Search

BACKGROUND: The diagnosis of constrictive bronchiolitis (CB) in previously deployed individuals, and evaluation of respiratory symptoms more broadly, presents considerable challenges, including using consistent histopathologic criteria and clinical assessments. RESEARCH QUESTION: What are the recommended diagnostic workup and associated terminology of respiratory symptoms in previously deployed individuals? STUDY DESIGN AND METHODS: Nineteen experts participated in a three-round modified Delphi study, ranking their level of agreement for each statement with an a priori definition of consensus. Additionally, rank-order voting on the recommended diagnostic approach and terminology was performed. RESULTS: Twenty-five of 28 statements reached consensus, including the definition of CB as a histologic pattern of lung injury that occurs in some previously deployed individuals while recognizing the importance of considering alternative diagnoses. Consensus statements also identified a diagnostic approach for the previously deployed individual with respiratory symptoms, distinguishing assessments best performed at a local or specialty referral center. Also, deployment-related respiratory disease (DRRD) was proposed as a broad term to subsume a wide range of potential syndromes and conditions identified through noninvasive evaluation or when surgical lung biopsy reveals evidence of multicompartmental lung injury that may include CB. INTERPRETATION: Using a modified Delphi technique, consensus statements provide a clinical approach to possible CB in previously deployed individuals. Use of DRRD provides a broad descriptor encompassing a range of postdeployment respiratory findings. Additional follow-up of individuals with DRRD is needed to assess disease progression and to define other features of its natural history, which could inform physicians better and lead to evolution in this nosology.

BackgroundOccupational cohort studies are often challenged by the Healthy Worker Survivor Effect, which may bias standard methods of analysis. G-estimation of structural failure time models is an approach for reducing this type of bias. Accelerated failure time models have recently been applied in an occupational cohort but cumulative failure time models have not.MethodsWe used g-estimation of a cumulative failure time model to assess the effect of working as a long-haul driver on ischaemic heart disease mortality in a cohort of 30 448 men employed in the unionised US trucking industry in 1985. Exposure was defined by job title and based on work records. We also applied g-estimation of an accelerated failure time model as a sensitivity analysis and approximated HRs from both models to compare them.ResultsThe risk ratio (RR) obtained from the cumulative failure time model, comparing the observed risk under no intervention to the risk had nobody ever been exposed as a long-haul driver, was 1.09 (95% CI 1.02 to 1.16). The RR comparing the risk had everyone been exposed as long-haul driver for 8 years to the risk had nobody ever been exposed was 1.20 (95% CI 1.04 to 1.46). After HR approximations, accelerated failure time model results were similar.ConclusionsThe cumulative failure time model can effectively control time-varying confounding by Healthy Worker Survivor Effect, and provides an easily interpretable effect estimate. RRs estimated from the cumulative failure time model indicate an elevated ischaemic heart disease mortality risk for long-haul drivers in the US trucking industry.

Abstract Background Experimental evidence suggests that inhaled particles from vehicle exhaust have systemic effects on inflammation, endothelial activation and oxidative stress. In the present study we assess the relationships of short-term exposures with inflammatory endothelial activation and oxidative stress biomarker levels in a population of trucking industry workers. Methods Blood and urine samples were collected pre and post-shift, at the beginning and end of a workweek from 67 male non-smoking US trucking industry workers. Concurrent measurements of microenvironment concentrations of elemental and organic carbon (EC & OC), and fine particulate matter (PM2.5) combined with time activity patterns allowed for calculation of individual exposures. Associations between daily and first and last-day average levels of exposures and repeated measures of intercellular and vascular cell adhesion molecule-1 (ICAM-1 & VCAM-1), interleukin 6 (IL-6) and C-reactive protein (CRP) blood levels and urinary 8-Hydroxy-2′-Deoxyguanosine (8-OHdG) were assessed using linear mixed effects models for repeated measures. Results There was a statistically significant association between first and last-day average PM2.5 and 8-OHdG (21% increase, 95% CI: 2, 42%) and first and last-day average OC and IL-6 levels (18% increase 95% CI: 1, 37%) per IQR in exposure. There were no significant findings associated with EC or associations suggesting acute cross-shift effects. Conclusion Our findings suggest associations between weekly average exposures of PM2.5 on markers of oxidative stress and OC on IL-6 levels.

Abstract Background Previous studies have suggested associations of adult exposures to secondhand smoke (SHS) with respiratory symptoms, but no study has focused on blue-collar industrial environments. We assessed the association between SHS and respiratory symptoms in 1,562 non-current smoking U.S. trucking industry workers. Methods Information on SHS exposure and respiratory health was obtained by questionnaire. Multiple logistic regression analyses were used to assess the associations of recent and lifetime exposures to SHS with chronic phlegm, chronic cough, and any wheeze, defined by American Thoracic Society criteria. Results In analyses adjusted for age, gender, race, childhood SHS exposure, former smoking, pack-years of smoking and years since quitting, body mass index, job title, region of the country, and urban residence, recent exposures to SHS were associated with all three respiratory symptoms (odds ratio (OR) = 1.46; 95% confidence interval (CI) = 1.00-2.13) for chronic cough, 1.55 (95% CI = 1.08-2.21) for chronic phlegm, and 1.76 (95% CI = 1.41-2.21) for any wheeze). Workplace exposure was the most important recent exposure. Childhood exposure to SHS was also associated with all three symptoms, but only statistically significantly for chronic phlegm (OR = 1.84; 95% CI = 1.24-2.75). Additional years of living with a smoker were associated with an increased risk, but there was no evidence of a dose–response, except for chronic phlegm. Conclusions In this group of trucking industry workers, childhood and recent exposures to SHS were related to respiratory symptoms.

BackgroundDiesel exhaust has been considered to be a probable lung carcinogen based on studies of occupationally exposed workers. Efforts to define lung cancer risk in these studies have been limited in part by lack of quantitative exposure estimates.ObjectiveWe conducted a retrospective cohort study to assess lung cancer mortality risk among U.S. trucking industry workers. Elemental carbon (EC) was used as a surrogate of exposure to engine exhaust from diesel vehicles, traffic, and loading dock operations.MethodsWork records were available for 31,135 male workers employed in the unionized U.S. trucking industry in 1985. A statistical model based on a national exposure assessment was used to estimate historical work-related exposures to EC. Lung cancer mortality was ascertained through the year 2000, and associations with cumulative and average EC were estimated using proportional hazards models.ResultsDuration of employment was inversely associated with lung cancer risk consistent with a healthy worker survivor effect and a cohort composed of prevalent hires. After adjusting for employment duration, we noted a suggestion of a linear exposure-response relationship. For each 1,000-µg/m³ months of cumulative EC, based on a 5-year exposure lag, the hazard ratio (HR) was 1.07 [95% confidence interval (CI): 0.99, 1.15] with a similar association for a 10-year exposure lag [HR = 1.09 (95% CI: 0.99, 1.20)]. Average exposure was not associated with relative risk.ConclusionsLung cancer mortality in trucking industry workers increased in association with cumulative exposure to EC after adjusting for negative confounding by employment duration.

BackgroundFew studies have directly assessed the association of secondhand smoke (SHS) with cardiovascular disease-related inflammatory markers, and the findings are inconsistent.ObjectivesWe assessed the association between SHS exposure and the inflammatory markers high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and soluble intercellular adhesion molecule-1 (sICAM-1) in 199 nonsmoking U.S. trucking industry workers.MethodsParticipants provided blood samples either by mail (blood drawn at local health care provider near home) or at the work site (blood drawn by research staff on-site) and completed a health and work history questionnaire at the time of blood draw. Exposure to SHS was measured by plasma cotinine concentrations. We used multivariate regression analyses to assess the associations between levels of cotinine and inflammatory markers.ResultsThe median cotinine level was 0.10 ng/mL (interquartile range, 0.04-0.23 ng/mL). The odds ratios of elevated hs-CRP (above highest CRP tertile, 1.5 mg/L) were 2.85 [95% confidence interval (CI), 1.03-7.89] for the high-cotinine group (> 0.215 ng/mL) and 2.80 (95% CI, 1.11-7.10) for the moderate-cotinine group (0.05-0.215 ng/mL), compared with the low-cotinine group (< 0.05 ng/mL), adjusting for age, sex, race, educational level, obesity, previous smoking history, job title, and medical history. Plasma cotinine levels were not associated with IL-6 or sICAM-1.ConclusionsSHS exposure, as assessed by plasma cotinine, was positively associated with hs-CRP in this group of blue-collar workers. The strength of the association with hs-CRP depended on the cut points selected for analysis.

BackgroundAlthough the smoking rate in the United States is declining because of an increase of smoke-free laws, among blue-collar workers it remains higher than that among many other occupational groups.ObjectivesWe evaluated the factors influencing workplace secondhand smoke (SHS) exposures in the U.S. unionized trucking industry.MethodsFrom 2003 through 2005, we measured workplace SHS exposure among 203 nonsmoking and 61 smoking workers in 25 trucking terminals. Workers in several job groups wore personal vapor-phase nicotine samplers on their lapels for two consecutive work shifts and completed a workplace SHS exposure questionnaire at the end of the personal sampling.ResultsMedian nicotine level was 0.87 microg/m3 for nonsmokers and 5.96 microg/m3 for smokers. As expected, smokers experienced higher SHS exposure duration and intensity than did nonsmokers. For nonsmokers, multiple regression analyses indicated that self-reported exposure duration combined with intensity, lack of a smoking policy as reported by workers, having a nondriver job, and lower educational level were independently associated with elevated personal nicotine levels (model R2 = 0.52). Nondriver job and amount of active smoking were associated with elevated personal nicotine level in smokers, but self-reported exposure, lack of a smoking policy, and lower educational level were not.ConclusionsDespite movements toward smoke-free laws, this population of blue-collar workers was still exposed to workplace SHS as recently as 2005. The perceived (reported by the workers), rather than the official (reported by the terminal managers), smoking policy was associated with measured SHS exposure levels among the nonsmokers. Job duties and educational level might also be important predictors of workplace SHS exposure.

We have addressed potential contamination of PM(2.5) filter samples by nicotine from cigarette smoke. We collected two nicotine samples - one nicotine sampling filter was placed inline after the collection of PM(2.5) and the other stood alone. The overall correlation between the two nicotine filter levels was 0.99. The nicotine collected on the stand-alone filter was slightly greater than that on the in-line filter (mean difference = 1.10 mug/m(3)), but the difference was statistically significant only when PM(2.5) was low (