The impact of alcohol and illicit substance use on the pharmacokinetics of first-line TB drugs.

BACKGROUND: In South Africa, an estimated 11% of the population have high alcohol use, a major risk factor for TB. Alcohol and other substance use are also associated with poor treatment response, with a potential mechanism being altered TB drug pharmacokinetics. OBJECTIVES: To investigate the impact of alcohol and illicit substance use on the pharmacokinetics of first-line TB drugs in participants with pulmonary TB. METHODS: We prospectively enrolled participants ≥15 years old, without HIV, and initiating drug-susceptible TB treatment in Worcester, South Africa. Alcohol use was measured via self-report and blood biomarkers. Other illicit substances were captured through a urine drug test. Plasma samples were drawn 1 month into treatment pre-dose, and 1.5, 3, 5 and 8 h post-dose. Non-linear mixed-effects modelling was used to describe the pharmacokinetics of rifampicin, isoniazid, pyrazinamide and ethambutol. Alcohol and drug use were tested as covariates. RESULTS: The study included 104 participants, of whom 70% were male, with a median age of 37 years (IQR 27-48). Alcohol use was high, with 42% and 28% of participants having moderate and high alcohol use, respectively. Rifampicin and isoniazid had slightly lower pharmacokinetics compared with previous reports, whereas pyrazinamide and ethambutol were consistent. No significant alcohol use effect was detected, other than 13% higher ethambutol clearance in participants with high alcohol use. Methaqualone use reduced rifampicin bioavailability by 19%. CONCLUSION: No clinically relevant effect of alcohol use was observed on the pharmacokinetics of first-line TB drugs, suggesting that poor treatment outcome is unlikely due to pharmacokinetic alterations. That methaqualone reduced rifampicin means dose adjustment may be beneficial.

Test characteristics for combining non-invasive liver fibrosis staging modalities in individuals with Hepatitis C virus.

Non-invasive methods have largely replaced biopsy to identify advanced fibrosis in hepatitis C virus (HCV). Guidelines vary regarding testing strategy to balance accuracy, costs and loss to follow-up. Although individual test characteristics are well-described, data comparing the accuracy of using two tests together are limited. We calculated combined test characteristics to determine the utility of combined strategies. This study synthesizes empirical data from fibrosis staging trials and the literature to estimate test characteristics for Fibrosis-4 (FIB4), APRI or a commercial serum panel (FibroSure®), followed by transient elastography (TE) or FibroSure®. We simulated two testing strategies: (1) second test only for those with intermediate first test results (staged approach), and (2) second test for all. We summarized empiric data with multinomial distributions and used this to estimate test characteristics of each strategy on a simulated population of 10,000 individuals with 4.2% cirrhosis prevalence. Negative predictive value (NPV) for cirrhosis from a single test ranged from 98.2% (95% CB 97.6-98.8%) for FIB-4 to 99.4% (95% CB 99.0-99.8%) for TE. Using a staged approach with TE second, sensitivity for cirrhosis rose to 93.3-96.9%, NPV to 99.7-99.8%, while PPV dropped to <32%. Using TE as a second test for all minimally changed estimated test characteristics compared with the staged approach. Combining two non-invasive fibrosis tests barely improves NPV and decreases or does not change PPV compared with a single test, challenging the utility of serial testing modalities. These calculated combined test characteristics can inform best methods to identify advanced fibrosis in various populations.

Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic.

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.

Intersectional inequities and longitudinal prevalence estimates of opioid use disorder in Massachusetts 2014-2020: a multi-sample capture-recapture analysis.

Background: As overdoses continue to increase worldwide, accurate estimates are needed to understand the size of the population at risk and address health disparities. Capture-recapture methods may be used in place of direct estimation at nearly any geographic level (e.g., city, state, country) to estimate the size of the population with opioid use disorder (OUD). We performed a multi-sample capture-recapture analysis with persons aged 18-64 years to estimate the prevalence of OUD in Massachusetts from 2014 to 2020, stratified by sex and race/ethnicity. Methods: We used seven statewide administrative data sources linked at the individual level. We developed log-linear models to estimate the unknown OUD-affected population. Uncertainty was characterized using 95% confidence intervals (95% CI) on the total counts and prevalence estimates. Findings: The estimated OUD prevalence increased from 5.47% (95% CI = 4.89%, 5.98%) in 2014 to 5.79% (95% CI = 5.34%, 6.19%) in 2020. Prevalence among Hispanic females doubled (2.46% in 2014 to 4.23% in 2020) and prevalence rose to nearly 10% among Black non-Hispanic males and Hispanic males from 2014 through 2019. Estimates for Black non-Hispanic females more than doubled from 2014 through 2019 (3.39% to 7.09%), and then decreased to 5.69% in 2020. Interpretation: This study is the first to provide OUD prevalence trend estimates by binary sex and race/ethnicity at a state level using capture-recapture methods. Using these methods as the international overdose crisis worsens can allow jurisdictions to appropriately allocate resources and targeted interventions to marginalised populations. Funding: NIDA.

Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.

PURPOSE: Identifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention. METHODS: We leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity. We compared the performance of each model using several performance measures and identified factors that were most predictive of opioid overdose within racial/ethnic groups and across models. RESULTS: We found that out of 44,246 prison releases in Massachusetts between 2015-2020, 2237 (5.1%) resulted in opioid overdose in the 90 days following release. The performance of the two predictive models varied. The single weight model had high sensitivity (79%) and low specificity (56%) for predicting opioid overdose and was more sensitive for White non-Hispanic individuals (sensitivity = 84%) than for racial/ethnic minority individuals. CONCLUSIONS: Stratified models had better balanced performance metrics for both White non-Hispanic and racial/ethnic minority groups and identified different predictors of overdose between racial/ethnic groups. Across racial/ethnic groups and models, involuntary commitment (involuntary treatment for alcohol/substance use disorder) was an important predictor of opioid overdose.

Medication for Opioid Use Disorder After Serious Injection-Related Infections in Massachusetts.

Importance: Serious injection-related infections (SIRIs) cause significant morbidity and mortality. Medication for opioid use disorder (MOUD) improves outcomes but is underused. Understanding MOUD treatment after SIRIs could inform interventions to close this gap. Objectives: To examine rehospitalization, death rates, and MOUD receipt for individuals with SIRIs and to assess characteristics associated with MOUD receipt. Design, Setting, and Participants: This retrospective cohort study used the Massachusetts Public Health Data Warehouse, which included all individuals with a claim in the All-Payer Claims Database and is linked to individual-level data from multiple government agencies, to assess individuals aged 18 to 64 years with opioid use disorder and hospitalization for endocarditis, osteomyelitis, epidural abscess, septic arthritis, or bloodstream infection (ie, SIRI) between July 1, 2014, and December 31, 2019. Data analysis was performed from November 2021 to May 2023. Exposure: Demographic and clinical factors potentially associated with posthospitalization MOUD receipt. Main Outcomes and Measures: The main outcome was MOUD receipt measured weekly in the 12 months after hospitalization. We used zero-inflated negative binomial regression to examine characteristics associated with any MOUD receipt and rates of treatment in the 12 months after hospitalization. Secondary outcomes were receipt of any buprenorphine formulation, methadone, and extended-release naltrexone examined individually. Results: Among 8769 individuals (mean [SD] age, 43.2 [12.0] years; 5066 [57.8%] male) who survived a SIRI hospitalization, 4305 (49.1%) received MOUD, 5919 (67.5%) were rehospitalized, and 973 (11.1%) died within 12 months. Of those treated with MOUD in the 12 months after hospitalization, the mean (SD) number of MOUD initiations during follow-up was 3.0 (1.7), with 956 of 4305 individuals (22.2%) receiving treatment at least 80% of the time. MOUD treatment after SIRI hospitalization was significantly associated with MOUD in the prior 6 months (buprenorphine: adjusted odds ratio [AOR], 16.51; 95% CI, 13.81-19.74; methadone: AOR, 28.46; 95% CI, 22.41-36.14; or naltrexone: AOR, 2.05; 95% CI, 1.56-2.69). Prior buprenorphine (incident rate ratio [IRR], 1.17; 95% CI, 1.11-1.24) or methadone (IRR, 1.89; 95% CI, 1.79-2.01) use was associated with higher treatment rates after hospitalization, and prior naltrexone use (IRR, 0.86; 95% CI, 0.77-0.95) was associated with lower rates. Conclusions and Relevance: This study found that in the year after a SIRI hospitalization in Massachusetts, mortality and rehospitalization were common, and only half of patients received MOUD. Treatment with MOUD before a SIRI was associated with posthospitalization MOUD initiation and time receiving MOUD. Efforts are needed to initiate MOUD treatment during SIRI hospitalizations and subsequently retain patients in treatment.

Accuracy of Inferences About the Reproductive Number and Superspreading Potential of SARS-CoV-2 with Incomplete Contact Tracing Data.

The basic reproductive number (R0) and superspreading potential (k) are key epidemiological parameters that inform our understanding of a disease's transmission. Often these values are estimated using the data obtained from contact tracing studies. Here we performed a simulation study to understand how incomplete data due to preferential contact tracing impacted the accuracy and inferences about the transmission of SARS-CoV-2. Our results indicate that as the number of positive contacts traced decreases, our estimates of R0 tend to decrease and our estimates of ktend to increase. Notably, when there are large amounts of positive contacts missed in the tracing process, we can conclude that there is no indication of superspreading even if we know there is. The results of this study highlight the need for a unified public health response to transmissible diseases.