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Patterns of use, dosing, and economic impact of biologic agent use in patients with rheumatoid arthritis: a retrospective cohort study.

BACKGROUND: Variability in dosing and costs of biologics among patients with rheumatoid arthritis (RA) is of interest to healthcare descision-makers. We examined dosing and costs among RA patients newly treated with infliximab or etanercept under conditions of typical clinical practice. METHODS: Integrated pharmacy and medical claims data were obtained from 61 U.S. health plans. RA patients newly treated with infliximab or etanercept between July 1999-June 2002 were selected. A maintenance number of infliximab vials was determined after the "loading period" (2-3 infusions); those with >or= 2 occurrences of an increase in vials or an interval between infusions of <49 days were considered to have had escalated. For etanercept patients, escalation was based on >or= 2 instances of increased average daily dose. Multiple logistic regression analyses were conducted to assess variables associated with dose escalation. RA-related costs at one year post-initiation also were examined; comparisons were made using generalized linear models. RESULTS: A total of 1,548 patients were identified (n = 598 and 950 for infliximab and etanercept respectively). Infliximab recipients were somewhat older (50.5 vs. 46.6 years for etanercept). Nearly 60% of infliximab patients increased their dose at one year, compared to 18% for etanercept. Infliximab patients who escalated dose incurred a 25% increase in mean one-year costs (20,915 dollars vs. 16,713 dollars for no increase; p < 0.0001). Costs among etanercept patients did not substantially differ based on dose escalation (14,482 dollars vs. 13,866 dollars respectively). CONCLUSIONS: Infliximab is associated with higher rates of dose escalation relative to etanercept, which contributes to substantially higher one-year medical costs.

Antibodies, Monoclonal↗

An exploration of district nurses' perception of occupational stress.

Many studies in nurse occupational stress have been carried out on high-dependency areas in general nursing, while community nursing has been neglected. District nurses, however, appear to be under increasing pressure, especially in the light of recent NHS reforms. This study aimed to explore district nurses' experiences in relation to the range and severity of stressful work-related events encountered in district nursing practice. The study was undertaken in a Yorkshire community healthcare NHS trust, with a convenience sample of 50 qualified district nurses, of which 38 successfully completed the questionnaire. The sample included F, G and H grade levels working both full-time and part-time. The research design adopted was a descriptive, non-experimental cross-sectional survey, integrating quantitative and qualitative approaches. Data were collected using the Community Health Nurses' Perceptions of Work-Related Stressors Questionnaire (Walcott-McQuigg and Ervin, 1992). Results revealed that the most stressful aspects of work for this group of district nurses were: work overload; climate of change; nursing patients with complex care needs; lack of teamwork with other departments, and family responsibilities (home/work interface). Results were consistent with much of the evidence reported in the literature, demonstrating that district nurses are a comparatively stressed group of healthcare professionals.

Adult↗

[Pesticide poisoning in Dourados, Mato Grosso do Sul State, Brazil, 1992/2002].

Reports of poisoning and suicide attempts involving pesticides in the micro region of Dourados, Mato Grosso do Sul State, Brazil, from 1992 to 2002, were evaluated, using data from the Integrated Center for Toxicological Surveillance under the State Health Department. A total of 475 reports were made during the period, of which 261 were accidental or occupational poisonings, 203 suicide attempts, and 11 undetermined. Dourados county had the highest prevalence of pesticide poisoning and suicide attempts per 100,000 inhabitants, considering the rural population, and Fatima do Sul the second highest prevalence of suicides within the micro region. Significant correlations were found between poisoning and suicide (r = 0.60; p < 0.05) and between poisoning and temporary crop area as a percentage of the county's total area (r = 0.68; p < 0.05). Poisoning occurred predominantly in men (87.0%), but the percentage of suicide attempts by men and women were similar (53 and 47.0%, respectively). Poisonings occurred mostly from October to March and the organophosphate insecticides monocrotophos and methamidophos were the main pesticides involved.

Adolescent↗

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer&#x2019;s disease↗