Economic claims after surgery may be regarded as an alternative surrogate outcome for long-term deprived quality of life. This study reports economic claims of chronic pain following inguinal hernia repair. Consecutive data on economic claims following inguinal hernia repair was collected from the nationwide Danish Patient Compensation Association. Patients' claims were stratified into three groups: 1) isolated chronic pain claims without claims of competing potential reasons for chronic pain (ICP); 2) diverse claims not involving claims of chronic pain (NCP); and 3) claims involving a combination of chronic pain and competing potential claim reasons for chronic pain (CCP). A total of 507 patients were included and 256 (50.5%) filed a claim involving chronic pain. Follow-up was 100% and median time from hernia repair to patient filing a claim in the ICP group was 1.5 years (IQR 0.6-2.6 years). ICP, NCP and CCP comprised 172 patients (33.9%), 251 patients (49.5%) and 84 patients (16.6%) respectively. Chronic pain was by far the most common claim reason (33.6% of all claim reasons). The median sum of granted compensation per patient in the ICP, NCP and CCP groups was €14,440 (IQR 7,233-100,600), €6,289 (4,024-12,094) and €7,777 (5,639-11,781) respectively. Long-term chronic pain alone, not involving other complications, was by far the most common reason for seeking economic compensation. Economic compensation of isolated chronic pain (ICP) was rare, but when awarded, was substantially higher than compensation for other claims.
Population health checkups support disease prevention and health management. Clarifying how routinely collected checkup factors relate to subsequent healthcare costs may inform population-level risk assessment and preventive planning. We aimed to characterize nonlinear and sex-specific associations between routine checkup factors and near-term claims-based healthcare costs in Japan and to explore whether composite metabolic indices provide additional information beyond routine measures. We linked Japan's Specific Health Checkups (FY2012-FY2022) to subsequent administrative insurance claims in Hakui City and analyzed adults aged 60-74 years. The outcome was annual claims-based healthcare costs in the third and fourth fiscal years after baseline, expressed as fee schedule points. We fitted generalized additive models with a Gamma distribution and log link in the overall sample and separately for men and women. Predictors included routine checkup measures (including glycated hemoglobin [HbA1c]) and lipid- and adiposity-related indices (atherogenic index of plasma, arteriosclerosis index, non-high-density lipoprotein cholesterol, fatty liver index, and visceral adiposity index). Model fit and the stability of the observed association patterns were assessed using 50 repeated 80/20 individual-level split validations. The dataset comprised 11,148 person-years from 6,757 residents. The models showed limited explanatory performance (mean R2=0.06), indicating that routine health checkup variables alone explained only a small proportion of variation in subsequent claims-based healthcare costs. Age and HbA1c showed consistent associations across groups. HbA1c showed a J-shaped association with costs, with a steeper increase above approximately 6.0%. Body mass index, waist circumference, and fatty liver index showed moderate association patterns, whereas the atherogenic index of plasma, arteriosclerosis index, and visceral adiposity index provided limited additional information beyond routine measures; the fatty liver index showed a notable association pattern in men. Routine health checkup factors exhibited nonlinear, sex-specific associations with near-term claims-based healthcare costs in a Japanese community. These findings may inform population-level hypothesis generation and future indicator selection. However, routine checkup variables alone appear insufficient for precise individual-level prediction. External validation and integration of additional information on comorbidities, medications, healthcare utilization, and socioeconomic factors are warranted.
The analysis of care trajectories derived from electronic health records and claims data has become increasingly common in biomedical informatics. This has enabled large-scale studies of care processes, yet widely used binary code representations result in high-dimensional, sparse data that fail to capture semantic relationships between medical concepts. Learning dense vector representations (embeddings) has emerged as a promising approach to address these limitations. We aimed to construct and share joint embeddings for the International Classification of Diseases (ICD-10) and the Anatomical Therapeutic Chemical (ATC) classification system, providing reusable semantic representations of diagnoses and treatments from real-world claims data. Using claims records from 1.5 million patients, we defined code co-occurrences within temporal windows and constructed a Positive Pointwise Mutual Information (PPMI) matrix spanning ICD-10 and ATC codes. Singular Value Decomposition (SVD) was applied to derive a low-dimensional embedding space. Evaluation combined UMAP visualization, nearest-neighbor retrieval, and a code-level classification task based on ICD chapters and ATC classes. The embeddings reflected the hierarchical organization of ICD-10 and ATC and revealed associations across coding systems, including clinically relevant diagnosis-treatment relationships. The classification task achieved mean AUCs of 0.93 for ICD-10 and 0.90 for ATC, indicating strong grouping of semantically related codes. The embeddings provide a reusable, code-level semantic representation that can support code retrieval, reduce manual code grouping, and be aggregated into patient-level features without training a task-specific model. We release the first openly available joint ICD-10-ATC embedding space derived from real-world claims data, providing a reusable resource for biomedical informatics research.
Quantify the performance of claims data in identifying diabetic ketoacidosis (DKA) cases and estimate healthcare resource utilization among newly diagnosed type 1 diabetes patients with and without DKA. We matched individuals <18 years of age from the Barbara Davis Center for Diabetes Registry to the Colorado all-payer claims database to estimate variation in the performance of claims (ie, type 1 diabetes and DKA codes in emergency department [ER] and inpatient [IP] settings) in identifying DKA cases from 2014 to 2019. We estimated the unadjusted and adjusted associations of laboratory-confirmed DKA with resource utilization using encounters and incidence rate ratios with 95% confidence intervals. After applying algorithms for identifying type 1 diabetes with continuous enrollment criteria, n = 728 (62% match of the sample) represented an insured population residing in Colorado. Mean age at onset was approximately 9.48 years, with a proportion of DKA events at diagnosis of 56%. Strict coding definitions of DKA (DKA and type 1 diabetes) had lower sensitivity (63%) and higher specificity (76%) than looser definitions (type 1 diabetes or DKA), which had higher sensitivity (89%) and lower specificity (60%). Laboratory-confirmed DKA was associated with over 5 times the rate of IP encounters compared with no confirmed DKA (incidence rate ratio [IRR], 5.49; 95% confidence interval, 4.03-7.47) but fewer ER-only encounters (IRR, 0.36; 95% confidence interval, 0.24-0.54). Claims data may not capture all DKA encounters at type 1 diabetes diagnosis. Future research should provide sensitivity analyses across coding and settings to estimate the burden of DKA.
Lumbar disc herniation (LDH) is a major cause of low back pain and may require surgical intervention when conservative or non-surgical treatment fails. In Korea, herbal medicine is commonly used as part of non-surgical care; however, its long-term effect on the risk of lumbar surgery has been difficult to assess because herbal decoctions have historically not been recorded in national insurance claims data. We conducted a retrospective cohort study by linking electronic health records (EHRs) from four Korean medical hospitals with National Health Insurance claims data. Patients newly diagnosed with LDH (ICD-10 M51) between 2016 and 2017 were included. The index date was defined as 1 year after the first hospital visit, and patients were followed until 31 July 2021. Exposure was defined as receiving herbal decoction for ≥30 days versus <30 days during the 1-year period between the entry date and index date. Patients with red-flag conditions or those who underwent lumbar surgery before the index date were excluded from the study. Propensity score matching (1:1) was performed using age, sex, insurance type, Charlson Comorbidity Index, and baseline leg pain numeric rating scale. Lumbar surgery was identified using procedure codes for discectomy, laminectomy, or spinal fusion. Kaplan-Meier analysis and Cox proportional hazards models were used to estimate the association between herbal decoction use and surgery. Among 6,669 eligible patients, 2,504 received herbal decoction for ≥30 days. After propensity score matching, 2,473 patients remained in each group, with good balance across baseline covariates (standardized mean differences <0.1). The ≥30-day group showed a significantly lower cumulative incidence of lumbar surgery compared with the <30-day group (2.63% vs. 3.64%; log-rank p = 0.033). Across sequential Cox regression models, longer herbal decoction use was consistently associated with a reduced risk of lumbar surgery, hazard ratio (HR) 0.71 (95% CI 0.51-0.97). In this real-world study linking EHR and claims data, herbal decoction use for ≥30 days was associated with a lower risk of subsequent lumbar spine surgery in patients with LDH. These findings suggest that herbal medicine may be an effective non-surgical treatment option and warrant confirmation in prospective and randomized studies.
Background: Nationwide epidemiologic data on comorbidity burden in early rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are limited. We compared coded diagnoses for concurrent disorders in incident RA and PsA based on administrative healthcare data (AHC). Methods: This retrospective cohort study used AHCs from the National Health Fund between 2009 and 2021. Using composite proxy definitions for RA and PsA diagnosis (combination of ICD-10 codes and prescription data), we identified all new cases of RA and PsA between 2019 and 2021. We utilized a ten-year lookback window for the accrual of concurrent disorder claims. Age-, sex-, serostatus- and calendar year-adjusted models were considered. Crude, relative and adjusted prevalence estimates were calculated using generalized linear models. Results: Using NHF data, we identified 36,285 and 1603 patients with incident RA/PsA, respectively. We estimated the burden of 31 multisystem comorbidities. Most disorders (N = 23, 74.2%) were more frequently coded among RA patients, while only liver diseases were significantly more prevalent in PsA. Chronic back pain (+21.2 pp) and osteoarthritis (+18.3 pp) were tied to the greatest absolute differences, likely mirroring medical contact patterns throughout the differential diagnostic process. Hospitalization due to heart failure and stroke, but not myocardial infarction, was more common in RA vs. PsA. Conclusions: Newly diagnosed patients with RA and PsA show distinct patterns of healthcare utilization for multiple organ disorders. Early RA may be tied to higher comorbidity rates not fully explained by age and sex, as compared to PsA; further studies are necessary to clarify these observations.
The etiology of pediatric leukemia remains incompletely understood. The delayed infection hypothesis suggests that reduced microbial exposure in early infancy may contribute to leukemia development through dysregulated immune responses. Because cesarean delivery may alter early-life microbial colonization and immune development, several studies have examined its association with pediatric leukemia, but findings remain inconsistent, particularly in Asian populations. We conducted a case-control study using a large Japanese administrative claims database (2008-2024). Children with Down syndrome were excluded from the primary analysis given its strong, well-established association with leukemia. Cases were matched 1:4 with controls by age, sex, birth year, and observation period using risk-set sampling. Multivariable conditional logistic regression was used to evaluate the association between cesarean delivery and pediatric leukemia, adjusting for maternal and perinatal factors. A sensitivity analysis including children with Down syndrome was also performed. In the primary analysis, 197 cases were matched with 778 controls. Cesarean delivery was recorded in 10 cases (5.1%) and 48 controls (6.2%; p = 0.56). In multivariable analysis, cesarean delivery was not significantly associated with pediatric leukemia (OR, 0.7; 95% CI, 0.3-1.4; p = 0.32). Pediatric complex chronic conditions were associated with leukemia risk (OR, 3.9; 95% CI, 2.6-6.0; p < 0.001), although this finding should be interpreted with caution given the heterogeneous nature of this construct. In the sensitivity analysis, 226 cases were matched with 894 controls. The association between cesarean delivery and leukemia remained non-significant, whereas Down syndrome was strongly associated with leukemia (OR, 30.3; 95% CI, 4.3-216; p = 0.001). In this Japanese case-control study, cesarean delivery was not significantly associated with pediatric leukemia after excluding children with Down syndrome and adjusting for maternal and perinatal factors. Further studies with detailed information on planned versus emergency cesarean delivery, genetic predisposition, and clinical factors are warranted.
Background/Objectives: Hyponatremia is a clinically important electrolyte disorder in older adults, yet early identification is hindered by complex, non-linear interactions between comorbidities and polypharmacy. This study aimed to develop and externally validate a machine learning (ML) prediction model for hyponatremia risk using nationwide claims data, focusing on medication patterns and clinical features. Methods: A retrospective cohort study was conducted using the South Korean Health Insurance Review and Assessment Service-Aged Patient Sample (HIRA-APS). Data from 2017 to 2018 were used for development, and 2019 data for temporal external validation (age ≥ 65). SHapley Additive exPlanations (SHAP)-based recursive feature elimination identified 33 high-impact predictors from 60 clinical features. Six ML algorithms, including LightGBM and CatBoost, were trained with 1:4 case-control matching and evaluated for discrimination and calibration. Results: The development and validation cohorts included 4810 and 648,586 patients, respectively. All models showed comparable discriminative performance (area under the receiver operating characteristic curve [AUROC] 0.741-0.746), with LightGBM achieving the highest (AUROC 0.746; 95% confidence interval (CI) 0.728-0.763). The models had very high negative predictive values (>0.999) for ruling out low-risk individuals. Tree-based ensemble matched linear models in discrimination but achieved better calibration. Conclusions: These validated, interpretable ML models can serve as clinical decision support tools that rule out low-risk patients and prioritize monitoring for high-risk individuals. Across sociodemographic subgroups, calibration was maintained after recalibration, whereas discrimination was lower in the oldest, most comorbid, frailest, highest-medication-burden, and lowest-socioeconomic groups-a gap to address before equitable deployment.
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Vutrisiran was approved by the US Food and Drug Administration for hereditary transthyretin amyloid polyneuropathy (ATTR-PN) in 2022 and for transthyretin amyloid cardiomyopathy in 2025. Real-world data on vutrisiran health care resource utilization (HCRU), costs, discontinuation, and mortality in the USA are limited. We conducted a retrospective analysis of patients initiating vutrisiran from 2022 to 2024 (when only approved for hereditary ATTR-PN) using Optum's de-identified Clinformatics® Data Mart (CDM) Database. Patients were followed until death, disenrollment, or last date of available data. Hospitalizations, emergency department (ED) visits, and associated costs (excluding vutrisiran itself) were assessed. Discontinuation analyses were also performed in the Komodo Healthcare Map® to serve as comparator. In Optum® CDM (n = 111), mean (standard deviation [SD]) age was 70.9 years (9.9), 62% were male, 41% were Black, 27% were White, and 70% had heart failure. Mean (SD) follow-up was 1.5 years (0.6). After vutrisiran initiation, 41 (37%) patients had hospitalizations with a median of 9.3 days hospitalized annually and mean (SD) cost of $80,536 ($83,907) per patient; 55 (50%) patients had ED visits not leading to hospitalization, with a mean of 2.4 visits and mean (SD) ED costs of $6339 ($7484) annually per patient. Within 12 months of vutrisiran initiation, 13 (12%) patients died and an additional 13 (12%) discontinued therapy, of which 3 (23%) restarted vutrisiran and 10 (77%) switched ATTR therapy. Results were generally similar in the Komodo database comparator analyses (n = 718); within 12 months of vutrisiran initiation, 22 (3%) patients died and 142 (20%) discontinued therapy, of which 53 (37%) restarted vutrisiran, 40 (28%) switched ATTR therapy, and 47 (33%) discontinued all treatments. Patients treated with vutrisiran continued to experience significant HCRU and costs related to disease progression despite treatment. New therapies are needed to further reduce burden and costs.
The objective of this retrospective study was to evaluate and compare the safety characteristics of patients with chronic low back pain (cLBP) without a recent positive history of opioid use disorder (OUD). This study was conducted using the Merative MarketScan® database (January 2019-December 2023). The first date of Belbuca®, buprenorphine patch, or oral schedule II (CII) opioid prescription was designated as the index date. The observational period covered a 6-month preindex period and a follow-up period that lasted until the end of index treatment or continuous healthcare coverage. Patients were required to have two low back pain diagnoses and no OUD in the preindex period and continuous healthcare coverage during the observational period. The primary outcomes were serious treatment-emergent adverse event (TEAE) rates reported as incidence rate ratios (IRR) or absolute incidence rate difference (IRD) per 1,000 person-years for TEAEs occurring in one cohort. Propensity-score matching was employed to balance differences in patient characteristics and minimize their impact on study outcomes. There were no serious TEAEs associated with higher occurrence in the Belbuca® cohort compared with oral CII opioids. Belbuca® treatment was associated with a significantly lower rate of serious opioid abuse/dependence (IRD -33.76 per 1,000 person-years, p = 0.032), osteoarthritis (IRD -78.77 per 1,000 person-years, p = 0.001), urinary discomfort (IRD -146.28 per 1,000 person-years, p < 0.001), seizures (IRR 0.11, p = 0.019), dehydration (IRR 0.13, p = 0.003), abdominal pain (IRR 0.25, p < 0.001), and nausea/vomiting (IRR 0.30, p = 0.001). The subanalysis compared incidence rates of serious TEAEs between Belbuca® and buprenorphine patch cohorts. Belbuca® demonstrated higher rates of serious coronary artery disease (IRD 39.01 per 1,000 person-years, p = 0.035), cholecystitis (IRD 39.01 per 1,000 person-years, p = 0.035), and headache (IRD 39.01 per 1,000 person-years, p = 0.035). However, the buprenorphine patch cohort had higher incidence rates of serious QT prolongation (IRD -52.78 per 1,000 person-years, p = 0.009), opioid abuse/dependence (IRD -184.75 per 1,000 person-years, p < 0.001), confusion (IRR 0.10, p = 0.007), hypertension (IRR 0.22, p = 0.043), and cellulitis (IRR 0.41, p = 0.011). The study findings suggest that Belbuca® may have a favorable safety profile relative to oral CII opioids and buprenorphine patch treatments in cLBP patients without a positive history of OUD.
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There are reports of oral nicotine pouches (ONPs) being marketed in high-income countries. The aim of this study was to report online promotions and marketing claims about ONPs in Malaysia, which currently does not regulate nicotine pouches. We did a content analysis of ONPs sold on e-stores. We searched the terms: 'Velo', 'Zyn', 'Onz', 'nicotine pouch', and 'oral nicotine pouch' on Google Malaysia, Facebook, Reddit, TikTok, and Instagram during March-April 2024. Three trained coders independently coded the e-stores, using a codebook developed based on previous research. Visual and textual information about brands, prices, discounts, flavors, nicotine strength, and marketing claims, was collected. Results are presented in both descriptive and free text formats. A sample of 41 e-stores was analyzed. ONP marketing was present on TikTok and e-commerce stores. Brands such as 'Velo', 'Zyn', and 'Boltbe' offered discounts, free deliveries, and an array of flavors, with nicotine strengths of 2-50 mg. All brands displayed nicotine strength (mg). Only 51% of the e-stores explicitly stated that the product contained 'nicotine', and 5% disclosed that nicotine is addictive; 41% of the stores displayed ONPs as 'tobacco-free'. About half of the e-stores had marketing claims of 'product appeal' (56%), 'convenient to use anywhere, everywhere' (46%), and 'smoking cessation' (49%). Age verifications and identification proofs were not available at all, whereas health warnings against usage by minors, non-smokers, and pregnant women, were present on only 5%. Online marketing and sales promotions were present on e-commerce websites and some social media platforms. The range of flavors and strengths, sales promotions, marketing claims such as appeal and convenience of use, the lack of nicotine disclaimers, and age verification were noted. Surveillance of ONPs is needed to inform regulatory policy.
Commercial foods for infants and young children are widely available and often introduced early, including before 6 months, raising concerns about their often high levels of sugar and sodium, limited micronutrient content, and sweet flavours that may shape future taste preferences. Product packaging further influences caregiver choices through child-appealing images and claims. This study examined changes in child-appealing marketing appearing on packaged commercial infant and young children's foods available in Australia in 2015 (n = 311) and 2024 (n = 298). Products were coded using a validated framework comprising 11 core techniques (child-targeted, e.g., bright colours, cartoon characters, fun themes) and 9 broad techniques (caregiver-targeted, e.g., nutrition/health, texture claims). Marketing power was calculated as the sum of techniques per product. The proportion of products featuring child-appealing marketing increased from 73.0% in 2015 to 89.9% in 2024, with significant increases in appeals to fun (48.2% to 79.2%), branded characters (33.8% to 62.4%), and child-appealing visuals on the package (60.5% to 72.5%). Claims regarding texture were high in both years (99.7% in 2015 and 100.0% in 2024), and there was a notable growth in messages promoting value, convenience, or sustainability (from 50.2% to 62.1%). Marketing power scores rose significantly over time, indicating an intensification of persuasive techniques on packaging. These findings highlight systemic commercial strategies that promote nutritionally poor foods to infants, young children and caregivers, undermining public health efforts. Stronger, government-led regulation is urgently needed to address the widespread use of both child-targeted and caregiver-targeted marketing on commercial foods for infants and young children.
While nutrition claims draw attention to desirable nutrient qualities, the Brazilian Front-of-Pack Nutrition Labeling (FOPNL) system serves a contradictory purpose by signaling excessive levels of added sugar, saturated fat, and sodium. Besides labeling, sociodemographic factors may influence the understanding of nutritional information and food choices. This experimental, controlled, and randomized study used secondary data derived from previously published research to evaluate the impact of sociodemographic variables, including region, sex, age, education, and income. Specifically, we analyzed how these factors influence the understanding of nutritional information, the perception of healthfulness, and the purchase intention of products with different FOPNL models and nutrition claims. A sample of 720 Brazilian adults completed an online questionnaire, being randomly assigned to one of four FOPNL conditions: control (without FOPNL), octagon, triangle, or magnifying glass. Participants evaluated 12 label panels in a 3×2×2 factorial design, considering 1) food category, 2) number of nutrients in excess and 3) presence/absence of nutrition claims. Understanding of nutritional information was measured as the ability to correctly identify nutrients in excess using a generalized linear model with binary logistic regression. Perception of healthfulness and purchase intention were assessed on a 7-point scale using mixed analysis of variance models, with the sociodemographic variables (region, sex, age, education, and income) as fixed effects and participants as random effects. The results indicate that sociodemographic variables did not significantly affect the understanding of nutritional information. However, participants aged 25 to 34 and male participants reported higher perception of healthfulness and greater purchase intention compared to other groups. These findings suggest that, although the provision of nutritional information on packages supports informed food choices across diverse sociodemographic contexts, age and sex specifically influenced how consumers perceived product healthfulness and their likelihood of purchasing the presented items in this sample.
Antimicrobial resistance (AMR) is a global health crisis shaped by complex ecological and evolutionary processes that often occur in polymicrobial communities. Metagenomics enables culture-independent profiling of microbial DNA directly from clinical or environmental samples, providing an unparalleled view of community composition, resistome content, and the mobile genetic elements that drive horizontal gene transfer (HGT). Yet, a recurring challenge is that metagenomic detection of antibiotic-resistance genes does not automatically translate into a mechanistic understanding of resistance phenotypes, nor does it replace culture-based functional validation. Here, we synthesize how modern metagenomics supports AMR research across three linked questions: (i) what resistance determinants are present and how do they change across time and space, (ii) which hosts and mobile genetic elements carry these determinants, and how gene flow can be inferred, and (iii) what evidence is required to move from "resistance potential" to robust mechanistic claims. We emphasize practical design principles (sampling, controls, and contamination management), analytical choices (database and parameter effects), and recent advances, including long-read sequencing for resolving antibiotic-resistance genes context, and rapid clinical metagenomic sequencing for time-sensitive decision support. We propose an evidence ladder for mechanistic inference that integrates metagenomics with targeted assays and culture-dependent experiments. Beyond synthesizing recent advances, this review provides operational tools for critical appraisal and study design: an evidence ladder for mechanistic inference, a decision-gated workflow that ties metagenomic outputs to allowable claim language, a minimum reporting checklist aligned to evidence strength, and a "pitfall → consequence → fix" guide to reduce over-interpretation. To support a more comprehensive, forward-looking view, we also summarize emerging directions that are rapidly reshaping AMR metagenomics-multi-omics integration, single-cell, and epigenetic linkage strategies, CRISPR-enabled enrichment/depletion, and AI-assisted discovery/mining-and clarify where these advances strengthen (or do not strengthen) mechanistic claims within the same evidence ladder.
This study aimed to identify demographic and clinical factors associated with advanced-stage pressure injury (PI) classification among hospitalised patients diagnosed with PIs using the Health Insurance Review and Assessment Service (HIRA) patient sample database. This secondary data analysis was approved by the Institutional Review Board of K University (IRB No. 2020-0266). Public data were analysed using SAS Enterprise Guide on a designated computer, and all procedures complied with data security and confidentiality requirements. A secondary analysis was conducted using the HIRA patient sample database from 2017 to 2019. Patients diagnosed with pressure injuries were classified into a mild-stage group (Stages 1-2) and an advanced-stage group (Stages 3-4). Multivariable logistic regression analysis was performed to identify factors associated with advanced-stage pressure injury classification (Stage 3-4 vs. Stage 1-2), and odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. Male patients had higher odds of being classified as having advanced-stage pressure injuries than female patients (OR = 1.25). The odds of advanced-stage pressure injury classification increased by 1.02 for each one-year increase in age and by 1.02 for each additional day of hospitalisation. Patients with urinary incontinence and malnutrition had 3.32-fold and 2.76-fold higher odds, respectively, of being classified as having advanced-stage pressure injuries than those without these conditions (p < 0.001). Sex, age, length of hospital stay and specific comorbidities were significantly associated with advanced-stage pressure injury classification among hospitalised patients with pressure injuries. Urinary incontinence and malnutrition showed particularly strong associations with advanced-stage classification. These findings may support the early identification of patients who are more likely to present with advanced-stage pressure injuries and inform targeted assessment and management strategies in clinical practice. Because this study was based on cross-sectional administrative claims data, the findings should be interpreted as associations with advanced-stage classification rather than evidence of temporal progression or causation. This study utilised secondary administrative data from a nationally representative sample provided by the Health Insurance Review and Assessment Service (2017-2019). No patients or members of the public were directly involved in the design or conduct of this study because the analyses were based on de-identified administrative claims data.
Patient narratives are foundational to diagnosis, yet clinicians frequently and unintentionally distort, minimize, or reinterpret these narratives during clinical encounters. These distortions - termed patient narrative distortion - are unmeasured contributors to diagnostic error [G.D. Schiff, O. Hasan, S. Kim, R. Abrams, K. Cosby, B.L. Lambert et al., Diagnostic error in medicine: analysis of 583 physician-reported errors, Arch Intern Med 169 (2009) 1881-1887; P. Croskerry, The importance of cognitive errors in diagnosis and strategies to minimize them, Acad Med 78 (2003) 775-780; H. Singh, A.N.D. Meyer, E.J. Thomas, The frequency of diagnostic errors in outpatient care, BMJ Qual Saf 23 (2014) 727-731; and M.L. Graber, N. Franklin, R. Gordon, Diagnostic error in internal medicine, Arch Intern Med 165 (2005) 1493-1499], emotional harm [J. Conway, F. Federico, K. Stewart, M.J. Campbell, Respectful Management of Serious Clinical Adverse Events, IHI Innovation Series White Paper, IHI, Cambridge, MA, 2011], and inequity [E.N. Chapman, A. Kaatz, M. Carnes, Physicians and implicit bias: how it affects clinical decision making, Acad Med 88 (2013) 354-360; and M. Marmot, R.G. Wilkinson (Eds.), Social Determinants of Health, 2nd ed., Oxford University Press, Oxford, 2005]. No existing safety tool captures the fidelity with which clinicians preserve patient stories [T. Greenhalgh, B. Hurwitz (Eds.), Narrative Based Medicine: Dialogue and Discourse in Clinical Practice, BMJ Books, London, 1998; and W. Levinson, D.L. Roter, J.P. Mullooly, V.T. Dull, R.M. Frankel, Physician-patient communication: the relationship with malpractice claims, JAMA 277 (1997) 553-559]. The stages at which narrative distortion emerges are illustrated in Figure 1. The objective of this article was to define patient narrative distortion as a measurable construct, develop a five-domain taxonomy, propose a scoring system (PNDI), and outline a workflow and validation strategy for clinical use. We conducted iterative conceptual modeling and structured synthesis of the diagnostic-safety and narrative-medicine literatures [G.D. Schiff, O. Hasan, S. Kim, R. Abrams, K. Cosby, B.L. Lambert et al., Diagnostic error in medicine: analysis of 583 physician-reported errors, Arch Intern Med 169 (2009) 1881-1887; P. Croskerry, The importance of cognitive errors in diagnosis and strategies to minimize them, Acad Med 78 (2003) 775-780; H. Singh, A.N.D. Meyer, E.J. Thomas, The frequency of diagnostic errors in outpatient care, BMJ Qual Saf 23 (2014) 727-731; and M.L. Graber, N. Franklin, R. Gordon, Diagnostic error in internal medicine, Arch Intern Med 165 (2005) 1493-1499] to identify core distortion modes, develop domain definitions, item-level anchors, and scoring thresholds. We propose a multi-phase validation plan including content validity, inter-rater reliability, construct validity, criterion validity, and responsiveness. The PNDI taxonomy includes five domains: Narrative Completeness Distortion, Meaning Substitution Distortion, Salience Distortion, Context Stripping Distortion, and Bias-Driven Distortion [E.N. Chapman, A. Kaatz, M. Carnes, Physicians and implicit bias: how it affects clinical decision making, Acad Med 88 (2013) 354-360]. Each domain includes 0-3 severity anchors and real-world clinical examples. The total PNDI score ranges from 0-15, with interpretation bands for narrative integrity and safety risk. Encounter-level, unit-level, and organizational-level workflows for implementation are outlined. The five-domain PNDI taxonomy is shown in Figure 2. PNDI operationalizes narrative integrity as a measurable dimension of diagnostic safety [The Joint Commission, National Patient Safety Goals: Improving Diagnosis in Health Care 2024-2026, The Joint Commission, Oakbrook Terrace, IL, 2024]. It provides clinicians, educators, and safety teams with a practical tool to detect narrative loss, reduce diagnostic error [G.D. Schiff, O. Hasan, S. Kim, R. Abrams, K. Cosby, B.L. Lambert et al., Diagnostic error in medicine: analysis of 583 physician-reported errors, Arch Intern Med 169 (2009) 1881-1887; P. Croskerry, The importance of cognitive errors in diagnosis and strategies to minimize them, Acad Med 78 (2003) 775-780; H. Singh, A.N.D. Meyer, E.J. Thomas, The frequency of diagnostic errors in outpatient care, BMJ Qual Saf 23 (2014) 727-731; and M.L. Graber, N. Franklin, R. Gordon, Diagnostic error in internal medicine, Arch Intern Med 165 (2005) 1493-1499], and strengthen patient trust [W. Levinson, D.L. Roter, J.P. Mullooly, V.T. Dull, R.M. Frankel, Physician-patient communication: the relationship with malpractice claims, JAMA 277 (1997) 553-559].
Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan-Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan-Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of 0.168 ± 0.017 PLI units and root mean squared error of 0.219 ± 0.045 (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096-0.114; t(59) = 22.61, p < 0.001). In the PhysioNet source-space benchmark, posterior Desikan-Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen's d = 0.85, p = 0.001; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test-retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made.
Alcohol exclusion provisions (AEPs) in the Uniform Accident and Sickness Policy Provision Law (UPPL) allow insurers to deny claims for alcohol-related injuries. Although many states have repealed AEPs, some enacted explicit prohibitions on intoxication-based claim denials while others did not. This review documents the evolution and current legal landscape of AEPs and evaluates how prior studies have classified these policies. Data were obtained from the Alcohol Policy Information System (APIS) accessed in December 2025 and verified using Westlaw and Nexis Uni. Targeted reviews of the literature on AEPs were conducted. As of 2024, 21 states retain AEPs. Fourteen states and the District of Columbia explicitly prohibit intoxication-based claim denials, 13 states have no UPPL-related provisions, four states limit AEP applicability to disability insurance, and two states maintain policy-specific exceptions. Prior studies frequently overlook these distinctions. Legal heterogeneity remains substantial and may contribute to policy misclassification and biased estimates in empirical evaluations of AEPs. By providing a comprehensive legal mapping of AEP regimes and identifying common methodological shortcomings in the literature, this review offers a framework for improving future research on alcohol-related insurance policies, alcohol screening practices, treatment utilization, and related health outcomes.