uri,href,identifier,attrs.Abstract,attrs.Author,attrs.DOI,attrs.Date,attrs.ISSN,attrs.Issue,attrs.Journal,attrs.Pages,attrs.Title,"attrs.Type of Article",attrs.Volume,attrs.Year,attrs._record_number,attrs._uuid,attrs.reftype,child_publication
/reference/200c4ff2-90da-45da-bc7a-f4565dbd2fbb,https://data.globalchange.gov/reference/200c4ff2-90da-45da-bc7a-f4565dbd2fbb,200c4ff2-90da-45da-bc7a-f4565dbd2fbb,"Heatwaves are divided between moderate, more common heatwaves and rare “high-mortality” heatwaves that have extremely large health effects per day, which we define as heatwaves with a 20 % or higher increase in mortality risk. Better projections of the expected frequency of and exposure to these separate types of heatwaves could help communities optimize heat mitigation and response plans and gauge the potential benefits of limiting climate change. Whether a heatwave is high-mortality or moderate could depend on multiple heatwave characteristics, including intensity, length, and timing. We created heatwave classification models using a heatwave training dataset created using recent (1987–2005) health and weather data from 82 large US urban communities. We built twenty potential classification models and used Monte Carlo cross-validations to evaluate these models. We ultimately identified several models that can adequately classify high-mortality heatwaves. These models can be used to project future trends in high-mortality heatwaves under different scenarios of a changing future (e.g., climate change, population change). Further, these models are novel in the way they allow exploration of different scenarios of adaptation to heat, as they include, as predictive variables, heatwave characteristics that are measured relative to a community’s temperature distribution, allowing different adaptation scenarios to be explored by selecting alternative community temperature distributions. The three selected models have been placed on GitHub for use by other researchers, and we use them in a companion paper to project trends in high-mortality heatwaves under different climate, population, and adaptation scenarios.","Anderson, G. Brooke; Oleson, Keith W.; Jones, Bryan; Peng, Roger D.",10.1007/s10584-016-1776-0,"February 01",1573-1480,3,"Climatic Change",439-453,"Classifying heatwaves: Developing health-based models to predict high-mortality versus moderate United States heatwaves","journal article",146,2018,25579,200c4ff2-90da-45da-bc7a-f4565dbd2fbb,"Journal Article",/article/10.1007/s10584-016-1776-0
/reference/26779560-dc50-4a6b-b555-d4075ce16af9,https://data.globalchange.gov/reference/26779560-dc50-4a6b-b555-d4075ce16af9,26779560-dc50-4a6b-b555-d4075ce16af9,"OBJECTIVE: To describe the incidence of occupational heat illness in Ontario. METHODS: Heat illness events were identified in two population-based data sources: work-related emergency department (ED) records and lost time claims for the period 2004-2010 in Ontario, Canada. Incidence rates were calculated using denominator estimates from national labour market surveys and estimates were adjusted for workers’ compensation insurance coverage. Proportional morbidity ratios were estimated for industry, occupation and tenure of employment. RESULTS: There were 785 heat illness events identified in the ED encounter records (incidence rate 1.6 per 1,000,000 full-time equivalent (FTE) months) and 612 heat illness events identified in the lost time claim records (incidence rate 1.7 per 1,000,000 FTE months) in the seven-year observation period with peak incidence observed in the summer months. The risk of heat illness was elevated for men, young workers, manual workers and those with shorter employment tenure. A higher proportion of lost time claims attributed to heat illness were observed in the government services, agriculture and construction sectors relative to all lost time claims. CONCLUSIONS: Occupational heat illnesses are experienced in Ontario’s population and are observed in ED records and lost time claims. The variation of heat illness incidence observed with worker and industry characteristics, and over time, can inform prevention efforts by occupational health services in Ontario.","Fortune, Melanie K.; Mustard, Cameron A.; Etches, Jacob J.C.; Chambers, Andrea G.",10.17269/cjph.104.3984,2013-09-12,1920-7476,5,"Canadian Journal of Public Health",7,"Work-attributed illness arising from excess heat exposure in Ontario, 2004-2010","Heat stress disorders; occupational exposure; epidemiology",104,2013,23611,26779560-dc50-4a6b-b555-d4075ce16af9,"Journal Article",/article/10.17269/cjph.104.3984
/reference/29960c69-6168-4fb0-9af0-d50bdd91acd3,https://data.globalchange.gov/reference/29960c69-6168-4fb0-9af0-d50bdd91acd3,29960c69-6168-4fb0-9af0-d50bdd91acd3,,"Vose, R.S.; D.R. Easterling; K.E. Kunkel; A.N. LeGrande; M.F. Wehner",10.7930/J0N29V45,,,,,185-206,"Temperature Changes in the United States",,,2017,21564,29960c69-6168-4fb0-9af0-d50bdd91acd3,"Book Section",/report/climate-science-special-report/chapter/temperature-change
/reference/2d3fe667-e18a-42ca-abf6-ae5261ac54e1,https://data.globalchange.gov/reference/2d3fe667-e18a-42ca-abf6-ae5261ac54e1,2d3fe667-e18a-42ca-abf6-ae5261ac54e1,"Several economic assessments of climate change build on the assumption that reductions of cold-related mortality will overcompensate increases in heat-related mortality at least for moderate levels of global warming. Due to the lack of suitable epidemiological studies with sufficient spatial coverage, many of these assessments rely on one particular dataset: projections of temperature-related mortality in 17 countries published almost 20 years ago. Here, we reanalyse this dataset with a focus on cardiovascular mortality and present evidence for two flaws in the original analysis, which would imply a significant bias towards finding net mortality benefits from climate change: (i) the combination of mortality data for all ages with data specific to the elderly and (ii) the confounding of seasonal effects with direct temperature effects on mortality. This bias appears to be further amplified in the integrated assessment models FUND and ENVISAGE, and related economic assessment tools relying on the same calibration scheme, because heat-related cardiovascular mortality is assumed to affect urban populations only in these models. In an exemplary calculation, we show that while FUND currently projects a net reduction of approximately 380,000 deaths from cardiovascular diseases globally per year at 1 °C of global warming, correcting for the two potential flaws and assuming equal vulnerability of urban and rural populations would result in a net increase of cardiovascular mortality, with approximately 150,000 net additional deaths globally per year. Our findings point to the urgent need of renewing damage functions on temperature-related mortality currently applied in some of the most widely used integrated assessment models.","Huber, Veronika; Ibarreta, Dolores; Frieler, Katja",10.1007/s10584-017-1956-6,"June 01",1573-1480,3,"Climatic Change",407-418," Cold- and heat-related mortality: A cautionary note on current damage functions with net benefits from climate change ","journal article",142,2017,23537,2d3fe667-e18a-42ca-abf6-ae5261ac54e1,"Journal Article",/article/10.1007/s10584-017-1956-6
/reference/3baf471f-751f-4d68-9227-4197fdbb6e5d,https://data.globalchange.gov/reference/3baf471f-751f-4d68-9227-4197fdbb6e5d,3baf471f-751f-4d68-9227-4197fdbb6e5d,,"Walthall, C.; Backlund, P.; Hatfield, J.; Lengnick, L.; Marshall, E.; Walsh, M.; Adkins, S.; Aillery, M.; Ainsworth, E.A.; Amman, C.; Anderson, C.J.; Bartomeus, I.; Baumgard, L.H.; Booker, F.; Bradley, B.; Blumenthal, D.M.; Bunce, J.; Burkey, K.; Dabney, S.M.; Delgado, J.A.; Dukes, J.; Funk, A.; Garrett, K.; Glenn, M.; Grantz, D.A.; Goodrich, D.; Hu, S.; Izaurralde, R.C.; Jones, R.A.C.; Kim, S-H.; Leaky, A.D.B.; Lewers, K.; Mader, T.L.; McClung, A.; Morgan, J.; Muth, D.J.; Nearing, M.; Oosterhuis, D.M.; Ort, D.; Parmesan, C.; Pettigrew, W.T.; Polley, W.; Rader, R.; Rice, C.; Rivington, M.; Rosskopf, E.; Salas, W.A.; Sollenberger, L.E.; Srygley, R.; Stockle, C.; Takle, E.S.; Timlin, D.; White, J.W.; Winfree, R.; Wright-Morton, L.; Ziska, L.H.",,,,,,186,"Climate Change and Agriculture in the United States: Effects and Adaptation",,,2012,3329,3baf471f-751f-4d68-9227-4197fdbb6e5d,Report,/report/usda-techbul-1935
/reference/53448a8f-22bd-4111-8212-b2204e4d4864,https://data.globalchange.gov/reference/53448a8f-22bd-4111-8212-b2204e4d4864,53448a8f-22bd-4111-8212-b2204e4d4864,"The probability that summer temperatures in the future will exceed the hottest on record during 1920–2014 is projected to increase at all land locations with global warming. Within the BRACE project framework we investigate the sensitivity of this projected change in probability to the choice of emissions scenario using two large ensembles of simulations with the Community Earth System Model. The large ensemble size allows for a robust assessment of the probability of record-breaking temperatures. Globally, the probability that any summer during the period 2061–2081 will be warmer than the hottest on record is 80 % for RCP 8.5 and 41 % for RCP 4.5. Hence, mitigation can reduce the risk of record-breaking temperatures by 39 %. The potential for risk reduction is greatest for some of the most populated regions of the globe. In Europe, for example, a potential risk reduction of over 50 % is projected. Model biases and future changes in temperature variance have only minor effects on the results, as their contribution stays well below 10 % for almost all locations.","Lehner, Flavio; Deser, Clara; Sanderson, Benjamin M.",10.1007/s10584-016-1616-2,"February 16",1573-1480,3-4,"Climatic Change",363-375,"Future risk of record-breaking summer temperatures and its mitigation","journal article",146,2018,23553,53448a8f-22bd-4111-8212-b2204e4d4864,"Journal Article",/article/10.1007/s10584-016-1616-2
/reference/75cf1c0b-cc62-4ca4-96a7-082afdfe2ab1,https://data.globalchange.gov/reference/75cf1c0b-cc62-4ca4-96a7-082afdfe2ab1,75cf1c0b-cc62-4ca4-96a7-082afdfe2ab1,,USGCRP,10.7930/J0J964J6,,,,,470,"Climate Science Special Report: Fourth National Climate Assessment, Volume I",,,2017,21557,75cf1c0b-cc62-4ca4-96a7-082afdfe2ab1,Report,/report/climate-science-special-report
/reference/79a8b35d-8f50-44c3-ba7d-a8c76f407620,https://data.globalchange.gov/reference/79a8b35d-8f50-44c3-ba7d-a8c76f407620,79a8b35d-8f50-44c3-ba7d-a8c76f407620,"Workers employed in outdoor occupations such as farming are exposed to hot and humid environments that put them at risk for heat-related illness or death. This report describes one such death and summarizes heat-related fatalities among crop production workers in the United States during 1992--2006. During this 15-year period, 423 workers in agricultural and nonagricultural industries were reported to have died from exposure to environmental heat; 68 (16%) of these workers were engaged in crop production or support activities for crop production. The heat-related average annual death rate for these crop workers was 0.39 per 100,000 workers, compared with 0.02 for all U.S. civilian workers. Data aggregated into 5-year periods indicated that heat-related death rates among crop workers might be increasing; however, trend analysis did not indicate a statistically significant increase. Prevention of heat-related deaths among crop workers requires educating employers and workers on the hazards of working in hot environments, including recognition of heat-related illness symptoms, and implementing appropriate heat stress management measures.","CDC,",,"Jun 20","1545-861X (Electronic)
0149-2195 (Linking)",24,"MMWR: Morbidity and Mortality Weekly Report",649-653,"Heat-related deaths among crop workers—United States, 1992–2006",,57,2008,16418,79a8b35d-8f50-44c3-ba7d-a8c76f407620,"Journal Article",/article/pmid-18566563
/reference/7d16ea3a-c4dc-4ebd-8d38-c3d6a64a3e66,https://data.globalchange.gov/reference/7d16ea3a-c4dc-4ebd-8d38-c3d6a64a3e66,7d16ea3a-c4dc-4ebd-8d38-c3d6a64a3e66,,"Hess, Jeremy J.; Saha, Shubhayu; Luber, George",10.1289/ehp.1306796,,1552-9924,11,"Environmental Health Perspectives",1209-1215,"Summertime acute heat illness in U.S. emergency departments from 2006 through 2010: Analysis of a nationally representative sample",,122,2014,16112,7d16ea3a-c4dc-4ebd-8d38-c3d6a64a3e66,"Journal Article",/article/10.1289/ehp.1306796
/reference/7e3a9127-81cd-46bf-99b8-e3538e982fea,https://data.globalchange.gov/reference/7e3a9127-81cd-46bf-99b8-e3538e982fea,7e3a9127-81cd-46bf-99b8-e3538e982fea,,"Jones, Bryan; O’Neill, Brian C.; McDaniel, Larry; McGinnis, Seth; Mearns, Linda O.; Tebaldi, Claudia",10.1038/nclimate2631,05/18/online,,,"Nature Climate Change",652-655,"Future population exposure to US heat extremes",,5,2015,23541,7e3a9127-81cd-46bf-99b8-e3538e982fea,"Journal Article",/article/10.1038/nclimate2631
/reference/831b4c27-416e-4b98-94e6-3969a3b34031,https://data.globalchange.gov/reference/831b4c27-416e-4b98-94e6-3969a3b34031,831b4c27-416e-4b98-94e6-3969a3b34031,"The global livestock industry is charged with providing sufficient animal source foods to supply the global population while improving the environmental sustainability of animal production. Improved productivity within dairy and beef systems has demonstrably reduced resource use and greenhouse gas emissions per unit of food over the past century through the dilution of maintenance effect. Further environmental mitigation effects have been gained through the current use of technologies and practices that enhance milk yield or growth in ruminants; however, the social acceptability of continued intensification and use of productivity-enhancing technologies is subject to debate. As the environmental impact of food production continues to be a significant issue for all stakeholders within the field, further research is needed to ensure that comparisons among foods are made based on both environmental impact and nutritive value to truly assess the sustainability of ruminant products.","Capper, Judith L.; Dale E. Bauman",10.1146/annurev-animal-031412-103727,,,1,"Annual Review of Animal Biosciences",469-489,"The role of productivity in improving the environmental sustainability of ruminant production systems",,1,2013,26135,831b4c27-416e-4b98-94e6-3969a3b34031,"Journal Article",/article/10.1146/annurev-animal-031412-103727
/reference/8e30bef3-ce8e-4df4-879b-21f809b02998,https://data.globalchange.gov/reference/8e30bef3-ce8e-4df4-879b-21f809b02998,8e30bef3-ce8e-4df4-879b-21f809b02998,"Extreme heat is a significant public health challenge in urban environments that disproportionally impacts vulnerable members of society. In this research, demographic, economic and climate projections are brought together with a statistical approach linking extreme heat and mortality in Houston, Texas. The sensitivity of heat-related non-accidental mortality to future changes of demographics, income and climate is explored. We compare climate change outcomes associated with two different Representative Concentration Pathways (RCPs), RCP4.5 and RCP8.5, which describe alternate future scenarios for greenhouse gas emissions and concentrations. For each RCP, we explore demographic and economic scenarios for two plausible Shared Socioeconomic Pathways (SSPs), SSP3 and SSP5. Our findings suggest that non-accidental mortality in 2061–2080 may increase for all combinations of RCP and SSP scenarios compared to a historical reference period spanning 1991–2010. Notably, increased heat-related non-accidental mortality is associated with changes in the size and age of the population, but the degree of sensitivity is highly uncertain given the breadth of plausible socioeconomic scenarios. Beyond socioeconomic changes, climate change is also important. For each socioeconomic scenario, non-accidental mortality associated with the lower emissions RCP4.5 scenario is projected to be 50 % less than mortality projected under the higher emissions RCP8.5 scenario.","Marsha, A.; Sain, S. R.; Heaton, M. J.; Monaghan, A. J.; Wilhelmi, O.V.",10.1007/s10584-016-1775-1,"August 30",1573-1480,,"Climatic Change",,"Influences of climatic and population changes on heat-related mortality in Houston, Texas, USA","journal article",,2016,23558,8e30bef3-ce8e-4df4-879b-21f809b02998,"Journal Article",/article/10.1007/s10584-016-1775-1
/reference/9d4b4e3f-1739-4e8f-ab0b-610dd5276da3,https://data.globalchange.gov/reference/9d4b4e3f-1739-4e8f-ab0b-610dd5276da3,9d4b4e3f-1739-4e8f-ab0b-610dd5276da3,"Variability of heat stress illness (HSI) by urbanicity and climate region has rarely been considered in previous HSI studies. We investigated temporal and geographic trends in HSI emergency department (ED) visits in CDC Environmental Public Health Tracking Network (Tracking) states for 2005–2010. We obtained county-level HSI ED visit data for 14 Tracking states. We used the National Center for Health Statistics Urban–Rural Classification Scheme to categorize counties by urbanicity as (1) large central metropolitan (LCM), (2) large fringe metropolitan, (3) small–medium metropolitan, or (4) nonmetropolitan (NM). We also assigned counties to one of six US climate regions. Negative binomial regression was used to examine trends in HSI ED visits over time across all counties and by urbanicity for each climate region, adjusting for pertinent variables. During 2005–2010, there were 98,462 HSI ED visits in the 14 states. ED visits for HSI decreased 3.0 % (p < 0.01) per year. Age-adjusted incidence rates of HSI ED visits increased from most urban to most rural. Overall, ED visits were significantly higher for NM areas (IRR = 1.41, p < 0.01) than for LCM areas. The same pattern was observed in all six climate regions; compared with LCM, NM areas had from 14 to 90 % more ED visits for HSI. These findings of significantly increased HSI ED visit rates in more rural settings suggest a need to consider HSI ED visit variability by county urbanicity and climate region when designing and implementing local HSI preventive measures and interventions.","Fechter-Leggett, Ethan D.; Vaidyanathan, Ambarish; Choudhary, Ekta",10.1007/s10900-015-0064-7,"February 01",1573-3610,1,"Journal of Community Health",57-69,"Heat stress illness emergency department visits in national environmental public health tracking states, 2005–2010","journal article",41,2016,23607,9d4b4e3f-1739-4e8f-ab0b-610dd5276da3,"Journal Article",/article/10.1007/s10900-015-0064-7
/reference/a0403ee4-f787-4078-bcba-64cdd6cc9cb1,https://data.globalchange.gov/reference/a0403ee4-f787-4078-bcba-64cdd6cc9cb1,a0403ee4-f787-4078-bcba-64cdd6cc9cb1,"Heat kills more people than any other weather-related event in the USA, resulting in hundreds of fatalities each year. In North Carolina, heat-related illness accounts for over 2,000 yearly emergency department admissions. In this study, data on emergency department (ED) visits for heat-related illness (HRI) were obtained from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool to identify spatiotemporal relationships between temperature and morbidity across six warm seasons (May–September) from 2007 to 2012. Spatiotemporal relationships are explored across different regions (e.g., coastal plain, rural) and demographics (e.g., gender, age) to determine the differential impact of heat stress on populations. This research reveals that most cases of HRI occur on days with climatologically normal temperatures (e.g., 31 to 35 °C); however, HRI rates increase substantially on days with abnormally high daily maximum temperatures (e.g., 31 to 38 °C). HRI ED visits decreased on days with extreme heat (e.g., greater than 38 °C), suggesting that populations are taking preventative measures during extreme heat and therefore mitigating heat-related illness.","Sugg, Margaret M.; Konrad, Charles E.; Fuhrmann, Christopher M.",10.1007/s00484-015-1060-4,"May 01",1432-1254,5,"International Journal of Biometeorology",663-675,"Relationships between maximum temperature and heat-related illness across North Carolina, USA","journal article",60,2016,23581,a0403ee4-f787-4078-bcba-64cdd6cc9cb1,"Journal Article",/article/10.1007/s00484-015-1060-4
/reference/a5d430bc-5756-42d1-924f-3dbc927e69c4,https://data.globalchange.gov/reference/a5d430bc-5756-42d1-924f-3dbc927e69c4,a5d430bc-5756-42d1-924f-3dbc927e69c4,"Previous studies examining future changes in heat/cold waves using climate model ensembles have been limited to grid cell-average quantities. Here, we make use of an urban parameterization in the Community Earth System Model (CESM) that represents the urban heat island effect, which can exacerbate extreme heat but may ameliorate extreme cold in urban relative to rural areas. Heat/cold wave characteristics are derived for U.S. regions from a bias-corrected CESM 30-member ensemble for climate outcomes driven by the RCP8.5 forcing scenario and a 15-member ensemble driven by RCP4.5. Significant differences are found between urban and grid cell-average heat/cold wave characteristics. Most notably, urban heat waves for 1981–2005 are more intense than grid cell-average by 2.1 °C (southeast) to 4.6 °C (southwest), while cold waves are less intense. We assess the avoided climate impacts of urban heat/cold waves in 2061–2080 when following the lower forcing scenario. Urban heat wave days per year increase from 6 in 1981–2005 to up to 92 (southeast) in RCP8.5. Following RCP4.5 reduces heat wave days by about 50 %. Large avoided impacts are demonstrated for individual communities; e.g., the longest heat wave for Houston in RCP4.5 is 38 days while in RCP8.5 there is one heat wave per year that is longer than a month with some lasting the entire summer. Heat waves also start later in the season in RCP4.5 (earliest are in early May) than RCP8.5 (mid-April), compared to 1981–2005 (late May). In some communities, cold wave events decrease from 2 per year for 1981–2005 to one-in-five year events in RCP4.5 and one-in-ten year events in RCP8.5.","Oleson, K. W.; Anderson, G. B.; Jones, B.; McGinnis, S. A.; Sanderson, B.",10.1007/s10584-015-1504-1,"September 23",1573-1480,,"Climatic Change",,"Avoided climate impacts of urban and rural heat and cold waves over the U.S. using large climate model ensembles for RCP8.5 and RCP4.5","journal article",,2015,23564,a5d430bc-5756-42d1-924f-3dbc927e69c4,"Journal Article",/article/10.1007/s10584-015-1504-1
/reference/a6714dce-b324-4324-a88e-d31d31fa2d95,https://data.globalchange.gov/reference/a6714dce-b324-4324-a88e-d31d31fa2d95,a6714dce-b324-4324-a88e-d31d31fa2d95,,"Anderson, G.B.
Bell, M.L.",10.1289/ehp.1002313,,1552-9924,2,"Environmental Health Perspectives",210-218,"Heat waves in the United States: Mortality risk during heat waves and effect modification by heat wave characteristics in 43 U.S. communities",,119,2011,837,a6714dce-b324-4324-a88e-d31d31fa2d95,"Journal Article",/article/10.1289/ehp.1002313
/reference/aa5c6ab0-74a3-40c4-83a3-0093480b9603,https://data.globalchange.gov/reference/aa5c6ab0-74a3-40c4-83a3-0093480b9603,aa5c6ab0-74a3-40c4-83a3-0093480b9603,"The standard US diet contributes to greenhouse gas emissions (GHGE) from both the food system, and from the health system through its contribution to non-communicable diseases. To estimate the potential for diet change to reduce GHGE and improve public health, we analyzed the effect of adopting healthier model diets in the USA on the risk of disease, health care costs, and GHGE. We found that adoption of healthier diets reduced the relative risk of coronary heart disease, colorectal cancer, and type 2 diabetes by 20–45%, US health care costs by US$B 77–93 per year, and direct GHGE by 222–826 kg CO2e capita−1 year−1 (69–84 kg from the health care system, 153–742 kg from the food system). Emission reductions were equivalent to 6–23% of the US Climate Action Plan’s target of a 17% reduction in 2005 GHGE by 2020, and 24–134% of California’s target of 1990 GHGE levels by 2020. However, there is potential for investment of health care savings to result in rebound up to and greater than 100%, which would increase net GHGE. Given the urgency of improving public health and of mitigating GHGE over the short term, the potential contribution of diet change, and the options for reducing rebound, deserve more research in support of policy.","Hallström, Elinor; Gee, Quentin; Scarborough, Peter; Cleveland, David A.",10.1007/s10584-017-1912-5,"May 01",1573-1480,1,"Climatic Change",199-212,"A healthier US diet could reduce greenhouse gas emissions from both the food and health care systems","journal article",142,2017,23526,aa5c6ab0-74a3-40c4-83a3-0093480b9603,"Journal Article",/article/10.1007/s10584-017-1912-5
/reference/c97a2716-9162-4e1d-ad39-ca1589a8d760,https://data.globalchange.gov/reference/c97a2716-9162-4e1d-ad39-ca1589a8d760,c97a2716-9162-4e1d-ad39-ca1589a8d760,"Estimating the impact of heat waves on human mortality is key when it comes to the design of effective climate change adaptation measures. As the usual approach—relying on detailed health data in form of hospital records—is not feasible for many countries, a different methodology is needed. This work presents such an approach. Based on singular spectrum analysis and using monthly mortality rates—partly ranging back to 1960—it derives excess mortality estimates for 27 European countries. Excess mortality is then regressed against a heat wave measure in order to assess the health impacts of extreme heat. The analysis demonstrates that many European countries are severely affected by heat waves: On average, 0.61%—and up to 1.14% in case of Portugal—of all deaths are caused by extreme heat events. This finding confirms the understanding that climate change is a major environmental risk to public health: In the 27 examined European countries, over 28,000 people die every year due to exposure to extreme heat.","Merte, Steffen",10.1007/s10584-017-1937-9,"June 01",1573-1480,3,"Climatic Change",321-330,"Estimating heat wave-related mortality in Europe using singular spectrum analysis","journal article",142,2017,23562,c97a2716-9162-4e1d-ad39-ca1589a8d760,"Journal Article",/article/10.1007/s10584-017-1937-9
/reference/ced8505a-f36f-4c7b-8a0d-ec7f08482297,https://data.globalchange.gov/reference/ced8505a-f36f-4c7b-8a0d-ec7f08482297,ced8505a-f36f-4c7b-8a0d-ec7f08482297,,"Houghton, Adele; Austin, Jessica; Beerman, Abby; Horton, Clayton",10.1155/2017/3407325,,,,"Journal of Environmental and Public Health",16,"An approach to developing local climate change environmental public health indicators in a rural district",,2017,2017,23534,ced8505a-f36f-4c7b-8a0d-ec7f08482297,"Journal Article",/article/10.1155/2017/3407325
/reference/d812667f-d643-497f-b969-be0acd154c4d,https://data.globalchange.gov/reference/d812667f-d643-497f-b969-be0acd154c4d,d812667f-d643-497f-b969-be0acd154c4d,,"Eisler, Mark C.; Michael R. F. Lee; John F. Tarlton; Graeme B. Martin; John Beddington; Jennifer A. J. Dungait; Henry Greathead; Jianxin Liu; Stephen Mathew; Helen Miller; Tom Misselbrook; Phil Murray; Valil K. Vinod; Robert Van Saun; Michael Winter",10.1038/507032a,,,,Nature,32-34,"Agriculture: Steps to sustainable livestock",,507,2014,23517,d812667f-d643-497f-b969-be0acd154c4d,"Journal Article",/article/10.1038/507032a
/reference/e518fff1-caa5-4ed1-8fdc-b512da7cbe3b,https://data.globalchange.gov/reference/e518fff1-caa5-4ed1-8fdc-b512da7cbe3b,e518fff1-caa5-4ed1-8fdc-b512da7cbe3b,"The disease burden due to heat-stress illness (HSI), which can result in significant morbidity and mortality, is expected to increase as the climate continues to warm. In the United States (U.S.) much of what is known about HSI epidemiology is from analyses of urban heat waves. There is limited research addressing whether HSI hospitalization risk varies between urban and rural areas, nor is much known about additional diagnoses of patients hospitalized for HSI.","Jagai, Jyotsna S.; Grossman, Elena; Navon, Livia; Sambanis, Apostolis; Dorevitch, Samuel",10.1186/s12940-017-0245-1,"April 07",1476-069X,1,"Environmental Health",38,"Hospitalizations for heat-stress illness varies between rural and urban areas: An analysis of Illinois data, 1987–2014","journal article",16,2017,21209,e518fff1-caa5-4ed1-8fdc-b512da7cbe3b,"Journal Article",/article/10.1186/s12940-017-0245-1