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Biomedical subjects

Lianne Sheppard

Publications and source records attributed to Lianne Sheppard.

29 records · Page 2Linked to original sources
PubMed2003

Effects of ambient air pollution on symptom severity and medication use in children with asthma.

BACKGROUND: Exposure to air pollutants has been investigated as a possible cause of asthma attacks in children. OBJECTIVE: To investigate the short-term effects of air pollutants on a panel of 133 children with asthma who enrolled in the Childhood Asthma Management Program. METHODS: During screening, the children completed daily diary cards for an average of 58 days to indicate their medication use and asthma severity. We used ordinal logistic regression to compare the odds of a more serious relative to a less serious asthma attack, and we used a Poisson model to analyze medication use. In both analyses we accommodate dependence in the data and different periods of observation for study subjects. RESULTS: Our results indicate that a 10-microg/m3 increase in particulate matter less than or equal to 2.5 microm (PM2.5) lagged 1 day was associated with a 1.20 times increased odds of having a more serious asthma attack [95% confidence interval (CI), 1.05 to 1.37] and a 1.08-fold increase in medication use (95% CI, 1.01 to 1.15). A 10-microg/m3 increase in particulate matter less than or equal to 10 microm (PM10) increased the odds of a more serious asthma attack (odds ratio = 1.12; 95% CI, 1.04 to 1.22) and also increased medication use (relative risk = 1.05; 95% CI, 1.00 to 1.09). CONCLUSIONS: Increases in PM2.5 and PM10 are significantly associated with an increased risk of more severe asthma attacks and medication use in Seattle area children with asthma. We also found associations with carbon monoxide, but we believe that carbon monoxide is a marker for exposure to combustion byproducts.

Adolescent↗
PubMed2003

Insights on bias and information in group-level studies.

Ecological and aggregate data studies are examples of group-level studies. Even though the link between the predictors and outcomes is not preserved in these studies, inference about individual-level exposure effects is often a goal. The disconnection between the level of inference and the level of analysis expands the array of potential biases that can invalidate the inference from group-level studies. While several sources of bias, specifically due to measurement error and confounding, may be more complex in group-level studies, two sources of bias, cross-level and model specification bias, are a direct consequence of the disconnection. With the goal of aligning inference from individual versus group-level studies, I discuss the interplay between exposure and study design. I specify the additional assumptions necessary for valid inference, specifically that the between- and within-group exposure effects are equal. Then cross-level inference is possible. However, all the information in the group-level analysis comes from between-group comparisons. Models where the group-level analysis provides even a small percentage of information about the within-group exposure effect are most susceptible to model specification bias. Model specification bias can be even more serious when the group-level model isn't derived from an individual-level model.

Analysis of Variance↗
PubMed2003

Exposure assessment of particulate matter for susceptible populations in Seattle.

In this article we present results from a 2-year comprehensive exposure assessment study that examined the particulate matter (PM) exposures and health effects in 108 individuals with and without chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), and asthma. The average personal exposures to PM with aerodynamic diameters < 2.5 microm (PM2.5) were similar to the average outdoor PM2.5 concentrations but significantly higher than the average indoor concentrations. Personal PM2.5 exposures in our study groups were lower than those reported in other panel studies of susceptible populations. Indoor and outdoor PM2.5, PM10 (PM with aerodynamic diameters < 10 microm), and the ratio of PM2.5 to PM10 were significantly higher during the heating season. The increase in outdoor PM10 in winter was primarily due to an increase in the PM2.5 fraction. A similar seasonal variation was found for personal PM2.5. The high-risk subjects in our study engaged in an equal amount of dust-generating activities compared with the healthy elderly subjects. The children in the study experienced the highest indoor PM2.5 and PM10 concentrations. Personal PM2.5 exposures varied by study group, with elderly healthy and CHD subjects having the lowest exposures and asthmatic children having the highest exposures. Within study groups, the PM2.5 exposure varied depending on residence because of different particle infiltration efficiencies. Although we found a wide range of longitudinal correlations between central-site and personal PM2.5 measurements, the longitudinal r is closely related to the particle infiltration efficiency. PM2.5 exposures among the COPD and CHD subjects can be predicted with relatively good power with a microenvironmental model composed of three microenvironments. The prediction power is the lowest for the asthmatic children.

Age Factors↗
PubMed2002

A hierarchical aggregate data model with spatially correlated disease rates.

The aggregate data study design (Prentice and Sheppard, 1995, Biometrika 82, 113-125) estimates individual-level exposure effects by regressing population-based disease rates on covariate data from survey samples in each population group. In this work, we further develop the aggregate data model to allow for residual spatial correlation among disease rates across populations. Geographical variation that is not explained by model predictors and has a spatial component often arises in studies of rare chronic diseases, such as breast cancer. We combine the aggregate and Bayesian disease-mapping models to provide an intuitive approach to the modeling of spatial effects while drawing correct inference regarding the exposure effect. Based on the results of simulation studies, we suggest guidelines for use of the proposed model.

Bayes Theorem↗
PubMed2002

Testing the metals hypothesis in Spokane, Washington.

A >7-year, time-series, epidemiologic study is ongoing in Spokane, Washington, to examine the associations between ambient particulate constituents or sources and health outcomes such as emergency department (ED) visits for asthma or respiratory problems. One of the hypotheses being tested is that particulate toxic metals are associated with these health outcomes. Spokane is a desirable city in which to conduct this study because of its relatively high concentrations of particulate matter, low concentrations of potentially confounding air pollutants, variability of particulate sources, and presence of several potential particulate metals sources. Daily fine- and coarse-fraction particulate samples are analyzed for metals via energy-dispersive X-ray fluorescence (EDXRF) and instrumental neutron activation analysis. Particulate sources are determined using receptor modeling, including chemical mass balancing and positive matrix factorization coupled with partial source contribution function analysis. Principal component analysis has also been used to examine the influence of sources on the daily variability of the chemical composition of particulate samples. Based upon initial analyses using the EDXRF elemental analyses, statistically significant associations were observed between ED visits for asthma and increased combustion products, air stagnation, and fine particulate Zn. Although there is a significant soil particulate component, increased crustal particulate levels were not found to be associated with ED visits for asthma. Further research will clarify whether there is an association between specific health outcomes and either coarse or fine particulate metal species.

Air Pollutants↗
PubMedDate not supplied

Chronic maternal methanol inhalation in nonhuman primates (Macaca fascicularis): exposure and toxicokinetics prior to and during pregnancy.

Toxicokinetic studies were conducted following daily inhalation exposure to methanol vapor prior to and throughout pregnancy in adult female Macaca fascicularis monkeys. They were part of a larger study to investigate the effects of chronic methanol exposure on maternal reproductive performance and early offspring effects. In a two-cohort study design, 48 females (24/cohort) were assigned to parallel exposure groups at 0 (control), 200, 600, or 1800 ppm methanol vapor for approximately 2.5 h/day, 7 days/week throughout breeding and pregnancy. Blood methanol at 30 min postexposure was monitored biweekly. The time course for the clearance of blood MeOH concentrations following exposure was characterized on four occasions: twice during the prebreeding period and during mid- and late pregnancy. Average blood methanol concentrations at 30 min postexposure were 5, 11, and 35 microg/ml across all four toxicokinetic studies in the 200, 600 and 1800 ppm groups, respectively. Blood concentrations in the 200 ppm group were barely above basal (preexposure) blood methanol concentrations or those observed in the control group (approximately 3 microg/ml). Nonlinear elimination kinetics were observed in most of the 1800 ppm group females. There was a decrease in elimination half-life (7-20%) and an increase in clearance (30%) after 3-months of daily MeOH exposure compared to the initial exposure. There were no statistically significant changes in the first-order blood methanol half-life or clearance during pregnancy, but the mean distribution volume per kilogram body weight decreased by 22% and 17% in the 600 and 1800 ppm groups. Plasma formate levels did not differ between the methanol and control exposure groups. Plasma formate and serum folate concentrations increased slightly over the course of this study in both the exposed and control groups but these increases were not related to methanol exposure.

Administration, Inhalation↗
PubMedDate not supplied

Chronic maternal methanol inhalation in nonhuman primates (Macaca fascicularis): reproductive performance and birth outcome.

The present study was designed to characterize maternal reproductive performance and early offspring effects following exposure to methanol (MeOH) vapor in a nonhuman primate model. The two-cohort study design used 48 adult female Macaca fascicularis (24/cohort) monkeys exposed to 0, 200, 600, or 1800 ppm MeOH vapor for approximately 2.5 h/day, 7 days/week prior to breeding and throughout pregnancy. Maternal body weight measurement, clinical observations and health assessments were conducted routinely throughout the study. Menstrual cyclicity was monitored during the pre-breeding and breeding periods and timed matings were conducted with nonexposed males. Females were monitored closely during the last month of pregnancy. At birth, infant physical characteristics were measured and a newborn health assessment was conducted. Methanol exposure did not alter menstrual cycles, the number of breedings to conception or conception rate. A total of 34 live-born infants were delivered (control=8, 200 ppm=9, 600 ppm=8, 1800 ppm=9). One female each in the control and 600-ppm group delivered a stillborn infant and a cesarean section (C-section) was required to deliver a hydrocephalic infant who died in utero in the maternal 1800-ppm group. Although not statistically significant, five MeOH-exposed females were C-sectioned due to pregnancy complications such as uterine bleeding and prolonged unproductive labor. These complications were not observed in the control group. The mean length of pregnancy in the MeOH-exposed groups was significantly decreased by 6 to 8 days when compared to controls. There were no MeOH-related effects on offspring birthweight or newborn health status. The consistent reduction in length of pregnancy observed in the MeOH females may reflect a treatment effect on the fetal neuroendocrine system. Given that the fetal hypothalamic--pituitary-adrenal axis controls pregnancy length in most species, these results suggest a modest but significant effect of MeOH on the biochemical events that control the timing of birth.

Administration, Inhalation↗
PubMedDate not supplied

Acute air pollution effects: consequences of exposure distribution and measurements.

Acute effect air pollution studies estimate the effect of short-term change in exposure on a health outcome. The two designs most commonly used in air pollution epidemiology are panel studies and time-series studies. Typically, both designs rely on ambient concentration measurements and not the personal exposures of individuals. This article discusses how panel studies and time-series studies are related and reviews the use of ambient concentrations versus personal exposure measurements in the analyses. This work suggests that for estimating acute effects, ambient concentration measurements are quite adequate in time-series studies. In addition, time-series studies have ample power relative to panel studies, in spite of the ecologic nature of their design. Panel studies have the benefit of being able to use all the information from personal exposures in the analysis, but they are much more costly and difficult to conduct. Furthermore, nontraditional panel studies, where multiple repeat panels are followed over time, require additional considerations in the analysis.

Air Pollutants↗
PubMedDate not supplied

Ambient woodsmoke and associated respiratory emergency department visits in Spokane, Washington.

Three multivariate receptor algorithms were applied to seven years of chemical speciation data to apportion fine particulate matter to various sources in Spokane, Washington. Source marker compounds were used to assess the associations between atmospheric concentration of these compounds and daily cardiac hospital admissions and/or respiratory emergency department visits. Total carbon and arsenic had high correlations with two different vegetative burning sources and were selected as vegetative burning markers, while zinc and silicon were selected as markers for the motor vehicle and airborne soil sources, respectively. The rate of respiratory emergency department visits increased 2% for a 3.0 microg/m3 interquartile range change in a vegetative burning source marker (1.023, 95% CI 1.009-1.038) at a lag of one day. The other source markers studied were not associated with the health outcomes investigated. Results suggest vegetative burning is associated with acute respiratory events.

Air Pollutants↗
PubMedDate not supplied

Comparison of task-based estimates with full-shift measurements of noise exposure.

Using a large data set of noise exposure measurements on construction workers, task-based (TB) and full-shift (FS) exposure levels were compared and analyzed for the sources and magnitudes of the error associated with this methodology. Data-logging dosimeters recorded A-weighted sound pressure levels in decibels using Occupational Safety and Health Administration criteria for every minute of monitoring and were combined with information from task cards completed by subjects. Task-related information included trade, construction site type, location, activity, and tool. A total of 502 FS measurements were made, including 248,677 min of exposure on five construction trades. Six TB models of varying degrees of specificity were fit to the minute-level data and the results used to obtain TB estimates of the daily FS exposure levels. The TB estimates were derived using the predictions alone and also including subject and shift-specific residual means and variances. The predictions alone, which ignore within-task variability, produced a significant negative bias that was corrected by incorporation of the residual variance. This bias is only an issue in this setting in which the exposure of interest is noise, which follows a nonlinear averaging relationship. These estimates explained 10 to 60% of the variability in FS measures; adding the residual mean produced estimates that explained about 90% of the variability. In summary, TB estimates are important for exposure estimation when task time varies substantially. However, TB estimates include a substantial degree of error when there is large interindividual or intershift variability in exposure levels for a given task. Methods to improve the prediction of task-associated exposure, or adjusting for individual and shift differences, are needed.

Data Collection↗