Costing of Surgical Site Infection

Surgical site infection (SSI) carries an enormous clinical and economic burden. The good news is that up to 35% of surgical wound infections could be prevented by an effective infection control programs.However, infection control programs (ICPs) are costly themselves.

Decisions, such as "Is it worthy to implement infection control measures?" and "How much resources can be utilized for this purpose?" are critical and should be informed.

Aim of the work

The aim of this calculator is helping policymakers to figure out how much they can allocate to implement an infection control program with a known efficacy in their hospitals; and to predict, in advance, the expected consequences of its implementation.

This calculator is formed of two tabs.

The first tab calculated the expected number of patients with surgical site infection(SSI) and the expected total cost paid by the hospital to treat them.

You have to feed four cells in the first tab

The first two cells are filled by data from the hospital surveillance system.

First cell: Enter the total number of surgical patients

Second cell:Enter the number of patients developed surgical site infection

Third cell:Enter the expected number of surgical patients admitted to the hospital; e.g., 1,000 surgical patients.

Forth cell: enter the number of simulations;e.g., recommended 10,000.

The data filled in the second tab is related to the infection control program (ICP).

Enter the cost of ICP per patient in the first cell on the right. If the cost of ICP is not known per patient, then divide the total cost of the ICP by the number of the surgical patients you entered in the third cell on the first tab.

Select the expected efficacy of the ICP using the slide bar on the right. It ranges from zero (no efficacy at all) to 1 (the ICP can prevent the occurrence of all the cases of surgical site infection). If you select 0.20, this means the ICP can reduced the number of surgical site infection by 20%.

In the first tab, we present the expected total cost the hospital will pay to treat the patients with SSI. We used the Burr distribution (shape1=0.21385, shape2=4.1144,scale=60.365) from a related sutudy to predict the cost of SSI per patient

  rburr(1, shape1=0.21385, shape2=4.1144,scale=60.365)

## [1] 119.5584

In the second tab, we present the expected savings resulting form implementing the ICP is shown