Andreas Neophytou

Traffic-related exposures and biomarkers of systemic inflammation, endothelial activation and oxidative stress: a panel study in the US trucking industry.

BackgroundExperimental 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.MethodsBlood 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.ResultsThere 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.ConclusionOur findings suggest associations between weekly average exposures of PM2.5 on markers of oxidative stress and OC on IL-6 levels.

Particulate matter concentrations during desert dust outbreaks and daily mortality in Nicosia, Cyprus

Ambient particulate matter (PM) has been shown to have short- and long-term effects on cardiorespiratory mortality and morbidity. Most of the risk is associated with fine PM (PM2.5); however, recent evidence suggests that desert dust outbreaks are major contributors to coarse PM (PM10–2.5) and may be associated with adverse health effects. The objective of this study was to investigate the risk of total, cardiovascular and respiratory mortality associated with PM concentrations during desert dust outbreaks. We used a time-series design to investigate the effects of PM10 on total non-trauma, cardiovascular and respiratory daily mortality in Cyprus, between 1 January 2004 and 31 December 2007. Separate PM10 effects for non-dust and dust days were fit in generalized additive Poisson models. We found a 2.43% (95% CI: 0.53, 4.37) increase in daily cardiovascular mortality associated with each 10-μg/m3 increase in PM10 concentrations on dust days. Associations for total (0.13% increase, 95% CI: −1.03, 1.30) and respiratory mortality (0.79% decrease, 95% CI: −4.69, 3.28) on dust days and all PM10 and mortality associations on non-dust days were not significant. Although further study of the exact nature of effects across different affected regions during these events is needed, this study suggests adverse cardiovascular effects associated with desert dust events.

Air Pollution and Lung Function in Minority Youth with Asthma in the GALA II (Genes–Environments and Admixture in Latino Americans) and SAGE II (Study of African Americans, Asthma, Genes, and Environments) Studies

RATIONALE Adverse effects of exposures to ambient air pollution on lung function are well documented, but evidence in racial/ethnic minority children is lacking. OBJECTIVES To assess the relationship between air pollution and lung function in minority children with asthma and possible modification by global genetic ancestry. METHODS The study population consisted of 1,449 Latino and 519 African American children with asthma from five different geographical regions in the mainland United States and Puerto Rico. We examined five pollutants (particulate matter ≤10 μm and ≤2.5 μm in diameter, ozone, nitrogen dioxide, and sulfur dioxide), derived from participant residential history and ambient air monitoring data, and assessed over several time windows. We fit generalized additive models for associations between pollutant exposures and lung function parameters and tested for interaction terms between exposures and genetic ancestry. MEASUREMENTS AND MAIN RESULTS A 5 μg/m(3) increase in average lifetime particulate matter less than or equal to 2.5 μm in diameter exposure was associated with a 7.7% decrease in FEV1 (95% confidence interval = -11.8 to -3.5%) in the overall study population. Global genetic ancestry did not appear to significantly modify these associations, but percent African ancestry was a significant predictor of lung function. CONCLUSIONS Early-life particulate exposures were associated with reduced lung function in Latino and African American children with asthma. This is the first study to report an association between exposure to particulates and reduced lung function in minority children in which racial/ethnic status was measured by ancestry-informative markers.

Occupational Diesel Exposure, Duration of Employment, and Lung Cancer: An Application of the Parametric G-Formula.

Background: If less healthy workers terminate employment earlier, thus accumulating less exposure, yet remain at greater risk of the health outcome, estimated health effects of cumulative exposure will be biased downward. If exposure also affects termination of employment, then the bias cannot be addressed using conventional methods. We examined these conditions as a prelude to a reanalysis of lung cancer mortality in the Diesel Exhaust in Miners Study. Methods: We applied an accelerated failure time model to assess the effect of exposures to respirable elemental carbon (a surrogate for diesel) on time to termination of employment among nonmetal miners who ever worked underground (n = 8,307). We then applied the parametric g-formula to assess how possible interventions setting respirable elemental carbon exposure limits would have changed lifetime risk of lung cancer, adjusting for time-varying employment status. Results: Median time to termination was 36% shorter (95% confidence interval = 33%, 39%), per interquartile range width increase in respirable elemental carbon exposure. Lung cancer risk decreased with more stringent interventions, with a risk ratio of 0.8 (95% confidence interval = 0.5, 1.1) comparing a limit of ⩽25 µg/m3 respirable elemental carbon to no intervention. The fraction of cases attributable to diesel exposure was 27% in this population. Conclusions: The g-formula controlled for time-varying confounding by employment status, the signature of healthy worker survivor bias, which was also affected by diesel exposure. It also offers an alternative approach to risk assessment for estimating excess cumulative risk, and the impact of interventions based entirely on an observed population.

A structural approach to address the healthy-worker survivor effect in occupational cohorts: an application in the trucking industry cohort

Background Occupational 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. Methods We 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. Results The 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. Conclusions The 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.

Incident Ischemic Heart Disease After Long-Term Occupational Exposure to Fine Particulate Matter: Accounting for 2 Forms of Survivor Bias.

Little is known about the heart disease risks associated with occupational, rather than traffic-related, exposure to particulate matter with aerodynamic diameter of 2.5 µm or less (PM2.5). We examined long-term exposure to PM2.5 in cohorts of aluminum smelters and fabrication workers in the United States who were followed for incident ischemic heart disease from 1998 to 2012, and we addressed 2 forms of survivor bias. Left truncation bias was addressed by restricting analyses to the subcohort hired after the start of follow up. Healthy worker survivor bias, which is characterized by time-varying confounding that is affected by prior exposure, was documented only in the smelters and required the use of marginal structural Cox models. When comparing always-exposed participants above the 10th percentile of annual exposure with those below, the hazard ratios were 1.67 (95% confidence interval (CI): 1.11, 2.52) and 3.95 (95% CI: 0.87, 18.00) in the full and restricted subcohorts of smelter workers, respectively. In the fabrication stratum, hazard ratios based on conditional Cox models were 0.98 (95% CI: 0.94, 1.02) and 1.17 (95% CI: 1.00, 1.37) per 1 mg/m3-year in the full and restricted subcohorts, respectively. Long-term exposure to occupational PM2.5 was associated with a higher risk of ischemic heart disease among aluminum manufacturing workers, particularly in smelters, after adjustment for survivor bias.

Ischemic Heart Disease Incidence in Relation to Fine versus Total Particulate Matter Exposure in a U.S. Aluminum Industry Cohort

Ischemic heart disease (IHD) has been linked to exposures to airborne particles with an aerodynamic diameter <2.5 μm (PM2.5) in the ambient environment and in occupational settings. Routine industrial exposure monitoring, however, has traditionally focused on total particulate matter (TPM). To assess potential benefits of PM2.5 monitoring, we compared the exposure-response relationships between both PM2.5 and TPM and incidence of IHD in a cohort of active aluminum industry workers. To account for the presence of time varying confounding by health status we applied marginal structural Cox models in a cohort followed with medical claims data for IHD incidence from 1998 to 2012. Analyses were stratified by work process into smelters (n = 6,579) and fabrication (n = 7,432). Binary exposure was defined by the 10th-percentile cut-off from the respective TPM and PM2.5 exposure distributions for each work process. Hazard Ratios (HR) comparing always exposed above the exposure cut-off to always exposed below the cut-off were higher for PM2.5, with HRs of 1.70 (95% confidence interval (CI): 1.11–2.60) and 1.48 (95% CI: 1.02–2.13) in smelters and fabrication, respectively. For TPM, the HRs were 1.25 (95% CI: 0.89–1.77) and 1.25 (95% CI: 0.88–1.77) for smelters and fabrication respectively. Although TPM and PM2.5 were highly correlated in this work environment, results indicate that, consistent with biologic plausibility, PM2.5 is a stronger predictor of IHD risk than TPM. Cardiovascular risk management in the aluminum industry, and other similar work environments, could be better guided by exposure surveillance programs monitoring PM2.5.

Biomechanical and psychosocial exposures are independent risk factors for carpal tunnel syndrome: assessment of confounding using causal diagrams

Background Between 2001 and 2010, six research groups conducted coordinated prospective studies of carpal tunnel syndrome (CTS) incidence among US workers from various industries to estimate exposure–response relationships. Objective This analysis examined the presence and magnitude of confounding between biomechanical and workplace psychosocial factors and incidence of dominant-hand CTS. Methods 1605 participants, without CTS at enrolment, were followed for up to 3.5 years (2471 person-years). Demographic information, medical history and workplace psychosocial stress measures were collected at baseline. Individual workplace biomechanical exposures were collected for each task and combined across the workweek using time-weighted averaging (TWA). CTS case criteria were based on symptoms and results of electrophysiological testing. HRs were estimated with Cox proportional hazard models. Confounding was assessed using causal diagrams and an empirical criterion of 10% or greater change in effect estimate magnitude. Results There were 109 incident CTS cases (IR=4.41/100 person-years; 6.7% cumulative incidence). The relationships between CTS and forceful repetition rate, % time forceful hand exertion and the Threshold Limit Value for Hand Activity Level (TLV-HAL) were slightly confounded by decision latitude with effect estimates being attenuated towards the null (10–14% change) after adjustment. The risk of CTS among participants reporting high job strain was attenuated towards the null by 14% after adjusting for the HAL Scale or the % time forceful hand exertions. Conclusions Although attenuation of the relationships between CTS and some biomechanical and work psychosocial exposures was observed after adjusting for confounding, the magnitudes were small and confirmed biomechanical and work psychosocial exposures as independent risk factors for incident CTS.

G-Estimation of Structural Nested Models: Recent Applications in Two Subfields of Epidemiology

Correct adjustment for time-varying confounding affected by prior exposure is often not straightforward. G-estimation of structural nested models is a method of data analysis that allows for estimation of the combined effects of exposures that vary over time in a longitudinal cohort study. The method has not been widely adopted, but its use has increased in recent years, particularly in two subfields of epidemiology. Pharmacoepidemiologists have explored its applications to randomized trials with non-adherence or treatment switching and to finding optimal dynamic treatment regimens. Occupational epidemiologists have used it to correct for healthy worker survivor bias. Pharmacoepidemiologists have used simulations to illustrate extensions and novel applications, while occupational epidemiologists have focused on practical applications to observational data, often with careful attention to the handling of exposures. In theory, g-estimation of an appropriate structural nested model should be considered in the context of any longitudinal cohort in which at least one time-varying confounder is affected by prior exposure.

Secondhand smoke exposure and asthma outcomes among African-American and Latino children with asthma

Background Secondhand smoke (SHS) exposures have been linked to asthma-related outcomes but quantitative dose–responses using biomarkers of exposure have not been widely reported. Objectives Assess dose–response relationships between plasma cotinine-determined SHS exposure and asthma outcomes in minority children, a vulnerable population exposed to higher levels of SHS and under-represented in the literature. Methods We performed analyses in 1172 Latino and African-American children with asthma from the mainland USA and Puerto Rico. We used logistic regression to assess relationships of cotinine levels ≥0.05 ng/mL with asthma exacerbations (defined as asthma-related hospitalisations, emergency room visits or oral steroid prescription) in the previous year and asthma control. The shape of dose–response relationships was assessed using a continuous exposure variable in generalised additive logistic models with penalised splines. Results The OR for experiencing asthma exacerbations in the previous year for cotinine levels ≥0.05 ng/mL, compared with <0.05 ng/mL, was 1.40 (95% CI 1.03 to 1.89), while the OR for poor asthma control was 1.53 (95% CI 1.12 to 2.13). Analyses for dose–response relationships indicated increasing odds of asthma outcomes related with increasing exposure, even at cotinine levels associated with light SHS exposures. Conclusions Exposure to SHS was associated with higher odds of asthma exacerbations and having poorly controlled asthma with an increasing dose–response even at low levels of exposure. Our results support the conclusion that there are no safe levels of SHS exposures.