Eur. J. Epidemiol. 0392-2990
Vol. 6, No. 1
EUROPEAN
JOURNAL
March 1990, p. 34-39
OF
EPIDEMIOLOGY
HOSPITAL STAY LENGTH AS AN EFFECT MODIFIER OF OTHER RISK FACTORS FOR NOSOCOMIAL INFECTION M. DELGADO-RODRiGUEZ1, A. BUENO-CAVANILLAS*, R. LOPEZ-GIGOSOS*, J. de DIOS LUNA-CAST1LLO**, J. GUILLI~N-SOLVAS*, O. MORENO-ABRIL*, B. RODR[GUEZ-TUlXIAS*, A. CUETO-ESPINAR* R. RODR[GUEZ-CONTRERAS* and R. GALVEZ-VARGAS* *Departments of Social and Preventive Medicine, and **Biostatisties University Hospital of Granada, Facultad de Medicina Avda, de Madrid 9, 18012-Granada, SPAIN. Key words: Hospital stay length - Nosocomial infection - Risk factors This paper addresses the problem of hospital stay length as a risk factor for nosocomial infection and as a modifier of the effect of other risk factors for hospital infection. Patients were selected form two cross-sectional studies done in two different seasons of 1986. Risk of infection rose fairly steadily as hospital stay lenght increased (correlation coefficient: 0,83, p 63 d.
62
44
0.7097
Stay length
RESULTS
Linear correlation coefficient between stay length and risk = 0.838, p < 0.01.
Two hundred eighty nosocomial infections were detected in the 786 patients of our study population. The infections ocurred in 212 patients, giving a multiplicity rate of 1.32 hospital infections per infected patient. The distribution of hospital infections by site is shown in Table 1. The most frequent infection site
The results of the crude analysis of risk factors are shown in Table 3. In Tables 4 to 6, the variables 35
Delgado-Rodriguez M. et al.
Eur. J. Epidemiol.
analyzed in Table 3 are stratified by the hospital stay length.
When the < ll-year group was taken as a reference, age was a risk factor: the greater the patient's age the higher the risk of infection. This relationship changed substantially when ORs were computed for different hospital stay lengths (Table 4); when the rising gradient of risk with age practically disappeared. Ages above 11 years were a risk factor in < 14-day and > 28-day stays. The summary parameters s'OR showed very similar increases in risk with increasing age. Despite the changes in OR values produced by length of stay, effect modification was not statistically significant in any age group. The odds ratio for operation, a risk factor in the crude analysis (Table 3), suffered important modifications when it was stratified by hospital stay length (Table 5). It became a significant risk factor in _< 14-day stays only. The s'OR was clearly lower than the OR obtained in the crude analysis. Effect modification of operation as a risk factor for nosocomial infection by stay length was significant.
TABLE 3 . - Risk factors for nosocomial infection crude analysis. Variable
Infected
Noninfected
OR
95% CL
Age: > 60 yr.
87
143
3.61
1.7-3.7
31-60 yr.
83
227
2.19
1.4-4.6
11-30 yr.
33
150
1.32
0.6-2.9
< 11 yr.
9
54
1"
152
297
2.40
59
277
1"
Operated vs. non-operated 1
1.7-3.4
Underlying disease 3
48
66
2.50
1.6-3.8
2
52
123
1.50
0.9-2.1
1
112
385
1'
TABLE 5 . - Odds ratio of operation for nosocomial infection in different hospital stay lengths ~.
*: Reference group 1: Information on operation was lost in an infected patient
Stay length
Operation
Infected
Noninfected
OR (95% CL)
< 14 d
Yes No
21
120
4.50 (1.9-10.9)
7
180
1"
Yes
32
121
No
15
60
Yes
99
56
1.77 (1.0-3.1)
No
37
37
1"
OR: odds ratio; 95% CL: OR 95% confidence interval For codes see text
TABLE 4 . - Odds ratios of age for nosocomial infection in different hospital lengths.
15-28 d
Stay length
> 28 d
< 2 wk.
2-4 wk.
> 4wk.
Age
Infected
Noninfected
OR (95% CL)
> 60 yr.
6
53
10.33 (0.6-188)
31-60 yr.
14
97
12.64 (0.7-216)
11-30 yr.
8
108
6.66 (0.4-118)
< 11 yr.
0
42
1"
> 60 yr.
13
54
0.84 (0.2-4.5)
31-60 yr.
21
89
0.83 (0.2-4.3)
11-30 yr.
11
31
1.24 (0.2-6.9)
< 11 yr.
2
7
1: Reference group *: Information on operation was lost in an infected patient s'OR = 1.91 95% CL 1.3-2.9 X2h = 6.41, 1 df, p < 0.025
Underlying disease ceased to be a risk factor (Table 3) in stratified analysis, as the summary parameter s'OR shows (Table 6), its values being close to one. On the basis of these results, the abovementioned variables, as well as change to another ward during hospital stay, preoperative stay, sex, and cause of admission, were analyzed by stepwise logistic regression (Table 7). All the variables formerly described were included in the model. Change to another ward and preoprative stay were dropped from the model in subsequent analyses since they showed a strong relationship with hospital stay length. Given the fact that the bivariate analysis revealed an effect modification of the risk factors by stay length, two more logistic regressions were undertaken: one for
1"
> 60 yr.
68
36
1.35 (0.4-4.6)
31-60 yr.
48
41
0.84 (0.2-2.8)
11-30 yr.
14
11
0.91 (0.2-3.7)
< 11 yr.
7
5
1"
*: Reference group s'OR: for > 60 yr. 1.71 95% CL 31-60 yr. 1.46 95% CL 11-60 yr. 1.28 950 CL X2h was non-significant for each
1.06 (0.5-2.1) 1"
0.5-5.5 0.4-5.9 0.4-3.9 age
36
Vol. 6, 1990
Stay length and hospital infection
variables failed to improve the model. The percentage of correct classifications (that is, the percentage of true infected plus true non-infected patients calculated after applying the logistic model) was 91%. The other logistic analysis selected age as the only factor in the model. In this case, however, the percentage of correct classifications was low, 56,4%.
TABLE 6. - Odds ratio of underlying disease (UD) for nosocomial infection in different hospital stay lengths. Stay length
UD
Infected
Noninfected
OR (95% CL)
28d
DISCUSSION Problems related to exposure time have generated numerous publications on theoretical epidemiology. In the field of nosocomial infection, however, the situation has been somewhat different. Most authors, when presenting their infection rates, fail to mention the stay lengths in their hospitals, nor are their rates adjusted by stay length (e.g. incidence density). This makes comparison of rates from different hospitals or services difficult. In the present study, we have tried to address one aspect of hospital infection epidemiology related to hospital stay lenght, e.g. exposure time before the development a nosocomial infection. It could be argued that a selection bias in our sample may affect the validity of the results (12). Selecting the patients by means of a cross-sectional survey means that patients with longer hospital stays have a higher probability of being included in the study. Since nosocomial infection prolongs hospital stay, a selection bias would produce a toward-the-null bias in the estimation of OR (e.g. lowering OR if the true value is >1). However, prevalence, or crosssectional survey, as a method for the study o f nosocomial infection has proven to be an useful tool (4, 13, 17). The present analysis stratified infection figures by stay length, a procedure which obviates the negative influence of selection bias since the recruiting method affects the proportions of different stay lengths in the sample but not the representativity of the sub-samples of different stay lengths. Selecting the patients by two cross-sectional surveys done in different seasons decreases the influence of seasonal diseases, such as chronicobstructive lung disease, which are related, in some cases, to a higher risk of nosocomial infection. It is thus possible to reduce susceptibility bias in this type of survey (10). The prevalence figure of nosocomial infection observed in this study seems quite high (280/786 = 35,6%). This is due to two facts: first, to a selection bias or Neyman bias (hospital infection prolongs hospital stay, and selecting patients on a crosssectional basis means we have a higher probability of selecting infected patients); second, each patient was studied for the entire hospital stay and not for just one day. Distribution by infection sites is somewhat different from that found in other studies (3, 7, 11, 15, 16, 19). Whereas most authors cite urinary tract infections as the most frequent, it ranked third in our study. The most frequent infection site among our patients was surgical wouds, which is ranked third by
1"
*: Reference group s'OR = for level 3 = 0.99 95% CL = 0.6-1.7 for level 2 = 0.92 95% CL = 0.6-1.5 x2h was non-significant for every level of UD
TABLE
7. -
Results of the logistic regression analysis.
Term in the model
Coefficient
SE
Texp
P
Constant Moving
-0.849 0.321
0.156 0.096
-5.44 3.36
< 0.001 < 0.001
0.034
0.011
3.10
< 0.001
0.276 0.235
0.089 0.103
3.11 2.29
< 0.001 < 0.05
0.229
0.136
1.69
< 0.10
Preoperative stay Age Operation Underlying disease
stays shorter than 15 days, and another for stays longer than or equal to 15 days. The results are presented in Table 8. For stays shorter than 15 days, only one variable - operation - was included. The remaining
TABLE 8 . - Results of the logistic regression analysis per different stay lengths. Stay length
Term in the model
1. < 15 days
Costant Operation
-2.52 0.774
0.222 0.222
2. > 15 days
Costant Age
-2.89 0.209
0.100 0.100
Coefficient SE
texp
P
-11.3 3.5
< 0.001 < 0.001
-2.90 < 0.01 2.09 < 0.05 37
Delgado-Rodriguez M. et aL
Eur. J. Epidemiol.
significant association with hospital stay length; given that the latter is associated with nosocomial infection, this may imply that hospital stay length is an intermediate variable, especially for long stays, which biases the assessment or other risk factors. In summary, our results show that hospital stay length may be considered, at least, an effect modifier of some risk factors for nosocomial infection. It would therefore seem advisable to analyse the role of risk factors for nosocomial infection using frequency measures which adjust for the influence of hospital stay length, i.e. using rates (incidence density) instead of probability measures (cumulative incidence).
the above-mentioned authors. This discrepancy may be explained by the excess of surgical patients in our sample and/or by the low frequency of urine cultures in surgical patients. This latter aspect reflects our surgeons concern over urinary tract infection. The percentage frequencies of infection in the remaining sites did nor differ substantially from those found in the other studies mentioned. As supported by the data in Table 2, hospital stay length is positively associated with hospital infection. Since risk increases with stay length, the effect of different hospital stay lengths on the relationship between several risk factors and nosocomial infection was also analyzed. The variables selected were identified as risk factors in crude analysis, and with the exception of age, have been previously recognized in the literature (5, 19). Tables 4 to 6 clearly suggest that hospital stay length modifies the effect of all the risk factors considered, although this influence was not statistically significant except in the case of operation. Age, operation, and underlying disease were clear risk factors in
Vol. 6, No. 1
EUROPEAN
JOURNAL
March 1990, p. 34-39
OF
EPIDEMIOLOGY
HOSPITAL STAY LENGTH AS AN EFFECT MODIFIER OF OTHER RISK FACTORS FOR NOSOCOMIAL INFECTION M. DELGADO-RODRiGUEZ1, A. BUENO-CAVANILLAS*, R. LOPEZ-GIGOSOS*, J. de DIOS LUNA-CAST1LLO**, J. GUILLI~N-SOLVAS*, O. MORENO-ABRIL*, B. RODR[GUEZ-TUlXIAS*, A. CUETO-ESPINAR* R. RODR[GUEZ-CONTRERAS* and R. GALVEZ-VARGAS* *Departments of Social and Preventive Medicine, and **Biostatisties University Hospital of Granada, Facultad de Medicina Avda, de Madrid 9, 18012-Granada, SPAIN. Key words: Hospital stay length - Nosocomial infection - Risk factors This paper addresses the problem of hospital stay length as a risk factor for nosocomial infection and as a modifier of the effect of other risk factors for hospital infection. Patients were selected form two cross-sectional studies done in two different seasons of 1986. Risk of infection rose fairly steadily as hospital stay lenght increased (correlation coefficient: 0,83, p 63 d.
62
44
0.7097
Stay length
RESULTS
Linear correlation coefficient between stay length and risk = 0.838, p < 0.01.
Two hundred eighty nosocomial infections were detected in the 786 patients of our study population. The infections ocurred in 212 patients, giving a multiplicity rate of 1.32 hospital infections per infected patient. The distribution of hospital infections by site is shown in Table 1. The most frequent infection site
The results of the crude analysis of risk factors are shown in Table 3. In Tables 4 to 6, the variables 35
Delgado-Rodriguez M. et al.
Eur. J. Epidemiol.
analyzed in Table 3 are stratified by the hospital stay length.
When the < ll-year group was taken as a reference, age was a risk factor: the greater the patient's age the higher the risk of infection. This relationship changed substantially when ORs were computed for different hospital stay lengths (Table 4); when the rising gradient of risk with age practically disappeared. Ages above 11 years were a risk factor in < 14-day and > 28-day stays. The summary parameters s'OR showed very similar increases in risk with increasing age. Despite the changes in OR values produced by length of stay, effect modification was not statistically significant in any age group. The odds ratio for operation, a risk factor in the crude analysis (Table 3), suffered important modifications when it was stratified by hospital stay length (Table 5). It became a significant risk factor in _< 14-day stays only. The s'OR was clearly lower than the OR obtained in the crude analysis. Effect modification of operation as a risk factor for nosocomial infection by stay length was significant.
TABLE 3 . - Risk factors for nosocomial infection crude analysis. Variable
Infected
Noninfected
OR
95% CL
Age: > 60 yr.
87
143
3.61
1.7-3.7
31-60 yr.
83
227
2.19
1.4-4.6
11-30 yr.
33
150
1.32
0.6-2.9
< 11 yr.
9
54
1"
152
297
2.40
59
277
1"
Operated vs. non-operated 1
1.7-3.4
Underlying disease 3
48
66
2.50
1.6-3.8
2
52
123
1.50
0.9-2.1
1
112
385
1'
TABLE 5 . - Odds ratio of operation for nosocomial infection in different hospital stay lengths ~.
*: Reference group 1: Information on operation was lost in an infected patient
Stay length
Operation
Infected
Noninfected
OR (95% CL)
< 14 d
Yes No
21
120
4.50 (1.9-10.9)
7
180
1"
Yes
32
121
No
15
60
Yes
99
56
1.77 (1.0-3.1)
No
37
37
1"
OR: odds ratio; 95% CL: OR 95% confidence interval For codes see text
TABLE 4 . - Odds ratios of age for nosocomial infection in different hospital lengths.
15-28 d
Stay length
> 28 d
< 2 wk.
2-4 wk.
> 4wk.
Age
Infected
Noninfected
OR (95% CL)
> 60 yr.
6
53
10.33 (0.6-188)
31-60 yr.
14
97
12.64 (0.7-216)
11-30 yr.
8
108
6.66 (0.4-118)
< 11 yr.
0
42
1"
> 60 yr.
13
54
0.84 (0.2-4.5)
31-60 yr.
21
89
0.83 (0.2-4.3)
11-30 yr.
11
31
1.24 (0.2-6.9)
< 11 yr.
2
7
1: Reference group *: Information on operation was lost in an infected patient s'OR = 1.91 95% CL 1.3-2.9 X2h = 6.41, 1 df, p < 0.025
Underlying disease ceased to be a risk factor (Table 3) in stratified analysis, as the summary parameter s'OR shows (Table 6), its values being close to one. On the basis of these results, the abovementioned variables, as well as change to another ward during hospital stay, preoperative stay, sex, and cause of admission, were analyzed by stepwise logistic regression (Table 7). All the variables formerly described were included in the model. Change to another ward and preoprative stay were dropped from the model in subsequent analyses since they showed a strong relationship with hospital stay length. Given the fact that the bivariate analysis revealed an effect modification of the risk factors by stay length, two more logistic regressions were undertaken: one for
1"
> 60 yr.
68
36
1.35 (0.4-4.6)
31-60 yr.
48
41
0.84 (0.2-2.8)
11-30 yr.
14
11
0.91 (0.2-3.7)
< 11 yr.
7
5
1"
*: Reference group s'OR: for > 60 yr. 1.71 95% CL 31-60 yr. 1.46 95% CL 11-60 yr. 1.28 950 CL X2h was non-significant for each
1.06 (0.5-2.1) 1"
0.5-5.5 0.4-5.9 0.4-3.9 age
36
Vol. 6, 1990
Stay length and hospital infection
variables failed to improve the model. The percentage of correct classifications (that is, the percentage of true infected plus true non-infected patients calculated after applying the logistic model) was 91%. The other logistic analysis selected age as the only factor in the model. In this case, however, the percentage of correct classifications was low, 56,4%.
TABLE 6. - Odds ratio of underlying disease (UD) for nosocomial infection in different hospital stay lengths. Stay length
UD
Infected
Noninfected
OR (95% CL)
28d
DISCUSSION Problems related to exposure time have generated numerous publications on theoretical epidemiology. In the field of nosocomial infection, however, the situation has been somewhat different. Most authors, when presenting their infection rates, fail to mention the stay lengths in their hospitals, nor are their rates adjusted by stay length (e.g. incidence density). This makes comparison of rates from different hospitals or services difficult. In the present study, we have tried to address one aspect of hospital infection epidemiology related to hospital stay lenght, e.g. exposure time before the development a nosocomial infection. It could be argued that a selection bias in our sample may affect the validity of the results (12). Selecting the patients by means of a cross-sectional survey means that patients with longer hospital stays have a higher probability of being included in the study. Since nosocomial infection prolongs hospital stay, a selection bias would produce a toward-the-null bias in the estimation of OR (e.g. lowering OR if the true value is >1). However, prevalence, or crosssectional survey, as a method for the study o f nosocomial infection has proven to be an useful tool (4, 13, 17). The present analysis stratified infection figures by stay length, a procedure which obviates the negative influence of selection bias since the recruiting method affects the proportions of different stay lengths in the sample but not the representativity of the sub-samples of different stay lengths. Selecting the patients by two cross-sectional surveys done in different seasons decreases the influence of seasonal diseases, such as chronicobstructive lung disease, which are related, in some cases, to a higher risk of nosocomial infection. It is thus possible to reduce susceptibility bias in this type of survey (10). The prevalence figure of nosocomial infection observed in this study seems quite high (280/786 = 35,6%). This is due to two facts: first, to a selection bias or Neyman bias (hospital infection prolongs hospital stay, and selecting patients on a crosssectional basis means we have a higher probability of selecting infected patients); second, each patient was studied for the entire hospital stay and not for just one day. Distribution by infection sites is somewhat different from that found in other studies (3, 7, 11, 15, 16, 19). Whereas most authors cite urinary tract infections as the most frequent, it ranked third in our study. The most frequent infection site among our patients was surgical wouds, which is ranked third by
1"
*: Reference group s'OR = for level 3 = 0.99 95% CL = 0.6-1.7 for level 2 = 0.92 95% CL = 0.6-1.5 x2h was non-significant for every level of UD
TABLE
7. -
Results of the logistic regression analysis.
Term in the model
Coefficient
SE
Texp
P
Constant Moving
-0.849 0.321
0.156 0.096
-5.44 3.36
< 0.001 < 0.001
0.034
0.011
3.10
< 0.001
0.276 0.235
0.089 0.103
3.11 2.29
< 0.001 < 0.05
0.229
0.136
1.69
< 0.10
Preoperative stay Age Operation Underlying disease
stays shorter than 15 days, and another for stays longer than or equal to 15 days. The results are presented in Table 8. For stays shorter than 15 days, only one variable - operation - was included. The remaining
TABLE 8 . - Results of the logistic regression analysis per different stay lengths. Stay length
Term in the model
1. < 15 days
Costant Operation
-2.52 0.774
0.222 0.222
2. > 15 days
Costant Age
-2.89 0.209
0.100 0.100
Coefficient SE
texp
P
-11.3 3.5
< 0.001 < 0.001
-2.90 < 0.01 2.09 < 0.05 37
Delgado-Rodriguez M. et aL
Eur. J. Epidemiol.
significant association with hospital stay length; given that the latter is associated with nosocomial infection, this may imply that hospital stay length is an intermediate variable, especially for long stays, which biases the assessment or other risk factors. In summary, our results show that hospital stay length may be considered, at least, an effect modifier of some risk factors for nosocomial infection. It would therefore seem advisable to analyse the role of risk factors for nosocomial infection using frequency measures which adjust for the influence of hospital stay length, i.e. using rates (incidence density) instead of probability measures (cumulative incidence).
the above-mentioned authors. This discrepancy may be explained by the excess of surgical patients in our sample and/or by the low frequency of urine cultures in surgical patients. This latter aspect reflects our surgeons concern over urinary tract infection. The percentage frequencies of infection in the remaining sites did nor differ substantially from those found in the other studies mentioned. As supported by the data in Table 2, hospital stay length is positively associated with hospital infection. Since risk increases with stay length, the effect of different hospital stay lengths on the relationship between several risk factors and nosocomial infection was also analyzed. The variables selected were identified as risk factors in crude analysis, and with the exception of age, have been previously recognized in the literature (5, 19). Tables 4 to 6 clearly suggest that hospital stay length modifies the effect of all the risk factors considered, although this influence was not statistically significant except in the case of operation. Age, operation, and underlying disease were clear risk factors in
