Background Health systems worldwide are facing shortages in health professional workforce. Several studies have demonstrated the direct correlation between the availability of health workers, coverage of health services, and population health outcomes. To address this shortage, online eLearning is increasingly being adopted in health professionals' education. To inform policy-making, in online eLearning, we need to determine its effectiveness.

ObjectivesTo assess the relationship between various social isolation indicators and loneliness, and to examine the differential associations that social isolation indicators, loneliness have with depressive symptoms.MethodsBaseline data for 1,919 adults (aged 21 years and above) from a representative health survey in the Central region of Singapore was used for this study. The association between social isolation indicators (marital status, living arrangement, social connectedness with relatives and friends) and loneliness (the three-item UCLA Loneliness) were assessed, and their differential associations with depressive symptoms (the Patient Health Questionnaire-9) were examined using multiple linear regression, controling for relevant covariates.ResultsThere was significant overlap between loneliness and social isolation. Social connectedness with relatives and friends were mildly correlated with loneliness score (|r| = 0.14~0.16). Social isolation in terms of weak connectedness with relatives and with friends and loneliness were associated with depressive symptoms even after controling for age, gender, employment status and other covariates. The association of loneliness with depressive symptoms (β = 0.33) was independent of and stronger than that of any social isolation indicators (|β| = 0.00~0.07).ConclusionsThe results of the study establishes a significant and unique association of different social isolation indicators and loneliness with depressive symptoms in community-dwelling adults aged 21 and above.

We conducted a study among healthcare workers (HCWs) exposed to patients with severe acute respiratory syndrome (SARS) before infection control measures were instituted. Of all exposed HCWs, 7.5% had asymptomatic SARS-positive cases. Asymptomatic SARS was associated with lower SARS antibody titers and higher use of masks when compared to pneumonic SARS.

Objectives: To be able to predict, at the time of triage, whether a need for hospital admission exists for emergency department (ED) patients may constitute useful information that could contribute to systemwide hospital changes designed to improve ED throughput. The objective of this study was to develop and validate a predictive model to assess whether a patient is likely to require inpatient admission at the time of ED triage, using routine hospital administrative data.Methods: Data collected at the time of triage by nurses from patients who visited the ED in 2007 and 2008 were extracted from hospital administrative databases. Variables included were demographics (age, sex, and ethnic group), ED visit or hospital admission in the preceding 3 months, arrival mode, patient acuity category (PAC) of the ED visit, and coexisting chronic diseases (diabetes, hypertension, and dyslipidemia). Chi-square tests were used to study the association between the selected possible risk factors and the need for hospital admission. Logistic regression was applied to develop the prediction model. Data were split for derivation (60%) and validation (40%). Receiver operating characteristic curves and goodness-of-fit tests were applied to the validation data set to evaluate the model. Results:Of 317,581 ED patient visits, 30.2% resulted in immediate hospital admission. In the developed predictive model, age, PAC status, and arrival mode were most predictive of the need for immediate hospital inpatient admission. The c-statistic of the receiver operating characteristic (ROC) curve was 0.849 (95% confidence interval [CI] = 0.847 to 0.851). The goodness-of-fit test showed that the predicted patients' admission risks fit the patients' actual admission status well. Conclusions:A model for predicting the risk of immediate hospital admission at triage for all-cause ED patients was developed and validated using routinely collected hospital data. Early prediction of the need for hospital admission at the time of triage may help identify patients deserving of early admission planning and resource allocation and thus potentially reduce ED overcrowding.

BackgroundAccurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning.MethodsData for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15.ResultsBy time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data.ConclusionTime series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.

Between 1 and 22 March 2003, a nosocomial outbreak of Severe Acute Respiratory Syndrome (SARS) occurred at the Communicable Disease Centre in Tan Tock Seng Hospital, Singapore, the national treatment and isolation facility for patients with SARS. A case-control study with 36 cases and 50 controls was conducted of factors associated with the transmission of SARS within the hospital. In univariate analysis, contact with respiratory secretions elevated the odds ratio to 6.9 (95 % CI 1.4-34.6, P= 0.02). Protection was conferred by hand washing (OR 0.06, 95% CI 0.007-0.5, P=0.03) and wearing of N95 masks (OR 0.1, 95% CI 0.03-0.4, P=0.001). Use of gloves and gowns had no effect. Multivariate analysis confirmed the strong role of contact with respiratory secretions (adjusted OR 21.8, 95 % CI 1.7 274.8, P=0.017). Both hand washing (adjusted OR 0.07, 95 % CI 0.008-0.66, P=0.02) and wearing of N95 masks (adjusted OR 0.1, 95% CI 0.02-0.86, P=0.04) remained strongly protective but gowns and gloves had no effect.

BackgroundPoor medication adherence can have negative consequences for the patients, the provider, the physician, and the sustainability of the healthcare system. To our knowledge, the association between medication adherence and glycemic control among newly diagnosed diabetes patients has not been studied. This study aims to bridge the gap.MethodThis is a retrospective cohort study of 2463 patients managed in the National Healthcare Group in Singapore with newly diagnosed diabetes. Patients were followed up for the first two years from their first medication dispensed for measuring medication adherence, proportion of days covered (PDC); and for another three years for investigating outcomes of glycemic control, emergency department visit, and hospitalization. Multivariable regressions were performed to study the association between medication adherence and the outcomes as well as the risk factors of poor adherence.ResultsThe prevalence of medication adherence (PDC≥80%) was 65.0% (95% CI 63.1% to 66.9%) among newly diagnosed diabetes patients in Singapore. Male, Indian, or patients without hypertension or dyslipidemia were associated with poorer medication adherence. The HbA1c level of poor adherent patients (PDC <40%) increased by 0.4 (95% CI 0.2 to 0.5) over the two years, and they were also more likely to have hospitalization (OR 2.6,95% CI 1.7 to 3.8) or emergency department visit (OR 2.4,95% CI 1.7 to 3.4) compared with the fully adherent patients (PDC=100%).ConclusionsThe medication adherence in the early stage of diabetes is important for maximizing the effectiveness of pharmaceutical therapy. Health policies or interventions targeting the improvement of medication adherence among newly diagnosed diabetes patients are in need.

BackgroundThe world is short of 7.2 million health-care workers and this figure is growing. The shortage of teachers is even greater, which limits traditional education modes. eLearning may help overcome this training need. Offline eLearning is useful in remote and resource-limited settings with poor internet access. To inform investments in offline eLearning, we need to establish its effectiveness in terms of gaining knowledge and skills, students' satisfaction and attitudes towards eLearning.