLUSION: Unplanned readmissions or early mortality occurred in 39.7% of patients with AML within 30 days postdischarge. Integrated risk stratification tools leveraging structured and unstructured EHR data may inform timely interventions to improve survival and reduce avoidable hospitalizations.

PMID:42585617 | DOI:10.1200/CCI-26-00018

J Clin Oncol. 2026 Aug 12:JCO2501846. doi: 10.1200/JCO-25-01846. Online ahead of print.

ABSTRACT

PURPOSE: Age ≥40 years is regarded as a high-risk feature and an indication for adjuvant chemoradiotherapy for patients with lower-grade glioma in clinical practice guidelines. It is unclear whether age remains a relevant prognostic factor for contemporary definitions of lower-grade gliomas in the molecular era.

METHODS: The Prospective Gliomas Research (PROGRES) database contains individual patient-level data from 11 prospective clinical trials or observational registries of histologically defined lower-grade 2-3 oligodendroglioma or astrocytoma. We determined the association of age (18-39 years v ≥40 years) with progression-free survival (PFS) stratified by isocitrate dehydrogenase 1 or 2 (IDH1/2) status, using log-rank tests and Cox regression models. We validated our findings in a separate multi-institutional retrospective cohort (Retrospective Glioma Research [REGRES] database).

RESULTS: We identified 1,619 and 1,292 eligible patients in the PROGRES and REGRES cohorts, respectively. IDH-wildtype tumors were more common in patients 40 years and older (38% v 5%, odds ratio: 11.3 [95% CI, 6.5 to 19.7]). Age was associated with PFS in IDH-wildtype (5-year PFS for ≥40 v 18-39 years: 6% v 24%, hazard ratio [HR], 1.74 [95% CI, 1.21 to 2.50]) but not in IDH-mutant glioma (60% v 59%, HR, 0.89 [95% CI, 0.76 to 1.05], P_interaction < .001). In IDH-wildtype tumors, older age predicted aggressive molecular features, including TERT promoter mutation (65% v 28%), EGFR amplification (41% v 15%), and chromosome +7/-10 alteration (57% v 25%). In a pooled analysis of four clinical trials, age was not predictive of a benefit from chemoradiotherapy versus radiotherapy alone for IDH-mutant glioma.

CONCLUSION: In the absence of additional clinical or molecular risk factors, age alone should not be considered an indication for administration or deferral of adjuvant treatment. Practice guidelines should be revised to reflect contemporary prognostic factors in the molecular era.

PMID:42585601 | DOI:10.1200/JCO-25-01846

J Cardiovasc Nurs. 2026 Aug 12. doi: 10.1097/JCN.0000000000001380. Online ahead of print.

ABSTRACT

BACKGROUND: Poor diabetes self-management is prevalent in individuals with diabetes and can result in macro- and micro-cardiovascular diabetes complications. Diabetes knowledge may impact diabetes self-management directly and indirectly via the impact on diabetes self-efficacy. However, these relationships have rarely been examined among individuals with diabetes, controlling for potential psychosocial, demographic, and clinical covariates.

OBJECTIVE: To investigate whether diabetes knowledge was associated with diabetes self-management directly and indirectly via diabetes self-efficacy, controlling for depressive symptoms, diabetes distress, self-esteem, self-compassion, resilience, social support, body mass index, and age.

METHODS: Baseline data from 2 studies were used in this cross-sectional, correlational study. Data on all study and demographic variables were collected from 228 adults with diabetes (mean age: 56.5 years) in 2023 and 2025. PROCESS Macro for the Statistical Package for Social Sciences (Model 4; 5000 bootstraps; 95% confidence intervals [CIs]) was used to examine the suggested relationships.

RESULTS: The mean diabetes knowledge score was 10.7 out of 13 (standard deviation [SD] = 1.9); the mean diabetes self-efficacy score was 28.0 (SD = 6.1); and the mean diabetes self-management score was 18.0 (SD = 6.3). Stronger diabetes knowledge showed a direct relationship with better diabetes self-management (effect(B) = 0.779, 95% bootstrap CI = 0.401, 1.157) and an indirect relationship through higher levels of diabetes self-efficacy (effect(B) = 0.193, 95% bootstrap CI = 0.078, 0.333).

CONCLUSIONS: Diabetes knowledge was associated with diabetes self-management directly and indirectly via diabetes self-efficacy. Clinicians and researchers may modify diabetes knowledge to improve diabetes self-efficacy, and, in turn, diabetes self-management.

PMID:42585591 | DOI:10.1097/JCN.0000000000001380

JMIR Form Res. 2026 Aug 12;10:e100764. doi: 10.2196/100764.

ABSTRACT

BACKGROUND: Consumer wearable activity monitors are increasingly being used as end points in exercise-oncology trials and in clinical decision-making, yet their accuracy is unvalidated in lung cancer, where slow, fragmented gait may challenge step-counting algorithms.

OBJECTIVE: This study assessed the criterion validity of the Fitbit Charge 6 device against video-recorded direct observation in adults with lung cancer under controlled laboratory conditions. We aimed to describe step-count agreement across walking bout durations and gait speeds, and to compare its accuracy in classifying active vs sedentary minutes and detecting spurious steps across nonwalking activities.

METHODS: Fourteen adults diagnosed with stage I-IV lung cancer completed a cross-sectional, in-laboratory validation study at The Ohio State Wexner Medical Center. Participants wore the Fitbit Charge 6 device on their nondominant wrist while completing variable-duration walking trials (5, 15, and 30 seconds); self-selected gait speed trials across 8 progressively faster speeds; and standing, sitting, lying, and fidgeting tasks. All activities were video recorded and coded at a 1-second resolution. Step count agreement was evaluated using repeated-measures Bland-Altman analysis (mean bias and 95% limits of agreement), supported by mean absolute percentage error (MAPE) and intraclass correlation coefficients (ICCs). Minute-level activity intensity classification was assessed via a pooled confusion matrix using a majority-rule active-minute threshold of ≥30 seconds. Spurious step detection was descriptively analyzed across nonwalking minutes, stratified by fidgeting status.

RESULTS: Across 126 walking trials, the Fitbit device undercounted steps by only a small absolute margin, which was consistent across bout durations (bias of 1-3 steps), but relative agreement was poor and strongly duration dependent (MAPE 50.6% at 5 seconds vs 16.3%-18.7% at 15-30 seconds). The ICC was low (overall ICC[A,1]=0.21). Across 111 gait speed trials, undercounting was the greatest at gait speeds below 0.6 m/s (bias of approximately 13 steps; MAPE 56.6%) and was minimized near 1.0-1.2 m/s, with a quadratic mixed-effects model confirming a nonlinear speed-error relationship (P<.001). For activity intensity classification, sensitivity was high (0.91), but specificity was modest (0.63), and the positive predictive value was low (0.31), reflecting frequent misclassification of sedentary minutes as active. Among 267 nonwalking minutes, 33 (12.4%) contained at least one spurious step, with higher false-positive rates during fidgeting (24/167, 14.4%) than nonfidgeting (9/100, 9.0%) periods.

CONCLUSIONS: The Fitbit Charge 6 device provides improved step counts during sustained, moderate-speed walking but introduces a clinically meaningful error during short bouts and at slower gait speeds, which are frequently noted in adults with lung cancer. High sensitivity but low specificity for activity intensity classification suggests systematic overestimation of active minutes. These findings have implications for the design and interpretation of exercise-oncology interventions relying on consumer wearable-derived end points in this population.

PMID:42585577 | DOI:10.2196/100764

Brief Bioinform. 2026 Jul 3;27(4):bbag434. doi: 10.1093/bib/bbag434.

ABSTRACT

Single-cell DNA methylation (scDNAm) profiling is revolutionizing our understanding of epigenetic control of gene expression, but its accurate analysis is severely hindered by extreme data sparsity. While imputation methods have undergone remarkable development in recent years, a rigorous benchmark to guide method selection remains absent. We established the first systematic benchmarking framework for scDNAm imputation, subjecting five state-of-the-art methods to a comprehensive evaluation across 13 published experimental scDNAm datasets. Performance was systematically assessed across seven critical dimensions: accuracy, sensitivity to data characteristics, scalability, robustness to data splitting strategies, inter-dataset generalizability, convergence behavior, and computational efficiency. Through rigorous statistical analysis, we dissected the influence of intrinsic data attributes and model architectures on the fidelity of scDNAm imputation to provide guidance for selecting appropriate methods for given scenarios. Furthermore, based on the benchmark-identified limitations, we proposed a dual-view strategy to address the performance bottlenecks of existing methods: at the model view, we developed BridgeCpG, an ensemble strategy to integrate complementary modeling strengths to overcome single-model limitations; at the data view, we introduced an adaptive divide-and-conquer strategy to partition highly heterogeneous datasets into several homogeneous subsets amenable to accurate imputation, followed by aggregating the sub-results. This integrated framework, spanning both model and data views, delivers quantitative analyses, scenario-aware selection guidelines, and targeted innovative strategies, establishing a rigorous, enabling foundation for accurate, high-throughput, and scalable next-generation single-cell epigenomic analysis.

PMID:42585575 | DOI:10.1093/bib/bbag434

Work. 2026 Aug 12:10519815261469529. doi: 10.1177/10519815261469529. Online ahead of print.

ABSTRACT

BackgroundRapid AI integration into healthcare necessitates proactive action to leverage benefits and reduce risks to clients and occupational therapy. Research on AI integration in occupational therapy shows promising outcomes.ObjectiveThis study assessed knowledge, use, and attitudes regarding AI in occupational therapy worldwide to inform development of guidance documents and resources for future policy, professional development, and practice in occupational therapy.MethodsThis cross-sectional descriptive study was conducted using a 28-item electronic survey. A global panel convened by the World Federation of Occupational Therapists (WFOT) reviewed the survey and translated it from English into French, German, and Spanish. WFOT distributed the survey via online communications and social media between September and November 2025. Analysis included descriptive statistics, nonparametric tests, and thematic analysis.ResultsResponses from 884 surveys representing 81 countries were analyzed. Most respondents were occupational therapists (87.8%), and one-third had over 20 years of experience. Two-thirds (68.4%) expressed positive views toward AI integration in occupational therapy. Over half (56.3%) reported using AI at work, most often for documentation, administrative tasks, education, research, intervention planning, and communication. Respondents learned about AI through online resources, peer networks, and continuing education. Qualitative analysis revealed three themes related to equity, implementation conditions, and ethical responsibilities.ConclusionsThe findings reveal high interest and increasing AI use for workflow support. Although attitudes were generally positive, respondents also raised important concerns. Professional organizations have an essential role in developing guidelines related to AI in occupational therapy, supporting education, ensuring ethical use, and advocating for research.

PMID:42585570 | DOI:10.1177/10519815261469529

Sex Med Rev. 2026 Jun 30;14(3):qeag057. doi: 10.1093/sxmrev/qeag057.

ABSTRACT

INTRODUCTION: Sexual dysfunction (SD) is an important complication in dialysis patients that affects quality of life but remains under-recognized in clinical practice. Previous reviews have mainly focused on prevalence, whereas systematic quantification of associated factors, sex-stratified comparisons, and evidence certainty evaluations remain limited.

OBJECTIVES: This meta-analysis aimed to identify factors associated with SD in dialysis patients and explore sex-specific differences.

METHODS: This systematic review and meta-analysis included observational studies of adult dialysis patients identified from database inception to April 20, 2025. Extracted data included adjusted odds ratios and 95% confidence intervals. Study quality was assessed using the Agency for Healthcare Research and Quality checklist or the Newcastle-Ottawa Scale. Sensitivity analyses, subgroup analyses, publication bias assessment, and GRADE evaluations were conducted to assess the robustness and certainty of the evidence.

RESULTS: A total of 19 studies involving 4989 patients were included. The primary analysis showed that factors associated with SD in males included age, diabetes mellitus, smoking history, dialysis vintage, depression, diabetic nephropathy, alcohol consumption, and inadequate dialysis. In females, factors associated with SD included age, depression, beta-blockers, low educational attainment, menopausal status, chronic diseases, and diabetic nephropathy, whereas recombinant human erythropoietin was associated with lower odds of SD. After REML+HKSJ adjustment, age, diabetes mellitus, smoking history, dialysis vintage, depression, and diabetic nephropathy in males, and depression, lower educational attainment, and diabetic nephropathy in females remained statistically significant.

CONCLUSIONS: Factors associated with SD in dialysis patients differed by sex. However, evidence certainty was limited by observational designs, heterogeneity, potential publication bias, and variations in measurement tools, diagnostic thresholds, and exposure definitions. Therefore, these findings reflect statistical associations rather than causal conclusions.

PMID:42585569 | DOI:10.1093/sxmrev/qeag057

Age Ageing. 2026 Aug 3;55(8):afag231. doi: 10.1093/ageing/afag231.

ABSTRACT

BACKGROUND: Falls are a major cause of morbidity and mortality in older adults. The World Falls Guidelines (WFG) propose a risk stratification algorithm using fall history, three key questions (3KQ), mobility tests and clinical criteria, but it has yet to be systematically evaluated.

OBJECTIVE: To assess whether the WFG algorithm, in original or modified form, effectively stratifies community-dwelling older adults into low, intermediate or high risk of future falls; whether it has been prospectively validated; and whether it has been adequately operationalised.

METHODS: We conducted a systematic review (PROSPERO CRD420251151506) by searching MEDLINE, Embase and Google Scholar. Two reviewers independently screened studies and extracted data on cohort characteristics, algorithm adaptations, baseline risk distribution and prospective falls outcomes over ≥6 months. Given the heterogeneity of the studies included, quantitative meta-analysis was not conducted.

RESULTS: Eight cohort studies (n = 25 027; mean age range 61-82 years) met the inclusion criteria. No study implemented the WFG algorithm as published; two used minor modifications and six had major adaptations due to dataset limitations. Common modifications included rephrasing the 3KQ, replacing ‘subjective unsteadiness’ question with objective measures, applying mobility test universally, adjusting mobility tests thresholds and omitting fall severity definition components. Across studies, 7.9%-82.6% of participants were classified as low risk, 0.4%-18.3% as intermediate risk and 10.5%-88.7% as high risk. High-risk classification showed modest sensitivity (26.5%-52.3%) and moderate-to-high specificity (34.2%-88.9%) for future falls (1-2 years), with an overall accuracy of 61.2%-74.2%. High-risk groups consistently had a two- to three-fold higher incidence of falls than low-risk groups, while intermediate-risk groups only showed a slightly elevated risk. Use of 3KQ alongside fall history and universal mobility testing improved discrimination and increased the proportion in the intermediate-risk category.

CONCLUSION: The operationalised versions of the WFG algorithm identify older adults at high falls risk with high specificity but limited sensitivity. Proposed pragmatic modifications improve stratification accuracy for intermediate risk group but require prospective validation.

PMID:42585559 | DOI:10.1093/ageing/afag231

Drug Dev Ind Pharm. 2026 Aug 12:1-20. doi: 10.1080/03639045.2026.2708900. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to design and evaluate pH-sensitive pellets loaded with 5-aminosalicylic acid (5-ASA) for targeted colon delivery in ulcerative colitis (UC) therapy.

SIGNIFICANCE: The proposed formulation leverages glyceryl monooleate (GMO) and linoleic acid (LA) as liquid crystal precursors to achieve site-specific drug release.

METHODS: Liquid crystal nanoparticles (LCNPs) were prepared from a GMO bulk phase using a top-down method, with LA added for pH sensitivity. The formulations were then evaluated for drug loading, encapsulation efficiency, and structure by polarised light microscopy, with drug release tested at pH 1.2 and 6.8. The LCNPs dispersions were then used to make pellets using the extrusion-spheronisation technique. Drug-excipient interactions were examined by FTIR and DSC. The pellet formulations were finally tested in a rat ulcerative colitis (UC) model by comparing colon damage scores across treatment groups.

RESULTS: The pH-sensitive pellets containing LCNPs exhibited a uniform morphology, desirable aspect ratio, and sufficient mechanical strength. The in vitro release profile demonstrated a 5.7-fold higher drug release at pH 6.8 compared to pH 1.2. This targeted release was ascribed to a pH-triggered phase transition of the LA and electrostatic repulsion between the ionised drug and the carrier. The in vivo results confirmed that the pH-sensitive pellets elicited the most pronounced and statistically significant (p < 0.05) reduction in colon damage scores.

CONCLUSION: The developed pellet system, which encapsulates 5-ASA within GMO/LA-based liquid crystal nanoparticles, presents a highly promising platform for targeted drug delivery in the treatment of ulcerative colitis.

PMID:42585548 | DOI:10.1080/03639045.2026.2708900