In order to investigate the etiology of and evaluate interventions for work-related back injuries, researchers need efficient, accurate occupational exposure assessment methods suitable for large samples. The chapters in this thesis examine critical decisions using electromyography (EMG): How should exposure be measured? For what duration? Who should be measured, and how many times? Low-back EMG, or muscle activity data, was collected during 138 full-shift field measurements over 30 different job titles at 50 different worksites in 5 heavy industries: forestry, transportation, wood products, construction, and warehousing. Observations and self-reports of posture, manual materials handling (MMH), and driving exposures were collected concurrently. 1) Variability of EMG calibration measurements was investigated on right/left sides, multiple trials, 4 positions, and pre/post-shift. Position accounts for the majority of explained variability; there is little to gain by measuring multiple trials or pre- and post-shift, but measuring both sides and multiple positions is worthwhile. 2) Observation and self-report data were easier to collect and cheaper than the EMG direct measure. Costs and successful field performance need to be weighed against the added data detail when making choices about exposure assessment techniques for epidemiological studies. 3) Observed and self-reported exposures were used to predict EMG exposure metrics using mixed multiple linear regression models. Regression models using observed variables predicted 43-50% of the variability in the EMG metrics, while self-reported variables predicted 21%-36%. The observation exposure model provides a low-cost alternative to direct measurement. The self-reported exposure model should be considered with more caution. 4) Full-shift EMG data was resampled for 4, 2, and 1 hour, and for 10 and 2 minute durations to determine the optimal sampling duration. Bias was consistently low, but shorter durations had higher absolute error, percentage error, and limits of agreement. Durations of 4 and 2 hours may be acceptable but those less than 1 hour had large errors. 5) Components of EMG variance were calculated between- and within-subject, and between- industry, company, job, and post hoc grouping. Resolution, contrast, and exposure-response relationship attenuation were calculated for each grouping scheme. The post hoc scheme had the highest contrast and lowest resolution.

Made available in DSpace on 2016-06-02T20:19:21Z (GMT). No. of bitstreams: 1 DissTOS.pdf: 1026563 bytes, checksum: 3084e32eb13447dd885b2643417f39a5 (MD5) Previous issue date: 2005-02-22 / Financiadora de Estudos e Projetos / Work-related musculoskeletal disorders (WMSDs) constitutes a group of disturbs, with multifactorial origin, mainly promoted by workplace factors (physical, organizational and psychosocial). WMSDs cause high human, social and economic costs, which justifies effort to determine more efficient prevention strategies. However, for an effective prevention is necessary to establish the main risk factors, and create or aprimorate assessment tools. It was proposed a new tool for risk assessment Checklist for musculoskeletal risk assessment (RARME). The objective of this study was to evaluate the consistency of this checklist in relation to other physical load indicators: discomfort and exertion ratings, sick leave and Ergonomic Workplace Analysis. Thirty-one subjects took part in this study. They performed fifteen different tasks involving repetitive motion pattern and manual material handling. Checklist was applied by direct observation in the workplace. No relation between the results from checklist and the other physical load indicators was identified. Several factors might have contributed to the lack of consistency between indicators. Exposure variability, cognitive overload of the observer, bias in observation methods, and instruments for risk measurement are important factors to be considered when analyzing the present results. Thus, although it was not possible to check the protocol validity, relevant methodological aspects when using theses types of checklists were discussed. Besides this, an improved version of RARME protocol is presented. / As Lesões por Esforços Repetitivos/Distúrbios Osteomusculares Relacionados ao Trabalho (LER/DORT) constituem um grupo de distúrbios, de origem multifatorial, promovidos ou agravados por características do local de trabalho (físicas, organizacionais ou psicossociais). As LER/DORT causam alto custo humano, social e econômico, o que justifica esforços para determinar estratégias de prevenção mais eficazes. Porém, para uma prevenção efetiva é necessário que se identifique as principais causas destes distúrbios, além de se criar ou aprimorar ferramentas de avaliação dos riscos presentes no trabalho. Diante disto foi proposto o Roteiro para Avaliação de Riscos Músculo-Esqueléticos (RARME). O objetivo deste estudo foi avaliar a consistência deste Roteiro em relação a outros indicadores de sobrecarga física: escalas de desconforto e esforço, afastamento do trabalho e Análise Ergonômica do Local de Trabalho. Foram avaliados 31 indivíduos que realizavam 15 atividades diferentes com padrões de movimento repetitivos e manuseio de cargas. A aplicação do RARME foi feita por observação direta. Não foi identificada relação entre o RARME e os outros indicadores de sobrecarga física. Vários fatores podem ter contribuído para esta inconsistência. A variabilidade da exposição; sobrecarga cognitiva do observador e erros inerentes à observação; e características dos instrumentos usados para medir o risco são fatores importantes a serem considerados quando se analisam os presentes resultados. Portanto, embora não tenha sido possível checar a validade deste novo Roteiro, foi possível determinar uma série de considerações metodológicas importantes para o uso de protocolos de registro postural. Além disso, uma versão aprimorada do RARME foi proposta.

In order to investigate the etiology of and evaluate interventions for work-related back injuries, researchers need efficient, accurate occupational exposure assessment methods suitable for large samples. The chapters in this thesis examine critical decisions using electromyography (EMG): How should exposure be measured? For what duration? Who should be measured, and how many times? Low-back EMG, or muscle activity data, was collected during 138 full-shift field measurements over 30 different job titles at 50 different worksites in 5 heavy industries: forestry, transportation, wood products, construction, and warehousing. Observations and self-reports of posture, manual materials handling (MMH), and driving exposures were collected concurrently. 1) Variability of EMG calibration measurements was investigated on right/left sides, multiple trials, 4 positions, and pre/post-shift. Position accounts for the majority of explained variability; there is little to gain by measuring multiple trials or pre- and post-shift, but measuring both sides and multiple positions is worthwhile. 2) Observation and self-report data were easier to collect and cheaper than the EMG direct measure. Costs and successful field performance need to be weighed against the added data detail when making choices about exposure assessment techniques for epidemiological studies. 3) Observed and self-reported exposures were used to predict EMG exposure metrics using mixed multiple linear regression models. Regression models using observed variables predicted 43-50% of the variability in the EMG metrics, while self-reported variables predicted 21%-36%. The observation exposure model provides a low-cost alternative to direct measurement. The self-reported exposure model should be considered with more caution. 4) Full-shift EMG data was resampled for 4, 2, and 1 hour, and for 10 and 2 minute durations to determine the optimal sampling duration. Bias was consistently low, but shorter durations had higher absolute error, percentage error, and limits of agreement. Durations of 4 and 2 hours may be acceptable but those less than 1 hour had large errors. 5) Components of EMG variance were calculated between- and within-subject, and between- industry, company, job, and post hoc grouping. Resolution, contrast, and exposure-response relationship attenuation were calculated for each grouping scheme. The post hoc scheme had the highest contrast and lowest resolution. / Medicine, Faculty of / Population and Public Health (SPPH), School of / Graduate

Die Europäische Wasserrahmenrichtlinie schuf einen rechtlichen Rahmen, der die Mitgliedsstaaten zum Schutz der Wasserressourcen verpflichtete. Das Ziel ist das Erreichen eines guten ökologischen und chemischen Zustandes der europäischen Gewässer. Ein besonderes Augenmerk fiel in jüngerer Zeit auf die Rolle von Arzneimitteln als unerwünschte Chemikalien in der Umwelt. Arzneimittelwirkstoffe und ihre Rückstände konnten in vielen europäischen Gewässern nachgewiesen werden. Über die Auswirkung einer chronischen Exposition von Wasserorganismen auf Arzneimittelwirkstoffen ist nur wenig bekannt. Das künstliche Östrogen 17a-Ethinylestradiol (EE2) sorgt für Kontroversen, da es zur Aufnahme in die Liste der prioritären Stoffe der Wasserrahmenrichtlinie vorgeschlagen wurde. Der dazugehörige vorgeschlagene Grenzwert für Oberflächengewässer beträgt 35 pg/L als Jahresdurchschnitts-Umweltqualitätsnorm (JD-UQN). Einerseits gibt es Zweifel, ob der Grenzwert eingehalten werden kann und andererseits ist die Analytik bisher nicht in der Lage, EE2-Konzentrationen in dieser Größenordnung verlässlich messen zu können. Da keine Messwerte zur Gewässerbelastung durch EE2 existieren, hatte meine Arbeit das Ziel, den Einfluss der Abflussvarianz auf die EE2-Konzentration im Wasser zu untersuchen und zu prüfen, ob die Einhaltung des Grenzwerts möglich ist. Der Eintrag und Verbleib von EE2 wurde mittels des räumlich expliziten Modellsystems GREAT-ER in den deutschen Einzugsgebieten Ruhr und Main modelliert. Die damit berechneten EE2-Frachten im Gewässer wurden benutzt, um an ausgewählten Messstellen eine Simulation der Variabilität der EE2-Konzentrationen zu ermöglichen. Dazu wurde die Abflussvariabilität an den Messstellen anhand von langjährigen Pegeldatenreihen geschätzt. Die Ergebnisse zeigen, dass insbesondere in den Hauptläufen der Flüsse mit deutlichen Überschreitungen der JD-UQN in den Monaten von Mai bis Oktober zu rechnen ist. Mittels Monte Carlo-Simulation wurde das Risiko quantifiziert, wie häufig die JD-UQN im Jahresmittel überschritten würde. Für die Messstellen im Hauptlauf des Mains und im Unterlauf der Ruhr wurde ermittelt, dass dort mit hoher Sicherheit die JD-UQN nicht eingehalten werden kann. Auch an Messstellen, die im Jahresmittel den Grenzwert nicht überschritten, konnten häufige Überschreitungen des Grenzwerts in den Sommermonaten beobachtet werden. Die Zeiträume waren dabei lang genug, um relevant für chronische Effekte auf Wasserorganismen zu sein. Die durch die Oberflächengewässerverordnung vorgegebene Überwachung der JD-UQN erscheint daher für EE2 nicht sinnvoll. In Deutschland werden große Mengen von jodhaltigen Röntgenkontrastmitteln (JRKM) verbraucht (rund 370 Tonnen in 2009), die unverändert und in gesamter Menge über das kommunale Abwasser in Oberflächengewässer gelangten. JRKM sind nach Stand der Forschung toxikologisch unbedenklich, stellen aber aufgrund der teilweise hohen Konzentrationen im Bereich von ug/L eine Gefahr für die Trinkwasserversorgung dar. Trotz der hohen Verbrauchsmenge war bisher wenig über Emissionsmuster und Verbrauch von JRKM bekannt. Ziel der Arbeit war es daher, Informationen über den Verbrauch in medizinischen Einrichtungen zu sammeln und ein geeignetes Modell zu identifizieren, mit dem der Eintrag von JRKM in die Umwelt modelliert werden kann. Zu diesem Zweck wurden Modellansätze aus der Literatur als auch ein selbstentwickelter Ansatz im Modellsystem GREAT-ER implementiert und in den Einzugsgebieten Main und Ruhr simuliert. Die Ergebnisse zeigen, dass die Anzahl der Computertomographen als Proxygrößen zur räumlichen Aufteilung von JRKM-Emissionen verwendet werden kann. Rund zwei Drittel der jährlichen in Deutschland verbrauchten JRKM entfallen dabei nach eigenen Abschätzungen auf Krankenhäuser. Außerdem wurde gezeigt, dass der Nachweis von JRKM mittels Stichprobenmessungen in Gewässern und Kläranlagen in vielen Fällen keine belastbaren Aussagen liefert, da der Eintrag von wenig genutzten JRKM stark ereignisgetriebenen ist. Darüber hinaus konnten regionale Unterschiede in den Verbrauchsmengen identifiziert werden, die vermutlich auf lokal vorherrschende Präferenzen für einzelne JRKM zurück zu führen sind.

No description available.

Ambient air pollution is among the top 10 health risk factors in the US. With increasing concerns about adverse health effects of ambient air pollution among stakeholders including environmental scientists, health professionals, urban planners and community residents, improving air quality is a crucial goal for developing healthy communities. The US Environmental Protection Agency (EPA) aims to reduce air pollution by regulating emissions and continuously monitoring air pollution levels. Local communities also benefit from crowd-sourced monitoring to measure air pollution, particularly with the help of rapidly developed low-cost sampling technologies. The shift from relying only on government-based regulatory monitoring to crowd-sourced effort has provided new opportunities for air quality data. In addition, the fast-growing data sciences (e.g., data mining) allow for leveraging open data from different sources to improve air pollution exposure assessment. My dissertation investigates how new data sources of air quality (e.g., community-based monitoring, low-cost sensor platform) and model predictor variables (e.g., non-government open data) based on emerging modeling approaches (e.g., machine learning [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring data based on neighboring land use and geographic variables. I explore the use of crowd-sourced low-cost monitoring data, new/open dataset from government and non-government sponsored platforms, and emerging modeling techniques to develop LUR models in the US. I focus on testing whether: (1) air quality data from community-based monitoring is feasible for developing LUR models, (2) air quality data from non-government crowd-sourced low-cost sensor platforms could supplement regulatory monitors for LUR development, and (3) new/open data extracted from non-government sponsored platforms could serve as alternative datasets to traditional predictor variable sources (e.g., land use and geographic features) in LUR models. In Chapter 3, I developed LUR models using community-based sampling (n = 50) for 60 volatile organic compounds (VOC) in the city of Minneapolis, US. I assessed whether adding area source-related features improves LUR model performance and compared model performance using variables featuring area sources from government vs. non-government sponsored platforms. I developed three sets of models: (1) base-case models with land use and transportation variables, (2) base-case models adding area source variables from local business permit data (government sponsored platform), and (3) base-case models adding Google point of interest (POI) data for area sources. Models with Google POI data performed the best; for example, the total VOC (TVOC) model had better goodness-of-fit (adj-R2: 0.56; Root Mean Square Error [RMSE]: 0.32 µg/m3) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). This work suggests that VOC LUR models can be developed using community-based samples and adding Google POI could improve model performance as compared to using local business permit data. In Chapter 4, I evaluated a national LUR model using annual average PM2.5 concentrations from low-cost sensors (i.e., PurpleAir platform) in 6 US urban areas (n = 149) and tested the feasibility of using low-cost sensor data for developing LUR models. I compared LUR models using only the PurpleAir sensors vs. hybrid LUR models (combining both the EPA regulatory monitors and the PurpleAir sensors). I found that the low-cost sensor network could serve as a promising alternative to fill the gaps of existing regulatory networks. For example, the national regulatory monitor-based LUR (i.e., CACES LUR developed as part of the Center for Air, Climate, and Energy Solutions) may fail to capture locations with high PM2.5 concentrations and the within-city spatial variability. Developing LUR models using the PurpleAir sensors was reasonable (PurpleAir sensors only: 10-fold CV R2 = 0.66, MAE = 2.01 µg/m3; PurpleAir and regulatory monitors: R2 = 0.85, MAE = 1.02 µg/m3). I also observed that incorporating PurpleAir sensor data into LUR models could help capture within-city variability and merit further investigation on areas of disagreement with the regulatory monitors. This work suggests that the use of crowd-sourced low-cost sensor networks for LUR models could potentially help exposure assessment and inform environmental and health policies, particularly for places (e.g., developing countries) where regulatory monitoring network is limited. In Chapter 5, I developed national LUR models to predict annual average concentrations of 6 criteria pollutants (NO2, PM2.5, O3, CO, SO2 and PM10) in the US to compare models using new data (Google POI, Google Street View [GSV] and Local Climate Zone [LCZ]) vs. traditional geographic variables (e.g., road lengths, area of built land) based on different modeling approaches (partial least square [PLS], stepwise regression and machine learning [ML] with and without Kriging effect). Model performance was similar for both variable scenarios (e.g., random 10-fold CV R2 of ML-kriging models for NO2, new vs. traditional: 0.89 vs. 0.91); whereas adding the new variables to the traditional LUR models didn't necessarily improve model performance. Models with kriging effect outperformed those without (e.g., CV R2 for PM2.5 using the new variables, ML-kriging vs. ML: 0.83 vs. 0.67). The importance of the new variables to LUR models highlights the potential of substituting traditional variables, thus enabling LUR models for areas with limited or no data (e.g., developing countries) and across cities. The dissertation presents the integration of new/open data from non-government sponsored platform and crowd-sourced low-cost sensor networks in LUR models based on different modeling approaches for predicting ambient air pollution. The analyses provide evidence that using new data sources of both air quality and predictor variables could serve as promising strategies to improve LUR models for tracking exposures more accurately. The results could inform environment scientists, health policy makers, as well as urban planners interested in promoting healthy communities. / Doctor of Philosophy / According to the US Centers for Disease Control and Prevention (CDC), a healthy community aims at preventing disease, reducing health gaps, and creating more accessible options for a wider population. Outdoor air pollution has been evidenced to cause a wide range of diseases (e.g., cardiovascular diseases, respiratory diseases, diabetes and adverse birth outcome), ranking as the top 10 health risks in the US. Thus, improving understanding of ambient air quality is one of the common goals among environmental scientists, urban planners, health professionals, and local residents to achieving healthy communities. To understand air pollution exposures in different areas, US Environmental Protection Agency (EPA) has regulatory monitors for outdoor air pollution measurements across the country. For locations without these regulatory monitors, land use regression (LUR) models (one type of air quality models) are commonly employed to make a prediction. Usually, information including number of people, location of bus stops, and type of roads are shared online from government websites. These datasets are often used as significant predictor variables for developing LUR models. Questions remain on whether new air quality data and alternative land use data from non-government sources could improve air quality modeling. In recent years, local communities have been actively involving in air pollution monitoring using rapidly developed low-cost sensors and sampling campaigns with the help of local residents. In the meantime, advances in data sciences make open data much easier to acquire and use, particularly from non-government sponsored platforms. My dissertation aims to explore the use of new data sources including community-based low-cost monitoring data and open dataset from non-government websites in LUR modes based on emerging modeling techniques (e.g. machine learning) to predict air pollution levels in the US. I first built LUR models for volatile organic compounds (VOC: organic chemicals with a high vapor pressure at room temperature [e.g., Benzene]) based on community-based sampling data in the City of Minneapolis, US. I added information on number of neighboring gas stations, dry cleaners, paint booths, and auto shops from both the local government and Google website into the model and compared the model performance for both data sources (Chapter 3). Then, I used PM2.5 data from a non-government website (PurpleAir low-cost sensors) for 6 US cities evaluating an existing air quality model that used air quality data from government websites. I further developed LUR models using the PurpleAir PM2.5 data to see whether this non-government source of low-cost sensor data could be as reasonable as the government data for LUR model development. I finally extracted new/open data from non-government sponsored platforms (e.g., Google products and local climate zone [LCZ: a map that describes the development patterns of land, such as high-rise vs. low-rise or trees vs. sands]) in the US to investigate if these data sources can be used to alternate the land use and geographic data often used in national LUR model development. I found that: (1) adding information (e.g., number of neighboring gas stations) from non-government sponsored sources (e.g., Google) could improve the air quality model performance for VOCs, (2) integrating non-government low-cost PM2.5 sensor data into government regulatory monitoring data to develop LUR models could improve model performance and offer more insights on the air pollution exposure, (3) new/open data from non-government sponsored platforms could be used to replace the land use and geographic data previous obtained from government websites for air quality models. These findings mean that air quality data and street-level land use characteristics could serve as alternative data sources and are capable of developing better air quality models for promoting healthy communities.

Antecedentes y objetivo: Un creciente número de estudios epidemiológicos han asociado la exposición prenatal a contaminación atmosférica urbana con un menor crecimiento fetal, pero pocos están basados en cohortes prospectivas con modelos de exposición que capten la variabilidad espacial a pequeña escala de la contaminación dentro de una misma ciudad. Métodos: A partir de modelos basados en medidas de contaminación con captadores pasivos y variables geográficas, se estimó la exposición prenatal a dióxido de nitrógeno (NO2) y compuestos orgánicos volátiles (COVs) en una cohorte de 611 embarazadas de Sabadell. El crecimiento fetal se midió como peso al nacer y también longitudinalmente mediante ecografías obstétricas.Resultados: Tras estratificar por determinados patrones de tiempo-actividad, se halló un efecto negativo de la exposición prenatal a COVs por incremento en el rango intercuartílico sobre el peso al nacer (-77 gr, p<0.05). Al evaluar el crecimiento fetal mediante ecografías se halló una asociación entre exposición a NO2 y COVs desde el inicio del embarazo y un menor crecimiento de varios parámetros fetales a partir de la semana 20 de gestación. Conclusiones: La variabilidad en la exposición a contaminación atmosférica asociada al tráfico dentro de una misma ciudad tiene un efecto negativo sobre el crecimiento fetal. Este efecto comienza a manifestarse hacia la mitad del embarazo y parece persisitir hasta el nacimiento. / Background and objective: A growing number of studies have found an association between prenatal exposure to urban air pollution and fetal growth, but few of them are based on prospective cohorts with exposure models developed to capture the small-scale spatial variability in air pollution levels within a city.Methods: We developed models based on air pollution measurements with passive samplers and geographic variables. They were applied to estimate prenatal exposure to nitrogen dioxide (NO2) and volatile organic compounds (VOCs) in a cohort of 611 pregnant women from Sabadell. Fetal growth was assessed as birth weight and also through obstetric ultrasounds.Results: After stratifying by some specific time-activity patterns, a negative effect of prenatal exposure to VOCs was found on birth weight (-77 gr, p<0.05 for an interquartilic range increase in VOCs levels). When fetal growth was longitudinally assessed through ultrasound examinations, an association was found between exposure to NO2 and VOCs from early pregnancy and impaired growth in several fetal parameters from week 20 onwards. Conclusiones: Within-city variations in exposure to traffic-related air pollution have an effect on fetal growth. This effect already manifests during mid-pregnancy and seems to persist until birth.

La presente tesi descrive i risultati del programma di controllo dei pesticidi in regione Lombardia da 1996 a 2008 ed analizza i dati per calcolarne, con metodi diversi, la valutazione dell’esposizione del consumatore. 9387 campioni sono stati analizzati con un numero di campioni irregolari pari all’1%. Il numero di campioni senza residuo era pari al 69% ed il numero di campioni con i residui al di sotto del valore limite stabilito per legge era del 30%. Successivamente per capire l'esposizione dei consumatori a residui di antiparassitari si è utilizzato un metodo deterministico sviluppato da EFSA (PRIMo). È stato trovato che fra i campioni irregolari analizzati, solo 31 potrebbero causare il danno alla salute del consumatore. Un’ ulteriore analisi è stata quella di effettuare una valutazione con metodo probabilistico (Creme) calcolando l'esposizione cumulativa di antiparassitari sulla salute dei consumatori. Coem primo passo, residui di uno stesso pesticida trovato su campioni di patate sono stati inseriti nel software. Inoltre, campioni contenenti residui di pesticidi organofosfati sono stati inseriti nel software accoppiati con i dati italiani di consumo. In entrambi i casi, la valutazione cumulativa probabilistica dimostrava un adeguato livello di sicurezza per adulti e bambini. / The presented thesis describes the results of the pesticide monitoring programme in Lombardy Region from 1996 to 2008 and analyses the data gathered to calculate consumer exposure assessment with different approaches. A total of 9387 samples were analysed and the number of irregular samples was equal to 1%. The number of samples without residues was 69% and the number of samples with residues within the MRL was 30%. A further step to understand the exposure of consumers to residue of pesticides was obtained with the use of a deterministic approach developed by EFSA (PRIMo Model). It was found that among the detected irregular samples, only 31 might cause harm to the health of the consumer. An additional step was constituted by the use of one probabilistic method (Creme Software) to calculate the cumulative exposure of pesticides for the consumers. As a first step, residues of Chlorprofam were plotted in the software on samples of potato. In addition, samples containing residues of Organophosphates were also plotted along with the Italian consumption data. In both the case studies, the probabilistic acute cumulative assessment indicated that the intake, for adults and toddlers was below the set toxicological endpoint.

Parkinson's disease (PD) is a progressive, degenerative, neurological disease. The progressive disability associated with PD results in substantial burdens for those with the condition, their families and society in terms of increased health resource use, earnings loss of affected individuals and family caregivers, poorer quality of life, caregiver burden, disrupted family relationships, decreased social and leisure activities, and deteriorating emotional well-being. Currently, no cure is available and the efficacy of available treatments, such as medication and surgical interventions, decreases with longer duration of the disease. Whilst the cause of PD is unknown, genetic and environmental factors are believed to contribute to its aetiology. Descriptive and analytical epidemiological studies have been conducted in a number of countries in an effort to elucidate the cause, or causes, of PD. Rural residency, farming, well water consumption, pesticide exposure, metals and solvents have been implicated as potential risk factors for PD in some previous epidemiological studies. However, there is substantial disagreement between the results of existing studies. Therefore, the role of environmental exposures in the aetiology of PD remains unclear. The main component of this thesis consists of a case-control study that assessed the contribution of environmental exposures to the risk of developing PD. An existing, previously unanalysed, dataset from a local case-control study was analysed to inform the design of the new case-control study. The analysis results suggested that regular exposure to pesticides and head injury were important risk factors for PD. However, due to the substantial limitations of this existing study, further confirmation of these results was desirable with a more robustly designed epidemiological study. A new exposure measurement instrument (a structured interviewer-delivered questionnaire) was developed for the new case-control study to obtain data on demographic, lifestyle, environmental and medical factors. Prior to its use in the case-control study, the questionnaire was assessed for test-retest repeatability in a series of 32 PD cases and 29 healthy sex-, age- and residential suburb-matched electoral roll controls. High repeatability was demonstrated for lifestyle exposures, such as smoking and coffee/tea consumption (kappas 0.70-1.00). The majority of environmental exposures, including use of pesticides, solvents and exposure to metal dusts and fumes, also showed high repeatability (kappas &gt0.78). A consecutive series of 163 PD case participants was recruited from a neurology clinic in Brisbane. One hundred and fifty-one (151) control participants were randomly selected from the Australian Commonwealth Electoral Roll and individually matched to the PD cases on age (± 2 years), sex and current residential suburb. Participants ranged in age from 40-89 years (mean age 67 years). Exposure data were collected in face-to-face interviews. Odds ratios and 95% confidence intervals were calculated using conditional logistic regression for matched sets in SAS version 9.1. Consistent with previous studies, ever having been a regular smoker or coffee drinker was inversely associated with PD with dose-response relationships evident for packyears smoked and number of cups of coffee drunk per day. Passive smoking from ever having lived with a smoker or worked in a smoky workplace was also inversely related to PD. Ever having been a regular tea drinker was associated with decreased odds of PD. Hobby gardening was inversely associated with PD. However, use of fungicides in the home garden or occupationally was associated with increased odds of PD. Exposure to welding fumes, cleaning solvents, or thinners occupationally was associated with increased odds of PD. Ever having resided in a rural or remote area was inversely associated with PD. Ever having resided on a farm was only associated with moderately increased odds of PD. Whilst the current study's results suggest that environmental exposures on their own are only modest contributors to overall PD risk, the possibility that interaction with genetic factors may additively or synergistically increase risk should be considered. The results of this research support the theory that PD has a multifactorial aetiology and that environmental exposures are some of a number of factors to contribute to PD risk. There was also evidence of interaction between some factors (eg smoking and welding) to moderate PD risk.

No description available.