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After being inspired by a research paper written in 1970 by an IBM researcher titled “A Relational Model of Data for Large Shared Data Banks” they decided to build a new type of database called a relational database system....
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In the framework of the multicenter ESCAPE (European Study of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health impacts–Integrated Methodologies for Assessing Particulate Matter) projects, we added standardized exposure assessment for air pollution to mortality data from 19 ongoing cohort studies across Europe. Associations of particle mass (PM2.5, PM10, PMcoarse, and PM2.5 absorbance) and nitrogen oxides (NO2 and NOx) with natural-cause mortality in the same cohorts have been reported previously (). We found a statistically significant elevated hazard ratio for PM2.5 of 1.07 [95% confidence interval (CI): 1.02, 1.13] per 5 μg/m3. In this paper we report associations with particle elemental composition in 19 European cohorts to assess whether specific components are associated with natural-cause mortality. A second aim was to assess whether the previously reported association with PM2.5 mass was explained by specific elements. Associations of particle composition and cardiovascular mortality have been published separately ().
Studies have shown associations between long-term exposure to particulate matter air pollution and mortality, with exposure characterized as the mass concentration of particles ≤ 10 μm (PM10) or ≤ 2.5 μm (PM2.5) (; ). Although these studies have identified associations between exposure to particulate matter mass and mortality, there is still uncertainty as to which particle components are the most harmful. In addition, particulate matter effect estimates for long-term studies on mortality have differed among studies, and an explanation for this might be differences in the chemical composition of particulate matter ().
Most studies that have assessed mortality in association with exposure to elemental components have been short-term exposure studies, and their results have varied considerably (; ). Few studies have investigated mortality in relation to long-term exposure to particle components. A lack of spatially resolved elemental composition measurement data and exposure models for elemental composition partly explains this (). The U.S. Six Cities and American Cancer Society cohort studies have suggested an association between long-term exposure to sulfate and mortality (; ; , ), but no other particle composition parameters have been evaluated in these studies. A cohort study, the California Teachers Study, found no statistically significant associations between all-cause mortality and long-term exposures to PM2.5 and several of its constituents, including elemental carbon, organic carbon (OC), sulfates, nitrates, iron, potassium, silicon, and zinc, although statistically significant associations were reported for more specific outcomes, especially ischemic heart disease mortality ().
Citation: Beelen R, Hoek G, Raaschou-Nielsen O, Stafoggia M, Andersen ZJ, Weinmayr G, Hoffmann B, Wolf K, Samoli E, Fischer PH, Nieuwenhuijsen MJ, Xun WW, Katsouyanni K, Dimakopoulou K, Marcon A, Vartiainen E, Lanki T, Yli-Tuomi T, Oftedal B, Schwarze PE, Nafstad P, De Faire U, Pedersen NL, Östenson C-G, Fratiglioni L, Penell J, Korek M, Pershagen G, Eriksen KT, Overvad K, Sørensen M, Eeftens M, Peeters PH, Meliefste K, Wang M, Bueno-de-Mesquita HB, Sugiri D, Krämer U, Heinrich J, de Hoogh K, Key T, Peters A, Hampel R, Concin H, Nagel G, Jaensch A, Ineichen A, Tsai MY, Schaffner E, Probst-Hensch NM, Schindler C, Ragettli MS, Vilier A, Clavel-Chapelon F, Declercq C, Ricceri F, Sacerdote C, Galassi C, Migliore E, Ranzi A, Cesaroni G, Badaloni C, Forastiere F, Katsoulis M, Trichopoulou A, Keuken M, Jedynska A, Kooter IM, Kukkonen J, Sokhi RS, Vineis P, Brunekreef B. 2015. Natural-cause mortality and long-term exposure to particle components: an analysis of 19 European cohorts within the Multi-Center ESCAPE Project. Environ Health Perspect 123:525–533;
A well-organized term paper is supposed to be interesting, informative and contain only up-to-date high-quality information from the reliable sources. One is supposed to explain the meaning of the term ‘regression analysis’, analyze the structure and the principles of the method and focus on its advantages and disadvantages. A student is supposed to define the role and the value of the method and prove that it is important for statistics and the methodology of all sciences. One should conclude the paper well and provide the professor with the well-structured logical paper which reveals the problem from all sides.
Regression analysis is an important topic for the analysis, because it provides scientists with the methods of processing of the required information, which can be used in statistics, decision making, etc. If one is going to connect his career with statistics, he will need to know much about various methods of data analysis and prediction of the possible results. A term paper on regression analysis is a good chance for students to improve their knowledge about the problem and achieve useful experience and professional skills.