Background Diabetes mellitus (DM) is a growing global health problem. In Singapore, the prevalence of Type 2 DM is rising, but comprehensive information about trends in DM-related complications is lacking. Objectives We utilized the Singapore Health Services (SingHealth) diabetes registry (SDR) to assess trends in DM micro and macro-vascular complications at the population level, explore factors influencing these trends. Methods We studied trends for ten DM-related complications: ischemic heart disease (IHD), acute myocardial infarction (AMI), peripheral arterial disease (PAD) and strokes, diabetic eye complications, nephropathy, neuropathy, diabetic foot, major and minor lower extremity amputation (LEA). The complications were determined through clinical coding in hospital (inpatient and outpatient) and primary care settings within the SingHealth cluster. We described event rates for the complications in 4 age-bands. Joinpoint regression was used to identify significant changes in trends. Results Among 222,705 patients studied between 2013 and 2020. 48.6% were female, 70.7% Chinese, 14.7% Malay and 10.6% Indian with a mean (SD) age varying between 64.6 (12.5) years in 2013 and 65.7 (13.2) years in 2020. We observed an increase in event rates in IHD, PAD, stroke, diabetic eye complications nephropathy, and neuropathy. Joinpoints was observed for IHD and PAD between 2016 to 2018, with subsequent plateauing of event rates. Major and minor LEA event rates decreased through the study period. Conclusion We found that DM and its complications represent an important challenge for healthcare in Singapore. Improvements in the trends of DM macrovascular complications were observed. However, trends in DM microvascular complications remain a cause for concern.
Objectives This scoping review evaluates the existing literature on clinical informatics (CI) training in medical schools. It aims to determine the essential components of a CI curriculum in medical schools, identify methods to evaluate the effectiveness of a CI-focused education, and understand its delivery modes. Materials and Methods This review was informed by the methodological guidance of the Joanna Briggs Institute. Three electronic databases including PubMed, Scopus, and Web of Science were searched for articles discussing CI between January 2010 and December 2021. Results Fifty-nine out of 3055 articles were included in our final analysis. Components of CI education include its utilization in clinical practice, ethical implications, key CI-related concepts, and digital health. Evaluation of educational effectiveness entails external evaluation by organizations external to the teaching institute, and internal evaluation from within the teaching institute. Finally, modes of delivery include various pedagogical strategies and teaching CI using a multidisciplinary approach. Discussion Given the broad discussion on the required competencies, we propose 4 recommendations in CI delivery. These include situating CI curriculum within specific contexts, developing evidence-based guidelines for a robust CI education, developing validated assessment techniques to evaluate curriculum effectiveness, and equipping educators with relevant CI training. Conclusion The literature reveals that CI training in the core curricula will complement if not enhance clinical skills, reiterating the need to equip students with relevant CI competencies. Furthermore, future research needs to comprehensively address current gaps in CI training in different contexts, evaluation methodologies, and delivery modes to facilitate structured training.
Background We investigate the association between mean HbA1c, HbA1c variability, and all-cause mortality and diabetes-related macrovascular complications in patients with diabetes. Methods We performed a retrospective cohort study using patients present in the Singapore Health Services diabetes registry (SDR) during 2013 to 2014. We assessed mean HbA1c using three models: a baseline mean HbA1c for 2013–14, the mean across the whole follow-up period, and a time-varying yearly updated mean. We assessed HbA1c variability at baseline using the patient’s HbA1c variability score (HVS) for 2013–14. The association between mean HbA1c, HVS, and 6 outcomes were assessed using Cox proportional hazard models. Results We included 43,837–53,934 individuals in the analysis; 99.3% had type 2 diabetes mellitus. The data showed a J-shaped distribution in adjusted hazard ratios (HRs) for all-cause mortality, ischemic heart disease, acute myocardial infarction, peripheral arterial disease, and ischemic stroke, with an increased risk of developing these outcomes at HbA1c <6% (42 mmol/mol) and ≥8% (64 mmol/mol). With the addition of HVS, the J-shaped distribution was maintained for the above outcomes, but HRs were greater at HbA1c <6.0% (42 mmol/mol) and reduced at HbA1c ≥8.0% (64 mmol/mol) when compared to models without HVS. The risk for all outcomes increased substantially with increasing glycaemic variability. Conclusion Both low (<6.0% [42 mmol/mol]) and high (≥8.0% [64 mmol/mol]) levels of glycaemic control are associated with increased all-cause mortality and diabetes-related macrovascular complications. Glycaemic variability is independently associated with increased risk for these outcomes. Therefore, patients with stable glycaemic level of 6–8% (42–64mmol/mol) are at lowest risk of all-cause mortality and diabetes-related macrovascular complications.
ObjectivePopulation health management involves risk characterisation and patient segmentation. Almost all population segmentation tools require comprehensive health information spanning the full care continuum. We assessed the utility of applying the ACG System as a population risk segmentation tool using only hospital data.DesignRetrospective cohort study.SettingTertiary hospital in central Singapore.Participants100 000 randomly selected adult patients from 1 January to 31 December 2017.InterventionHospital encounters, diagnoses codes and medications prescribed to the participants were used as input data to the ACG System.Primary and Secondary Outcome MeasuresHospital costs, admission episodes and mortality of these patients in the subsequent year (2018) were used to assess the utility of ACG System outputs such as resource utilisation bands (RUBs) in stratifying patients and identifying high hospital care users.ResultsPatients placed in higher RUBs had higher prospective (2018) healthcare costs, and were more likely to have healthcare costs in the top five percentile, to have three or more hospital admissions, and to die in the subsequent year. A combination of RUBs and ACG System generated rank probability of high healthcare costs, age and gender that had good discriminatory ability for all three outcomes, with area under the receiver-operator characteristic curve (AUC) values of 0.827, 0.889 and 0.876, respectively. Application of machine learning methods improved AUCs marginally by about 0.02 in predicting the top five percentile of healthcare costs and death in the subsequent year.ConclusionA population stratification and risk prediction tool can be used to appropriately segment populations in a hospital patient population even with incomplete clinical data.
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