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Detecting and localizing leakages in underground water pipelines continue to be challenging in large-scale water distribution networks (WDNs) having a low density of acoustic sensors per unit pipeline. Many previous acoustic research analyses have been restricted to either (1) lab-scale WDN setups having simulated leakage conditions, or (2) field-scale WDNs with high densities of acoustic sensors. To address the current challenges, this study develops a generic and practical acoustic data analysis framework to analyze acoustics signals for leakage detection and localization in large-scale WDNs, where the proposed framework encompasses multi-stage systematic analyses, namely: (1) data quality assessment; (2) acoustic features generation and analysis; (3) leakage event detection; and (4) leakage event localization. In collaboration with Public Utility Board (PUB), Singapore's National Water Agency, our proposed framework has been verified in large-scale WDNs in Singapore having more than 1,100 km of underground water pipelines and 82 permanently installed monitoring stations, each of which is instrumented to measure acoustics, via hydrophone equipment, pressure, and water quality parameters, and by using historical data collected between August 1, 2019 and August 31, 2020, where numerous leakage events were reported to within 1,000 m, or less, connected pipelines from independent hydrophones. [ABSTRACT FROM AUTHOR]

Background: Despite the improvements in the incidence rate and mortality of colorectal cancer in Singapore, most cases are still diagnosed at later stages. This suggests limitations in the uptake of cancer screening, relating to suboptimal population awareness, unfamiliarity with tests, or cost concerns. Objective: We report our experience with a charity colonoscopy event to assist patients with endoscopy costs, raise public awareness of endoscopic cancer screening, and demonstrate the safety of colonoscopy. Design and Settings: The Singapore General Hospital (SGH) Preventing Rectal and Colon Cancer through Endoscopy (SPRUCE) fund was established in 2019. A charity endoscopic event was held to kickstart the official launch of the fund with corporate, individual, and industry support. Patients and Methods: The recruitment criteria were first-visit patients who required colonoscopic evaluation following specialist evaluation and those in possession of a Community Health Assist Scheme card, for which only Singapore citizens whose annual household per capita income was less than Singapore dollar (SGD) 24,000 (USD 17,500) were eligible. This enabled meaningful selection of patients who would most benefit from financial assistance. Main Outcome Measures: To determine the feasibility of a charity colonoscopy event while raising colorectal cancer public awareness. Sample Size: Forty-eight patients underwent colonoscopies by accredited endoscopists for the SPRUCE endoscopy event. Results: Of the 48 participants, 19 (39.5%) were men and 29 (60.4%) were women, with a median age of 64 years. Seven patients (14.6%) had a normal colonoscopy, while 42 (85.4%) had at least one abnormality documented. Forty-one benign polyps were detected and removed in 22 patients (45.8%), with an overall adenoma detection rate of 85.4%. One patient (2.1%) was diagnosed with colon cancer. No complications resulted from the procedures performed during the charity event. Conclusion: A charity colonoscopy event conducted in the form of a health fair for needy patients is feasible and may help raise colorectal cancer awareness. This framework may serve as a useful basis for similar future events held in Singapore or elsewhere. Limitations: Lack of longitudinal assessment of colorectal cancer awareness among the public, following the charity colonoscopy event. Conflict of Interest: The authors have no conflict of interest to declare. [ABSTRACT FROM AUTHOR]

Introduction: We aimed to report the ten-year incidence and trend of sharps injuries and splash exposures (SISE), and analyse the causative instruments and risk factors leading to these injuries to identify potential areas of improvement.Methods: A retrospective review of self-reported SISE incidents from 2005 to 2014 was conducted in Singapore General Hospital. Data was extracted from the electronic database of the Department of Infection Prevention and Control. Incidence of SISE was calculated per 1,000 healthcare workers (HCWs) per year.Results: Over the ten-year period, a total of 1,901 SISE were reported. The average SISE incidence per year was 110.5 per 1,000 doctors and 22.8 per 1,000 nursing staff, with an overall incidence of 28.9 per 1,000 HCWs. The incidence of SISE decreased from 30.3 to 22.0 per 1,000 HCWs per year from 2005 to 2014, while that for splash exposures increased from 1.9 to 3.7 per 1,000 HCWs per year. Doctors reported the highest number of SISE (43.7%), followed by nurses (37.7%). Top mechanisms of injury were intraoperative procedures (22.8%), drawing blood (14.4%) and splash exposures (13.7%).Conclusion: Overall incidence of SISE decreased over the decade. Improved training and increasing use of safety devices, education and awareness could have contributed to the fall in incidence of sharps injuries; these measures should be reinforced and continued. However, the incidence of splash exposures increased over the same period. Preventive measures such as the use of protective goggles and face shields, together with personal protective equipment, should be emphasised and encouraged. [ABSTRACT FROM AUTHOR]

Background: Factors associated with place of death inform policies with respect to allocating end-of-life care resources and tailoring supportive measures. Objective: To determine factors associated with non-hospital deaths among cancer patients. Design: Retrospective cohort study of cancer decedents, examining factors associated with non-hospital deaths using multinomial logistic regression with hospital deaths as the reference category. Setting/subjects: Cancer patients (n = 15254) in Singapore who died during the study period from January 1, 2012 till December 31, 2105 at home, acute hospital, long-term care (LTC) or hospice were included. Results: Increasing age (categories ≥65 years: RRR 1.25–2.61), female (RRR 1.40; 95% CI 1.28–1.52), Malays (RRR 1.67; 95% CI 1.47–1.89), Brain malignancy (RRR 1.92; 95% CI 1.15–3.23), metastatic disease (RRR 1.33–2.01) and home palliative care (RRR 2.11; 95% CI 1.95–2.29) were associated with higher risk of home deaths. Patients with low socioeconomic status were more likely to have hospice or LTC deaths: those living in smaller housing types had higher risk of dying in hospice (1–4 rooms apartment: RRR 1.13–3.17) or LTC (1–5 rooms apartment: RRR 1.36–4.11); and those with Medifund usage had higher risk of dying in LTC (RRR 1.74; 95% CI 1.36–2.21). Patients with haematological malignancies had increased risk of dying in hospital (categories of haematological subtypes: RRR 0.06–0.87). Conclusions: We found key sociodemographic and clinical factors associated with non-hospital deaths in cancer patients. More can be done to enable patients to die in the community and with dignity rather than in a hospital. [ABSTRACT FROM AUTHOR]

• Developed a generic digital twin (DT) framework for smart water grids (SWG). • Integrated data analytics and hydraulic model calibration for constructing high-fidelity SWG DT. • Automated model training including data cleansing, outlier detection and event classification. • Applied DT-based approach to three supply zone with pipeline length of more than 1000 km. • Achieved event detection accuracy of greater than 80% with lead time of 1 to 3 days. Smart Water Grid (SWG) plays a critical role in sustaining cities economic and social development, but challenges remain in fully realizing the benefits of SWG. While Digital twin (DT) has been discussed in some literature for possible SWG applications, there has been limited, or no technical framework developed to facilitate SWG operation and management. In this paper, a generic framework is developed for constructing SWG high fidelity Digital Twin (DT) by integrating digital thread with various digital models for visualization, data-driven analysis, physics-based simulations, and decision-making support. Both the physics-based models and data-driven models are trained/retrained and calibrated/re-calibrated respectively by using the data collected with the sensors installed throughout a SWG. The information derived from the SWG DT can be diagnostic, predictive, and prescriptive to significantly augment users' intelligence for improving SWG operation and management. One important application of digital twin augmented intelligence is illustrated to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. The solution is tested on the selected areas in Singapore to construct the ever-green DT by calibrating and recalibrating the models for near real-time SWG operation management. The case study was conducted for three supply zones with the total pipeline length of more than 1000 km and 40 weeks of monitoring data, collected by 89 pressure monitoring stations and 8 flow meters at inlets and boundary. More than 3300 data-driven models are trained for optimizing the model performance to achieve accuracy of greater than 80% F1 score for detecting anomaly events, which are subsequently localized within 400 m with 2–3 days lead time. [ABSTRACT FROM AUTHOR]

Introduction: Singapore is a developed country with a population of 5.6 million and an acutecentric healthcare system. While the acute hospitals deliver extensive care, patients with multiple care needs face challenges in managing their conditions post-discharge, due to various reasons, such as weak caregiver support. This phenomenon is especially evident in older and frail patients who are frequently readmitted as their condition(s) deteriorate post-discharge. Methods: In April 2017, Singapore's Ministry of Health (MOH) launched a nationwide Hospital to Home (H2H) care model to provide holistic patient-centric care to support patients discharging from public hospitals, so that they can transit back home, and stay well in the community. H2H targets high healthcare utilizers who are frail with complex care needs. The service includes medication reconciliation, case management, care coordination, telephonic support and caregiver training. To facilitate the development and deployment of the predictive model to risk stratify and segment the patients for enrolment assessment into H2H, a workgroup consisting of clinicians and data scientists across the various public hospitals was formed. The dependent variable for the model was three or more non-elective inpatient admissions within a 1-year period. Independent variables, generated based on literature review and input from clinicians and care managers, were segmented into 3 primary categories: sociodemographic, past hospital utilization and past medical conditions. Results: The top important variable was "total LOS in the past 12 months" followed by "number of days from previous non-elective admission" which lifted the model performance significantly. The best performing machine learning algorithm has an AUC of 0.79, recall of 39%, with precision set at 70% after consultation with the H2H clinical committee to capture very high risk patients. The prediction list, generated on daily basis, is now an integrated part of patient assessment workflow. The list helps the care team to support and follow up with the high healthcare utilizers and their caregivers after discharge. As of Oct 2018, more than 23,000 patients had benefited. Conclusions: The predictive model exemplifies the benefit of augmenting prediction model with clinical assessment. By having the prediction tool act as a bigger "sieve" to do the first level of filtering and the subsequent removal of potential false positives by clinicians when they assessed the patient in-ward provides a targeted intervention with minimal clinical resources. Lessons learnt: To manage the multidisciplinary development team in this endeavour efficiently, we adopted an iterative development process that allow us to fail fast and often. To convince clinicians to shift their decision making process to be augmented with a predictive model, we engaged reputable clinicians in the H2H clinical committee and analytics teams from different public hospitals to co-develop the model. Limitations: In this discussion, we had not been able to cover in depth on areas such as comparing the predictive model performance with different set of features. Future research: Future enhancements include the use of Natural Language Processing on unstructured clinical notes to extract care-giver and non-clinical information as additional features for the prediction model. [ABSTRACT FROM AUTHOR]

Improving the quality of primary care may reduce avoidable hospital admissions. Avoidable admissions for conditions such as diabetes are used as a quality metric in the Health Care Quality Indicators of the Organization for Economic Cooperation and Development (OECD). Using the OECD indicators, we compared avoidable admission rates and spending for diabetes-related complications in Japan, Singapore, Hong Kong, and rural and peri-urban Beijing, China, in the period 2008-14. We found that spending on diabetes-related avoidable hospital admissions was substantial and increased from 2006 to 2014. Annual medical expenditures for people with an avoidable admission were six to twenty times those for people without an avoidable admission. In all of our study sites, when we controlled for severity, we found that people with more outpatient visits in a given year were less likely to experience an avoidable admission in the following year, which implies that primary care management of diabetes has the potential to improve quality and achieve cost savings. Effective policies to reduce avoidable admissions merit investigation. [ABSTRACT FROM AUTHOR]

Introduction: We investigated sex and ethnic differences in the incidence, clinical characteristics and 1-year mortality of patients with newly diagnosed AF in a multi-ethnic population., Method: This retrospective cohort study of patients diagnosed with AF from 2008 to 2015 was based on medical claims, casemix and subvention data submitted to the Ministry of Health. Patients with AF were matched with controls without AF for age (3-year bands), sex and ethnicity, and categorised as middle-aged (45-64 years) or elderly (≥65 years) among major ethnic groups in Singapore (Chinese, Malay and Indian)., Results: Among 40,602 adults with AF (elderly 74%), Malays had the highest age-standardised incidence rate of AF, followed by Chinese and Indians; and the rate was higher in men. Despite having the worst cardiovascular risk profile, Indians had the lowest prevalence and incidence of AF. The 1-year mortality rate after newly diagnosed AF was 22-26 deaths per 100 people. Newly diagnosed AF was independently associated with increased 1-year all-cause mortality among middle-aged (adjusted odds ratio [AOR] 9.08, 95% confidence interval [CI] 7.36-11.20) and elderly adults (AOR 3.60, 95% CI 3.40-3.80) compared with those without AF. Sex differences in mortality among patients with AF were limited to elderly adults (men: AOR 1.17, 95% CI 1.11-1.24), while Indians were associated with a 30% increased odds of mortality compared with Chinese regardless of age (middle-aged: AOR 1.27, 95% CI 1.09-1.548 elderly: AOR 1.33, 95% CI 1.22-1.45)., Conclusion: Variations in incidence, clinical profile and 1-year mortality of patients with AF in a nationwide cohort were influenced by sex and ethnicity. Newly diagnosed AF portends a worse prognosis and is a marker of high mortality within the first year.

Introduction: Air pollution is associated with adverse health outcomes. However, its impact on emergency health services is less well understood. We investigated the impact of air pollution on nation-wide emergency department (ED) visits and hospital admissions to public hospitals in Singapore., Materials and Methods: Anonymised administrative and clinical data of all ED visits to public hospitals in Singapore from January 2010 to December 2015 were retrieved and analysed. Primary and secondary outcomes were defined as ED visits and hospital admissions, respectively. Conditional Poisson regression was used to model the effect of Pollutant Standards Index (PSI) on each outcome. Both outcomes were stratified according to subgroups defined a priori based on age, diagnosis, gender, patient acuity and time of day., Results: There were 5,791,945 ED visits, of which 1,552,187 resulted in hospital admissions. No significant association between PSI and total ED visits (Relative risk [RR], 1.002; 99.2% confidence interval [CI], 0.995-1.008; P = 0.509) or hospital admissions (RR, 1.005; 99.2% CI, 0.996-1.014; P = 0.112) was found. However, for every 30-unit increase in PSI, significant increases in ED visits (RR, 1.023; 99.2% CI, 1.011-1.036; P = 1.24 × 10 -6 ) and hospital admissions (RR, 1.027; 99.2% CI, 1.010-1.043; P = 2.02 × 10 -5 ) for respiratory conditions were found., Conclusion: Increased PSI was not associated with increase in total ED visits and hospital admissions, but was associated with increased ED visits and hospital admissions for respiratory conditions in Singapore.

Context: Risk scores for cardiovascular and mortality outcomes have not been commonly applied in Chinese populations., Objective: To develop and externally validate a set of parsimonious risk scores [University of Hong Kong-Singapore (HKU-SG)] to predict the risk of mortality, cerebrovascular disease, and ischemic heart disease among Chinese people with type 2 diabetes and compare HKU-SG risk scores to other existing ones., Design: Retrospective population-based cohorts drawn from Hong Kong Hospital Authority health records from 2006 to 2014 for development and Singapore Ministry of Health records from 2008 to 2016 for validation. Separate five-year risk scores were derived using Cox proportional hazards models for each outcome., Setting: Study participants were adults with type 2 diabetes aged 20 years or over, consisting of 678,750 participants from Hong Kong and 386,425 participants from Singapore., Main Outcome Measures: Performance was evaluated by discrimination (Harrell C-index), and calibration plots comparing predicted against observed risks., Results: All models had fair external discrimination. Among the risk scores for the diabetes population, ethnic-specific risk scores (HKU-SG and Joint Asia Diabetes Evaluation) performed better than UK Prospective Diabetes Study and Risk Equations for Complications Of type 2 Diabetes models. External validation of the HKU-SG risk scores for mortality, cerebrovascular disease, and ischemic heart disease had corresponding C-indices of 0.778, 0.695, and 0.644. The HKU-SG models appeared well calibrated on visual plots, with predicted risks closely matching observed risks., Conclusions: The HKU-SG risk scores were developed and externally validated in two large Chinese population-based cohorts. The parsimonious use of clinical predictors compared with previous risk scores could allow wider implementation of risk estimation in diverse Chinese settings., (Copyright © 2019 Endocrine Society.)

Introduction: We aimed to report the ten-year incidence and trend of sharps injuries and splash exposures (SISE), and analyse the causative instruments and risk factors leading to these injuries to identify potential areas of improvement., Methods: A retrospective review of self-reported SISE incidents from 2005 to 2014 was conducted in Singapore General Hospital. Data was extracted from the electronic database of the Department of Infection Prevention and Control. Incidence of SISE was calculated per 1,000 healthcare workers (HCWs) per year., Results: Over the ten-year period, a total of 1,901 SISE were reported. The average SISE incidence per year was 110.5 per 1,000 doctors and 22.8 per 1,000 nursing staff, with an overall incidence of 28.9 per 1,000 HCWs. The incidence of SISE decreased from 30.3 to 22.0 per 1,000 HCWs per year from 2005 to 2014, while that for splash exposures increased from 1.9 to 3.7 per 1,000 HCWs per year. Doctors reported the highest number of SISE (43.7%), followed by nurses (37.7%). Top mechanisms of injury were intraoperative procedures (22.8%), drawing blood (14.4%) and splash exposures (13.7%)., Conclusion: Overall incidence of SISE decreased over the decade. Improved training and increasing use of safety devices, education and awareness could have contributed to the fall in incidence of sharps injuries; these measures should be reinforced and continued. However, the incidence of splash exposures increased over the same period. Preventive measures such as the use of protective goggles and face shields, together with personal protective equipment, should be emphasised and encouraged., (Copyright: © Singapore Medical Association.)

Objectives: Readmission after acute care is a significant contributor to health care costs, and has been proposed as a quality indicator. Our earlier studies showed that patients aged ≥55 years who are injured by falls from heights of ≤0.5 m were at increased risk for long-term mortality, compared to patients by high-velocity blunt trauma (higher fall heights, road injuries, and other blunt trauma). We hypothesized that these patients are also at higher risk of readmission, compared to patients injured by high-velocity mechanisms., Design and Measures: Competing risks regression (all-cause unplanned readmission or death) was performed., Setting and Participants: Data for 5671 patients from the Singapore National Trauma Registry data who were injured from 2011-2013 and aged 55 and over were matched to Ministry of Health admissions data. The registry uses standardized conversion metrics to convert patient histories to fall heights., Results: Patients injured after a low fall were more likely to be readmitted to a hospital, compared to those sustaining injuries by high-velocity blunt trauma. On competing risks analysis, low fall [subdistribution hazard ratio (SHR) 1.52, 95% confidence interval (CI) 1.20-1.93, P < .01], Charlson Comorbidity Score (CCS≥3 relative to CCS = 0, SHR 1.46, 95% CI 1.04-2.04, P = .03), and Modified Frailty Index (MFI≥3 relative to MFI = 0, SHR 1.98, 95% CI 1.44-2.72, P < .001) were associated with higher risk of 30-day readmission. Rehabilitation was associated with reduced 30-day (SHR 0.64, 95% CI 0.48-0.86, P < .001) and 1-year (SHR 0.84, 95% CI 0.72-0.99, P = .04) readmission., Conclusions/implications: Our study sheds light on the interpretation of trauma data in aging populations. The detailed fall height information in our registry makes it uniquely placed to facilitate understanding of the paradoxical finding that injuries sustained by low-energy falls are higher risk than those sustained by higher-velocity mechanisms. Low-fall patients should be prioritized for rehabilitation and postdischarge support. The proportion of low-fall patients in a trauma registry should be included in the factors considered for benchmarking., (Copyright © 2018 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)