--- - attributes: ~ caption: 'Conceptual diagram illustrating the exposure pathways by which climate change affects human health. Exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Key factors that influence vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Key factors that influence vulnerability at larger scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors can affect an individual’s or a community’s vulnerability through changes in exposure, sensitivity, and adaptive capacity and may also be affected by climate change.' chapter: display_name: 'Chapter 1: Climate Change and Human Health' chapter_identifier: climate-change-and-human-health create_dt: 2014-10-10T10:00:00 description: 'Conceptual diagram illustrating the exposure pathways by which climate change affects human health. Exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Key factors that influence vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Key factors that influence vulnerability at larger scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors can affect an individual’s or a community’s vulnerability through changes in exposure, sensitivity, and adaptive capacity and may also be affected by climate change.' display_name: '1.5: Climate Change and Health' identifier: climate-change-and-health lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 5 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/climate-change-and-health url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Major U.S. national and regional climate trends. Shaded areas are the U.S. regions defined in the 2014 NCA.dd5b893d-4462-4bb3-9205-67b532919566,bfc00315-ccea-4e7c-8a05-2650a07e4252' chapter: display_name: 'Chapter 1: Climate Change and Human Health' chapter_identifier: climate-change-and-human-health create_dt: 2014-11-25T01:00:00 description: 'Major U.S. national and regional climate trends. Shaded areas are the U.S. regions defined in the 2014 NCA.dd5b893d-4462-4bb3-9205-67b532919566,bfc00315-ccea-4e7c-8a05-2650a07e4252' display_name: '1.1: Major U.S. Climate Trends' identifier: major-us-climate-trends lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.8 ordinal: 1 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Major U.S. Climate Trends type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/major-us-climate-trends url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: Examples of sources of uncertainty in projecting impacts of climate change on human health. The left column illustrates the exposure pathway through which climate change can affect human health. The right column lists examples of key sources of uncertainty surrounding effects of climate change at each stage along the exposure pathway. chapter: display_name: 'Chapter 1: Climate Change and Human Health' chapter_identifier: climate-change-and-human-health create_dt: 2015-08-24T11:20:00 description: Examples of sources of uncertainty in projecting impacts of climate change on human health. The left column illustrates the exposure pathway through which climate change can affect human health. The right column lists examples of key sources of uncertainty surrounding effects of climate change at each stage along the exposure pathway. display_name: '1.6: Sources of Uncertainty' identifier: sources-of-uncertainty lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 6 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Sources of Uncertainty type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/sources-of-uncertainty url: ~ usage_limits: ~ - attributes: ~ caption: 'Conceptual diagram illustrating the exposure pathways by which climate change affects human health. Here, the center boxes list some selected examples of the kinds of changes in climate drivers, exposure, and health outcomes explored in this report. Exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Some of the key factors that influence vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Some key factors that influence vulnerability at larger scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors can affect an individual’s or a community’s vulnerability through changes in exposure, sensitivity, and adaptive capacity and may also be affected by climate change.' chapter: display_name: Executive Summary chapter_identifier: executive-summary create_dt: 2014-10-10T10:00:00 description: 'Conceptual diagram illustrating the exposure pathways by which climate change affects human health. Here, the center boxes list some selected examples of the kinds of changes in climate drivers, exposure, and health outcomes explored in this report. Exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Some of the key factors that influence vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Some key factors that influence vulnerability at larger scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors can affect an individual’s or a community’s vulnerability through changes in exposure, sensitivity, and adaptive capacity and may also be affected by climate change.' display_name: '2: Climate Change and Health' identifier: es-climate-change-and-health lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 2 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-climate-change-and-health url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'The food system involves a network of interactions with our physical and biological environments as food moves from production to consumption, or from “farm to table.” Rising CO2 and climate change will affect the quality and distribution of food, with subsequent effects on food safety and nutrition (see Ch. 7: Food Safety).' chapter: display_name: Executive Summary chapter_identifier: executive-summary create_dt: 2014-11-20T00:00:00 description: 'The food system involves a network of interactions with our physical and biological environments as food moves from production to consumption, or from “farm to table.” Rising CO2 and climate change will affect the quality and distribution of food, with subsequent effects on food safety and nutrition (see Ch. 7: Food Safety).' display_name: '8: Farm to Table' identifier: es-farm-to-table lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 8 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Farm to Table type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-farm-to-table url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Defining the determinants of vulnerability to health impacts associated with climate change, including exposure, sensitivity, and adaptive capacity. (Figure source: adapted from Turner et al. 2003)b6a2f8d3-a113-4e46-b62c-7fbaf90b4f59' chapter: display_name: 'Chapter 9: Climate-Health Risk Factors and Populations of Concern' chapter_identifier: populations-of-concern create_dt: 2015-10-06T11:03:00 description: 'Defining the determinants of vulnerability to health impacts associated with climate change, including exposure, sensitivity, and adaptive capacity. (Figure source: adapted from Turner et al. 2003)b6a2f8d3-a113-4e46-b62c-7fbaf90b4f59' display_name: '9.1: Determinants of Vulnerability' identifier: determinants-of-vulnerability lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 1 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Determinants of Vulnerability type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/determinants-of-vulnerability url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'The diagram shows specific examples of how climate change has already affected or will continue to affect human health in the United States. The examples listed in the first column are those described in each underlying chapter’s Exposure Pathway diagram (see Guide to the Report). Moving from left to right along one health impact row, the three middle columns show how climate drivers affect an individual or a community’s exposure to a health threat and the resulting change in health outcome. The overall climate impact is summarized in the final gray column. For a more comprehensive look at how climate change affects health, and to see the environmental, institutional, social, and behavioral factors that play an interactive role in determining health outcomes, see chapters 2–8.' chapter: display_name: Executive Summary chapter_identifier: executive-summary create_dt: 2015-09-09T10:00:00 description: 'The diagram shows specific examples of how climate change has already affected or will continue to affect human health in the United States. The examples listed in the first column are those described in each underlying chapter’s Exposure Pathway diagram (see Guide to the Report). Moving from left to right along one health impact row, the three middle columns show how climate drivers affect an individual or a community’s exposure to a health threat and the resulting change in health outcome. The overall climate impact is summarized in the final gray column. For a more comprehensive look at how climate change affects health, and to see the environmental, institutional, social, and behavioral factors that play an interactive role in determining health outcomes, see chapters 2–8.' display_name: '1: Examples of Climate Impacts on Human Health' identifier: examples-of-climate-impacts-on-human-health lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 1 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Examples of Climate Impacts on Human Health type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/examples-of-climate-impacts-on-human-health url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'This conceptual diagram for a Salmonella example illustrates the key pathways by which humans are exposed to health threats from climate drivers, and potential resulting health outcomes (center boxes). These exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Key factors that influence vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Key factors that influence vulnerability at larger scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors can affect an individual’s or a community’s vulnerability through changes in exposure, sensitivity, and adaptive capacity and may also be affected by climate change. See Ch. 1: Introduction for more information.' chapter: display_name: 'Chapter 7: Food Safety, Nutrition, and Distribution' chapter_identifier: food-safety-nutrition-and-distribution create_dt: 2015-03-05T00:00:00 description: 'This conceptual diagram for a Salmonella example illustrates the key pathways by which humans are exposed to health threats from climate drivers, and potential resulting health outcomes (center boxes). These exposure pathways exist within the context of other factors that positively or negatively influence health outcomes (gray side boxes). Key factors that influence vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Key factors that influence vulnerability at larger scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors can affect an individual’s or a community’s vulnerability through changes in exposure, sensitivity, and adaptive capacity and may also be affected by climate change. See Ch. 1: Introduction for more information.' display_name: '7.2: Climate Change and Health--Salmonella' identifier: climate-change-and-health-salmonella lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 2 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health--Salmonella type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/climate-change-and-health-salmonella url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Direct effect of rising atmospheric carbon dioxide (CO2) on the concentrations of protein and minerals in crops. The top figure shows that the rise in CO2 concentration from 293 ppm (at the beginning of the last century) to 385 ppm (global average in 2008) to 715 ppm (projected to occur by 2100 under the RCP8.5 and RCP6.0 pathways),30b72411-16f2-400d-a1f1-deddf0ef757b progressively lowers protein concentrations in wheat flour (the average of four varieties of spring wheat). The lower figure—the average effect on 125 plant species and cultivars—shows that a doubling of CO2 concentration from preindustrial levels diminishes the concentration of essential minerals in wild and crop plants, including ionome (all the inorganic ions present in an organism) levels, and also lowers protein concentrations in barley, rice, wheat and potato. (Figure source: Experimental data from Ziska et al. 2004 (top figure), Taub et al. 2008, and Loladze 2014 (bottom figure)).de07adc8-7f48-4455-8b2a-6707520acd59,d763a364-656a-4a46-96cc-82800edc3ac2,6f0fe842-95ce-481a-b3f6-473975719843' chapter: display_name: 'Chapter 7: Food Safety, Nutrition, and Distribution' chapter_identifier: food-safety-nutrition-and-distribution create_dt: 2014-11-21T08:00:00 description: 'Direct effect of rising atmospheric carbon dioxide (CO2) on the concentrations of protein and minerals in crops. The top figure shows that the rise in CO2 concentration from 293 ppm (at the beginning of the last century) to 385 ppm (global average in 2008) to 715 ppm (projected to occur by 2100 under the RCP8.5 and RCP6.0 pathways),30b72411-16f2-400d-a1f1-deddf0ef757b progressively lowers protein concentrations in wheat flour (the average of four varieties of spring wheat). The lower figure—the average effect on 125 plant species and cultivars—shows that a doubling of CO2 concentration from preindustrial levels diminishes the concentration of essential minerals in wild and crop plants, including ionome (all the inorganic ions present in an organism) levels, and also lowers protein concentrations in barley, rice, wheat and potato. (Figure source: Experimental data from Ziska et al. 2004 (top figure), Taub et al. 2008, and Loladze 2014 (bottom figure)).de07adc8-7f48-4455-8b2a-6707520acd59,d763a364-656a-4a46-96cc-82800edc3ac2,6f0fe842-95ce-481a-b3f6-473975719843' display_name: '7.4: Effects of Carbon Dioxide on Protein and Minerals' identifier: effects-of-carbon-dioxide-on-protein-and-minerals lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 4 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Effects of Carbon Dioxide on Protein and Minerals type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/effects-of-carbon-dioxide-on-protein-and-minerals url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'The food system involves a network of interactions with our physical and biological environments as food moves from production to consumption, or from “farm to table.” Rising CO2 and climate change will affect the quality and distribution of food, with subsequent effects on food safety and nutrition.' chapter: display_name: 'Chapter 7: Food Safety, Nutrition, and Distribution' chapter_identifier: food-safety-nutrition-and-distribution create_dt: 2014-11-20T00:00:00 description: 'The food system involves a network of interactions with our physical and biological environments as food moves from production to consumption, or from “farm to table.” Rising CO2 and climate change will affect the quality and distribution of food, with subsequent effects on food safety and nutrition.' display_name: '7.1: Farm to Table' identifier: farm-to-table lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 1 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Farm to Table type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/farm-to-table url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Mississippi River gauge height at St. Louis, MO, from October 2007 through October 2014 showing low water conditions during the 2012 drought and water levels above flood stage in 2013. (Figure source: adapted from USGS 2015)b2c1fa72-8eb0-4983-9281-331db52c5b8e' chapter: display_name: 'Chapter 7: Food Safety, Nutrition, and Distribution' chapter_identifier: food-safety-nutrition-and-distribution create_dt: 2014-10-08T19:00:00 description: 'Mississippi River gauge height at St. Louis, MO, from October 2007 through October 2014 showing low water conditions during the 2012 drought and water levels above flood stage in 2013. (Figure source: adapted from USGS 2015)b2c1fa72-8eb0-4983-9281-331db52c5b8e' display_name: '7.5: Mississippi River Level at St. Louis, Missouri' identifier: mississippi-river-level-at-st-louis-missouri lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 5 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2014-10-08T23:59:59 time_start: 2007-10-01T00:00:00 title: 'Mississippi River Level at St. Louis, Missouri' type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mississippi-river-level-at-st-louis-missouri url: ~ usage_limits: ~ - attributes: ~ caption: ~ chapter: display_name: 'Chapter 7: Food Safety, Nutrition, and Distribution' chapter_identifier: food-safety-nutrition-and-distribution create_dt: 2009-08-31T12:00:00 description: ~ display_name: '7.: Mycotoxin in Corn' identifier: mycotoxin-in-corn lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: ~ report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Mycotoxin in Corn type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mycotoxin-in-corn url: ~ usage_limits: ~ - attributes: ~ caption: 'A review of the published literature from 1960 to 2010 indicates a summertime peak in the incidence of illnesses associated with infection from a) Campylobacter, b) Salmonella, and c) Escherichia coli (E. coli). For these three pathogens, the monthly seasonality index shown here on the y-axis indicates the global disease incidence above or below the yearly average, which is denoted as 100. For example, a value of 145 for the month of July for Salmonellosis would mean that the proportion of cases for that month was 45% higher than the 12 month average. Unlike these three pathogens, incidence of norovirus, which can be attained through food, has a wintertime peak. The y-axis of the norovirus incidence graph (d) uses a different metric than (a–c): the monthly proportion of the annual sum of norovirus cases in the northern hemisphere between 1997 and 2011. For example, a value of 0.12 for March would indicate that 12% of the annual cases occurred during that month). Solid line represents the average; confidence intervals (dashed lines) are plus and minus one standard deviation. (Figure sources: a, b, and c: adapted from Lal et al. 2012; d: adapted from Ahmed et al. 2013)84097f67-e3ee-4293-a657-b7f7d2b91e29,04230d65-7ec8-4b53-a59a-fa960649b9c4' chapter: display_name: 'Chapter 7: Food Safety, Nutrition, and Distribution' chapter_identifier: food-safety-nutrition-and-distribution create_dt: 2015-01-07T01:00:00 description: 'A review of the published literature from 1960 to 2010 indicates a summertime peak in the incidence of illnesses associated with infection from a) Campylobacter, b) Salmonella, and c) Escherichia coli (E. coli). For these three pathogens, the monthly seasonality index shown here on the y-axis indicates the global disease incidence above or below the yearly average, which is denoted as 100. For example, a value of 145 for the month of July for Salmonellosis would mean that the proportion of cases for that month was 45% higher than the 12 month average. Unlike these three pathogens, incidence of norovirus, which can be attained through food, has a wintertime peak. The y-axis of the norovirus incidence graph (d) uses a different metric than (a–c): the monthly proportion of the annual sum of norovirus cases in the northern hemisphere between 1997 and 2011. For example, a value of 0.12 for March would indicate that 12% of the annual cases occurred during that month). Solid line represents the average; confidence intervals (dashed lines) are plus and minus one standard deviation. (Figure sources: a, b, and c: adapted from Lal et al. 2012; d: adapted from Ahmed et al. 2013)84097f67-e3ee-4293-a657-b7f7d2b91e29,04230d65-7ec8-4b53-a59a-fa960649b9c4' display_name: '7.3: Seasonality of Human Illnesses Associated With Foodborne Pathogens' identifier: seasonality-of-human-illnesses-associated-with-foodborne-pathogens lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 3 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Seasonality of Human Illnesses Associated With Foodborne Pathogens type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/seasonality-of-human-illnesses-associated-with-foodborne-pathogens url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'The center boxes include selected examples of climate drivers, the primary pathways by which humans are exposed to health threats from those drivers, and the key health outcomes that may result from exposure. The left gray box indicates examples of the larger environmental and institutional context that can affect a person’s or community’s vulnerability to health impacts of climate change. The right gray box indicates the social and behavioral context that also affects a person’s vulnerability to health impacts of climate change. This path includes factors such as race, gender, and age, as well as socioeconomic factors like income and education or behavioral factors like individual decision making. The examples listed in these two gray boxes can increase or reduce vulnerability by influencing the exposure pathway (changes in exposure) or health outcomes (changes in sensitivity or adaptive capacity). The diagram shows that climate change can affect health outcomes directly and by influencing the environmental, institutional, social, and behavioral contexts of health.' chapter: display_name: Front Matter chapter_identifier: front-matter create_dt: ~ description: 'The center boxes include selected examples of climate drivers, the primary pathways by which humans are exposed to health threats from those drivers, and the key health outcomes that may result from exposure. The left gray box indicates examples of the larger environmental and institutional context that can affect a person’s or community’s vulnerability to health impacts of climate change. The right gray box indicates the social and behavioral context that also affects a person’s vulnerability to health impacts of climate change. This path includes factors such as race, gender, and age, as well as socioeconomic factors like income and education or behavioral factors like individual decision making. The examples listed in these two gray boxes can increase or reduce vulnerability by influencing the exposure pathway (changes in exposure) or health outcomes (changes in sensitivity or adaptive capacity). The diagram shows that climate change can affect health outcomes directly and by influencing the environmental, institutional, social, and behavioral contexts of health.' display_name: understanding-the-exposure-pathway-diagrams identifier: understanding-the-exposure-pathway-diagrams lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: ~ report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: ~ type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/front-matter/figure/understanding-the-exposure-pathway-diagrams url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Vulnerability to heat-related illness in Georgia extends beyond urban zones. The map on the left shows a composite measure of social vulnerability for the Atlanta, Georgia Metropolitan Area (darkest colors indicate the most vulnerable areas). The six state-wide maps on the right show the following six vulnerability factors: 1) percent population below the poverty level, 2) percent aged 65 and older living alone, 3) heat event exposure with Heat Index over 100¼F for two consecutive days, 4) percent dialysis patients on Medicare, 5) hospital insufficiency based upon accessibility of hospital infrastructure, and 6) percent impervious surface. Areas located in rural southern Georgia experienced more hazardous heat events, had less access to health care, and had a higher percentage of people living alone. (Figure source: adapted from Manangan et al. 2014)399cfb21-5e6d-425a-98ec-55f42e32401a' chapter: display_name: 'Chapter 9: Climate-Health Risk Factors and Populations of Concern' chapter_identifier: populations-of-concern create_dt: 2014-11-01T01:00:00 description: 'Vulnerability to heat-related illness in Georgia extends beyond urban zones. The map on the left shows a composite measure of social vulnerability for the Atlanta, Georgia Metropolitan Area (darkest colors indicate the most vulnerable areas). The six state-wide maps on the right show the following six vulnerability factors: 1) percent population below the poverty level, 2) percent aged 65 and older living alone, 3) heat event exposure with Heat Index over 100¼F for two consecutive days, 4) percent dialysis patients on Medicare, 5) hospital insufficiency based upon accessibility of hospital infrastructure, and 6) percent impervious surface. Areas located in rural southern Georgia experienced more hazardous heat events, had less access to health care, and had a higher percentage of people living alone. (Figure source: adapted from Manangan et al. 2014)399cfb21-5e6d-425a-98ec-55f42e32401a' display_name: '9.5: Mapping Communities Vulnerable to Heat in Georgia' identifier: mapping-communities-vulnerable-to-heat-in-georgia lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 5 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Mapping Communities Vulnerable to Heat in Georgia type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-communities-vulnerable-to-heat-in-georgia url: ~ usage_limits: ~ - attributes: ~ caption: 'CDC Social Vulnerability Index (SVI): This interactive web map shows the overall social vulnerability of the U.S. Southwest in 2010. The SVI provides a measure of four social vulnerability elements: socioeconomic status; household composition; race, ethnicity, and language; and housing/transportation. Each census tract receives a separate ranking for overall vulnerability at the census-tract level. Dark blue indicates the highest overall vulnerability (the top quartile) with the lowest quartile in pale yellow. (Figure source: ATSDR 2015)90ee72cf-ab21-486c-bb40-45780e31b45f' chapter: display_name: 'Chapter 9: Climate-Health Risk Factors and Populations of Concern' chapter_identifier: populations-of-concern create_dt: 2014-12-01T01:00:00 description: 'CDC Social Vulnerability Index (SVI): This interactive web map shows the overall social vulnerability of the U.S. Southwest in 2010. The SVI provides a measure of four social vulnerability elements: socioeconomic status; household composition; race, ethnicity, and language; and housing/transportation. Each census tract receives a separate ranking for overall vulnerability at the census-tract level. Dark blue indicates the highest overall vulnerability (the top quartile) with the lowest quartile in pale yellow. (Figure source: ATSDR 2015)90ee72cf-ab21-486c-bb40-45780e31b45f' display_name: '9.4: Mapping Social Vulnerability' identifier: mapping-social-vulnerability lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 4 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2010-12-31T23:59:59 time_start: 2010-01-01T00:00:00 title: Mapping Social Vulnerability type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-social-vulnerability url: ~ usage_limits: ~ - attributes: ~ caption: 'Top: Illustration of eastern North American topography in a resolution of 68 miles x 68 miles (110 x 110 km). Bottom: Illustration of eastern North America at a resolution of 19 miles x 19 miles (30 x 30 km). Global climate models are constantly being enhanced as scientific understanding of climate improves and as computational power increases. For example, in 1990, the average model divided up the world into grid cells measuring more than 300 miles per side. Today, most models divide the world up into grid cells of about 60 to 100 miles per side, and some of the most recent models are able to run short simulations with grid cells of only 15 miles per side. Supercomputer capabilities are the primary limitation on grid cell size. Newer models also incorporate more of the physical processes and components that make up the Earth’s climate system. (Figure source: Melillo et al. 2014)dd5b893d-4462-4bb3-9205-67b532919566' chapter: display_name: 'Appendix 1: Technical Support Document' chapter_identifier: appendix-1--technical-support-document create_dt: 2014-05-01T12:00:00 description: 'Top: Illustration of eastern North American topography in a resolution of 68 miles x 68 miles (110 x 110 km). Bottom: Illustration of eastern North America at a resolution of 19 miles x 19 miles (30 x 30 km). Global climate models are constantly being enhanced as scientific understanding of climate improves and as computational power increases. For example, in 1990, the average model divided up the world into grid cells measuring more than 300 miles per side. Today, most models divide the world up into grid cells of about 60 to 100 miles per side, and some of the most recent models are able to run short simulations with grid cells of only 15 miles per side. Supercomputer capabilities are the primary limitation on grid cell size. Newer models also incorporate more of the physical processes and components that make up the Earth’s climate system. (Figure source: Melillo et al. 2014)dd5b893d-4462-4bb3-9205-67b532919566' display_name: '2: Example of Increasing Spatial Resolution of Climate Models' identifier: example-increasing-spatial-resolution-of-climate-models lat_max: 52 lat_min: 30 lon_max: 106 lon_min: 80 ordinal: 2 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2011-12-31T23:59:59 time_start: 1995-01-01T00:00:00 title: Example of Increasing Spatial Resolution of Climate Models type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/example-increasing-spatial-resolution-of-climate-models url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: Examples of sources of uncertainty in projecting impacts of climate change on human health. The left column illustrates the exposure pathway through which climate change can affect human health. The right column lists examples of key sources of uncertainty surrounding effects of climate change at each stage along the exposure pathway. chapter: display_name: 'Appendix 1: Technical Support Document' chapter_identifier: appendix-1--technical-support-document create_dt: 2015-08-24T11:20:00 description: Examples of sources of uncertainty in projecting impacts of climate change on human health. The left column illustrates the exposure pathway through which climate change can affect human health. The right column lists examples of key sources of uncertainty surrounding effects of climate change at each stage along the exposure pathway. display_name: '4: Sources of Uncertainty' identifier: tsd-sources-of-uncertainty lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 4 report: display_name: 'The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment' report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Sources of Uncertainty type: figure uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/tsd-sources-of-uncertainty url: ~ usage_limits: Free to use with credit to the original figure source.