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Comparison of methods for calculation and depiction of incidence infection rates in long-term care facilities
0895-4356/88 S3.00+0.00
Copyright 0 1988Pcrgamon Pressplc
COMPARISON OF METHODS FOR CALCULATION AND DEPICTION OF INCIDENCE INFECTION RATES IN LONG-TERM CARE FACILITIES JEFFREY
M. SCHICKER, TIMOTHYR. FRANSON,‘* EDMUND H. DUTHIE JR? and SUSAN M. LECLAIR
Sections of ‘Infectious Disease and Geriatrics/Gerontology, Department of Medicine, Zablocki Veterans Administration Medical Center, Medical College of Wisconsin, Milwaukee, WI 53295, U.S.A. (Received in revised form 4 January 1988)
Ahatraet-Assessment of changes in endemic infections in health care facilities are often based on comparison of infection rates over time. This study compared two methods for calculating and depicting infection data at a hospital-based nursing home care unit. Prospective incidence surveillance of nursing home acquired infections was conducted over a 12-month period, during which time denominator information on census and patient care days was also collected. Monthly infection rates were calculated based on (a) census (number infections per month/average monthly census), and (b) care duration (number infections per month/average monthly resident stay days). Results showed average monthly infection incidence of 27.4 episodes, (range 19-37), with average monthly census of 166.2 (range 160.0-180.0) and average monthly patient days of 5056 (range 4631-5583). The average census based monthly infection rate was 16.5 (range 11.9-22.4); average care duration based monthly infection rate was 5.4 (range 3.8-7.2) episodes per 1000 patient care days. Results indicate care duration based rates demonstrate less marked rate fluctuation than census based fnndings, and that duration based rates are more similar to valuesobservedin acute ,
carehospitalnosocomialinfectionrates,thus are lesslikelyto be misunderstoodor misinterpreted
by &aft’.Institutionsshould consider analyzingtheir methods for depictinginfection data to provideconsistencyand clarity in data reporting. Nursing home acquired infection Geriatric infectious diseases
Infection
rates
Infection control in nursing homes
Infection surveillance data are usually depicted as infection rates, which are comPrevention and control of nursing-homeputations involving a numerator parameter of acquired infections are major goals of institunumber of infections at a given point in time tional risk management programs. Implicit in (prevalence), or of new infections over a preventive planning is the baseline knowledge of specified period of time (incidence). The denomfrequency and types of infections in a specific inator parameter used in these calculations has nursing home, which are detected through infec- classically been the number of admissions or tion surveillance projects. discharges in a facility for acute care hospital infection rates [I]. Using such parameters yields average hospital-acquired infection incidence rates 1of 5-lO%, which has been used as a *Current address Lilly Research Laboratories, Indianapolis, Indiana, U.S.A. comparative index by some programs. HowtAddress correspondence and reprint requests to: Edmund ever, due to low resident turnover in long-term H. Duthie Jr M.D., Geriatrics/Gerontology, Medical care settings, a denominator of admissions or Service/l llP, VA Medical Center, 5000 W. National Ave., Milwaukee, WI 53295, U.S.A. discharges yields rates much higher than those Supported by the National Institute on Aging Grant in acute hospitals, and is not typically used by Rol-AGO6403-02 and the National Institute on Aging Geriatric Medicine Academic Award 5-K07-AGOO178. long-term care settings. INTRODUCTION
757
158
JEFFREYM. SCHICKER et al.
Denominator parameters used for incidence rate calculations in long-term care facilities are not standardized, and alternatives used include end-of-month census, average monthly census, total bed complement and total resident care days. This variability in calculations makes rate comparisons from institution-to-institution difficult and often confuses nursing home staff who are used to gauging rates in comparison to published acute care hospital rates. Furthermore, depending on the denominator parameter chosen, rates may drastically fluctuate from month-to-month due to changes in census rather than major shifts in infection occurrence, lending to further confusion in rate interpretations. Therefore, universal use of a consistent reliable method for calculating infection rates in long-term care facilities would greatly facilitate comparisons and interpretations. This study describes a one year surveillance project of infections in a long-term care facility, in which rates of nursing-home acquired infections were depicted and compared using two calculation methods.
with the NHCU pharmacist, review of laboratory and radiological reports, review of 24 hr nursing reports, review of vital sign flow sheets for abnormal values, and review of nursing and medical progress notes in the resident chart. The nurse only recorded those nursing-home acquired infections that were validated in the’ chart by physician diagnosis, or supported by documentation of lab work, X-ray reports, or patient symptoms. At no time did the nurse epidemiologist individually assess the resident to determine the presence or absence of infection. All infected residents had further data documented as to demographic features, chronic illness factors, chronic drug and treatment therapy, infection features, isolation practices and outcome of infection and immunization data, as shown in Table 1. Infections were recorded in eight categoriesurinary tract infections (UTI), pneumonia, upper respiratory infection, wound and skin infections, gastrointestinal infection, bacteremia source unknown, and other (prostatitis, epididymitis, thrush, etc.). Questionable infection cases were discussed METHODS bi-weekly with the hospital epidemiologist as to Daily prospective infection surveillance was possible inclusion in the study. Monthly reports carried out by a trained geriatric nurse epi- were also discussed with the hospital epidemiologist over a one year period from 1 demiologist and the NHCU Director for review August 1985 through 31 July 1986 at a hospitaland validation. based Veterans Administration Medical Center Monthly resident census was determined by Nursing Home Care Unit (NHCU). The nurse obtaining the daily nursing home census. The epidemiologist recorded only those infections denominator consisted of the total of resident that occurred or were diagnosed in the nursing care days for the month (calculated as the sum home with onset at least 48 hr after admission. of care days for every resident present at least Residents admitted from an acute care hospital one day of the given month) and average or the community who were undergoing treat- monthly census of residents (calculated by divment for infections or were diagnosed with iding the sum of daily census by the number of infections within 48 hr of admission were not days in the given month). Infection rates were calculated on a monthly recorded in the numerator. Infections were debasis using the following formulas: tected by two main methods: (1) a documented “physician diagnosis” in the resident’s chart (1) census based me = number of infections that an infection was present, and (2) by criteria per month/average monthly census x 100; and adopted from CDC definitions for detection of (2) care duration based rate = number of innosocomial infections [2]. Resident charts were fections per month/average monthly resident reviewed for signs and symptoms of infection, care days x 1000. then compared to infection criteria. The study was observational only and no attempt was RESULTS made to influence physician or nurse documentation of infection events. During this one year survey, 330 nursingThe nurse epidemiologist collected infection home acquired infections were detected, during data through consultation with the nursing staff which time the average monthly census was and doctors concerning resident health status, 166.21 residents, with average monthly resident review of antibiotics prescribed via consultation care days of 5056. Results for each month are
Incidence Infection Rates
759
Table 1. Nursing care facility surveillance data base Patient survev factors A. Demographic features: Name, age, sex, race, date of admission to nursing care facility, residence prior to admission,
date of most recent acute care hospitalization, ward site, room status (l-bed, 2-bed, 3&d, Cbed, or ward). B. Chronic illness factors: Prior urinary tract infections, diabetes mellitus, hypertension, congestive heart failure, chronic pulmonary disease, ethanolism (by history), peripheral vascular disease, neurovascular disease, dementia, organic brain syndrome, immobility, nutritional status, (obesity-malnutrition), neoplastic disease (type and site), heart disease (type) and renal disease. C. Chronic drug therapy and treatments: Psychotropic agents, immunosuppressive therapy (corticosteroids, chemotherapy, antibiotics, radiation therapy, antacids, and sedatives). D. Chronic treatment factors: Intravascular devices, respiratory devices (tracheostomy), urinary devices (indwelling genitourinary catheter, intermittent catheterization, condom catheter, suprapubic catheter, recent instrumentation, genitourinary irrigation, nasogastric tube, surgical drains), antibiotic therapy preceding infection (agent, route, dosage, duration). E. Infection features : 1. Date of onset 2. Acquisition source: community, hospital, nursing home 3. Anatomic site: upper respiratory, lower respiratory, wound/skin, gastroinestinal,
eye, blood, urinary tract (with vs without catheter association) and other 4. Signs-symptoms: general (asymptomatic, chills, rigors, inflammation, pain, WBC > 10,000, tachypnea, tachycardia, confusion--acute, behavior change-acute, lethargy, falls, Md-Dx, fever > 100.4); respiratory (cough, sputum, coryxal symptoms, rales, rhonchi, positive chest X-ray); gastroinestinal (diarrhea); genitourinary (frequency, burning, hematuria, dysuria, pyuria, CVA tenderness, incontinence); wound-decub (drainage, ulcer, furuncle, bum); and eye (discharge, chemosis, conjunctivitis, pain) 5. Microbiologic information: culture date, specimen source, culture isolate, antibiotic sensitivities 6. Therapy: antibiotics administered, agent, dose, route F. Isolation practices: respiratory, blood, contact, strict, enteric, drainage/secretion, protective G. Outcome of infection: resolved, resolved-relapse, failure, failure retreatment, transfer for hospitalization (number of days, outcome, complications, mortality and morbidity) H. Immunizations: pneumonia, influenxae, diptheria, tetanus, allergies
shown in Table 2. Of 330 total infections, 118 (35.8%) incidents of UT! were detected-of which 31 cases were urinary-catheter associated; 100 cases (30.3%) of pneumonia and bronchitis; 40 incidents (12.1%) of wound/skin infection; 20 cases (6.1%) of upper respiratory infection; 15 cases (4.5%) of gastrointestinal infection; 12 cases (3.6%) of conjunctivitis, 2 cases (0.6%) of bacteremia, source unknown, and 25 cases (7.0%) listed as others occurred. The overall average monthly infection rate calculated via the average monthly census (27.4 of 166.2 x 100) was 16.5% and using infections per 1000 resident care days (27.4 of 5056 x 1000) was 5.4. -
Avera.~e
-
Resident cam days
1985
cenrur _
1986
Fig. 1. Monthly infection rate depiction using denominator parameters of resident care days (per 1000) vs average monthly resident census (%).
The average census based monthly infection rate fluctuated from 11.9 to 22.8%, and the average resident care day duration based monthly infection rate varied from 3.8 to 7.2. Figure 1 depicts variance and comparison of the two rates by month, and Table 3 shows variance of monthly census, monthly resident care days and actual number of infections over the survey period. DISCUSSION
This study summarizes a one year infection surveillance project with a comparison of two methods used to calculate infection rates in long-term care facilities. When examining rates using the monthly census denominator, the average monthly infection rate average was 16.5%, with a range from 11.9 to 22.8% Such rate findings give an impression of a high density of infections, with wide fluctuations suggesting sporadic problems, when viewed in the context of routine acute hospital rate expectations. However when using the same infection data with a denominator of resident care day duration, the average monthly infection rate was 5.4 with a range of 3.8-7.2. These figures appear to vary less drastically than census-based figures, and led to less confusion
Urinary tract infection Pneumonia/bronchitis Wound/skin infections Upper respiratory inf&ons Gastrointestinal infections Racmmmiasource unknown Conjunctivitis other Totals
10 7 6 0 0 0 0 2 25
21 10 3
0
1
0 0 0 35
0 2 1 19
0
0
4 9 3
Oct.
Dec. 11 12 4 0 0 0 1 3 31
Nov. 11 9 2 4 1 0 0 3 30
0 1 3 25
0
1
5 12 3
Jan.
0 1 3 28
2
2
6 10 4
Feb.
0 2 0 4 2;
2 1 1 3;
6 6 1
14 7 4 6
April
Mar.
3:
1 _ 1
2
-4
11 6 5
May
60,672 5056
1994.5 166.2
Total Monthly average
5583 4994 4%1 4865 4%8 5283 4631 5124 4803 5127 5023 5310
180.0 167.0 160.0 162.0 160.2 170.4 165.4 165.3 160.1 165.4 167.4 171.3
August September October November December Jamlaty February March April May June July
=dvS
CenSUS
Resident
Month
Average monthly
27.5
330
:: 28 37 20 33 ;:
19 30
::
Number of infections
5.4
6.3 5.0 3.8 6.2 6.2 4.7 6.0 7.2 4.2 6.4 4.2 4.9
19.0 15.0 11.9 18.5 19.3 14.7 16.9 22.8 12.5 19.9 12.5 15.2
16.5
Resident care days (per 1000)
2:
0 1
2
2
6 6 2
June
Monthly census (%)
Infection rates using denominator OE
Table 3. Monthly infection rate by monthly census and patient care days
Sept.
Aug.
Table 2. Jnfections by month and site
13 6 -3
3:
2 12
0 0 2:
15
3
20
118 100 40
July
1
Yearly total
0.6 3.6 7.0 100.0
4.5
6.1
35.8 30.3 12.1
Percent of total
Incidence Infection Rates
when presented to NHCU personnel in routine reporting sessions. In hospital-based nursing homes, where care personnel may have rotating duties between acute care and long-term units, the potential for misunderstanding infection control data would be minimized by use of rate depictions which are “familiar” to both settings. Individuals with prolonged stays are more likely to be exposed to events leading to infection than patients with shorter stays, and their consideration should be incorporated as a factor in rate calculations. Only the care day formula provides adjustment for such a variance and therefore allows a more accurate rate depiction. As shown in a recent report, use of a formula for calculating infections per thousand patientcare days in acute care hospitals corrects for long hospital stays, avoids falsely high infection rates, and allows better comparison of infection rates over time and across locations [3]. The variability in this literature can be seen among four recent reports which also prospectively described the incidence of infections in nursing homes. Magnussen over a 2-month period reported her rates of infection as a percent of patients at risk [4]. A rate of 18.2% was reported for the 2 month period for nosocomial infections. Using comparable methods we noted a 16.5% rate over a 12 month period. Scheckler in a 6 month study of rural Wisconsin nursing homes found an infection rate of 10.7 infections per 100 resident months [5]. Using an average of 30 days per month, this would correspond to a rate of 3.56 infections per 1000 resident care days compared to 5.4 infections per 1000 resident care days in the .current study. In another VA facility, Farber reported the incidence of infections over a one year period. In this nursing home, an infection rate of 0.67 infections per
761
100 patient care days was noted [6]. Finally, Nicolle found the infection rate in a Canadian veterans facility to be 192.7 infections per 100 patient years [q. Using 365 days per year, we would calculate an infection rate of 5.2 infections per 1000 patient care days, a figure similar to our results and those of the other authors. In summary, this study illustrates the utility of using a denominator of “thousand resident care ,days” in calculating infection rates and depicting results for the personnel in a hospitalbased nursing home care unit. Long-term care facilities should carefully reexamine their methods used in compiling and presenting infection data to enhance accuracy and clarity of those efforts, as well as to aid rate comparisons with similar facilities.
REFERENCES 1. Haley RN (Sonic Project, CDC, Atlanta, GA 30333) Culver DH. White JW er al. The nationwide nosocomial infection rate: A new need for vital statistics. Am J R@der&I 1985; 121: 159-167. 2. Center for Disease Control. Outline for surveillance and control of nosccomial infections. Appendix II. -forDete~Reaenceuld-~n of fnfectioll1976; pp. 19-31. 3. Madison R, A6fl AA. Detlnition and comparability of nosocomial infection rates. Am J Infect Contr 1982; 10: 49-52. 4. Magnussen MH, Robb SS. Nosocomial infections in a long-term care facility. Am J I&et Cootr 1980; 8: 12-17. 5. Scheckler WE, Peterson PJ. Infections and infection control among residents of eight rural Wisconsin nursing homes. Arch Intern Med 1986; 146: 1981-1985. 6. Farber BF, Bremtan C, Puntereri AI, Brody JP. A prospective study of nosocomial infections in a chronic care facility. J Am Cerbttr !Soe 1984; 32: 499-502. 7. Nicolle LE, McIntyre M, Zacharias H, MacDonell JA. Twelve-month surveillance of infections in institutionalixed elderly men. J Am GerIatr Boc 1984; 32: 513-519.
Copyright 0 1988Pcrgamon Pressplc
COMPARISON OF METHODS FOR CALCULATION AND DEPICTION OF INCIDENCE INFECTION RATES IN LONG-TERM CARE FACILITIES JEFFREY
M. SCHICKER, TIMOTHYR. FRANSON,‘* EDMUND H. DUTHIE JR? and SUSAN M. LECLAIR
Sections of ‘Infectious Disease and Geriatrics/Gerontology, Department of Medicine, Zablocki Veterans Administration Medical Center, Medical College of Wisconsin, Milwaukee, WI 53295, U.S.A. (Received in revised form 4 January 1988)
Ahatraet-Assessment of changes in endemic infections in health care facilities are often based on comparison of infection rates over time. This study compared two methods for calculating and depicting infection data at a hospital-based nursing home care unit. Prospective incidence surveillance of nursing home acquired infections was conducted over a 12-month period, during which time denominator information on census and patient care days was also collected. Monthly infection rates were calculated based on (a) census (number infections per month/average monthly census), and (b) care duration (number infections per month/average monthly resident stay days). Results showed average monthly infection incidence of 27.4 episodes, (range 19-37), with average monthly census of 166.2 (range 160.0-180.0) and average monthly patient days of 5056 (range 4631-5583). The average census based monthly infection rate was 16.5 (range 11.9-22.4); average care duration based monthly infection rate was 5.4 (range 3.8-7.2) episodes per 1000 patient care days. Results indicate care duration based rates demonstrate less marked rate fluctuation than census based fnndings, and that duration based rates are more similar to valuesobservedin acute ,
carehospitalnosocomialinfectionrates,thus are lesslikelyto be misunderstoodor misinterpreted
by &aft’.Institutionsshould consider analyzingtheir methods for depictinginfection data to provideconsistencyand clarity in data reporting. Nursing home acquired infection Geriatric infectious diseases
Infection
rates
Infection control in nursing homes
Infection surveillance data are usually depicted as infection rates, which are comPrevention and control of nursing-homeputations involving a numerator parameter of acquired infections are major goals of institunumber of infections at a given point in time tional risk management programs. Implicit in (prevalence), or of new infections over a preventive planning is the baseline knowledge of specified period of time (incidence). The denomfrequency and types of infections in a specific inator parameter used in these calculations has nursing home, which are detected through infec- classically been the number of admissions or tion surveillance projects. discharges in a facility for acute care hospital infection rates [I]. Using such parameters yields average hospital-acquired infection incidence rates 1of 5-lO%, which has been used as a *Current address Lilly Research Laboratories, Indianapolis, Indiana, U.S.A. comparative index by some programs. HowtAddress correspondence and reprint requests to: Edmund ever, due to low resident turnover in long-term H. Duthie Jr M.D., Geriatrics/Gerontology, Medical care settings, a denominator of admissions or Service/l llP, VA Medical Center, 5000 W. National Ave., Milwaukee, WI 53295, U.S.A. discharges yields rates much higher than those Supported by the National Institute on Aging Grant in acute hospitals, and is not typically used by Rol-AGO6403-02 and the National Institute on Aging Geriatric Medicine Academic Award 5-K07-AGOO178. long-term care settings. INTRODUCTION
757
158
JEFFREYM. SCHICKER et al.
Denominator parameters used for incidence rate calculations in long-term care facilities are not standardized, and alternatives used include end-of-month census, average monthly census, total bed complement and total resident care days. This variability in calculations makes rate comparisons from institution-to-institution difficult and often confuses nursing home staff who are used to gauging rates in comparison to published acute care hospital rates. Furthermore, depending on the denominator parameter chosen, rates may drastically fluctuate from month-to-month due to changes in census rather than major shifts in infection occurrence, lending to further confusion in rate interpretations. Therefore, universal use of a consistent reliable method for calculating infection rates in long-term care facilities would greatly facilitate comparisons and interpretations. This study describes a one year surveillance project of infections in a long-term care facility, in which rates of nursing-home acquired infections were depicted and compared using two calculation methods.
with the NHCU pharmacist, review of laboratory and radiological reports, review of 24 hr nursing reports, review of vital sign flow sheets for abnormal values, and review of nursing and medical progress notes in the resident chart. The nurse only recorded those nursing-home acquired infections that were validated in the’ chart by physician diagnosis, or supported by documentation of lab work, X-ray reports, or patient symptoms. At no time did the nurse epidemiologist individually assess the resident to determine the presence or absence of infection. All infected residents had further data documented as to demographic features, chronic illness factors, chronic drug and treatment therapy, infection features, isolation practices and outcome of infection and immunization data, as shown in Table 1. Infections were recorded in eight categoriesurinary tract infections (UTI), pneumonia, upper respiratory infection, wound and skin infections, gastrointestinal infection, bacteremia source unknown, and other (prostatitis, epididymitis, thrush, etc.). Questionable infection cases were discussed METHODS bi-weekly with the hospital epidemiologist as to Daily prospective infection surveillance was possible inclusion in the study. Monthly reports carried out by a trained geriatric nurse epi- were also discussed with the hospital epidemiologist over a one year period from 1 demiologist and the NHCU Director for review August 1985 through 31 July 1986 at a hospitaland validation. based Veterans Administration Medical Center Monthly resident census was determined by Nursing Home Care Unit (NHCU). The nurse obtaining the daily nursing home census. The epidemiologist recorded only those infections denominator consisted of the total of resident that occurred or were diagnosed in the nursing care days for the month (calculated as the sum home with onset at least 48 hr after admission. of care days for every resident present at least Residents admitted from an acute care hospital one day of the given month) and average or the community who were undergoing treat- monthly census of residents (calculated by divment for infections or were diagnosed with iding the sum of daily census by the number of infections within 48 hr of admission were not days in the given month). Infection rates were calculated on a monthly recorded in the numerator. Infections were debasis using the following formulas: tected by two main methods: (1) a documented “physician diagnosis” in the resident’s chart (1) census based me = number of infections that an infection was present, and (2) by criteria per month/average monthly census x 100; and adopted from CDC definitions for detection of (2) care duration based rate = number of innosocomial infections [2]. Resident charts were fections per month/average monthly resident reviewed for signs and symptoms of infection, care days x 1000. then compared to infection criteria. The study was observational only and no attempt was RESULTS made to influence physician or nurse documentation of infection events. During this one year survey, 330 nursingThe nurse epidemiologist collected infection home acquired infections were detected, during data through consultation with the nursing staff which time the average monthly census was and doctors concerning resident health status, 166.21 residents, with average monthly resident review of antibiotics prescribed via consultation care days of 5056. Results for each month are
Incidence Infection Rates
759
Table 1. Nursing care facility surveillance data base Patient survev factors A. Demographic features: Name, age, sex, race, date of admission to nursing care facility, residence prior to admission,
date of most recent acute care hospitalization, ward site, room status (l-bed, 2-bed, 3&d, Cbed, or ward). B. Chronic illness factors: Prior urinary tract infections, diabetes mellitus, hypertension, congestive heart failure, chronic pulmonary disease, ethanolism (by history), peripheral vascular disease, neurovascular disease, dementia, organic brain syndrome, immobility, nutritional status, (obesity-malnutrition), neoplastic disease (type and site), heart disease (type) and renal disease. C. Chronic drug therapy and treatments: Psychotropic agents, immunosuppressive therapy (corticosteroids, chemotherapy, antibiotics, radiation therapy, antacids, and sedatives). D. Chronic treatment factors: Intravascular devices, respiratory devices (tracheostomy), urinary devices (indwelling genitourinary catheter, intermittent catheterization, condom catheter, suprapubic catheter, recent instrumentation, genitourinary irrigation, nasogastric tube, surgical drains), antibiotic therapy preceding infection (agent, route, dosage, duration). E. Infection features : 1. Date of onset 2. Acquisition source: community, hospital, nursing home 3. Anatomic site: upper respiratory, lower respiratory, wound/skin, gastroinestinal,
eye, blood, urinary tract (with vs without catheter association) and other 4. Signs-symptoms: general (asymptomatic, chills, rigors, inflammation, pain, WBC > 10,000, tachypnea, tachycardia, confusion--acute, behavior change-acute, lethargy, falls, Md-Dx, fever > 100.4); respiratory (cough, sputum, coryxal symptoms, rales, rhonchi, positive chest X-ray); gastroinestinal (diarrhea); genitourinary (frequency, burning, hematuria, dysuria, pyuria, CVA tenderness, incontinence); wound-decub (drainage, ulcer, furuncle, bum); and eye (discharge, chemosis, conjunctivitis, pain) 5. Microbiologic information: culture date, specimen source, culture isolate, antibiotic sensitivities 6. Therapy: antibiotics administered, agent, dose, route F. Isolation practices: respiratory, blood, contact, strict, enteric, drainage/secretion, protective G. Outcome of infection: resolved, resolved-relapse, failure, failure retreatment, transfer for hospitalization (number of days, outcome, complications, mortality and morbidity) H. Immunizations: pneumonia, influenxae, diptheria, tetanus, allergies
shown in Table 2. Of 330 total infections, 118 (35.8%) incidents of UT! were detected-of which 31 cases were urinary-catheter associated; 100 cases (30.3%) of pneumonia and bronchitis; 40 incidents (12.1%) of wound/skin infection; 20 cases (6.1%) of upper respiratory infection; 15 cases (4.5%) of gastrointestinal infection; 12 cases (3.6%) of conjunctivitis, 2 cases (0.6%) of bacteremia, source unknown, and 25 cases (7.0%) listed as others occurred. The overall average monthly infection rate calculated via the average monthly census (27.4 of 166.2 x 100) was 16.5% and using infections per 1000 resident care days (27.4 of 5056 x 1000) was 5.4. -
Avera.~e
-
Resident cam days
1985
cenrur _
1986
Fig. 1. Monthly infection rate depiction using denominator parameters of resident care days (per 1000) vs average monthly resident census (%).
The average census based monthly infection rate fluctuated from 11.9 to 22.8%, and the average resident care day duration based monthly infection rate varied from 3.8 to 7.2. Figure 1 depicts variance and comparison of the two rates by month, and Table 3 shows variance of monthly census, monthly resident care days and actual number of infections over the survey period. DISCUSSION
This study summarizes a one year infection surveillance project with a comparison of two methods used to calculate infection rates in long-term care facilities. When examining rates using the monthly census denominator, the average monthly infection rate average was 16.5%, with a range from 11.9 to 22.8% Such rate findings give an impression of a high density of infections, with wide fluctuations suggesting sporadic problems, when viewed in the context of routine acute hospital rate expectations. However when using the same infection data with a denominator of resident care day duration, the average monthly infection rate was 5.4 with a range of 3.8-7.2. These figures appear to vary less drastically than census-based figures, and led to less confusion
Urinary tract infection Pneumonia/bronchitis Wound/skin infections Upper respiratory inf&ons Gastrointestinal infections Racmmmiasource unknown Conjunctivitis other Totals
10 7 6 0 0 0 0 2 25
21 10 3
0
1
0 0 0 35
0 2 1 19
0
0
4 9 3
Oct.
Dec. 11 12 4 0 0 0 1 3 31
Nov. 11 9 2 4 1 0 0 3 30
0 1 3 25
0
1
5 12 3
Jan.
0 1 3 28
2
2
6 10 4
Feb.
0 2 0 4 2;
2 1 1 3;
6 6 1
14 7 4 6
April
Mar.
3:
1 _ 1
2
-4
11 6 5
May
60,672 5056
1994.5 166.2
Total Monthly average
5583 4994 4%1 4865 4%8 5283 4631 5124 4803 5127 5023 5310
180.0 167.0 160.0 162.0 160.2 170.4 165.4 165.3 160.1 165.4 167.4 171.3
August September October November December Jamlaty February March April May June July
=dvS
CenSUS
Resident
Month
Average monthly
27.5
330
:: 28 37 20 33 ;:
19 30
::
Number of infections
5.4
6.3 5.0 3.8 6.2 6.2 4.7 6.0 7.2 4.2 6.4 4.2 4.9
19.0 15.0 11.9 18.5 19.3 14.7 16.9 22.8 12.5 19.9 12.5 15.2
16.5
Resident care days (per 1000)
2:
0 1
2
2
6 6 2
June
Monthly census (%)
Infection rates using denominator OE
Table 3. Monthly infection rate by monthly census and patient care days
Sept.
Aug.
Table 2. Jnfections by month and site
13 6 -3
3:
2 12
0 0 2:
15
3
20
118 100 40
July
1
Yearly total
0.6 3.6 7.0 100.0
4.5
6.1
35.8 30.3 12.1
Percent of total
Incidence Infection Rates
when presented to NHCU personnel in routine reporting sessions. In hospital-based nursing homes, where care personnel may have rotating duties between acute care and long-term units, the potential for misunderstanding infection control data would be minimized by use of rate depictions which are “familiar” to both settings. Individuals with prolonged stays are more likely to be exposed to events leading to infection than patients with shorter stays, and their consideration should be incorporated as a factor in rate calculations. Only the care day formula provides adjustment for such a variance and therefore allows a more accurate rate depiction. As shown in a recent report, use of a formula for calculating infections per thousand patientcare days in acute care hospitals corrects for long hospital stays, avoids falsely high infection rates, and allows better comparison of infection rates over time and across locations [3]. The variability in this literature can be seen among four recent reports which also prospectively described the incidence of infections in nursing homes. Magnussen over a 2-month period reported her rates of infection as a percent of patients at risk [4]. A rate of 18.2% was reported for the 2 month period for nosocomial infections. Using comparable methods we noted a 16.5% rate over a 12 month period. Scheckler in a 6 month study of rural Wisconsin nursing homes found an infection rate of 10.7 infections per 100 resident months [5]. Using an average of 30 days per month, this would correspond to a rate of 3.56 infections per 1000 resident care days compared to 5.4 infections per 1000 resident care days in the .current study. In another VA facility, Farber reported the incidence of infections over a one year period. In this nursing home, an infection rate of 0.67 infections per
761
100 patient care days was noted [6]. Finally, Nicolle found the infection rate in a Canadian veterans facility to be 192.7 infections per 100 patient years [q. Using 365 days per year, we would calculate an infection rate of 5.2 infections per 1000 patient care days, a figure similar to our results and those of the other authors. In summary, this study illustrates the utility of using a denominator of “thousand resident care ,days” in calculating infection rates and depicting results for the personnel in a hospitalbased nursing home care unit. Long-term care facilities should carefully reexamine their methods used in compiling and presenting infection data to enhance accuracy and clarity of those efforts, as well as to aid rate comparisons with similar facilities.
REFERENCES 1. Haley RN (Sonic Project, CDC, Atlanta, GA 30333) Culver DH. White JW er al. The nationwide nosocomial infection rate: A new need for vital statistics. Am J R@der&I 1985; 121: 159-167. 2. Center for Disease Control. Outline for surveillance and control of nosccomial infections. Appendix II. -forDete~Reaenceuld-~n of fnfectioll1976; pp. 19-31. 3. Madison R, A6fl AA. Detlnition and comparability of nosocomial infection rates. Am J Infect Contr 1982; 10: 49-52. 4. Magnussen MH, Robb SS. Nosocomial infections in a long-term care facility. Am J I&et Cootr 1980; 8: 12-17. 5. Scheckler WE, Peterson PJ. Infections and infection control among residents of eight rural Wisconsin nursing homes. Arch Intern Med 1986; 146: 1981-1985. 6. Farber BF, Bremtan C, Puntereri AI, Brody JP. A prospective study of nosocomial infections in a chronic care facility. J Am Cerbttr !Soe 1984; 32: 499-502. 7. Nicolle LE, McIntyre M, Zacharias H, MacDonell JA. Twelve-month surveillance of infections in institutionalixed elderly men. J Am GerIatr Boc 1984; 32: 513-519.