Environmental damage affects many aspects of healthcare, from extreme weather events to evolving population disease. Singapore's healthcare sector has the world's second highest healthcare emissions per capita, hampering the nation's pledge to reduce emissions by 2030 and achieve net zero emissions by 2050. In this review, we provide an overview of the impact environmental damage has on healthcare, including facilities, supply chain and human health, and examine measures to address healthcare's impact on the environment. Utilising the 'R's of sustainability - rethinking, reducing/refusing, reusing/repurposing/reprocessing, repairing, recycling and research - we have summarised the opportunities and challenges across medical disciplines. Awareness and advocacy to adopt strategies at institutional and individual levels is needed to revolutionise our environmental footprint and improve healthcare sustainability. By leveraging evidence from ongoing trials and integrating sustainable practices, our healthcare system can remain resilient against environment-driven challenges and evolving healthcare demands while minimising further impacts of environmental destruction.
Humans ; Climate Change ; Delivery of Health Care ; Singapore ; Conservation of Natural Resources ; Sustainable Development ; Environment

INTRODUCTION: Degenerative mitral stenosis (DMS) is frequently cited as increasing in prevalence in the developed world, although comparatively little is known about DMS in comparison to rheumatic mitral stenosis (RMS). METHOD: A retrospective observational study was conducted on 745 cases of native-valve mitral stenosis (MS) with median follow-up time of 7.25 years. Clinical and echocardiographic parameters were compared. Univariate and multivariate Cox regression analyses were performed for a composite of all-cause mortality and heart failure hospitalisation. RESULTS: Patients with DMS compared to RMS were older (age, mean ± standard deviation: 69.6 ± 12.3 versus [vs] 51.6 ± 14.3 years, respectively; P<0.001) and a greater proportion had medical comorbidities such as diabetes mellitus (78 [41.9%] vs 112 [20.0%], P<0.001). The proportion of cases of degenerative aetiology increased from 1.1% in 1991-1995 to 41.0% in 2016-2017. In multivariate analysis for the composite outcome, age (hazard ratio [HR] 95% confidence interval [CI] of 1.032 [1.020-1.044]; P<0.001), diabetes mellitus (HR 1.443, 95% CI 1.068-1.948; P=0.017), chronic kidney disease (HR 2.043, 95% CI 1.470-2.841; P<0.001) and pulmonary artery systolic pressure (HR 1.019, 95% CI 1.010- 1.027; P<0.001) demonstrated significant indepen-dent associations. The aetiology of MS was not independently associated with the composite outcome. CONCLUSION DMS is becoming an increasingly common cause of native-valve MS. Despite numerous clinical differences between RMS and DMS, the aetiology of MS did not independently influence a composite of mortality or heart failure hospitalisation.
Humans ; Mitral Valve Stenosis/etiology* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Rheumatic Heart Disease/mortality* ; Echocardiography ; Hospitalization/statistics & numerical data* ; Heart Failure/epidemiology* ; Singapore/epidemiology* ; Proportional Hazards Models ; Diabetes Mellitus/epidemiology*

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Construct Validity and Reliability of the Malay Version of Rosenberg Self Esteem Scale (RSES) among youth in Southern Malaysia: A Confirmatory Factor Analysis IntroductionThe Rosenberg Self-Esteem Scale (RSES) is widely used to measure self-esteem among adults and youth. This study aims to determine the construct validity and reliability of the Rosenberg Self-Esteem Scale Malay version (RSES-M) among Malaysian youth using Confirmatory Factor Analysis (CFA).MethodologyWe administered the Malay-language RSES to 378 Form Four students in the Kota Tinggi District, selected through multistage sampling. The construct validity of RSES-M was assessed using confirmatory factor analysis (CFA), while internal consistency was measured using Cronbach alpha. AMOS version 26 and SPSS version 20 were used for statistical analysis. We compared three measurement models of the RSES-M for the best relative fit: one uni-dimensional model and two different two-domain models (with different items assigned to each domain for each model).ResultsThe findings indicate that the best model for the RSES-M was a two-domain model, with domain one representing positive self-esteem and domain two representing negative self-esteem. The item “I wish I could respect myself more” demonstrated a strong fit within the CFA model when included under the positive domain of self-esteem (Model 3) compared to negative domain ((Model 2) (Chi-Square/degree of freedom (df) = 3.341, goodness of fit (GFI) = 0.967, Comparative Fit Index (CFI) = 0.905, Incremental Fit Index (IFI) = 0.906, and the Root Mean Squared Error of Approximation (RMSEA) = 0.079 and substantial reliability (Cronbach's alpha for domain one = 0.765, and domain two = 0.648). This finding diverges from the original RSES developed by Morris Rosenberg in 1965, which conceptualised the RSES as a unidimensional construct, and other studies that categorised the item "I wish I could respect myself more" under the negative self-esteem domain.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact in Asia and has placed significant burden on already stretched healthcare systems. We examined the impact of COVID-19 on the safety attitudes among healthcare workers (HCWs), as well as their associated demographic and occupational factors, and measures of burnout, depression and anxiety. METHODS: A cross-sectional survey study utilising snowball sampling was performed involving doctors, nurses and allied health professions from 23 hospitals in Singapore, Malaysia, India and Indonesia between 29 May 2020 and 13 July 2020. This survey collated demographic data and workplace conditions and included three validated questionnaires: the Safety Attitudes Questionnaire (SAQ), Oldenburg Burnout Inventory and Hospital Anxiety and Depression Scale. We performed multivariate mixed-model regression to assess independent associations with the SAQ total percentage agree rate (PAR). RESULTS: We obtained 3,163 responses. The SAQ total PARs were found to be 35.7%, 15.0%, 51.0% and 3.3% among the respondents from Singapore, Malaysia, India and Indonesia, respectively. Burnout scores were highest among respondents from Indonesia and lowest among respondents from India (70.9%-85.4% vs. 56.3%-63.6%, respectively). Multivariate analyses revealed that meeting burnout and depression thresholds and shifts lasting ≥12 h were significantly associated with lower SAQ total PAR. CONCLUSION Addressing the factors contributing to high burnout and depression and placing strict limits on work hours per shift may contribute significantly towards improving safety culture among HCWs and should remain priorities during the pandemic.
Humans ; Cross-Sectional Studies ; Pandemics ; COVID-19/epidemiology* ; Burnout, Psychological ; Health Personnel