Objective: Despite the high prevalence of alcohol use disorder (AUD) in the United States, limited research is focused on the associations among AUD, pain, and opioids/benzodiazepine use. In addition, little is known regarding individuals with a history of AUD and their potential risk for pain diagnoses, pain prescriptions, and subsequent misuse. Moreover, the potential risk of pain diagnoses, prescriptions, and subsequent misuse among individuals with a history of AUD is not well known. The objective was to develop a tailored dataset by linking data from 2 New York State (NYS) administrative databases to investigate a series of hypotheses related to AUD and painful medical disorders.
Methods: Data from the NYS Office of Addiction Services and Supports (OASAS) Client Data System (CDS) and Medicaid claims data from the NYS Department of Health Medicaid Data Warehouse (MDW) were merged using a stepwise deterministic method. Multiple patient-level identifier combinations were applied to create linkage rules. We included patients aged 18 and older from the OASAS CDS who initially entered treatment with a primary substance use of alcohol and no use of opioids between January 1, 2003, and September 23, 2019. This cohort was then linked to corresponding Medicaid claims.
Results: A total of 177,685 individuals with a primary AUD problem and no opioid use history were included in the dataset. Of these, 37,346 (21.0%) patients had an OUD diagnosis, and 3,365 (1.9%) patients experienced an opioid overdose. There were 121,865 (68.6%) patients found to have a pain condition.
Conclusion: The integrated database allows researchers to examine the associations among AUD, pain, and opioids/benzodiazepine use, and propose hypotheses to improve outcomes for at-risk patients. The findings of this study can contribute to the development of a prognostic prediction model and the analysis of longitudinal outcomes to improve the care of patients with AUD.
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http://dx.doi.org/10.1016/j.jbi.2023.104443 | DOI Listing |
Viruses
December 2024
Wadsworth Center, David Axelrod Institute, New York State Department of Health, Albany, NY 12208, USA.
A historical perspective of more than one hundred years of influenza surveillance in New York State demonstrates the progression from anecdotes and case counts to next-generation sequencing and electronic database management, greatly improving pandemic preparedness and response. Here, we determined if influenza virologic surveillance at the New York State public health laboratory (NYS PHL) tests sufficient specimen numbers within preferred confidence limits to assess situational awareness and detect novel viruses that pose a pandemic risk. To this end, we analyzed retrospective electronic data on laboratory test results for the influenza seasons 1997-1998 to 2021-2022 according to sample sizes recommended in the Influenza Virologic Surveillance Right Size Roadmap issued by the Association of Public Health Laboratories and Centers for Disease Control and Prevention.
View Article and Find Full Text PDFVaccines (Basel)
November 2024
U.S. Centers for Disease Control and Prevention, Global Immunization Division, Global Health Center, Atlanta, GA 30329, USA.
Uganda's Integrated Child Health Day (ICHD) initiative aims to improve children's access to vaccinations. Although widely used as a catch-up vaccination strategy, the effectiveness of the ICHD program in increasing immunization coverage, especially among vulnerable populations, has not been recently evaluated. This study assessed the reach and uptake of ICHD for immunizations in Uganda.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science, University of Bristol, Bristol BS8 1QU, UK.
Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions.
View Article and Find Full Text PDFPharmaceutics
December 2024
University of Belgrade-Faculty of Chemistry, Studentski trg 12-16, 11000 Belgrade, Serbia.
Background/objectives: Clofazimine (CFZ) is a Biopharmaceutics Classification System (BCS) II drug introduced in the US market in 1986 for the treatment of leprosy. However, CFZ was later withdrawn from the market due to its extremely low aqueous solubility and low absorption. In the literature, the intrinsic solubility of CFZ has been estimated to be <0.
View Article and Find Full Text PDFPharmaceutics
December 2024
Wits Advanced Drug Delivery Platform, Department of Pharmacy and Pharmacology, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, 7 York Road, Parktown, Johannesburg 2193, South Africa.
The effectiveness of paclitaxel (PTX) in treating non-small-cell lung carcinoma (NSCLC) is restricted by its poor pharmacokinetic profile and side effects. This limitation stems from the lack of a suitable delivery vector to efficiently target cancer cells. Therefore, there is a critical need to develop an efficient carrier for the optimised delivery of PTX in NSCLC therapy.
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