Nurses/' Contacts And Potential For Infectious Disease Transmission
DOI: 10.3201/eid1509.081475
Suggested citation for this article: Bernard H, Fischer R, Mikolajczyk RT, Kretzschmar M,
Wildner M. Nurses’ contacts and potential for infectious disease transmission. Emerg Infect Dis.
2009 Sep; [Epub ahead of print]
Nurses’ Contacts and Potential for
Infectious Disease Transmission
Helen Bernard, Richela Fischer, Rafael T. Mikolajczyk,
Mirjam Kretzschmar, and Manfred Wildner
Author affiliations: Robert Koch Institute, Berlin, Germany (H. Bernard); Bavarian Health and Food Safety Authority,
Oberschleissheim, Germany (H. Bernard, R. Fischer, M. Wildner); University of Bielefeld, Bielefeld, Germany (R.T.
Mikolajczyk); National Institute for Public Health and the Environment, Bilthoven, the Netherlands (M. Kretzschmar);
and University Medical Center Utrecht, Utrecht, the Netherlands (M. Kretzschmar).
Nurses’ contacts with potentially infectious persons probably place them at higher risk than the general
population for infectious diseases. During an influenza pandemic, illness among nurses might result in
staff shortage. We aimed to show the value of individual data from the healthcare sector for mathematical
modeling of infectious disease transmission. Using a paper diary approach, we compared nurses’ daily
contacts (2-way conversation with >2 words or skin-to-skin contact) with those of matched controls from a
representative population survey. Nurses (n = 129) reported a median of 40 contacts (85% work related),
and controls (n = 129) reported 12 contacts (33% work related). For nurses, 51% of work-related contacts
were with patients (74% involving skin-to-skin contact, and 63% lasted <15 minutes); 40% were with staff
members (29% and 36%, respectively). Our data, used with simulation models, can help predict staff
availability and provide information for pandemic preparedness planning.
During past influenza epidemics, hospital staff have been confronted with a surge of
inpatients (1–5), and modeling studies predict collapse of the healthcare system if resources are
not allocated carefully (6,7). To ensure the availability of healthcare during a pandemic,
maintaining qualified staff capacity is crucial. Some pandemic preparedness plans therefore
prioritize healthcare workers (HCWs) for preventive interventions, such as prophylaxis with
antiviral drugs or vaccination (8–10). In addition to their indispensability, HCWs most likely are
Page 1 of 15
at higher risk than the general public for influenza because of their close interaction with infected
patients and, presumably, more overall contacts.
Some simulation models use data on social contacts to account for disease spread (11–
15). Models also have been used to assess the effectiveness of vaccinating HCWs against
influenza in nursing homes (16). Several recently published studies reported contact rates for
different population subgroups (17–22) and even representative population samples from
different countries (23–25), but HCWs’ contacts were not explicitly assessed. This lack of
information curtails planning for pandemic preparedness. The current approach to identifying
critical threshold parameters for a pandemic is to model disease spread in the population.
However, the focus on the general population and an uncritical generalization of model
parameters could potentially bias the assessment of disease spread and of available staff capacity
within the healthcare sector. We aimed, therefore, to compare social contact data from nurses
with data from matched controls in the general population.
Methods
We conducted a prospective contact survey of nurses in charge of inpatient care for 5
hospitals in the German federal state of Bavaria by using a paper diary approach. We compared
the data with data from a matched sample of the German general population obtained in a
previously conducted survey of contacts (25). In both surveys, a contact was defined as a 2-way
conversation of >2 words (not by telephone) or skin-to-skin contact (17,18) as a surrogate for
exposure to disease. Actual rates of disease transmission were not measured.
Study Population
Nurse Sample
Hospitals in Germany are classified by level of care as basic, intermediate, and
maximum. We selected 5 hospitals and 5 substitute hospitals representing the distribution of
available hospital beds in different Bavarian regions and across the levels of care (26). If the
head of a hospital refused consent for participation, we approached the head of a substitute
hospital.
Page 2 of 15
We included only nurses from the departments of internal medicine or surgery because
these departments provide the majority of hospital beds in Germany. We assumed that during an
influenza pandemic, most hospitalized persons would be admitted to these departments.
To equally represent work shifts (morning, afternoon, and night) and days of the week,
we assigned combinations of work shifts and days for data collection to each hospital and
department. Before the survey began, we visited the study hospitals and briefed the nurses on the
study aims and methods. We asked the heads of the nursing departments to randomly select 1 of
the nurses on duty during each assigned work shift on the assigned day. If the selected nurse
refused to participate, another nurse from the same shift was randomly chosen. All nurses
provided written informed consent before participating in the study.
We assumed a dropout rate of 20% of initial study participants and accounted for that
proportion of declining participants. The calculated sample size for a normally distributed
variable with a type I error of 5%, a type II error of 20%, and a difference of 5 in the mean
number of contacts between nurses and an equally sized control group (SD = 15 in both groups)
was adapted for nonparametric testing by a 15% increase (27). Hence, the estimated sample size
for our study was 160 participants.
Matched Controls
We matched 1 control to each nurse by age (±3 years), sex, and day of data collection
(Monday–Friday vs. the weekend [i.e., Saturday–Sunday]). Controls, who were not necessarily
from Bavaria, originated from a contact survey of the German population conducted as part of
the Improving Public Health Policy in Europe through the Modeling and Economic Evaluation
of Interventions for the Control of Infectious Diseases (POLYMOD) project, which is described
elsewhere (25). In brief, survey participants were recruited by an independent market research
company. A representative household sample was selected by using a random-walk technique. In
each household, the person with the birthday nearest the date was interviewed. After a face-to-
face interview (multitheme survey), respondents filled out a contact diary during the following
day. The diaries were collected in person. No incentives were given. We restricted the sample
used for comparison with nurses to the summer round of the survey (May–July 2006) to
correspond with the period of the survey of nurses. This subsample comprised 340 participants.
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Data Collection
We used a modified version of both the questionnaire and the contact diary designed for
the POLYMOD contact survey (25). We collected sociodemographic information about each
study participant. Participants were asked to complete the diary during a 24-hour period starting
at 5 AM on the assigned day. The diary was organized as a table in which participants recorded
the following features of their contacts during work and leisure time: age (or age range) and sex
of the contact person; location where the contact occurred (multiple locations possible);
indication of whether physical contact was involved; and length of time the contact lasted. For
each contact person, 1 row of the table had to be completed. If a participant had repeated contact
with 1 person during the 24 hours of data collection, the characteristics of the contacts with this
person were summarized. Additionally, we asked nurses to specify the contact person at work
(patient, hospital staff, or other [e.g., visitor]) and, for contacts during travel, means of
transportation (public or private).
If controls estimated their total number of contact persons at work to be >10, they were
asked to report them aggregated, without specifying contact duration and other characteristics. If
controls completed the diary on Monday–Friday, the aggregated contacts were added to those
reported in the diary. Therefore, duration and other characteristics of controls’ work contacts
were not always available for analysis.
Data Analysis
We compared median number of contacts by using the Wilcoxon rank sum test or the
Kruskal-Wallis 1-way analysis of variance. To compare numbers of contacts for nurses and
controls, we used the Wilcoxon signed rank test for matched pairs. These analyses were
completed with Stata 10.0 (StataCorp, College Station, TX, USA). In addition, to compare age
mixing matrices, we grouped nurses, controls, and the persons they contacted by age and
calculated mean number of contacts for each age group.
Results
Nurses
We selected 2 hospitals providing basic care, 1 providing intermediate care, and 2
providing maximum care. We replaced 1 of the selected hospitals providing maximum care
Page 4 of 15
because the head of the hospital did not consent to study participation. A total of 131 (82%
response) nurses completed the diary during April–July 2007.
Nurses reported a median of 40 contacts (range 12–80) during 24 hours (Table 1). Total
numbers of contacts did not differ by nurses’ sex or by hospital department (surgery or internal
medicine) or level of care. However, nurses had more contacts during Monday–Friday than
during the weekend. A median of 34 contacts (range 3–66) were work related.
Nurses reported a total of 5,161 contacts. Most took place at work, more than half lasted
<15 minutes, more than half involved skin-to-skin contact, and more than one third were with
persons >60 years of age (Table 2). Ages of contact persons differed for work compared with
other places. At work, most contacts were with persons >60 years of age; a small percentage was
with persons <20 years. At other places, most contacts were with persons 20–39 years of age,
and the proportion with persons <20 years was higher than the proportion at work.
Most work-related contacts were with patients (51%) or other staff members (40%); 9%
were with other persons. Of those with patients, 74% involved skin-to-skin contact, and 63%
lasted <15 minutes. Of contacts with staff members, 67% were nonphysical, and 62% lasted >15
minutes. Most contacts with other persons were nonphysical (79%) and lasted <15 minutes
(87%) (Table 3).
Matched Comparison
We matched controls to 129 (98%) nurses; 2 nurses could not be matched because day of
data collection was unknown. Twelve percent of controls were housewives or were unemployed
or retired.
Matched nurses reported more total contacts than did controls (5,071 vs. 2,741; median
40 vs. 12; p<0.0001) and more contacts at work (4,288 vs. 1,996; median 34 vs. 4; p<0.0001)
(Figure). In other locations, numbers of contacts were similar (783 vs. 745; both medians 5, p =
0.73). In both samples, more contacts occurred during Monday–Friday than during the weekend.
Regardless of day of data collection (Monday–Friday or weekend), nurses had more contacts
than did controls (Monday–Friday: total contacts median 41.5 vs. 21, p<0.0001; work median
35.5 vs. 16, p<0.0001; other, both medians 5, p = 0.79; on weekends: total contacts median 32
vs. 6, p<0.0001; work median 27 vs. 0, p<0.0001; other median 5 vs. 4, p = 0.85).
Page 5 of 15
We calculated mean number of individually reported contacts by nurses’ ages or controls’
ages and by contact persons’ ages (excluding those at work reported by controls only in
aggregated manner) (Table 4). Whereas controls tended to interact with persons from their own
age group, nurses interacted with persons from a wider variety of age groups, primarily because
of the inclusion of older age groups among patients.
Discussion
Our findings correspond with the nature of contact between HCWs and patients. Nurses
have a high frequency of close contact with patients, but the time they can dedicate to each
patient is limited. This pattern differed fundamentally from that of their work contacts with staff
members or other persons, the characteristics of which agreed with those described by Mossong
et al. (25), who found that contact intensity (i.e., whether physical contact was involved)
correlated positively with contact duration in a general population sample.
Contact data are important for modeling the spread of infectious diseases. Our results
show that the use of general population data might lead to inaccurate modeling results for the
healthcare sector because HCWs’ patterns of contact with patients differ fundamentally from the
day-to-day contacts of the general population. However, although contact data form the basis of
simulation models, other parameters may modify the influence of contact patterns. Our data
might change current modeling predictions with respect to 1) spread of disease within the nurse
population that results in staff shortage and 2) spread of disease from nurses to the general
population.
Our findings suggest that the risk for infectious diseases by airborne transmission might
be greater for nurses than for the general population because of nurses’ more frequent and more
intensive professional contacts with potentially infected patients. In addition, nurses’ high
proportion of work-related skin-to-skin contact highlights the potential for fecal–oral
transmission of disease (e.g., norovirus infection).
Whether increased risk for infection in nurses during an influenza pandemic would lead
to an earlier peak in the number of infections in HCWs than in the general population needs to be
investigated in modeling studies. During a pandemic, maintaining sufficient staff during peak
hospitalizations of the general population will be crucial. Our data may help guide public health
Page 6 of 15
interventions to prevent the infections in hospital staff and in the general population from
peaking simultaneously.
Nurses most likely would take preventive measures, such as wearing personal protective
equipment, when handling patients with clinically manifested influenza. However, their risk for
infection from patients hospitalized for other reasons and already infected with influenza, but not
yet clinically ill or with influenza diagnosis, might be more important for initial spread of disease
in the healthcare sector. We collected our data during the interpandemic period, so we cannot
predict how HCWs’ contact behavior might change during a pandemic. Models using our data
will need to account for this uncertainty.
Nurses can potentially spread infection from infected patients to the general population.
In Germany, nurses represent 0.5% of the population (28). After infection is introduced into the
nurse population, further contact with patients, other staff members, and relatives might result in
faster spread to the general population during the early phase of a pandemic. The combination of
contact with persons from older age groups at work and from younger age groups at home might
facilitate spread among different age groups that would not otherwise occur.
These points support the need to add a separate healthcare component to models, i.e.,
modeling both HCWs and general population by using self-reported contact data instead of
assuming similar contact rates for nurses and the general population. Additional data are required
for modeling studies to determine the extent to which nurses and patients would respectively
benefit from vaccination of HCWs against influenza.
Our contact definition captures only the amount of social contact with other persons.
Because we did not measure rates of transmission or disease, use of our data for modeling relies
on the assumption that the number of contacts correlates with the amount of exposure to disease.
Regarding the ongoing discussion about the major route of transmission of influenza
(29,30), we might overestimate exposure to disease by counting conversational contacts if
influenza is transmitted predominantly by droplet spread. On the other hand, accounting only for
skin-to-skin contact might considerably underestimate exposure because, among other reasons,
transmission by contaminated surfaces or fomites would not be included. To provide data for
modeling the spread of influenza and other infectious diseases with various routes of
transmission, we dichotomized the data.
Page 7 of 15
Furthermore, for nurses, the distribution of conversational and skin-to-skin contacts
differs among contacts with patients and with others. Consequently, different contact matrices
should be used for modeling when different transmission probabilities are assumed for
conversational and skin-to-skin contacts.
Our study has some limitations. First, our nurse sample might not be entirely random
because potential participants could refuse participation. If nurses willing to participate in the
study had more contacts than nurses not willing to participate, our results could overestimate
nurses’ contact numbers. Second, nurses’ motivation to participate might have been higher than
that of controls, resulting in more reported contacts; a reason for nurses’ increased motivation
might be our visits to study hospitals to inform nurses about the aims of the study and the
potential benefits of participating for their occupation. The method used to recruit controls from
the POLYMOD study required conduct of the survey in study participants’ homes. Even though
home visits were timed to minimize this possible selection bias, the sampling still might have
resulted in a higher proportion of housewives and unemployed and retired persons, all of whom
may have fewer contacts than the general population. However, because patients’ needs largely
determine the nurses’ number of work contacts and because the numbers of non–work-related
contacts were comparable for nurses and controls, we do not expect that a large selection bias
accounts for the difference.
A third potential limitation is the difference in work patterns of nurses and controls.
Nurses worked on days they were surveyed, even on weekends, and controls might not have had
work contacts during some days of diary completion. This difference is regarded, not as bias, but
as potentially meaningful information that needs to be considered when modeling nurses’
contacts. Still, the separate analysis by day of the week persistently differed between the groups.
The difference in work contacts reported by nurses and controls also might be affected by
controls’ reporting of aggregated work contacts instead of single diary entries if they usually had
>10 contacts at work. Not considering these aggregated work contacts for weekend days might
have underestimated controls’ contact numbers. However, again, the separate comparison by day
points to more contacts of nurses than controls. Evidence conflicts about whether disaggregated
contact reporting is more complete than aggregated reporting. One study showed higher median
contact numbers comparing prospective to retrospective reporting among university students
(22). By contrast, another group that compared a diary approach with a 1 time-point estimation
Page 8 of 15
of contacts decided in favor of aggregated contacts because most diaries were completed
retrospectively (31). However, most respondents in the POLYMOD sample from Germanyof the
POLYMOD study (25) stated that they completed the diary during the day and not
retrospectively. The large difference in work contacts between nurses and controls is difficult to
explain solely by different methods of contact reporting.
Finally, we perhaps missed types of contacts that do not include conversation or physical
contact. Missed contacts might include crowds (e.g., during public transportation), which have
been discussed in the context of airborne spread of infections (29,30). However, the average risk
associated with a conversation or with physical contact can be reasonably assumed to be
substantially higher than just presence in the same room.
Hospital structure and tasks of nurses in charge of inpatient care are identical in the
different German federal states. Therefore, we expect our results from hospitals in the German
federal state of Bavaria to be representative of all of Germany. However, because structures and
standards of care might differ in other countries, our data require country-specific validation.
Furthermore, patterns of contact for hospital workers other than nurses in charge of inpatient care
(e.g., physicians, technical assistants, cleaning personnel, or nurses in emergency departments)
might differ substantially and influence the patterns of infectious disease spread in the hospital.
However, because most routine daily patient contacts are with inpatient nurses, we are confident
our data reflect the pattern of a large proportion of contacts in German hospitals.
Our survey did not account for length of hospital stay. Repeated contacts with long-term
patients might not bear the same risk for infection as contacts with newly admitted patients
because long-term patients are less likely to import an infection to the hospital. Future modeling
studies might investigate the impact of length of hospital stay on disease spread.
In our study, nurses’ patterns of contact differed from those of the general population.
Our findings support the need for explicit modeling of the healthcare sector to assess the spread
of epidemics. To this aim, our study provides quantitative estimates of contact patterns. On the
basis of results of modeling approaches for the healthcare sector, public health policies should be
reassessed and revised as necessary.
Page 9 of 15
Acknowledgments
We thank the nurses from the 5 study hospitals for participating in this survey. We also thank Martin
Eichner, Wolfgang Hautmann, Maria-Sabine Ludwig, and Markus Schwehm for stimulating discussions; Christiane
Hoeller and Andreas Sing for their advice in the conceptual phase of the study; Katharina Alpers, Viviane Bremer,
Udo Buchholz, and Martin Eichner for helpful comments on the study protocol; the participants of the 2008
POLYMOD meeting and of the 2008 European Program for Intervention Epidemiology Training project review
module for fruitful discussions of study results; and Udo Buchholz and Doris Radun for helpful comments on the
manuscript.
The work of H.B. was financed by an influenza research grant provided by the Federal Ministry of
Education and Research and the Federal Ministry of Health. Contact data on the matched controls were collected in
the framework of the POLYMOD project. POLYMOD was financially supported by grant SP22-CT-2004-502084
of the European Commission, DG Research.
Dr Bernard is a medical epidemiologist at the Robert Koch Institute. Her current work focuses on
surveillance and control of gastrointestinal infections and zoonoses.
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Address for correspondence: Helen Bernard, Robert Koch Institute, Department for Infectious Disease
Epidemiology, Gastrointestinal Infections, Zoonoses and Tropical Infections Unit, DGZ-Ring 1, 13086 Berlin,
Germany; email: bernardh@rki.de
Table 1. Characteristics of 131 nurses surveyed to determine extent of work and other contacts, Bavaria, Germany, April–July 2007*
Characteristic
No. (%) nurses
Median no. reported contacts
p value
Sex
Female
115 (88)
40
0.35
Male
16 (12)
41
Leisure activities >1×/wk in group of >5 persons
68 (52)
Use of public transportation
Any
86 (66)
Daily
7 (8)
Day of diary completion
Weekday (Monday–Friday)
96 (73)
41.5
<0.05
Weekend (Saturday/Sunday)
33 (25)
32
Unknown
2 (2)
Hospital department
Internal medicine
60 (46)
40
0.46
Surgery
60 (46)
41.5
Both
11 (8)
Hospital level of care
I (basic)
60 (46)
42
0.35
II (intermediate)
20 (15)
31.5
III (maximum)
51 (39)
39
*Mean age of nurses was 35 y (range 18–59 y). Mean number of household members was 2 (range 1–7).
Page 13 of 15
Table 2. Characteristics of 5,161 contacts reported by surveyed
nurses, Bavaria, Germany, April–July 2007
Reported contacts,
Characteristic
no. (%)
Place of contact
Work
4,207 (82)
Home
226 (4)
Transportation
32 (<1)
Other places
349 (7)
Multiple locations
232 (4)
Not specified
115 (2)
Contact duration
<15 minutes
2,650 (51)
>15–60
minutes
1,244 (24)
>1 hour
1,202 (23)
Not specified
65 (1)
Contact intensity
Skin-to-skin
2,646 (51)
Not skin-to-skin
2,331 (45)
Not specified
184 (4)
Age of contact person, y
<20
284 (6)
20–39
1,535 (30)
40–59
1,558 (30)
>60
1,776 (34)
Not specified
8 (<1)
Age of person contacted at work, y*
<20
160 (4)
20–39
1,171 (28)
40–59
1,264 (30)
>60
1,611 (38)
Not specified
1 (<1)
Age of person contacted outside work, y†
<20
94 (15)
20–39
226 (37)
40–59
204 (34)
>60
83 (14)
*n = 4,207.
†n = 607.
Table 3. Work-related contacts of nurses, Bavaria, Germany, April–July 2007*
Contact duration,
No. nonphysical contacts with
No. of physical contacts with
min
Patients
Staff
Other
All
Patients
Staff
Other
All
<15
441
507
279
1,227
927
114
37
1,078
15–60
69
332
12
413
483
135
10
628
>60
7
319
5
331
226
247
17
490
Total
517
1,158
296
1,971
1,636
496
64
2,196
*Only contacts with information on all 3 variables (n = 4,167).
Page 14 of 15
Table 4. Mean number of individually reported contacts between nurses or controls and other persons, by age group, Bavaria,
Germany, April–July 2007*
Age
No. nurses/
Mean no. contacts by age group of contact person
group, y
controls
0–4 y
5–14 y 15–24 y 25–34 y 35–44 y 45–54 y 55–64 y 65–74 y 75–84 y 85–94 y
Nurses
15–24
32
0.00
0.13
2.91
4.09
2.94
2.53
2.22
3.00
2.88
0.81
25–34
29
0.28
0.55
6.21
9.31
9.69
7.24
6.79
5.97
5.17
1.45
35–44
31
0.29
1.03
4.13
4.74
6.16
6.13
3.68
5.81
4.94
1.23
45–54
30
0.30
0.63
3.47
5.03
6.40
5.93
3.90
3.87
2.83
1.13
55–64
7
0.29
0.29
2.71
4.57
5.43
3.29
3.43
4.43
3.86
0.43
Controls
15–24
32
0.00
0.29
6.10
2.38
1.43
1.71
0.76
0.14
0.14
0.00
25–34
29
0.29
0.45
0.60
1.48
1.17
0.74
0.64
0.14
0.07
0.02
35–44
31
0.13
0.26
1.29
1.00
2.29
1.48
0.52
0.42
0.26
0.00
45–54
30
0.07
0.18
0.86
0.96
1.61
1.93
0.71
0.36
0.25
0.00
55–64
7
0.00
0.57
0.57
1.43
0.71
1.86
1.71
0.43
0.14
0.00
*Total number of contacts was 5,071 for nurses and 993 for controls. Controls were not all from Bavaria.
Figure. Total numbers of contacts for surveyed nurses and their matched controls from the general
population, Bavaria, Germany, April–July 2007.
Page 15 of 15
