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.
High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is ...pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups.
The index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups' persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history.
A total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment.
HUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.
We demonstrate the performance and workload impact of incorporating a natural language model, pretrained on citations of biomedical literature, on a workflow of abstract screening for studies on ...prognostic factors in end-stage lung disease. The model was optimized on one-third of the abstracts, and model performance on the remaining abstracts was reported. Performance of the model, in terms of sensitivity, precision, F1 and inter-rater agreement, was moderate in comparison with other published models. However, incorporating it into the screening workflow, with the second reviewer screening only abstracts with conflicting decisions, translated into a 65% reduction in the number of abstracts screened by the second reviewer. Subsequent work will look at incorporating the pre-trained BERT model into screening workflows for other studies prospectively, as well as improving model performance.
ObjectivesChallenges with manual methodologies to identify frailty, have led to enthusiasm for utilising large-scale administrative data, particularly standardised diagnostic codes. However, concerns ...have been raised regarding coding reliability and variability. We aimed to quantify variation in coding frailty syndromes within standardised diagnostic code fields of an international dataset.SettingPooled data from 37 hospitals in 10 countries from 2010 to 2014.ParticipantsPatients ≥75 years with admission of >24 hours (N=1 404 671 patient episodes).Primary and secondary outcome measuresFrailty syndrome groups were coded in all standardised diagnostic fields by creation of a binary flag if the relevant diagnosis was present in the 12 months leading to index admission. Volume and percentages of coded frailty syndrome groups by age, gender, year and country were tabulated, and trend analysis provided in line charts. Descriptive statistics including mean, range, and coefficient of variation (CV) were calculated. Relationship to in-hospital mortality, hospital readmission and length of stay were visualised as bar charts.ResultsThe top four contributors were UK, US, Norway and Australia, which accounted for 75.4% of the volume of admissions. There were 553 595 (39.4%) patient episodes with at least one frailty syndrome group coded. The two most frequently coded frailty syndrome groups were ‘Falls and Fractures’ (N=3 36 087; 23.9%) and ‘Delirium and Dementia’ (N=221 072; 15.7%), with the lowest CV. Trend analysis revealed some coding instability over the frailty syndrome groups from 2010 to 2014. The four countries with the lowest CV for coded frailty syndrome groups were Belgium, Australia, USA and UK. There was up to twofold, fourfold and twofold variation difference for outcomes of length of stay, 30-day readmission and inpatient mortality, respectively, across the countries.ConclusionsVariation in coding frequency for frailty syndromes in standardised diagnostic fields are quantified and described. Recommendations are made to account for this variation when producing risk prediction models.
Abstract Context Hospice care can be delivered in different settings, but many patients choose to receive it at home because of familiar surroundings. Despite their preferences, not every home ...hospice patient manages to die at home. Objective To examine the independent factors associated with home hospice patient dying at home. Methods Retrospective analysis of Hospice Care Association's database. Hospice Care Association is the largest home hospice provider in Singapore. The study included all patients who were admitted into home hospice service from January 1, 2004 to December 31, 2013. Cox proportional hazards modeling with time as constant was used to study the relationship between independent variables and home death. Results A total of 19,721 patients were included in the study. Females (adjusted risk ratio ARR 1.09, 95% CI 1.04–1.15), older patients (ARR 1.01, 95% CI 1.00–1.01), shorter duration of home hospice stay (ARR 0.88, 95% CI 0.82–0.94), fewer episodes of hospitalization (ARR 0.81, 95% CI 0.75–0.86), living with caregivers (ARR 1.54, 95% CI 1.05–2.26), doctor (ARR 1.05, 95% CI 1.01–1.08) and nurse (ARR 1.06, 95% CI 1.04–1.08) visits were positive predictors of dying-at-home. Diagnosis of cancer (ARR 0.93, 95% CI 0.86–1.00) was a negative predictor of dying-at-home. Conclusion Female, older age, living with a caregiver, non-cancer diagnosis, more doctor and nurse visits, shorter duration of home hospice stays, and fewer episodes of acute hospitalizations are predictive of dying-at-home for home hospice patients.
As healthcare expenditure and utilization continue to rise, understanding key drivers of hospital expenditure and utilization is crucial in policy development and service planning. This study aims to ...investigate micro drivers of hospital expenditure and length of stay (LOS) in an Academic Medical Centre.
Data corresponding to 285,767 patients and 207,426 inpatient visits was extracted from electronic medical records of the National University of Hospital in Singapore between 2005 to 2013. Generalized linear models and generalized estimating equations were employed to build patient and inpatient visit models respectively. The patient models provide insight on the factors affecting overall expenditure and LOS, whereas the inpatient visit models provide insight on how expenditure and LOS accumulate longitudinally.
Although adjusted expenditure and LOS per inpatient visit were largely similar across socio-economic status (SES) groups, patients of lower SES groups accumulated greater expenditure and LOS over time due to more frequent visits. Admission to a ward class with greater government subsidies was associated with higher expenditure and LOS per inpatient visit. Inpatient death was also associated with higher expenditure per inpatient visit. Conditions that drove patient expenditure and LOS were largely similar, with mental illnesses affecting LOS to a larger extent. These observations on condition drivers largely held true at visit-level.
The findings highlight the importance of distinguishing the drivers of patient expenditure and inpatient utilization at the patient-level from those at the visit-level. This allows better understanding of the drivers of healthcare utilization and how utilization accumulates longitudinally, important for health policy and service planning.
Abstract
Objectives
emergency department interventions for frailty (EDIFY) delivers frailty-centric interventions at the emergency department (ED). We evaluated the effectiveness of a multicomponent ...frailty intervention (MFI) in improving functional outcomes among older persons.
Design
a quasi-experimental study.
Setting
a 30-bed ED observation unit within a 1,700-bed acute tertiary hospital.
Participants
patients aged ≥65 years, categorised as Clinical Frailty Scale 4–6, and planned for discharge from the unit.
Methods
we compared patients receiving the MFI versus usual-care. Data on demographics, function, frailty, sarcopenia, comorbidities and medications were gathered. Our primary outcome was functional status—Modified Barthel Index (MBI) and Lawton’s iADL. Secondary outcomes include hospitalisation, ED re-attendance, mortality, frailty, sarcopenia, polypharmacy and falls. Follow-up assessments were at 3, 6 and 12 months.
Results
we recruited 140 participants (mean age 79.7 ± 7.6 years; 47% frail and 73.6% completed the study). Baseline characteristics between groups were comparable (each n = 70). For the intervention group, MBI scores were significantly higher at 6 months (mean: 94.5 ± 11.2 versus 88.5 ± 19.5, P = 0.04), whereas Lawton’s iADL scores experienced less decline (change-in-score: 0.0 ± 1.7 versus −1.1 ± 1.8, P = 0.001). Model-based analyses revealed greater odds of maintaining/improving MBI in the intervention group at 6 months odds ratio (OR) 2.51, 95% confidence interval (CI) 1.04–6.03, P = 0.04 and 12 months (OR 2.98, 95% CI 1.18–7.54, P = 0.02). This was similar for Lawton’s iADL at 12 months (OR 4.01, 95% CI 1.70–9.48, P = 0.002). ED re-attendances (rate ratio 0.35, 95% CI 0.13–0.90, P = 0.03) and progression to sarcopenia (OR 0.19, 95% CI 0.04–0.94, P = 0.04) were also lower at 6 months.
Conclusions
the MFI delivered to older persons at the ED can possibly improve functional outcomes and reduce ED re-attendances while attenuating sarcopenia progression.
This paper aims to describe and compare the characteristics of 2 stroke populations in Singapore and in St. Louis, USA, and to document thrombolysis rates and contrast factors associated with its ...uptake in both populations.
The stroke populations described were from the Singapore Stroke Registry (SSR) in -Singapore and the Cognitive Rehabilitation Research Group Stroke Registry (CRRGSR) in St. Louis, MO, USA. The registries were compared in terms of demographics and stroke risk factor history. Logistic regression was used to determine factors associated with thrombolysis uptake.
A total of 39,323 and 8,106 episodes were recorded in SSR and CRRGSR, respectively, from 2005 to 2012. Compared to CRRGSR, patients in SSR were older, male, and from the ethnic majority. Thrombolysis rates in SSR and CRRGSR were 2.5 and 8.2%, respectively, for the study period. History of ischemic heart disease or atrial fibrillation was associated with increased uptake in both populations, while history of stroke was associated with lower uptake. For SSR, younger age and males were associated with increased uptake, while having a history of smoking or diabetes was associated with decreased uptake. For CRRGSR, ethnic minority status was associated with decreased uptake.
The comparison of stroke populations in Singapore and St Louis revealed distinct differences in clinicodemographics of the 2 groups. Thrombolysis uptake was driven by nonethnicity demographics in Singapore. Ethnicity was the only demographic driver of uptake in the CRRGSR population, highlighting the need to target ethnic minorities in increasing access to thrombolysis.
Abstract Background Patients with chronic lung diseases (CLDs), defined as progressive and life-limiting respiratory conditions, experience a heavy symptom burden as the conditions become more ...advanced, but palliative referral rates are low and late. Prognostic tools can help clinicians identify CLD patients at high risk of deterioration for needs assessments and referral to palliative care. As current prognostic tools may not generalize well across all CLD conditions, we aim to develop and validate a general model to predict one-year mortality in patients presenting with any CLD. Methods A retrospective cohort study of patients with a CLD diagnosis at a public hospital from July 2016 to October 2017 was conducted. The outcome of interest was all-cause mortality within one-year of diagnosis. Potential prognostic factors were identified from reviews of prognostic studies in CLD, and data was extracted from electronic medical records. Missing data was imputed using multiple imputation by chained equations. Logistic regression models were developed using variable selection methods and validated in patients seen from January 2018 to December 2019. Discriminative ability, calibration and clinical usefulness of the model was assessed. Model coefficients and performance were pooled across all imputed datasets and reported. Results Of the 1000 patients, 122 (12.2%) died within one year. Patients had chronic obstructive pulmonary disease or emphysema (55%), bronchiectasis (38%), interstitial lung diseases (12%), or multiple diagnoses (6%). The model selected through forward stepwise variable selection had the highest AUC (0.77 (0.72–0.82)) and consisted of ten prognostic factors. The model AUC for the validation cohort was 0.75 (0.70, 0.81), and the calibration intercept and slope were − 0.14 (-0.54, 0.26) and 0.74 (0.53, 0.95) respectively. Classifying patients with a predicted risk of death exceeding 0.30 as high risk, the model would correctly identify 3 out 10 decedents and 9 of 10 survivors. Conclusions We developed and validated a prognostic model for one-year mortality in patients with CLD using routinely available administrative data. The model will support clinicians in identifying patients across various CLD etiologies who are at risk of deterioration for a basic palliative care assessment to identify unmet needs and trigger an early referral to palliative medicine. Trial registration Not applicable (retrospective study).
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