Mieke Koehoorn

Bio: Mieke Koehoorn is an academic researcher from University of British Columbia. The author has contributed to research in topics: Population & Poison control. The author has an hindex of 35, co-authored 170 publications receiving 5430 citations. Previous affiliations of Mieke Koehoorn include Health Canada.

A Cohort Study of Traffic-Related Air Pollution Impacts on Birth Outcomes

Abstract: Numerous studies have indicated associations between exposure to ambient air pollution and adverse pregnancy outcomes. Such associations, if determined to be causal, are likely to result in significant public health impacts given the widespread exposure to air pollution and the fact that low birth weight (LBW) or preterm births are subsequently associated with long-term sequelae such as developmental disability and chronic lung disease (Cano et al. 2001; Dik et al. 2004). Determination of a causal relationship between air pollution and adverse pregnancy outcomes would have implications for burden of disease measures and add to the importance of strategies to mitigate the health effects of air pollution exposure. Previous studies have been reviewed in detail. Sram et al. (2005) concluded that evidence is sufficient to support a causal association between ambient concentrations of particulate matter and LBW, but evidence of effects for other pollutants and for other outcomes such as preterm birth is less robust. Maisonet et al. (2004) concluded that studies to date support small effects of air pollution on preterm birth and small for gestational age birth (SGA), but not full-term LBW. In a systematic review, Glinianaia et al. (2004) suggested that evidence of associations with air pollution and fetal growth or pregnancy duration is limited and inconclusive and argued for population-based cohort designs using high-quality individual exposure estimates. These reviews highlight the difficulties in interpreting an evidence base with differences among methods and with important limitations. First, most studies are either time-series studies (Dugandzic et al. 2006; Liu et al. 2003, 2007; Mannes et al. 2005; Sagiv et al. 2005) that relate relatively short-term changes in air pollution concentrations to temporal changes in rates of adverse pregnancy outcomes or, less frequently, cohort analyses that compare outcomes between locations with differing levels of ambient air pollution (Salam et al. 2005) based on interpolated ambient monitoring network data. Between-city comparisons are subject to potential confounding because covariates may be highly correlated with air pollution, whereas time-series studies are problematic to interpret because they relate short-term changes in air pollution that are driven primarily by meteorology to outcomes. They inherently assume that the impact of air pollution on birth outcomes is acute, require knowledge of the relevant periods of pregnancy during which air pollution may have impacts, and are subject to potential confounding by seasonally varying factors. As reviewed by Glinianaia et al. (2004), a number of studies have suggested stronger relationships between birth outcomes and exposure during specific periods of pregnancy based on comparison of statistical effect sizes. However, results across studies have not consistently identified specific periods of exposure that are most closely linked to adverse pregnancy outcomes. Increasingly, air pollution researchers have identified important spatial variability in air pollution concentrations within airsheds (Hoek et al. 2002b; Lewne et al. 2004; Zhang et al. 2004; Zhu et al. 2004). In many situations these contrasts are of greater magnitude than between-city or temporal contrasts (Jerrett et al. 2005). Such spatial contrasts, primarily related to measures of proximity to traffic corridors, have been associated with a number of health impacts including mortality (Hoek et al. 2002a; Maynard et al. 2007; Miller et al. 2007; Nafstad et al. 2004; Roemer and van Wijnen 2001), asthma and respiratory symptoms (Bayer-Oglesby et al. 2006; Brauer et al. 2002, 2007; Gauderman et al. 2005, 2007; McConnell et al. 2006; Ryan et al. 2005; Smargiassi et al. 2006), and otitis media (Brauer et al. 2006). Application of within-airshed spatial contrasts in birth outcome studies are few (Leem et al. 2006; Parker et al. 2005; Ritz and Yu 1999; Ritz et al. 2000; Slama et al. 2007; Wilhelm and Ritz 2003, 2005). These studies, though provocative, have been limited largely to Southern California—a metropolitan area with relatively high levels of ambient air pollution. They relied on interpolated ambient monitoring data or simple road proximity measures rather than high-resolution spatial contrasts in concentrations. We sought to assess the relationship between reproductive outcomes and spatial and temporally varying levels of air pollution in the metropolitan area of Vancouver, British Columbia, Canada, a city with relatively low levels of ambient air pollution. We estimated exposures at the individual level, for a population-based cohort using both monitor-based methods and land use regression models based on proximity to traffic sources, land use, population density, and topographic features. Even in Vancouver, an area with a dense ambient monitoring network, exposure assessment based on regulatory monitoring network data is more suited to characterizing temporal variability. Land use regression models, even those with temporal components, as in this analysis, focus on high-resolution spatial variability in air pollutant concentrations. The literature describing associations between air pollution and birth outcomes has focused on clinically defined outcomes of LBW and preterm birth, defined in a variety of ways, which complicates comparisons. The underlying biological processes—fetal growth restriction and inadequate gestational length—are incompletely understood and imperfectly represented in routinely available perinatal measurements available in vital statistic records. We elected to focus on SGA births as a primary outcome measure, because birth weight as a function of gestational age has a direct effect on perinatal morbidity and mortality (Pollack and Divon 1992). LBW may result from complex and multiple pathways of fetal growth restriction attributed to maternal, fetal, or placental factors. Three broad categories of biological factors have been suggested to play a role in inadequate fetal gestation: abnormality of the biological clock, abnormal implantation, and infection and inflammation (Mattison et al. 2003). The current theories provide multiple sites at which environmental factors may influence biological factors to modulate fetal growth and induce preterm birth. However, specific toxicologic mechanisms including relevant timing during gestational development are not known. We explored each of these processes, fetal growth restriction and inadequate gestational length, separately, and explored the influences of exposure timing in early and late pregnancy.

Effect of Early Life Exposure to Air Pollution on Development of Childhood Asthma

Abstract: BackgroundThere is increasing recognition of the importance of early environmental exposures in the development of childhood asthma. Outdoor air pollution is a recognized asthma trigger, but it is ...

Association of Long-term Exposure to Community Noise and Traffic-related Air Pollution With Coronary Heart Disease Mortality

Abstract: In metropolitan areas, road traffic is a major contributor to ambient air pollution and the dominant source of community noise. The authors investigated the independent and joint influences of community noise and traffic-related air pollution on risk of coronary heart disease (CHD) mortality in a population-based cohort study with a 5-year exposure period (January 1994-December 1998) and a 4-year follow-up period (January 1999-December 2002). Individuals who were 45-85 years of age and resided in metropolitan Vancouver, Canada, during the exposure period and did not have known CHD at baseline were included (n = 445,868). Individual exposures to community noise and traffic-related air pollutants, including black carbon, particulate matter less than or equal to 2.5 μm in aerodynamic diameter, nitrogen dioxide, and nitric oxide, were estimated at each person's residence using a noise prediction model and land-use regression models, respectively. CHD deaths were identified from the provincial death registration database. After adjustment for potential confounders, including traffic-related air pollutants or noise, elevations in noise and black carbon equal to the interquartile ranges were associated with 6% (95% confidence interval: 1, 11) and 4% (95% confidence interval: 1, 8) increases, respectively, in CHD mortality. Subjects in the highest noise decile had a 22% (95% confidence interval: 4, 43) increase in CHD mortality compared with persons in the lowest decile. These findings suggest that there are independent effects of traffic-related noise and air pollution on CHD mortality.

Long-Term Exposure to Traffic-Related Air Pollution and the Risk of Coronary Heart Disease Hospitalization and Mortality

Abstract: BackgroundEpidemiologic studies have demonstrated that exposure to road traffic is associated with adverse cardiovascular outcomes.ObjectivesWe aimed to identify specific traffic-related air pollut...

Evaluating non-randomised intervention studies.

Abstract: OBJECTIVES: To consider methods and related evidence for evaluating bias in non-randomised intervention studies. DATA SOURCES: Systematic reviews and methodological papers were identified from a search of electronic databases; handsearches of key medical journals and contact with experts working in the field. New empirical studies were conducted using data from two large randomised clinical trials. METHODS: Three systematic reviews and new empirical investigations were conducted. The reviews considered, in regard to non-randomised studies, (1) the existing evidence of bias, (2) the content of quality assessment tools, (3) the ways that study quality has been assessed and addressed. (4) The empirical investigations were conducted generating non-randomised studies from two large, multicentre randomised controlled trials (RCTs) and selectively resampling trial participants according to allocated treatment, centre and period. RESULTS: In the systematic reviews, eight studies compared results of randomised and non-randomised studies across multiple interventions using meta-epidemiological techniques. A total of 194 tools were identified that could be or had been used to assess non-randomised studies. Sixty tools covered at least five of six pre-specified internal validity domains. Fourteen tools covered three of four core items of particular importance for non-randomised studies. Six tools were thought suitable for use in systematic reviews. Of 511 systematic reviews that included non-randomised studies, only 169 (33%) assessed study quality. Sixty-nine reviews investigated the impact of quality on study results in a quantitative manner. The new empirical studies estimated the bias associated with non-random allocation and found that the bias could lead to consistent over- or underestimations of treatment effects, also the bias increased variation in results for both historical and concurrent controls, owing to haphazard differences in case-mix between groups. The biases were large enough to lead studies falsely to conclude significant findings of benefit or harm. Four strategies for case-mix adjustment were evaluated: none adequately adjusted for bias in historically and concurrently controlled studies. Logistic regression on average increased bias. Propensity score methods performed better, but were not satisfactory in most situations. Detailed investigation revealed that adequate adjustment can only be achieved in the unrealistic situation when selection depends on a single factor. CONCLUSIONS: Results of non-randomised studies sometimes, but not always, differ from results of randomised studies of the same intervention. Non-randomised studies may still give seriously misleading results when treated and control groups appear similar in key prognostic factors. Standard methods of case-mix adjustment do not guarantee removal of bias. Residual confounding may be high even when good prognostic data are available, and in some situations adjusted results may appear more biased than unadjusted results. Although many quality assessment tools exist and have been used for appraising non-randomised studies, most omit key quality domains. Healthcare policies based upon non-randomised studies or systematic reviews of non-randomised studies may need re-evaluation if the uncertainty in the true evidence base was not fully appreciated when policies were made. The inability of case-mix adjustment methods to compensate for selection bias and our inability to identify non-randomised studies that are free of selection bias indicate that non-randomised studies should only be undertaken when RCTs are infeasible or unethical. Recommendations for further research include: applying the resampling methodology in other clinical areas to ascertain whether the biases described are typical; developing or refining existing quality assessment tools for non-randomised studies; investigating how quality assessments of non-randomised studies can be incorporated into reviews and the implications of individual quality features for interpretation of a review's results; examination of the reasons for the apparent failure of case-mix adjustment methods; and further evaluation of the role of the propensity score.