Browsing by Author "Bertazzon, Stefania"

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    A Geographical Exploration of Inflammatory Bowel Disease
    (2013-01-25) Underwood, Fox; Bertazzon, Stefania; Kaplan, Gilaad
    A chronic disease that afflicts 200 000 Canadians of all ages, inflammatory bowel disease has no known cure or aetiology. Using socioeconomic Census variables, population-level models of inflammatory bowel diseases – Crohn's disease and ulcerative colitis – were made for two large cities in Alberta as well as for the entire province. For modelling, a rule was made to link the imperfectly overlapping dissemination areas (a census unit in which patient data were provided in) and municipal boundaries that were used in provincial-wide analysis. The models assessed statistical relationships between inflammatory bowel disease and socioeconomic population-level characteristics. Factors found to be positively correlated with inflammatory bowel disease were the presence of older buildings (1970 and older), low socioeconomic status in the form of less than high school education and proportion of one parent families, and certain occupational work- forces: waste management, accommodation and tourism, and construction work-forces. Conversely, negatively correlated factors consisted of the presence of newer buildings (1990 and newer), apartment buildings, and the proportion of people working in the mining and oil industry. These findings indicate the need to consider the potential links between inflammatory bowel disease and three factors: occupational exposures, socioeconomic status, and environmental exposures in different building types and age of buildings.
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    Analyzing Physician and Patient factors in optimizing Cervical Cancer Screening in Alberta: Progress, Barriers, and Enablers
    (2023-07-17) Sayed, Sayeeda Amber; Dickinson, James; Naugler Christopher; Chen, Guanmin; Bertazzon, Stefania; McDougall, Laura
    Cervical cancer screening (CCS) program effectiveness is well-accepted; however, inappropriate CCS results in either a woman being tested too often or not being tested at the recommended intervals. Widespread disparities in CCS uptake also exist, even when screening is offered without cost through a publicly funded and organized CCS in Alberta. This thesis has three main objectives: Study 1.To describe temporal trends in screening and outcomes for women, after changes in guidelines in Alberta, Canada, that raised the starting age for screening to 21, then to 25 years of age, and reduced frequency to 3-yearly Study 2. To identify family physicians’ (FP) characteristics that are associated with over- and under-screening for 25-69-year-old women in Calgary, Alberta. Study 3. To study spatial and temporal associations of CCS and sociodemographic variables in Calgary, Canada using Census Canada datasets (2006, 2011, 2016). Methods Study 1: Calgary Laboratory Information System data were used to examine CCS, follow-up procedures, and cancer among women 10 to 29 years from 2007 to 2016 in the whole population of Calgary. Interrupted time-series analyses were used to assess changes in screening and subsequent diagnostic procedures over the ten-year period. Study 2: A population-based retrospective observational study was performed by linking the College of Physicians and Surgeons Alberta physicians’ database of FPs practicing in Calgary with the Calgary Laboratory Services database. We matched physicians’ sex, country and year of medical school graduation, years since medical school graduation, city quadrant of practice, and their estimated patient panel size. Logistic regression models were applied to analyze the over-screening and under-screening patterns. Study 3: CCS rates were obtained from a population-wide laboratory administrative database for Calgary, Alberta for the years 2006, 2011, and 2016 for women 25-69 years of age. These years coincide with Census Canada years, allowing comparison with sociodemographic factors Ordinary Least Square regression (OLS) and geographically weighted regression models (GWR) were used to examine sociodemographic variables associated with CCS rates. Results Study 1: Annual Screening rates dropped by around 10% for all ages over 15 after the 2009 Alberta cervical cancer screening guidelines, followed by a steady decrease. The rates of abnormal test results and biopsies did not increase with decreased screening. Likewise, no increases in Cervical intra-epithelial neoplasias (CIN I, CIN II/III), or invasive cervical cancer rates were observed after reduced testing. Study 2: Among 807 physicians included in the over-screening analysis, 43% of physicians had over-screened their screen-eligible patients. Among the 317 physicians included in the underscreening analysis, 42% had under-screened during the three-year study period. Physician characteristics significantly associated with over-screening included more years of practice and having more female patients in the practice. Female physicians were less likely to under-screen their eligible female patients. Physicians practicing in the Northeast quadrant of the city also had lower odds of screening. Study 3: We analyzed approximately 200,000 cervical cancer screening tests for each year and noted a considerable decrease in screening rates between 2006 and 2011, consistent with changes in screening guidelines. The OLS results showed that a high median household income and university education were strongly associated with higher screening rates in all three census years. 2006 and 2011 OLS models showed negative associations with screening of Aboriginals, Blacks, and recent immigrant women. Conclusions Study 1: The largest decrease in screening and follow-up procedures occurred in the period immediately following the implementation of 2009 Alberta screening guidelines. The number of consequent procedures also decreased in proportion to decreased screening, but there was no increase in cancer rates. Starting screening at age 25 and reducing intervals appears to be safe. Study 2: Screening patterns of family physicians indicate both overuse and underuse, which indicates inconsistencies in adherence to screening guideline recommendations. Identifying strategies and addressing disparities to improve guideline adherence among different physician demographic groups is critical for the success of screening programs. More education and guideline publicity are required to encourage compliance with screening guidelines. Study 3: There were significant sociodemographic differences associated with cervical cancer screening in Calgary. Understanding these sociodemographic associations could form the basis of future education or outreach screening programs, targeting underserved populations, such as women with low income and education.
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    Comparison of distance measures in spatial analytical modeling for health service planning
    (BioMed Central, 2009-11-06) Shahid, Rizwan; Bertazzon, Stefania; Knudtson, Merril L.; Ghali, William A.
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    Environmental Risk Mapping for Contamination of Drinking Water Wells Post Flood in Southern Alberta
    (2014-08-26) Eccles, Kristin; Bertazzon, Stefania; Checkley, Sylvia
    The objective of this research was to determine if there were more cases of contamination in 2013 than in previous years. To determine private groundwater wells in the Calgary Health Zone were impacted by the flood in June 2013, and finally determine what environmental variables influence contamination during a flooding event. The analysis utilizes, test results of total coliform and E.coli of private water wells were obtained though Alberta Health Services’ Provincial Laboratory (ProvLab) for the period of June 19th to September 30th, 2013. The analysis was completed using ArcGIS 10.2 and R 3.0.2. The results of the regression indicate that total coliform contamination was not impacted by the flood, however, E.coli contamination was impacted by floodways, flood fringe, farms, and intermittent water (sloughs).
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    European Starlings as Sentinels for Health Effects of Urban Air Pollution
    (2017) North, Michelle Alison; Smits, Judit E. G.; Kaplan, Gilaad; Kinniburgh, David W.; Bertazzon, Stefania
    The consequences of exposure to air pollution are widely studied in humans, with urban pollutants associated with a suite of adverse health outcomes. With the complexity of air pollutant mixtures thwarting our full understanding of effects in humans, the consequences to urban wildlife are even less well-understood. The intricate, highly efficient respiratory system of birds makes them more sensitive to airborne toxicants than other vertebrates. The motivation for this study is to identify sensitive, reliable biomarkers of biological effects of air pollutants using wild European starlings (Sturnus vulgaris). This was achieved using two approaches: a field study investigation disclosed the effects of ambient exposure on nestling starlings, whereas experimental exposure of adult starlings to vehicle emissions provided insights under controlled conditions. In both studies, pollutant exposures were measured using several techniques to provide as accurate information as possible. Passive air samplers measured the concentrations of nitrogen dioxide, sulphur dioxide, and volatile organic compounds in ten urban parks in Calgary, Canada during May and June of 2013 to 2015, and during the experiment in May 2016. For the field study, the reproductive success of adult starlings, growth and development of their offspring, biomarkers of oxidative stress, hepatic detoxification effort, and T-cell mediated immune response were evaluated as biomarkers of contaminant effects. The relative contributions of confounding predictors were assessed, while clustering within nest and location were included during analyses. For the experimental study, the same biomarkers were measured in adult, wild-caught, non-breeding starlings, with additional measurements of B-cell immunity, thyroid hormones and histology. Several responses in nestlings indicated that higher exposures to benzene, toluene, ethylbenzene, xylenes (BTEX) and hexane had physiological costs, which, although subtle, may reduce the resilience of nestlings to cope with additional stressors such as fledging. Similarly, subclinical endocrine and immune changes in experimental birds suggest that higher exposures, or the exposure at sensitive life stages, may have population-level consequences.
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    Geospatial patterns of comorbidity prevalence among people with osteoarthritis in Alberta Canada
    (2020-10-15) Liu, Xiaoxiao; Shahid, Rizwan; Patel, Alka B; McDonald, Terrence; Bertazzon, Stefania; Waters, Nigel; Seidel, Judy E; Marshall, Deborah A
    Abstract Background Knowledge of geospatial pattern in comorbidities prevalence is critical to an understanding of the local health needs among people with osteoarthritis (OA). It provides valuable information for targeting optimal OA treatment and management at the local level. However, there is, at present, limited evidence about the geospatial pattern of comorbidity prevalence in Alberta, Canada. Methods Five administrative health datasets were linked to identify OA cases and comorbidities using validated case definitions. We explored the geospatial pattern in comorbidity prevalence at two standard geographic areas levels defined by the Alberta Health Services: descriptive analysis at rural-urban continuum level; spatial analysis (global Moran’s I, hot spot analysis, cluster and outlier analysis) at the local geographic area (LGA) level. We compared area-level indicators in comorbidities hotspots to those in the rest of Alberta (non-hotspots). Results Among 359,638 OA cases in 2013, approximately 60% of people resided in Metro and Urban areas, compared to 2% in Rural Remote areas. All comorbidity groups exhibited statistically significant spatial autocorrelation (hypertension: Moran’s I index 0.24, z score 4.61). Comorbidity hotspots, except depression, were located primarily in Rural and Rural Remote areas. Depression was more prevalent in Metro (Edmonton-Abbottsfield: 194 cases per 1000 population, 95%CI 192–195) and Urban LGAs (Lethbridge-North: 169, 95%CI 168–171) compared to Rural areas (Fox Creek: 65, 95%CI 63–68). Comorbidities hotspots included a higher percentage of First Nations or Inuit people. People with OA living in hotspots had lower socioeconomic status and less access to care compared to non-hotspots. Conclusions The findings highlight notable rural-urban disparities in comorbidities prevalence among people with OA in Alberta, Canada. Our study provides valuable evidence for policy and decision makers to design programs that ensure patients with OA receive optimal health management tailored to their local needs and a reduction in current OA health disparities.
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    Integrated analysis of primary health care accessibility for aboriginal communities in Alberta
    (2010) Elikan, Olesya; Bertazzon, Stefania
    The purpose of this study is to investigate accessibility of primary health care services for Aboriginal people in Alberta in two dimensions: spatial and non-spatial. The spatial dimension is represented by travel time and geographical distance to health services. The non-spatial dimension is based on a number of different economic and socio-demographic factors and emphasizes the importance of non-geographic barriers between consumers and providers of health care. An integrated approach consisting of both quantitative and qualitative methods is employed in order to better assess spatial accessibility; this is measured by travel time from reserves to the nearest health care provider using GIS (Geographic Information Systems) techniques. Finally, a spatial regression model is estimated in order to explore the correlation between population health status and accessibility to health care provision -as measured through the above method-, in conjunction with a set of relevant socio-economic and demographic factors.
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    Integrating Spatial Analysis and System Dynamics to Model Childhood Overweight and Obesity Prevalence
    (2014-05-16) Shahid, Rizwan; Bertazzon, Stefania
    Weight is an important indicator of current and future health and it is more crucial in children who are tomorrow’s adults. This research analyzes physical activity, food environment and socioeconomic factors but recognizes that there may be other factors, not included in the analyses that are influencing overweight and obesity. Contrary to the conventional thinking of global analysis, this research suggests localized analysis and need-based interventions. The one-size-fit-all strategy may not be effective in controlling obesity rates since each neighbourhood or set of neighbourhoods has unique characteristics that need to be addressed individually. This thesis offers an innovative framework combining local analysis with simulation modeling to analyze childhood overweight and obesity for children 4.5 to 6 years old. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by combining geographically weighted regression (GWR) to identify vulnerable neighbourhoods and critical factors for childhood overweight and obesity, and simulation modeling to evaluate the impact of suggested interventions on the targeted neighbourhoods. Walkability was chosen as a potential intervention to test the framework. Simulation results suggest that some walkability interventions would achieve measurable declines in childhood obesity rates. The result appears encouraging, and the improvement will likely compound over time. Moreover, the significant association between obesity and walkability decreases over time, exposing other factors that can be targeted at a later stage. The research further addresses an outstanding issue in the emerging GWR method, local multicollinearity, by proposing a novel solution. Another contribution in the GWR and cartography literature is the introduction of an innovative way of mapping t-values and R2. Overall, the results demonstrate that the integration of GWR and simulation modeling is cost effective, flexible and general in nature that can be applied to different areas and to address other health issues. The innovative framework has great potential for health professionals and policy makers to design obesity control and prevention programs that meet the unique characteristics of each neighbourhood.
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    Making Movement Matter: Modelling Connectivity with Spatial Interaction Models
    (2018-07-10) Koenig, Shantel Julene; Bender, Darren J.; Bertazzon, Stefania; Galpern, Paul; Alexander, Shelley M.
    While the idea of landscape connectivity is conceptually straightforward, practically assessing connectivity is substantially more complicated. Behavioural interactions and the composition and configuration of the landscape ultimately determine an animal’s movement between locations; however, when it comes to modelling connectivity, the comprehensive integration of these key determinants into models is lacking. Interestingly, models used by geographers to model human movement and connectivity are centered on the similar key themes of composition and configuration, but also incorporate the components commonly missing in ecological connectivity models. Specifically, spatial interaction models (SIMs) include variables to describe locational traits and offer a model structure that can account for many of the factors that may influence movement across the landscape. Therefore, the aim of this dissertation was to examine to what extent the SIM framework could be adapted and applied to more fully model landscape connectivity. Using both theoretical and empirical approaches, connectivity modelling using SIMs was explored in several ways. A simulation-based study explored the effect that matrix generalization has on assessments of connectivity at both the landscape and patch scale. As well, the ability of SIMs to model the movement landscape was explored using a simulation model and a case study on Ord’s kangaroo rat, and methods for interpreting and assessing model outputs were developed and presented. Overall, SIMs were successful at assessing connectivity and provided new insights into how connectivity varies across landscape and patch scales. SIM outputs showed that as connectivity models were varied, there were no consistent trends or patterns in the differences in assessments, suggesting that connectivity assessments can be highly sensitive to the landscape representation and the model inputs used. Especially when there is uncertainty in the landscape representation or in understanding how a species interacts with the landscape during movement, uncertainty analysis is required, and SIMs provide a meaningful way to do so. Ultimately, and overall, SIMs proved to be a useful, straightforward, and flexible way to model connectivity, especially in cases that warrant incorporating more information than just distance and patch area.
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    Measuring Accessibility to Primary Health Care Across the Urban-rural Continuum in the Province of Alberta
    (2016) Barrett, Olesya; Bertazzon, Stefania; Miller, Byron; Hemmelgarn, Brenda; Patel, Alka; Yiannakoulias, Nikolaos
    The proper estimation of accessibility is necessary to understand the issue of the urban-rural divide in access health care. The main concern with the previously developed accessibility measures is that the urban-rural continuum is treated as a dichotomous variable, while many degrees of rurality exist. While many studies attempted to include a distance decay function to address local travel patterns, it is usually arbitrary and may not exactly reflect the reality. This project is an attempt to improve the current methodology and make it more suitable for provincial settings that include various degrees of rurality. The past records of health care utilization are analyzed and distance decay coefficients are extracted for each type of urban and rural area. These coefficients are used to enhance a commonly used two-step floating catchment area (2SFCA) method to estimate accessibility scores. In addition, health needs and mobility measures are added to the model. The result of this enhanced methodology is compared to other methods where distance decay is used. The proposed method consistently shows better and more accurate results. The alternative methods often underestimate accessibility, especially in remote areas. The distance decay function derived from actual utilization data also allows use of this approach for large areas such as a Canadian province.
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    Multiscale Spatial and Temporal Modelling of Fine Particulate Matter (PM2.5) from Wildfire Smoke Using Remote Sensing and Statistical Methods
    (2020-04-22) Mirzaei, Mojgan; Bertazzon, Stefania; Yackel, John J.; Farjad, Babak
    Wildfire smoke exposure is increasingly recognized as a critical public health problem due to the increase in the frequency and severity of wildfires in recent decades. Wildfire smoke-generated PM2.5 is considered the most concerning particulate, as it can be inhaled deep into the lungs, penetrate the human respiratory system, and enter the bloodstream. Studies assessing wildfire PM2.5 exposure and population health have traditionally employed three main approaches: (1) in situ measurements (2) satellite information of atmospheric aerosol, and (3) atmospheric models. The objective of the present study was to identify models to accurately predict PM2.5 concentration over space and time. Spatiotemporal models were built to perform a comprehensive analysis of wildfire PM2.5 concentrations, for each recent year over the study region: Land Use Regression (LUR), Linear Mixed Effect (LME), and Artificial Neural Network (ANN). Predictor variables were MODIS AOD images, ground PM2.5 measurements, and ancillary land use and meteorological data. LUR models were used to predict PM2.5 in three distinct periods: before, during, and after a wildfire. Results showed a major difference in predictors between the during-fire and the other models, due to the different contribution of traffic and industrial emissions. Daily estimation of PM2.5 concentration was derived by incorporating nested period-zone-specific random effects of the AOD-PM2.5 relationship over the province of Alberta, Canada using LME models. The LME model’s predictions also improved when additional variables were integrated with AOD measures in a multivariate framework. ANN models were used as a multivariate and non-parametric approach to empirically predict wildfire smoke using AOD along with other predictors. Daily PM2.5 concentrations were predicted using temporal and spatial ANN for the 2014 to 2017 fire seasons and each airshed zone in Alberta. The study demonstrated how MODIS AOD could be incorporated within statistical models to provide reliable predictions of daily PM2.5 concentrations over wildfire events, to feed health and epidemiological studies. The results demonstrated that mixed effect models outperformed the LUR models owing to their ability to adjust the varying relationship of AOD-PM2.5. ANN models also outperformed them owing to their advantage in modelling non-parametric and non-linear behaviours.
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    NO2 Exposure and Myocardial Infarction Hospitalizations in Calgary, Canada
    (2018-01-02) Liu, Xiaoxiao; Bertazzon, Stefania; Draper, Dianne Louise; Kaplan, Gilaad; TownShend, Ivan J.
    Air pollution is a leading public health concern. This research investigated the health effects of nitrogen dioxide (NO2) on myocardial infarction (MI) in Calgary, Canada. Seasonal trend decomposition and hot spot analysis were applied exploring the spatial and seasonal pattern of MI. Both males and females followed a seasonal pattern with MI peaks in winter and summer. MI exhibited spatial clustering over communities with a larger proportion of older people, lower socioeconomic status, and proximity to industrial areas. A space-time model integrating harmonic regression and land use regression model was applied to estimate air pollution at fine spatial and temporal resolutions. providing accurate air pollution exposure for health risk studies. The varying association between MI and NO2 was examined with spatial autoregressive model (SAR) and geographically weighted regression (GWR), which accounts for spatial autocorrelation and spatial non-stationarity in MI. Result indicated that NO2 had a positive significant association with MI hospitalizations. However, the size and significance declined when age and gender were accounted for. By identifying potential factors in the hot spots, new evidence may be found to aid understanding of MI etiology. The advantage of this research is the space-time air pollution estimates at DA level, which enables reliable risk assessment at fine spatial scale. Further research shall be done with approach of multilevel model or hierarchic model to account for both individual and contextual factors. The research provides important information for health promotion. By raising the awareness regarding the spatial variation of air pollution, people may be able to further protect themselves from areas with relatively higher air pollution concentrations, especially for those vulnerable population such as elderly and people with asthma. It is helpful for policy makers and health researchers in targeting efforts and resources to areas in most needs. Overall, the air quality in Calgary is good, under the World Health Organization air pollution guidelines. Air pollution research in a cleaner area such as Calgary shows further indications about health impacts of air pollution at an acceptable level, which may shed new light regarding threshold research and further improvement of the existed air pollution guidelines.
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    Schools, Air Pollution, and Active Transportation: An Exploratory Spatial Analysis of Calgary, Canada
    (International Journal of Environmental Research and Public Health, 2017-07-25) Bertazzon, Stefania; Shahid, Rizwan
    An exploratory spatial analysis investigates the location of schools in Calgary (Canada) in relation to air pollution and active transportation options. Air pollution exhibits marked spatial variation throughout the city, along with distinct spatial patterns in summer and winter; however, all school locations lie within low to moderate pollution levels. Conversely, the study shows that almost half of the schools lie in low walkability locations; likewise, transitability is low for 60% of schools, and only bikability is widespread, with 93% of schools in very bikable locations. School locations are subsequently categorized by pollution exposure and active transportation options. This analysis identifies and maps schools according to two levels of concern: schools in car-dependent locations and relatively high pollution; and schools in locations conducive of active transportation, yet exposed to relatively high pollution. The findings can be mapped and effectively communicated to the public, health practitioners, and school boards. The study contributes with an explicitly spatial approach to the intra-urban public health literature. Developed for a moderately polluted city, the methods can be extended to more severely polluted environments, to assist in developing spatial public health policies to improve respiratory outcomes, neurodevelopment, and metabolic and attention disorders in school-aged children.
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    Topographic and Geographic Influences on Near-surface Temperature under Different Seasonal Weather Types in Southwestern Alberta
    (2017) Wood, Wendy Helen; Marshall, Shawn; Bertazzon, Stefania; Yackel, John
    Near-surface temperature variability is influenced by geographic and terrain characteristics. My research examines how these influences vary by weather type. This knowledge is used to determine the best methods for modelling temperature in the mountains and prairies in southwestern Alberta, using data collected as part of the Foothills Climate Array (FCA) study. A weather classification system was developed for the area using multivariate statistical analysis, and six weather patterns were identified. Missing temperature data in the FCA are gap-filled using regression equations generated using the most closely correlated station for each site, where correlations are calculated by seasonal weather type. Seasonal weather type correlations improve estimates by ~7% over monthly correlations. The biggest improvements (10 to 20%) occur for chinook and cool-wet days. Cold Arctic air days and hot anticyclonic days in summer show the lowest improvement, indicating strong within-type variability for these weather types. These weather types also show the most variable temperature lapse rates, with frequent inversions. Local weighted regression models outperform multivariate regression models by between 4 and 8% in the mountains. Daily temperature and elevation are not always strongly correlated, most notably during Arctic cold spells. This is true for both minimum and maximum temperatures in the mountains. Therefore, regression models using elevation as the only predictor perform poorly, particularly in winter months. Vertical and horizontal separation are the most important factors in choosing local neighbours, with vertical separation being most important for minimum temperatures and for winter months. Relative elevation and slope, as indictors of cold air pooling potential, influence the selection of local neighbours for minimum and mean temperature models. Spatial proximity is the most important factor determining temperature relatedness in the prairies. Minimum temperatures are strongly influenced by urban and relative elevation effects. Sites located within the city of Calgary are warmer than those in the outlying areas, and temperatures are warmer away from low lying areas. Seasonal variability is stronger than weather type variability in the prairies. Therefore, kriging is suggested as an appropriate method for estimating temperature in the prairies, with models parameterised monthly.