Tag: statistics

Can J Public Health. 2025 Nov 10. doi: 10.17269/s41997-025-01128-z. Online ahead of print.

ABSTRACT

OBJECTIVE: SARS-CoV-2 serological surveillance used blood donors, research cohorts, and residual patient samples. Differences in socio-demographic characteristics across these sources may bias seroprevalence estimates, necessitating statistical adjustment.

METHODS: We re-analyzed data from six serosurveillance sources, comparing the estimated percent of the population positive for SARS-CoV-2 anti-nucleocapsid antibodies for six regions during periods when the sources’ sample collection overlapped. We assessed the concordance between sources with and without using multilevel regression and poststratification (MRP) to adjust for differences in representation by age, sex, and race.

RESULTS: Across regions and timepoints, unadjusted seroprevalence differed between sources by up to 20%. MRP did not consistently improve comparability of seroprevalence across sources. In 2022, seroprevalence was consistently highest among blood donors, and MRP increased regional seroprevalence across all sources (except in Manitoba during January-April 2022 in ABC Study). In a secondary regression analysis, immunoassay kit and sample type (dried blood spot or venous blood draw) strongly influenced the odds that a sample was classified as seropositive.

CONCLUSION: Adjusting for representativeness using common socio-demographic variables did not systematically improve concordance in seropositivity estimates between serosurveillance sources. While discrepancies between sources might be influenced by studies’ representativeness of characteristics we did not assess, methods for measuring seropositivity appear to explain much of the differences between sources. Serosurveillance findings are influenced by many aspects of study design beyond representativeness, such as sample type (venous blood draw or dried blood spots), choice of immunoassay, and laboratory procedures such as dilution or immunoassay calibration.

PMID:41214282 | DOI:10.17269/s41997-025-01128-z

Eur Phys J E Soft Matter. 2025 Nov 10;48(10-12):67. doi: 10.1140/epje/s10189-025-00527-x.

ABSTRACT

We examine the influence of an external orienting field on the director orientation and fluid flow of an active nematic liquid crystal confined in a channel, subject to infinite anchoring of the director and no-slip conditions at the channel walls. A mathematical model based on the Ericksen-Leslie dynamic equations for nematic liquid crystals is employed, with an additional active stress tensor accounting for the activity of the fluid. By solving the fully coupled nonlinear equations numerically, we investigate the dynamic response and the steady state of the active nematic when an orienting field is switched on. The dynamic behaviour when an orienting field is switched off is also examined, with our model demonstrating how the activity of the liquid crystal can enhance or hinder the classically observed kickback immediately after switch-off and generate nontrivial steady-state solutions. Specifically, we find that kickback, which can delay relaxation of the system to a steady state, can be made less pronounced, and eventually completely avoided, for contractile agents with a high activity parameter, even with a high magnitude orienting field value.

PMID:41214270 | DOI:10.1140/epje/s10189-025-00527-x

Forensic Sci Int. 2025 Nov 7;378:112710. doi: 10.1016/j.forsciint.2025.112710. Online ahead of print.

ABSTRACT

Friction ridge skin patterns, including latent fingerprints (LFs), have long been essential for human identification. However, traditional ridge examinations do not convey temporal information. The ability to estimate the Time-since-Deposition (TsDp) of LFs could provide valuable chronological context in criminal investigations, helping to reconstruct timelines and corroborate alibis. A recent study explored LF microbiota as potential biological “clocks” for TsDp estimations at the Phylum taxonomical rank. In that instance, it was revealed that the composition, relative abundance, and succession patterns of microorganisms varied over time. This dynamic nature made the transferred skin microbiome a promising candidate for investigating predictable temporal changes of LFs in semi-controlled environments, such as indoor locations. The present article further expands the taxonomic resolution of the original study by identifying time-dependent microbial taxa at the Family rank and suggesting specific temporal signatures through statistical analyses. The same experimental conditions were considered: three donors, hand washing conditions, and aging for 1, 7, 14, and 21 days. For this analysis, the relative abundance, presence, and temporal shifts were examined with a focus on time-variant taxa. The 16S rRNA gene (V4 region) sequencing revealed distinct temporal signatures across the observed time points and handwashing conditions. For example, in unwashed hands, the combined presence of Mycosphaerellaceae and Coxiellaceae indicated a freshly deposited LF. In contrast, under washed conditions, the presence of Ruminococcaceae and Beijerinckiaceae was associated with a recent deposition. These preliminary findings further demonstrate the potential of microbiome analysis as a forensic tool for estimating TsDp in LFs and are a feasibility study for further work.

PMID:41213205 | DOI:10.1016/j.forsciint.2025.112710

Vaccine. 2025 Nov 9;68:127933. doi: 10.1016/j.vaccine.2025.127933. Online ahead of print.

ABSTRACT

INTRODUCTION: In the UK, adults 65 years and over and those in clinical risk groups are among those eligible for seasonal influenza vaccination. While vaccine effectiveness for reducing cases of influenza is well documented, less is known about impact on wider hospital and post-discharge outcomes in the UK. We investigated whether vaccinated adults hospitalised with confirmed influenza infection had different outcomes to non-vaccinated adults during contact with health services.

METHODS: A retrospective cohort study using the Combined Intelligence for Population Health Action platform, linking primary care, secondary care and laboratory data for Cheshire and Merseyside (2.7 M population), UK. We accessed 2081 laboratory-confirmed influenza hospital admissions for adults ≥16 years (October 2018-April 2024). We studied the association of influenza vaccination with several hospital and post-discharge outcomes, considering competing risks and potential confounding factors. We included age-based subgroup analyses.

RESULTS: Vaccination uptake was recorded as 38.8 %, 52.7 % and 20.9 % among ≥16, ≥65 and 16-64 years respectively. Among the full cohort and ≥ 65 years cohort, vaccination was associated with a reduction in length of hospital stay in competing risk models (17 %, 95 %CI 7-26 %; 19 %, 95 % CI 7-31 %), risk of death up to six months after discharge (aHR 0.66, 95 %CI 0.48-0.90; aHR 0.67, 95 %CI 0.48-0.92) and change in vaccination status in the next season (aOR 0.19, 95 %CI 0.13-0.27; aOR 0.07 95 %CI 0.04-0.13). No statistically significant difference was detected for admission to critical care or other post-discharge outcomes (readmission, attendance to general practice or emergency department).

CONCLUSIONS: Influenza vaccination was associated with benefits beyond acute illness, reducing length of hospital stay and mortality among adults hospitalised with laboratory-confirmed influenza. Findings support policy decisions, including greater outreach to high-risk, low-uptake groups and advocate for the national integration of laboratory data with comprehensive healthcare data to enable more robust vaccine evaluations.

PMID:41213184 | DOI:10.1016/j.vaccine.2025.127933

Clin Chem Lab Med. 2025 Nov 11. doi: 10.1515/cclm-2025-0728. Online ahead of print.

ABSTRACT

OBJECTIVES: Laboratories are required to routinely verify reported reference intervals (RIs), but common verification methods like the CLSI-EP28-A3c binomial test are often impractical due to sample collection requirements. Indirect verification methods like equivalence limits (ELs) use routine data from patient care but lack systematic evaluation. This study aimed to develop and evaluate a novel indirect verification method: verification of reference intervals based on the uncertainty of sampling (VeRUS).

METHODS: VeRUS compares the to-be-verified candidate RI to an RI estimated from local routine data. Acceptable differences are based on the sampling uncertainty intrinsic to the nonparametric method for establishing RIs with n=120 samples. The three verification methods were systematically compared with simulated test sets resembling 10 differently distributed biomarkers and a wide range of plausible candidate RIs.

RESULTS: The binomial test is inherently unable to reject too wide RIs; e.g. the 99.8 %-interval, for which ELs and VeRUS showed high rejection rates (mean 89.2 %, SD 31.5 % and mean 95.8 %, SD 2.3 %, respectively). Moreover, the binomial test incorrectly accepts 29.3 % of “too narrow” 80%-intervals, whereas the false acceptance rates of ELs and VeRUS were lower (mean 21.7 %, SD 40.9 % and mean 7.2 %, SD 4.7 %, respectively). Overall, both indirect verification methods demonstrated increased statistical power, while ELs were least consistent among different biomarker distributions.

CONCLUSIONS: Its robust performance without the need for sample collection makes VeRUS an attractive tool for RI verification. By enabling routine verification of previously practically unverifiable RIs (e.g., in pediatrics), VeRUS may enhance clinical decision-making and improve patient care.

PMID:41213183 | DOI:10.1515/cclm-2025-0728

Stat Med. 2025 Nov;44(25-27):e70283. doi: 10.1002/sim.70283.

ABSTRACT

To advance our understanding of Alzheimer’s Disease (AD), especially during the preclinical stage when patients’ brain functions are mostly intact, recent research has shifted towards studying AD biomarkers across the disease continuum. A widely adopted framework in AD research, proposed by Jack and colleagues, maps the progression of these biomarkers from the preclinical stage to symptomatic stages, linking their changes to the underlying pathophysiological processes of the disease. However, most existing studies rely on clinical diagnoses as a proxy for underlying AD status, potentially overlooking early stages of disease progression where biomarker changes occur before clinical symptoms appear. In this work, we develop a novel Bayesian approach to directly model the underlying AD status as a latent disease process and biomarker trajectories as nonlinear functions of disease progression. This allows for more data-driven exploration of AD progression, reducing potential biases due to inaccurate clinical diagnoses. We address the considerable heterogeneity among individuals’ biomarker measurements by introducing a subject-specific latent disease trajectory as well as incorporating random intercepts to further capture additional inter-subject differences in biomarker measurements. We evaluate our model’s performance through simulation studies. Applications to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study yield interpretable clinical insights, illustrating the potential of our approach in facilitating the understanding of AD biomarker evolution.

PMID:41213170 | DOI:10.1002/sim.70283

Stat Med. 2025 Nov;44(25-27):e70303. doi: 10.1002/sim.70303.

ABSTRACT

Empirical substantive research, such as in the life or social sciences, is commonly categorized into the two modes exploratory and confirmatory, both of which are essential to scientific progress. The former is also referred to as hypothesis-generating or data-contingent research, while the latter is also called hypothesis-testing research. In the context of empirical methodological research in statistics, however, the exploratory-confirmatory distinction has received very little attention so far. Our paper aims to fill this gap. First, we revisit the concept of empirical methodological research through the lens of the exploratory-confirmatory distinction. Second, we examine current practice with respect to this distinction through a literature survey including 115 articles from the field of biostatistics. Third, we provide practical recommendations toward a more appropriate design, interpretation, and reporting of empirical methodological research in light of this distinction. In particular, we argue that both modes of research are crucial to methodological progress, but that most published studies-even if sometimes disguised as confirmatory-are essentially exploratory in nature. We emphasize that it may be adequate to consider empirical methodological research as a continuum between “pure” exploration and “strict” confirmation, recommend transparently reporting the mode of conducted research within the spectrum between exploratory and confirmatory, and stress the importance of study protocols written before conducting the study, especially in confirmatory methodological research.

PMID:41213159 | DOI:10.1002/sim.70303

Int J Occup Saf Ergon. 2025 Nov 10:1-10. doi: 10.1080/10803548.2025.2575603. Online ahead of print.

ABSTRACT

Objectives. Professionals in the perfume industry are routinely exposed to numerous chemical substances during olfactory evaluations, some of which may pose inhalation hazards. Existing qualitative risk assessment tools, such as Control of Substances Hazardous to Health (COSHH) Essentials, provide approximate estimates and may not be well suited to industries with highly specific exposure conditions like perfumery. This study evaluates the applicability and limitations of COSHH Essentials in perfume laboratories and proposes an improved qualitative framework tailored to perfumers’ exposure scenarios. Methods. A total of 626 substances from a perfumer’s palette were assessed using COSHH Essentials, which classifies substances into risk levels based on hazard, volatility and quantity. A complementary method incorporating molecular-level hazard analysis, exposure patterns, occupational exposure limits and conservative inhalation dose estimations was developed. Statistical agreement between both methods was examined using Cohen’s κ, and McNemar’s test assessed significant differences. Results. COSHH Essentials identified 76 hazardous substances, while the enhanced method identified 81 substances, including five additional requiring local exhaust ventilation. Agreement was moderate (κ = 0.58; p = 0.031). Conclusion. COSHH Essentials provides a useful baseline but lacks the specificity needed in industries with intentional close-range exposure. The enhanced method enables more precise, context-sensitive assessment and better protection in fragrance laboratories.

PMID:41213145 | DOI:10.1080/10803548.2025.2575603

JMIR Biomed Eng. 2025 Nov 10;10:e66691. doi: 10.2196/66691.

ABSTRACT

BACKGROUND: Diagnostic errors and administrative burdens, including medical coding, remain major challenges in health care. Large language models (LLMs) have the potential to alleviate these problems, but their adoption has been limited by concerns regarding reliability, transparency, and clinical safety.

OBJECTIVE: This study introduces and evaluates 2 LLM-based frameworks, implemented within the Rhazes Clinician platform, designed to address these challenges: generation-assisted retrieval-augmented generation (GARAG) for automated evidence-based treatment planning and generation-assisted vector search (GAVS) for automated medical coding.

METHODS: GARAG was evaluated on 21 clinical test cases created by medically qualified authors. Each case was executed 3 times independently, and outputs were assessed using 4 criteria: correctness of references, absence of duplication, adherence to formatting, and clinical appropriateness of the generated management plan. GAVS was evaluated on 958 randomly selected admissions from the Medical Information Mart for Intensive Care (MIMIC)-IV database, in which billed International Classification of Diseases, Tenth Revision (ICD-10) codes served as the ground truth. Two approaches were compared: a direct GPT-4.1 baseline prompted to predict ICD-10 codes without constraints and GAVS, in which GPT-4.1 generated diagnostic entities that were each mapped onto the top 10 matching ICD-10 codes through vector search.

RESULTS: Across the 63 outputs, 62 (98.4%) satisfied all evaluation criteria, with the only exception being a minor ordering inconsistency in one repetition of case 14. For GAVS, the 958 admissions contained 8576 assigned ICD-10 subcategory codes (1610 unique). The vanilla LLM produced 131,329 candidate codes, whereas GAVS produced 136,920. At the subcategory level, the vanilla LLM achieved 17.95% average recall (15.86% weighted), while GAVS achieved 20.63% (18.62% weighted), a statistically significant improvement (P<.001). At the category level, performance converged (32.60% vs 32.58% average weighted recall; P=.99).

CONCLUSIONS: GARAG demonstrated a workflow that grounds management plans in diagnosis-specific, peer-reviewed guideline evidence, preserving fine-grained clinical detail during retrieval. GAVS significantly improved fine-grained diagnostic coding recall compared with a direct LLM baseline. Together, these frameworks illustrate how LLM-based methods can enhance clinical decision support and medical coding. Both were subsequently integrated into Rhazes Clinician, a clinician-facing web application that orchestrates LLM agents to call specialized tools, providing a single interface for physician use. Further independent validation and large-scale studies are required to confirm generalizability and assess their impact on patient outcomes.

PMID:41213118 | DOI:10.2196/66691

JMIR Med Inform. 2025 Nov 10;13:e70753. doi: 10.2196/70753.

ABSTRACT

BACKGROUND: The diagnosis of sleep disorders presents a challenging landscape, characterized by the complex nature of their assessment and the often divergent views between objective clinical assessment and subjective patient experience. This study explores the interplay between these perspectives, focusing on the variability of individual perceptions of sleep quality and latency.

OBJECTIVE: Our primary goal was to investigate the alignment, or lack thereof, between subjective experiences and objective measures in the assessment of sleep disorders.

METHODS: To study this, we developed an aspect-based sentiment analysis method for clinical narratives: using large language models (Falcon 40B and Mixtral 8X7B), we are identifying entity groups of 3 aspects related to sleep behavior (day sleepiness, sleep quality, and fatigue). To phrases referring to these aspects, we are assigning sentiment values between 0 and 1 using a BERT-BiLSTM-based approach (accuracy 78%) and a fine-tuned GPT-2 sentiment classifier (accuracy 87%).

RESULTS: In a cohort of 100 patients with complete subjective (Karolinska Sleepiness Scale [KSS]) and objective (Multiple Sleep Latency Test [MSLT]) assessments, approximately 15% exhibited notable discrepancies between perceived and measured levels of daytime sleepiness. A paired-sample t test comparing KSS scores to MSLT latencies approached statistical significance (t99=2.456; P=.06), suggesting a potential misalignment between subjective reports and physiological markers. In contrast, the comparison using text-derived sentiment scores revealed a statistically significant divergence (t99=2.324; P=.047), indicating that clinical narratives may more reliably capture discrepancies in sleepiness perception. These results underscore the importance of integrating multiple subjective sources, with an emphasis on narrative free text, in the assessment of domains such as fatigue and daytime sleepiness-where standardized measures may not fully reflect the patient’s lived experience.

CONCLUSIONS: Our method has potential in uncovering critical insights into patient self-perception versus clinical evaluations, which enables clinicians to identify patients requiring objective verification of self-reported symptoms.

PMID:41213114 | DOI:10.2196/70753