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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. [ABSTRACT FROM AUTHOR]

BackgroundOzone is a ubiquitous air pollutant with increasing concentrations in many populous regions. Toxicological studies show that ozone can cause oxidative stress and increase insulin resistance. These pathways may contribute to metabolic changes and diabetes formation. In this paper, we investigate the association between ozone and incident type 2 diabetes in a large cohort of African American women.MethodsWe used Cox proportional hazards models to calculate hazard ratios (HRs) for incident type 2 diabetes associated with exposure to ozone in a cohort of 45,231 African American women living in 56 metropolitan areas across the United States. Ozone levels were estimated using the U.S. EPA Models-3/Community Multiscale Air Quality (CMAQ) predictions fused with ground measurements at a resolution of 12km for the years 2007-2008.ResultsThe HR per interquartile range increment of 6.7ppb of ozone was 1.18 (95% CI 1.04-1.34) for incident diabetes in adjusted models. This association was unaltered in models that controlled for fine particulate matter with diameter

IntroductionSubstantial research has been dedicated to understanding the reasons for the dramatic rise in obesity rates in the U.S. in the last 2 decades. Animal studies and epidemiologic studies in children have suggested that air pollution might contribute to weight gain. This study investigates the association between ambient air pollution and weight gain over 16 years of follow-up (1995-2011) in a large cohort of African-American women in the U.S.MethodsThis study assessed associations of fine particulate matter, ozone, and nitrogen dioxide with weight gain using a linear random effects model. All analyses were conducted in 2015.ResultsThere was no statistically significant association between weight change and fine particulate matter (mean weight change over 16 years per interquartile range [2.9 μg/m(3)], 0.12 kg; 95% CI=-0.10, 0.35) and ozone (0.16 kg per interquartile range [6.7 ppb]; 95% CI=-0.11, 0.43). There was a small decrease in weight associated with nitrogen dioxide (-0.50 per interquartile range [9.7 ppb]; 95% CI=-0.77, -0.23).ConclusionsThe results do not provide support for an association of air pollution with weight gain in African-American adult women.

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. [ABSTRACT FROM AUTHOR]

While laboratory studies show that air pollutants can potentiate insulin resistance, the epidemiologic evidence regarding the association of air pollution with diabetes incidence is conflicting. The purpose of the present study was to assess the association of the traffic-related nitrogen dioxide (NO2) with the incidence of diabetes in a longitudinal cohort study of African American women. We used Cox proportional hazards models to calculate hazard ratios and 95% confidence intervals (CI) for diabetes associated with exposure to NO2 among 43,003 participants in the Black Women's Health Study (BWHS). Pollutant levels at participant residential locations were estimated with 1) a land use regression model for participants living in 56 metropolitan areas, and 2) a dispersion model for participants living in 27 of the cities. From 1995 to 2011, 4387 cases of diabetes occurred. The hazard ratios per interquartile range of NO2 (9.7 ppb), adjusted for age, metropolitan area, education, vigorous exercise, body mass index, smoking, and diet, were 0.96 (95% CI 0.88-1.06) using the land use regression model estimates and 0.94 (95% CI 0.80, 1.10) using the dispersion model estimates. The present results do not support the hypothesis that exposure to NO2 contributes to diabetes incidence in African American women.

Numerous observational studies have assessed the association between ambient air pollution and chronic disease incidence, but there is no uniform approach to create an exposure metric that captures the variability in air pollution through time and determines the most relevant exposure window. The purpose of the present study was to assess ways of modeling exposure to air pollution in relation to incident hypertension. We simulated data on incident hypertension to assess the performance of six air pollution exposure metrics, using characteristics from the Black Women's Health Study. Each metric made different assumptions about how to incorporate time trends in pollutant data, and the most relevant window of exposure. We use observed values for particulate matter ≤ 2.5 microns (PM2.5) for this cohort to create the six exposure metrics and fit Cox proportional hazards models to the simulated data using the six metrics. The optimal exposure metric depends on the underlying association between PM2.5 and disease, which is unknown. Metrics that incorporate exposure information from multiple years tend to be more robust and suffer from less bias. This study provides insight into factors that influence the metric used to quantifying exposure to PM2.5 and suggests the need for careful sensitivity analyses.

BackgroundClinical studies have shown that exposure to fine particulate matter (PM2.5) can increase insulin resistance and blood pressure. The epidemiologic evidence for an association of PM2.5 exposure with the incidence of type 2 diabetes or hypertension is inconsistent. Even a modest association would have great public health importance given the ubiquity of exposure and high prevalence of the conditions.MethodsWe used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident type 2 diabetes and hypertension associated with exposure to PM2.5 in a large cohort of African American women living in 56 metropolitan areas across the US, using data from the Black Women's Health Study. Pollutant levels were estimated at all residential locations over follow-up with a hybrid model incorporating land use regression and Bayesian Maximum Entropy techniques.ResultsDuring 1995 to 2011, 4,387 cases of diabetes and 9,570 cases of hypertension occurred. In models controlling for age, questionnaire cycle, and metro area, there were positive associations with diabetes (HR = 1.13, 95% CI = 1.04, 1.24) and hypertension (HR = 1.06, 95% CI = 1.00, 1.12) per interquartile range of PM2.5 (2.9 μg/m). Multivariable HRs, however, were 0.99 (95% CI = 0.90, 1.09) for diabetes and 0.99 (95% CI = 0.93, 1.06) for hypertension.ConclusionsOur results provide little support for an association of PM2.5 with diabetes or hypertension incidence.

Introduction: The mean emergency department (ED) length of stay (LOS) is considered a measure of crowding. This paper measures the association between LOS and factors that potentially contribute to LOS measured over consecutive shifts in the ED: shift 1 (7:00 AM to 3:00 PM), shift 2 (3:00 PM to 11:00PM), and shift 3 (11:00 PM to 7:00 AM).Methods: Setting: University, inner-city teaching hospital. Patients: 91,643 adult ED patients between October 12, 2005 and April 30, 2007. Design: For each shift, we measured the numbers of (1) ED nurses on duty, (2) discharges, (3) discharges on the previous shift, (4) resuscitation cases, (5) admissions, (6) intensive care unit (ICU) admissions, and (7) LOS on the previous shift. For each 24-hour period, we measured the (1) number of elective surgical admissions and (2) hospital occupancy. We used autoregressive integrated moving average time series analysis to retrospectively measure the association between LOS and the covariates.Results: For all 3 shifts, LOS in minutes increased by 1.08 (95% confidence interval 0.68, 1.50) forevery additional 1% increase in hospital occupancy. For every additional admission from the ED, LOS in minutes increased by 3.88 (2.81, 4.95) on shift 1, 2.88 (1.54, 3.14) on shift 2, and 4.91 (2.29, 7.53) onshift 3. LOS in minutes increased 14.27 (2.01, 26.52) when 3 or more patients were admitted to the ICU on shift 1. The numbers of nurses, ED discharges on the previous shift, resuscitation cases, andelective surgical admissions were not associated with LOS on any shift.Conclusion: Key factors associated with LOS include hospital occupancy and the number of hospital admissions that originate in the ED. This particularly applies to ED patients who are admitted to the ICU. [West J Emerg Med. 2012;13(2):163–168.]

Background: The National Survey on Drug Use and Health (NSDUH) estimated the prevalence of opioid use disorder (OUD) among the civilian, noninstitutionalized people aged 12 years or older in Massachusetts as 1.2% between 2015 and 2017. Accurate estimation of the prevalence of OUD is critical to the success of treatment and resource planning. Various indirect estimation approaches have been used but are subject to data availability and infrastructurerelated issues. Methods: We used 2015 data from the Massachusetts Public Health Data Warehouse (PHD) to compare the results of two approaches to estimating OUD prevalence in the Massachusetts population. First, we used a seven-dataset capture-recapture analysis under log-linear model parameterization, controlling for the source dependence and effects of age, sex, and county through stratification. Second, we applied a benchmark-multiplier method in a Bayesian framework by linking health care claims data to death certificate data assuming an extrapolation of death rates from observed untreated OUD to unobserved OUD. Results: Our estimates for OUD prevalence among Massachusetts residents (aged 18-64 years) were 4.62% (95% CI = 4.59%, 4.64%) in the capture-recapture approach and 4.29% (95% CrI = 3.49%, 5.32%) in the Bayesian model. Both estimates were approximately four times higher than NSDUH estimates. Conclusion: The synthesis of our findings suggests that the disease surveillance system misses a large portion of the population with OUD. Our study also suggests that concurrent use of multiple methods improves the justification and facilitates the triangulation and interpretation of the resulting estimates. [ABSTRACT FROM AUTHOR]

Four different endemic coronaviruses (eCoVs) are etiologic agents for the seasonal common cold, and these eCoVs share extensive sequence homology with human SARS coronavirus 2 (SARS-CoV-2). Here, we show that individuals with, as compared with those without, a recent documented infection with eCoV were tested at greater frequency for respiratory infections but had a similar rate of SARS-CoV-2 acquisition. Importantly, the patients with a previously detected eCoV had less-severe coronavirus disease 2019 (COVID-19) illness. Our observations suggest that preexisting immune responses against endemic human coronaviruses can mitigate disease manifestations from SARS-CoV-2 infection., Introduction While SARS coronavirus 2 (SARS-CoV-2) emerged recently, other coronaviruses are endemic in the human population. Four different human coronaviruses (HCoV-OC43, HCoV-HKU1, HCoV-NL63, and HCoV-229E) are among the most common [...]