--- - attributes: ~ caption: "This conceptual diagram illustrates the exposure pathways by which climate change could affect 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. The extent to which climate change could alter the burden of disease in any location at any point in time will depend not just on the magnitude of local climate change but also on individual and population vulnerability, exposure to changing weather patterns, and capacity to manage risks, which may also be affected by climate change. Source: Balbus et al. 2016.{{< tbib '2' '6b118a80-8335-4c02-91cf-762c8bb14301' >}}" chapter_identifier: human-health create_dt: 2018-04-06T19:09:27 href: https://data.globalchange.gov/report/nca4/chapter/human-health/figure/climate-change-and-health.yaml identifier: climate-change-and-health lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 1 report_identifier: nca4 source_citation: ~ submission_dt: 2018-11-23T14:45:50 time_end: ~ time_start: ~ title: Climate Change and Health uri: /report/nca4/chapter/human-health/figure/climate-change-and-health url: ~ usage_limits: Figure may be copyright protected and permission may be required. Contact original figure source for information - attributes: ~ caption: 'Examples of populations at higher risk of exposure to adverse climate-related health threats are shown along with adaptation measures that can help address disproportionate impacts. When considering the full range of threats from climate change as well as other environmental exposures, these groups are among the most exposed, most sensitive, and have the least individual and community resources to prepare for and respond to health threats. White text indicates the risks faced by those communities, while dark text indicates actions that can be taken to reduce those risks. Source: EPA.' chapter_identifier: human-health create_dt: 2018-03-30T23:52:32 href: https://data.globalchange.gov/report/nca4/chapter/human-health/figure/fig--14-x-vulnerable-populations-km2.yaml identifier: fig--14-x-vulnerable-populations-km2 lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 2 report_identifier: nca4 source_citation: ~ submission_dt: 2018-11-23T14:45:44 time_end: ~ time_start: ~ title: Vulnerable Populations uri: /report/nca4/chapter/human-health/figure/fig--14-x-vulnerable-populations-km2 url: ~ usage_limits: Figure may be copyright protected and permission may be required. Contact original figure source for information - attributes: ~ caption: "These maps shows the locations of hospitals in Charleston County, South Carolina, and Miami-Dade County, Florida, with respect to storm surge inundation for different categories of hurricanes making landfall at high tide. Colors indicate the lowest category hurricane affecting a given location, with darker blue shading indicating areas with the greatest susceptibility to flooding and darker red dots indicating the most vulnerable hospitals. Four of the 38 (11%) hospitals in Miami-Dade County face possible storm surge inundation following a Category 2 hurricane; this could increase to 26 (68%) following a Category 5 hurricane. Charleston hospitals are more exposed to inundation risks. Seven of the 11 (64%) hospitals in Charleston County face possible storm surge inundation following a Category 2; this could increase to 9 (82%) following a Category 4. The impacts of a storm surge will depend on the effectiveness of resilience measures, such as flood walls, deployed by the facilities. Data from National Hurricane Center 2018{{< tbib '152' '6507ef2b-a68a-420c-9aac-cd1d5c0fc210' >}} and the Department of Homeland Security 2018.{{< tbib '153' 'f40f0493-f23a-476c-9900-2dd34eb7fd6a' >}}" chapter_identifier: human-health create_dt: 2017-06-23T15:27:43 href: https://data.globalchange.gov/report/nca4/chapter/human-health/figure/hospitals_floodplain_sandy.yaml identifier: hospitals_floodplain_sandy lat_max: 32 lat_min: 25 lon_max: -80 lon_min: -79 ordinal: 3 report_identifier: nca4 source_citation: ~ submission_dt: 2018-12-03T20:35:18 time_end: ~ time_start: ~ title: Hospitals at Risk from Storm Surge by Tropical Cyclones uri: /report/nca4/chapter/human-health/figure/hospitals_floodplain_sandy url: ~ usage_limits: Figure may be copyright protected and permission may be required. Contact original figure source for information - attributes: ~ caption: "The maps show estimated changes in annual net mortality due to extremely hot and cold days in 49 U.S. cities for 2080–2099 as compared to 1989–2000. Across these cities, the change in mortality is projected to be an additional 9,300 deaths each year under a higher scenario (RCP8.5) and 3,900 deaths each year under a lower scenario (RCP4.5). Assuming a future in which the human health response to extreme temperatures in all 49 cities was equal to that of Dallas today (for example, as a result of availability of air conditioning or physiological adaptation) results in an approximate 50% reduction in these mortality estimates. For example, in Atlanta, an additional 349 people are projected to die from extreme temperatures each year by the end of century under RCP8.5. Assuming residents of Atlanta in 2090 have the adaptive capacity of Dallas residents today, this number is reduced to 128 additional deaths per year. Cities without circles should not be interpreted as having no extreme temperature impact. Data not available for the U.S. Caribbean, Alaska, or Hawai‘i & U.S.-Affiliated Pacific Islands regions. Source: adapted from EPA 2017.{{< tbib '157' '0b30f1ab-e4c4-4837-aa8b-0e19faccdb94' >}}" chapter_identifier: human-health create_dt: 2018-04-06T19:11:01 href: https://data.globalchange.gov/report/nca4/chapter/human-health/figure/projected-extreme-temperature-mortality.yaml identifier: projected-extreme-temperature-mortality lat_max: ~ lat_min: ~ lon_max: ~ lon_min: ~ ordinal: 4 report_identifier: nca4 source_citation: ~ submission_dt: 2018-11-23T14:46:00 time_end: ~ time_start: ~ title: Projected Change in Annual Extreme Temperature Mortality uri: /report/nca4/chapter/human-health/figure/projected-extreme-temperature-mortality url: ~ usage_limits: Figure may be copyright protected and permission may be required. Contact original figure source for information