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Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulating and transforming point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fitting and interpreting spatial and spatio-temporal models with the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches, Creating interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policymakers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modelling, and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners. I Geospatial health data and INLA 1. Geospatial health Geospatial health data Disease mapping Communication of results 2. Spatial data and R packages for mapping Types of spatial data Areal data Geostatistical data Point patterns Coordinate Reference Systems (CRS) Geographic coordinate systems Projected coordinate systems Setting Coordinate Reference Systems in R Shapefiles Making maps with R ggplot2 leaflet mapview tmap 3. Bayesian inference and INLA Bayesian inference Integrated Nested Laplace Approximations (INLA) 4. The R-INLA package Linear predictor The inla() function Priors specification Example Data Model Results Control variables to compute approximations II Modeling and visualization 5. Areal data Spatial neighborhood matrices Standardized Incidence Ratio (SIR) Spatial small area disease risk estimation Spatial modeling of lung cancer in Pennsylvania Spatio-temporal small area disease risk estimation Issues with areal data 6. Spatial modeling of areal data. Lip cancer in Scotland Data and map Data preparation Adding data to map Mapping SIRs Modeling Model Neighborhood matrix Inference using INLA Results Mapping relative risks Exceedance probabilities 7. Spatio-temporal modeling of areal data. Lung cancer in Ohio Data and map Data preparation Observed cases Expected cases SIRs Adding data to map Mapping SIRs Time plots of SIRs Modeling Model Neighborhood matrix Inference using INLA Mapping relative risks 8. Geostatistical data Gaussian random fields Stochastic Partial Differential Equation approach (SPDE) Spatial modeling of rainfall in Paraná, Brazil Model Mesh construction Building the SPDE model on the mesh Index set Projection matrix Prediction data Stack with data for estimation and prediction Model formula inla() call Results Projecting the spatial field Disease mapping with geostatistical data 9. Spatial modeling of geostatistical data. Malaria in The Gambia Data Data preparation Prevalence Transforming coordinates Mapping prevalence Environmental covariates Modeling Model Mesh construction Building the SPDE model on the mesh Index set Projection matrix Prediction data Stack with data for estimation and prediction Model formula inla() call Mapping malaria prevalence Mapping exceedance probabilities 10. Spatio-temporal modeling of geostatistical data. Air pollution in Spain Map Data Modeling Model Mesh construction Building the SPDE model on the mesh Index set Projection matrix Prediction data Stack with data for estimation and prediction Model formula inla() call Results Mapping air pollution predictions III Communication of results 11. Introduction to R Markdown R Markdown YAML Markdown syntax R code chunks Figures Tables Example 12. Building a dashboard to visualize spatial data with flexdashboard The R package flexdashboard R Markdown Layout Dashboard components A dashboard to visualize global air pollution Data Table using DT Map using leaflet Histogram using ggplot2 R Markdown structure. YAML header and layout R code to obtain the data and create the visualizations 13. Introduction to Shiny Examples of Shiny apps Structure of a Shiny app Inputs Outputs Inputs, outputs and reactivity Examples of Shiny apps Example 1 Example 2 HTML Content Layouts Sharing Shiny apps 14. Interactive dashboards with flexdashboard and Shiny An interactive dashboard to visualize global air pollution 15. Building a Shiny app to upload and visualize spatio-temporal data Shiny Setup Structure of app.R Layout HTML content Read data Adding outputs Table using DT Time plot using dygraphs Map using leaflet Adding reactivity Reactivity in dygraphs Reactivity in leaflet Uploading data Inputs in ui to upload a CSV file and a shapefile Uploading CSV file in server() Uploading shapefile in server() Accessing the data and the map Handling missing inputs Requiring input files to be available using req() Checking data are uploaded before creating the map Conclusion 16. Disease surveillance with SpatialEpiApp Installation Use of SpatialEpiApp ‘Inputs’ page ‘Analysis’ page ‘Help’ page Appendix A R installation and packages used in the book A.1 Installing R and RStudio A.2 Installing R packages A.3 Packages used in the book \" The stress is on practical usage of INLA modelling in a spatial context and hence the author shows the full code for several carefully selected examples. Essentially all the steps from the beginning (necessary data manipulation and preparation) via INLA analysis itself (often in several alternatives) to the results (plots and maps) are explained carefully and commented. This is very useful for anybody who wants to start with the powerful INLA but did not dare to go through the very powerful but notalways- fully-documented environment.\" ~Marek Brabec, ISCB News Paula Moraga is a Lecturer in the Department of Mathematical Sciences at the University of Bath. She received her Master’s in Biostatistics from Harvard University and her Ph.D. in Statistics from the University of Valencia. Dr. Moraga develops innovative statistical methods and open-source software for disease surveillance including R packages for spatio-temporal modeling, detection of clusters, and travel-related spread of disease. Her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries.

Localization is involved everywhere in epidemiology: health phenomena often involve spatial relationships among individuals and risk factors related to geography and environment. Therefore, the use of localization in the analysis and comprehension of health phenomena is essential. This book describes the objectives, principles, methods and tools of spatial analysis and geographic information systems applied to the field of health, and more specifically to the study of the spatial distribution of disease and health-environment relationships. It is a practical introduction to spatial and spatio-temporal analysis for epidemiology and health geography, and takes an educational approach illustrated with real-world examples.Epidemiology and Geography presents a complete and straightforward overview of the use of spatial analysis in epidemiology for students, public health professionals, epidemiologists, health geographers and specialists in health-environment studies.

Looking at health and health care in a new way, this book examines health risks and benefits as encountered 'on the move' rather than focusing on the risks and benefits incurred at fixed locations. The provision and utilization of health care is also investigated, as produced/delivered and consumed/accessed in mobile settings. Engaging with the contemporary concern with 'mobilities' this book covers many forms of movement and flow, including movements of people, disease, information and health care. The issues and problems which are considered - whether re-emerging infections, displaced persons, or the 'risks' of globalised travel - are of current and ongoing concern. Drawing on three main disciplines, geography, sociology, and epidemiology, author Tony Gatrell makes strong connections between these areas of inquiry, drawing on (for example) social theorising, geographical concepts, and epidemiological methods and data. The book will be of interest to the growing number of geographers working on the geography of health, along with social scientists involved in the mobilities 'turn'. More broadly, as issues of global public health that invariably involve the movements of people, goods, viruses and information continue to hit the headlines, the book is both timely and of policy relevance.

Endemicity mapping is required to determining whether a district requires mass drug administration (MDA). Current guidelines for mapping LF require that two sites be selected per district and within each site a convenience sample of 100 adults be tested for antigenemia or microfilaremia. One or more confirmed positive tests in either site is interpreted as an indicator of potential transmission, prompting MDA at the district-level. While this mapping strategy has worked well in high-prevalence settings, imperfect diagnostics and the transmission potential of a single positive adult have raised concerns about the strategy's use in low-prevalence settings. In response to these limitations, a statistically rigorous confirmatory mapping strategy was designed as a complement to the current strategy when LF endemicity is uncertain. Under the new strategy, schools are selected by either systematic or cluster sampling, depending on population size, and within each selected school, children 9-14 years are sampled systematically. All selected children are tested and the number of positive results is compared against a critical value to determine, with known probabilities of error, whether the average prevalence of LF infection is likely below a threshold of 2%. This confirmatory mapping strategy was applied to 45 districts in Ethiopia and 10 in Tanzania, where initial mapping results were considered uncertain. In 42 Ethiopian districts, and all 10 of the Tanzanian districts, the number of antigenemic children was below the critical cutoff, suggesting that these districts do not require MDA. Only three Ethiopian districts exceeded the critical cutoff of positive results. Whereas the current World Health Organization guidelines would have recommended MDA in all 55 districts, the present results suggest that only three of these districts requires MDA. By avoiding unnecessary MDA in 52 districts, the confirmatory mapping strategy is estimated to have saved a total of $9,293,219.

In recent years, the role of medical subspecialty textbooks as sources of information for students, trainees, and practicing clinicians has been challenged. Although the structure of textbooks continues to evolve from standard, printed versions to digital formats, including e-books and online texts, we maintain that the authoritative compilation of clinical and scientific material by experts in the field (i.e., a modern-day textbook) remains central to the education, training, and practice of subspecialists. Regardless of format, an effective medical subspecialty textbook is authoritative, comprehensive, and integrated in its coverage of the subject. Textbook content represents a unique synthesis of clinical and scientific material of real educational and clinical value. Incorporation of illustrations, including figures, tables, videos, and audios, bolsters the presentation and further solidifies the reader's understanding of the subject. The textbook, both printed and digital, reinforces the many widely available online resources and serves as a platform from which to evaluate other sources of information and to launch additional scientific and clinical inquiry.

Location-dependent OA development,1 topographical differences within individual subregions,2,3 all influenced by the meniscus coverage2,3 highlight the urgent need to precisely reproduce pathological alterations at high quantitative detail in appropriate in vivo models.4,5 We performed a detailed comprehensive analysis of the zonal characteristics of the subchondral bone of mice, rats, rabbits, minipigs, and sheep, the most common animal models in orthopaedic research, applying the human tibial plateau as a model (Figure 1A), to identify the species with the highest morphological agreement. Minipigs, in contrast, had expanded trabeculae in the lateral tibial plateau. Since the medial tibiofemoral compartment is involved in 67% of all OA cases, and the load distribution can be (non)surgically modified,7 such lateral-medial dissimilarities are of major translational relevance. The subarticular spongiosa displays structural lateral-to-medial differences in humans, sheep, rats and minipigs, largely absent in rabbits and mice. (iv) Most of the osteochondral parameters show strong and significant correlations with joint size. (v) The declining rate of analogy in macroscopic anatomy and microstructure of the tibial plateau of the animal species to humans is: sheep ≈ minipigs > rabbits > > rats > > mice.

A revolutionary book that presents analytical tools for understanding why a particular disease is transmitted within a specific geographic area. A. Townsend Peterson, one of the pioneers of ecological niche modeling, presents a synthesis that illuminates new and more effective infectious disease mapping methods. His work—the culmination of twelve years of refinement—breaks new ground by integrating biogeographic and ecological factors with spatial models. Aimed at seasoned epidemiologists and public health experts, this interdisciplinary book explains the conceptual and technical underpinnings of Peterson's approach while simultaneously describing the potentially enormous benefits of his modeling method. Peterson treats disease transmission areas for what they are—distributions of species. The book argues that complex, fragmented, and highly irregular disease patterns can only be understood when underlying environmental drivers are considered. The result is an elegant modeling approach that challenges static spatial models and provides a framework for recasting disease mapping. Anyone working in the area of disease transmission, particularly those employing predictive maps, will find Peterson's book both inspiring and indispensable.

The World Bank Loan Project (WBLP) for controlling schistosomiasis in China was implemented during 1992-2001. Its short-term impact has been assessed from non-spatial perspective, but its long-term impact remains unclear and a spatial evaluation has not previously been conducted. Here we compared the spatial distribution of schistosomiasis risk using national datasets in the lake and marshland regions from 1999-2001 and 2007-2008 to evaluate the long-term impact of WBLP strategy on China's schistosomiasis burden. A hierarchical Poisson regression model was developed in a Bayesian framework with spatially correlated and uncorrelated heterogeneities at the county-level, modeled using a conditional autoregressive prior structure and a spatially unstructured Gaussian distribution, respectively. There were two important findings from this study. The WBLP strategy was found to have a good short-term impact on schistosomiasis control, but its long-term impact was not ideal. It has successfully reduced the morbidity of schistosomiasis to a low level, but can not contribute further to China's schistosomiasis control because of the current low endemic level. A second finding is that the WBLP strategy could not effectively compress the spatial distribution of schistosomiasis risk. To achieve further reductions in schistosomiasis-affected areas, and for sustainable control, focusing on the intermediate host snail should become the next step to interrupt schistosomiasis transmission within the two most affected regions surrounding the Dongting and Poyang Lakes. Furthermore, in the lower reaches of the Yangtze River, the WBLP's morbidity control strategy may need to continue for some time until snails in the upriver provinces have been well controlled. It is difficult to further reduce morbidity due to schistosomiasis using a chemotherapy-based control strategy in the lake and marshland regions of China because of the current low endemic levels of infection. The future control strategy for schistosomiasis should instead focus on a snail-based integrated control strategy to maintain the program achievements and sustainably reduce the burden of schistosomiasis in China.

Among the most far-reaching effects of the modern environmental movement was the widespread acknowledgment that human beings were inescapably part of a larger ecosystem. With this book, Linda Nash gives us a wholly original and much longer history of \"ecological\" ideas of the body as that history unfolded in California's Central Valley. Taking us from nineteenth-century fears of miasmas and faith in wilderness cures to the recent era of chemical pollution and cancer clusters, Nash charts how Americans have connected their diseases to race and place as well as dirt and germs. In this account, the rise of germ theory and the pushing aside of an earlier environmental approach to illness constituted not a clear triumph of modern biomedicine but rather a brief period of modern amnesia. As Nash shows us, place-based accounts of illness re-emerged in the postwar decades, galvanizing environmental protest against smog and toxic chemicals. Carefully researched and richly conceptual, Inescapable Ecologies brings critically important insights to the histories of environment, culture, and public health, while offering a provocative commentary on the human relationship to the larger world.

Lower respiratory infections are a leading cause of morbidity and mortality around the world. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, provides an up-to-date analysis of the burden of lower respiratory infections in 195 countries. This study assesses cases, deaths, and aetiologies spanning the past 26 years and shows how the burden of lower respiratory infection has changed in people of all ages. We used three separate modelling strategies for lower respiratory infections in GBD 2016: a Bayesian hierarchical ensemble modelling platform (Cause of Death Ensemble model), which uses vital registration, verbal autopsy data, and surveillance system data to predict mortality due to lower respiratory infections; a compartmental meta-regression tool (DisMod-MR), which uses scientific literature, population representative surveys, and health-care data to predict incidence, prevalence, and mortality; and modelling of counterfactual estimates of the population attributable fraction of lower respiratory infection episodes due to Streptococcus pneumoniae, Haemophilus influenzae type b, influenza, and respiratory syncytial virus. We calculated each modelled estimate for each age, sex, year, and location. We modelled the exposure level in a population for a given risk factor using DisMod-MR and a spatio-temporal Gaussian process regression, and assessed the effectiveness of targeted interventions for each risk factor in children younger than 5 years. We also did a decomposition analysis of the change in LRI deaths from 2000–16 using the risk factors associated with LRI in GBD 2016. In 2016, lower respiratory infections caused 652 572 deaths (95% uncertainty interval [UI] 586 475–720 612) in children younger than 5 years (under-5s), 1 080 958 deaths (943 749–1 170 638) in adults older than 70 years, and 2 377 697 deaths (2 145 584–2 512 809) in people of all ages, worldwide. Streptococcus pneumoniae was the leading cause of lower respiratory infection morbidity and mortality globally, contributing to more deaths than all other aetiologies combined in 2016 (1 189 937 deaths, 95% UI 690 445–1 770 660). Childhood wasting remains the leading risk factor for lower respiratory infection mortality among children younger than 5 years, responsible for 61·4% of lower respiratory infection deaths in 2016 (95% UI 45·7–69·6). Interventions to improve wasting, household air pollution, ambient particulate matter pollution, and expanded antibiotic use could avert one under-5 death due to lower respiratory infection for every 4000 children treated in the countries with the highest lower respiratory infection burden. Our findings show substantial progress in the reduction of lower respiratory infection burden, but this progress has not been equal across locations, has been driven by decreases in several primary risk factors, and might require more effort among elderly adults. By highlighting regions and populations with the highest burden, and the risk factors that could have the greatest effect, funders, policy makers, and programme implementers can more effectively reduce lower respiratory infections among the world's most susceptible populations. Bill & Melinda Gates Foundation.