--- - 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_identifier: climate-change-and-human-health create_dt: 2014-11-25T01:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/major-us-climate-trends.yaml identifier: major-us-climate-trends lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.8 ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Major U.S. Climate Trends 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: 'Time series of 5-year averages of the number of extreme 2-day duration precipitation events, averaged over the United States from 1900 to 2014. The number is expressed as the percent difference from the average for the entire period. This is based on 726 stations that have precipitation data for at least 90% of the days in the period. An event is considered extreme if the precipitation amount exceeds a threshold for a once-per-year recurrence. (Figure source: adapted from Mellilo et al. 2014)dd5b893d-4462-4bb3-9205-67b532919566' chapter_identifier: climate-change-and-human-health create_dt: 2014-10-29T08:35:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/change-in-number-of-extreme-precipitation-events.yaml identifier: change-in-number-of-extreme-precipitation-events lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2014-12-31T23:59:59 time_start: 1900-01-01T00:00:00 title: Change in Number of Extreme Precipitation Events uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/change-in-number-of-extreme-precipitation-events url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Projected changes in annual average temperature (top) and precipitation (bottom) for 2021–2050 (left) and 2041–2070 (right) with respect to the average for 1971­–2000 for the RCP6.0 scenario. The RCP6.0 pathway projects an average global temperature increase of 5.2°F in 2100 over the 1901–1960 global average temperature (the RCPs are described in more detail in Appendix 1: Technical Support Document). Temperature increases in the United States for this scenario (top panels) are in the 2°F to 3°F range for 2021 to 2050 and 2°F to 4°F for 2041­ to 2070. This means that the increase in temperature projected in the United States over the next 50 years under this scenario would be larger than the 1°F to 2°F increase in temperature that has already been observed over the previous century. Precipitation is projected to decrease in the Southwest and increase in the Northeast (bottom panels). These projected changes are statistically significant (95% confidence) in small portions of the Northeast, as indicated by the hatching. (Figure source: adapted from Sun et al. 2015)b63c9720-f770-4718-89cc-53b3616e2bec' chapter_identifier: climate-change-and-human-health create_dt: 2014-07-21T12:51:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/projected-changes-in-temperature-and-precipitation-by-mid-century.yaml identifier: projected-changes-in-temperature-and-precipitation-by-mid-century lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2070-12-31T23:59:59 time_start: 2021-01-01T00:00:00 title: Projected Changes in Temperature and Precipitation by Mid-Century uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/projected-changes-in-temperature-and-precipitation-by-mid-century url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Projected changes in several climate variables for 2046–2065 with respect to the 1981–2000 average for the RCP6.0 scenario. These include the coldest night of the year (top left) and the hottest day of the year (top right). By the middle of this century, the coldest night of the year is projected to warm by 6°F to 10°F over most of the country, with slightly smaller changes in the south. The warmest day of the year is projected to be 4°F to 6°F warmer in most areas. Also shown above are projections of the wettest day of the year (bottom left) and the annual longest consecutive dry day spell (bottom right). Extreme precipitation is projected to increase, with an average change of 5% to 15% in the precipitation falling on the wettest day of the year. The length of the annual longest dry spell is projected to increase in most areas, but these changes are small: less than two days in most areas. (Figure source: adapted from Sun et al. 2015)b63c9720-f770-4718-89cc-53b3616e2bec' chapter_identifier: climate-change-and-human-health create_dt: 2014-07-21T12:51:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/projected-changes-in-hottest-coldest-and-wettest-driest-day-of-the-year.yaml identifier: projected-changes-in-hottest-coldest-and-wettest-driest-day-of-the-year lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 4 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2065-12-31T23:59:59 time_start: 1981-01-01T00:00:00 title: Projected Changes in Hottest/Coldest and Wettest/Driest Day of the Year uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/projected-changes-in-hottest-coldest-and-wettest-driest-day-of-the-year url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - 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_identifier: climate-change-and-human-health create_dt: 2014-10-10T10:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/climate-change-and-health.yaml identifier: climate-change-and-health lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 5 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health 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: 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_identifier: climate-change-and-human-health create_dt: 2015-08-24T11:20:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/sources-of-uncertainty.yaml identifier: sources-of-uncertainty lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 6 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Sources of Uncertainty uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/climate-change-and-human-health/figure/sources-of-uncertainty url: ~ usage_limits: ~ - attributes: ~ caption: 'This conceptual diagram illustrates the key pathways by which climate change influences human health during an extreme heat event, 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 Chapter 1: Introduction for more information.' chapter_identifier: temperature-related-death-and-illness create_dt: 2015-03-05T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/temperature-related-death-and-illness/figure/climate-change-and-health-extreme-heat.yaml identifier: climate-change-and-health-extreme-heat lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health--Extreme Heat uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/temperature-related-death-and-illness/figure/climate-change-and-health-extreme-heat url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'This figure shows the relationship between high temperatures and deaths observed during the 1995 Chicago heat wave. The large spike in deaths in mid-July of 1995 (red line) is much higher than the average number of deaths during that time of year (orange line), as well as the death rate before and after the heat wave. This increase in the rate of deaths occurred during and after the heat wave, as shown here by temperatures exceeding 100°F during the day (green line). Humidity and high nighttime temperatures were also key contributing factors to this increase in deaths.4f9edf45-db7c-4e87-b1ab-af8856388760 The number of excess deaths has been estimated to be about 700 based on statistical methods, but only 465 deaths in Cook County were classified as “heat-related” on death certificates during this same period,e4b23502-00d8-4f34-8da8-3bb61ece107d demonstrating the tendency of direct attribution to undercount total heat-related deaths. (Figure source: EPA 2014)bfc00315-ccea-4e7c-8a05-2650a07e4252' chapter_identifier: temperature-related-death-and-illness create_dt: 2014-05-01T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/temperature-related-death-and-illness/figure/heat-related-deaths-during-the-1995-chicago-heat-wave.yaml identifier: heat-related-deaths-during-the-1995-chicago-heat-wave lat_max: 42.2 lat_min: 41.5 lon_max: 88.3 lon_min: 87.1 ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 1995-08-30T23:59:59 time_start: 1995-06-01T00:00:00 title: Heat-Related Deaths During the 1995 Chicago Heat Wave uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/temperature-related-death-and-illness/figure/heat-related-deaths-during-the-1995-chicago-heat-wave url: ~ usage_limits: ~ - attributes: ~ caption: 'This figure shows the projected decrease in death rates due to warming in colder months (October–March, top left), the projected increase in death rates due to warming in the warmer months (April–September, top right), and the projected net change in death rates (combined map, bottom), comparing results for 2100 to those for a 1990 baseline period in 209 U.S. cities. These results are from one of the two climate models (GFDL–CM3 scenario RCP6.0) studied in Schwartz et al. (2015). In the study, mortality data for a city is based on county-level records, so the borders presented reflect counties corresponding to the study cities. Geographic variation in the death rates are due to a combination of differences in the amount of projected warming and variation in the relationship between deaths and temperatures derived from the historical health and temperature data. These results are based on holding the 2010 population constant in the analyses, with no explicit assumptions or adjustment for potential future adaptation. Therefore, these results reflect only the effect of the anticipated change in climate over time. (Figure source: Schwartz et al. 2015)e805bfdc-c4c2-43a0-b2e5-5a66945c74e4' chapter_identifier: temperature-related-death-and-illness create_dt: 2014-10-31T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/temperature-related-death-and-illness/figure/projected-changes-in-temperature-related-death-rates.yaml identifier: projected-changes-in-temperature-related-death-rates lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2100-12-31T23:59:59 time_start: 1975-01-01T00:00:00 title: Projected Changes in Temperature-Related Death Rates uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/temperature-related-death-and-illness/figure/projected-changes-in-temperature-related-death-rates url: ~ usage_limits: ~ - attributes: ~ caption: 'This figure shows the projected increase in deaths due to warming in the summer months (hot season, April–September), the projected decrease in deaths due to warming in the winter months (cold season, October–March), and the projected net change in deaths for the 209 U.S. cities examined. These results compare projected deaths for future reporting years to results for the year 1990 while holding the population constant at 2010 levels and without any quantitative adjustment for potential future adaptation, so that temperature–death relationships observed in the last decade of the available data (1997–2006) are assumed to remain unchanged in projections over the 21st century. With these assumptions, the figure shows an increasing health benefit in terms of reduced deaths during the cold season (October–March) over the 21st century from warming temperatures, while deaths during the hot season (April–September) increase. Overall, the additional deaths from the warming in the hot season exceed the reduction in deaths during the cold season, resulting in a net increase in deaths attributable to temperature over time as a result of climate change. The baseline and future reporting years are based on 30-year periods where possible, with the exception of 2100: 1990 (1976–2005), 2030 (2016–2045), 2050 (2036–2065), and 2100 (2086–2100). (Figure source: adapted from Schwartz et al. 2015)e805bfdc-c4c2-43a0-b2e5-5a66945c74e4' chapter_identifier: temperature-related-death-and-illness create_dt: 2014-10-31T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/temperature-related-death-and-illness/figure/projected-changes-in-deaths-in-us-cities-by-season.yaml identifier: projected-changes-in-deaths-in-us-cities-by-season lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 4 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2100-12-31T23:59:59 time_start: 2030-01-01T00:00:00 title: Projected Changes in Deaths in U.S. Cities by Season uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/temperature-related-death-and-illness/figure/projected-changes-in-deaths-in-us-cities-by-season url: ~ usage_limits: ~ - attributes: ~ caption: 'This conceptual diagram for an outdoor air quality 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 Chapter 1: Introduction for more information.' chapter_identifier: air-quality-impacts create_dt: 2015-03-05T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/air-quality-impacts/figure/climate-change-and-health-outdoor-air-quality.yaml identifier: climate-change-and-health-outdoor-air-quality lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health--Outdoor Air Quality uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/air-quality-impacts/figure/climate-change-and-health-outdoor-air-quality url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Projected changes in average daily maximum temperature (degrees Fahrenheit), summer average maximum daily 8-hour ozone (parts per billion), and excess ozone-related deaths (incidences per year by county) in the year 2030 relative to the year 2000, following two global climate models and two greenhouse gas concentration pathways, known as Representative Concentration Pathways, or RCPs (see van Vuuren et al. 201144124472-4a1d-4fbd-b86e-91cca108b938). Each year (2000 and 2030) is represented by 11 years of modeled data for May through September, the traditional ozone season in the United States.

The top panels are based on the National Center for Atmospheric Research/Department of Energy (NCAR/DOE) Community Earth System Model (CESM) following RCP8.5 (a higher greenhouse gas concentration pathway), and the bottom panels are based on the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) ModelE2-R following RCP6.0 (a moderate greenhouse gas concentration pathway).

The leftmost panels are based on dynamically downscaled regional climate using the NCAR Weather Research and Forecasting (WRF) model, the center panels are based on air quality simulations from the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model, and the rightmost panels are based on the U.S. EPA Environmental Benefits and Mapping Program (BenMAP).

Fann et al. 2015 reports a range of mortality outcomes based on different methods of computing the mortality effects of ozone changes—the changes in the number of deaths shown in the rightmost panels were computed using the method described in Bell et al. 2004.54a66159-1675-43bb-b5d3-a9b7f283e4de,a02f25a1-29c1-4564-9b41-7d974e8ce6b5 (Figure source: adapted from Fann et al. 2015)54a66159-1675-43bb-b5d3-a9b7f283e4de' chapter_identifier: air-quality-impacts create_dt: 2014-10-31T12:34:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/air-quality-impacts/figure/projected-change-in-temperature-ozone-and-ozone-related-premature-deaths-in-2030.yaml identifier: projected-change-in-temperature-ozone-and-ozone-related-premature-deaths-in-2030 lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2030-12-31T23:59:59 time_start: 2030-01-01T00:00:00 title: 'Projected Change in Temperature, Ozone, and Ozone-Related Premature Deaths in 2030' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/air-quality-impacts/figure/projected-change-in-temperature-ozone-and-ozone-related-premature-deaths-in-2030 url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Projected change in ozone-related premature deaths from 2000 to 2030 by U.S. region and based on CESM/RCP8.5. Each year (2000 and 2030) is represented by 11 years of modeled data. Ozone-related premature deaths were calculated using the risk coefficient from Bell et al. (2004).a02f25a1-29c1-4564-9b41-7d974e8ce6b5 Boxes indicate 25th, 50th, and 75th percentile change over 11-year sample periods, and vertical lines extend to 1.5 times the interquartile range. U.S. regions follow geopolitical boundaries shown in Figure 3.2. (Figure source: Fann et al. 2015)54a66159-1675-43bb-b5d3-a9b7f283e4de' chapter_identifier: air-quality-impacts create_dt: 2014-11-07T12:30:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/air-quality-impacts/figure/projected-change-in-ozone-related-premature-deaths.yaml identifier: projected-change-in-ozone-related-premature-deaths lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2035-12-31T23:59:59 time_start: 2025-01-01T00:00:00 title: Projected Change in Ozone-related Premature Deaths uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/air-quality-impacts/figure/projected-change-in-ozone-related-premature-deaths url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Ragweed pollen season length has increased in central North America between 1995 and 2011 by as much as 11 to 27 days in parts of the United States and Canada, in response to rising temperatures. Increases in the length of this allergenic pollen season are correlated with increases in the number of days before the first frost. The largest increases have been observed in northern cities. (Figure source: Melillo et al. 2014. Photo credit: Lewis Ziska, USDA).dd5b893d-4462-4bb3-9205-67b532919566' chapter_identifier: air-quality-impacts create_dt: 2014-05-01T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/air-quality-impacts/figure/ragweed-pollen-season-lengthens.yaml identifier: ragweed-pollen-season-lengthens lat_max: 52 lat_min: 30 lon_max: 106 lon_min: 89 ordinal: 4 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: Ragweed Pollen Season Lengthens uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/air-quality-impacts/figure/ragweed-pollen-season-lengthens url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'This figure provides 10-year estimates of fatalities related to extreme events from 2004 to 2013,a1b08f2f-e94c-4628-b82a-a646e71116ec as well as estimated economic damages from 58 weather and climate disaster events with losses exceeding $1 billion (see Smith and Katz 2013 to understand how total losses were calculated).4fe32146-a968-4dde-8a2b-df2aa2eabdd4 These statistics are indicative of the human and economic costs of extreme weather events over this time period. Climate change will alter the frequency, intensity, and geographic distribution of some of these extremes,dd5b893d-4462-4bb3-9205-67b532919566 which has consequences for exposure to health risks from extreme events. Trends and future projections for some extremes, including tornadoes, lightning, and wind storms are still uncertain.' chapter_identifier: extreme-events create_dt: 2015-02-23T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/extreme-events/figure/estimated-deaths-and-billion-dollar-losses-from-extreme-weather-events-in-the-us-2004-2013.yaml identifier: estimated-deaths-and-billion-dollar-losses-from-extreme-weather-events-in-the-us-2004-2013 lat_max: 49.28 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2013-12-31T00:00:00 time_start: 2004-01-01T00:00:00 title: Estimated Deaths and Billion Dollar Losses from Extreme Weather Events in the U.S. 2004-2013 uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/extreme-events/figure/estimated-deaths-and-billion-dollar-losses-from-extreme-weather-events-in-the-us-2004-2013 url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'This conceptual diagram for a flooding event 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 health outcomes and vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Key factors that influence health outcomes and vulnerability at larger community or societal scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors may also be affected by climate change. See Chapter 1: Introduction for more information.' chapter_identifier: extreme-events create_dt: 2015-03-05T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/extreme-events/figure/climate-change-and-health-flooding.yaml identifier: climate-change-and-health-flooding lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health--Flooding uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/extreme-events/figure/climate-change-and-health-flooding url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Composite map of floods associated with landfalling hurricanes over the past 31 years, based on stream gauge data. The Flood Ratio (Q) refers to maximum hurricane-related flood peaks compared to 10-year flood peaks (expected to occur, on average, once every 10 years and corresponds to the 90th percentile of the flood peak distribution) calculated for the same area. See Villarini et al. 2014 for a detailed description of how Q values are calculated.7f29d739-b00c-4140-a922-be8d211ecc5e Q values between 0.6 and 1 (light blue and yellow) generally indicate minor to moderate flooding, while values above 1 (orange and red) generally indicate major flooding larger than the 10-year flood peak. The dark gray areas of the map represent the extent of the 500-km buffer around the center of circulation of the hurricanes included during the study period (the light gray areas of the map fall outside of the study area). Figure 4.3 shows that hurricanes are important contributors to flooding in the eastern United States, as well as large areas of the central United States. Land use/land cover properties and soil moisture conditions are also important factors for flooding. (Figure source: adapted from Villarini et al. 2014)7f29d739-b00c-4140-a922-be8d211ecc5e' chapter_identifier: extreme-events create_dt: 2014-01-10T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/extreme-events/figure/hurricane-induced-flood-effects-in-eastern-and-central-united-states.yaml identifier: hurricane-induced-flood-effects-in-eastern-and-central-united-states lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2011-12-31T23:59:59 time_start: 1981-01-01T00:00:00 title: Hurricane-Induced Flood Effects in Eastern and Central United States uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/extreme-events/figure/hurricane-induced-flood-effects-in-eastern-and-central-united-states url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Based on 17 climate model simulations for the continental United States using a higher emissions pathway (RCP8.5), the map shows projected percentage increases in weeks with risk of very large fires by mid-century (2041-2070) compared to the recent past (1971-2000). The darkest shades of red indicated that up to a 6-fold increase in risk is projected for parts of the West. This area includes the Great Basin, Northern Rockies, and parts of Northern California. Gray represents areas within the continental United States where there is either no data or insufficient historical observations on very large fires to build robust models. The potential for very large fire events is also expected to increase along the southern coastline and in areas around the Great Lakes. (Figure source: adapted from Barbero et al. 2015 by NOAA)ca5c4b38-9aa8-4edc-9aea-42f1625cc45b' chapter_identifier: extreme-events create_dt: 2015-09-10T09:33:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/extreme-events/figure/projected-increases-in-very-large-fires.yaml identifier: projected-increases-in-very-large-fires lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 4 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Projected Increases in Very Large Fires uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/extreme-events/figure/projected-increases-in-very-large-fires url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'This conceptual diagram illustrates the key pathways by which climate change influences human exposure to Lyme disease and the 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_identifier: vectorborne-diseases create_dt: 2015-03-05T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/climate-change-and-health-lyme-disease.yaml identifier: climate-change-and-health-lyme-disease lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health--Lyme Disease uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/climate-change-and-health-lyme-disease url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Maps show the reported cases of Lyme disease in 2001 and 2014 for the areas of the country where Lyme disease is most common (the Northeast and Upper Midwest). Both the distribution and the numbers of cases have increased. (Figure source: adapted from CDC 2015)6066212c-7cfd-46af-8255-e6c75647167a' chapter_identifier: vectorborne-diseases create_dt: 2015-10-01T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/changes-in-lyme-disease-case-report-distribution.yaml identifier: changes-in-lyme-disease-case-report-distribution lat_max: -67.0 lat_min: -98.0 lon_max: 48.0 lon_min: 36.0 ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2014-12-31T23:59:59 time_start: 2001-01-01T00:00:00 title: Changes in Lyme Disease Case Report Distribution uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/changes-in-lyme-disease-case-report-distribution url: ~ usage_limits: ~ - attributes: ~ caption: 'Figure depicts the life cycle of blacklegged ticks, including the phases in which humans can be exposed to Lyme disease, and some of the changes in seasonality expected with climate change. (Figure source: adapted from CDC 2015)f4eeeade-8af7-4d4a-9a9c-af712338b208' chapter_identifier: vectorborne-diseases create_dt: 2010-09-09T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/life-cycle-of-blacklegged-ticks-ixodes-scapularis.yaml identifier: life-cycle-of-blacklegged-ticks-ixodes-scapularis lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: 'Life Cycle of Blacklegged Ticks, Ixodes scapularis' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/life-cycle-of-blacklegged-ticks-ixodes-scapularis url: ~ usage_limits: ~ - attributes: ~ caption: 'Box plots comparing the distributions of the national-level historical observed data for annual Lyme onset week (1992–2007 in green) with the distributions of AOGCM multi-model mean projections of Lyme onset week for each of four Representative Concentration Pathways (RCP2.6, 4.5, 6.0, and 8.5) and two future time periods (2025–2040 in blue, 2065–2080 in red). Each box plot shows the values of Lyme disease onset week for the maximum (top of dashed line), 75th percentile (top of box), average (line through box), 25th percentile (bottom of box), and minimum (bottom of dashed line) of the distribution. All distributions are comprised of values for 12 eastern states and 16 years (N = 192). Additional details can be found in Monaghan et al. (2015). (Figure source: adapted from Monaghan et al. 2015).953d1436-e0d0-426c-8dcc-68e5c02eef30' chapter_identifier: vectorborne-diseases create_dt: 2014-10-30T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/projected-change-in-lyme-disease-onset-week.yaml identifier: projected-change-in-lyme-disease-onset-week lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 4 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2080-12-31T23:59:59 time_start: 1992-01-01T00:00:00 title: Projected Change in Lyme Disease Onset Week uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/projected-change-in-lyme-disease-onset-week url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Maps show the incidence of West Nile neuroinvasive disease in the United States for 2010 through 2013. Shown as cases per 100,000 people. (Data source: CDC 2014)2eb0f9eb-b977-49c5-88b6-a1e513414225' chapter_identifier: vectorborne-diseases create_dt: 2014-11-17T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/incidence-of-west-nile-neuroinvasive-disease-by-county-in-the-united-states.yaml identifier: incidence-of-west-nile-neuroinvasive-disease-by-county-in-the-united-states lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 5 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2013-12-31T23:59:59 time_start: 2010-01-01T00:00:00 title: Incidence of West Nile Neuroinvasive Disease by County in the United States uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/incidence-of-west-nile-neuroinvasive-disease-by-county-in-the-united-states url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: ~ chapter_identifier: vectorborne-diseases create_dt: 2014-12-03T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/climate-impacts-on-west-nile-virus-transmission.yaml identifier: climate-impacts-on-west-nile-virus-transmission lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 6 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Impacts on West Nile Virus Transmission uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/vectorborne-diseases/figure/climate-impacts-on-west-nile-virus-transmission url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'This conceptual diagram for an example of infection by Vibrio species (V. vulnificus, V. parahaemolyticus, or V. alginolyticus) illustrates the key pathways by which humans are exposed to health threats from climate drivers. These climate drivers create more favorable growing conditions for these naturally occurring pathogens in coastal environments through their effects on coastal salinity, turbidity (water clarity), or plankton abundance and composition. Longer seasons for growth and expanding geographic range of occurrence increase the risk of exposure to Vibrio, which can result in various potential 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_identifier: water-related-illnesses create_dt: 2015-03-16T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/climate-change-and-health-vibrio.yaml identifier: climate-change-and-health-vibrio lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: 'Climate Change and Health - Vibrio' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/climate-change-and-health-vibrio url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Precipitation and temperature changes affect fresh and marine water quantity and quality primarily through urban, rural, and agricultural runoff. This runoff in turn affects human exposure to water-related illnesses primarily through contamination of drinking water, recreational water, and fish and shellfish.' chapter_identifier: water-related-illnesses create_dt: 2014-10-30T14:42:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness.yaml identifier: links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: 'Links between Climate Change, Water Quantity and Quality, and Human Exposure to Water-Related Illness' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/links-between-climate-change-water-quantity-and-quality-and-human-exposure-to-water-related-illness url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'This figure compares the geographic distribution of chicken, cattle, and hog and pig densities to the projected change in annual maximum 5-day precipitation totals (2046–2065 compared to 1981–2000, multi-model average using RCP8.5) across the continental United States. Increasing frequency and intensity of precipitation and subsequent increases in runoff are key climate factors that increase the potential for pathogens associated with livestock waste to contaminate water bodies. (Figure sources: adapted from Sun et al. 2015 and USDA 2014).b63c9720-f770-4718-89cc-53b3616e2bec,1002d699-e8a9-4572-aec0-16524400e7a5' chapter_identifier: water-related-illnesses create_dt: 2015-12-09T14:13:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/locations-of-livestock-and-projections-of-heavy-precipitation.yaml identifier: locations-of-livestock-and-projections-of-heavy-precipitation lat_max: 49.38 lat_min: 24.50 lon_max: -66.95 lon_min: -124.80 ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2012-12-31T23:59:59 time_start: 2012-12-31T00:00:00 title: Locations of Livestock and Projections of Heavy Precipitation uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/locations-of-livestock-and-projections-of-heavy-precipitation url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Seasonal and decadal projections of abundance of V. parahaemolyticus in oysters of Chesapeake Bay (top) and probability of occurrence of V. vulnificus in Chesapeake Bay surface waters (bottom). The circles show average values in the baseline period (1985–2000) and future years averaged by decadal period: 2030 (2025–2034), 2050 (2045–2054), and 2095 (2090–2099). (Figure source: adapted from Jacobs et al. 2015).8640a3db-35fa-4089-8fb5-d52dc8b35c71' chapter_identifier: water-related-illnesses create_dt: 2015-12-11T10:51:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projections-of-vibrio-occurrence-and-abundance-in-chesapeake-bay.yaml identifier: projections-of-vibrio-occurrence-and-abundance-in-chesapeake-bay lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 4 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2099-12-31T23:59:59 time_start: 2014-12-31T00:00:00 title: Projections of Vibrio Occurrence and Abundance in Chesapeake Bay uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projections-of-vibrio-occurrence-and-abundance-in-chesapeake-bay url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Vibrio growth increases in temperatures above 15°C (59°F). These maps show the low and high end of the ranges for projected area of Alaskan coastline with water temperature averages in August that are greater than this threshold. The projections were made for the following future time periods: 2030 (2026–2035), 2050 (2046–2055), and 2090 (2086–2095). On average, the models project that by 2090, nearly 60% of the Alaskan shoreline in August will become suitable Vibrio habitat. (Figure source: adapted from Jacobs et al. 2015)8640a3db-35fa-4089-8fb5-d52dc8b35c71' chapter_identifier: water-related-illnesses create_dt: 2015-10-21T14:22:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/changes-in-suitable-coastal-vibrio-habitat-in-alaska.yaml identifier: changes-in-suitable-coastal-vibrio-habitat-in-alaska lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 5 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2090-12-31T23:59:59 time_start: 2030-01-01T00:00:00 title: Changes in Suitable Coastal Vibrio Habitat in Alaska uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/changes-in-suitable-coastal-vibrio-habitat-in-alaska url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - attributes: ~ caption: 'Water temperature data from 1990–2013 were collected or reconstructed for buoy sites in the western Gulf of Mexico, Yucatan channel, and eastern Caribbean Sea. These data were then used in calculations to project average annual water temperature and average growth rates for three Caribbean Gambierdiscus species (G. caribaeus, G. belizeanus, G. carolinianus) for the period 2014–2099. (Figure source: adapted from Kibler et al. 2015).1dfd14e0-eae8-46d9-9c3e-0fa3f0c37da4' chapter_identifier: water-related-illnesses create_dt: 2015-12-11T12:54:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projected-changes-in-caribbean-gambierdiscus-species.yaml identifier: projected-changes-in-caribbean-gambierdiscus-species lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 6 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Projected Changes in Caribbean Gambierdiscus Species uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projected-changes-in-caribbean-gambierdiscus-species url: ~ usage_limits: ~ - attributes: ~ caption: 'Seasonal and decadal projections of growth of Alexandrium in Puget Sound. The circles show average values in the baseline period (2006–2013) and future years averaged by decadal period: 2030 (2025–2035), 2050 (2045–2055), and 2095 (2090–2099). Growth rate values above 0.25_d-1 constitute a bloom of Alexandrium (Figure source: adapted from Jacobs et al. 2015)8640a3db-35fa-4089-8fb5-d52dc8b35c71' chapter_identifier: water-related-illnesses create_dt: 2015-12-11T12:57:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projections-of-growth-of-alexandrium-in-puget-sound.yaml identifier: projections-of-growth-of-alexandrium-in-puget-sound lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 7 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2099-12-31T23:59:59 time_start: 2014-12-31T00:00:00 title: Projections of Growth of Alexandrium in Puget Sound uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/water-related-illnesses/figure/projections-of-growth-of-alexandrium-in-puget-sound 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_identifier: food-safety-nutrition-and-distribution create_dt: 2014-11-20T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/farm-to-table.yaml identifier: farm-to-table lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Farm to Table 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: '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_identifier: food-safety-nutrition-and-distribution create_dt: 2015-03-05T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/climate-change-and-health-salmonella.yaml 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_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health--Salmonella 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: '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_identifier: food-safety-nutrition-and-distribution create_dt: 2015-01-07T01:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/seasonality-of-human-illnesses-associated-with-foodborne-pathogens.yaml 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_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Seasonality of Human Illnesses Associated With Foodborne Pathogens 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: '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_identifier: food-safety-nutrition-and-distribution create_dt: 2014-11-21T08:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/effects-of-carbon-dioxide-on-protein-and-minerals.yaml 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_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Effects of Carbon Dioxide on Protein and Minerals 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: '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_identifier: food-safety-nutrition-and-distribution create_dt: 2014-10-08T19:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mississippi-river-level-at-st-louis-missouri.yaml 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_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' 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_identifier: food-safety-nutrition-and-distribution create_dt: 2009-08-31T12:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mycotoxin-in-corn.yaml identifier: mycotoxin-in-corn lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 6 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Mycotoxin in Corn uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/mycotoxin-in-corn url: ~ usage_limits: ~ - attributes: ~ caption: ~ chapter_identifier: food-safety-nutrition-and-distribution create_dt: ~ href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/low-water-conditions-on-mississippi-river.yaml identifier: low-water-conditions-on-mississippi-river lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 7 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Low Water Conditions on Mississippi River uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/food-safety-nutrition-and-distribution/figure/low-water-conditions-on-mississippi-river url: ~ usage_limits: ~ - attributes: ~ caption: 'This conceptual diagram illustrates the key pathways by which humans are exposed to health threats from climate drivers, and potential resulting mental health and well-being 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 health outcomes and vulnerability for individuals are shown in the right box, and include social determinants of health and behavioral choices. Key factors that influence health outcomes and vulnerability at larger community or societal scales, such as natural and built environments, governance and management, and institutions, are shown in the left box. All of these influencing factors may also be affected by climate change. See Chapter 1: Introduction for more information.' chapter_identifier: mental-health-and-well-being create_dt: 2015-02-05T00:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/mental-health-and-well-being/figure/climate-change-and-mental-health.yaml identifier: climate-change-and-mental-health lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Mental Health uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/mental-health-and-well-being/figure/climate-change-and-mental-health url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'At the center of the diagram are human figures representing adults, children, older adults, and people with disabilities. The left circle depicts climate impacts including air quality, wildfire, sea level rise and storm surge, heat, storms, and drought. The right circle shows the three interconnected health domains that will be affected by climate impacts—Medical and Physical Health, Mental Health, and Community Health. (Figure source: adapted from Clayton et al. 2014).f66b946f-c672-4a4b-8f71-1b05738e029e' chapter_identifier: mental-health-and-well-being create_dt: 2014-12-05T01:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/mental-health-and-well-being/figure/the-impact-of-climate-change-on-physical-mental-and-community-health.yaml identifier: the-impact-of-climate-change-on-physical-mental-and-community-health lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: 'The Impact of Climate Change on Physical, Mental, and Community Health' uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/mental-health-and-well-being/figure/the-impact-of-climate-change-on-physical-mental-and-community-health 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_identifier: populations-of-concern create_dt: 2015-10-06T11:03:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/determinants-of-vulnerability.yaml identifier: determinants-of-vulnerability lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Determinants of Vulnerability 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: 'Social determinants of health interact with the three elements of vulnerability. The left side boxes provide examples of social determinants of health associated with each of the elements of vulnerability. Increased exposure, increased sensitivity and reduced adaptive capacity all affect vulnerability at different points in the causal chain from climate drivers to health outcomes (middle boxes). Adaptive capacity can influence exposure and sensitivity and also can influence the resilience of individuals or populations experiencing health impacts by influencing access to care and preventive services. The right side boxes provide illustrative examples of the implications of social determinants on increased exposure, increased sensitivity, and reduced adaptive capacity.' chapter_identifier: populations-of-concern create_dt: 2015-10-19T08:49:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/intersection-of-social-determinants-of-health-and-vulnerability.yaml identifier: intersection-of-social-determinants-of-health-and-vulnerability lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Intersection of Social Determinants of Health and Vulnerability uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/intersection-of-social-determinants-of-health-and-vulnerability url: ~ usage_limits: Free to use with credit to the original figure source. - attributes: ~ caption: 'Children’s vulnerability to climate change results from distinct exposures, biological sensitivities (developing bodies and immune systems), and limitations to adaptive capacity (dependency on caregivers) at different life stages.' chapter_identifier: populations-of-concern create_dt: 2014-11-17T12:55:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/vulnerability-to-the-health-impacts-of-climate-change-at-different-lifestages.yaml identifier: vulnerability-to-the-health-impacts-of-climate-change-at-different-lifestages lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 3 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Vulnerability to the Health Impacts of Climate Change at Different Lifestages uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/vulnerability-to-the-health-impacts-of-climate-change-at-different-lifestages url: ~ usage_limits: Free to use with credit to the original figure source. - 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_identifier: populations-of-concern create_dt: 2014-12-01T01:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-social-vulnerability.yaml identifier: mapping-social-vulnerability lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 4 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 uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-social-vulnerability url: ~ usage_limits: ~ - 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_identifier: populations-of-concern create_dt: 2014-11-01T01:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/populations-of-concern/figure/mapping-communities-vulnerable-to-heat-in-georgia.yaml identifier: mapping-communities-vulnerable-to-heat-in-georgia lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 5 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Mapping Communities Vulnerable to Heat in Georgia 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: '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_identifier: executive-summary create_dt: 2015-09-09T10:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/examples-of-climate-impacts-on-human-health.yaml identifier: examples-of-climate-impacts-on-human-health lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Examples of Climate Impacts on Human Health 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: 'Projected global average temperature rise for specific emissions pathways (left) and concentration pathways (right) relative to the 1901_1960 average. Shading indicates the range (5thto 95th percentile) of results from a suite of climate models. Projections in 2099 are indicated by the bars to the right of each panel. In all cases, temperatures are expected to rise, although the difference between lower and higher pathways is substantial.

The left panel shows the two main CMIP3 scenarios (SRES) used in this assessment: A2 assumes continued increases in emissions throughout this century, and B1 assumes significant emissions reductions beginning around 2050. The right panel shows the newer CMIP5 scenarios using Representative Concentration Pathways (RCPs). CMIP5 includes both lower and higher pathways than CMIP3. The lowest concentration pathway shown here, RCP2.6, assumes immediate and rapid reductions in emissions and would result in about 2.5°F of warming in this century. The highest pathway, RCP8.5, roughly similar to a continuation of the current path of global emissions increases, is projected to lead to more than 8°F warming by 2100, with a high-end possibility of more than 11°F. (Data from CMIP3, CMIP5, and NOAA NCEI). (Figure source: adapted from Melillo et al. 2014)dd5b893d-4462-4bb3-9205-67b532919566' chapter_identifier: appendix-1--technical-support-document create_dt: 2014-07-02T08:38:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/scenarios-of-future-temperature-rise.yaml identifier: scenarios-of-future-temperature-rise lat_max: 90.00 lat_min: -90.00 lon_max: 180.00 lon_min: -180.00 ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: 2100-12-31T23:59:59 time_start: 1900-01-01T00:00:00 title: Scenarios of Future Temperature Rise uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/appendix-1--technical-support-document/figure/scenarios-of-future-temperature-rise url: ~ usage_limits: ~ - 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_identifier: front-matter create_dt: ~ href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/front-matter/figure/understanding-the-exposure-pathway-diagrams.yaml identifier: understanding-the-exposure-pathway-diagrams lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 1 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Understanding the Exposure Pathway Diagrams 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: 'Defining the determinants of vulnerability to health impacts associated with climate change, including exposure, sensitivity, and adaptive capacity (see Ch. 9: Populations of Concern). (Figure source: adapted from Turner et al. 2003)b6a2f8d3-a113-4e46-b62c-7fbaf90b4f59' chapter_identifier: executive-summary create_dt: 2014-11-01T01:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-determinants-of-vulnerability.yaml identifier: es-determinants-of-vulnerability lat_max: N/A lat_min: N/A lon_max: N/A lon_min: N/A ordinal: 10 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Determinants of Vulnerability uri: /report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-determinants-of-vulnerability url: ~ usage_limits: Copyright protected. Obtain permission from the original figure source. - 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_identifier: executive-summary create_dt: 2014-10-10T10:00:00 href: https://data.globalchange.gov/report/usgcrp-climate-human-health-assessment-2016/chapter/executive-summary/figure/es-climate-change-and-health.yaml identifier: es-climate-change-and-health lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 2 report_identifier: usgcrp-climate-human-health-assessment-2016 source_citation: ~ submission_dt: ~ time_end: ~ time_start: ~ title: Climate Change and Health 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.