Answer

Factors associated with Hospital-Acquired Urinary Tract Infection in Catheterized Patients

Data Analysis Plan

The data analysis plan will be based on the study research questions outlined below

1. What is the proportion of patients at hospital X presenting with CAUTI in hospital x?
2. What is the age, gender, type of patient and length of catheterization of patients at hospital X presenting with CAUTI in hospital x?
3. Is there a difference in the length of catheterization for the patients who acquire CAUTI and those who do not in Hospital X?

Study variables

Dependent (response)

• Prevalence of CAUTI

Independent (covariates)

• Age
• Gender
• Length of catheter use
• Type of patient

Analysis plan for objective 1 and 2 is outlined in the table below.

Analysis plan for objective 3

To determine association between length of catheterization and CAUTI among hospitalized patients at hospital x

Null hypothesis

There is no difference in the number of days in a catheter between those with CAUTI and those without.

Research hypothesis

The mean difference in the length of catheterization between those with CAUTI and those without is not equal to 0

Analysis plan

Inferential statistics will be applied. Since we want to determine whether there is an association between length of catheterization and presence of CAUTI, independent samples t test will be applied to check whether the mean number of days of catheterization of those with UTI varies significantly from those without. This is an appropriate test because it allows for the comparison of means between two independent groups (Frankfort-Nachmias & Leon-Guerrero, 2015). The independent groups in this case are those with and those without CAUTI. An independent sample t test is applicable when there is a dichotomous variable (with or without UTI) and the dependent variable is metric (length of catheterization).

Decision criteria

A 95% level of significance will be set giving us an alpha of 0.05. After data analysis, if the p value obtained is less than α, the null hypothesis will be rejected (Wagner, 2016), and the conclusion made is that there is a statistically significant difference in the mean number of days with a catheter of those with and those without CAUTI. If p is more than α, we shall fail to reject the null and conclude that whether or not a patient has CAUTI is not dependent on how long they have had a catheter, and any variations could have been brought about by chance. If a statistically significant association is found, a binary logistic regression analysis can be applied to come up with a regression equation showing how the length of catheterization predicts CAUTI.

succeeding [] ]