Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-26T07:01:24.480Z Has data issue: false hasContentIssue false
  • English
  • Français

Standardized Infection Surveillance in Long-Term Care Interfacility Comparisons From a Regional Cohort of Facilities

Published online by Cambridge University Press:  21 June 2016

Kurt B. Stevenson ,
James Moore ,
Holly Colwell  and
Barbara Sleeper
Kurt B. Stevenson*
Affiliation:
Division of Clinical Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, Utah
James Moore
Affiliation:
Qualis Health, Boise, Idaho
Holly Colwell
Affiliation:
Qualis Health, Boise, Idaho
Barbara Sleeper
Affiliation:
Qualis Health, Boise, Idaho
*
Qualis Health, 720Park Boulevard, Suite 120, Boise, ID 83712-7756kurts@qualishealtk.org
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

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.

Setting:

Seventeen LTCFs in Idaho.

Design:

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.

Results:

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.

Conclusion:

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

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 26 , Issue 3 , March 2005 , pp. 231 - 238
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2005

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Smith, PW, Rusnak, PG. Infection prevention and control in the long-term-care facility. Infect Control Hosp Epidemiol 1997;18:831–849.Google Scholar
2.Haley, RW, Culver, DH, White, JW. The efficacy of infection surveillance and control programs in preventing nosocomial infections in U.S. hospitals. Am J Epidemiol 1985;121:182–205.Google Scholar
3. Centers for Disease Control and Prevention. Public health focus: surveillance, prevention, and control of nosocomial infections. MMWR 1992;41:783–787.Google Scholar
4.Roy, MC, Perl, TM. Basics of surgical-site infection surveillance. Infect Control Hosp Epidemiol 1997;18:659–668.Google Scholar
5.Misset, B, Timsit, JF, Dumay, MF, et al.A continuous quality-improvement program reduces nosocomial infection rates in the ICU. Intensive Care Med 2004;30:395–400.Google Scholar
6.Smith, PW. Development of nursing home infection control. Infect Control Hosp Epidemiol 1999;20:303–305.Google Scholar
7.Goldrick, BA. Infection control programs in skilled nursing long-term care facilities: an assessment, 1995. Am J Infect Control 1999;27:4–9.Google Scholar
8.Strausbaugh, L, Joseph, CL. Epidemiology and prevention of infections in residents of long term care facilities. In: Mayhall, CG, ed. Hospital Epidemiology and Infection Control. Philadelphia: Lippincott Williams & Wilkins; 1999:1461–1482.Google Scholar
9.Strausbaugh, LJ. Infection control in long-term care: news from the front. Am J Infect Control 1999;27:1–3.Google Scholar
10.Loeb, M. Antibiotic use in long-term-care facilities: many unanswered questions. Infect Control Hosp Epidemiol 2000;21:680–683.Google Scholar
11.Goldrick, BA. Infection control programs in long-term-care facilities: structure and process. Infect Control Hosp Epidemiol 1999;20:764–769.CrossRefGoogle ScholarPubMed
12.Archibald, LK, Gaynes, RP. Hospital-acquired infections in the United States: the importance of interhospital comparisons. Infect Dis Clin North Am 1997;11:245–255.Google Scholar
13.Stevenson, KB. Regional data set of infection rates for long-term care facilities: description of a valuable benchmarking tool. Am J Infect Control 1999;27:20–26.Google Scholar
14.White, C, Pizer, SD, White, AJ. Assessing the RUG-III resident classification system for skilled nursing facilities. Health Care Financing Review 2002;24:7–15.Google Scholar
15.Kane, RL. Improving the quality of long-term care. JAMA 1995;273: 1376–1380.Google Scholar
16.McGeer, A, Campbell, B, Emori, TG, et al.Definitions of infection for surveillance in long-term care facilities. Am J Infect Control 1991;19:1–7.Google Scholar
17.Greenland, S, Rothman, KJ. Introduction to categorical statistics. In: Rothman, KJ, Greenland, S, eds. Modern Epidemiology. Philadelphia: Lippincott Williams & Wilkins; 1998.Google Scholar
18.Duncan, AJ. Quality Control and Industrial Statistics. Columbus, OH: McGraw-Hill; 1986.Google Scholar
19.Tufte, ER. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press; 1983.Google Scholar
20.Centers for Disease Control and Prevention. National Nosocomial Infections Surveillance (NNIS) System report: data summary from January 1992 through June 2003, issued August 2003. Am J Infect Control 2003;31:481–498.Google Scholar
21.Gaynes, RP, Culver, DH, Emori, TG, et al.The National Nosocomial Infections Surveillance System: plans for the 1990s and beyond. Am J Med 1991;91(suppl 3B):116S–120S.Google Scholar
22.Centers for Medicare & Medicaid Services. Nursing Home Compare. Baltimore, MD: Centers for Medicare & Medicaid Services. Available at www.medicare.gov/NHCompare/home.asp.Google Scholar
23.Emori, TG, Edwards, JR, Culver, DH, et al.Accuracy of reporting nosocomial infections in intensive-care-unit patients to the National Nosocomial Infections Surveillance System: a pilot study. Infect Control Hosp Epidemiol 1998;19:308–316.Google Scholar
24.Morton, AP, Whitby, M, McLaws, ML, et al.The application of statistical process control charts to the detection and monitoring of hospital-acquired infections. Journal of Quality in Clinical Practice 2001;21:112–117.Google Scholar
25.Sellick, JA Jr.The use of statistical process control charts in hospital epidemiology. Infect Control Hosp Epidemiol 1993;14:649–656.Google Scholar
26.Benneyan, JC. Statistical quality control methods in infection control and hospital epidemiology: Part II. Chart use, statistical properties, and research issues. Infect Control Hosp Epidemiol 1998;19:265–283.CrossRefGoogle ScholarPubMed