Karen Y. Fung

This study aimed at establishing the relationship between annoyance scores and modelled air pollution in “Chemical Valley”, Sarnia, Ontario (Canada). Annoyance scores were taken from a community health survey (N = 774); and respondents’ exposure to nitrogen dioxide (NO₂) and sulphur dioxide (SO₂) were estimated using land use regression (LUR) models. The associations were examined by univariate analysis while multivariate logistic regression was used to examine the determinants of odour annoyance. The results showed that odour annoyance was significantly correlated to modelled pollutants at the individual (NO₂, r = 0.15; SO₂, r = 0.13) and census tract (NO₂, r = 0.56; SO₂, r = 0.67) levels. The exposure-response relationships show that residents of Sarnia react to very low pollution concentrations levels even if they are within the Ontario ambient air quality criteria. The study found that exposure to high NO₂ and SO₂ concentrations, gender, and perception of health effects were significant determinants of individual odour annoyance reporting. The observed association between odour annoyance and modelled ambient pollution suggest that individual and census tract level annoyance scores may serve as proxies for air quality in exposed communities because they capture the within area spatial variability of pollution. However, questionnaire-based odour annoyance scores need to be validated longitudinally and across different scales if they are to be adopted for use at the national level.

Purpose This study examined whether race/ethnicity had differential effects on breast cancer care and survival across age strata and cohorts within stages of disease. Methods The Detroit Cancer Registry provided 25,997 breast cancer cases. African American and non-Hispanic white, older Medicare-eligible and younger non-eligible women were compared. Successive historical cohorts (1975–1980 and 1990–1995) were, respectively, followed until 1986 and 2001. Results African American disadvantages on survival and treatments increased significantly, particularly among younger women who were much more likely to be uninsured. Within node positive disease all treatment disadvantages among younger African American women disappeared with socioeconomic adjustment. Conclusions Growth of this racial divide implicates social, rather than biological, forces. Its elimination will require high quality health care for all.

Southwestern Ontario (SWO) in Canada has been known as a 'hot spot' in terms of environmental exposure and potential effects. We chose to study 3 major cities in SWO in this paper. We compared age-standardized hospital admission ratios of Sarnia and Windsor to London, and to generate hypotheses about potential pollutant-induced health effects in the 'Chemical Valley', Sarnia.

Recurrent events such as repeated hospital admissions for the same health outcome occur frequently in environmental health studies. Dewanji and Moolgavkar proposed a flexible parametric model and a conditional likelihood analysis for recurrent events based on a Poisson process formulation. In this paper, we examine the statistical properties of the Dewanji–Moolgavkar (DM) estimator of the risk of an adverse health outcome associated with environmental exposures based on recurrent event data using computer simulation. We also compare the DM approach with both case–crossover analysis for multiple observations and time series analysis when there are no subject‐specific covariates. When using a correctly specified model, the DM method produced better estimates with respect to relative mean square error when each subject had constant or curved baseline intensity functions than it did when baseline intensities were increasing or decreasing in a linear fashion. For under‐specified models, the DM method outperformed case–crossover analysis for decreasing straight line intensity functions, was outperformed by case–crossover analysis for increasing straight line intensity functions, and was roughly equivalent to case–crossover analysis for constant and curved intensity functions. Case–crossover analysis produced superior risk estimates more frequently than the other two methods in the cases considered here, especially for linear representations of the baseline intensities. Copyright © 2006 John Wiley & Sons, Ltd.

Recurrent events, such as repeated hospital admissions for the same health outcome, occur frequently in environmental health studies. In this study, we conducted an analysis of data on repeated respiratory hospitalizations among the elderly in Vancouver, Canada, for the period of June 1, 1995, to March 31, 1999, using a new method proposed by (Dewanji and Moolgavkar 2000, 2002) for recurrent events, and compared it with some traditional methods. In particular, we assessed the impact of ambient gaseous (SO2, NO2, CO, and O3) and particulate pollutants (PM10, PM2.5, and PM10-2.5) as well as the coefficient of haze (CoH) on recurrent respiratory hospital admissions. Using the new procedure, significant associations were found between admissions and 3-day, 5-day, and 7-day moving averages of the ambient SO2 concentrations, with the strongest association observed at the 7-day lag (RR = 1.044, 95% CI: 1.018-1.070). We also found PM10-2.5 for 3-day and 5-day lag to be significant, with the strongest association at 5-day lag (RR = 1.020, 95% CI: 1.001-1.039). No significant associations with admission were found with current day exposure.

In this paper, we examine the role that ambient air pollution plays in exacerbating cardiovascular and respiratory disease hospitalization in London, Ontario from 1 November 1995 to 31 December 2000. The number of daily cardiac and respiratory admissions was linked to concentrations of air pollutants (sulphur dioxide, nitrogen dioxide, ozone, carbon monoxide, coefficient of haze, PM₁₀) and weather variables (maximum and minimum of temperature and humidity). Results showed that current day carbon monoxide and coefficient of haze produced significant percentage increase in daily cardiac admissions of 8.0% (95% CI: 1.5–11.5%) and 5.7% (95% CI: 0.9–10.8%) for people < 65 years old. PM₁₀ was found to be significantly associated with asthma admission in the > 65 group, with percentage increase in cardiac admission of 25% (95% CI: 2.8–52.3%) and 26.0% (95% CI: 5.3–50.9%) for current day and 2‐day means, respectively.

This article extends the previous work of Fung et al. (2004) investigating the ability of the time-scale log-linear regression model, proposed by Dominici et al. (2003) Dominici, F. , McDermott, A. and Zeger, S. L. 2003. Airborne particulate matter and mortality: Timescale effects in four US cities. Am. J. Epidemiol., 157: 1055–1065. [PUBMED] [INFOTRIEVE] [CSA] [Crossref], [PubMed], [Web of Science ®] [Google Scholar] , to detect mortality displacement (sometimes known as harvesting) in time-series data relating air pollution to excess mortality. We conducted a simulation study based on two different compartment models of the death process: pure frailty model and mixed frailty model. We assume that nonaccidental death only affects frail population in a pure frailty model and affects both frail people and other individuals in the mixed frailty model. With a pure frailty model and a moderate-size pollution effect, we identified a characteristic mortality displacement pattern in the different time-scale coefficients of log relative risk. However, once a covariate like temperature was introduced into the model, such a mortality displacement pattern disappeared. Furthermore, a false mortality displacement effect was present in the incorrectly specified model, when temperature was not taken into account. We believe that time-scale regression has limited value for detecting mortality displacement in time-series data.

Objective To examine the role that ambient air pollution plays in exacerbating cardiovascular disease hospitalization in Windsor, Ontario. Methods The number of daily cardiac hospital admissions was obtained from all Windsor hospitals from April 1, 1995 to December 31, 2000 and linked to concentrations of ambient air pollutants and weather variables. The logarithm of daily counts of hospitalization was regressed on the levels of pollutants, after adjusting for seasonal, weekly cycles, and weather variables using time series analysis with natural splines as smoothing functions. Results Of all the pollutants considered, sulphur dioxide (SO₂) had the strongest effect on cardiac hospitalization among the >65 age group. The percentage increase in daily admission was 2.6% for current day sulphur dioxide level (95% CI: 0.5-6.4), 4.0% for 2-day mean level (95% CI: 0.1-6.9), and 5.6% (95% CI: 1.5–9.9) for 3-day mean level for an increase in interquartile range of 19.3 ppb. When particulate PM₁₀ was included in the model, the contributing effect of sulphur dioxide remained significant for the >65 age group for all three levels. Conclusions Short-term effects of sulphur dioxide are associated significantly to daily cardiac hospital admissions for people >65 years of age living in Windsor. Since Windsor is a border city, additional monitoring and assessment is recommended to determine if air quality and resultant health effects have deteriorated since traffic congestion at the border has increased following the events of September 11, 2001.

Background Time series analysis is the most commonly used technique for assessing the association between counts of health events over time and exposure to ambient air pollution. Recently, case-crossover analysis has been proposed as an alternative analytical approach. While each technique has its own advantages and disadvantages, there remains considerable uncertainty as to which statistical methodology is preferable for evaluating data of this type. Methods The objective of this paper is to evaluate the performance of different variations of these two procedures using computer simulation. Hospital admission data were generated under realistic models with known parameters permitting estimates based on time series and case-crossover analyses to be compared with these known values. Results While accurate estimates can be achieved with both methods, both methods require some decisions to be made by the researcher during the course of the analysis. With time series analysis, it is necessary to choose the time span in the LOESS smoothing process, or degrees of freedom when using natural cubic splines. For case-crossover studies using either uni- or bi-directional control selection strategies, the choice of time intervals needs to be made. Conclusions We prefer the times series approach because the best estimates of risk that can be obtained with time series analysis are more precise than the best estimates based on case-crossover analysis.