Objective: Understanding the health and health service utilization of the population is critical for Regional Health System's (RHS) population health management (PHM) initiatives in Singapore. The RHS database is a collaborative effort toward developing a national architecture for healthcare utilization data across diverse clinical systems with disparate data models. This manuscript describes the setup of an RHS database which would facilitate big data analytics for proactive population health management and health services research. Materials and methods:The RHS database is a conglomeration of four isolated databases from the three RHSs. It contains linked National Healthcare Group (NHG) polyclinic visit records, specialist outpatient clinic visit records, hospital discharge records from Tan Tock Seng Hospital (TTSH), National University Hospital (NUH) and Alexandra Hospital (AH), chronic disease management system (CDMS) records and mortality records from local registries. The data linkage process was conducted using the unique identification number (NRIC) as the linking variable. The final anonymized database has multiple interconnected tables that includes patient demographics, chronic disease and healthcare utilization information. Results: Over 2.8 million patients had contact with the three RHSs from 2008 to 2013. The database facilitated risk stratification of patients based on their past healthcare utilization and chronic disease information. This database aids in understanding the cross-utilization of healthcare services across the three RHSs and can help address the challenges of setting up a distinct geographical boundary for individual RHSs. Conclusions: The RHS database has been established with the intention to support the secondary use of administrative health data in health services research and proactive PHM in Singapore.

Background Hearing aids (HA) is the primary medical intervention aimed to reduce hearing handicap. This study assessed the cost-effectiveness of HA for older adults who were volunteered to be screened for hearing loss in a community-based mobile hearing clinic (MHC). Methods Participants with (1) at least moderate hearing loss (≥40 dB HL) in at least one ear, (2) no prior usage of HA, (3) no ear related medical complications, and (4) had a Mini-Mental State Examination score ≥ 18 were eligible for this study. Using a delayed-start study design, participants were randomized into the immediate-start (Fitted) group where HA was fitted immediately or the delayed-start (Not Fitted) group where HA fitting was delayed for three months. Cost utility analysis was used to compare the cost-effectiveness of being fitted with HA combined with short-term, aural rehabilitation with the routine care group who were not fitted with HA. Incremental cost effectiveness ration (ICER) was computed. Health Utility Index (HUI-3) was used to measure utility gain, a component required to derive the quality adjusted life years (QALY). Total costs included direct healthcare costs, direct non-healthcare costs and indirect costs (productivity loss of participant and caregiver). Demographic data was collected during the index visit to MHC. Cost and utility data were collected three months after index visit and projected to five years. Results There were 264 participants in the Fitted group and 163 participants in the Not Fitted group. No between-group differences in age, gender, ethnicity, housing type and degree of hearing loss were observed at baseline. At 3 months, HA fitting led to a mean utility increase of 0.12 and an ICER gain of S$42,790/QALY (95% CI: S$32, 793/QALY to S$62,221/QALY). At five years, the ICER was estimated to be at S$11,964/QALY (95% CI: S$8996/QALY to S$17,080/QALY) assuming 70% of the participants continued using the HA. As fewer individuals continued using their fitted HA, the ICER increased. Conclusions HA fitting can be cost-effective and could improve the quality of life of hearing-impaired older individuals within a brief period of device fitting. Long term cost-effectiveness of HA fitting is dependent on its continued usage.

We estimated the generation interval distribution for coronavirus disease on the basis of serial intervals of observed infector–infectee pairs from established clusters in Singapore. The short mean generation interval and consequent high prevalence of presymptomatic transmission requires public health control measures to be responsive to these characteristics of the epidemic.

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Background Challenges in prognosticating patients diagnosed with advanced dementia (AD) hinders timely referrals to palliative care. We aim to develop and validate a prognostic model to predict one-year all-cause mortality (ACM) in patients with AD presenting at an acute care hospital. Methods This retrospective cohort study utilised administrative and clinical data from Tan Tock Seng Hospital (TTSH). Patients admitted to TTSH between 1st July 2016 and 31st October 2017 and identified to have AD were included. The primary outcome was ACM within one-year of AD diagnosis. Multivariable logistic regression was used. The PROgnostic Model for Advanced Dementia (PRO-MADE) was internally validated using a bootstrap resampling of 1000 replications and externally validated on a more recent cohort of AD patients. The model was evaluated for overall predictive accuracy (Nagelkerke’s R2 and Brier score), discriminative [area-under-the-curve (AUC)], and calibration [calibration slope and calibration-in-the-large (CITL)] properties. Results A total of 1,077 patients with a mean age of 85 (SD: 7.7) years old were included, and 318 (29.5%) patients died within one-year of AD diagnosis. Predictors of one-year ACM were age > 85 years (OR:1.87; 95%CI:1.36 to 2.56), male gender (OR:1.62; 95%CI:1.18 to 2.22), presence of pneumonia (OR:1.75; 95%CI:1.25 to 2.45), pressure ulcers (OR:2.60; 95%CI:1.57 to 4.31), dysphagia (OR:1.53; 95%CI:1.11 to 2.11), Charlson Comorbidity Index ≥ 8 (OR:1.39; 95%CI:1.01 to 1.90), functional dependency in ≥ 4 activities of daily living (OR: 1.82; 95%CI:1.32 to 2.53), abnormal urea (OR:2.16; 95%CI:1.58 to 2.95) and abnormal albumin (OR:3.68; 95%CI:2.07 to 6.54) values. Internal validation results for optimism-adjusted Nagelkerke’s R2, Brier score, AUC, calibration slope and CITL were 0.25 (95%CI:0.25 to 0.26), 0.17 (95%CI:0.17 to 0.17), 0.76 (95%CI:0.76 to 0.76), 0.95 (95% CI:0.95 to 0.96) and 0 (95%CI:-0.0001 to 0.001) respectively. When externally validated, the model demonstrated an AUC of 0.70 (95%CI:0.69 to 0.71), calibration slope of 0.64 (95%CI:0.63 to 0.66) and CITL of -0.27 (95%CI:-0.28 to -0.26). Conclusion The PRO-MADE attained good discrimination and calibration properties. Used synergistically with a clinician’s judgement, this model can identify AD patients who are at high-risk of one-year ACM to facilitate timely referrals to palliative care.

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