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To estimate the effects on pain of two medications (low-dose naltrexone and gabapentin) compared to placebo among people with HIV (PWH) with heavy alcohol use and chronic pain. We conducted a pilot, randomized, double-blinded, 3-arm study of PWH with chronic pain and past-year heavy alcohol use in 2021. Participants were recruited in St. Petersburg, Russia, and randomized to receive daily low-dose naltrexone (4.5mg), gabapentin (up to 1800mg), or placebo. The two primary outcomes were change in self-reported pain severity and pain interference measured with the Brief Pain Inventory from baseline to 8 weeks. Participants (N = 45, 15 in each arm) had the following baseline characteristics: 64% male; age 41 years (SD±7); mean 2 (SD±4) heavy drinking days in the past month and mean pain severity and interference were 3.2 (SD±1) and 3.0 (SD±2), respectively. Pain severity decreased for all three arms. Mean differences in change in pain severity for gabapentin vs. placebo, and naltrexone vs. placebo were -0.27 (95% confidence interval [CI] -1.76, 1.23; p = 0.73) and 0.88 (95% CI -0.7, 2.46; p = 0.55), respectively. Pain interference decreased for all three arms. Mean differences in change in pain interference for gabapentin vs. placebo, and naltrexone vs. placebo was 0.16 (95% CI -1.38, 1.71; p = 0.83) and 0.40 (95% CI -1.18, 1.99; p = 0.83), respectively. Neither gabapentin nor low-dose naltrexone appeared to improve pain more than placebo among PWH with chronic pain and past-year heavy alcohol use. ClinicalTrials.gov (NCT4052139).

Background Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately. Methods In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP. Results In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered. Conclusions We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years.

Biomarkers of monocyte activation (soluble CD14 [sCD14]), inflammation (interleukin-6 [IL-6]), and altered coagulation (D-dimer) are associated with increased mortality risk in people with HIV. The objective of the Russia Alcohol Research Collaboration on HIV/AIDS (ARCH) study was to evaluate the association between heavy alcohol use and inflammatory biomarkers over time. The study sought antiretroviral therapy naive participants with HIV (n = 350) and assessed them at baseline, 12 and 24 months. Linear mixed effects models were used to determine whether heavy drinking (self-report augmented by phosphatidylethanol [PEth], an alcohol biomarker) was longitudinally associated with IL-6, sCD14 and D-dimer adjusting for potential confounders (e.g., demographics, HIV factors, comorbid conditions). Participants' baseline characteristics were as follows: 71% male; mean age of 34 years; 87% self-reported hepatitis C; and 86% current smokers. Mean log10 (HIV RNA) was 4.3 copies/mL. Heavy alcohol use, based on National Institute of Alcohol Abuse and Alcoholism risky drinking criteria and PEth (versus non-heavy alcohol use) was associated with higher sCD14 (adjusted mean difference 125 ng/mL [95% CI: 42, 209]), IL-6 (ratio of means 1.35 [95% CI: 1.17, 1.55] pg/mL), and D-dimer (ratio of means 1.20 [95% CI: 1.06, 1.37] ug/mL) across the two-year follow-up. Among HIV+ adults, current heavy alcohol use is associated with higher sCD14, IL-6 and D-dimer over time. Since these biomarkers are associated with mortality, interventions to mitigate effects of heavy drinking on these immune processes merit consideration.

Liver disease in people living with HIV co-infected with hepatitis C virus is a source of morbidity and mortality in Russia. HIV accelerates liver fibrosis in the setting of HCV co-infection and alcohol use. Zinc deficiency is common among people living with HIV and may be a factor that facilitates the underlying mechanisms of liver fibrosis. We investigated the association between zinc deficiency and advanced liver fibrosis in a cohort of HIV/HCV co-infected persons reporting heavy drinking in Russia. This is a secondary data analysis of baseline data from 204 anti-retroviral treatment naïve HIV/HCV co-infected Russians with heavy drinking that were recruited into a clinical trial of zinc supplementation. The primary outcome of interest in this cross-sectional study was advanced liver fibrosis. Zinc deficiency, the main independent variable, was defined as plasma zinc <0.75 mg/L. Exploratory analyses were performed examining continuous zinc levels and fibrosis scores. Analyses were conducted using multivariable regression models adjusted for potential confounders. The prevalence of advanced liver fibrosis was similar for those with zinc deficiency compared to those with normal zinc levels, (27.7% vs. 23.0%, respectively). We did not detect an association between zinc deficiency and advanced liver fibrosis in the adjusted regression model (aOR: 1.28, 95% CI: 0.62-2.61, p = 0.51) nor in exploratory analyses. In this cohort of Russians with HIV/HCV co-infection, who are anti-retroviral treatment naïve and have heavy alcohol use, we did not detect an association between zinc deficiency or zinc levels and advanced liver fibrosis.

Tuberculosis (TB) is the leading cause of death among people living with HIV (PLWH), and current evidence suggests that heavy alcohol users have an increased risk of developing TB disease compared to non-drinkers. Not known is whether the increased risk for TB disease among alcohol users may reflect higher rates of latent TB infection (LTBI) among this population. We assessed the latent TB infection prevalence based on tuberculin skin testing (TST) and examined association with current alcohol use among HIV-infected persons on antiretroviral therapy (ART) in south-western Uganda. We included PLWH at the Mbarara Regional Hospital HIV clinic, who were either current alcohol consumers (prior 3 months) or past year abstainers (2:1 enrolment ratio). Participants were recruited for a study of isoniazid preventive therapy for LTBI. TST was performed using 5 tuberculin units of purified protein derivative. The primary outcome was a positive TST reading (≥5mm induration), reflecting LTBI. We used logistic regression analyses to assess the cross-sectional association between self-reported current alcohol use and a positive TST. Of the 295 of 312 (95%) who returned for TST reading, 63% were females and 63% were current alcohol drinkers. The TST positive prevalence was 27.5% (95% confidence interval [CI]: 22.6% - 32.9%). The odds of a positive TST for current alcohol users compared to abstainers was 0.76 (95% CI: 0.41, 1.41), controlling for gender, age, body mass index, history of smoking, and prior unhealthy alcohol use. The prevalence of LTBI among PLWH on ART in south-western Uganda was moderate and LTBI poses a risk for future infectious TB. Although alcohol use is common, we did not detect an association between current drinking or prior unhealthy alcohol use and LTBI. Further studies to evaluate the association between LTBI and different levels of current drinking (heavy versus not) are needed.

Background Alcohol use is common among persons living with HIV (PLWH), who often experience chronic pain, yet its impact on pain and opioid misuse is not fully characterized. Methods We assessed associations between hazardous alcohol use and pain interference, defined as the self-reported impact of pain on daily living, pain severity, and risk for opioid misuse among PLWH who were on long-term opioid therapy (LTOT). A cohort was recruited as part of the “Targeting Effective Analgesia in Clinics for HIV” (TEACH) study, a randomized controlled trial to improve LTOT in HIV clinics. The Alcohol Use Disorders Test (AUDIT), Brief Pain Inventory (BPI) and the Current Opioid Misuse Measure (COMM) were administered at both baseline and 12-months. Linear mixed and generalized estimating equation models, incorporating data from both time points, evaluated associations between hazardous alcohol use (AUDIT ≥8) and: pain interference (0–10), pain severity (0–10), and opioid misuse risk (COMM ≥13), adjusting for age, gender, depressive symptoms, use of non-alcohol substances, time-point, and study-arm. Results The sample was comprised of 166 participants, of which 31 (19%) reported hazardous alcohol use. The majority were male (65%), black (72%), and the mean age was 54 (range: 29–77). Hazardous alcohol use was significantly associated with higher pain interference (adjusted mean difference [AMD]: 1.02; 95% CI: 0.08, 1.96) and higher odds of opioid misuse risk (AOR: 3.73, 95% CI: 1.88–7.39), but not pain severity (AMD: 0.47, 95% CI: − 0.35, 1.29). Conclusions Hazardous alcohol use was associated with greater functional impairment in daily living from their pain and higher odds for prescription opioid misuse in this study of PLWH on LTOT. Providers should be attentive to alcohol use among PLWH who are prescribed opioids given associations with pain and opioid misuse. Trial registration ClinicalTrials.gov NCT02564341 (Intervention, September 30, 2015) and NCT02525731 (Patient Cohort, August 17, 2015). Both prospectively registered.

Introduction More than 70% of new HIV diagnoses in the United States were among men who have sex with men (MSM) in 2019. Pre‐exposure prophylaxis (PrEP) is a transformative innovation for reducing human immunodeficiency virus (HIV) infections. Structural stigma against sexual minorities, including in the form of state‐level policies, may affect PrEP implementation. We evaluated whether lower structural stigma reflected by earlier year of state same‐sex marriage legalization was associated with increased male PrEP prescriptions and male PrEP‐to‐need ratio (PnR), a ratio of PrEP prescriptions to new HIV diagnoses. Methods We used 2012−2019 AIDSVu data on male PrEP prescriptions and male PnR in each US state and year. We used generalized estimating equations to evaluate the relationship between the timing of implementing state same‐sex marriage policies and the outcomes of male PrEP prescriptions per 100,000 people and the male PnR. We adjusted for calendar year, Medicaid expansion and the political party of the governor in each state. Results State implementation of same‐sex marriage policies in earlier, relative to later, periods was associated with increases in the rate of male PrEP prescriptions and in the male PnR. Specifically, implementing state same‐sex marriage policies between 2004 and 2011 and between 2012 and 2013 were each associated with greater rates of male PrEP prescriptions relative to implementing same‐sex marriage policies between 2014 and 2015. Implementing state same‐sex marriage policies between 2004 and 2011 as well as between 2012 and 2013 were both significantly associated with a greater male PnR relative to implementing same‐sex marriage policies between 2014 and 2015. By 2019, the difference in male PrEP prescriptions was 137.9 (97.3−175.5) per 100,000 in states that implemented same‐sex marriage in 2004−2011 and 27.2 (23.3−30.5) per 100,000 in states that implemented same‐sex marriage from 2012 to 2013, relative to states that implemented same‐sex marriage in 2014−2015. Conclusions Earlier implementation of state same‐sex marriage policies was associated with greater rates of male PrEP prescriptions. Reducing state‐level structural stigma may improve HIV prevention among MSM in the United States.

Background Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years. Conclusions Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Background The HEALing (Helping to End Addiction Long-term SM ) Communities Study (HCS) is a multi-site parallel group cluster randomized wait-list comparison trial designed to evaluate the effect of the Communities That Heal (CTH) intervention compared to usual care on opioid overdose deaths. Covariate-constrained randomization (CCR) was applied to balance the community-level baseline covariates in the HCS. The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the HCS setting. We conducted a simulation study to evaluate type I error rates and power for model-based and permutation tests for the multi-site HCS as well as for a subgroup analysis of a single state (Massachusetts). We also investigated whether the maximum degree of imbalance in the CCR design has an impact on the performance of the tests. Methods The primary outcome, the number of opioid overdose deaths, is count data assessed at the community level that will be analyzed using a negative binomial regression model. We conducted a simulation study to evaluate the type I error rates and power for 3 tests: (1) Wald-type t -test with small-sample corrected empirical standard error estimates, (2) Wald-type z -test with model-based standard error estimates, and (3) permutation test with test statistics calculated by the difference in average residuals for the two groups. Results Our simulation results demonstrated that Wald-type t -tests with small-sample corrected empirical standard error estimates from the negative binomial regression model maintained proper type I error. Wald-type z -tests with model-based standard error estimates were anti-conservative. Permutation tests preserved type I error rates if the constrained space was not too small. For all tests, the power was high to detect the hypothesized 40% reduction in opioid overdose deaths for the intervention vs. comparison group both for the overall HCS and the subgroup analysis of Massachusetts (MA). Conclusions Based on the results of our simulation study, the Wald-type t -test with small-sample corrected empirical standard error estimates from a negative binomial regression model is a valid and appropriate approach for analyzing cluster-level count data from the HEALing Communities Study. Trial registration ClinicalTrials.gov http://www.clinicaltrials.gov ; Identifier: NCT04111939

Globally, persons with HIV infection, depression and substance use disorders have a higher smoking prevalence and smoke more heavily than other populations. These associations have not been explored among Russian smokers with HIV infection and substance use disorders. The purpose of this study was to examine the relationship between the presence of depressive symptoms and smoking outcomes in an HIV-positive cohort of Russian smokers with a history of substance use disorders (alcohol and/or drug use disorders). We performed a cross-sectional secondary data analysis of a cohort of HIV-positive regular smokers with a history of substance use disorders recruited in St. Petersburg, Russia in 2012-2015. The primary outcome was heavy smoking, defined as smoking > 20 cigarettes per day. Nicotine dependence (moderate-very high) was a secondary outcome. The main independent variable was a high level of depressive symptoms in the past 7 days (defined as CES-D > = 24). We used multivariable logistic regression to examine associations between depressive symptoms and the outcomes, controlling for age, sex, education, income, running out of money for housing/food, injection drug use, and alcohol use measured by the AUDIT. Among 309 regular smokers, 79 participants (25.6%) had high levels of depressive symptoms, and 65 participants (21.0%) were heavy smokers. High levels of depressive symptoms were not significantly associated with heavy smoking (adjusted odds ratio [aOR] 1.50, 95% CI 0.78-2.89) or with moderate-very high levels of nicotine dependence (aOR 1.35, 95% CI 0.75-2.41). This study did not detect an association between depressive symptoms and smoking outcomes among HIV-positive regular smokers in Russia.