ANALYSIS OF INFECTION-CONTROL SURVEILLANCE DATA IN A LONG-TERM-CARE FACILITY - USE OF THRESHOLD TESTING

OBJECTIVE: To describe long-term trends in nosocomial infection rates and the threshold testing method for evaluating nosocomial surveillanc e data in a long-term-care facility (LTCF). DESIGN: Descriptive epidem iology of prospectively collected infection control surveillance data and application of threshold testing for detecting possible outbreaks. Threshold testing uses the binomial distribution to calculate probabi lities of infection frequency at selected endemic levels (mean number of infections per month) and compares these probabilities to observed infection frequency. SETTING: One hundred twenty-bed LTCF located with in a public, university-affiliated hospital. PATIENTS AND METHODS: The study period was 1987 through 1994. Yearly endemic levels of specific types of infection were calculated and threshold levels were determin ed using a previously published method. In this study, a probability o f P=.01 was chosen to determine the threshold at a specific endemic le vel; if the observed number of infections in a month reached or exceed ed the threshold level, the likelihood that this occurred by chance al one was 1% or less. INTERVENTIONS: None. RESULTS: The overall mean nos ocomial infection rate ranged from three to five episodes per 1,000 re sident care days per year; mean yearly rates from 1990 onward were hig her and more stable than those from 1987 to 1989. The most common infe ctions identified were lower respiratory tract, skin and soft tissue, urinary tract, and conjunctivitis. For each of these infections, thres hold levels were calculated periodically, using only the monthly frequ ency of infection. Despite variations in the yearly mean endemic level of various infections, threshold levels were stable except for skin a nd soft-tissue infection. CONCLUSIONS: Threshold testing for analysis of infection control surveillance data in the LTCF setting is straight forward and does not require knowledge of statistics, special computer software, or calculation of rates; given the stable population in the typical LTCF, threshold testing can be based on variations in the mon thly count of infections and provides an objective evaluation of surve illance data and a method to identify when outbreaks may be occurring.