ANOVA is an acronym that stands for Analysis of Variance. It is a method applied during the test of hypothesis to show the relationship between a dependent and one or more independent variables. ANOVA differs from the regression in two ways in that no assumptions are made about the relationship between the variables and the independent variables are qualitative in nature (Cardinal et al. 2013 p.8).
Wisplinghoff et al. (2004) conducted research about Nosocomial Bloodstream Infections within hospitals in the United States. The case study involved forty-nine hospitals across the country. The Nosocomial bloodstream infections are some of the leading causes of deaths in many parts of the world, but people disregard them due to the widespread occurrence of chronic diseases such as cancer (El Helou et al. 2013 p.167). At the end of the research, there were twenty-four thousand, one hundred seventy-nine cases of persons suffering from the disease in these hospitals. The forty-nine hospitals varied in size, but all had a bed capacity of between sixty and one thousand two hundred beds. Patients involved in the study had particular characteristics that included blood detected to have pathogenic infections and abnormal blood pressure (Wisplinghoff et al. 2004 p.309). The researchers included demographic factors of the patients such as age, sex, and location of the hospital.
To calculate the rate of incidence of the disease, the researchers first collected and summarized the admission data of the participating hospitals. Prevalence was calculated as the number of persons with Bloodstream Infections per ten thousand hospital admissions. So as to arrive at reliable results, the researchers excluded seven hospitals that had participated for less than one year thereby leaving forty-two eligible participating health facilities. The statistical analysis was represented as mean plus or minus the standard deviation. As for the continuous variables involved in the study, the mean measures were pared using two t-tests for the independent variables. The variations in proportions were contrasted by use of a fisher's exact test and x2 test as for where applicable (Wisplinghoff et al. 2004 p.310). Also, a Mann-Whitney test was conducted to examine the equity of the continuous variables. The statistical analysis for this study was carried out using the Statistical Package for the Social Science (SPSS) software (Wisplinghoff et al. 2004 p.311).
For the seven and a half years that the research lasted, fifteen percent of the total patients in these hospitals were found to have bloodstream infections. Most of these patients (approximately half), were male with a mean age of fifty-one years. Of these patients, more than half were in the Intensive Care Unit (Wisplinghoff et al. 2004 p.311). The most prevalent condition was malignancy which accounted for about eighteen percent of the total. However, four thousand three hundred patients were recorded to have underlying conditions that were not specified. Cardiac conditions in the study population were three thousand six hundred twenty-six which accounted for fifteen percent.
In conclusion, the results of the study indicated that one and a half of all cases of Nosocomial Bloodstream Infections occur in the Intensive Care Unit of the hospital setting. Many of these patients develop a high resistance to antibiotics administered in the hospitals (Wisplinghoff et al. 2004 p.315). The study necessitates that the health care experts should first explore the species of the disease before administering any medication.
Cardinal, R. N., & Aitken, M. R. (2013). ANOVA for the behavioral sciences researcher. Psychology Press.El Helou, G., Viola, G. M., Hachem, R., Han, X. Y., & Raad, I. I. (2013). Rapidly growingmycobacterial bloodstream infections. The Lancet Infectious Diseases, 13(2), 166-174.
Wisplinghoff, H., Bischoff, T., Tallent, S. M., Seifert, H., Wenzel, R. P., & Edmond, M. B.(2004). Nosocomial bloodstream infections in US hospitals: analysis of 24,179 casesfrom a prospective nationwide surveillance study. Clinical infectious diseases, 39(3),309-317.
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