MATERIALS AND METHODS: PM2.5 was measured as a marker of SHS levels in a total of 61 restaurants, entertainment centres, internet cafes and pubs in Kuala Lumpur, Malaysia.
RESULTS: Under the current smoke-free laws smoking was prohibited in 42 of the 61 premises. Active smoking was observed in nearly one-third (n=12) of these. For premises where smoking was prohibited and no active smoking observed, the mean (standard deviation) indoor PM2.5 concentration was 33.4 (23.8) μg/m3 compared to 187.1 (135.1) μg/m3 in premises where smoking was observed The highest mean PM2.5 was observed in pubs [361.5 (199.3) μg/m3].
CONCLUSIONS: This study provides evidence of high levels of SHS across a range of hospitality venues, including about one-third of those where smoking is prohibited, despite 8 years of smoke-free legislation. Compliance with the legislation appeared to be particularly poor in entertainment centres and internet cafes. Workers and non-smoking patrons continue to be exposed to high concentrations of SHS within the hospitality industry in Malaysia and there is an urgent need for increased enforcement of existing legislation and consideration of more comprehensive laws to protect health.
Objective: This study assessed the impact of heat on the health and productivity among maize farmers in a hot tropical country.
Methods: A cross-sectional study was conducted among 396 maize farmers, randomly selected across Gombe province, Nigeria. The wet bulb globe temperature monitor (WBGT) Model QuesTemp036 was used in determining the heat index. Health was determined using a validated questionnaire, while productivity was determined by recording work output based on the number of ridges cultivated during the working hours.
Results: The farms recorded mean heat index with standard deviation (SD) of 31.56 (2.19) and 34.08 (1.54) in the hours of 9 am to 12 pm and 12-3 pm respectively, which exceeded the threshold level set by the ACGIH. Heavy sweating (93.2%), tiredness (48.5%), dizziness (34.1%), and headache (40.4%) were experienced by the respondents almost on daily basis. The finding further showed a significant difference in the farmers' productivity during the three time duration of the work day (p < 0.001). The productivity was significantly higher between the hours of 6-9 am (p < 0.001) and 12-3 pm (p < 0.001), compared to the hours of 9 am to 12 pm (p < 0.001). The factors that significantly predict the productivity outcome include temperature (p < 0.001), gender (p < 0.001), age (p=0.033), and BMI (p=0.008).
Conclusion: The farmers were frequently experiencing heat exhaustion which decreased their productivity.