Abstract
Introduction
Cell phones are susceptible to bacterial contamination. The aim of this study was to characterize the bacterial isolates and to explore their dispersion in five Intensive Care Units (ICUs) over the time.
Methods
We performed a secondary analysis of non-fermenting Gram-negative bacteria and Gram-positive cocci isolated from a 5-month observational cohort study developed among health care workers’ cell phones in five ICUs. Cell phones were sampled using a swab every 15 days. Antimicrobial resistance was determined by the minimum inhibitory concentration method. We constructed resistance phenotypes to group the isolates according to species and antimicrobial resistance pattern to explore dispersion through time.
Results
A total of 35 P. aeruginosa, 16 Acinetobacter spp., 30 S. aureus and 26 Enterococcus spp. were isolated from 491 phone samples. Multidrug resistance was 2.9% for P. aeruginosa, 31.3% for Acinetobacter spp., 46.7% for S. aureus and 80.8% for Enterococcus spp. The resistance to methicillin in S. aureus and to vancomycin in Enterococcus spp. was 26.7% and 42.3%, respectively. We did not observe distribution patterns or clusters over the time for P. aeruginosa, Acinetobacter spp. and Enterococcus spp. isolates. All the S. aureus isolates grouped into eight phenotypes. Interestingly, we observed S. aureus isolates with the same phenotype in consecutive and separate sampling dates in the same cell phone.
Conclusion
Cell phones are contaminated with highly harmful bacteria and potentially can maintain them for prolonged periods of time. These devices could be considered as a potential source of nosocomial infections in ICUs.
Keywords: Fomites, cell phones, drug resistance, bacterial, intensive care units
Introduction
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 Kirby-Bauer 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.
Methods
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 °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 (bioMérieux Inc., Durham, NC, USA). The antibiotic susceptibility testing was done using the AST-GP67 and AST-N249 VITEK cards (bioMérieux Inc.) for CGP and NFGNB, respectively. Results for penicillin, β-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’ guideline16 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 ≥1 agent in ≥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.
Results
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.
Table 1. Antibiotic resistance rates of non-fermenting Gram-negative bacteria isolated from health care workers’ cell phones.
| Resistance to agent | Pseudomonas aeruginosa (n=35) | Acinetobacter 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 acid | 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 categories | Median (min-max) | 1 (1-4) | 1 (0-8)€ | 0.767¥ |
MDR – multidrug resistance; NA – not applicable; NT – not tested.
Note: Values are % (n/N).
p-values calculated using the Fisher’s exact test.
One Acinetobacter spp. was resistant to all the antimicrobial categories listed plus the folate pathway inhibitor, resulting in resistance to 8 categories.
p-value calculated using the Mann Whitney test, effect size = 0.041
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).
Table 2. Antibiotic resistance rates of Gram-positive cocci isolated from health care workers’ cell phones.
| Resistance to category/agent | Staphylococcus aureus (n=30) | Enterococcus spp. (n=26) | χ2 | p-value* | |
|---|---|---|---|---|---|
| Penicillin | 93.3 (28/30) | 30.8 (8/26) | 23.8 | <0.001 | |
| Vancomycin | 0.0 (0/30) | 42.3 (11/26) | 15.8 | <0.001 | |
| Erythromycin | 40.0 (12/30) | 92.3 (24/26) | 16.6 | <0.001 | |
| Ciprofloxacin | 23.3 (7/30) | 46.2 (12/26) | 3.24 | 0.072 | |
| Chloramphenicol | 0.0 (0/30) | 15.4 (4/26) | 4.97 | 0.026 | |
| Rifampicin | 13.3 (4/30) | 73.1 (19/26) | 20.5 | <0.001 | |
| Linezolid | 0.0 (0/30) | 30.8 (8/26) | 10.8 | 0.001 | |
| MDR Resistance to categories | Yes | 46.7 (14/30)€ | 80.8 (21/26) | 6.91 | 0.009 |
| Resistance to categories | median (min-max) | 2 (0-5)¥ | 5 (1-7) | - | <0.001£ |
MDR – multidrug resistance
Note: Values are % (n/N).
All p-values were calculated using the Chi2 test.
8 MRSA isolates + 6 non-MRSA that were MDR.
Includes all the antimicrobial categories listed plus aminoglycoside (gentamicin) and lincosamides (clindamycin).
p-value calculated using the Mann Whitney test, effect size = 0.526.
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.
Table 3. Resistance phenotypes of S. aureus isolates.
| Phenotype* | n (%) | PEN | GEN | RIF | CIP | CLI | ERY | MRSA | D-test | Hospital | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A (n=18) | B (n=7) | C (n=5) | ||||||||||
| S | 2 (6.7) | S | S | S | S | S | S | - | - | . | . | X |
| R1 | 11 (36.7) | R | S | S | S | S | S | - | - | X | X | X |
| R2a | 3 (10.0) | R | R | S | S | S | S | - | - | X | X | . |
| R2b | 1 (3.3) | R | S | S | S | R | S | + | - | X | . | |
| R3a | 5 (16.7) | R | S | S | S | R | R | - | + | X | . | X |
| R3b | 1 (3.3) | R | S | R | S | R | S | - | - | . | X | . |
| R4 | 4 (13.3) | R | S | S | R | R | R | + | + | X | . | . |
| R5 | 3 (10.0) | R | S | R | R | R | R | + | + | 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.
| Code* | Sampling date | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1st | 2nd | 3rd | 4th | 5th | 6th | 7th | 8th | 9th | 10th | |
| AP004 | R1 | . | . | - | . | . | - | . | - | - |
| AP007 | . | R1 | . | . | - | - | . | - | . | . |
| AP010 | . | R4 | . | . | . | . | . | . | R4 | . |
| AP012 | . | - | . | - | - | . | . | R3a | . | - |
| AP016 | - | - | . | R3a | . | . | - | . | . | - |
| AN002 | . | - | . | - | . | - | R1 | . | - | . |
| AN003 | . | R4 | . | R5 | . | . | R4 | . | . | - |
| AN004 | . | . | . | - | . | - | - | . | R1 | . |
| AN005 | . | - | . | . | - | - | . | . | R3a | . |
| AN009 | . | . | - | . | R2b | . | - | . | . | . |
| AN011 | . | - | . | - | . | - | . | . | . | R2a |
| AN015 | - | R1 | R1 | . | . | - | . | - | . | - |
| AN017 | - | R5 | . | . | - | . | - | - | . | . |
| AN019 | - | . | . | . | R5 | . | - | . | - | . |
| BP002 | . | . | - | - | . | - | R1 | . | . | . |
| BP003 | R3b | . | . | R1 | . | - | . | . | - | . |
| BP011 | . | - | - | . | . | . | R2a | . | - | - |
| BP016 | . | . | - | . | R2a | . | - | . | . | - |
| BP018 | R1 | . | . | - | - | . | - | . | - | . |
| BP033 | - | - | - | . | R1 | . | . | . | - | . |
| CP010 | . | . | - | - | . | - | . | . | . | S |
| CP011 | . | . | . | - | . | - | . | - | R1 | . |
| CN003 | . | S | . | . | . | . | . | . | . | . |
| CN005 | . | . | - | - | . | . | - | . | R3a | . |
| CN022 | - | . | . | . | R3a | . | . | . | . | . |
Code: Hospital, “P” for pediatric ICU or “N” for neonatology ICU, and cell phone ID
“.” – no S. aureus isolation; “-” – no sample collected.
Discussion
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 results12 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 non-resistant 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.
Conclusion
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.
Footnotes
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 Investigación 2011” awarded to SL, LG and JT. The grant was offered by the School of Medicine of the Universidad Peruana Cayetano Heredia.
References
- 1.Labarca JA, Salles MJ, Seas C, Guzmán-Blanco M. Carbapenem resistance in Pseudomonas aeruginosa and Acinetobacter baumannii in the nosocomial setting in Latin America. Crit Rev Microbiol. 2016;42:276–92. doi: 10.3109/1040841X.2014.940494. [DOI] [PubMed] [Google Scholar]
- 2.Becerra MR, Tantaleán JA, Suárez VJ, Alvarado MC, Candela JL, Urcia FC. Epidemiologic surveillance of nosocomial infections in a Pediatric Intensive Care Unit of a developing country. BMC Pediatr. 2010;10:66. doi: 10.1186/1471-2431-10-66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Cuellar LE, Fernandez-Maldonado E, Rosenthal VD, et al. Device-associated infection rates and mortality in intensive care units of Peruvian hospitals: findings of the International Nosocomial Infection Control Consortium. Rev Panam Salud Publica. 2008;24:16–24. doi: 10.1590/s1020-49892008000700002. [DOI] [PubMed] [Google Scholar]
- 4.Panesso D, Reyes J, Rincon S, et al. Molecular epidemiology of vancomycin-resistant Enterococcus faecium: a prospective, multicenter study in South American hospitals. J Clin Microbiol. 2010;48:1562–9. doi: 10.1128/JCM.02526-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Diaz-Tello J, Rojas-Jaimes J, Ibarra-Trujillo J, Tárraga-Gonzales D. [Antimicrobial sensitivity of the environmental microbiota in the intensive care units of a peruvian hospital] Rev Peru Med Exp Salud Publica. 2017;34:93–7. doi: 10.17843/rpmesp.2017.341.2709. [DOI] [PubMed] [Google Scholar]
- 6.Ulger F, Dilek A, Esen S, Sunbul M, Leblebicioglu H. Are healthcare workers’ mobile phones a potential source of nosocomial infections? Review of the literature. J Infect Dev Ctries. 2015;9:1046–53. doi: 10.3855/jidc.6104. [DOI] [PubMed] [Google Scholar]
- 7.Pathare NA, Asogan H, Tejani S, et al. Prevalence of methicillin resistant Staphylococcus aureus [MRSA] colonization or carriage among health-care workers. J Infect Public Health. 2016;9:571–6. doi: 10.1016/j.jiph.2015.12.004. [DOI] [PubMed] [Google Scholar]
- 8.Chang CH, Chen SY, Lu JJ, Chang CJ, Chang Y, Hsieh PH. Nasal colonization and bacterial contamination of mobile phones carried by medical staff in the operating room. PLoS One. 2017;12:e0175811. doi: 10.1371/journal.pone.0175811. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Heyba M, Ismaiel M, Alotaibi A, et al. Microbiological contamination of mobile phones of clinicians in intensive care units and neonatal care units in public hospitals in Kuwait. BMC Infect Dis. 2015;15:434. doi: 10.1186/s12879-015-1172-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Pal S, Juyal D, Adekhandi S, et al. Mobile phones: reservoirs for the transmission of nosocomial pathogens. Adv Biomed Res. 2015;4:144. doi: 10.4103/2277-9175.161553. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Chao Foong Y, Green M, Zargari A, et al. Mobile phones as a potential vehicle of infection in a hospital setting. J Occup Environ Hyg. 2015;12:D232–5. doi: 10.1080/15459624.2015.1060330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Loyola S, Gutierrez LR, Horna G, et al. Extended-spectrum beta-lactamase-producing Enterobacteriaceae in cell phones of health care workers from Peruvian pediatric and neonatal intensive care units. Am J Infect Control. 2016;44:910–6. doi: 10.1016/j.ajic.2016.02.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Borer A, Gilad J, Smolyakov R, et al. Cell phones and Acinetobacter transmission. Emerg Infect Dis. 2005;11:1160–1. doi: 10.3201/eid1107.050221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Nwankwo EO, Ekwunife N, Mofolorunsho KC. Nosocomial pathogens associated with the mobile phones of healthcare workers in a hospital in Anyigba, Kogi state, Nigeria. J Epidemiol Glob Health. 2014;4:135–40. doi: 10.1016/j.jegh.2013.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Al-Talib H, Yean CY, Al-Khateeb A, et al. A pentaplex PCR assay for the rapid detection of methicillin-resistant Staphylococcus aureus and Panton-Valentine Leucocidin. BMC Microbiol. 2009;9:113. doi: 10.1186/1471-2180-9-113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Clinical and Laboratory Standards Institute . Performance standards for antimicrobial susceptibility testing: twenty-third informational supplement M100-S23. Wayne (PA): Clinical Laboratory Standards Institute; 2015. [Google Scholar]
- 17.Steward CD, Raney PM, Morrell AK, et al. Testing for induction of clindamycin resistance in erythromycin-resistant isolates of Staphylococcus aureus. J Clin Microbiol. 2005;43:1716–21. doi: 10.1128/JCM.43.4.1716-1721.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Magiorakos AP, Srinivasan A, Carey RB, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–81. doi: 10.1111/j.1469-0691.2011.03570.x. [DOI] [PubMed] [Google Scholar]
- 19.Selim HS, Abaza AF. Microbial contamination of mobile phones in a health care setting in Alexandria, Egypt. GMS Hyg Infect Control. 2015;10:Doc03. doi: 10.3205/dgkh000246. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Nübel U, Nachtnebel M, Falkenhorst G, et al. MRSA transmission on a neonatal intensive care unit: epidemiological and genome-based phylogenetic analyses. PLoS One. 2013;8:e54898. doi: 10.1371/journal.pone.0054898. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Shakir IA, Patel NH, Chamberland RR, Kaar SG. Investigation of cell phones as a potential source of bacterial contamination in the operating room. J Bone Joint Surg Am. 2015;97:225–31. doi: 10.2106/JBJS.N.00523. [DOI] [PubMed] [Google Scholar]
- 22.García C, Horna G, Linares E, et al. Antimicrobial drug resistance in Peru. Emerg Infect Dis. 2012;18:520–1. doi: 10.3201/eid1803.100878. [DOI] [PMC free article] [PubMed] [Google Scholar]