Social disparities in heart disease risk and survivor bias among autoworkers: an examination based on survival models and g-estimation

Metalworking Fluid Exposure and Stroke Mortality Among US Autoworkers

Although air pollution is an important risk factor for stroke, few studies have considered the impact of workplace exposure to particulate matter (PM). We examined implications of exposure to PM composed of metalworking fluids (MWFs) for stroke mortality in the United Autoworkers-General Motors cohort. Cox proportional hazards models with age as the timescale were used to estimate the association of cumulative straight, soluble, and synthetic MWF exposure with stroke mortality, controlling for sex, race, plant, calendar year, and hire year. Among 38,553 autoworkers followed during 1941-1995, we identified 114 ischemic stroke deaths and 113 hemorrhagic stroke deaths. Overall stroke mortality risk was increased among workers in the middle exposure category for straight MWF (hazard ratio (HR) = 1.31, 95% confidence interval (CI): 0.87, 1.98) and workers in the highest exposure category for synthetic MWF (HR = 1.94, 95% CI: 1.13, 3.16) compared with workers who had no direct exposure. Ischemic stroke mortality risk was increased among workers in the highest exposure categories for straight MWF (HR = 1.45, 95% CI: 0.83, 2.52) and synthetic MWF (HR = 2.39, 95% CI: 1.39, 4.50). We observed no clear relationship between MWF exposure and hemorrhagic stroke mortality. Our results support a potentially important role for occupational PM exposures in stroke mortality and indicate the need for further studies of PM exposure and stroke in varied occupational settings.

Social and environmental risks as contributors to the clinical course of heart failure

Heart failure is a major contributor to healthcare expenditures. Many clinical risk factors for the development and exacerbation of heart failure had been reported, including diabetes, renal dysfunction, and respiratory disease. In addition to these clinical parameters, the effects of social factors, such as occupation or lifestyle, and environmental factors may have a great impact on disease development and progression of heart failure. However, the current understanding of social and environmental factors as contributors to the clinical course of heart failure is insufficient. To present the knowledge of these factors to date, this comprehensive review of the literature sought to identify the major contributors to heart failure within this context. Social factors for the risk of heart failure included occupation and lifestyle, specifically in terms of the effects of specific occupations, occupational exposure to toxicities, work style, and sleep deprivation. Socioeconomic factors focused on income and education level, social status, the neighborhood environment, and marital status. Environmental factors included traffic and noise, air pollution, and other climate factors. In addition, psychological stress and behavior traits were investigated. The development of heart failure may be closely related to these factors; therefore, these data should be summarized for the context to improve their effects on patients with heart failure. The present study reviews the literature to summarize these influences.

Estimating Effect of Obesity on Stroke Using G-Estimation: The ARIC study

OBJECTIVE

This study quantified the obesity-stroke relationship by appropriately adjusting for time-varying confounders using G-estimation.

METHODS

A total of 13,975 participants in the Atherosclerosis Risk in Communities (ARIC) study were included. General obesity (GOB) was defined as BMI ≥ 30 kg/m² ; abdominal obesity (AOB) was defined as waist circumference  ≥ 102 cm in men and ≥ 88 cm in women and waist to hip ratio  ≥ 0.9 in men and ≥ 0.85 in women. The effects of obesity on stroke were estimated using G-estimation and compared with accelerated failure time models using three modeling strategies.

RESULTS

The first accelerated failure time model adjusted for baseline covariates excluding metabolic mediators of obesity showed increased risk of stroke for all measures of obesity. Further adjustment for hypertension, diabetes mellitus, and lipid profiles resulted in decreasing hazard ratios (HRs) with intervals that included the null value for all measures of obesity. G-estimated HRs were 1.60 (95% CI: 1.08-2.40), 1.43 (95% CI: 1.14-1.99), and 1.99 (95% CI: 1.50-2.91) for GOB and AOB based on waist circumference and waist to hip ratio.

CONCLUSIONS

Both GOB and AOB affected the risk of stroke. The magnitude of the estimates was larger when modeled by G-estimation than when using standard models, suggesting that bias from mishandling of time-varying confounding was toward the null.

Blue-collar work and women's health: A systematic review of the evidence from 1990 to 2015

Despite the implications of gender and sex differences for health risks associated with blue-collar work, adverse health outcomes among blue-collar workers has been most frequently studied among men. The present study provides a "state-of-the-field" systematic review of the empiric evidence published on blue-collar women's health. We systematically reviewed literature related to the health of blue-collar women published between January 1, 1990 and December 31, 2015. We limited our review to peer-reviewed studies published in the English language on the health or health behaviors of women who were presently working or had previously worked in a blue-collar job. Studies were eligible for inclusion regardless of the number, age, or geographic region of blue-collar women in the study sample. We retained 177 studies that considered a wide range of health outcomes in study populations from 40 different countries. Overall, these studies suggested inferior health among female blue-collar workers as compared with either blue-collar males or other women. However, we noted several methodological limitations in addition to heterogeneity in study context and design, which inhibited comparison of results across publications. Methodological limitations of the extant literature, alongside the rapidly changing nature of women in the workplace, motivate further study on the health of blue-collar women. Efforts to identify specific mechanisms by which blue-collar work predisposes women to adverse health may be particularly valuable in informing future workplace-based and policy-level interventions.

Accounting for Time-Varying Confounding in the Relationship Between Obesity and Coronary Heart Disease: Analysis With G-Estimation: The ARIC Study

In longitudinal studies, standard analysis may yield biased estimates of exposure effect in the presence of time-varying confounders that are also intermediate variables. We aimed to quantify the relationship between obesity and coronary heart disease (CHD) by appropriately adjusting for time-varying confounders. This study was performed in a subset of participants from the Atherosclerosis Risk in Communities (ARIC) Study (1987-2010), a US study designed to investigate risk factors for atherosclerosis. General obesity was defined as body mass index (weight (kg)/height (m)2) ≥30, and abdominal obesity (AOB) was defined according to either waist circumference (≥102 cm in men and ≥88 cm in women) or waist:hip ratio (≥0.9 in men and ≥0.85 in women). The association of obesity with CHD was estimated by G-estimation and compared with results from accelerated failure-time models using 3 specifications. The first model, which adjusted for baseline covariates, excluding metabolic mediators of obesity, showed increased risk of CHD for all obesity measures. Further adjustment for metabolic mediators in the second model and time-varying variables in the third model produced negligible changes in the hazard ratios. The hazard ratios estimated by G-estimation were 1.15 (95% confidence interval (CI): 0.83, 1.47) for general obesity, 1.65 (95% CI: 1.35, 1.92) for AOB based on waist circumference, and 1.38 (95% CI: 1.13, 1.99) for AOB based on waist:hip ratio, suggesting that AOB increased the risk of CHD. The G-estimated hazard ratios for both measures were further from the null than those derived from standard models.

Assessment of the healthy worker survivor effect in cancer studies of the United Autoworkers-General Motors cohort

OBJECTIVE

The healthy worker survivor effect (HWSE) can affect the validity of occupational studies when data are analysed incorrectly. HWSE depends on three underlying conditions: (1) leaving work predicts future exposure, (2) leaving work is associated with disease outcome and (3) prior exposure increases probability of leaving work. If all these conditions are satisfied, then employment status is a time-varying confounder affected by prior exposure, and standard regression will produce bias. We assessed these conditions for cancer outcomes in a cohort of autoworkers exposed to metalworking fluids (MWF).

METHODS

The cohort includes 31 485 workers followed for cancer incidence from 1985 to 1994. As occupational exposures to straight, soluble and synthetic MWFs are necessarily zero after leaving work, condition (1) is satisfied. Cox models for cancer incidence and for employment termination were used to assess conditions (2) and (3), respectively. Employment termination by select ages was examined to better gauge the presence of condition (2).

RESULTS

The HR for leaving work as a predictor of all cancers combined and prostate cancer was null, but elevated for lung and colorectal cancers among men. Condition (2) was more clearly satisfied for all cancer outcomes when leaving work occurred by age 50. Higher exposures to all three MWF types were associated with increased rates of leaving work (condition (3)), with the exception of straight MWF among women.

CONCLUSIONS

We found evidence for the structural conditions underlying HWSE in a cohort of autoworkers. G-methods should be applied to reduce HWSE bias in studies of all cancers presently examined.

Healthy Worker Effect Phenomenon: Revisited with Emphasis on Statistical Methods - A Review

Known since 1885 but studied systematically only in the past four decades, the healthy worker effect (HWE) is a special form of selection bias common to occupational cohort studies. The phenomenon has been under debate for many years with respect to its impact, conceptual approach (confounding, selection bias, or both), and ways to resolve or account for its effect. The effect is not uniform across age groups, gender, race, and types of occupations and nor is it constant over time. Hence, assessing HWE and accounting for it in statistical analyses is complicated and requires sophisticated methods. Here, we review the HWE, factors affecting it, and methods developed so far to deal with it.

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/m(3)-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.

Straight Metalworking Fluids and All-Cause and Cardiovascular Mortality Analyzed by Using G-Estimation of an Accelerated Failure Time Model With Quantitative Exposure: Methods and Interpretations

Straight metalworking fluids have been linked to cardiovascular mortality in analyses using binary exposure metrics, accounting for healthy worker survivor bias by using g-estimation of accelerated failure time models. A cohort of 38,666 Michigan autoworkers was followed (1941-1994) for mortality from all causes and ischemic heart disease. The structural model chosen here, using continuous exposure, assumes that increasing exposure from 0 to 1 mg/m(3) in any single year would decrease survival time by a fixed amount. Under that assumption, banning the fluids would have saved an estimated total of 8,468 (slope-based 95% confidence interval: 2,262, 28,563) person-years of life in this cohort. On average, 3.04 (slope-based 95% confidence interval: 0.02, 25.98) years of life could have been saved for each exposed worker who died from ischemic heart disease. Estimates were sensitive to both model specification for predicting exposure (multinomial or logistic regression) and characterization of exposure as binary or continuous in the structural model. Our results provide evidence supporting the hypothesis of a detrimental relationship between straight metalworking fluids and mortality, particularly from ischemic heart disease, as well as an instructive example of the challenges in obtaining and interpreting results from accelerated failure time models using a continuous exposure in the presence of competing risks.