The floors of hospital rooms are quickly and frequently contaminated with antibiotic-resistant bacteria within hours of patient admission, creating a route of transfer of potentially dangerous organisms to patients, according to a study published today as part of the proceedings from Decennial 2020: The Sixth International Conference on Healthcare-Associated Infections.
Decennial 2020, an initiative of the Centers for Disease Control and Prevention and the Society for Healthcare Epidemiology of America, was cancelled in March due to the pandemic.
All abstracts accepted for the meeting have been published as a supplement issue in the journal Infection Control & Hospital Epidemiology.
“If bacteria stayed on floors this wouldn’t matter, but we’re seeing clear evidence that these organisms are transferred to patients, despite our current control efforts,” said Curtis Donskey, MD, senior author of the study and hospital epidemiologist at the Cleveland VA Medical Center. “Hand hygiene is critical, but we need to develop practical approaches to reduce underappreciated sources of pathogens to protect patients.”
Researchers with the Northeast Ohio VA Healthcare System closely tracked contamination in hospital rooms of 17 newly admitted patients to identify the timing and route of transfer of bacteria within patients’ rooms.
Before testing, rooms were thoroughly cleaned and sanitized and all patients screened negative for methicillin-resistant Staphylococcus aureus (MRSA) and other healthcare-associated bacteria. Researchers then observed patients’ interactions with healthcare personnel and portable equipment, collecting cultures one-to-three times per day from patients, their socks, beds and other high-touch surfaces, as well as key sections of the floor.
Nearly half of rooms tested positive for MRSA within the first 24 hours, and MRSA, C. difficile, and vancomycin-resistant enterococci (VRE) pathogens were identified in 58% of patient rooms within four days of admission.
Contamination often started on the floors, but ultimately moved to patients’ socks, bedding, and nearby surfaces.
“While we’re showing that these scary sounding bugs can make their way into a patient’s room and near them, not everyone who encounters a pathogen will get an infection,” said Sarah Redmond, lead author and a medical student at Case Western Reserve University School of Medicine. “With that in mind, are there simple ways to address these areas of exposure without placing too much emphasis on the risk?”
In a related study published in August in Infection Control & Hospital Epidemiology, the authors reported similar findings of frequent detection of SARS-CoV-2 nucleic acid on floors and on shoes of personnel on a COVID-19 ward.
The authors note that further research is needed to clarify the role of floor contamination in transmission of both bacterial and viral pathogens and to identify practical approaches to address contamination.
On the COVID-19 ward, contamination was reduced with simple modifications of floor cleaning and disinfection protocols.
Researchers noted several limitations of the study, including the small sample size and variables in characteristics among patients and healthcare personnel that may impact how generalizable the study findings are to other hospitals.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has spread globally and many countries are experiencing ongoing local transmission despite varying levels of control efforts. Understanding the different transmission routes of SARS-CoV-2 is crucial in planning effective interventions to break the chain of transmission.
Although extensive surface contamination with SARS-CoV-2 by a symptomatic patient has been demonstrated1, little is known about airborne transmission of SARS-CoV-2. It is also unknown if asymptomatic individuals pose the same environmental contamination risk as symptomatic ones, although viral shedding has been demonstrated to continue even after clinical recovery of COVID-19 patients2.
There are multiple reports of asymptomatic patients testing positive for SARS-CoV-23,4, and the potential transmission of the virus by an asymptomatic person has been described5.
Therefore, viral contamination of the air and surfaces surrounding asymptomatic or recovering COVID-19 patients could have serious implications for outbreak control strategies. This knowledge gap is recognized in the Report of the WHO-China Joint Mission on Coronavirus 20196.
The primary objective of our study is to identify potential patient-level risk factors for environmental contamination by SARS-CoV-2 by sampling the air and surfaces surrounding hospitalized COVID-19 patients at different stages of illness.Go to:
Results
Air and environmental sampling
Environmental sampling was conducted in three airborne infection isolation rooms (AIIRs) in the ICU and 27 AIIRs in the general ward. Air sampling was performed in three of the 27 AIIRs in the general ward. All patients reported COVID-19 symptoms. Seven patients (23%) were asymptomatic at the time of environmental sampling. Of the 23 symptomatic patients, 18 (78%) had respiratory symptoms, 1 had gastrointestinal symptoms, 1 had both respiratory and gastrointestinal symptoms, and 3 patients (10%) had fever or myalgia only (Supplementary Table 1).
Air samples from two (66.7%) of three AIIRs tested positive for SARS-CoV-2, in particle sizes >4 µm and 1–4 µm in diameter (Table 1). Samples from the fractionated size <1 µm were all negative, as were all non-size-fractionated SKC polytetrafluoroethylene (PTFE) filter cassette samples. Total SARS-CoV-2 concentrations in air ranged from 1.84 × 103 to 3.38 × 103 RNA copies per m3 air sampled. Rooms with viral particles detected in the air also had surface contamination detected.
Table 1
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detections in the air of hospital rooms of infected patient.
| Patient | Day of illness | Symptoms reported on day of air sampling | Clinical Ct valuea | Airborne SARS-CoV-2 concentrations (RNA copies m−3 air) | Aerosol particle size | Samplers used |
|---|---|---|---|---|---|---|
| 1 | 9 | Cough, nausea, dyspnea | 33.22 | ND | >4 μm | NIOSH |
| ND | 1–4 μm | |||||
| ND | <1 μm | |||||
| ND | – | SKC filters | ||||
| 2 | 5 | Cough, dyspnea | 18.45 | 2,000 | >4 μm | NIOSH |
| 1,384 | 1–4 μm | |||||
| ND | <1 μm | |||||
| 3 | 5 | Asymptomaticb | 20.11 | 927 | >4 μm | NIOSH |
| 916 | 1–4 μm | |||||
| ND | <1 μm |
ND none detected.
aPCR cycle threshold value from patient’s clinical sample.
bPatient reported fever, cough, and sore throat until the day before the sampling. Patient reported no symptoms on the day of sampling, however was observed to be coughing during sampling.
There were no baseline differences between patients with environmental surface contamination and those without, in terms of age, comorbidities, and positive clinical sample on the day of sampling. Median cycle threshold (Ct) values of the clinical specimens for patients with and without environmental surface contamination were 25.69 (IQR 20.37–34.48) and 33.04 (28.45–35.66), respectively (Table 2).
Table 2
Baseline clinical characteristics of COVID-19 patients with environmental contamination.
| Characteristics of COVID-19 patients | Rooms with surface environment contamination (n = 17) | Rooms without surface environment contamination (n = 13) | P value |
|---|---|---|---|
| Median age (IQR) | 52 (42–62) | 44 (36–55) | 0.75 |
| Male Sex (%) | 6 (46%) | 8 (47%) | 0.96 |
| Median Age Adjusted Charlson’s Comorbidity Index (IQR) | 1 (0–2) | 1 (0–1) | 0.69 |
| Median day of Illness (IQR) | 5 (4–9) | 13 (5–20) | 0.17 |
| Median day of stay in room (IQR) | 3 (3–8) | 4 (2–16) | 0.95 |
| Oxygen requirement (%) | 0 | 4 (31) | 0.03 |
| Symptomatic (%) | 12 (71) | 11 (85) | 0.43 |
| Respiratory symptoms (%) | 11 (65) | 7 (54) | 0.55 |
| Gastrointestinal symptoms (%) | 1 (6) | 1 (8) | >0.99 |
| Clinical Cycle threshold value, median (IQR)a | 25.69 (20.37–34.48) | 33.04 (28.45–35.66) | 0.06 |
aPCR cycle threshold value from patient’s clinical sample.
χ2 or Fisher’s exact test was used to compare categorial variables; and Student’s t test or nonparametric Wilcoxon rank-sum was used to compare continuous variables.
Of the rooms with environmental contamination, the floor was most likely to be contaminated (65%), followed by the air exhaust vent (60%, n = 5), bed rail (59%), and bedside locker (47%) (Fig. 1). Contamination of toilet seat and automatic toilet flush button was detected in 5 out of 27 rooms, and all 5 occupants had reported gastrointestinal symptoms within the preceding 1 week of sampling. We did not detect surface contamination in any of the three ICU rooms.
Fig. 1 Percentage of contaminated swabs from surface samples, in rooms with any contamination.
All sites were n = 17, except for air exhaust vents where n = 5.
Presence of environmental surface contamination was higher in week 1 of illness (Fig. 2) and showed association with the clinical cyclical threshold (P = 0.06, Wilcoxon rank-sum test). Surface environment contamination was not associated with the presence of symptoms (Table 2).
In a subgroup analysis, the presence and extent of high-touch surface contamination were significantly higher in rooms of patients in their first week of illness (Fig. 2).
The best fit curve with the least-squares fit (Fig. 3) showed that the extent of high-touch surface contamination declined with increasing duration of illness and Ct values. There was also no correlation between the Ct values of clinical samples and the Ct values of environmental samples across the days of illness (Supplementary Fig. 3).
Fig. 2 Extent of environmental contamination correlated with day of illness timepoint.
a Percentage of patients with contamination of high-touch surfaces in the first week of illness compared with more than first week of illness, n = 15 in both groups. b Percentage of surfaces contaminated across weeks of illness with median and 95% confidence intervals. c. Percentage of high-touch surfaces contaminated across weeks of illness with median and 95% confidence intervals.
Fig. 3 – Patient and disease factors affecting percentage of high-touch contamination.
a Mean percentage of high-touch surface contaminated by day of illness with 95% confidence interval with best fit curve, n = 30. b Percentage of high-touch surfaces contaminated by clinical cycle threshold values with 95% confidence interval with bestfit curve, n = 30. c Mean percentage of high-touch surface contaminated by day of illness with 95% confidence interval grouped by symptoms, n = 30.
Discussion
Surface sampling revealed that the PCR-positivity high-touch surfaces was associated with nasopharyngeal viral loads and peaked at approximately day 4–5 of symptoms. Air sampling of the AIIR environments of two COVID-19 patients (both day 5 of illness with high nasopharyngeal swab viral loads) detected the presence of SARS-CoV-2 particles sized 1–4 µm and >4 µm. The absence of any detection of SARS-CoV-2 in air samples of the third patient (day 9 of illness with lower nasopharyngeal viral load concentration) suggests that the presence of SARS-CoV-2 in the air is possibly highest in the first week of illness.
Recent aggregated environmental sampling and laboratory experiments have examined the particle size distribution of SARS-CoV-2 in the air. A study from Wuhan, China sampled three different environmental settings and detected aerosol size range particles.7
Additionally, a recent laboratory study demonstrated the ability of SARS-CoV-2 to remain viable in aerosols for up to 3 h8. Although limited in subject numbers, our study examined this issue at the individual patient-level, thus enabling correlation of particle size distribution in the air with symptoms duration and nasopharyngeal viral loads.
The absence of aerosol-generating procedures or intranasal oxygen supplementation reduces the possibility of our current findings being iatrogenic in nature. Larger individual patient-level studies examining the droplet and aerosolizing potential of SARS-CoV-2 over different distances and under different patient and environmental conditions are rapidly needed to determine the generalizability of our current findings.
Contrary to the study from Wuhan, China that detected SARS-CoV-2 in aerosols 0.25–1.0 µm in diameter7, the smallest aerodynamic size fraction that contained detectable levels of SARS-CoV-2 in our study was 1–4 µm.
The non-detection of SARS-CoV-2 in particles <1 µm could have been due to the reduced efficiency of extracting viruses from filters as compared with extracting viruses adhered to the wall of the 1.5 mL and 15 mL centrifuge tubes, where particles 1–4 µm and >4 µm in diameter are captured using the NIOSH aerosol sampler.
Furthermore, to our knowledge, this is the first time the NIOSH samplers have been used to capture coronaviruses. Therefore, no baseline data exist for airborne coronavirus sampling using these samplers, limiting our understanding of the negative results in the <1 µm size fraction.
The extent of environmental contamination we found in our study could be attributable to direct touch contamination by either the patient or healthcare workers after contact with infected respiratory fluids. However, contamination through respiratory droplets emitted through coughing and sneezing, as well as through respiratory aerosols, is also plausible. Contamination of surface sites not frequently touched (air exhaust vents and floor) support this latter hypothesis.
In the current analysis, the presence and concentration of SARS-CoV-2 in air and high-touch surface samples correlated with the day of illness and nasopharyngeal viral loads of COVID-19 patients. This finding is supported by multiple observational clinical studies, which have demonstrated that SARS-CoV-2 viral loads peak in the first week among COVID-19 patients2,9,10, with active viral replication in the upper respiratory tract in the first 5 days of illness11.
This finding could help inform public health and infection prevention measures in prioritizing resources by risk stratifying COVID-19 patients by their potential to directly or indirectly transmit the SARS-CoV-2 virus to others.
Our study was limited in that it did not determine the ability of SARS-CoV-2 to be cultured from the environmental swabs and the differentially sized air particles, which would be vital to determining the infectiousness of the detected particles. Another study from Nebraska attempted virus culture on SARS-CoV-2 PCR-positive air samples, however could not isolate viable virus12.
The difficulty in culturing virus from air samples arises from low-virus concentrations, as well as the compromised integrity of the virus due to air sampling stressors. Future studies using enhanced virus culture techniques could be considered13, and efforts to design a culture method to isolate virus from our samples is underway. Second, sampling in an AIIR environment may not be representative of community settings and further work is needed to generalize our current findings.
Third, we sampled each room at a single timepoint during the course of illness and did not track environmental contamination over the course of illness for individual patients. Fourth, as clinical results were within 72 h of environmental testing, it is plausible that during the day of testing, viral load was actually low or negligible, hence limiting environmental contamination.
Current evidence does not seem to point to aerosolization as the key route of transmission of SARS-CoV-2, and there have been reports of healthcare workers not being infected after exposure to confirmed patients despite not using airborne precautions14. Detailed epidemiologic studies of outbreaks, in both healthcare and non-healthcare settings, should be carried out to determine the relative contribution of various routes of transmission and their correlation with patient-level factors.
In conclusion, in a limited number of AIIR environments, our current study involving individual COVID-19 patients not undergoing aerosol-generating procedures suggests that SARS-CoV-2 can be shed in the air from a patient in particles sized between 1 and 4 microns.
Even though particles in this size range have the potential to linger longer in the air, more data on viability and infectiousness of the virus would be required to confirm the potential airborne spread of SARS-CoV-2. Additionally, the concentrations of SARS-CoV-2 in the air and high-touch surfaces could be highest during the first week of COVID-19 illness.
Further work is urgently needed to examine these findings in larger numbers and different settings to better understand the factors affecting air and surface spread of SARS-CoV-2 and inform effective infection prevention policies.
Supplementary information
Supplementary Information(459K, pdf)
Peer Review File(364K, pdf)
Reporting Summary(207K, pdf)
Source data
Source Data(27K, xlsx)
References
- 1. Ong Sean Wei Xiang, Tan Yian Kim, Chia Po Ying, Lee Tau Hong, Ng Oon Tek, Wong Michelle Su Yen, Marimuthu Kalisvar. Air, Surface Environmental, and Personal Protective Equipment Contamination by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) From a Symptomatic Patient. JAMA. 2020;323(16):1610. doi: 10.1001/jama.2020.3227. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- 2. Young Barnaby Edward, Ong Sean Wei Xiang, Kalimuddin Shirin, Low Jenny G., Tan Seow Yen, Loh Jiashen, Ng Oon-Tek, Marimuthu Kalisvar, Ang Li Wei, Mak Tze Minn, Lau Sok Kiang, Anderson Danielle E., Chan Kian Sing, Tan Thean Yen, Ng Tong Yong, Cui Lin, Said Zubaidah, Kurupatham Lalitha, Chen Mark I-Cheng, Chan Monica, Vasoo Shawn, Wang Lin-Fa, Tan Boon Huan, Lin Raymond Tzer Pin, Lee Vernon Jian Ming, Leo Yee-Sin, Lye David Chien. Epidemiologic Features and Clinical Course of Patients Infected With SARS-CoV-2 in Singapore. JAMA. 2020;323(15):1488. doi: 10.1001/jama.2020.3204. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- 3. Chan Jasper Fuk-Woo, Yuan Shuofeng, Kok Kin-Hang, To Kelvin Kai-Wang, Chu Hin, Yang Jin, Xing Fanfan, Liu Jieling, Yip Cyril Chik-Yan, Poon Rosana Wing-Shan, Tsoi Hoi-Wah, Lo Simon Kam-Fai, Chan Kwok-Hung, Poon Vincent Kwok-Man, Chan Wan-Mui, Ip Jonathan Daniel, Cai Jian-Piao, Cheng Vincent Chi-Chung, Chen Honglin, Hui Christopher Kim-Ming, Yuen Kwok-Yung. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet. 2020;395(10223):514–523. doi: 10.1016/S0140-6736(20)30154-9. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- 4. Pan Xingfei, Chen Dexiong, Xia Yong, Wu Xinwei, Li Tangsheng, Ou Xueting, Zhou Liyang, Liu Jing. Asymptomatic cases in a family cluster with SARS-CoV-2 infection. The Lancet Infectious Diseases. 2020;20(4):410–411. doi: 10.1016/S1473-3099(20)30114-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- 5. Bai Yan, Yao Lingsheng, Wei Tao, Tian Fei, Jin Dong-Yan, Chen Lijuan, Wang Meiyun. Presumed Asymptomatic Carrier Transmission of COVID-19. JAMA. 2020;323(14):1406. doi: 10.1001/jama.2020.2565. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- 6. WHO. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) (2020).
- 7. Liu, Y. et al. Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals. Nature (2020). [Epub ahead of print]. [PubMed]
- 8. van Doremalen, N. et al. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. N. Engl. J. Med.382, 1564–1567 (2020). [PMC free article] [PubMed]
- 9. Zou Lirong, Ruan Feng, Huang Mingxing, Liang Lijun, Huang Huitao, Hong Zhongsi, Yu Jianxiang, Kang Min, Song Yingchao, Xia Jinyu, Guo Qianfang, Song Tie, He Jianfeng, Yen Hui-Ling, Peiris Malik, Wu Jie. SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients. New England Journal of Medicine. 2020;382(12):1177–1179. doi: 10.1056/NEJMc2001737. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- 10. Wang, W. et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA323, 1843–1844 (2020). [PMC free article] [PubMed]
- 11. Wölfel, R. et al. Virological assessment of hospitalized patients with COVID-2019. Nature (2020). [Epub ahead of print]. [PubMed]
- 12. Santarpia, J. L. et al. Transmission potential of SARS-CoV-2 in viral shedding observed at the University of Nebraska Medical Center. medRxiv (2020). [Epub ahead of print].
- 13. Cao G, Noti J, Blachere F, Lindsley W, Beezhold D. Development of an improved methodology to detect infectious airborne influenza virus using the NIOSH bioaerosol sampler. J. Environ. Monit. 2011;13:3321–3328. doi: 10.1039/c1em10607d. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- 14. Ng, K. et al. COVID-19 and the risk to health care workers: a case report. Ann. Intern. Med.16, L20–0175 (2020). [PMC free article] [PubMed]
- 15. Coleman KK, Sigler WV. Airborne influenza A virus exposure in an elementary school. Sci. Rep. 2020;10:1859. doi: 10.1038/s41598-020-58588-1. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
- 16. Corman VM, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance. 2020;25:2000045. [PMC free article] [PubMed] [Google Scholar
- ]17. Drosten C, et al. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N. Engl. J. Med. 2003;348:1967–1976. doi: 10.1056/NEJMoa030747. [PubMed] [CrossRef] [Google Scholar]
- 18. Young Barnaby Edward, Ong Sean Wei Xiang, Kalimuddin Shirin, Low Jenny G., Tan Seow Yen, Loh Jiashen, Ng Oon-Tek, Marimuthu Kalisvar, Ang Li Wei, Mak Tze Minn, Lau Sok Kiang, Anderson Danielle E., Chan Kian Sing, Tan Thean Yen, Ng Tong Yong, Cui Lin, Said Zubaidah, Kurupatham Lalitha, Chen Mark I-Cheng, Chan Monica, Vasoo Shawn, Wang Lin-Fa, Tan Boon Huan, Lin Raymond Tzer Pin, Lee Vernon Jian Ming, Leo Yee-Sin, Lye David Chien. Epidemiologic Features and Clinical Course of Patients Infected With SARS-CoV-2 in Singapore. JAMA. 2020;323(15):1488. doi: 10.1001/jama.2020.3204. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
More information: Epidemiology (2020). DOI: 10.1017/ice.2020.1066
