## Services on Demand

## Article

## Indicators

## Related links

## Share

## Revista de Saúde Pública

*Print version* ISSN 0034-8910

### Rev. Saúde Pública vol.37 n.6 São Paulo Dec. 2003

#### http://dx.doi.org/10.1590/S0034-89102003000600012

**ORIGINAL
ARTICLES**

**Use of statistical
process control charts in the epidemiological surveillance of nosocomial infections
**

**Aglai Arantes ^{I}; Eduardo
da Silva Carvalho^{II,} ^{*}; Eduardo Alexandrino
Servolo Medeiros^{II}; Calil Kairalla Farhat^{II}; Orlando César
Mantese^{I} **

^{I}Universidade Federal
de Uberlândia. Uberlândia, MG, Brazil

^{II}Escola Paulista de Medicina^{ }da Universidade Federal
de São Paulo. São Paulo, SP, Brasil

**ABSTRACT**

**OBJECTIVE:** To monitor occurrence
trends and identify clusters of nosocomial infection (NI) using statistical
process control (SPC) charts.

**METHODS: **Between January 1998 and
December 2000 nosocomial infection occurrence was evaluated in a cohort of 460
patients admitted to the Pediatric Intensive Care Unit of a university hospital,
according to the concepts and criteria proposed by the National Nosocomial Infection
Surveillance System of the Centers for Disease Control, in the United States.
Graphs were plotted using Poisson statistical distribution, including three
horizontal lines: center line (CL), upper warning limit (UWL) and upper control
limit (UCL). CL was the arithmetic mean NI rate calculated for the studied period;
UWL and UCL were drawn at 2 and 3 standard deviations above average NI rates,
respectively. Clusters were identified when NI rates remained above UCL.

**RESULTS: **Mean NI incidence was 20
per 1,000 patient days. One urinary tract infection cluster was identified in
July 2000, with an infection rate of 63 per 1,000 patient days, exceeding UCL
and characterizing a period of epidemic.

**CONCLUSIONS: **The use of SPC charts
for controlling endemic levels of NI, through both global and site-specific
evaluation, allowed for the identification of uncommon variations in NI rates,
such as outbreaks and epidemics, and for their distinction from the natural
variations observed in NI occurrence rates, without the need for calculations
and hypothesis testing.

**Keywords:** Epidemiological
surveillance. Cross infection. Infection control. Endemic diseases. Statistical
graphs. Incidence. Inpatients. Intensive care units, pediatric. Hospitals, university.

**INTRODUCTION**

Traditionally, three different categories of risk factors associated with nosocomial infection (NI) acquisition have been described: factors inherent to patient, to invasive procedures and to hospital environment. The study of these factors can guide the selection, implementation, and evaluation of control measures for this type of infection.

One of the goals of epidemiological
surveillance and of NI control programs is to establish the endemic rates of
this type of infection. Consequently, the continuous monitoring of endemic levels
can identify increases in baseline NI rates, which, in a small proportion of
cases, may be significant, constituting outbreaks or epidemics.^{8,9}

Variations in nosocomial infection
incidence rates are common. In order to demonstrate significant differences
in NI occurrence during different time periods, statistical models that include
tests for means, variance, and proportions, among others, are usually employed.^{
3,11,12} However, if frequent analyses of clustered data are to be carried
out, repetitive hypothesis testing is not practical, especially if there is
no evident outbreak. Thus, there is a need for statistical procedures capable
of simplifying acute variation detection and of continually evaluating event
occurrence trends.^{14}

Graphic statistic methodology consists
in building and statistically analyzing control diagrams in order to study variations
in collected data.^{1,2,14} Control diagrams are graphs based on the
Theory of Probabilities, which allow for comparisons between the observed incidence
of a given event and the maximum and minimum incidence expected. The underlying
principle of control diagrams as applied to nosocomial infections is that NI
rates present natural variations around the mean value, and that more distant
values have smaller probability of representing random events.

The observation of NI occurrence
and the evaluation of its variability show that NI incidence in a given time
period tends to follow a statistical probability of occurrence frequently resembling
normal distribution. These observations also reveal that significance tests
have the ability to determine whether or not chance is a likely explanation
for the difference in the values obtained.^{1,2,14}

There are many different types of
variables with different probabilities of distribution, such as measured values,
counts, fractions, and rates. For each of these situations there is an appropriate
graph model, usually referred to using letters *np*, *p*, *c*,
*u*, *X*, and *S*. Graph type selection depends on the statistical
distribution of the probabilities described by the studied variable: *X*
and *S *for data with normal distribution, *c *and* u* for Poisson
distribution, and *np *and* p* for binomial distribution.

Poisson distribution consists in
the probability of distribution of the number of occurrences of a random event
in a given interval of time or space. It frequently offers good statistical
models when the number of occurrences is small, such as bacteremia/central vascular
catheter day, or NI/patient day. Graphs *c *or *u* are the choice
for monitoring total number or rate of occurrences per time period, respectively.^{1,2,14}

The present study describes the construction and interpretation of a control diagram for the endemic level of NI/patient day rates, in an pediatric intensive care unit of a university hospital.

**METHODS**

A prospective cohort study was conducted in a pediatric intensive care unit (PICU) of a public general university hospital, between January 1998 and December 2000.

This hospital has 461 beds, of which 105 are reserved for children. The PICU is a general four-bed unit designed to care for children ages 29 days to 13 years, with a yearly admission rate of roughly 200 children.

Epidemiological surveillance of
nosocomial infections was carried out systematically. Diagnostic concepts and
criteria used for case identification were those proposed by the National Nosocomial
Infection Surveillance System (NNIS) of the Centers for Disease Control and
Prevention (CDC), in Atlanta in the United States,^{7,10} and by the
Brazilian Ministry of Health.^{13}

Data collection was carried out between January 1998 and December 2000. General NI rates per 1,000 patients and site-specific infections per 1,000 procedures day were determined on a monthly basis.

Graph *u*, based on a Poisson
probabilistic distribution, was selected for the monitoring of the rates of
NI per thousand patient days. Diagram construction stages included:^{ 14}

Stage 1

Mean NI incidence rate in the studied period (X) calculation, X= total n. Of infections/ total n. Of patient days.

Stage 2

Monthly mean patient days calculation.

Mean patient days = n. of patient days/ total n. of months.

Stage 3

Standard deviation of NI rates (s)

s = Ö X / n where: X = mean incidence rate, n = number of patient days for each month of the study

Since the number of patient days for each month varied more than 20% in relation to mean patient days, s was calculated for each month of the study.

Stage 4

Parallel line calculation

Central line representing the mean NI incidence rate (CL).

Upper warning limit (UWL) representing X + 2s

Upper control limit (UCL) representing + 3s

Lower control limit (LCL) representing a X - 3s. When the values obtained for LCL were negative (<0), they were limited by the x-axis (abscissa zero).

Stage 5

Monthly incidence calculation and plotting in graph.

Stage 6

Diagram interpretation. Statistical stability of NI rates was verified through the absence of the following criteria:

- one of the rates was above UCL or below LCL;
- two or three consecutive rates between 2s and 3s , on the same side of CL:
- nine consecutive rates on either side of CL;
- six consecutive rates with decrease or increase;
- fourteen consecutive rates alternating between above and below CL;
- fifteen consecutive rates below CL;

The presence of any of these parameters
indicated the need for investigating and reevaluating epidemiological surveillance,
since the NI rate distributions described in each of the criteria above have
little probability of occurring by chance alone.^{ 1,2,14}

Epidemic periods were defined when
the NI rate rose above UCL. The presence of outbreaks lead to the construction
of new diagrams, excluding such periods from the NI incidence means, standard
deviations, and control limits.^{1,2,14}

**RESULTS**

Between January 1998 and December 2000, 50 of the 460 patients admitted to the PICU had 60 episodes of nosocomial infection.

Monthly average was 83 patients. Mean NI incidence was 20.0 per thousand patient days, as seen in Table 1. Incidences of ventilator-associated pneumonia, central vascular catheter-associated bloodstream infection, and urinary catheter-associated urinary tract infection was 9.1 per thousand ventilator days, 7.0 per thousand central vascular catheter days, and 7.3 per thousand urinary catheter days, respectively. Invasive device utilization density was 0.66 for ventilator, 0.77 for central vascular catheter, and 0.51 for urinary catheter.

Figure 1 presents the control diagram demonstrating the endemic limits of NI incidence per thousand patient days.

UCL upper control limit (3s + X), UWL upper warning limit (2s + X), X central line (mean NI rate = 0.020). |

The NI rate in July 2000 was 63.0 per thousand patient days, exceeding UCL, and characterizing a period of epidemic. This lead to the construction of a second control diagram, in which the month of the outbreak was excluded from mean NI incidence calculation, as shown by Figure 2. Mean NI rate in this evaluation was 18.0/1,000 patient days. No new outbreaks or other deviations from the expected NI rate distribution around the mean value were identified.

UCL upper control limit (3 |

During the period of epidemic, seven cases of NI were identified; three of catheter-associated urinary tract infection, one of ventilator-associated pneumonia, one of suppurated middle-ear otitis, also ventilator-associated, one of tracheitis in patients under mechanical ventilation, and one of central vascular catheter-associated phlebitis. Graphical evaluation of endemic level site-specific occurrence identified urinary tract infection (UTI) as the source for the July 2000 NI outbreak.

Table 2 presents
an evaluation of UTI incidence in the 36 months of the study. Roughly one-half
of the patients required urinary catheters utilization density and mean catheterization
period for this device were 0.51 and 6.8 days, respectively. Eleven cases of
UTI were identified, all of which were associated to urinary catheterization.
*Candida* spp was the microorganism most commonly isolated, through urine
culture, occurring in nine patients, followed by *E. faecalis *identified
in one patient, and *K. pneumoniae *associated with* E. coli *in another.

Mean UTI incidence for the 36-month period was 7.3 episodes per thousand urinary catheter days. Between February and November 1999, UTI rates remained constantly below mean incidence. There was an increase in occurrences during the December 1999-March 2000 period, albeit not statistically significant. An UTI outbreak occurred in July 2000, when urinary catheter-associated UTI rate rose to 50.9 per thousand catheter days. Mean UTI incidence excluding the month of the epidemic was 5.5 episodes/1,000 urinary catheter days; no further outbreak periods were identified.

**DISCUSSION**

Variations in nosocomial infection incidence are common during epidemiological surveillance. There is a constant concern with outbreaks and deviations above and below the rates considered as normal, and the observation of incidence rates alone does not provide enough evidence for deciding whether or not they are within the normal values expected.

In order to statistically assess
NI indicators, the methodology proposed by NNIS constructs models based on the
distribution of events in medians and percentiles.^{4} However, the
absence of indicators for sets of Brazilian pediatric intensive care units prevents
the construction of a national distribution model according to these parameters.
Thus, NI rate monitoring is carried out through external comparisons, using
data from the annual NNIS System reports.

During the three years of the present
study, there was a high utilization density for invasive devices, above Percentile
(P) 90.0% when compared to those published by the NNIS System, probably reflecting
the gravity of the cases admitted to the PICUs.^{4} The analysis of
incidences of bloodstream infection per thousand central vascular catheter days,
pneumonias per thousand ventilator days, and urinary tract infection per thousand
urinary catheter days showed values constantly above P 50% and below P 90%,
therefore not indicating significant deviations in the occurrence of these infections.
Nevertheless, annual evaluation demonstrated that the urinary infection rate
for the year 2000 was above P 90%, suggesting the existence of control problems
for this infection.

A comparison of results obtained
in different studies must be approached cautiously, even when dealing with similar
methodologies. Differences in laboratory testing availability and utilization
for NI diagnosis purposes and in the intensity of infection surveillance and
accuracy of reports, as well as the lack of an index by which to adjust infections
to the severity of patients diseases, ought to be considered.^{5,6}

Among the parameters used for evaluating
alterations in NI occurrence trends, endemic level determination is a fairly
simple resource. According to some authors,^{1,2,14} the detection of
an uncommon statistical pattern for NI rate distribution around the mean value
suggests the need for a more in-depth epidemiological investigation.

In the present study, the NI occurrence rate for July 2000 exceeded the control limit, established at 3s above mean incidence, and, influenced by other factors in addition to those responsible for the endemic variations in these infection rates, probably constituted an outbreak.

The use of the 3s above mean as
an upper control limit has been questioned when SPC charts are applied to healthcare.
For this reason, more sensitive and less specific criteria, such as 2s control
limits, have been used. According to Sellick^{14} (1993), the adoption
of such a pattern results in a diminution of specificity and, consequently,
in a large number of outbreak investigations, based on false-positive warning
signs. Supplementary-test application (Stage 6 of Methods) increases control
chart sensitivity and slightly reduces specificity in the identification of
NI occurrence rate patterns, when compared to the application of a single criterion,
such as graph points above the control limits.

The construction of graphs that
exclude outbreak periods was based on the fact that such periods increase baseline
NI occurrence rates, consequently widening endemic control limits, and thus
obscuring other probable outbreaks and uncommon trends, as described by Sellick^{14}
(1993) and Benneyan^{1,2} (1993). In the present study, the exclusion
of July 2000 from NI rate calculation reduced mean incidence from 20.0 to 18.0
per thousand patient days. However, the determination of new control limits
did not identify any further periods of epidemic. NI rates were considered as
statistically stable, submitted only to variations explainable by chance. Nevertheless,
one must note that stable rates, and occurrence predictability, signify only
statistical stability, and do not imply the acceptance of the NI levels found,
nor the absence of a need for the implementation of measures aimed at reducing
the incidence of such infections.

The study of the epidemic period
identified an increase in urinary tract infection rates. The analysis of the
endemic levels of this infection revealed a statistically significant uncommon
occurrence pattern between February and November 1999, when no UTIs were detected.
No justification was found for such an occurrence. In the seven months prior
to the outbreak, there was an increase in the number of UTI occurrences; however,
none of them were statistically significant, even after UCL was set at 2s above
mean incidence. Epidemic period evaluation failed to identify any cause-effect
relationships responsible for the increase in UTIs, and the outbreak was controlled
spontaneously. According to CDC^{3 }data, the probability of identifying
such relationships is small, even when an excessive number of cases are confirmed.

Despite its simplicity, very few
NI control studies have used the graphical methodology employed in the present
study, probably due to the relative disadvantage inherent to SPC-chart use when
the number of occurrences is relatively small. In this case, assuring control
method and NI incidence rate stability requires monitoring for at least 25 consecutive
months.^{1,2,14}

In conclusion, the use of SPC charts for controlling endemic levels of NI, through both global and site-specific evaluation, allowed for the identification of uncommon variations in NI rates, such as outbreaks and epidemics, and for their distinction from the natural variations observed in NI occurrence rates, without the need for calculations or hypothesis testing.

**REFERENCES**

1. Benneyan JC. Statistical quality control methods in infection control and hospital epidemiology, part I: introduction and basic theory. *Infect Control Hosp Epidemiol* 1993;19:194-214. [ Links ]

2. Benneyan JC. Statistical quality control methods in infection control and hospital epidemiology, part II: Chart uses, statistical properties, and research isses. *Infect Control Hosp Epidemiol* 1993;19:265-83. [ Links ]

3. Center for Disease Control and Prevention. Guidelines for investigating clusters of health events. *MMWR* 1990;39:1-22. [ Links ]

4. Center for Disease Control and Prevention. Semiannual report. Aggregated data from the National Nosocomial Infection Surveillance (NNIS) System. Available from: URL: Http://www.cdc.gov/ncidod/hip/surveill/nnis.htm [2000] [ Links ]

5. Emori TG, Edwards JR, Culver DH, Sartor C, Stroud LA, Gaunt EE, Horan TC, Gaynes RP. Accuracy of reporting nosocomial infections in intensive-care-unit patient to the National Nosocomial Infections Surveillance System: a pilot study. *Infect Control Hosp Epidemiol* 1998;19:308-16. [ Links ]

6. Garner JS, Jarvis WR, Emori TG, Horan TC, Hughes JM. CDC definitions for nosocomial infections, 1988. *Am J Infect Control* 1988;16:128-40. [ Links ]

7. Gaynes RP, Culver DH, Emori TG, Horan TC, Benarjee SN, Edwards JR, et al. The National Nosocomial Infections Surveillance System: plans for the 1990s and beyond. *Am J Med* 1991;91(Suppl 3B):116-20. [ Links ]

8. Haley RW, Tenney JH, Lindsey JO, Garner JS, Bennett JV. How frequent are outbreaks of nosocomial infection in community hospital? *Infect Control Hosp Epidemiol* 1985;6:233-6. [ Links ]

9. Haley RW, Gaynes RP, Aber RC, Bennett JV. Surveillance of nosocomial infections. In Bennett JV, Brachmann PS. *Hospital Infections*. 3^{rd} ed. Boston: Litlle Brown; 1992 p. 79 -109. [ Links ]

10. Horan TC, Emori TG. Definitions of key terms used in the NNIS system. *Am J Infect Control* 1997;25:112-6. [ Links ]

11. Jacquez GM, Waller LA, Grimson R, Wartenberg D. The analysis of disase clusters, part I: state of the art. *Infect Control Hosp Epidemiol* 1996;17:310-27. [ Links ]

12. Jacquez GM, Grimson R, Waller LA, Watenberg D. The analisys of disease clusters, part II: introduction to techniques. *Infect Control Hosp Epidemiol* 1996;17:385-97. [ Links ]

13. Ministério da Saúde. Portaria n° 2616 de 12 de maio de 1998. *Diário Oficial da União*, Brasília (DF): 13 de maio; 1998. [ Links ]

14. Sellick JÁ. The use of statistical process control charts in hospital epidemiology. *Infect Control Hosp Epidemiol* 1993;14:649-56. [ Links ]

** ****Correspondence to**

Aglai Arantes

Av. Estrela do sul, 2699

38401-139 Uberlândia, MG, Brasil

E-mail: aglai@triang.com.br

Received on 17/1/2003. Approved on 11/6/2003.

This study was carried out at the
Pediatrics Department of the *Hospital de Clínicas* *Universidade Federal
de Uberlândia.
*This study is part of the masters
dissertation presented at the

*Universidade Federal de São Paulo, Escola Paulista de Medicina*, 2001.

Presented at the

*IX Congresso Latino Americano de Infectologia Pediátrica*, San Salvador, 2001.

*

*In memoriam*