Testing Air Quality of Primary Health Care Centers in Qatar

Abstract

Background: Poor indoor air quality results in significant adverse effects on human health. In particular, the hospital atmospheric environment requires high air quality to protect patients and health care workers against airborne disease including nosocomial infections. Monitoring and surveillance programs of air pollutants and communicable diseases are essential as they provide information on the effectiveness of occupational hygiene and hazard control, and beneficial in assessing risks to community and environment. Objectives: This study aims to identify, monitor and report the level of air borne bacteria at four PHCC canters in Doha. Methodology: Four primary Health Centers (HC) were selected for testing air quality namely, Qatar University HC (North of Doha), AlRayan HC (West of Doha), Um Ghualina HC (Centre of Doha) and Old airport HC (South of Doha). Three sublocations were tested in each health center including a triage room, lobby and outdoor sample; each centre was visited once a month. Two sampling methods were used in this study: Anderson impactor (viable method) and filtration method (non-viable method). Anderson six stages impactor (TISCH Environmental, USA) was used to collect airborne bacteria on nutrient agar plates. Then the samples were incubated at 37 o C for 24 - 48 hours. The average colony- forming units (CFU) of bacteria was calculated per cubic meter of air (CFU/m3). On the other hand, the SKC Button Sampler (SKC Inc. PA, USA) was used to collect the airborne bacteria using cellulose ester filters. The collected isolates will be identified by sequencing 16srRNA (Miseq) later. Preliminary results: According to our preliminary results, the smallest average number of bacteria in the air was detected in QU HC, 3.2 (CFU/m3). While the highest average number was 44.7 CFU/m3 detected in Old Airport HC. Al-Rayyan HC and Om Ghuilina HC exhibited 30 and 20 CFU/m3 sequentially. Conclusions: Our preliminary results depicted that the occupancy pattern, size, and age of the building affect the number of bacteria in the air. However, more samples will be collected for better statistical sample size and analysis. .In addition, the captured airborne bacteria will be identified by 16s r RNA sequencing