2 resultados para Data-Information-Knowledge Chain


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The EMECAM Project demonstrated the short-term effect of air pollution on the death rate in 14 cities in Spain throughout the 1990-1995 period. The Spanish Multicentre Study on Health Effects of Air Pollution (EMECAS) is broadening these objectives by incorporating more recent data, information on hospital disease admissions and totaling 16 Spanish cities. This is an ecological time series study in which the response variables are the daily deaths and the emergency hospitalizations due to circulatory system diseases and respiratory diseases among the residents in each city. Pollutants analyses: suspended particles, SO2, NO2, CO and O3. Control variables: meteorological, calendar, seasonality and influenza trend and incidence. Statistical analysis: estimate of the association in each city by means of the construction of generalized additive Poisson regression models and metanalysis for obtaining combined estimators. The EMECAS Project began with the creation of three working groups (Exposure, Epidemiology and Analysis Methodology) which defined the protocol. The average levels of pollutants were below those established under the current regulations for sulfur dioxide, carbon monoxide and ozone. The NO2 and PM10 values were around those established under the regulations (40 mg/m3). This is the first study of the relationship between air pollution and disease rate among one group of Spanish cities. The pollution levels studied are moderate for some pollutants, although for others, especially NO2 and particles, these levels could entail a problem with regard to complying with the regulations in force.

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Background. The use of hospital discharge administrative data (HDAD) has been recommended for automating, improving, even substituting, population-based cancer registries. The frequency of false positive and false negative cases recommends local validation. Methods. The aim of this study was to detect newly diagnosed, false positive and false negative cases of cancer from hospital discharge claims, using four Spanish population-based cancer registries as the gold standard. Prostate cancer was used as a case study. Results. A total of 2286 incident cases of prostate cancer registered in 2000 were used for validation. In the most sensitive algorithm (that using five diagnostic codes), estimates for Sensitivity ranged from 14.5% (CI95% 10.3-19.6) to 45.7% (CI95% 41.4-50.1). In the most predictive algorithm (that using five diagnostic and five surgical codes) Positive Predictive Value estimates ranged from 55.9% (CI95% 42.4-68.8) to 74.3% (CI95% 67.0-80.6). The most frequent reason for false positive cases was the number of prevalent cases inadequately considered as newly diagnosed cancers, ranging from 61.1% to 82.3% of false positive cases. The most frequent reason for false negative cases was related to the number of cases not attended in hospital settings. In this case, figures ranged from 34.4% to 69.7% of false negative cases, in the most predictive algorithm. Conclusions. HDAD might be a helpful tool for cancer registries to reach their goals. The findings suggest that, for automating cancer registries, algorithms combining diagnoses and procedures are the best option. However, for cancer surveillance purposes, in those cancers like prostate cancer in which care is not only hospital-based, combining inpatient and outpatient information will be required.