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Multidrug-resistant bacteria isolated from cell phones in five intensive care units: Exploratory dispersion analysis.


Non-fermenting Gram-negative bacteria (NFGNB) such as Pseudomonas aeruginosa and Acinetobacter species, as well as Gram-positive cocci (GPC) like Staphylococcus aureus and Enterococcus species, are human pathogens that frequently cause nosocomial infections in developing countries. (1-4) These pathogens have been isolated from the environment and fomites in critical care settings. (5-11)

Cell phones are frequently used by health care workers during working hours and are not properly disinfected. (6,12) Cell phones are susceptible to contamination by bacterial pathogens and could be involved in nosocomial transmission. (10,13,14) Therefore, cell phones probably represent a constant and mobile source of infection, for hospitalized patients in critical care settings due to their portability and frequent use by providers.

From February to June 2012, we enrolled 114 health care workers' cell phones in three Pediatric and two Neonatology Intensive Care Units (ICUs) at three Peruvian Hospitals to investigate bacterial contamination. (12) We isolated multiple NFGNB and GPC during the study period. Due to high levels of antimicrobial resistance observed in the screening method based on the KirbyBauer disk diffusion susceptibility test, we characterized the NFGNB and GPC using the minimum inhibitory concentration (MIC) method. The objective of this study was to describe the antimicrobial resistance profiles of NFGNB and GPC isolated from cell phones and to explore their dispersion through time.


Bacterial isolation and identification

The trained study personnel collected the samples by swabbing the phone every 15 days as previously described. (12) Each swab was placed in a tube with 3 mL of trypticase soy broth and incubated aerobically for 18-24 hours at 35 [degrees]C. Then, swabs were plated to MacConkey, Mannitol salt and Blood agar. NFGNB were characterized using standard biochemical and enzymatic microbiologic procedures. The S. aureus isolates were characterized using the coagulase test and confirmed by a genus-specific PCR assay that targets a fragment of the 16S rRNA gene. (15) Enterococcus spp. isolates were classified using the catalase test and the esculin degradation on Bile esculin agar.

Antimicrobial resistance and virulence genes

Antimicrobial resistance was determined by the MIC method using the VITEK 2 automated system (bioMerieux Inc., Durham, NC, USA). The antibiotic susceptibility testing was done using the AST-GP67 and AST-N249 VITEK cards (bioMerieux Inc.) for CGP and NFGNB, respectively. Results for penicillin, [beta]-lactam inhibitor combination, glycopeptides, aminoglycoside, macrolides, fluoroquinolones, lincosamides, folate pathway inhibitors, phenicols, ansamycins, oxazolidinones, tetracycline, cephems, monobactams, carbapenems and lipopeptides were interpreted according to the breakpoints described in the Clinical Laboratory Standards Institute' guideline (16) and results emitted by the VITEK 2. ATCC strains were used for quality control for the susceptibility testing.

Methicillin-resistant S. aureus (MRSA) isolates were screened using cefoxitin, and confirmed by a PCR that targets the mecA gene. (15) Additionally, all S. aureus isolates were screened for the erythromycin-induced resistance to clindamycin and for the Panton-Valentine leukocidin (PVL) production. (15,17)

Statistical analysis

Antimicrobial resistance was analyzed as a binary categorical variable, considering the intermediate-level resistance as resistant. Multidrug resistance (MDR) was defined as resistance to [greater than or equal to]1 agent in [greater than or equal to]3 different antimicrobial categories. (18) Also, all the MRSA isolates were considered as MDR according to a proposed standard definition. (18) The Chi-square test or Fisher's exact test was used for the analysis of resistant status between described species of NFGNB and GPC, as well as among MRSA and non-MRSA, and vancomycin-resistant enterococci (VRE) and non-VRE. Resistance to at least one agent was sufficient to be resistant to the antimicrobial category. Then, the Mann-Whitney U test was used to evaluate the resistance to antimicrobial categories. The resistance phenotypes were constructed using aggregated resistance information according to MIC results. We explored the dispersion of phenotypes through time (sampling dates) according to study sites (ICUs and hospitals). Data analysis was performed in Stata v. 14.0 (StataCorp, College Station, TX, USA) considering p<0.05 as significant.


A total of 35 P. aeruginosa, 16 Acinetobacter spp., 30 S. aureus and 26 Enterococcus spp. were isolated from 491 phone samples of ICU health care workers.

Antimicrobial resistance of non-fermenting Gram-negative bacteria (NFGNB)

A percentage of 12.5% (2/16) of the Acinetobacter spp. isolates were resistant to trimethoprim-sulfamethoxazole. The observed resistance to piperacillin, piperacillin/ tazobactam, netilmicin, fluoroquinolones, cephems, aztreonam and imipenem was more frequent and associated to Acinetobacter spp. isolates (Table 1), in comparison to P. aeruginosa isolates. The frequency of MDR Acinetobacter spp. isolates was higher than that of P. aeruginosa isolates (p=0.009, Table 1). Nearly all the P. aeruginosa isolates (33/35, 94.3%) were susceptible to all antimicrobials tested in this study. Additionally, all the NFGNB were susceptible to colistin and to the aminoglycosides gentamicin, tobramycin and amikacin.

Antimicrobial resistance of Gram-positive cocci (GPC)

The high-level aminoglycoside resistance in Enterococcus spp. was 30.8% (8/26) to gentamicin and 3.9% (1/26) to streptomycin. The resistance to tetracycline was 73.1% (19/26), and 30.8% (8/26) for linezolid. The resistance to vancomycin, erythromycin, chloramphenicol, rifampicin and linezolid was higher in Enterococcus spp. than in S. aureus (Table 2). Also, the frequency of MDR Enterococcus spp. was statistically higher compared to that of MDR S. aureus (p=0.009, Table 2). The resistance to vancomycin in Enterococcus spp. was 42.3% (11/26) and also associated with greater resistance to ciprofloxacin (p=0.045), rifampicin (p=0.010) and tetracycline (p=0.010).

The resistance to gentamicin in S. aureus was 10.0% (3/30) and 46.7% (14/30) to clindamycin. Of the 30 S. aureus isolates, 8 (26.7%) were classified as MRSA. The methicillin-resistant status was associated to an increased resistance to erythromycin (p=0.001), ciprofloxacin (p<0.001), clindamycin (p<0.001) and rifampicin (p=0.019). Nearly half of the S. aureus isolates encoded erythromycin-induced resistance to clindamycin (12/30, 40.0%). Additionally, all S. aureus were susceptible to trimethoprim-sulfamethoxazole. No PVL-producers were found.

Resistance phenotypes distribution

The 35 P. aeruginosa, 16 Acinetobacter spp. and 26 Enterococcus spp. were grouped into 3, 13 and 23 phenotypes, respectively. Neither clusters nor consecutive bacterial isolations with the same phenotype were observed in the same cell phone during the follow-up for those bacteria.

All S. aureus were grouped into 8 phenotypes (Table 3) and isolated through the study period (Table 4). The R1 phenotype was isolated in multiple sampling dates (Table 4), from all hospitals (Table 3) and even at consecutive dates in the same cell phone (code: AN015, Table 4). Phenotypes R2b, R4 and R5 were MRSA strains and were exclusively isolated at hospital A (Table 3). The R4 phenotype was isolated four times from two cell phones (codes: AP010 and AN003, Table 4) in two different sampling dates spaced at least 75 days.


We isolated resistant P. aeruginosa, Acinetobacter spp., S. aureus and Enterococcus spp. from cell phones used by health care workers in ICUs, as previously described by other studies. (6,9) (11,14,19) The MDR status varied from 2.9% to 80.8%, with P. aeruginosa being the least resistant and Enterococcus spp. the most resistant isolates of the four bacterial species described in this study. Also, we identified several MRSA, VRE, and imipenem-resistant Acinetobacter spp. strains. Those isolates represent a threat in ICUs and have been described as causative agents of outbreaks and nosocomial infections in developing countries, (3,4,20) although, there is not enough evidence to conclude direct transmission from cell phones to hospitalized patients. (6)

Several studies have highlighted the role of the cell phone as potential reservoir of resistant bacteria and its capacity to spread pathogens. (6,11,19) According to our previous results (12) and the findings reported here, the bacterial contamination in cell phones was frequent and distributed across the study time. This finding suggests that cell phones are devices susceptible to pathogen contamination. The maintenance of bacterial contamination over time was not assessed in this study due to the design, wide-ranging interval between sampling dates and incomplete sampling for each cell phone enrolled. Further studies with more continuous sampling are needed to determine if cell phones can maintain bacteria for prolonged periods of times.

The bacterial contamination in cell phones occurs in short periods of time generating a continuous turnover of bacterial populations. (21) Also, cell phones may be involved in cross-contamination between hospital wards. (11,19) We hypothesized that the continuous contamination from several sources, not specifically within the ICUs, might explain the high diversity of antimicrobial resistance patterns observed in Enterococcus spp. isolates. An extensive surveillance network conducted during 2008 to 2009 in Peru showed that MDR P. aeruginosa are frequently isolated from blood cultures. (22) In this study, the less variable phenotypes observed in nonresistant P. aeruginosa isolates could reflect a constant, unique and non-hospital source of contamination. Despite not having determined the contamination source in this study, we recommend that further studies explore the epidemiological role of cell phones within ICUs. In the meantime, the use of cell phones should be controlled or banned within the ICU to avoid bacterial threats.

The integration of epidemiological data in terms of time and the application of molecular tools allow testing transmission routes in clinical care settings. (20) Health care workers have been described as carriers of S. aureus in nares, hands, or both. (6-8) Recently, Chang et al. (8) found that 87.5% of S. aureus isolated from health care workers and their cell phones were clonally related. In this study, we observed phones that harbored bacteria with the same phenotype in consecutive sampling dates and also on separate sampling dates. We did not characterize the isolates at the molecular level to infer their clonal relationship or to compare them to other strains like USA300, USA100 or other clonal clusters defined by multilocus sequence typing. Also, we did not collect samples from health care workers. However, because the phenotypes were constructed using information derived from MIC results for multiple antimicrobial agents, the MRSA status and one mechanism of antimicrobial resistance, the findings suggest that strains may be related to each other. Further comprehensive studies are needed to determine if clonal pathogens can remain over the time on the cell phone surface.


In summary, our data suggest that cell phones are susceptible to be contaminated with highly harmful pathogens such as methicillin-resistant S. aureus, vancomycin-resistant Enterococcus, and MDR P. aeruginosa and Acinetobacter spp. Additional studies are needed to evaluate the cell phone's capacity to preserve bacterial pathogens over time in order to elucidate its role as a reservoir.

doi: 10.18683/germs.2018.1135

Authors' contributions statement: SL, LG and JT designed and supervised the study. SL, LG, EA, NS and JT participated in sample and data collection, and carried out the laboratory work. SL and JT wrote the manuscript. All the authors read and approved the final version of the manuscript.

Conflicts of interest: All authors--none to disclose.

Funding: The study was funded by a competitive grant "Fondo de Apoyo a la Investigacion 2011" awarded to SL, LG and JT. The grant was offered by the School of Medicine of the Universidad Peruana Cayetano Heredia.

Received: 12 December 2017; revised: 20 March 2018; accepted: 23 March 2018.


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Steev Loyola [1] *, Luz Gutierrez [2], Estrella Avendano [3], Nixon Severino [4], Jesus Tamariz [5]

[1] MSc, MT, School of Medicine, Universidad Peruana Cayetano Heredia, 430 Honorio Delgado Ave., Lima 31, Peru; [2] MT, School of Medicine, Universidad Peruana Cayetano Heredia, 430 Honorio Delgado Ave., Lima 31, Peru; [3] MT, Universidad Peruana Cayetano Heredia, 430 Honorio Delgado Ave., Lima 31, Peru; [4] MT, Universidad Peruana Cayetano Heredia, 430 Honorio Delgado Ave., Lima 31, Peru; [5] PhD, Universidad Peruana Cayetano Heredia, 430 Honorio Delgado Ave., Lima 31, Peru.

* Corresponding author: Steev Loyola, MSc, MT, School of Medicine, Universidad Peruana Cayetano Heredia, 430 Honorio Delgado Ave., Lima 31, Peru. /

Article downloaded from

Published June 2018
Table 1. Antibiotic resistance rates of non-
fermenting Gram-negative bacteria isolated
from health care workers' cell phones

                          aeruginosa    Acinetobacter
Resistance to agent         (n=35)       spp. (n=16)      p-value *

Piperacillin              5.7 (2/35)    31.3 (5/16)        0.025
Piperacillin-tazobactam   2.9 (1/35)    31.3 (5/16)        0.009
Ticarcillin-clavulanic    5.7 (2/35)        NT              --
Netilmicin                0.0 (0/35)    18.8 (3/16)        0.027
Ciprofloxacin             0.0 (0/35)    31.3 (5/16)        0.002
Levofloxacin              0.0 (0/35)    31.3 (5/16)        0.002
Ceftazidime               2.9 (1/35)    31.3 (5/16)        0.009
Cefepime                  2.9 (1/35)    31.3 (5/16)        0.009
Aztreonam                 2.9 (1/35)    75.0 (12/16)      <0.001
Imipenem                  0.0 (0/35)    25.0 (4/16)        0.007
MDR Yes                   2.9 (1/35)    31.3 (5/16)        0.009
Resistance to Median        1 (1-4)      1 (0-8)           0.767
categories (min-max)                      ([euro])         ([yen])

MDR--multidrug resistance; NA-not applicable;
NT--not tested.

Note: Values are % (n/N). * p-values calculated using
the Fisher's exact test. ([euro]) One Acinetobacter spp. was
resistant to all the antimicrobial categories listed plus
the folate pathway inhibitor, resulting in resistance to
8 categories. ([yen]) p-value calculated using the Mann
Whitney test, effect size = 0.041

Table 2. Antibiotic resistance rates of Gram-positive
cocci isolated from health care workers' cell phones

Resistance to                   Staphylococcus      Enterococcus
category/agent                   aureus (n=30)      spp. (n=26)

Penicillin                       93.3 (28/30)       30.8 (8/26)
Vancomycin                        0.0 (0/30)        42.3 (11/26)
Erythromycin                     40.0 (12/30)       92.3 (24/26)
Ciprofloxacin                     23.3 (7/30)       46.2 (12/26)
Chloramphenicol                   0.0 (0/30)        15.4 (4/26)
Rifampicin                        13.3 (4/30)       73.1 (19/26)
Linezolid                         0.0 (0/30)        30.8 (8/26)
MDR               Yes            46.7 (14/30)       80.8 (21/26)
Resistance to     median         2 (0-5) ([yen])       5 (1-7)
  categories      (min-max)

Resistance to
category/agent    [chi square]     p-value *

Penicillin            23.8          <0.001
Vancomycin            15.8          <0.001
Erythromycin          16.6          <0.001
Ciprofloxacin         3.24           0.072
Chloramphenicol       4.97           0.026
Rifampicin            20.5          <0.001
Linezolid             10.8           0.001
MDR                   6.91           0.009
Resistance to          --        <0.001 ([pounds
   categories                       sterling)

MDR--multidrug resistance
Note: Values are % (n/N).

* All p-values were calculated using the Chi2 test.

([euro]) 8 MRSA isolates + 6 non-MRSA that were MDR.

([yen]) Includes all the antimicrobial categories listed
plus aminoglycoside (gentamicin) and lincosamides (clindamycin).

([pounds sterling]) p-value calculated using the Mann Whitney test,
effect size = 0.526.

Table 3. Resistance phenotypes of S. aureus isolates

                  n (%)     PEN   GEN   RIF   CIP   CLI   ERY   MRSA

Phenotype *

S              2 (6.7)      S     S     S     S     S     S     -
R1            11 (36.7)     R     S     S     S     S     S     -
R2a            3 (10.0)     R     R     S     S     S     S     -
R2b            1 (3.3)      R     S     S     S     R     S     +
R3a            5 (16.7)     R     S     S     S     R     R     -
R3b            1 (3.3)      R     S     R     S     R     S     -
R4             4 (13.3)     R     S     S     R     R     R     +
R5             3 (10.0)     R     S     R     R     R     R     +


Phenotype *            A (n=18)   B (n=7)    C (n=5)

S               -         .          .          X
R1              -         X          X          X
R2a             -         X          X          .
R2b             -         X          .
R3a             +         X          .          X
R3b             -         .          X          .
R4              +         X          .          .
R5              +         X          .          .

CIP--ciprofloxacin; CLI-clindamycin; D-test-erythromycin-induced
resistance to clindamycin test; ERY-erythromycin; GEN-gentamicin;
MRSA-methicillin-resistant S. aureus; PEN-penicillin; RIF-rifampicin;
R-resistant; S-susceptible; "-"-negative; "+"-positive;
"."-no S. aureus isolation; "X"-S. aureus isolation.

* Only antimicrobial agents to which S. aureus was resistant.

Table 4. Distribution of S aureus phenotypes according
to " sampling date, hospital and intensive care units

                   Sampling date
Code *

           1st   2nd   3rd   4th   5th

AP004    R1     .     .     -     .
AP007     .    R1     .     .     -
AP010     .    R4     .     .     .
AP012     .     -     .     -     -
AP016     -     -     .    R3a    .
AN002     .     .     .     -     .
AN003     .    R4     .    R5     .
AN004     .     .     .     -     .
AN005     .     .     .     .     -
AN009     .     .     .     .    R2b
AN011     .     .     .     -     .
AN015     -    R1    R1     .     .
AN017     -    R5     .     .     -
AN019     -     .     .     .    R5
BP002     .     .     .     -     .
BP003    R3b    .     .    R1     .
BP011     .     .     .     .     .
BP016     .     .     .     .    R2a
BP018    R1     .     .     -     -
BP033     -     .     .     .    R1
CP010     .     .     .     -     .
CP011     .     .     .     -     .
CN003     .     S     .     .     .
CN005     .     .     .     -     .
CN022     -     .     .     .    R3a

                  Sampling date

Code *

           6th   7th   8th   9 *   10th

AP004     .     -     .     -     -
AP007     -     .     .     .     .
AP010     .     .     .    R4     .
AP012     .     .    R3a    .     -
AP016     .     -     .     .     -
AN002     -    R1     .     -     .
AN003     .    R4     .     .     -
AN004     -     -     .    R1     .
AN005     -     .     .    R3a
AN009     .     -     .     .     .
AN011     -     .     .     .    R2a
AN015     -     .     .     .     -
AN017     .     -     .     .     .
AN019     .     -     .     -     .
BP002     -    R1     .     .     .
BP003     -     .     .     -     .
BP011     .    R2a    .     -     -
BP016     .     -     .     .     -
BP018     .     -     .     -     .
BP033     .     .     .     -     .
CP010     -     .     .     .     S
CP011     -     .     .    R1     .
CN003     .     .     .     .     .
CN005     .     -     .    R3a    .
CN022     .     .     .     .     .

* Code: Hospital, "P" for pediatric ICU
or "N" for neonatology ICU, and cell phone ID

"."--no S. aureus isolation; "-"--no sample collected.
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Title Annotation:Original article
Author:Loyola, Steev; Gutierrez, Luz; Avendano, Estrella; Severino, Nixon; Tamariz, Jesus
Article Type:Report
Date:Jun 1, 2018
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