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Article Excerpt
In this study we assessed the potential effects of climate variability and change on population health in Cuba. We describe the climate of Cuba as well as the patterns of climate-sensitive diseases of primary concern, particularly dengue fever. Analyses of the associations between climatic anomalies and disease patterns highlight current vulnerability to climate variability. We describe current adaptations, including the application of climate predictions to prevent disease outbreaks. Finally, we present the potential economic costs associated with future impacts due to climate change. The tools used in this study can be useful in the development of appropriate and effective adaptation options to address the increased climate variability associated with climate change. Key words: climate change, climate indices, climate variability, human health, impacts. Environ Health Perspect 114:1942-1949 (2006). doi:10.1289/ehp.8434 available via http://dx.doi.org/ [Online 11 July 2006]
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Concern about the potential health efffects of climate change began in the mid-1980s, with indications that emission of greenhouse gases from human activities could influence the climate system and result in intensification of the greenhouse effect. Given the clear evidence that many health outcomes are highly sensitive to climate variations, it is inevitable that long-term climate change will have some effect on global population health. Climate variability (CV) and change will influence all natural, human, and socioeconomic systems, thus affecting not only health but also many aspects of ecologic and social systems. Climate is one factor that may create conditions that facilitate the development of some disease-causing microorganisms (McMichael and Kovats 1999)
It is important for the health sector to understand current vulnerability to CV because this increasing variability may have a greater impact on health than gradual changes in mean temperature, precipitation, and other climatic variables. Assessing current vulnerability includes understanding both disease exposure--response relationships and current interventions implemented to reduce the burden of climate-sensitive diseases. Additional interventions that can be implemented within the time frame of decision makers (5-10 years) need to be identified to reduce the health effects projected to occur with climate change.
Climate-sensitive diseases have been identified that have important health burdens, particularly vectorborne diseases. Virus and bacteria quickly mutate, thus allowing for environmental adaptation (McMichael and Kovats 1999). CV and climate change may be additional stresses that increase mutation rates of different microorganisms, thus increasing emerging and reemerging diseases. Climate is not the only factor that affects the incidence and range of vectorborne diseases; recent increases are due at least in part to the collapse of vector-control programs (Burton and van Aalst 1999; Michael and Trtanj 1999).
We included acute respiratory infections (ARIs), acute diarrheal diseases (ADDs), bacterial meningitis, viral meningitis, dengue fever, and bronchial asthma (BA) in the vulnerability assessment because these diseases are known to be climate sensitive and because they have relatively high burdens of disease in Cuba.
We analyzed interactions between CV and disease burdens, taking into account that epidemic processes are multicausal (Chan et al. 1999; Kovats et al. 2003). We also explored the uses of a climate index in the prediction of disease outbreaks (Michael and Trtanj 1999; World Meteorological Organization 2001). We then synthesized this information to describe current vulnerabilities to CV, including descriptions of the adaptation baseline. Finally, we estimated the potential economic costs of the projected health impacts of climate change.
Materials and Methods
Data sources. Weather and climate data. We obtained meteorologic data from 1961 to 2003 from the Climate Center at the Institute of Meteorology; data are available from 52 stations across the country. Data included monthly series of maximum and minimum mean temperatures ([degrees]C), precipitation (millimeter), atmospheric pressure (hectopascal, i.e., [10.sup.-2] pascal), vapor pressure (millimeter of mercury), percent relative humidity (percent), thermal oscillation, days with precipitation, solar radiation in (megajoules per square meter), and isolation (hours of light). The period 1961-1990 constituted the climate baseline.
Three monthly variables were included to account for interannual and decadal variability: multivariate ENSO (El Nino Southern Oscillation) index (MEI), quasi-biennial oscillation (QBO), and North Atlantic Oscillation (NAO). Data were available since 1950 from the Climate Diagnostic Center.
Epidemiologic data. The Ministry of Public Health provided epidemiologic data that were obtained from the National Statistical Branch for 1961-2003, including the number of cases and rate of primary health care visits for ARIs, ADDs, viral hepatitis (VH), varicella (chicken pox), meningococcal disease, meningitis caused by Streptococcus pneumoniae, and Plasmodium falciparum and Plasmodium vivax malaria.
Ecologic data. We obtained an ecologic database from the Unit of Fight and Vector Control in the Ministry of Public Health. This database contained information from 1981 to 2004 on its Aedes aegypti [vector for yellow fever, dengue fever, and dengue hemorrhagic fever (DHF)] monitoring and surveillance system. Data included the larval density, biting density per hour of the vector, and the positive house index, which is the ratio of the number of houses positive for larvae to the number of houses inspected.
Socioeconomic data. We obtained data from the National Planification Center from 1981 to 2003, including the percentage of houses without potable water, percentage of houses with dirt floors, the adult ([greater than or equal to] 16 years of age) illiteracy rate, monthly bird rates, and an index of monthly infestations of A. aegypti based on the number of houses where a foci of A. aegypti mosquitoes was observed.
Statistical methods. The empiric orthogonal function (EOF) analysis method has been used extensively in meteorologic and climatologic studies [for more information on EOF, see Hair et al. (1999)]. The EOF is designed to derive the dominant variability patterns from sets of fields...
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