Indoor air quality at selected wards at a South African government hospital. *N.J. Malebo 1 and K. Shale 2

Transcription

1 Indoor air quality at selected wards at a South African government hospital *N.J. Malebo 1 and K. Shale 2 1. Central University of Technology, Bloemfontein, Republic of South Africa 2. Central University of Technology, Bloemfontein, Republic of South Africa * Corresponding nmalebo@cut.ac.za Abstract Indoor air quality is hardly monitored at South African hospitals. Investigations at hospitals are vital as hospitals accommodate people with compromised immune systems and could acquire other infections if air quality is not monitored. Monitoring of air quality is important as it may assist in identifying and controlling possible microbial threats such as viruses, fungi and drug resistant microbes. In addition, rapid identification of airborne microbes is essential in this environment. Molecular methods are frequently used for rapid identification of microorganisms. However, Matrix-Assisted Laser Desorption/Ionization time-of-flight Mass Spectrometry has proved over recent years to be a rapid, accurate, easy-to-use, and inexpensive method for the identification of microorganisms. The aim of this study was to monitor air quality at different wards by quantifying and identifying (using MALDI-TOF MS) airborne microorganisms at a South African hospital. In general, counts for samples collected with the SAS super 90 were below 95 cfu/m 3, this was observed for fungi and bacteria. Bacterial counts ranged from 2 to 95 cfu/m 3 and fungal counts 5 to 25 cfu/m 3. Samples were also collected at all ventilation systems in different wards using swabs, counts were dominated by bacteria, counts ranged from 100 to more than 300 cfu/ml -1. Results from the study indicate that bacteria from vents were not aerolised in significant quantities. However, their presence is of great concern due to the possibility of aerial transmission related with their presence. Fungi and bacteria identified using MALDI-TOF MS included yeast genera Candida, Cryptococcus and Rhodotorula and bacterial genera Bacillus, Staphylococcus, Arthrobacter, and Micrococcus. Even though counts from the air were low there is a need for frequent indoor air quality monitoring as some of these microorganisms are opportunistic pathogens. Key words: Bioaerosols, Hospitals, Indoor Air Quality, MALDI-TOF MS

2 Introduction Bioaerosols are airborne particles that are living (bacteria, viruses and fungi) or originate from living organisms and in most cases affect indoor air quality. Indoor air monitoring is essential especially at hospitals as they accommodate people with compromised immune systems who could acquire other infections if air quality is not monitored. Exposure to bioaerosols may cause adverse health effects such as infectious diseases, acute toxic effects, allergies, stress, and other disorders. Air quality monitoring may assist in identifying and controlling possible microbial threats that could lead to hospital acquired infections and outbreaks (Araujo et al., 2008). Furthermore, bioaerosol monitoring in hospitals is essential as it can ensure safety to patients and employees (Qudiesat et al., 2009). Although, various means such as contact-spread have been suggested to describe the transmission of hospital acquired infections, a small number of studies have placed emphasis on the contribution of bioaerosols in hospital rooms to these infections (Beggs 2003; Gilbert et al., 2010). According to Beggs (2003) the involvement of bioaerosols in the spread of hospital infections is probably greater than currently recognized. Factors such as inadequate ventilation, chemical use in the building, outdoor pollutants, or growth of mould or other microorganisms inside the building or in the heating and ventilation (HVAC) system can compromise air quality (Dascalaki et al., 2008). In most cases the interchange between indoor and outdoor air in hospitals is limited and the primary source of contamination could be the filtration system of the ventilation system. Furthermore, Maus et al. [7] demonstrated that microbial spores collected in air filter media were able to survive over a prolonged period of time and as a result pose the potential of microbial growth. Sampling of ventilation systems is essential as they control indoor environment quality (Dascalaki et al., 2008). Studies in the United States have described the colonization of filtration systems by fungi in seven hospitals (Simmons et al., 1997). Since they are enclosed environments, indoor environments were reported to possibly place patients at greater risk than outside environments as they can confine and allow aerosols to build up to infectious levels (Ekhaise, 2008). Although monitoring is essential, quantification and rapid identification of airborne contaminants is imperative. Identification of bacteria and fungi by conventional phenotypic methods is often difficult, time consuming and may sometimes be inconclusive (Marklein et al., 2009; Verroken et al., 2010). MALDI-TOF MS has been shown as a rapid and cheap method for identification of microorganisms when compared to molecular and conventional techniques. There is limited information regarding bioaerosols at South African hospitals indicating that this is an area that is hardly investigated. There is also a need for rapid identification of bioaerosols when found. The aims of this study were to quantify and identify bioaerosols at a South African Hospital.

3 Materials and methods Sample collection Air samples were collected using a single-stage SAS super 90 viable impactor on Plate Count Agar (PCA) for bacteria and Potato Dextrose Agar (PDA) for fungi at an airflow rate of 28.3L/min for 2 minutes. All removable components of the air sampler we autoclaved before sampling and disinfected with 70% ethanol between each sampling run. The air sampler was placed 1.5m above the floor, the height of the human breathing zone. Air samples impacted on PDA and PCA were incubated at 25 C and 38 C respectively for hours. The positive-hole conversion table and the sampled air volume were used to calculate concentrations (reported as cfu/m 3 ). Colonies on agar plates were identified using the Matrix-Assisted Laser Desorption/Ionization time-of-flight Mass Spectrometry (MALDI-TOF MS). Mass Spectrometry Identification of colonies by MALDI-TOF MS was performed on a Microflex LT instrument (Bruker Daltonics GmbH, Leipzig, Germany) with flex control (version 3.0) software (Bruker Daltonics). Colonies were smeared as a thin film directly on a MALDI steel target. Following this, smeared colonies were overlaid with 1µl of HCCA matrix solution (a saturated solution of α-cyano-4-hydroxycinnamic acid in 50% acetonitrile / 2.5% tri-flour-acetic-acid) and left to air dry at room temperature. The MALDI steel plate was placed in the MALDI-TOF-MS for analysis. Criteria for identification of isolates The MALDI-TOF MS scores are reported as a logarithmic value, the value is obtained by alignment of peaks of the unknown raw spectrum and the best matching database spectrum. Acquired spectra were interpreted using the manufacturer-recommended scores (genus, 1.7; species, 2.0).

4 Results Table1. Showing counts and identified microbial species at different rooms in selected wards. WARD COUNTS IN SAMPLED WARDS MALDI-TOF MS Scores IDENTIFIED ORGANISMS A 1-6 cfu/ml -1 in 5 rooms B 2-6 cfu/ml -1 in 5 rooms C 2-5 cfu/ml -1 in 5 rooms D 2-95 cfu/ml -1 in 5 rooms E 2-12 cfu/ml -1 in 5 rooms Staphylococcus hominis 18 ESL Candida sp DSM1247_DSM Micrococcus luteus 59 PIM Micrococcus luteus IMET HKJ Arthrobacter ilicis DSM 20138T DSM Staphylococcus simulans DSM DSM Staphylococcus epidermidis CHB Anaerococcus sp HU40261 PNU Staphylococcus haemolyticus DSM DSM Micrococcus luteus IMET HKJ Rhodotorula mucilaginosa DSM DSM Kocuria rhizophila DSM 11926T DSM Staphylococcus hominis 18 ESL Cryptococcus uniguttulatus DSM 4652 DSM Arthrobacter oxydans IMET 10684T HKJ Micrococcus luteus BK_01140_09 ERL Staphylococcus hominis 18 ESL Staphylococcus haemolyticus CHB

5 Table 2. Ward MALDI-TOF MS Score Organisms Ward A Ventilation system Ward B Ventilation system Ward C Ventilation system 1.7 Arthrobacter oxydans DSM 20119T DSM Arthrobacter pigmenti DSM 16403T DSM Rhodotorula minuta DSM 4043 DSM Anaerococcus sp HU40261 PNU Ward D ventilation system 2.2 Arthrobacter polychromogenes DSM 20136T DSM Ward E ventilation system 2.2 Arthrobacter polychromogenes DSM 20136T DSM Discussion In general, bacterial and fungal counts were low ranging between 2-95 cfu/m 3 for bacteria and 5-25 cfu/m 3 for fungi. Identification of microorganisms was done using MALDI-TOF MS. Table one shows identified organisms from different rooms in wards A-E. MALDI-TOF MS scores 1.7 indicates secure genus identification and scores 2.0 indicates secure species identification. Identified airborne microbes included yeast genera Candida, Cryptococcus and Rhodotorula and bacterial genera Bacillus, Staphylococcus, Arthrobacter, and Micrococcus. Counts from vents ranged from cfu/ml -1. Table 2 shows identified microbial species collected using swabs from ventilation systems in selected wards; MALDI-TOF MS scores 1.7 indicates secure genus identification and scores 2.0 indicates secure species identification. The low microbial counts in the wards indicate that microbial contaminants were not aerosolized in significant quantities. However, the identified organisms could have negative health implications of immunocompromised patients. Possible aerosolization of genera Bacillus and Arthrobacter was observed in wards A, B and E. Conclusion This study indicates that MALDI-TOF MS is a cost-effective and rapid alternative to conventional or molecular methods for microbial identification. Rapid identification of airborne contaminants in hospitals is required to prevent possible outbreaks. Furthermore, frequent indoor air monitoring is essential in order to ensure patient safety as some of these microorganisms are opportunistic pathogens.

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