Graphical models for multivariate spatial data

In this thesis we focus on a graphical model for multivariate spatially correlated data--isomorphic chain graphs (ICG; Gitelman and Herlihy, 2007). We feel ICG allow flexibility for modeling spatial correlation and are intuitively appealing because each model has an associated graph that visually represents a complex multivariate system. We examine three ICG models: one (IsoY) that assumes the residuals follow a Gaussian spatial process with an independent predictor, another (IsoX) assumes that the predictor follows a Gaussian spatial process and the residuals are independent, and a third (IsoXY) that assumes the response and predictor have a joint spatial process. We enumerate the conditional and marginal independencies and the resulting likelihood factorization for each model. We are able to rely on results provided in Andersson et al. (2001) and Gitelman and Herlihy (2007) to formulate statistical models for the three ICG. However, parameterizing these models as valid spatial models is not as straightforward. Thus, in Chapter 2, we verify for IsoX and IsoY that parametrizations using the available valid univariate spatial covariances do not violate the assumptions needed to use the results in Andersson et al. (2001). In Chapter 3, we demonstrate that IsoXY can be parameterized using multivariate spatial covariance functions. Chapters 2 and 3 provide the groundwork for Chapter 4, in which we apply the three models to a stream sulfate dataset. These results raise several questions that we address via simulations, and we consider how an analyst would select among the ICG models. Also, we investigate whether the skewness in the effective range posterior intervals is related to the strength of spatial correlation--similar to the REML results in Irvine et al. (2007)--or whether it can be attributed to fitting an incorrect model. And finally, we explore the consequences of assuming an incorrect ICG on parameter estimation. Our work contributes to the field of spatial statistics by providing an additional way to visually display multivariate spatial models. Also, we present accessible suggestions for how to select among the ICG models. Code is provided such that one can implement these models under the Bayesian paradigm using the available freeware, Winbugs (Lunn et al., 2000)

Evaluating Aspen Responses to Changes in Elk Abundance, Distribution and Behavior Following Wolf Reestablishment in West-Central Yellowstone National Park

The reintroduction of wolves to Yellowstone National Park created a unique “natural experiment” to study trophic interactions in a large-scale terrestrial system among wolves, elk, and aspen. This study utilized data from a long-term elk demography study that was established prior to wolf reintroduction. Significant changes in the abundance and distribution of the Madison headwaters elk herd were observed following wolf reestablishment. The spatial arrangement of these changes made it possible to directly test for the occurrence of a density-mediated trophic cascade. The objectives of this study were to answer the following questions: 1) was there a marked decrease in browsing pressure on aspen where elk densities declined, and 2) was there a corresponding plant-growth response indicating that aspen were released from browsing pressure? Historical browsing conditions and aspen height were observed for 31 aspen stands to assess the occurrence of a density-mediated trophic cascade following wolf reintroduction. Browse conditions and aspen morphology in stands where elk densities declined dramatically following wolf reintroduction were compared to stands that experienced persistent heavy browsing throughout this period. A major decline in browsing pressure along with a modest increase in aspen height and leader longevity was detected, supporting the hypothesis of a density-mediated trophic cascade. However, the magnitude of the growth response was weak, suggesting that browsing was not the dominant limiting factor to aspen growth in the study area and that aspen may be more strongly limited by bottom-up regulation

Estimating Temporal Trend in the Presence of Spatial Complexity: A Bayesian Hierarchical Model for a Wetland Plant Population Undergoing Restoration

Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed Bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas]) population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations (“zones”) with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity—a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach

Right place. Right time. Right tool: guidance for using target analysis to increase the likelihood of invasive species detection

In response to the National Invasive Species Council’s 2016–2018 Management Plan, this paper provides guidance on applying target analysis as part of a comprehensive framework for the early detection of and rapid response to invasive species (EDRR). Target analysis is a strategic approach for detecting one or more invasive species at a specific locality and time, using a particular method and/or technology(ies). Target analyses, which are employed across a wide range of disciplines, are intended to increase the likelihood of detection of a known target in order to maximize survey effectiveness and cost-efficiency. Although target analyses are not yet a standard approach to invasive species management, some federal agencies are employing target analyses in principle and/or in part to improve EDRR capacities. These initiatives can provide a foundation for a more standardized and comprehensive approach to target analyses. Guidance is provided for improving computational information. Federal agencies and their partners would benefit from a concerted effort to collect the information necessary to perform rigorous target analyses and make it available through open access platforms

Elk Contact Patterns and Potential Disease Transmission

Understanding the drivers of contact rates among individuals is critical to understanding disease dynamics and implementing targeted control measures. We studied the interaction patterns of 149 female elk (Cervus elaphus) distributed across five different regions of western Wyoming over three years, defining a contact as an approach within one body length (~2m). Using hierarchical models that account for correlations within individuals, pairs and groups, we found that pairwise contact rates within a group declined by a factor of three as group sizes increased 30-fold. Meanwhile, per capita contact rates increased with group size due to the increasing number of potential pairs. We found similar patterns for the duration of contacts. Supplemental feeding of elk had a limited impact on pairwise interaction rates and durations, but increased per capita rates more than two times higher. Variation in contact patterns were driven more by environmental factors such as group size than either individual or pairwise differences. Female elk in this region fall between the expectation of contact rates that linearly increase with group size (as assumed by pseudo-mass action models of disease transmission) or are constant with changes in group size (as assumed by frequency dependent transmission models). Our statistical approach decomposes the variation in contact rate into individual, dyadic, and environmental effects, which provides insight into those factors that are important for effective disease control programs

Estimating the incidence of acute infectious intestinal disease in the community in the UK:A retrospective telephone survey

Objectives: To estimate the burden of intestinal infectious disease (IID) in the UK and determine whether disease burden estimations using a retrospective study design differ from those using a prospective study design. Design/Setting: A retrospective telephone survey undertaken in each of the four countries comprising the United Kingdom. Participants were randomly asked about illness either in the past 7 or 28 days. Participants: 14,813 individuals for all of whom we had a legible recording of their agreement to participate Outcomes: Self-reported IID, defined as loose stools or clinically significant vomiting lasting less than two weeks, in the absence of a known non-infectious cause. Results: The rate of self-reported IID varied substantially depending on whether asked for illness in the previous 7 or 28 days. After standardising for age and sex, and adjusting for the number of interviews completed each month and the relative size of each UK country, the estimated rate of IID in the 7-day recall group was 1,530 cases per 1,000 person-years (95% CI: 1135 – 2113), while in the 28-day recall group it was 533 cases per 1,000 person-years (95% CI: 377 – 778). There was no significant variation in rates between the four countries. Rates in this study were also higher than in a related prospective study undertaken at the same time. Conclusions: The estimated burden of disease from IID varied dramatically depending on study design. Retrospective studies of IID give higher estimates of disease burden than prospective studies. Of retrospective studies longer recall periods give lower estimated rates than studies with short recall periods. Caution needs to be exercised when comparing studies of self-reported IID as small changes in study design or case definition can markedly affect estimated rates

BRCA1/2 mutation testing in breast cancer patients: a prospective study of the long-term psychological impact of approach during adjuvant radiotherapy

This study assessed psychological distress during the first year after diagnosis in breast cancer patients approached for genetic counseling at the start of adjuvant radiotherapy and identified those vulnerable to long-term high distress. Of the approached patients some chose to receive a DNA test result (n = 58), some were approached but did not fulfill criteria for referral (n = 118) and some declined counseling and/or testing (n = 44). The comparative group consisted of patients not eligible for genetic counseling (n = 182) and was therefore not approached. Patients actively approached for genetic counseling showed no more long-term distress than patients not eligible for such counseling. There were no differences between the subgroups of approached patients. Predictors for long-term high distress or an increase in distress over time were pre-existing high distress and a low quality of life, having children, and having no family members with breast cancer. It is concluded that breast cancer patients can be systematically screened and approached for genetic counseling during adjuvant radiotherapy without imposing extra psychological burden. Patients vulnerable to long-term high distress already displayed high distress shortly after diagnosis with no influence of their medical treatment on their level of distress at long-term

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

The contribution of environmental science to mental health research: a scoping review

Mental health is influenced by multiple complex and interacting genetic, psychological, social, and environmental factors. As such, developing state-of-the-art mental health knowledge requires collaboration across academic disciplines, including environmental science. To assess the current contribution of environmental science to this field, a scoping review of the literature on environmental influences on mental health (including conditions of cognitive development and decline) was conducted. The review protocol was developed in consultation with experts working across mental health and environmental science. The scoping review included 202 English-language papers, published between 2010 and 2020 (prior to the COVID-19 pandemic), on environmental themes that had not already been the subject of recent systematic reviews; 26 reviews on climate change, flooding, air pollution, and urban green space were additionally considered. Studies largely focused on populations in the USA, China, or Europe and involved limited environmental science input. Environmental science research methods are primarily focused on quantitative approaches utilising secondary datasets or field data. Mental health measurement was dominated by the use of self-report psychometric scales. Measures of environmental states or exposures were often lacking in specificity (e.g., limited to the presence or absence of an environmental state). Based on the scoping review findings and our synthesis of the recent reviews, a research agenda for environmental science’s future contribution to mental health scholarship is set out. This includes recommendations to expand the geographical scope and broaden the representation of different environmental science areas, improve measurement of environmental exposure, prioritise experimental and longitudinal research designs, and giving greater consideration to variation between and within communities and the mediating pathways by which environment influences mental health. There is also considerable opportunity to increase interdisciplinarity within the field via the integration of conceptual models, the inclusion of mixed methods and qualitative approaches, as well as further consideration of the socio-political context and the environmental states that can help support good mental health. The findings were used to propose a conceptual model to parse contributions and connections between environmental science and mental health to inform future studies

The Contribution of Environmental Science to Mental Health Research: A Scoping Review

Mental health is influenced by multiple complex and interacting genetic, psychological, social, and environmental factors. As such, developing state-of-the-art mental health knowledge requires collaboration across academic disciplines, including environmental science. To assess the current contribution of environmental science to this field, a scoping review of the literature on environmental influences on mental health (including conditions of cognitive development and decline) was conducted. The review protocol was developed in consultation with experts working across mental health and environmental science. The scoping review included 202 English-language papers, published between 2010 and 2020 (prior to the COVID-19 pandemic), on environmental themes that had not already been the subject of recent systematic reviews; 26 reviews on climate change, flooding, air pollution, and urban green space were additionally considered. Studies largely focused on populations in the USA, China, or Europe and involved limited environmental science input. Environmental science research methods are primarily focused on quantitative approaches utilising secondary datasets or field data. Mental health measurement was dominated by the use of self-report psychometric scales. Measures of environmental states or exposures were often lacking in specificity (e.g., limited to the presence or absence of an environmental state). Based on the scoping review findings and our synthesis of the recent reviews, a research agenda for environmental science’s future contribution to mental health scholarship is set out. This includes recommendations to expand the geographical scope and broaden the representation of different environmental science areas, improve measurement of environmental exposure, prioritise experimental and longitudinal research designs, and giving greater consideration to variation between and within communities and the mediating pathways by which environment influences mental health. There is also considerable opportunity to increase interdisciplinarity within the field via the integration of conceptual models, the inclusion of mixed methods and qualitative approaches, as well as further consideration of the socio-political context and the environmental states that can help support good mental health. The findings were used to propose a conceptual model to parse contributions and connections between environmental science and mental health to inform future studies