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Hospitalization data

Sheikh A. Alves B Age. sex, geographical and socioeconomic variations in admissions for anaphylaxis dS analysis of four years of Enghsh hospital data. Clin Exp Allergy 2001 31 1571 -1576. [Pg.21]

Berkson, Joseph. 1946. Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics Bulletin 2 47-53. [Pg.85]

Hospital data collected by the Substance Abuse and Mental Health Services Administration, a division of the U.S. Department of Health and Human Services, suggests the age of the typical American club drug user is between 18 and 25. Whereas only 20% of all drug-related emergency room visits involved patients 25 and under, this age group accounts for 58% of ketamine incidents, 67% of all recorded MDMA incidents, 50% of recorded GHB incidents, and 46% of all LSD incidents. [Pg.272]

Smith, T.C., Gray, G.C., Weir, J.C., Heller, J.M., Ryan, M.A. (2003). Gulf War veterans and Iraqi nerve agents at Khami-siyah postwar hospitalization data revisited. Am. J. Epidemiol. 158 457-67. [Pg.479]

An interesting discussion of how faulty conclusions can easily be icached from 2x2 table is by J. Berkson on hospital data Biometrics Bulletin, Vol. 2, No. 3, page 47, 1946. [Pg.40]

Table 2. SURVEY OF THE HOSPITAL DATA ON VICTIMS OF TOKYO SUBWAY SARIN POISONING... Table 2. SURVEY OF THE HOSPITAL DATA ON VICTIMS OF TOKYO SUBWAY SARIN POISONING...
Stationary Hospitals, Data Center Backup Power Generation 1,500/kW... [Pg.39]

In a case-control study using drug-dispensing and hospitalization data from more than 2 million residents in The Netherlands, subjects with a first hospitalization for acute myocardial infarction, cardiovascular and gastrointestinal events were identified [14 J. Use of coxibs and non-selective NSAIDs was classified into remote, recent, and current use. Compared with remote use, the risk of acute myocardial infarction was increased in current users of all coxibs (adjusted OR = 1.73 95% Cl = 1.37, 2.19) and all non-selective NSAIDs (adjusted OR = 1.41 95% Cl = 1.23, 1.61). Analysis by separate agents showed that the risk of acute myocardial infarction was increased with celecoxib (OR = 2.53 95% Cl = 1.53, 4.18), rofecoxib (OR = 1.60 95% Cl = 1.22, 2.10), ibuprofen (OR = 1.56 95% Cl — 1.19, 2.05), and diclofenac (OR = 1.51 95% Cl = 1.22, 1.87), but not with naproxen (OR = 1.21 95% Cl = 0.87,1.68). [Pg.242]

For this study, we analyzed all records of injirry acciderrts from the natiortal Austrian accident database, where cyclists were involved. The data covers the years 2002 until 2011 since data for 2012 was not available at the time of writing. While studies [ELV 99, LAN 03] recommend the use of hospital data for safety assessments, police data for accidents was used in this study since hospital data is not available for analysis in Austria. While accident insurance providers do collect data from hospitals, current data privacy laws do not allow for a consolidation of accident data from police and hospital sources. For the city of Vienna, the resulting dataset contains a total of 6,287 accidents. The database consists of several tables which describe the accidents. The accident table contains information about the location of the accident, the weather conditions, as well as the date and type of the accident. The participants table contains information about the participants such as age, degree of injury and type of vehicle. Table 10.4 shows the trends of the yearly nnmber of accidents. Fignre 10.1 presents a comparison of the trends of accident counts and bicycle counts based on the initial values for 2002. In the analysis time frame between 2002 and 2011, the data show no correlation between trends of accident counts and bicycle counts (R = 0.03). This suggests the validity of the concept of safety in numbers , which states, that an increase in the modal share of bicycles leads to a decrease in the number of accidents per cycled kilometer. [Pg.151]

Based on a comprehensive identification of situations for VRUs that are critical in view of traffic safety, making use of accident data, hospital data and in-depth studies, a taxonomy of the major critical scenarios for VRUs is derived, providing essential irqiut to the development of ITS aimed at VRU safety. [Pg.254]


See other pages where Hospitalization data is mentioned: [Pg.283]    [Pg.131]    [Pg.267]    [Pg.128]    [Pg.38]    [Pg.86]    [Pg.861]    [Pg.291]    [Pg.77]    [Pg.486]    [Pg.131]    [Pg.528]    [Pg.216]    [Pg.234]    [Pg.160]    [Pg.656]   
See also in sourсe #XX -- [ Pg.656 ]




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Hospitalism

Hospitalization data rates

Hospitalized

Hospitals

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