OBJECTIVES: This study aimed to evaluate the determinants of health-related quality of life (HRQoL) among workers in Singapore. METHODS: We analysed data from a cross-sectional study of 464 participants from 4 companies in Singapore. Physical and mental components of HRQoL were assessed using the Short-Form 36 version 2.0 survey. A generalized linear model was used to determine factors associated with the physical component summary (PCS) and mental component summary (MCS) scores of HRQoL. RESULTS: The overall mean PCS and MCS scores were mean±standard deviation 51.6±6.7 and 50.2±7.7, respectively. The scores for subscales ranged from 62.7±14.7 for vitality to 83.5±20.0 for role limitation due to emotional problems. Ethnicity, overweight/obesity, and years working at the company were significantly associated with physical HRQoL, and age and stress at work were significantly associated with mental HRQoL. Moreover, sleep quality was significantly associated with both physical and mental HRQoL. CONCLUSIONS These findings could help workplaces in planning strategies and initiatives for employees to maintain a worklife balance that encompasses their physical, emotional, and social well-being.

OBJECTIVES: This study aimed to evaluate the determinants of health-related quality of life (HRQoL) among workers in Singapore. METHODS: We analysed data from a cross-sectional study of 464 participants from 4 companies in Singapore. Physical and mental components of HRQoL were assessed using the Short-Form 36 version 2.0 survey. A generalized linear model was used to determine factors associated with the physical component summary (PCS) and mental component summary (MCS) scores of HRQoL. RESULTS: The overall mean PCS and MCS scores were mean±standard deviation 51.6±6.7 and 50.2±7.7, respectively. The scores for subscales ranged from 62.7±14.7 for vitality to 83.5±20.0 for role limitation due to emotional problems. Ethnicity, overweight/obesity, and years working at the company were significantly associated with physical HRQoL, and age and stress at work were significantly associated with mental HRQoL. Moreover, sleep quality was significantly associated with both physical and mental HRQoL. CONCLUSIONS These findings could help workplaces in planning strategies and initiatives for employees to maintain a worklife balance that encompasses their physical, emotional, and social well-being.

Background: The optimal screening interval for diabetic retinopathy (DR) remains controversial. This study aimed to develop a risk algorithm to predict the individual risk of referable sight-threatening diabetic retinopathy (STDR) in a mainly Chinese population and to provide evidence for risk-based screening intervals. Methods: The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. The risk algorithm can be used to predict the individual risk of STDR within a specific time interval, or the time to reach a specific risk margin and thus to allocate a screening interval. The calibration performance was assessed by comparing the cumulative STDR events versus predicted risk over 2 years, and discrimination by using receiver operative characteristics (ROC) curve. Results: Duration of diabetes, glycosylated hemoglobin, systolic blood pressure, presence of chronic kidney disease, diabetes medication, and age were included in the risk algorithm. The validation of prediction performance showed that there was no significant difference between predicted and observed STDR risks in males (5.6% vs. 5.1%, P=0.724) or females (4.8% vs. 4.6%, P=0.099). The area under the receiver operating characteristic curve was 0.80 (95% confidence interval [CI], 0.78 to 0.81) for males and 0.81 (95% CI, 0.79 to 0.83) for females. Conclusion The risk algorithm has good prediction performance for referable STDR. Using a risk-based screening interval allows us to allocate screening visits disproportionally more to those at higher risk, while reducing the frequency of screening of lower risk people.

Background: The optimal screening interval for diabetic retinopathy (DR) remains controversial. This study aimed to develop a risk algorithm to predict the individual risk of referable sight-threatening diabetic retinopathy (STDR) in a mainly Chinese population and to provide evidence for risk-based screening intervals. Methods: The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. The risk algorithm can be used to predict the individual risk of STDR within a specific time interval, or the time to reach a specific risk margin and thus to allocate a screening interval. The calibration performance was assessed by comparing the cumulative STDR events versus predicted risk over 2 years, and discrimination by using receiver operative characteristics (ROC) curve. Results: Duration of diabetes, glycosylated hemoglobin, systolic blood pressure, presence of chronic kidney disease, diabetes medication, and age were included in the risk algorithm. The validation of prediction performance showed that there was no significant difference between predicted and observed STDR risks in males (5.6% vs. 5.1%, P=0.724) or females (4.8% vs. 4.6%, P=0.099). The area under the receiver operating characteristic curve was 0.80 (95% confidence interval [CI], 0.78 to 0.81) for males and 0.81 (95% CI, 0.79 to 0.83) for females. Conclusion The risk algorithm has good prediction performance for referable STDR. Using a risk-based screening interval allows us to allocate screening visits disproportionally more to those at higher risk, while reducing the frequency of screening of lower risk people.

Background: The optimal screening interval for diabetic retinopathy (DR) remains controversial. This study aimed to develop a risk algorithm to predict the individual risk of referable sight-threatening diabetic retinopathy (STDR) in a mainly Chinese population and to provide evidence for risk-based screening intervals. Methods: The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. The risk algorithm can be used to predict the individual risk of STDR within a specific time interval, or the time to reach a specific risk margin and thus to allocate a screening interval. The calibration performance was assessed by comparing the cumulative STDR events versus predicted risk over 2 years, and discrimination by using receiver operative characteristics (ROC) curve. Results: Duration of diabetes, glycosylated hemoglobin, systolic blood pressure, presence of chronic kidney disease, diabetes medication, and age were included in the risk algorithm. The validation of prediction performance showed that there was no significant difference between predicted and observed STDR risks in males (5.6% vs. 5.1%, P=0.724) or females (4.8% vs. 4.6%, P=0.099). The area under the receiver operating characteristic curve was 0.80 (95% confidence interval [CI], 0.78 to 0.81) for males and 0.81 (95% CI, 0.79 to 0.83) for females. Conclusion The risk algorithm has good prediction performance for referable STDR. Using a risk-based screening interval allows us to allocate screening visits disproportionally more to those at higher risk, while reducing the frequency of screening of lower risk people.

Background: The optimal screening interval for diabetic retinopathy (DR) remains controversial. This study aimed to develop a risk algorithm to predict the individual risk of referable sight-threatening diabetic retinopathy (STDR) in a mainly Chinese population and to provide evidence for risk-based screening intervals. Methods: The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. The risk algorithm can be used to predict the individual risk of STDR within a specific time interval, or the time to reach a specific risk margin and thus to allocate a screening interval. The calibration performance was assessed by comparing the cumulative STDR events versus predicted risk over 2 years, and discrimination by using receiver operative characteristics (ROC) curve. Results: Duration of diabetes, glycosylated hemoglobin, systolic blood pressure, presence of chronic kidney disease, diabetes medication, and age were included in the risk algorithm. The validation of prediction performance showed that there was no significant difference between predicted and observed STDR risks in males (5.6% vs. 5.1%, P=0.724) or females (4.8% vs. 4.6%, P=0.099). The area under the receiver operating characteristic curve was 0.80 (95% confidence interval [CI], 0.78 to 0.81) for males and 0.81 (95% CI, 0.79 to 0.83) for females. Conclusion The risk algorithm has good prediction performance for referable STDR. Using a risk-based screening interval allows us to allocate screening visits disproportionally more to those at higher risk, while reducing the frequency of screening of lower risk people.

The development of underground workspaces is a strategic effort towards healthy urban growth in cities with ever-increasing land scarcity. Despite the growth in underground workspaces, there is limited information regarding the impact of this environment on workers’ health. The Health Effects of Underground Workspaces (HEUW) study is a cohort study that was set up to examine the health effects of working in underground workspaces. In this paper, we describe the rationale for the study, study design, data collection, and baseline characteristics of participants. The HEUW study recruited 464 participants at baseline, of whom 424 (91.4%) were followed-up at 3 months and 334 (72.0%) at 12 months from baseline. We used standardized and validated questionnaires to collect information on socio-demographic and lifestyle characteristics, medical history, family history of chronic diseases, sleep quality, health-related quality of life, chronotype, psychological distress, occupational factors, and comfort levels with indoor environmental quality parameters. Clinical and anthropometric parameters including blood pressure, spirometry, height, weight, and waist and hip circumference were also measured. Biochemical tests of participants’ blood and urine samples were conducted to measure levels of glucose, lipids, and melatonin. We also conducted objective measurements of individuals’ workplace environment, assessing air quality, light intensity, temperature, thermal comfort, and bacterial and fungal counts. The findings this study will help to identify modifiable lifestyle and environmental parameters that are negatively affecting workers’ health. The findings may be used to guide the development of more health-promoting workspaces that attempt to negate any potential deleterious health effects from working in underground workspaces.