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Lianne Sheppard

Publications and source records attributed to Lianne Sheppard.

At least 19 recordsLinked to original sources
PubMed2006

Evaluation of the recursive model approach for estimating particulate matter infiltration efficiencies using continuous light scattering data.

Quantifying particulate matter (PM) infiltration efficiencies (F(inf)) in individual homes is an important part of PM exposure assessment because individuals spend the majority of time indoors. While F(inf) of fine PM has most commonly been estimated using tracer species such as sulfur, here we evaluate an alternative that does not require particle collection, weighing and compositional analysis, and can be applied in situations with indoor sources of sulfur, such as environmental tobacco smoke, gas pilot lights, and humidifier use. This alternative method involves applying a recursive mass balance model (recursive model, RM) to continuous indoor and outdoor concentration measurements (e.g., light scattering data from nephelometers). We show that the RM can reliably estimate F(inf), a crucial parameter for determining exposure to particles of outdoor origin. The RM F(inf) estimates showed good agreement with the conventional filter-based sulfur tracer approach. Our simulation results suggest that the RM F(inf) estimates are minimally impacted by measurement error. In addition, the average light scattering response per unit mass concentration was greater indoors than outdoors; after correcting for differences in light scattering response the median deviation from sulfur F(inf) was reduced from 15 to 11%. Thus, we have verified the RM applied to light scattering data. We show that the RM method is unable to provide satisfactory estimates of the individual components of F(inf) (penetration efficiency, air exchange rate, and deposition rate). However, this approach may allow F(inf) to be estimated in more residences, including those with indoor sources of sulfur. We show that individual homes vary in their infiltration efficiencies, thereby contributing to exposure misclassification in epidemiological studies that assign exposures using ambient monitoring data. This variation across homes indicates the need for home-specific estimation methods, such as the RM or sulfur tracer, instead of techniques that give average estimates of infiltration across homes.

Air Pollution↗
PubMed2006

Ambient air pollution and asthma exacerbations in children: an eight-city analysis.

The authors investigated the relation between ambient concentrations of five of the Environmental Protection Agency's criteria pollutants and asthma exacerbations (daily symptoms and use of rescue inhalers) among 990 children in eight North American cities during the 22-month prerandomization phase (November 1993-September 1995) of the Childhood Asthma Management Program. Short-term effects of carbon monoxide, nitrogen dioxide, particulate matter less than 10 mum in aerodynamic diameter (PM10), sulfur dioxide, and warm-season ozone were examined in both one-pollutant and two-pollutant models, using lags of up to 2 days. Lags in carbon monoxide and nitrogen dioxide were positively associated with both measures of asthma exacerbation, and the 3-day moving sum of sulfur dioxide levels was marginally related to asthma symptoms. PM10 and ozone were unrelated to exacerbations. The strongest effects tended to be seen with 2-day lags, where a 1-parts-per-million change in carbon monoxide and a 20-parts-per-billion change in nitrogen dioxide were associated with symptom odds ratios of 1.08 (95% confidence interval (CI): 1.02, 1.15) and 1.09 (95% CI: 1.03, 1.15), respectively, and with rate ratios for rescue inhaler use of 1.06 (95% CI: 1.01, 1.10) and 1.05 (95% CI: 1.01, 1.09), respectively. The authors believe that the observed carbon monoxide and nitrogen dioxide associations can probably be attributed to mobile-source emissions, though more research is required.

Air Pollutants↗
PubMed2006

Worker recovery expectations and fear-avoidance predict work disability in a population-based workers' compensation back pain sample.

STUDY DESIGN: Prospective, population-based cohort study. OBJECTIVES: To examine whether worker demographic, pain, disability, and psychosocial variables, assessed soon after work-related back pain disability onset, predict 6-month work disability. SUMMARY OF BACKGROUND DATA: Greater age, pain, and physical disability, and certain psychosocial characteristics may be risk factors for prolonged back pain-related work disability, although many studies have been small, findings have been inconsistent, and some psychosocial variables have not been examined prospectively. METHODS: Workers (N = 1,068) completed telephone interviews assessing demographic, pain, disability, and psychosocial variables 18 days (median) after submitting Workers' Compensation back pain disability claims. Administrative measures of work disability 6 months after claim submission were obtained. RESULTS: At 6 months, 196 workers (18.4%) were receiving work disability compensation. Age, race, education, and baseline pain and disability were significant predictors of 6-month disability. Adjusting for baseline demographics, pain, disability, and other psychosocial variables, high work fear-avoidance (odds ratio, 4.6; 95% confidence interval, 1.6-13.7) and very low recovery expectations (odds ratio, 3.1, 95% confidence interval, 1.5-6.5) were significant independent predictors. CONCLUSIONS: Among individuals with acute work-related back pain, high pain and disability, low recovery expectations, and fears that work may increase pain or cause harm are risk factors for chronic work disability.

Adolescent↗
PubMed2006

Factors associated with early opioid prescription among workers with low back injuries.

UNLABELLED: Prescription of opioids for nonmalignant musculoskeletal pain has increased substantially in recent years, but there is little information on the incidence of, or factors associated with, such prescription for work-related back pain. In a prospective cohort study (N = 1,067), we examined associations between worker sociodemographic and other characteristics and opioid prescription within six weeks of the first medical visit for workers' compensation claims for work loss due to back injury. We examined administrative, pharmacy, and worker-reported data. In bivariate logistic regression models, Hispanics were less likely than non-Hispanic whites to receive opioid prescriptions, and very high body mass index, daily tobacco use, greater pain and physical disability, pain radiating below the knee, injury severity categorizations (from medical records) of major sprain and radiculopathy, and worse mental health were associated with opioid prescription. Adjusting for demographics, pain intensity, and physical disability, opiate prescription was significantly associated with daily tobacco use, pain radiating below the knee, and injury severity categories (major sprain and radiculopathy). Knowledge of worker characteristics associated with early opioid prescription may be useful in future studies of the role of early pain treatment in influencing subsequent course of pain and disability among workers with back injuries. PERSPECTIVE: Little is known about patient characteristics that may influence physicians' decisions concerning prescription of opioids for acute back pain. Not surprisingly, workers with more severe back injuries are more likely to be prescribed opioids, but reasons for prescription disparities based on ethnicity and tobacco use warrant further study.

Adult↗
PubMed2006

Modeling distortion product otoacoustic emission input/output functions using segmented regression.

Distortion product otoacoustic emissions (DPOAEs) are low-level acoustic signals, the detection of which involves extraction from a background of noise. Boege and Janssen [J. Acoust. Soc. Am. 111, 1810-1818 (2002)] described a method for modeling the presence and growth of these responses. While improving growth function parameter estimation, this technique excludes a significant fraction of the data (especially low-level responses), and relies on ad hoc model fit acceptance criteria. The statistical difficulties associated with these limitations are described, and a weighted segmented linear regression model that avoids them is proposed. A simple test is presented for the presence of DPOAE growth. This technique is compared to that of Boege and Janssen in a dataset of 9 556 input/output (I/O) functions collected over 4 years on 866 ears from 379 construction apprentices and 63 age-matched controls. Comparisons are made on the entire dataset and within audiometric hearing loss categories. Segmented regression avoids the statistical pitfalls of the previous method, allows estimation of the threshold and slope of auditory response on a far greater number of I/O functions, and improves estimation of these parameters in this dataset. The potential for this method to yield more sensitive metrics of hearing function and compromise is discussed.

Acoustic Impedance Tests↗
PubMed2005

Alternative metrics for noise exposure among construction workers.

Although the exposure-response relationships for noise-induced hearing loss are relatively well established, there is not complete agreement on which metrics of noise exposure best represent risk of hearing damage. In particular, while L(eq), based on a 3 dB exchange rate (ER) is used by most agencies, US OSHA's standard is based on the L(avg), which uses a 5 dB ER. In addition, peak levels of exposure, which are commonly found in some industries, including construction, are believed to increase risk above that predicted by the L(eq). This paper presents an analysis of a large database of noise exposures among construction workers, comparing several noise metrics, and their application to a cohort of construction workers. Metrics examined were the L(avg), L(eq) and L(max), expressing average levels of exposure across an exposure interval. Two novel metrics were derived from these monitored metrics, L(eq)/L(avg) and L(max)/L(eq), as measures of exposure variability and 'peakiness', respectively. A total of 730 workshifts, including data on 361 492 min of exposure to workers in nine trades were examined. Correlations between average metrics (L(eq), L(avg) and L(max)) are generally very high, while the variability metrics are poorly correlated with either average levels, or with each other, indicating that they characterize different aspects of exposure. Alternative models for estimating exposure for the cohort were considered and the use of a task-within-trade specific mean level was adopted. The task-specific estimates of exposure using the various metrics will be applied to the cohort's work history to explore the importance of these alternative metrics in estimating risk of noise-induced damage.

Hearing Loss, Noise-Induced↗
PubMed2005

Overlap bias in the case-crossover design, with application to air pollution exposures.

The case-crossover design uses cases only, and compares exposures just prior to the event times to exposures at comparable control, or 'referent' times, in order to assess the effect of short-term exposure on the risk of a rare event. It has commonly been used to study the effect of air pollution on the risk of various adverse health events. Proper selection of referents is crucial, especially with air pollution exposures, which are shared, highly seasonal, and often have a long-term time trend. Hence, careful referent selection is important to control for time-varying confounders, and in order to ensure that the distribution of exposure is constant across referent times, a key assumption of this method. Yet the referent strategy is important for a more basic reason: the conditional logistic regression estimating equations commonly used are biased when referents are not chosen a priori and are functions of the observed event times. We call this bias in the estimating equations overlap bias. In this paper, we propose a new taxonomy of referent selection strategies in order to emphasize their statistical properties. We give a derivation of overlap bias, explore its magnitude, and consider how the bias depends on properties of the exposure series. We conclude that the bias is usually small, though highly unpredictable, and easily avoided.

Air Pollution↗
PubMed2005

Association between particulate matter and emergency room visits, hospital admissions and mortality in Spokane, Washington.

There is conflicting evidence regarding the association between different size fractions of particulate matter (PM) and cardiac and respiratory morbidity and mortality. We investigated the short-term associations of four size fractions of particulate matter (PM(1), PM(2.5), PM(10), and PM(10-2.5)) and carbon monoxide with hospital admissions and emergency room (ER) visits for respiratory and cardiac conditions and mortality in Spokane, Washington. We used a log-linear generalized linear model to compare daily averages of PM and carbon monoxide with daily counts of the morbidity and mortality outcomes from January 1995 to June 2001. We examined pollution lags ranging from 0 to 3 days and compared our results to a similar log-linear generalized additive model. Effect estimates tended to be smaller and have larger standard errors for the generalized linear model. Overall, we saw no association with respiratory ER visits and any size fraction of PM. However, there was a suggestion of greater respiratory effect from fine PM when compared to coarse fraction. Carbon monoxide was associated with both all respiratory ER visits and visits for asthma at the 3-day lag. We feel that carbon monoxide may be serving as a marker for combustion-derived pollutants, which is one large component of the diverse air pollutant mixture. We also found no association with any size fraction of PM or CO with cardiac hospital admissions or mortality at the 0- to 3-day lag. We found no consistent associations between any size fraction of PM and cardiac or respiratory ER visits or hospital admissions.

Air Pollutants↗
PubMed2005

Exposure and measurement contributions to estimates of acute air pollution effects.

Air pollution health effect studies are intended to estimate the effect of a pollutant on a health outcome. The definition of this effect depends upon the study design, disease model parameterization, and the type of analysis. Further limitations are imposed by the nature of exposure and our ability to measure it. We define a plausible exposure model for air pollutants that are relatively nonreactive and discuss how exposure varies. We discuss plausible disease models and show how their parameterizations are affected by different exposure partitions and by different study designs. We then discuss a measurement model conditional on ambient concentrations and incorporate this into the disease model. We use simulation studies to show the impact of a range of exposure model assumptions on estimation of the health effect in the ecologic time series design. This design only uses information from the time-varying ambient source exposure. When ambient and nonambient sources are independent, exposure variation due to nonambient source exposures behaves like Berkson measurement error and does not bias the effect estimates. Variation in the population attenuation of ambient concentrations over time does bias the estimates with the bias being either positive or negative depending upon the association of this parameter with ambient pollution. It is not realistic to substitute measured average personal exposures into time series studies because so much of the variation in personal exposures comes from nonambient sources that do not contribute information in the time series design. We conclude that general statements about the implications of measurement error need to be conditioned on the health effect study design and the health effect parameter to be estimated.

Air Pollutants↗
PubMed2005

Relation between short-term fine-particulate matter exposure and onset of myocardial infarction.

BACKGROUND: Epidemiologic studies have reported increases in the incidence of cardiovascular morbidity and myocardial infarction (MI) associated with increases in short-term and daily levels of fine-particulate matter air pollution, suggesting a role for particulate matter in triggering an MI. METHODS: We studied the association between onset time of MI and preceding hourly measures of fine-particulate matter using a case-crossover study of 5793 confirmed cases of acute MI. We linked data from a community-wide database on acute MI from 1988-1994 in King County, Washington, with central site air pollution monitoring data on fine-particulate matter determined by nephelometry. We compared air pollution exposure levels averaged 1 hour, 2 hours, 4 hours, and 24 hours before MI onset to a set of time-stratified referent exposures from the same day of the week in the month of the case event. RESULTS: : The estimated relative risk for a 10-microg/m increase in fine-particulate matter the hour before MI onset was 1.01 (95% CI=0.98-1.05). Analyses of pollutant levels at the other time points demonstrated a similar lack of association. No increased risk was found in all cases with preexisting cardiac disease (odds ratio = 1.05; 0.95-1.16). Stratification by known cardiovascular risk factors (hypertension, diabetes, and smoking status) also did not modify the relation between fine-particulate matter and MI onset. CONCLUSION: Although a very small effect cannot be excluded, there was no consistent association between ambient levels of fine-particulate matter and risk of MI onset.

Aged↗
PubMed2005

Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias.

The case-crossover design has been widely used to study the association between short-term air pollution exposure and the risk of an acute adverse health event. The design uses cases only; for each individual case, exposure just before the event is compared with exposure at other control (or "referent") times. Time-invariant confounders are controlled by making within-subject comparisons. Even more important in the air pollution setting is that time-varying confounders can also be controlled by design by matching referents to the index time. The referent selection strategy is important for reasons in addition to control of confounding. The case-crossover design makes the implicit assumption that there is no trend in exposure across the referent times. In addition, the statistical method that is used-conditional logistic regression-is unbiased only with certain referent strategies. We review here the case-crossover literature in the air pollution context, focusing on key issues regarding referent selection. We conclude with a set of recommendations for choosing a referent strategy with air pollution exposure data. Specifically, we advocate the time-stratified approach to referent selection because it ensures unbiased conditional logistic regression estimates, avoids bias resulting from time trend in the exposure series, and can be tailored to match on specific time-varying confounders.

Acute Disease↗
PubMed2004

Prediction of chronic disability in work-related musculoskeletal disorders: a prospective, population-based study.

BACKGROUND: Disability associated with work-related musculoskeletal disorders is an increasingly serious societal problem. Although most injured workers return quickly to work, a substantial number do not. The costs of chronic disability to the injured worker, his or her family, employers, and society are enormous. A means of accurate early identification of injured workers at risk for chronic disability could enable these individuals to be targeted for early intervention to promote return to work and normal functioning. The purpose of this study is to develop statistical models that accurately predict chronic work disability from data obtained from administrative databases and worker interviews soon after a work injury. Based on these models, we will develop a brief instrument that could be administered in medical or workers' compensation settings to screen injured workers for chronic disability risk. METHODS: This is a population-based, prospective study. The study population consists of workers who file claims for work-related back injuries or carpal tunnel syndrome (CTS) in Washington State. The Washington State Department of Labor and Industries claims database is reviewed weekly to identify workers with new claims for work-related back injuries and CTS, and these workers are telephoned and invited to participate. Workers who enroll complete a computer-assisted telephone interview at baseline and one year later. The baseline interview assesses sociodemographic, employment-related, biomedical/health care, legal, and psychosocial risk factors. The follow-up interview assesses pain, disability, and work status. The primary outcome is duration of work disability over the year after claim submission, as assessed by administrative data. Secondary outcomes include work disability status at one year, as assessed by both self-report and work disability compensation status (administrative records). A sample size of 1,800 workers with back injuries and 1,200 with CTS will provide adequate statistical power (0.96 for low back and 0.85 for CTS) to predict disability with an alpha of.05 (two-sided) and a hazard ratio of 1.2. Proportional hazards regression models will be constructed to determine the best combination of predictors of work disability duration at one year. Regression models will also be developed for the secondary outcomes.

Activities of Daily Living↗
PubMed2004

Effect of ambient air pollution on pulmonary exacerbations and lung function in cystic fibrosis.

Information concerning the impact of environmental factors on cystic fibrosis (CF) is limited. We conducted a cohort study to assess the impact of air pollutants in CF. The study included patients over the age of 6 years enrolled in the Cystic Fibrosis Foundation National Patient Registry in 1999 and 2000. Exposure was assessed by linking air pollution values from the Aerometric Information Retrieval System with the patients' home zip code. After adjusting for confounders, a 10 microg/m(3) rise in particulate matter (both with a median aerodynamic diameter of 10 microm (PM(10)) or less and with an aerodynamic diameter of 2.5 microm or less (PM(2.5)) was associated with an 8% (95% confidence interval [CI], 2-15%) and 21% (95% CI, 7-33%) increase in the odds of two or more exacerbations, respectively; a 10-ppb rise in ozone was associated with a 10% (95% CI, 3-17%) increase in odds of two or more exacerbations. For every increase in PM(2.5) of 10 microg/m(3), there was an associated fall in FEV(1) of 24 ml (7-40) (95% CI) after adjusting for confounders. PM(2.5)'s association with mortality did not achieve statistical significance (adjusted RR = 1.32 per 10 microg/m(3) 0.91-1.93; 95% CI). Annual average exposures to particulate air pollution was associated with an increased risk of pulmonary exacerbations and a decline in lung function, suggesting a role of environmental exposures on prognosis in CF.

Adolescent↗
PubMed2004

Estimated hourly personal exposures to ambient and nonambient particulate matter among sensitive populations in Seattle, Washington.

Epidemiological studies of particulate matter (PM) routinely use concentrations measured with stationary outdoor monitors as surrogates for personal exposure. Despite the frequently reported poor correlations between ambient concentrations and total personal exposure, the epidemiologic associations between ambient concentrations and health effects depend on the correlation between ambient concentrations and personal exposure to ambient-generated PM. This paper separates personal PM exposure into ambient and nonambient components and estimates the outdoor contribution to personal PM exposures with continuous light scattering data collected from 38 subjects in Seattle, WA. Across all subjects, the average exposure encountered indoors at home was lower than in all other microenvironments. Cooking and being at school were associated with elevated levels of exposure. Previously published estimates of particle infiltration (Finf) were combined with time-location data to estimate an ambient contribution fraction (alpha, mean = 0.66+/-0.21) for each subject. The mean alpha was significantly lower for subjects monitored during the heating season (0.55+/-0.16) than for those monitored during the nonheating season (0.80+/-0.17). Our modeled alpha estimates agreed well with those estimated with the sulfur-tracer method (slope = 1.08; R2 = 0.67). We modeled exposure to ambient and nonambient PM with both continuous light scattering and 24-hr gravimetric data and found good agreement between the two methods. On average, ambient particles accounted for 48% of total personal exposure (range = 21-80%). The personal activity exposure was highly influenced by time spent away from monitored microenvironments. The median hourly longitudinal correlation between central site concentrations and personal exposures was 0.30. Although both alpha and the nonambient sources influence the personal-central relationship, the latter seems to dominate. Thus, total personal exposure may be poorly predicted by stationary outdoor monitors, particularly among persons whose PM exposure is dominated by nonambient exposures, for example, those living in tightly sealed homes, those who cook, and children.

Air Pollutants↗
PubMed2003

Use of real-time light scattering data to estimate the contribution of infiltrated and indoor-generated particles to indoor air.

The contribution of outdoor particulate matter (PM) to residential indoor concentrations is currently not well understood. Most importantly, separating indoor PM into indoor- and outdoor-generated components will greatly enhance our knowledge of the outdoor contribution to total indoor and personal PM exposures. This paper examines continuous light scattering data at 44 residences in Seattle, WA. A newly adapted recursive model was used to model outdoor-originated PM entering indoor environments. After censoring the indoor time-series to remove the influence of indoor sources, nonlinear regression was used to estimate particle penetration (P, 0.94 +/- 0.10), air exchange rate (a, 0.54 +/- 0.60 h(-1)), particle decay rate (k, 0.20 +/- 0.16 h(-1)), and particle infiltration (F(inf), 0.65 +/- 0.21) for each of the 44 residences. All of these parameters showed seasonal differences. The F(inf) estimates agree well with those estimated from the sulfur-tracer method (R2 = 0.78). The F(inf) estimates also showed robust and expected behavior when compared against known influencing factors. Among our study residences, outdoor-generated particles accounted for an average of 79 +/- 17% of the indoor PM concentration, with a range of 40-100% at individual residences. Although estimates of P, a, and k were dependent on the modeling technique and constraints, we showed that a recursive mass balance model combined with our censoring algorithms can be used to attribute indoor PM into its outdoor and indoor components and to estimate an average P, a, k, and F(inf), for each residence.

Air Pollution, Indoor↗