Category: Statistics

Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2025 Jan;37(1):17-22. doi: 10.3760/cma.j.cn121430-20240103-00009.

ABSTRACT

OBJECTIVE: To analyze the clinical characteristics of elderly patients with sepsis, identify the key factors affecting their clinical outcomes, construct a death risk assessment scale for elderly patients with sepsis, and evaluate its predictive value.

METHODS: A retrospective case-control study was conducted. The clinical data of sepsis patients admitted to intensive care unit (ICU) of the First Affiliated Hospital of Wenzhou Medical University from September 2021 to September 2023 were collected, including basic information, clinical characteristics, and clinical outcomes. The patients were divided into non-elderly group (age ≥ 65 years old) and elderly group (age < 65 years old) based on age. Additionally, the elderly patients were divided into survival group and death group based on their 30-day survival status. The clinical characteristics of elderly patients with sepsis were analyzed. Univariate and multivariate Logistic regression analyses were used to screen the independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed. The regression equation was simplified, and the death risk assessment scale was established. The predictive value of different scores for the prognosis of elderly patients with sepsis was compared.

RESULTS: (1) A total of 833 patients with sepsis were finally enrolled, including 485 in the elderly group and 348 in the non-elderly group. Compared with the non-elderly group, the elderly group showed significantly lower counts of lymphocyte, T cell, CD8⁺ T cell, and the ratio of T cells and CD8⁺ T cells [lymphocyte count (×10⁹/L): 0.71 (0.43, 1.06) vs. 0.83 (0.53, 1.26), T cell count (cells/μL): 394.0 (216.0, 648.0) vs. 490.5 (270.5, 793.0), CD8⁺ T cell count (cells/μL): 126.0 (62.0, 223.5) vs. 180.0 (101.0, 312.0), T cell ratio: 0.60 (0.48, 0.70) vs. 0.64 (0.51, 0.75), CD8⁺ T cell ratio: 0.19 (0.13, 0.28) vs. 0.24 (0.16, 0.34), all P < 0.01], higher natural killer cell (NK cell) count, acute physiology and chronic health evaluation II (APACHE II) score, ratio of invasive mechanical ventilation (IMV) during hospitalization, and 30-day mortality [NK cell count (cells/μL): 112.0 (61.0, 187.5) vs. 95.0 (53.0, 151.0), APACHE II score: 16.00 (12.00, 21.00) vs. 13.00 (8.00, 17.00), IMV ratio: 40.6% (197/485) vs. 31.9% (111/348), 30-day mortality: 28.9% (140/485) vs. 19.5% (68/348), all P < 0.05], and longer length of ICU stay [days: 5.5 (3.0, 10.0) vs. 5.0 (3.0, 8.0), P < 0.05]. There were no statistically significant differences in the levels of inflammatory markers such as C-reactive protein (CRP), procalcitonin (PCT), tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), and interleukins (IL-2, IL-4, IL-6, IL-10) between the two groups. (2) In 485 elderly patients with sepsis, 345 survived in 30 days, and 140 died with the 30-day mortality of 28.9%. Compared with the survival group, the patients in the death group were older, and had lower body mass index (BMI), white blood cell count (WBC), PCT, platelet count (PLT) and higher IL-6, IL-10, N-terminal pro-brain natriuretic peptide (NT-proBNP), total bilirubin (TBil), blood lactic acid (Lac), and ratio of in-hospital IMV and continuous renal replacement therapy (CRRT). Multivariate Logistic regression analysis indicated that BMI [odds ratio (OR) = 0.783, 95% confidence interval (95%CI) was 0.678-0.905, P = 0.001], IL-6 (OR = 1.073, 95%CI was 1.004-1.146, P = 0.036), TBil (OR = 1.009, 95%CI was 1.000-1.018, P = 0.045), Lac (OR = 1.211, 95%CI was 1.072-1.367, P = 0.002), and IMV during hospitalization (OR = 6.181, 95%CI was 2.214-17.256, P = 0.001) were independent risk factors for 30-day death in elderly patients with sepsis, and the regression equation was constructed (Logit P = 1.012-0.244×BMI+0.070×IL-6+0.009×TBil+0.190×Lac+1.822×IMV). The regression equation was simplified to construct a death risk assessment scale, namely BITLI score. Receiver operator characteristic curve (ROC curve) analysis showed that the area under the ROC curve (AUC) of BITLI score for predicting death risk was 0.852 (95%CI was 0.769-0.935), and it was higher than APACHE II score (AUC = 0.714, 95%CI was 0.623-0.805) and sequential organ failure assessment (SOFA) score (AUC = 0.685, 95%CI was 0.578-0.793). The determined cut-off value of BITLI score was 1.50, while achieving a sensitivity of 83.3% and specificity of 74.0%.

CONCLUSIONS: Elderly patients with sepsis often have reduced lymphocyte counts, severe conditions, and poor prognosis. BMI, IL-6, TBil, Lac, and IMV during hospitalization were independent risk factors for 30-day death in elderly patients with sepsis. The BITLI score constructed based above risk factors is more precise and reliable than traditional APACHE II and SOFA scores in predicting the outcomes of elderly patients with sepsis.

PMID:39968581 | DOI:10.3760/cma.j.cn121430-20240103-00009

Stud Health Technol Inform. 2025 Feb 18;322:89-90. doi: 10.3233/SHTI250031.

ABSTRACT

Canada’s healthcare system faces a critical choice between two futures: “Cyborgville,” driven by advanced medical technologies and AI, or a wellness-focused approach inspired by Blue Zones, which emphasize healthy lifestyles and environments. This commentary paper explores the benefits and challenges of each path. Blue Zones promote longevity through natural practices like plant-based diets and physical activity but face adaptation challenges in Canada’s diverse climate and culture. Cyborg technologies offer cutting-edge healthcare but raise ethical concerns and high costs. Health informatics is key to both models, supporting personalized care, data-driven health interventions, and population management. A balanced, hybrid approach combining Blue Zone principles with technological advancements could provide a sustainable, equitable healthcare system, positioning Canada as a leader in global health innovation.

PMID:39968563 | DOI:10.3233/SHTI250031

Stud Health Technol Inform. 2025 Feb 18;322:83-84. doi: 10.3233/SHTI250028.

ABSTRACT

The increasing prevalence of preventable chronic disease in Canada poses significant challenges to both healthcare budgets and individual financial stability. New treatments and predictive technologies are creating an urgent need to evaluate the impact of these innovations on population health and healthcare costs. This paper explores the use of system dynamics modeling to analyze the effects of artificial intelligence (AI)-driven predictive tools, life-prolonging treatments, and digital behavior change applications on T2D prevalence and healthcare expenditures. Our model simulates three scenarios over a 50-year period, revealing that while AI and novel treatments can reduce complications, they may paradoxically increase T2D prevalence and overall costs unless combined with preventive measures. The study demonstrates the utility of system dynamics models in forecasting the secondary effects of policy decisions, providing policymakers with a valuable tool for evaluating trade-offs and optimizing health outcomes. The findings underscore the need for new tools to effectively manage the evolving landscape of chronic disease treatment and prevention.

PMID:39968560 | DOI:10.3233/SHTI250028

Stud Health Technol Inform. 2025 Feb 18;322:81-82. doi: 10.3233/SHTI250027.

ABSTRACT

Health information technology implementations frequently fail despite extensive research on success factors over the past three decades. This paper introduces the Playing-to-Win Digital Health Strategy Canvas, an adaptation of Martin and Lafley’s framework, tailored for healthcare. The canvas integrates business strategy principles with evidence-based insights to address unique challenges in digital health implementation. Key elements include prioritizing high-risk populations, co-designing solutions with stakeholders, and aligning with the Quintuple Aim to ensure sustainable, impactful outcomes. Developed through systematic reviews and stakeholder consultations, the framework serves as a practical tool for early-career planners and implementers. While promising, further research is needed to optimize its application to scalability and sustainability in complex healthcare systems.

PMID:39968559 | DOI:10.3233/SHTI250027

Stud Health Technol Inform. 2025 Feb 18;322:12-16. doi: 10.3233/SHTI250005.

ABSTRACT

Guided by interviews with end-users and in collaboration with lived-experience advisors, the Fairness Dashboard is being co-designed to promote the standardized and responsible utilization of sociodemographic data in statistical and machine learning models. This initiative aims to mitigate the potential for harm and to advance the equitable and compassionate interpretation of knowledge derived from Artificial Intelligence.

PMID:39968539 | DOI:10.3233/SHTI250005

Infect Dis Model. 2024 Dec 10;10(2):591-627. doi: 10.1016/j.idm.2024.12.005. eCollection 2025 Jun.

ABSTRACT

We analyze COVID-19 surveillance data from Ontario, Canada, using state-space modelling techniques to address key challenges in understanding disease transmission dynamics. The study applies component linear Gaussian state-space models to capture periodicity, trends, and random fluctuations in case counts. We explore the relationships between COVID-19 cases, hospitalizations, workdays, and wastewater viral loads through dynamic regression models, offering insights into how these factors influence public health outcomes. Our analysis extends to multivariate covariance estimation, utilizing a novel methodology to provide time-varying correlation estimates that account for non-stationary data. Results demonstrate the significance of incorporating environmental covariates, such as wastewater data, in improving model robustness and uncovering the complex interplay between epidemiological factors. This work highlights the limitations of simpler models and emphasizes the advantages of state-space approaches for analyzing dynamic infectious disease data. By illustrating the application of advanced modelling techniques, this study contributes to a deeper understanding of disease transmission and informs public health interventions.

PMID:39968529 | PMC:PMC11834045 | DOI:10.1016/j.idm.2024.12.005

Explor Res Clin Soc Pharm. 2025 Jan 20;17:100565. doi: 10.1016/j.rcsop.2025.100565. eCollection 2025 Mar.

ABSTRACT

BACKGROUND: The number of prescription medicines prescribed to older adults is increasing in Ireland and other countries. This is leading to higher out-of-pocket prescription medicine expenditure for older adults, which has several negative consequences including cost-related non-adherence. This study aimed to characterise out-of-pocket prescription medicine payments, and examine their relationship with entitlements, multimorbidity and adherence.

METHODS: This cross-sectional study used 2016 data from a nationally-representative sample of adults in Ireland aged ≥50 years. Descriptive statistics and regression models were used to describe out-of-pocket prescription medicine payments and assess the association between out-of-pocket prescription medicine payments and the following variables: healthcare entitlements, multimorbidity, and cost-related non-adherence.

RESULTS: There were 5,668 eligible participants. Median annual out-of-pocket prescription medicine expenditure was €144 (IQR: €0-€312). A generalised linear model showed that, amongst those with out-of-pocket prescription medicine expenditure, having fewer healthcare entitlements was associated with 4.74 (95%CI: 4.37-5.15) times higher out-of-pocket prescription medicine expenditure. Overall, 1.7% (n = 89) of participants reported cost-related non-adherence in the previous year. A multivariable model examining cost-related non-adherence found a significant association only for those prescribed 4-5 regular medications (compared to 3 medications) (OR: 1.87, 95%CI: 1.02-3.42).

CONCLUSIONS: Those with entitlements to subsidised prescription medicines had much lower out-of-pocket prescription medicine expenditure. This highlights the benefits of expanding healthcare entitlements and ensuring uptake of entitlements by those with eligibility.

PMID:39968511 | PMC:PMC11833648 | DOI:10.1016/j.rcsop.2025.100565

BMC Nurs. 2025 Feb 18;24(1):186. doi: 10.1186/s12912-025-02826-2.

ABSTRACT

BACKGROUND: Medication errors, mainly in the administration phase are, one of the most prevalent and critical problems in healthcare, so it is crucial to examine the factors that influence the incidence of medication administration errors among nurses. Nurses’ burnout caused by emotional exhaustion often results in frequent errors, compromising patient safety. Conversely, nurses’ resilience level has been linked to promoting professional development and enhancing the level of patient safety and care. This study aimed to ascertain whether nurse emotional exhaustion and resilience can predict medication administration errors.

METHODS: A cross-sectional descriptive correlational study was conducted on 272 nurses from February 2024 to April 2024 in the teaching hospitals affiliated to Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. The data of the study was collected through the demographic information questionnaire, the medication administration errors questionnaire, the short version of the Resilience Scale (RS-14), and the emotional exhaustion scale. Data were analyzed using descriptive and analytical statistics such as independent t-test, one-way ANOVA, Pearson’s correlation coefficient, and multiple linear regression in SPSS-22.

RESULTS: nurses’ mean scores for medication administration errors, emotional exhaustion, and resilience were 10.29 ± 10.02, 29.97 ± 7.92, and 56.65 ± 8.28, respectively. The regression model indicated that the rise in resilience, age, and work experiences are associated with decreased levels of medication administration errors as much as 0.42, 0.51, and 0.80 times respectively. This model explained 23% of the variance in medication administration errors in nurses (F = 18.054, p < 0.001).

CONCLUSIONS: The level of resilience among nurses was found to play a very important role not only in preventing medication administration errors but also in preventing nurse emotional exhaustion. Accordingly, teaching positive coping methods when dealing with stressful situations must be given top priority in all healthcare settings to promote nurses’ standard of care, reduce the likelihood of medical errors, and prevent emotional exhaustion. Additionally, nurses must receive continuous, dedicated training on drug knowledge, including side effects, as well as the correct techniques of drug administration.

PMID:39966856 | DOI:10.1186/s12912-025-02826-2

Chiropr Man Therap. 2025 Feb 18;33(1):8. doi: 10.1186/s12998-025-00575-2.

ABSTRACT

BACKGROUND: Evidence has been reported that spinal manipulation therapy (SMT) leads to spine segmental hypoalgesia through neurophysiological and peripheral mechanisms related to regulating inflammatory biomarker function. However, these studies also showed substantial inter-individual variability in the biomarker responses. Such variability may be due to the incomplete understanding of the fundamental effects of force-based manipulations (e.g., patient-specific force-time characteristics) on a person’s physiology in health and disease. This study investigated the short-term effects of distinct SMT force-time characteristics on blood oxidative stress and pro-resolution of inflammation biomarkers.

METHODS: Nineteen healthy adults between 18 and 45 years old were recruited between February and March 2020 before the COVID-19 pandemic and clustered into three groups: control (preload only), target total peak force of 400 N, and 800 N. A validated force-sensing table technology (FSTT^®) determined the SMT force-time characteristics. Blood samples were collected at pre-intervention, immediately after SMT, and 20 min post-intervention. Parameters of the oxidant system (total oxidant status, lipid peroxidation and lipid hydroperoxide), the antioxidant system (total antioxidant capacity and bilirubin), and lipid-derived resolvin D1 were evaluated in plasma and erythrocytes through enzyme-linked immunosorbent assay and colorimetric assays.

RESULTS: The COVID-19 global pandemic impacted recruitment, and our pre-established target sample size could not be reached. As a result, there was a small sample size, which decreased the robustness of the statistical analysis. Despite the limitations, we observed that 400 N seemed to decrease systemic total oxidant status and lipid peroxidation biomarkers. However, 800 N appeared to transitorily increase these pro-oxidant parameters with a further transitory reduction in plasma total antioxidant capacity and resolvin D1 mediator.

CONCLUSION: Despite the small sample size, which elevates the risk of type II error (false negatives), and the interruption of recruitment caused by the pandemic, our findings appeared to indicate that different single SMT force-time characteristics presented contrasting effects on the systemic redox signalling biomarkers and pro-resolution of inflammation mediators in healthy participants. The findings need to be confirmed by further research; however, they provide baseline information and guidance for future studies in a clinical population.

PMID:39966844 | DOI:10.1186/s12998-025-00575-2

BMC Endocr Disord. 2025 Feb 18;25(1):45. doi: 10.1186/s12902-025-01835-1.

ABSTRACT

OBJECTIVE: This systematic review and meta-analysis assess the effects of Ramadan fasting in adolescents with type 1 diabetes mellitus (T1DM), on blood sugar factors such as hemoglobin A1C and problems caused by its lack of control such as hypoglycemia and DKA, and metabolic outcomes.

METHODS: Electronic databases including MEDLINE, Embase, and SINOMED were searched up to February 13, 2024, without language, region, or publication time restrictions. The outcomes were Acute complications, changes in Hemoglobin A1c (HbA1c) and weight changes. Meta-analyses used random-effects models to compute weighted Relative risk (RR) and standard mean differences (SMD). And to check the risk of bias of included studies, the Newcastle-Ottawa scale was used.

RESULTS: Nine studies were included, comprising 458 participants, with studies varying in quality from high to low. Meta-analysis showed no significant reduction in HbA1c levels post-Ramadan (SMD: -0.12; 95% CI: -0.38 to 0.14), indicating minimal impact on long-term glycemic control. The incidence of hypoglycemia was notably high (50.79 events per 100 observations), with hyperglycemia and diabetic ketoacidosis (DKA) also reported but less frequently. The variability in complication rates among studies was significant, reflecting the high heterogeneity across the data. Weight changes during Ramadan were minimal and not statistically significant, suggesting fasting’s negligible effect on body weight among participants.

CONCLUSIONS: Ramadan fasting among adolescents with T1DM does not significantly alter HbA1c levels, suggesting potential feasibility under careful monitoring and management. However, the high incidence of hypoglycemia underscores the need for vigilant glucose monitoring and tailored adjustments to diabetes management plans during fasting periods.

PMID:39966830 | DOI:10.1186/s12902-025-01835-1