Background: Reporting nosocomial surveillance data can be difficult because the quantity of statistics, graphics, tables, and numeric data may confuse people. Another issue related to feedback regarding healthcare infections rate is that gaps exist between collecting data, the analysis, and implementation of actions based on the information produced. Even when a statistical process control chart (SPC) is used, it is interpreted retrospectively. Here, we present 3 epidemiological tools: (1) a cobweb chart for infection rates, (2) the infectometer, and (3) an outbreak alert system. Methods: For the cobweb chart, the first step is to choose how many and which infection rates will be summarized. Thereafter, all infection rates, respective benchmarks, endemic level, and actual values are placed in a spreadsheet. Although each infection rate has different units (eg, %, rates per 100 discharges, and/or rates per 1,000 denominator days), when we compare the respective endemic level and actual rate with the benchmark, dimensionless quantities are generated for each indicator, making it possible to build the cobweb graph. Using the infectometer for calculations, we (1) built an SPC chart for each infection or microorganism; (2) estimated the average month and standard deviation of the infection cases, excluding outlier data, and (3) calculated the monthly expected incidence, assuming that nosocomial infection occurrence follows a normal distribution. If the supposition of normal distribution fails, a percentile method is used. The outbreak alert system predicts outbreaks using the infectometer parameters, the last month’s observed infection cases, and a Poisson model for predicting the chance of new cases of each infection above monthly expected incidence. Results: With the adapted radar chart, we can report many infection rates in only 1 chart (Fig. 1). The SPC charts for infection rates, stratified by all the types of healthcare infections or by microorganism, can be built, and the infectometer can then be produced, showing weekly and monthly expected cases of an endemic condition. The outbreak alert system is presented as a speedometer that is analyzed at the beginning of each month (Fig. 2). Conclusions: The idea behind the cobweb chart for infection rate method is to report all infection rates in only 1 graph. With the infectometer, it is not necessary to wait until the end of the month to analyze the surveillance data; the analysis becomes prospective and timely. The outbreak alert system brings the future to the present, showing the risk of an outbreak.

Funding: None

Disclosures: None