Tag: statistics

Eur J Health Econ. 2025 Jul 8. doi: 10.1007/s10198-025-01809-z. Online ahead of print.

ABSTRACT

INTRODUCTION: To ensure fair competition and prevent risk selection by sickness funds, Germany employs a morbidity-based risk-adjustment scheme, primarily using diagnostic data to record insured persons’ morbidity. However, concerns about the manipulability and quality of diagnostic coding have sparked discussions. This study proposes and evaluates an alternative risk-adjustment model based on pharmaceutical data, assessing its potential as an extension or an alternative to the diagnosis-based status quo.

METHODS: We adapted an existing pharmacy-based model to German conditions and simulated various models. In order to create comparability to the status quo, we constructed a representative sample for the German statutory health insurance (SHI), using claims data of about 4.5 million insured persons. We evaluated the sample by assessing the standardized differences of the weighted means of the relevant covariates. For a quantitative assessment of the models we used the coefficients of determination (R²), Cumming’s Predictive Measure (CPM), and the mean absolute prediction error (MAPE). Under- and overcompensation within different risk groups were also analysed.

RESULTS: The sample closely matched SHI data (overall effect size after matching < 0.0001). Substituting diagnostic data with pharmacy cost groups (PCGs) showed comparable model quality, but worsened under- and overcompensation for groups vulnerable to risk selection. Conversely, integrating PCGs into the status quo improved nearly all performance measures.

CONCLUSION: Introducing pharmacy-based models into the German risk compensation scheme demonstrates significant potential. Extending the current model with PCGs enhances statistical performance, improves morbidity measurement, and offers a viable approach to mitigate coding manipulation incentives.

PMID:40627257 | DOI:10.1007/s10198-025-01809-z

Eur J Neurol. 2025 Jul;32(7):e70283. doi: 10.1111/ene.70283.

ABSTRACT

AIMS: The present systematic review and meta-analysis study aimed to investigate the putative relationship between pre-diabetes and neuropathy.

METHODS: Original studies that assessed the association of pre-diabetes patients with neuropathy disorders in humans without setting and country were selected. The methodological quality of the included articles was evaluated using NHLBI quality assessment tools for observational studies. The meta-analysis was conducted using the relevant effect sizes to compare outcomes and the random-effects restricted model. An I² value > 50% and a p < 0.05 indicated substantial heterogeneity. Galbraith plot is demonstrated for heterogeneity. Egger’s and Begg’s test was used to evaluate publication bias. A non-parametric trim-and-fill analysis of publication bias was used to assess the number of missing studies.

RESULTS: According to the standardized mean difference (SMD) of included articles, there was a statistically significant association between pre-diabetes and the occurrence of peripheral neuropathy in the increasing neuropathy assessment metrics (e.g., Impaired unilateral vibration perception, Neuropathic pain, Sensory nerve dysfunction) 0.23[0.14; 0.33] and in decreasing neuropathy assessment metrics (e.g., corneal nerve fiber density, corneal nerve fiber length, warm threshold, cold threshold) -1.04[-1.05; -0.57].

CONCLUSION: Policymakers should give special attention to preventive strategies and effective lifestyle interventions for these patients to reduce the risk of neuropathy and its consequences.

PMID:40626353 | DOI:10.1111/ene.70283

J Chin Med Assoc. 2025 Jul 8. doi: 10.1097/JCMA.0000000000001265. Online ahead of print.

ABSTRACT

BACKGROUND: The Montreal Cognitive Assessment (MoCA) may not be appropriately interpreted in Taiwan because of the lack of large-scale normative data. Moreover, examinees’ demographic characteristics may influence their MoCA scores. However, previous studies have not adequately adjusted for these effects. This study aimed to use regression-based methods to establish demographically adjusted MoCA norms.

METHODS: Participants were recruited from six hospitals and neighboring communities from all geographic areas of Taiwan. Multiple regression analyses were conducted to quantify the effects of age, education, and sex on MoCA total and domain scores, resulting in correction equations and adjusted cutoff scores.

RESULTS: A total of 2,310 cognitively healthy participants were included in the analysis. Age and education significantly affected the total and all domain scores. Sex affected naming, language, and abstract thinking domain scores. Correction equations and corresponding cutoffs were proposed for MoCA total and domain scores to support more precise clinical interpretations.

CONCLUSION: This study provides regression-adjusted norms for the MoCA, improving its accuracy and clinical utility in Taiwan. An adjusted total MoCA score of 23 points is recommended as the cutoff for identifying potential cognitive impairment, with domain-specific cutoffs further supporting individualized interpretation.

PMID:40626349 | DOI:10.1097/JCMA.0000000000001265

Glob Chang Biol. 2025 Jul;31(7):e70341. doi: 10.1111/gcb.70341.

ABSTRACT

The physical environment of nearshore Southern Ocean (coastal Antarctica) is altering rapidly in response to climate change, but also has other long cyclicity due to El Nino Southern Oscillation and Southern Annular Mode. Detecting biological responses to such physical change, which is complex in time and space, is very challenging not least because of remoteness, difficulty of access, frequency of iceberg destruction and short funding cycles but also the paucity of research stations with SCUBA (or ROV) facilities. At one of those few, Rothera, Adelaide Island on the West Antarctic Peninsula, we immersed arrays of artificial substrata (settlement panels) for 1 year repeatedly for over two decades. Whilst many ‘mature assemblages’ are monitored at nearshore sites around the world, there are few of similar duration for recruitment and colonisation. We report the variability in annual biodiversity descriptive statistics with the crucial context of also recording adjacent long (here defined as > 1 decade) term seabed disturbance and biophysical oceanography at Rothera. We ask how variable is annual colonisation, recruitment and early community development in Antarctica’s shallows, what aspect of recruitment changes over two decades and in what way? Of 40 recorded, most species recruiting to our panels at 12 m depth at Adelaide Island (67.568° S, 68.127° W) were rare, comprising cheilostome and cyclostome bryozoans, polychaetes, calcarea and demosponge sponges, hydroid cnidarians and ascidians. The most striking finding was a sustained decrease in total richness of recruits over time, mainly due to loss of rare species. Unlike losses of seasonal sea ice, iceberg disturbance and benthos mortality, such findings are unlikely to be climate-forced responses. This raises important questions of whether this is a chance finding, (the data only spans 20 years), driven by a recent complex of stressors and most of all is losing rare species a wider polar problem?

PMID:40626345 | DOI:10.1111/gcb.70341

J Cosmet Dermatol. 2025 Jul;24(7):e70326. doi: 10.1111/jocd.70326.

ABSTRACT

BACKGROUND: Hyperpigmentation and periorbital dark circles remain challenging dermatological concerns due to their multifactorial etiology, including vascular, pigmentary, and structural components. This study evaluates the efficacy of a novel multi-action topical formulation designed to target hyperpigmentation and skin aging through a synergistic blend of active ingredients.

OBJECTIVE: To assess the objective and clinical effects of a dermatological composition containing niacinamide, arbutin, tranexamic acid, ubiquinone, a DHEA-like component, curcuma oily extract, and marine exopolysaccharides on periorbital hyperpigmentation and skin quality.

METHODS: Subjects with visible under-eye dark circles applied the formulation twice daily over a treatment period of 6 weeks. The evaluation included instrumental quantitative assessments of skin pigmentation along with standardized imaging. A separate safety study and cosmetic efficacy questionnaire of patient experience were completed.

RESULTS: The formulation demonstrated statistically significant improvements in pigmentation intensity. The twice daily use of the under-eye serum resulted in an average overall reduction in under-eye hyperpigmentation of 47.94%, with no adverse effects observed.

CONCLUSION: This novel topical composition offers a safe and effective multi-targeted approach for improving periorbital hyperpigmentation and promoting overall skin rejuvenation. The synergistic action of its active components supports its use in clinical and aesthetic dermatology for the treatment of dark circles and age-related changes.

PMID:40626342 | DOI:10.1111/jocd.70326

Clin Trials. 2025 Jul 8:17407745251350289. doi: 10.1177/17407745251350289. Online ahead of print.

ABSTRACT

Dose selection is a key component of drug development, yet inadequate dose-response characterization remains a major challenge, contributing to late-stage attrition and post-marketing regulatory commitments. Effective dose-response characterization for both efficacy and safety supports benefit-risk assessments of therapeutic interventions and relies on two main elements: Trial design and trial analysis. In trial design, selecting an appropriate dose range, determining the number of dose levels, and ensuring proper dose spacing are essential to capture both the steep and plateau regions of a dose-response curve. Adaptive trial designs provide additional flexibility to address uncertainties during trial planning and execution, increasing the chances of identifying optimal doses and improving trial efficiency. In trial analysis, modeling approaches support dose-response characterization by utilizing data across dose levels to fit a continuous curve rather than analyzing each dose level separately. Model-based methods, such as Emax modeling or MCP-Mod (which combines multiple comparison procedures and modeling), incorporate assumptions about the dose-response relationship to improve the precision of dose-response and target dose estimation. Additional precision can often be achieved by modeling dose-exposure-response relationships, recognizing that exposure (e.g. drug concentration in the plasma) often mediates the relationship between dose and clinical response. Dose-exposure response models may also enable the prediction of dose-response relationships of alternative regimens (e.g. when applying a different frequency of administration than the tested ones). This article reviews key considerations for the design and analysis of dose-response trials, focusing on strategies to improve decision-making and regulatory alignment.

PMID:40626332 | DOI:10.1177/17407745251350289

JAMIA Open. 2025 Jul 4;8(4):ooaf059. doi: 10.1093/jamiaopen/ooaf059. eCollection 2025 Aug.

ABSTRACT

OBJECTIVES: To determine whether a novel digital tool, the Community Vulnerability Compass (CVC), built using large datasets, can accurately measure neighborhood- and individual-level social determinants of health (SDOH) at scale. Existing SDOH indexes fall short of this dual requirement.

MATERIALS AND METHODS: Setting: A cross-sectional study by Parkland Health (Parkland) and Parkland Center for Clinical Innovation (PCCI) to design, build, deploy, and validate CVC in Dallas County/across Texas (2018-2024). Data Sources: Parkland Electronic Health Records; population-level data from diverse national datasets. Statistical Analysis: CVC’s Community Vulnerability Index (CVI), and 4 subindexes were used to classify all 18 638 Texas census-block groups as Very-High, High, Moderate, Low, and Very-Low social vulnerability. Individuals were assigned the vulnerability of their home address census-block group. CVC’s classifications were compared against 3 existing SDOH neighborhood tools (Area Deprivation Index [ADI], Social Vulnerability Index [SVI], or Environmental Justice Index [EJI]) and validated against individual-level SDOH screening tools or Z-code documentation. Spearman rank correlation was used for neighborhood-level comparisons and precision/recall, for individual-level comparisons.

RESULTS: Neighborhood-level CVI measurement of social vulnerability strongly correlated with EJI (r = 0.83), SVI (r = 0.82), and ADI (r = 0.79). Individual-level CVI measurement had higher recall than ADI (68% vs 39%, respectively; P < .001) and high recall across self-reported SDOH (77%-79.6%). Precision was highest for food needs (75.1%); lowest for safety needs (1.2%).

DISCUSSION: CVC measured a cross-cutting range of neighborhood social vulnerabilities and accurately approximated individual-level SDOH, outperforming existing indexes.

CONCLUSION: CVC can be leveraged as an accurate and scalable SDOH digital measurement tool.

PMID:40626323 | PMC:PMC12231598 | DOI:10.1093/jamiaopen/ooaf059

Int J Chron Obstruct Pulmon Dis. 2025 Jul 3;20:2143-2159. doi: 10.2147/COPD.S504774. eCollection 2025.

ABSTRACT

PURPOSE: This study aims to comparatively evaluate the efficacy and safety profiles of biologic agents targeting type 2 inflammation in COPD.

METHODS: As of September 1, 2024, we identified and screened eight clinical studies evaluating biologic agents targeting type 2 inflammation for COPD treatment from multiple databases. Following data extraction, we conducted a network meta-analysis using R software to indirectly compare the efficacy and safety profiles of the five included biologic agents, incorporating visualization of the analytical results.

RESULTS: In COPD patients with elevated eosinophil levels (peripheral blood eosinophil count ≥200 cells/μL), dupilumab demonstrated significant therapeutic efficacy by: (1) reducing the annualized rate of acute exacerbations (versus placebo: -0.44; 95% CI -0.77 to -0.10), (2) decreasing SGRQ total scores (versus placebo: -3.41; 95% CI -6.00 to -0.82), and (3) increasing pre-bronchodilator FEV1 (versus placebo: 0.06 L; 95% CI 0.00 to 0.12). Benralizumab also showed clinical benefits in reducing acute exacerbation rates (10 mg versus placebo: -0.21; 95% CI -0.39 to -0.04) and improving SGRQ scores (100 mg versus placebo: -1.70; 95% CI -3.35 to -0.04). Furthermore, all five biologic agents evaluated in this network meta-analysis exhibited favorable safety profiles.

CONCLUSION: This NMA demonstrates that both dupilumab and benralizumab show statistically significant efficacy in COPD management, particularly among patients with eosinophilic inflammation. And these biological agents maintain favorable safety profiles. Future research should focus on large-scale multicenter clinical trials, biomarker-based patient stratification, optimization of drug delivery regimens, and development of multi-target combination therapies.

PMID:40626315 | PMC:PMC12232952 | DOI:10.2147/COPD.S504774

Int J Chron Obstruct Pulmon Dis. 2025 Jul 3;20:2173-2182. doi: 10.2147/COPD.S518455. eCollection 2025.

ABSTRACT

BACKGROUND: The gut-lung axis, representing the communication between gut microbiota and the lungs, has been hypothesized to influence chronic obstructive pulmonary disease (COPD) development through modulation of the immune response. However, the causal role of gut microbiota in COPD and the potential mediating role of immune cells remain largely undetermined. This study aimed to uncover the causal relationship between gut microbiota and COPD and explore the potential mediating role of immune cells in this connection.

METHODS: This study employed a two-step Mendelian randomization (MR) analysis to investigate the causal effect of gut microbiota on COPD and explore the potential mediating role of immune cells in this relationship. The inverse variance weighted method served as the primary MR analysis method.

RESULTS: MR analyses revealed statistically significant genetic associations between 28 gut microbiota and COPD. Among these, the genus Coprococcus demonstrated the strongest causal effect on COPD risk, exhibiting a significant positive association (odds ratio (OR) = 1.18, 95% confidence interval (CI): 1.03-1.36, P = 0.03). Additionally, 15 immune cell traits displayed significant associations with Coprococcus. Notably, CD27 expressed on IgD^– CD38^– B cells emerged as a potential contributor to COPD development (OR = 1.04, 95% CI: 1.00-1.07, P = 0.03). We further explored the potential mediating effect of CD27 on IgD^– CD38^– B cells in the relationship between Coprococcus and COPD.

CONCLUSION: Our MR analysis provided evidence for a causal association between gut microbiota and COPD, potentially mediated by immune cells.

PMID:40626314 | PMC:PMC12232946 | DOI:10.2147/COPD.S518455

Front Pharmacol. 2025 Jun 23;16:1554650. doi: 10.3389/fphar.2025.1554650. eCollection 2025.

ABSTRACT

INTRODUCTION: Drug safety has increasingly become a serious public health problem that threatens health and damages social economy. The common detection methods have the problem of high false positive rate. This study aims to introduce deep learning models into the adverse drug reaction (ADR) signal detection and compare different methods.

METHODS: The data are based on adverse events collected by Center for ADR Monitoring of Guangdong. Traditional statistical methods were used for data preliminary screening. We transformed data into free text, extracted text information and made classification prediction by using the Long Short-Term Memory (LSTM) model. We compared it with the existing signal detection methods, including Logistic Regression, Random Forest, K-NearestNeighbor, and Multilayer Perceptron. The feature importance of the included variables was analyzed.

RESULTS: A total of 2,376 ADR signals were identified between January 2018 and December 2019, comprising 448 positive signals and 1,928 negative signals. The sensitivity of the LSTM model based on free text reached 95.16%, and the F1-score was 0.9706. The sensitivity of Logistic Regression model based on feature variables was 86.83%, and the F1-score was 0.9063. The classification results of our model demonstrate superior sensitivity and F1-score compared to traditional methods. Several important variables “Reasons for taking medication”, “Serious ADR scenario 4”, “Adverse reaction analysis 5”, and “Dosage” had an important influence on the result.

CONCLUSION: The application of deep learning models shows potential to improve the detection performance in ADR monitoring.

PMID:40626311 | PMC:PMC12230008 | DOI:10.3389/fphar.2025.1554650