67 research outputs found

    Long-term Patterns of Self-reported Opioid Use, VACS Index, and Mortality Among People with HIV Engaged in Care.

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    Longitudinal analyses of opioid use and overall disease severity among people with HIV (PWH) are lacking. We used joint-trajectory and Cox proportional hazard modeling to examine the relationship between self-reported opioid use and the Veterans Aging Cohort Study (VACS) Index 2.0, a validated measure of disease severity and mortality, among PWH engaged in care. Using data from 2002 and 2018, trajectory modeling classified 20% of 3658 PWH in low (i.e., lower risk of mortality), 40% in moderate, 28% in high, and 12% in extremely high VACS Index trajectories. Compared to those with moderate VACS Index trajectory, PWH with an extremely high trajectory were more likely to have high, then de-escalating opioid use (adjusted odds ratio [AOR], 95% confidence interval [CI] 5·17 [3·19-8·37]) versus stable, infrequent use. PWH who report high frequency opioid use have increased disease severity and mortality risk over time, even when frequency of opioid use de-escalates

    Trajectories of Self-Reported Opioid Use Among Patients With HIV Engaged in Care: Results From a National Cohort Study.

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    BACKGROUND: No prior studies have characterized long-term patterns of opioid use regardless of source or reason for use among patients with HIV (PWH). We sought to identify trajectories of self-reported opioid use and their correlates among a national sample of PWH engaged in care. SETTING: Veterans Aging Cohort Study, a prospective cohort including PWH receiving care at 8 US Veterans Health Administration (VA) sites. METHODS: Between 2002 and 2018, we assessed past year opioid use frequency based on self-reported "prescription painkillers" and/or heroin use at baseline and follow-up. We used group-based trajectory models to identify opioid use trajectories and multinomial logistic regression to determine baseline factors independently associated with escalating opioid use compared to stable, infrequent use. RESULTS: Among 3702 PWH, we identified 4 opioid use trajectories: (1) no lifetime use (25%); (2) stable, infrequent use (58%); (3) escalating use (7%); and (4) de-escalating use (11%). In bivariate analysis, anxiety; pain interference; prescribed opioids, benzodiazepines and gabapentinoids; and marijuana use were associated with escalating opioid group membership compared to stable, infrequent use. In multivariable analysis, illness severity, pain interference, receipt of prescribed benzodiazepine medications, and marijuana use were associated with escalating opioid group membership compared to stable, infrequent use. CONCLUSION: Among PWH engaged in VA care, 1 in 15 reported escalating opioid use. Future research is needed to understand the impact of psychoactive medications and marijuana use on opioid use and whether enhanced uptake of evidence-based treatment of pain and psychiatric symptoms can prevent escalating use among PWH

    A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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    Accounting for historical injustices in mathematical models of infectious disease transmission: An analytic overview

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    Differences in infectious disease risk, acquisition, and severity arise from intersectional systems of oppression and resulting historical injustices that shape individual behavior and circumstance. We define historical injustices as distinct events and policies that arise out of intersectional systems of oppression. We view historical injustices as a medium through which structural forces affect health both directly and indirectly, and are thus important to study in the context of infectious disease disparities. In this critical analysis we aim to highlight the importance of incorporating historical injustices into mathematical models of infectious disease transmission and provide context on the methodologies to do so. We offer two illustrations of elements of model building (i.e., parameterization, validation and calibration) that can allow for a better understanding of health disparities in infectious disease outcomes. Mathematical models that do not recognize the historical forces that underlie infectious disease dynamics inevitably lead to the individualization of our focus and the recommendation of untenable individual-behavioral prescriptions to address the burden of infectious disease
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