To measure infection rates in a regional cohort of long-term-care facilities (LTCFs) using standard surveillance methods and to analyze different methods for interfacility comparisons.

Seventeen LTCFs in Idaho.

Prospective, active surveillance for LTCF-acquired infections using standard definitions and case-finding methods was conducted from July 2001 to June 2002. All surveillance data were combined and individual facility performance was compared with the aggregate employing a variety of statistical and graphic methods.

The surveillance data set consisted of 472,019 resident-days of care with 1,717 total infections for a pooled mean rate of 3.64 infections per 1,000 resident-days. Specific infections included respiratory (828; rate, 1.75), skin and soft tissue (520; rate, 1.10), urinary tract (282; rate, 0.60), gastrointestinal (77; rate, 0.16), unexplained febrile illnesses (6; rate, 0.01), and bloodstream (4; rate, 0.01). Initially, methods adopted from the National Nosocomial Infections Surveillance System were used comparing individual rates with pooled means and percentiles of distribution. A more sensitive method appeared to be detecting statistically significant deviations (based on chi-square analysis) of the individual facility rates from the aggregate of all other facilities. One promising method employed statistical process control charts (U charts) adjusted to compare individual rates with aggregate monthly rates, providing simultaneous visual and statistical comparisons. Small multiples graphs were useful in providing images valid for rapid concurrent comparison of all facilities.

Interfacility comparisons have been demonstrated to be valuable for hospital infection control programs, but have not been studied extensively in LTCFs.

To consider statistical methods for estimating transmission rates for colonization of patients with methicillin-resistant Staphylococcus aureus (MRSA) in an intensive care unit (ICU) from three different sources: background contamination, non-isolated patients, and isolated patients.

We developed statistical methods that allowed for the analysis of interval-censored, routine surveillance data and extended the general epidemic model for the flow of patients through the ICU.

Within this ICU, the rate of transmission to susceptible patients from a background source of MRSA (0.0092 case per day; 95% confidence interval [CI95], 0.0062–0.0126) is approximately double the rate of transmission from a non-isolated patient (0.0052 case per day; CI95, 0.0013–0.0096) and six times the rate of transmission from an isolated patient (0.0015 case per day; CI95, 0.0001–0.0043). We used the methodology to investigate whether transmission rates vary with workload.

Our methodology has general application to infection by and transmission of pathogens in a hospital setting and is appropriate for quantifying the effect of infection control interventions. (Infect Control Hosp Epidemiol 2005;26:598-606)