uri,href,identifier,attrs.Abstract,attrs.Author,"attrs.Book Title",attrs.Chapter,attrs.DOI,attrs.ISBN,attrs.Keywords,attrs.Language,attrs.Pages,"attrs.Place Published",attrs.Publisher,attrs.Title,attrs.Year,attrs.\.reference_type,attrs._record_number,attrs._uuid,attrs.reftype,child_publication
/reference/e51f35c4-b5ba-4e95-8090-582e2897754b,https://data.globalchange.gov/reference/e51f35c4-b5ba-4e95-8090-582e2897754b,e51f35c4-b5ba-4e95-8090-582e2897754b,"As the human population grows--tripling in the past century while, simultaneously, quadrupling its demand for water--Earth's finite freshwater supplies are increasingly strained, and also increasingly contaminated by domestic, agricultural, and industrial wastes. Today, approximately one-third of the world's population lives in areas with scarce water resources. Nearly one billion people currently lack access to an adequate water supply, and more than twice as many lack access to basic sanitation services. It is projected that by 2025 water scarcity will affect nearly two-thirds of all people on the planet. Recognizing that water availability, water quality, and sanitation are fundamental issues underlying infectious disease emergence and spread, the Institute of Medicine held a two-day public workshop, summarized in this volume. Through invited presentations and discussions, participants explored global and local connections between water, sanitation, and health; the spectrum of water-related disease transmission processes as they inform intervention design; lessons learned from water-related disease outbreaks; vulnerabilities in water and sanitation infrastructure in both industrialized and developing countries; and opportunities to improve water and sanitation infrastructure so as to reduce the risk of water-related infectious disease.","Beach, Michael J.; Roy, Sharon; Brunkard, Joan; Yoder, Jonathan; Hlavsa, Michele C.","Global Issues in Water, Sanitation, and Health: Workshop Summary",3,10.17226/12658,978-0-309-13872-7,"Health and Medicine; Earth Sciences",English,156-168,"Washington, D.C.","Institute of Medicine. The National Academies Press","The changing epidemiology of waterborne disease outbreaks in the United States: Implications for system infrastructure and future planning",2009,7,18853,e51f35c4-b5ba-4e95-8090-582e2897754b,"Book Section",/report/iom-water-sanitation-2009
/reference/e52c6b87-47d7-47dc-9018-f78cad2a35af,https://data.globalchange.gov/reference/e52c6b87-47d7-47dc-9018-f78cad2a35af,e52c6b87-47d7-47dc-9018-f78cad2a35af,,"Corrarino, Jane E.",,,10.1097/01.NMC.0000326079.26870.e3,,,,242-248,,,"Disaster-related mental health needs of women and children",2008,0,16356,e52c6b87-47d7-47dc-9018-f78cad2a35af,"Journal Article",/article/10.1097/01.NMC.0000326079.26870.e3
/reference/e55635c1-c252-49c8-a402-a7701fda2c3f,https://data.globalchange.gov/reference/e55635c1-c252-49c8-a402-a7701fda2c3f,e55635c1-c252-49c8-a402-a7701fda2c3f,,"Polin, R. A.; Abman, S. H.","Fetal and Neonatal Physiology",,,,,,615-670,"Philadelphia, PA",Elsevier,Thermoregulation,2011,7,18944,e55635c1-c252-49c8-a402-a7701fda2c3f,"Book Section",/book/2eb47ed7-2182-4f32-96e5-52e9869a7d56
/reference/e573afb0-9fee-45a5-bbd6-e3abdf6e5bd8,https://data.globalchange.gov/reference/e573afb0-9fee-45a5-bbd6-e3abdf6e5bd8,e573afb0-9fee-45a5-bbd6-e3abdf6e5bd8,,"Ravel, André; Smolina, E.; Sargeant, Jan M.; Cook, Angela; Marshall, Barbara; Fleury, Manon D.; Pollari, Frank",,,10.1089/fpd.2009.0460,,,,785-794,,,"Seasonality in human salmonellosis: Assessment of human activities and chicken contamination as driving factors",2010,0,18329,e573afb0-9fee-45a5-bbd6-e3abdf6e5bd8,"Journal Article",/article/10.1089/fpd.2009.0460
/reference/e5970e79-3be9-4add-afcb-fc6e00192589,https://data.globalchange.gov/reference/e5970e79-3be9-4add-afcb-fc6e00192589,e5970e79-3be9-4add-afcb-fc6e00192589,"Air-temperature and relative humidity data were used to explain variation in behavioral activity of In odes scapularis Sap nymphs. We estimated behavioral activity as the residual variation in drag-sample data after seasonal changes in population density were removed by regression. The seasonal decline in drag samples between June and August 1995 on field plots at Morristown National Historical Park, NJ, can be described by a simple negative exponential function. Residuals around a fitted exponential were significantly correlated with temperature and with relative humidity measured at the leaf-litter surface, and explained 34 and 44% of the variance, respectively. Multiple regression on temperature and relative humidity explained 51% of the variance. These regressions estimated the explanatory power of microclimate, independent of seasonal correlations, and might provide a basis for day-to-day prediction of human exposure to Lyme disease.","Vail, S. G.; Smith, G.",,,10.1093/jmedent/35.6.1025,,"ixodes scapularis; lyme disease; microclimate; behavioral activity; questing; ixodes-pacificus acari; scapularis acari; vegetation; survival",English,1025-1028,,,"Air temperature and relative humidity effects on behavioral activity of blacklegged tick (Acari: Ixodidae) nymphs in New Jersey",1998,0,17756,e5970e79-3be9-4add-afcb-fc6e00192589,"Journal Article",/article/10.1093/jmedent/35.6.1025
/reference/e5aaf711-68bc-468c-8120-7e61732c14ae,https://data.globalchange.gov/reference/e5aaf711-68bc-468c-8120-7e61732c14ae,e5aaf711-68bc-468c-8120-7e61732c14ae,,"Wenden, A.L.","Climate Change and Human Well-being: Global challenges and opportunities",,10.1007/978-1-4419-9742-5,978-1-4419-9741-8,,,119-133,"New York",Springer-Verlag,"Women and climate change: Vulnerabilities and challenges",2011,7,18206,e5aaf711-68bc-468c-8120-7e61732c14ae,"Book Section",/book/ff08562c-49aa-4b2f-b7be-aaf93d86487b
/reference/e5b2c774-27de-4166-b5aa-0e6dc2e5f4d7,https://data.globalchange.gov/reference/e5b2c774-27de-4166-b5aa-0e6dc2e5f4d7,e5b2c774-27de-4166-b5aa-0e6dc2e5f4d7,"The summer abundance of Culex tarsalis in Kern County, California, during 1990-98 was related quantitatively to rainfall, snow depth and water content, and runoff of the Kern River. Total monthly rain that fell during winter, lagged by 4-6 months, explained only 13% of the variability in the number of host-seeking females collected per trap night per month during summer. In contrast, regression analysis showed that river runoff 1 month earlier explained 67% of the variability in mosquito abundance. The water content of snowpack measured within the Kern River watershed during winter explained 70% of the variation in average mosquito abundance during the following summer. After being absent from Kern County since 1983, western equine encephalomyelitis virus (WEE) returned during the wet years of 1996-98 after the flow of the Kern River exceeded 150,000 acre-ft (450 hectare-meters) per month. Water content of snow in the Sierra Nevada during winter provided an excellent early warning of vernal river runoff, mosquito abundance, and enzootic WEE activity levels on the floor of the San Joaquin Valley.","Wegbreit, J.; Reisen, W. K.",,,,,"Animals; California/epidemiology; *Culex; Encephalitis Virus, St. Louis/*pathogenicity; Encephalitis Virus, Western Equine/*pathogenicity; Encephalitis, St. Louis/epidemiology/transmission; Encephalomyelitis, Western Equine/epidemiology/transmission; *Insect Vectors; Population Dynamics; Rain; Retrospective Studies; Seasons; Weather",,22-27,,,"Relationships among weather, mosquito abundance, and encephalitis virus activity in California: Kern County 1990-98",2000,0,18042,e5b2c774-27de-4166-b5aa-0e6dc2e5f4d7,"Journal Article",/article/pmc-10757487
/reference/e5e8a22b-a7eb-4e98-a9b2-56301b9a02de,https://data.globalchange.gov/reference/e5e8a22b-a7eb-4e98-a9b2-56301b9a02de,e5e8a22b-a7eb-4e98-a9b2-56301b9a02de,,"Paterson, R. Russell M.; Lima, Nelson",,,10.1016/j.foodres.2009.07.010,,"Mycotoxins; Climate change; Fungi; Aflatoxins; Deoxynivalenol; Ochratoxin A; Temperature; Water activity",,1902-1914,,,"How will climate change affect mycotoxins in food?",2010,0,14956,e5e8a22b-a7eb-4e98-a9b2-56301b9a02de,"Journal Article",/article/10.1016/j.foodres.2009.07.010
/reference/e60cb47e-4a48-4e92-a2d3-97516836e8f3,https://data.globalchange.gov/reference/e60cb47e-4a48-4e92-a2d3-97516836e8f3,e60cb47e-4a48-4e92-a2d3-97516836e8f3,,"Gosling, Simon N.; Lowe, Jason A.; McGregor, Glenn R.; Pelling, Mark; Malamud, Bruce D.",,,10.1007/s10584-008-9441-x,,,,299-341,,,"Associations between elevated atmospheric temperature and human mortality: A critical review of the literature",2009,0,17596,e60cb47e-4a48-4e92-a2d3-97516836e8f3,"Journal Article",/article/10.1007/s10584-008-9441-x
/reference/e65c896d-395c-4347-80ed-64af1333f3a7,https://data.globalchange.gov/reference/e65c896d-395c-4347-80ed-64af1333f3a7,e65c896d-395c-4347-80ed-64af1333f3a7,,"Petkova, Elisaveta P.; Gasparrini, Antonio; Kinney, Patrick L.",,,10.1097/ede.0000000000000123,,,,554-560,,,"Heat and mortality in New York City since the beginning of the 20th century",2014,0,17615,e65c896d-395c-4347-80ed-64af1333f3a7,"Journal Article",/article/10.1097/ede.0000000000000123
/reference/e6a7e8cd-c43d-4208-aa71-0604e8710b01,https://data.globalchange.gov/reference/e6a7e8cd-c43d-4208-aa71-0604e8710b01,e6a7e8cd-c43d-4208-aa71-0604e8710b01,"We have identified environmental and demographic variables, available in January, that predict the relative magnitude and spatial distribution of West Nile virus (WNV) for the following summer. The yearly magnitude and spatial distribution for WNV incidence in humans in the United States (US) have varied wildly in the past decade. Mosquito control measures are expensive and having better estimates of the expected relative size of a future WNV outbreak can help in planning for the mitigation efforts and costs. West Nile virus is spread primarily between mosquitoes and birds; humans are an incidental host. Previous efforts have demonstrated a strong correlation between environmental factors and the incidence of WNV. A predictive model for human cases must include both the environmental factors for the mosquito-bird epidemic and an anthropological model for the risk of humans being bitten by a mosquito. Using weather data and demographic data available in January for every county in the US, we use logistic regression analysis to predict the probability that the county will have at least one WNV case the following summer. We validate our approach and the spatial and temporal WNV incidence in the US from 2005 to 2013. The methodology was applied to forecast the 2014 WNV incidence in late January 2014. We find the most significant predictors for a county to have a case of WNV to be the mean minimum temperature in January, the deviation of this minimum temperature from the expected minimum temperature, the total population of the county, publicly available samples of local bird populations, and if the county had a case of WNV the previous year.","Manore, C. A.; Davis, J.K.; Christofferson, R. C.; Wesson, D.M.; Hyman, J. M.; Mores, C. N.",,,10.1371/currents.outbreaks.f0b3978230599a56830ce30cb9ce0500,,,,,,,"Towards an early warning system for forecasting human west nile virus incidence",2014,0,18014,e6a7e8cd-c43d-4208-aa71-0604e8710b01,"Journal Article",/article/10.1371/currents.outbreaks.f0b3978230599a56830ce30cb9ce0500
/reference/e6acc684-b3e6-4713-90a1-ddb08e7467b3,https://data.globalchange.gov/reference/e6acc684-b3e6-4713-90a1-ddb08e7467b3,e6acc684-b3e6-4713-90a1-ddb08e7467b3,,"Paerl, Hans W.; Hall, Nathan S.; Calandrino, Elizabeth S.",,,10.1016/j.scitotenv.2011.02.001,,"Cyanobacteria l blooms; Nutrients; Eutrophication; Hydrology; Climate change; Water quality management",,1739-1745,,,"Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change",2011,0,19037,e6acc684-b3e6-4713-90a1-ddb08e7467b3,"Journal Article",/article/10.1016/j.scitotenv.2011.02.001
/reference/e6e1907d-2807-424a-890a-96a076d5db86,https://data.globalchange.gov/reference/e6e1907d-2807-424a-890a-96a076d5db86,e6e1907d-2807-424a-890a-96a076d5db86,,USDA,,,,,,,,"Washington, DC","U.S. Department of Agriculture Economic Research Service","Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences. Report to Congress.",2009,10,18230,e6e1907d-2807-424a-890a-96a076d5db86,Report,/report/ers-ap-036
/reference/e76ac40a-1b89-497e-be9c-83d7b1a636a1,https://data.globalchange.gov/reference/e76ac40a-1b89-497e-be9c-83d7b1a636a1,e76ac40a-1b89-497e-be9c-83d7b1a636a1,,"Clement, Jan; Vercauteren, Jurgen; Verstraeten, Willem W.; Ducoffre, Geneviève; Barrios, José M.; Vandamme, Anne-Mieke; Maes, Piet; Van Ranst, Marc",,,10.1186/1476-072x-8-1,,,,1,,,"Relating increasing hantavirus incidences to the changing climate: The mast connection",2009,0,16296,e76ac40a-1b89-497e-be9c-83d7b1a636a1,"Journal Article",/article/10.1186/1476-072x-8-1
/reference/e76c9e93-c2a7-406e-b0b7-6dbebadab0ff,https://data.globalchange.gov/reference/e76c9e93-c2a7-406e-b0b7-6dbebadab0ff,e76c9e93-c2a7-406e-b0b7-6dbebadab0ff,,"Harris-Kojetin, L.; Sengupta, M.; Park-Lee, E.; Valverde, R.",,,,"Vital and Health Statistics 3(37)",,,107,"Hyattsville, MD","National Center for Health Statistics","Long-Term Care Services in the United States: 2013 Overview",2013,10,19349,e76c9e93-c2a7-406e-b0b7-6dbebadab0ff,Report,/report/cdc-dhhs-2014-1040
/reference/e7927819-0782-42ff-a491-6e125f61600e,https://data.globalchange.gov/reference/e7927819-0782-42ff-a491-6e125f61600e,e7927819-0782-42ff-a491-6e125f61600e,,"Bouchama, A.
Dehbi, M.
Mohamed, G.
Matthies, F.
Shoukri, M.
Menne, B.",,,10.1001/archinte.167.20.ira70009,,,,2170-2176,,,"Prognostic factors in heat wave-related deaths: A meta-analysis",2007,0,1326,e7927819-0782-42ff-a491-6e125f61600e,"Journal Article",/article/10.1001/archinte.167.20.ira70009
/reference/e7bab6e2-1287-4e13-a669-6620a37f1c5a,https://data.globalchange.gov/reference/e7bab6e2-1287-4e13-a669-6620a37f1c5a,e7bab6e2-1287-4e13-a669-6620a37f1c5a,,"Masten, Ann S.; Narayan, Angela J.",,,10.1146/annurev-psych-120710-100356,,,,227-257,,,"Child development in the context of disaster, war, and terrorism: Pathways of risk and resilience",2012,0,19217,e7bab6e2-1287-4e13-a669-6620a37f1c5a,"Journal Article",/article/10.1146/annurev-psych-120710-100356
/reference/e805bfdc-c4c2-43a0-b2e5-5a66945c74e4,https://data.globalchange.gov/reference/e805bfdc-c4c2-43a0-b2e5-5a66945c74e4,e805bfdc-c4c2-43a0-b2e5-5a66945c74e4,,"Schwartz, J.D.; Lee, M.; Kinney, P.L.; Yang, S.; Mills, D.; Sarofim, M.; Jones, R.; Streeter, R.; Juliana, A. St.; Peers, J.; Horton, R.M.",,,10.1186/s12940-015-0071-2,,,,,,,"Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach",2015,0,18811,e805bfdc-c4c2-43a0-b2e5-5a66945c74e4,"Journal Article",/article/10.1186/s12940-015-0071-2
/reference/e82bef4a-e0f7-4eab-96d6-080301942c14,https://data.globalchange.gov/reference/e82bef4a-e0f7-4eab-96d6-080301942c14,e82bef4a-e0f7-4eab-96d6-080301942c14,,"Paerl, Hans W.; Paul, Valerie J.",,,10.1016/j.watres.2011.08.002,,"Cyanobacteria; Eutrophication; Climate change; Hydrology; Nutrients; Freshwater; Marine; Water quality management",,1349-1363,,,"Climate change: Links to global expansion of harmful cyanobacteria",2012,0,19036,e82bef4a-e0f7-4eab-96d6-080301942c14,"Journal Article",/article/10.1016/j.watres.2011.08.002
/reference/e839bc70-12c5-48fa-9083-798cf367eefc,https://data.globalchange.gov/reference/e839bc70-12c5-48fa-9083-798cf367eefc,e839bc70-12c5-48fa-9083-798cf367eefc,,"Redsteer, M.H.; Bogle, R.C.; Vogel, J.M.",,,,"U.S. Geological Survey Fact Sheet 2011-3085",,,2,"Reston, VA","U.S. Geological Survey","Monitoring and Analysis of Sand Dune Movement and Growth on the Navajo Nation, Southwestern United States",2011,10,18270,e839bc70-12c5-48fa-9083-798cf367eefc,Report,/report/usgs-factsheet-2011-3085