Tag: pubmed

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

BMC Oral Health. 2025 Feb 18;25(1):249. doi: 10.1186/s12903-025-05650-x.

ABSTRACT

BACKGROUND: Global studies have reported varying malocclusion prevalence, highlighting its dependence on age, gender, and population characteristics. This study aims to determine the prevalence of malocclusion in randomly selected public school children and to identify the most common type of malocclusion in this population.

METHODS: This study is a cross-sectional study covering school-age children in Bolu, Turkey A total of 1144 students (591 females, 553 males) aged 10-12 participated in this study. Orthodontic anomalies such as anterior and posterior crossbite, overjet, overbite, open bite, deep bite, midline diastema, presence of wedge lateral teeth, crowding, presence of diastema, Angle malocclusion classification, and abnormal habits were recorded in detail. In the statistical analysis, descriptive analyses were performed, Pearson chi-square test was used to evaluate the differences between the groups, and Kappa test was used to determine the intra-observer consistency.

RESULTS: Posterior crossbite prevalence was found to be higher in females than in males. Moderate overjet and deep bite prevalence were found to be higher in males. The most common malocclusion was Class I, followed by Class II Division 2, Class II Division 1, and Class III malocclusions. Abnormal habits were more common in females, with nail-biting being the most common abnormal habit.

CONCLUSIONS: This study provides basic data on orthodontic variables in school-age children. In order to meet the increasing aesthetic and functional needs, more importance should be given to interceptive orthodontic treatments and prevalence studies in this regard.

PMID:39966826 | DOI:10.1186/s12903-025-05650-x

BMC Vet Res. 2025 Feb 18;21(1):76. doi: 10.1186/s12917-025-04545-x.

ABSTRACT

Phoenixin (PNX), well-conserved but newly discovered neuropeptide, is involved in various physiological processes, such as food intake, cardiovascular functions, reproductive functions, and stress regulation. PNX is the predicted ligand of GPR173 receptor, but due to its relatively recent discovery in 2013, there is a lack of studies describing the exact mechanism of action of the peptide. In addition, the protein was not been well-studied in specific organs, particularly in the gastrointestinal tract (GIT) of ruminants, including domestic cattle, which are among the world’s main livestock animals. Therefore, this study aimed to investigate the immunolocalization and quantification of PNX and GPR173 in the GIT of domestic cattle. Study material, including GIT sections of two age groups, calves and adult bulls (n = 6 per group), was obtained from a slaughterhouse. Enzyme-linked immunosorbent assay (ELISA) and immunohistochemical (IHC) analyses were performed. Analyses revealed low levels of PNX in the GIT of both age groups, with localization restricted to epithelial cells across all examined GIT segments, with statistically significant differences between age groups and GIT segments, which may result from the delayed development of forestomachs in calves. On the other hand, GPR173 levels were shown to be higher than those of PNX and to have a wider distribution extending beyond the epithelium to the blood vessels wall and the intrinsic nervous system. This may suggests that PNX is not the only ligand for this receptor. Overall, the results may suggest that both PNX and GPR173 could possibly play protective roles related to the immune response, regulate digestive and absorptive functions, and due to receptor presence in nerve fibres, may play a role in regulating GIT secretion and motility. These findings could potentially facilitate further research into the therapeutic potential of targeting PNX and GPR173 in managing gastrointestinal disorders in domestic cattle and other species, and can also be further used for experimental, clinical or pharmacological research into the treatment of eating disorders not only in humans, but also in farm animals.

PMID:39966825 | DOI:10.1186/s12917-025-04545-x

Cardiovasc Diabetol. 2025 Feb 18;24(1):80. doi: 10.1186/s12933-025-02640-9.

ABSTRACT

BACKGROUND: Digitalization and big health system data open new avenues for targeted prevention and treatment strategies. We aimed to develop and validate prediction models for stroke and myocardial infarction (MI) in patients with type 2 diabetes based on routinely collected high-dimensional health insurance claims and compared predictive performance of traditional regression with state-of-the-art machine learning including deep learning methods.

METHODS: We used German health insurance claims from 2014 to 2019 with 287 potentially relevant literature-derived variables to predict 3-year risk of MI and stroke. Following a train-test split approach, we compared the performance of logistic methods with and without forward selection, LASSO-regularization, random forests (RF), gradient boosting (GB), multi-layer-perceptrons (MLP) and feature-tokenizer transformers (FTT). We assessed discrimination (Areas Under the Precision-Recall and Receiver-Operator Curves, AUPRC and AUROC) and calibration.

RESULTS: Among n = 371,006 patients with type 2 diabetes (mean age: 67.2 years), 3.5% (n = 13,030) had MIs and 3.4% (n = 12,701) strokes. AUPRCs were 0.035 (MI) and 0.034 (stroke) for a null model, between 0.082 (MLP) and 0.092 (GB) for MI, and between 0.061 (MLP) and 0.073 (GB) for stoke. AUROCs were 0.5 for null models, between 0.70 (RF, MLP, FTT) and 0.71 (all other models) for MI, and between 0.66 (MLP) and 0.69 (GB) for stroke. All models were well calibrated.

CONCLUSIONS: Discrimination performance of claims-based models reached a ceiling at around 0.09 AUPRC and 0.7 AUROC. While for AUROC this performance was comparable to existing epidemiological models incorporating clinical information, comparison of other, potentially more relevant metrics, such as AUPRC, sensitivity and Positive Predictive Value was hampered by lack of reporting in the literature. The fact that machine learning including deep learning methods did not outperform more traditional approaches may suggest that feature richness and complexity were exploited before the choice of algorithm could become critical to maximize performance. Future research might focus on the impact of different feature derivation approaches on performance ceilings. In the absence of other more powerful screening alternatives, applying transparent regression-based models in routine claims, though certainly imperfect, remains a promising scalable low-cost approach for population-based cardiovascular risk prediction and stratification.

PMID:39966813 | DOI:10.1186/s12933-025-02640-9