Elizabeth J. Malloy

The Strain Index (SI) and Threshold Limit Value (TLV) for Hand Activity Level (HAL): risk of carpal tunnel syndrome (CTS) in a prospective cohort.

A cohort of 536 workers was enrolled from 10 diverse manufacturing facilities and was followed monthly for six years. Job physical exposures were individually measured. Worker demographics, medical history, psychosocial factors, current musculoskeletal disorders (MSDs) and nerve conduction studies (NCS) were obtained. Point and lifetime prevalence of carpal tunnel syndrome (CTS) at baseline (symptoms + abnormal NCS) were 10.3% and 19.8%. During follow-up, there were 35 new CTS cases (left, right or both hands). Factors predicting development of CTS included: job physical exposure (American conference of governmental industrial hygienists Threshold Limit Value (ACGIH TLV) for Hand Activity Level (HAL) and the Strain Index (SI)), age, BMI, other MSDs, inflammatory arthritis, gardening outside of work and feelings of depression. In the adjusted models, the TLV for HAL and the SI were both significant per unit increase in exposure with hazard ratios (HR) increasing up to a maximum of 5.4 (p = 0.05) and 5.3 (p = 0.03), respectively; however, similar to other reports, both suggested lower risk at higher exposures. Data suggest that the TLV for HAL and the SI are useful metrics for estimating exposure to biomechanical stressors. Practitioner Summary: This study was conducted to determine how well the TLV for HAL and the SI predict risk of CTS using a prospective cohort design with survival analysis. Both the TLV for HAL and the SI were found to predict risk of CTS when adjusted for relevant covariates.

Reducing healthy worker survivor bias by restricting date of hire in a cohort study of Vermont granite workers

Objective: To explore the healthy worker survivor effect (HWSE) in a study of Vermont granite workers by distinguishing “prevalent” from “incident” hires based on date of hire before or after the start of follow-up. Methods: Records of workers between 1950 and 1982 were obtained from a medical surveillance programme. Proportional hazards models were used to model the association between silica exposure and lung cancer mortality, with penalised splines used to smooth the exposure-response relationship. A sensitivity analysis compared results between the original cohort and subcohorts defined by restricting date of hire to include varying proportions of prevalent hires. Results: Restricting to incident hires reduced the 213 cases by 74% and decreased the exposure range. The maximum mortality rate ratio (MRR) was close to twofold in all subcohorts. However, the exposure at which the maximum MRR was achieved decreased from 4.0 to 0.6 mg-year/m3 as the proportion of prevalent hires decreased from 50% in the original cohort to 0% in the subcohort of incident hires. Conclusion: Despite loss in power and restricted exposure range, decreasing the relative proportion of prevalent to incident hires reduced HWSE bias, resulting in stronger evidence for a dose-response between silica exposure and lung cancer mortality.

Left Truncation, Susceptibility, and Bias in Occupational Cohort Studies

Background: Left truncation occurs when subjects who otherwise meet entry criteria do not remain observable for a later start of follow-up. We investigated left truncation in occupational studies due to inclusion of workers hired before the start of follow-up in a simulation study. Methods: Using Monte Carlo methods, we simulated null and positive associations between exposure (work duration) and mortality for 500 datasets of 5000 subjects, assuming the absence and presence of heterogeneity in susceptibility to disease and to the effect of exposure. We examined incident hires (followed since hire) and left-truncated prevalent hires (those hired before baseline and remained employed at baseline). We estimated the association (&OV0404;1*) as the mean slope using Cox proportional hazards with a linear term for exposure, under scenarios with and without susceptibility. Results: With homogeneous susceptibility, there were no differences between incident and prevalent hires. Introducing only disease susceptibility did not change results. However, with heterogeneous susceptibility to the effect of exposure, downward bias was observed among prevalent hires under both the true null and positive exposure-response scenarios. The bias increased with time between hire and baseline (null: &OV0404;1* = 0.05 [SD = 0.08], &OV0404;1* = −0.08 [SD = 0.24], &OV0404;1* = −0.18 [SD = 0.98] if hired <15, 15 to <30, and ≥30 years before baseline, respectively), coincident with a decreasing percentage of susceptible subjects. Conclusions: Prevalent hires induce downward bias in an occupational cohort. This occurs because subjects who are less susceptible to the exposure remain exposed the longest, thereby underestimating the association.

The Comparison of Alternative Smoothing Methods for Fitting Non-Linear Exposure-Response Relationships with Cox Models in a Simulation Study

We examined the behavior of alternative smoothing methods for modeling environmental epidemiology data. Model fit can only be examined when the true exposure-response curve is known and so we used simulation studies to examine the performance of penalized splines (P-splines), restricted cubic splines (RCS), natural splines (NS), and fractional polynomials (FP). Survival data were generated under six plausible exposure-response scenarios with a right skewed exposure distribution, typical of environmental exposures. Cox models with each spline or FP were fit to simulated datasets. The best models, e.g. degrees of freedom, were selected using default criteria for each method. The root mean-square error (rMSE) and area difference were computed to assess model fit and bias (difference between the observed and true curves). The test for linearity was a measure of sensitivity and the test of the null was an assessment of statistical power. No one method performed best according to all four measures of performance, however, all methods performed reasonably well. The model fit was best for P-splines for almost all true positive scenarios, although fractional polynomials and RCS were least biased, on average.

Rectal cancer and exposure to metalworking fluids in the automobile manufacturing industry.

Background: Rectal cancer has been previously associated with exposure to metalworking fluids in a cohort mortality study of autoworkers. Objective: To better specify the exposure–response relationship with straight metalworking fluids (mineral oils) by applying non-parametric regression methods that avoid linearity constraints and arbitrary exposure cut points and by lagging exposure to account for cancer latency, in a nested case–control analysis. Methods: In addition to the classical Poisson regression with categorical exposure, survival models with penalised splines were used to estimate the exposure–response relationship between cumulative exposure to straight metalworking fluid and mortality from rectal cancer. Exposures to water-based metalworking fluids were treated as potential confounders, and all exposures were lagged by 5, 10, 15 and 20 years to account for cancer latency. The influence of the highest exposures was dealt with by a log transformation and outlier removal. The sensitivity of the penalised splines to alternative criteria for model selection and to the placement of knots was also examined. Results: The hazard ratio for mortality from rectal cancer increased essentially linearly with cumulative exposure to straight metalworking fluid (with narrow confidence bands) up to a maximum of 2.2 at the 99th centile of exposure and then decreased (with wide confidence bands). Lagging exposure up to 15 years increased the initial steepness of the curve and raised the maximum hazard ratio to 3.2. Conclusions: Non-parametric smoothing of lagged exposures has shown stronger evidence for a causal association between straight metalworking fluid and rectal cancer than was previously described using standard analytical methods. This analysis suggests an exposure–response trend that is close to linear and statistically significant over most of the exposure range and that increases further with lagged exposures. Smoothing should be regularly applied to environmental studies with quantitative exposure estimates to refine characterisation of the dose–response relationship.

The NIOSH Lifting Equation and Low-Back Pain, Part 1 Association With Low-Back Pain in the Backworks Prospective Cohort Study

Objective: The aim of this study was to evaluate relationships between the revised NIOSH lifting equation (RNLE) and risk of low-back pain (LBP). Background: The RNLE is commonly used to quantify job physical stressors to the low back from lifting and/or lowering of loads. There is no prospective study on the relationship between RNLE and LBP that includes accounting for relevant covariates. Method: A cohort of 258 incident-eligible workers from 30 diverse facilities was followed for up to 4.5 years. Job physical exposures were individually measured. Worker demographics, medical history, psychosocial factors, hobbies, and current LBP were obtained at baseline. The cohort was followed monthly to ascertain development of LBP and quarterly to determine changes in job physical exposure. The relationship between LBP and peak lifting index (PLI) and peak composite lifting index (PCLI) were tested in multivariate models using proportional hazards regression. Results: Point and lifetime prevalences of LBP at baseline were 7.1% and 75.1%, respectively. During follow-up, there were 123 incident LBP cases. Factors predicting development of LBP included job physical exposure (PLI and PCLI), history of LBP, psychosocial factors, and housework. In adjusted models, risk (hazard ratio [HR]) increased per-unit increase in PLI and PCLI (p = .05 and .02; maximum HR = 4.3 and 4.2, respectively). PLI suggested a continuous increase in risk with an increase in PLI, whereas the PCLI showed elevated, but somewhat reduced, risk at higher exposures. Conclusion: Job physical stressors are associated with increased risk of LBP. Data suggest that the PLI and PCLI are useful metrics for estimating exposure to job physical stressors.

Stepped care in the treatment of trichotillomania.

OBJECTIVE There are effective treatments of trichotillomania (TTM), but access to expert providers is limited. This study tested a stepped care model aimed at improving access. METHOD Participants were 60 (95% women, 75% Caucasian, 2% Hispanic) adults (M = 33.18 years) with TTM. They were randomly assigned to immediate versus waitlist (WL) conditions for Step 1 (10 weeks of web-based self-help via StopPulling.com). After Step 1, participants chose whether to engage in Step 2 (8 sessions of in-person habit reversal training [HRT]). RESULTS In Step 1, the immediate condition had a small (d = .21) but significant advantage, relative to WL, in reducing TTM symptom ratings by interviewers (masked to experimental condition but not to assessment point); there were no differences in self-reported TTM symptoms, alopecia, functional impairment, or quality of life. Step 1 was more effective for those who used the site more often. Stepped care was highly acceptable: Motivation did not decrease during Step 1; treatment satisfaction was high, and 76% enrolled in Step 2. More symptomatic patients self-selected into HRT, and on average they improved significantly. Over one third (36%) made clinically significant improvement in self-reported TTM symptoms. Considering the entire stepped care program, participants significantly reduced symptoms, alopecia, and impairment, and increased quality of life. For quality of life and symptom severity, there was some relapse by 3-month follow-up. CONCLUSIONS Stepped care is acceptable, and HRT was associated with improvement. Further work is needed to determine which patients with TTM can benefit from self-help and how to reduce relapse.

The Strain Index and ACGIH TLV for HAL Risk of Trigger Digit in the WISTAH Prospective Cohort

Objective: The objective of this study was to investigate the association between job physical exposure (JPE) and incidence of flexor tendon entrapment of the digits (FTED). Background: FTED, commonly known as trigger digit, is associated with age, gender, and certain health disorders. Although JPE has been suggested as a risk factor for FTED, there are no prospective cohort studies. Method: A cohort of 516 workers was enrolled from 10 diverse manufacturing facilities and followed monthly for 6 years. Worker demographics, medical history, and symptoms of FTED were assessed. JPE was individually measured using the Strain Index (SI) and American Conference of Governmental Industrial Hygienists (ACGIH) threshold limit value for hand activity level (TLV for HAL). Changes in JPE (assessed quarterly) and symptoms (assessed monthly) were recorded during follow-up. FTED was defined as demonstrated triggering on examination. Results: Point prevalence of FTED at baseline was 3.6%. During follow-up there were 23 incident FTED cases (left and/or right hands). The incident rate for first occurrence of FTED from enrollment was 1.38 per 100 person-years. Risk factors were JPE, age, gender, diabetes mellitus, carpometacarpal osteoarthrosis, and rheumatoid arthritis. In multivariate models, the SI showed strong association with risk of FTED when treated as a continuous variable and marginal association when dichotomized (SI > 6.1). TLV for HAL showed a statistical trend of increasing risk of FTED using the ACGIH limits, but no association as a continuous variable. Conclusions: Both JPE and personal risk factors are associated with FTED development. The SI and TLV for HAL are useful tools for estimating JPE.

The Strain Index and TLV for HAL: Risk of lateral epicondylitis in a prospective cohort

BACKGROUND This studys objective was to quantify exposure-response relationships between job physical exposure (JPE) and incidence of lateral epicondylitis (LE). METHODS A cohort of 536 workers was enrolled from 10 manufacturing facilities and followed monthly for 6 years to ascertain changes in JPE and health status. JPE was individually measured and quantified using the Strain Index (SI) and TLV for HAL. Worker demographics, medical history, psychosocial factors, and current musculoskeletal disorders were obtained. RESULTS Fifty-six workers developed LE. In multivariate models JPE, age, family problems, and swimming were associated with increased risk of LE. SI showed an exposure-response relationship with maximum hazard ratio (HR) of 4.5(P = 0.04). TLV for HAL showed a non-statistically significant trend for increased risk of LE (P = 0.19). CONCLUSION JPE is associated with increased risk of LE. The SI and TLV for HAL are useful metrics for estimating JPE.

Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1-7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.