Author
Chris Giovis
Other affiliations: West Health
Bio: Chris Giovis is an academic researcher from McMaster University. The author has contributed to research in topics: Population & Exposure assessment. The author has an hindex of 5, co-authored 5 publications receiving 1591 citations. Previous affiliations of Chris Giovis include West Health.
Papers
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TL;DR: In this article, a review of models for assessing intraurban exposure under six classes, including proximity-based assessments, statistical interpolation, land use regression models, line dispersion models, integrated emission-meteorological models, and hybrid models combining personal or household exposure monitoring with one of the preceding methods is presented.
Abstract: The development of models to assess air pollution exposures within cities for assignment to subjects in health studies has been identified as a priority area for future research. This paper reviews models for assessing intraurban exposure under six classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorological models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. We enrich this review of the modelling procedures and results with applied examples from Hamilton, Canada. In addition, we qualitatively evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space analysis will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.
1,023 citations
TL;DR: This paper used a universal kriging procedure to interpolate data from twenty-three monitoring stations in Hamilton (1985-94) to develop an estimate of likely pollution values across the city based on annual geometric means and extreme events.
Abstract: The authors address two research questions: (1) Are populations with lower socioeconomic status, compared with people of higher socioeconomic status, more likely to be exposed to higher levels of particulate air pollution in Hamilton, Ontario, Canada? (2) How sensitive is the association between levels of particulate air pollution and socioeconomic status to specification of exposure estimates or statistical models? Total suspended particulate (TSP) data from the twenty-three monitoring stations in Hamilton (1985–94) were interpolated with a universal kriging procedure to develop an estimate of likely pollution values across the city based on annual geometric means and extreme events. Comparing the highest with the lowest exposure zones, the interpolated surfaces showed more than a twofold increase in TSP concentrations and more than a twentyfold difference in the probability of exposure to extreme events. Exposure estimates were related to socioeconomic and demographic data from census tract areas by usi...
326 citations
TL;DR: Low educational attainment and high manufacturing employment in the zones significantly and positively modified the acute mortality effects of air pollution exposure, and increased mortality was associated with air pollution Exposure in a citywide model and in intra-urban zones with lower socioeconomic characteristics.
Abstract: Study objective: To assess the short term association between air pollution and mortality in different zones of an industrial city. An intra-urban study design is used to test the hypothesis that socioeconomic characteristics modify the acute health effects of ambient air pollution exposure. Design: The City of Hamilton, Canada, was divided into five zones based on proximity to fixed site air pollution monitors. Within each zone, daily counts of non-trauma mortality and air pollution estimates were combined. Generalised linear models (GLMs) were used to test mortality associations with sulphur dioxide (SO 2 ) and with particulate air pollution measured by the coefficient of haze (CoH). Main results: Increased mortality was associated with air pollution exposure in a citywide model and in intra-urban zones with lower socioeconomic characteristics. Low educational attainment and high manufacturing employment in the zones significantly and positively modified the acute mortality effects of air pollution exposure. Discussion: Three possible explanations are proposed for the observed effect modification by education and manufacturing: (1) those in manufacturing receive higher workplace exposures that combine with ambient exposures to produce larger health effects; (2) persons with lower education are less mobile and experience less exposure measurement error, which reduces bias toward the null; or (3) manufacturing and education proxy for many social variables representing material deprivation, and poor material conditions increase susceptibility to health risks from air pollution.
215 citations
TL;DR: To familiarize researchers and policymakers with this increasingly important approach, this work reviews spatial-analytic methods under three headings: visualization, exploration, and modeling to assist readers in understanding the strengths and weaknesses of specific methods.
Abstract: Spatial analysis can illuminate environmental health research in two ways. First, spatial analysis may suggest possible causal factors in disease pathogenesis. Association between disease and place may imply that the population living there either possesses inherent traits that make it more susceptible to disease or experiences elevated exposure to a risk factor such as air pollution. Second, spatial analysis can help identify how populations adapt and relate to their environment. This knowledge may lead to improved understanding of how people perceive and avoid health risks of environmental origin. The potential for spatial analysis to uncover these aspects of the association between health and the environment is limited by data and methodological problems that are discussed in the article. To familiarize researchers and policymakers with this increasingly important approach, we review spatial-analytic methods under three headings: visualization, exploration, and modeling. We use illustrative examples to assist readers in understanding the strengths and weaknesses of specific methods.
73 citations
TL;DR: In this article, a mixed-methods approach was employed to study the determinants of health at the local level using specific neighborhoods in Hamilton, Ontario, Canada, and the results reveal a pattern of distinct neighbourhoods that will be used in subsequent quantitative and qualitative stages in the larger research program.
Abstract: This paper is part of a larger research program which employs a mixed-methods approach to study the determinants of health at the local level using specific neighborhoods in Hamilton, Ontario, Canada. In this paper, multivariate, spatial statistical techniques and geographic information systems are used to address questions about the characterization of neighbourhoods, based on socioeconomic determinants of health and risk factors such as smoking. While neighbourhood characterization has been a component of public health surveillance for some time, geostatistical techniques can now be used to derive more accurate representation of neighbourhoods for use in subsequent analysis. We utilize principal components analysis to reduce the data and extract the components that represent the underlying local processes. Principal components are also overlayed on comparative mortality figures to visualize where the socio-demographic determinants of health correspond spatially with mortality patterns. Predicted values from the components are then analysed for spatial clustering using local indicators of spatial association. The findings reveal a pattern of distinct neighbourhoods that will be used in subsequent quantitative and qualitative stages in the larger research programme. The results can also be used to inform public health policy and to target public health interventions.
58 citations
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6,278 citations
TL;DR: It is the opinion of the writing group that the overall evidence is consistent with a causal relationship between PM2.5 exposure and cardiovascular morbidity and mortality.
Abstract: In 2004, the first American Heart Association scientific statement on “Air Pollution and Cardiovascular Disease” concluded that exposure to particulate matter (PM) air pollution contributes to card...
5,227 citations
TL;DR: The association between income deprivation and mortality differed significantly across the groups of exposure to green space for mortality from all causes and circulatory disease, but not from lung cancer or intentional self-harm, which suggests physical environments that promote good health might be important to reduce socioeconomic health inequalities.
1,540 citations
Journal Article•
TL;DR: In this paper, the authors investigated the relationship between income deprivation and mortality in the UK and found that those living in the greenest areas had the lowest levels of health inequality related to income deprivation.
1,272 citations
TL;DR: Land-use regression (LUR) models have been increasingly used in the past few years to assess the health effects of long-term average exposure to outdoor air pollution as mentioned in this paper.
1,114 citations