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Health care in the United States is more expensive than in other developed countries, costing $2.7 trillion in 2011, or 17.9 percent of the national gross domestic product. Increasing costs strain budgets at all levels of government and threaten the solvency of Medicare, the nation's largest health insurer. At the same time, despite advances in biomedical science, medicine, and public health, health care quality remains inconsistent. In fact, underuse, misuse, and overuse of various services often put patients in danger. Many efforts to improve this situation are focused on Medicare, which mainly pays practitioners on a fee-for-service basis and hospitals on a diagnoses-related group basis, which is a fee for a group of services related to a particular diagnosis. Research has long shown that Medicare spending varies greatly in different regions of the country even when expenditures are adjusted for variation in the costs of doing business, meaning that certain regions have much higher volume and/or intensity of services than others. Further, regions that deliver more services do not appear to achieve better health outcomes than those that deliver less. Variation in Health Care Spending investigates geographic variation in health care spending and quality for Medicare beneficiaries as well as other populations, and analyzes Medicare payment policies that could encourage high-value care. This report concludes that regional differences in Medicare and commercial health care spending and use are real and persist over time. Furthermore, there is much variation within geographic areas, no matter how broadly or narrowly these areas are defined. The report recommends against adoption of a geographically based value index for Medicare payments, because the majority of health care decisions are made at the provider or health care organization level, not by geographic units. Rather, to promote high value services from all providers, Medicare and Medicaid Services should continue to test payment reforms that offer incentives to providers to share clinical data, coordinate patient care, and assume some financial risk for the care of their patients. Medicare covers more than 47 million Americans, including 39 million people age 65 and older and 8 million people with disabilities. Medicare payment reform has the potential to improve health, promote efficiency in the U.S. health care system, and reorient competition in the health care market around the value of services rather than the volume of services provided. The recommendations of Variation in Health Care Spending are designed to help Medicare and Medicaid Services encourage providers to efficiently manage the full range of care for their patients, thereby increasing the value of health care in the United States.

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.

Americans live three years less than their counterparts in France or Sweden. Scottish men survive two years less than English men. Across Europe, women in the poorest communities live up to ten years less than those in the richest. Revealing gaps in life expectancy of up to 25 years between places just a few miles apart, this important book demonstrates that where you live can kill you. Clare Bambra, a leading expert in public health, draws on case studies from across the globe to examine the social environmental, economic and political causes of these health inequalities, how they have evolved over time and what they are like today. Bambra concludes by considering how health divides might develop in the future and what should be done, so that where you live is not a matter of life and dealth. -- Provided by publisher.

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.

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.

Geographic Disparities in Rising Firearm Homicide RatesRates of firearm-related homicide in the United States have risen since 2014. The increases have varied across states and demographic groups, exacerbating existing disparities.